A gantry crane whole life cycle risk assessment and management system and method

Abstract

The application discloses a gantry crane full life cycle risk assessment and management system and method, relates to the technical field of hoisting machinery safety monitoring and management, and comprises a data acquisition and treatment module, a risk assessment engine module, an early warning and ranking module, an archive management module and a cockpit display module. The method comprises the following steps: collecting multi-source heterogeneous data for standardized processing to establish a full life cycle data view; calculating a basic risk score obtained by weighting a running monitoring score, a state monitoring score and a collision avoidance early warning score; acquiring full life cycle correction factor parameters and calculating a service age-fatigue coupling correction factor, a running intensity correction factor, a maintenance quality correction factor and a spare part health correction factor; and calculating a final risk score according to the basic risk score and each correction factor, and performing risk grade determination, ranking and early warning. The application realizes full life cycle risk assessment and intelligent management of a group-level gantry crane group.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring and management technology for lifting machinery, and more specifically, to a risk assessment and management system and method for the entire life cycle of a gantry crane. Background Technology

[0002] With the advancement of infrastructure construction and the overall development of heavy industry in my country, large gantry cranes have become key equipment in important sectors of the national economy such as shipbuilding and logistics transportation. As cranes develop towards larger size, higher speed, and greater automation, their safe operation and maintenance are evolving from traditional localized single-machine management to group-level remote centralized control management.

[0003] For group enterprises with multiple production bases, the existing technical solution is usually to deploy an independent gantry crane monitoring system in each factory area, and then upload the alarm and operation data generated by each factory system to the group headquarters server through the network, where the group-level management system will display and statistically analyze the data.

[0004] At the single-machine evaluation level, relevant technical solutions already exist. For example, Chinese patent application CN121051889A discloses a method for evaluating the stability and safety of shipbuilding gantry cranes. By acquiring equipment design data and structural measurement data, a geometric model is established and finite element simulation calculations are performed to obtain a safety assessment ratio and risk level. Chinese patent application CN121809225A discloses a method and system for real-time monitoring of crane health status. By acquiring raw operating data, time-domain and frequency-domain features are extracted, and feature fusion is performed in conjunction with the physical topology diagram to generate fault diagnosis results and predict remaining service life. Chinese patent CN120097222A discloses a monitoring and early warning method and system for container gantry cranes. By continuously acquiring monitoring time-series data and performing risk extraction processing, a multi-dimensional risk assessment index is obtained, including structural risk, crane operation risk, and interference from the operating environment. In addition, the academic community has also proposed a risk assessment method for gantry cranes based on fuzzy mathematics, establishing a multi-level evaluation system from the perspective of the main beam and outriggers.

[0005] However, the aforementioned existing technologies still have the following shortcomings: 1. Data silos and inconsistent standards: Subsystems of various subordinate units may be built by different vendors, resulting in inconsistencies in alarm classification standards, data formats, and reporting frequencies. Key lifecycle data of cranes, such as design parameters, manufacturing information, and operation and maintenance history, are scattered across different systems, making it impossible for the group to correlate and analyze pre-commissioning design data with post-commissioning operational data. 2. Limited Risk Assessment Dimensions: Existing group-level assessment schemes rely heavily on fragmented information such as alarm counts, failing to deeply integrate real-time data from multiple dimensions, including operation monitoring, status monitoring, and collision avoidance warnings, with key information reflecting the crane's lifecycle status, such as its commissioning years, cumulative operating hours, cumulative work cycles, and historical fault repair records. This results in biased assessment results. 3. Lack of correlation between remaining service life and risk assessment: Although existing patents have proposed methods for estimating the remaining service life of a single crane, the assessment results have not been effectively integrated into the group-level risk assessment system. Group management cannot intuitively understand the impact of the remaining service life of each crane on current safety risks. Summary of the Invention

[0006] To achieve the above objectives, the present invention provides the following technical solution: On the one hand, this invention provides a method for risk assessment and management of the entire life cycle of a gantry crane, including the following steps: S1. Collect multi-source heterogeneous data of gantry cranes. The multi-source heterogeneous data includes real-time operation data, condition monitoring and evaluation results data, working condition statistics data and full life cycle archive data. Perform format verification, missing value processing and unit unification on the multi-source heterogeneous data, and associate the processed data with the equipment identification information of the gantry crane to establish a full life cycle data view. S2. Calculate the basic risk score for the gantry crane based on the full lifecycle data view. Basic risk score Based on operation monitoring score Status monitoring score Collision avoidance warning score Weighted summation yields: in, , , These are the weighting coefficients for the operation monitoring score, condition monitoring score, and collision avoidance warning score, respectively. S3. Obtain the full life cycle correction factor parameters of the gantry crane. The full life cycle correction factor parameters include: service life. Design lifespan Cumulative fatigue damage value Damage limit value When the station is in operation this month Average monthly operating hours of similar cranes Actual load spectral coefficients Design load spectral coefficients Number of preventative maintenance Total number of maintenance times Mean Time To Repair (MTBT) Average fault repair time for similar cranes Total number of replacements of key components ; S4. Calculate the full lifecycle correction factor, including: Service life-fatigue coupling correction factor: Operational intensity correction factor: Maintenance quality correction factor: Spare parts health correction factor: in, These are the weighting coefficients; S5. Based on the basic risk score The final risk score of the gantry crane is calculated using the life-cycle correction factor. : S6. Based on the final risk score Risk levels of gantry cranes are assessed and ranked, and the final risk score is determined. When the preset safety threshold is exceeded, a risk warning message is generated and pushed. S7. The system monitors environmental data, equipment alarm events, historical statistical trends, and remaining lifespan distribution in real time, and automatically adjusts the weighting coefficients when preset trigger conditions are met. , , , The preset triggering conditions include severe weather warnings, concentrated outbreaks of fault clusters, periodic calibrations, and concentrated expiration of remaining lifespan. When multiple triggering conditions are met simultaneously, the adjustment range is determined according to preset priorities, with severe weather warnings having absolute priority.

[0007] Preferably, in step S1: Real-time operational data includes online / offline status, real-time alarm information, and alarm levels; Condition monitoring and assessment results include health scores, stress spectrum coefficients, cumulative fatigue damage values, and remaining service life obtained from the health assessment system; Operating statistics include monthly operating hours, cumulative number of work cycles, and load spectrum coefficients; The full lifecycle archive data includes data from the design and manufacturing stage, the installation and commissioning stage, the operation and maintenance stage, and the scrapping and decommissioning stage; The unification of dimensions includes mapping all alarm levels to a unified three levels: "Critical", "General", and "Alert".

[0008] Preferably, in step S2: Operational monitoring score The calculation is based on the number of alarms and alarm levels for overspeed alarms, overload alarms, and over-limit alarms, with different severity penalty coefficients corresponding to different alarm levels; Status monitoring score The crane health index is mapped from the output of the lower-level health assessment system; Collision avoidance warning score Calculation of trigger counts based on the soft-leg laser collision avoidance warning system.

[0009] Preferably, in step S4, the service life-fatigue coupling correction factor In the middle, the cumulative fatigue damage value Calculated based on Miner's linear cumulative damage theory; When the cumulative fatigue damage value When the damage exceeds the preset warning value or the remaining service life of the gantry crane is lower than the preset service life warning value, a life warning message will be automatically triggered.

[0010] Preferably, in step S7, the preset triggering condition includes: when a severe weather warning with wind speeds exceeding 12 m / s is received, the collision avoidance warning weight is increased. Increase to 25%-30%, and return to normal 24 hours after the warning is lifted; When the same type of fault alarm occurs in the same factory area within a rolling 7-day time window, the weight of the scoring dimension associated with the fault type is automatically increased, and the threshold of the association correction factor is simultaneously reduced by 10%. Periodic calibration is performed on the 1st of each month. If the month-on-month growth rate of a certain type of alarm exceeds 30%, the weight of the corresponding scoring dimension will be increased by 5-10 percentage points. If the month-on-month decrease exceeds 50%, it will be reverted to the baseline value. When ≥30% of the equipment in the plant area has a remaining service life less than 20% of its design life, the service life-fatigue coupling correction factor for that plant area will be automatically adjusted. The coefficient was increased by 20%, and the area was marked as a key area of ​​concern due to concentrated lifespan expiration. The preset priorities are: severe weather warning > concentrated outbreak of fault clusters > concentrated expiration of remaining lifespan > periodic calibration, with the adjustment range taking the larger value but not superimposed.

[0011] Preferably, in step S6, the risk level determination also includes performing aggregate calculations on multiple gantry cranes within the same factory area to obtain a factory-level risk score. : in, This represents the total number of gantry cranes within the factory area. For the first The final risk score for gantry cranes. For the first Number of days the gantry crane was offline in the current month. The total number of days in the month. For the first Number of serious alarms for gantry cranes in the current month These are the weighting coefficients.

[0012] Preferably, the remaining service life information is displayed in conjunction with the lifespan warning message, and the amplification effect of the remaining service life on the risk score is corrected by the service life-fatigue coupling factor. The cumulative fatigue damage term is realized.

[0013] On the other hand, the present invention also provides a gantry crane full life cycle risk assessment and management system for implementing the above method, including: The data acquisition and governance module includes a data interface unit, a data cleaning unit, and a data standardization unit. It is used to collect multi-source heterogeneous data of gantry cranes, perform format verification, missing value processing, and unit unification on the multi-source heterogeneous data, and associate the processed data with the equipment identification information of the gantry crane to establish a full life cycle data view. The risk assessment engine module includes a parameter configuration unit, a correction factor calculation unit, and a comprehensive scoring unit, which are used to calculate a basic risk score based on a full lifecycle data view. And obtain the full life cycle correction factor parameters, and calculate the service life-fatigue coupling correction factor. Operational intensity correction factor Maintenance quality correction factor and spare parts health correction factor Then according to the formula Calculate the final risk score for the gantry crane; The early warning and ranking module is used to determine the risk score based on the final risk rating. Risk levels of gantry cranes are assessed and ranked, and the final risk score is determined. A risk warning message will be generated and pushed when the preset safety threshold is exceeded; The archive management module is used to store and manage the archive data of the gantry crane throughout its entire life cycle in a structured manner according to the equipment identification information of the gantry crane. The archive data includes design data, manufacturing and installation data, operation and maintenance records and decommissioning management data. The cockpit display module is used to display the final risk score, correction factor values, remaining service life, and key information throughout the entire life cycle of the gantry crane.

[0014] Preferably, the data acquisition and management module establishes a data interface with the crane monitoring platform of each subordinate unit and collects data according to a unified data dictionary; The equipment identification information is a unified code, which includes the equipment type code, the factory area code, the equipment serial number, and the year of commissioning, serving as a unique identifier for the gantry crane throughout its entire life cycle; The archive management module is also used to automatically associate and intelligently retrieve archive data throughout its entire lifecycle based on a unified code.

[0015] Preferably, the early warning and ranking module is further used to calculate a plant-level risk score according to an aggregation formula. And generate a risk ranking for the factory area; When factory-level risk assessment When the risk level rises continuously and exceeds the preset trend threshold, a risk trend warning message for the factory area will be pushed. The system also includes a report generation module, which responds to the user's one-click operation command, retrieves data within a specified time range from the database, and automatically generates an assessment report containing risk scores, rankings, and key information throughout the entire life cycle of the gantry crane according to a preset template.

[0016] The technical effects and advantages of this invention are as follows: 1. This invention establishes a unified coding system and data dictionary for the group, performs format verification, missing value processing, and dimension unification on multi-source heterogeneous data, and constructs a full life cycle data view that runs through all stages of crane design, manufacturing, installation, operation and maintenance, and scrapping. It links all data such as design parameters, operating data, evaluation results, and maintenance records that are scattered in different systems of various subordinate units to the same equipment code, solving the problem of data silos in multiple factories and systems, realizing the queryability, manageability, and traceability of crane full life cycle data, and transforming crane archives from static data into core data assets that can be dynamically linked. 2. Based on the basic risk score, this invention innovatively introduces service life-fatigue coupling correction factors, operating intensity correction factors, maintenance quality correction factors, and spare parts health correction factors. It quantifies key parameters throughout the entire life cycle of the crane, such as service life, cumulative fatigue damage, deviation of actual load spectrum coefficient from design value, proportion of preventive maintenance, mean time to repair failure, and frequency of replacement of key components, into correction coefficients. This multi-dimensional correction of the basic risk score overcomes the limitations of existing technologies that rely solely on fragmented information such as the number of alarms for risk assessment, making the assessment results more comprehensive, objective, and accurate. 3. This invention integrates the remaining service life information output by the lower-level health assessment system into the group-level risk assessment system. By using the cumulative fatigue damage item in the service life-fatigue coupling correction factor, it achieves a quantitative amplification effect of remaining service life on risk score. Based on the final risk score, it automatically generates risk rankings for each gantry crane and plant area within the group. When the risk score exceeds a preset safety threshold or the remaining service life is lower than a preset warning value, it automatically pushes an early warning message. This allows the group's management to intuitively grasp the impact of the remaining service life of each crane on the current safety risk, realizing a shift from passively responding to failures to proactively predicting risks, and providing data support for the group's safety management decisions. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention.

[0018] Figure 2 This is a system diagram of the present invention.

[0019] The attached diagram is labeled as follows: 1. Data Acquisition and Governance Module; 2. Risk Assessment Engine Module; 3. Early Warning and Ranking Module; 4. File Management Module; 5. Cockpit Display Module. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0021] Example 1 As attached Figure 1 As shown, this embodiment provides a method for risk assessment and management of the entire life cycle of gantry cranes. Taking a group of gantry cranes from multiple shipyards under a group as an application scenario, the specific implementation process of the method of the present invention is described in detail, including the following steps: S1. Multi-source heterogeneous data acquisition and establishment of a full lifecycle data view First, the system establishes a connection with the crane monitoring platforms of each subordinate unit through a data interface, in accordance with the "Crane Data Dictionary" uniformly formulated by the group, to collect multi-source heterogeneous data of gantry cranes.

[0022] The multi-source heterogeneous data collected in this step includes the following four categories: The first category is real-time operational data: including the online or offline status of the gantry crane, real-time alarm information, and alarm levels. Alarm levels are classified into three levels according to the group's unified standards: "Severe," "General," and "Important." Specifically, alarms involving safe operation, such as overspeed and overload alarms, are classified as "Severe"; alarms exceeding limits that deviate from the normal operating range but do not directly endanger safety are classified as "General"; and alarms with slight sensor signal fluctuations that do not affect safe operation are classified as "Important."

[0023] The second category is condition monitoring and assessment results data: including health scores, stress spectrum coefficients, cumulative fatigue damage values, and remaining service life obtained from the health assessment system. The health assessment system is a crane health assessment system deployed in various subordinate units. It assesses the health status of the crane based on strain data, vibration data, and operating condition data collected by a multi-dimensional sensor network deployed on the crane's structural components and mechanisms, using a combination of physical-driven and data-driven methods.

[0024] The third category is operational statistics: including monthly operating hours, cumulative work cycles, and load spectrum coefficients. The system collects the above data from the operation and maintenance management platforms of each subordinate unit.

[0025] The fourth category is full lifecycle archive data, including data from the design and manufacturing phase, installation and commissioning phase, operation and maintenance phase, and scrapping / retirement phase. Design and manufacturing phase data includes crane design documents, manufacturing parameters, rated load, design life, design load spectrum coefficient, design stress spectrum coefficient, design working level, and factory test reports. Installation and commissioning phase data includes installation date, commissioning records, and initial state parameters. Operation and maintenance phase data includes commissioning date, cumulative operating hours, cumulative work cycles, downtime records, maintenance and repair records, and spare parts usage log. Downtime records include downtime type, downtime duration, and corrective actions; maintenance and repair records include repair date, repair content, and replaced parts; and the spare parts usage log includes the name, quantity, and replacement cycle of replaced parts. Scrapping / retirement phase data includes crane condition assessment conclusions, scrapping date, and reason for retirement.

[0026] Next, the collected multi-source heterogeneous data will be processed as follows: Format validation: Check whether each data field conforms to the format requirements defined in the group's unified data dictionary. Data that does not conform to the format requirements will be marked and a data quality alarm will be triggered.

[0027] Missing value handling: For temporarily missing data, linear interpolation is used to fill in the missing data; for long-term missing data or missing critical data, a data missing report is generated and the relevant responsible person is notified to fill in the missing data.

[0028] Standardized Dimensions: Alarm levels reported by all subordinate units will be mapped to three levels: "Severe," "General," and "Alert," according to the group's unified standards. Specifically, the different alarm classification standards of each subordinate unit will be uniformly converted into the group's standard classification. For example, Unit A's Level 1 alarm and Unit B's Emergency alarm will both be mapped to the "Severe" level; Unit A's Level 2 alarm and Unit B's Important alarm will both be mapped to the "General" level; and Unit A's Level 3 alarm and Unit B's Alert alarm will both be mapped to the "Alert" level.

[0029] Finally, the processed data is associated with the equipment identification information of the gantry cranes. The equipment identification information consists of a pre-assigned unified code for each gantry crane, which includes the equipment type code, the factory area code, the equipment serial number, and the year of commissioning, serving as a unique identifier throughout the entire lifecycle of the gantry crane. Based on this code, the system establishes a full lifecycle data view, linking all data, including design parameters, operational data, evaluation data, and maintenance records, to the same equipment code, enabling data to be searchable, manageable, and traceable.

[0030] S2, Calculate the basic risk score Based on the full lifecycle data view established in step S1, calculate the basic risk score for the gantry crane. The basic risk score is composed of the operational monitoring score. Status monitoring score Collision avoidance warning score The weighted summation is obtained, and the calculation formula is: in, , , These are the weighting coefficients for the operation monitoring score, condition monitoring score, and collision avoidance warning score, with default values ​​of 60%, 30%, and 10%, respectively. The weighting coefficients mentioned above, as well as the weighting coefficients in subsequent correction factors, are also specified. All of these are dynamically adjustable parameters, and the specific adjustment methods are detailed in step S7 below, which describes the adaptive weight adjustment mechanism.

[0031] Operational monitoring score Based on the number of alarms and alarm levels for overspeed, overload, and over-limit alarms, different severity penalty coefficients are assigned to different alarm levels. Specifically, the penalty coefficient for a "serious" alarm is 3, for a "normal" alarm it is 1, and for a "notice" alarm it is 0.3.

[0032] The operation monitoring score uses a 100-point scale, with a base score of 100 points. Points are deducted based on the number of alarms of various types and their corresponding penalty coefficients within the month. The deduction rules are as follows: 15 points are deducted for a single "serious" alarm, 5 points for a single "general" alarm, and 1.5 points for a single "notice" alarm. Furthermore, if the system detects a long-term operational deviation trend in the crane, such as a load spectrum coefficient consistently exceeding the design value or continuous exceedances of operating parameters, an additional trend deviation deduction is applied to the alarm deduction to reflect the impact of continuous operational deviation on risk. A final score below 0 is recorded as 0 points, and a score above 100 is recorded as 100 points.

[0033] Status monitoring score The health index of the crane is mapped from the output of the underlying health assessment system. The health assessment system, based on data collected by a multi-dimensional sensor network deployed on the crane's structural components and mechanisms, uses a combination of physical and data-driven methods to assess the crane's health status, outputting quantitative assessment results including a health score, stress spectrum coefficient, cumulative fatigue damage value, and remaining service life. The condition monitoring score directly maps to this health index. Health Score The value ranges from 0 to 1, with values ​​closer to 1 indicating a worse health status. When the health score... When the score is between 0 and 0.2, the condition monitoring score is mapped to 80-100 points; when... When the value is between 0.2 and 0.4, the mapping is 60-80 points; when... When the value is between 0.4 and 0.6, the mapping is 40-60 points; when When the value is between 0.6 and 0.8, the mapping is 20-40 points; when When the value is between 0.8 and 1.0, it corresponds to a score of 0-20. Those skilled in the art can adjust the segmentation intervals and corresponding scoring ranges of the above mapping relationship according to actual needs.

[0034] Collision avoidance warning score The collision avoidance warning system is calculated based on the number of triggers. The collision avoidance warning score uses a 100-point scale, with 100 points as the base score. Each trigger of the collision avoidance warning deducts a set number of points, such as 10 points each time. If the final score is lower than 0 points, it is 0 points.

[0035] Substituting the scores of each of the above sub-items into the basic risk scoring formula, we obtain the basic risk score for the gantry crane. .

[0036] In this embodiment, each weight , , , In addition to allowing administrators to manually adjust the weights through the management interface, the system also has a built-in adaptive weight adjustment mechanism that can automatically adjust the weight coefficients based on various triggering conditions such as environmental changes, equipment operation events, historical statistical trends, and remaining lifespan distribution, without requiring manual intervention.

[0037] S3. Obtain the full lifecycle correction factor parameters The system obtains the following lifecycle correction factor parameters from the lifecycle data view established in step S1: service life Design lifespan Cumulative fatigue damage value Damage limit value When the station is in operation this month Average monthly operating hours of similar cranes Actual load spectral coefficients Design load spectral coefficients Number of preventative maintenance Total number of maintenance times Mean Time To Repair (MTBT) Average fault repair time for similar cranes Total number of replacements of key components Among them, the damage limit value Based on Miner's linear cumulative damage theory, the value is 1, and fatigue failure occurs when the cumulative damage reaches 1.

[0038] S4. Calculate the life-cycle correction factor. Based on the correction factor parameters obtained in step S3, calculate the service life-fatigue coupling correction factor, operating intensity correction factor, maintenance quality correction factor, and spare parts health correction factor respectively.

[0039] Service life-fatigue coupling correction factor The calculation formula is as follows: in, , This is a weighting coefficient used to adjust the degree to which service life and cumulative fatigue damage amplify the basic risk. The value range is 0.10-0.40. The value range is 0.20-0.60. This correction factor also takes into account the service life of the crane. With design lifespan The ratio and cumulative fatigue damage value With damage limit The ratio is smoothed using a logarithmic function to account for the amplification effect of service life, causing it to gradually slow down with increasing service life, conforming to the general law of aging rate of mechanical equipment. When When the damage exceeds a preset warning value, such as 0.5, the correction factor calculation unit automatically triggers a lifespan warning signal.

[0040] Operational intensity correction factor The calculation formula is as follows: in, , These are weighting coefficients used to adjust the degree to which the deviation of the running table and the deviation of the load spectrum coefficient amplify the basic risk. The value range is 0.05-0.20. The value range is 0.10-0.40. This correction factor also considers the crane's monthly operating hours. Average monthly operating hours of similar cranes The degree of deviation and the actual load spectral coefficient With design load spectral coefficient The degree of deviation. When the monthly operating hours are higher than the average level of similar products or the actual load spectrum coefficient is higher than the design value, the correction factor is greater than 1, and the basic risk is amplified.

[0041] Maintenance quality correction factor The calculation formula is as follows: in, , , which is a weighting coefficient used to adjust the degree to which the proportion of preventive maintenance and the mean time to repair (MTBT) affect the underlying risk. The value range is 0.08-0.20. The value range is 0.05-0.15. This correction factor comprehensively considers the crane's maintenance initiative and maintenance efficiency, where maintenance initiative is measured by the number of preventive maintenance operations. Total number of maintenance Maintenance efficiency is measured by the ratio of mean time to repair (MTBL). Average time to repair (MTB) compared to similar cranes The ratio of preventative maintenance to routine maintenance is used to measure risk. A higher proportion of preventative maintenance indicates more proactive and efficient maintenance, which reduces risk; a longer mean time to repair (MTBT) indicates lower maintenance efficiency and higher risk.

[0042] Spare parts health correction factor The calculation formula is as follows: in, This is a weighting coefficient used to adjust the degree to which the frequency of replacement of key components amplifies the underlying risk. The value ranges from 0.05 to 0.15. This correction factor reflects the systemic defect risk of the crane based on the replacement frequency of key components per unit time. A higher replacement frequency indicates that the crane may have systemic defects or be improperly selected. Key components include wire ropes, brakes, and bearings.

[0043] S5. Calculate the final risk score. Based on the basic risk score obtained in step S2 Using the life-cycle correction factors obtained in step S4, calculate the final risk score for the gantry crane. : Each correction factor acts independently on the base risk score, and the final risk score comprehensively reflects the overall risk level of the crane under the influence of multiple dimensions such as current operating status, service life, cumulative damage, maintenance quality, and spare parts health. The system correlates and displays the remaining service life information obtained in step S3 with the final risk score. The amplification effect of the remaining service life on the risk score is achieved through the service life-fatigue coupling correction factor. The cumulative fatigue damage term in the middle is realized, that is The higher the cumulative fatigue damage value, the shorter the remaining service life. The higher the value, the greater the final risk score.

[0044] S6. Risk Level Assessment, Ranking, and Early Warning The system calculates the final risk score based on step S5. The risk level of all gantry cranes within the group was assessed and ranked. The risk level classification criteria are as follows: A score below the first preset threshold of 40 indicates low risk; a score greater than or equal to the first preset threshold of 40 but less than the second preset threshold of 70 indicates medium risk; and a score greater than or equal to the second preset threshold of 70 indicates high risk. These thresholds can be adjusted by the system administrator according to actual management needs.

[0045] When a certain gantry crane When the score exceeds a preset safety threshold of 70 points, the system automatically generates and pushes a risk warning message. The warning message includes device identification information, current risk score, risk level, ranking position, and a description of the main risk factors.

[0046] In step S6, the risk level determination also includes performing aggregate calculations on multiple gantry cranes within the same factory area to obtain a factory-level risk score. The formula for calculating the factory-level risk score is as follows: in, This represents the total number of gantry cranes within the factory area. For the first The final risk score for gantry cranes. For the first Number of days the gantry crane was offline in the current month. The total number of days in the month. For the first Number of serious alarms for gantry cranes in the current month This is the weighting coefficient, with a default value of 0.20.

[0047] This formula comprehensively considers three factors: the average risk level of gantry cranes within the plant area, the online rate, and the concentration of severe alarms. The first term reflects the overall risk level of the plant area through an arithmetic mean; the second term discounts the average risk based on the proportion of offline days—longer offline days indicate lower equipment availability and a smaller contribution to actual operational risk; the third term amplifies the average risk through the concentration of severe alarms—more severe alarms indicate a higher concentration of high-risk events within the plant area and a higher overall risk level. If multiple cranes within the plant area have long offline periods or severe alarms occur in a concentrated manner, the plant-level risk score will be adjusted accordingly.

[0048] The system generates a risk ranking of each factory area based on the calculated factory area-level risk scores. When the factory area-level risk score of a certain factory area rises continuously above a preset trend threshold, the system determines that the risk of that factory area is showing a continuous deterioration trend and automatically pushes a factory area risk trend warning message to the group's safety management department.

[0049] In this embodiment, each weighting coefficient , , , All of these are configurable, dynamically adjustable parameters. The adjustment methods include manual adjustment and the adaptive weight adjustment mechanism described above. For details on adaptive weight adjustment, please refer to step S7 below.

[0050] Manual adjustment: The group administrator receives adjustment instructions from external sources through the management interface to change the values ​​of various weighting coefficients. For example, when the group focuses on structural safety at a certain stage, it can increase the weighting through the management interface. and The weight value; when the group focuses on operational violations, it can increase the weight value through the management interface. The weight value; Adaptive weight adjustment: The system automatically optimizes the weight coefficients dynamically based on the four rules described in step S7 below (severe weather warning, concentrated outbreak of fault clusters, periodic calibration, and concentrated expiration of remaining lifespan), without the need for manual intervention.

[0051] S7, Adaptive Weight Adjustment In this step, the system monitors environmental data, equipment alarm events, historical statistical trends, and remaining lifespan distribution in real time, and automatically adjusts the weighting coefficients when preset trigger conditions are met. , , , The preset trigger conditions include severe weather warnings, concentrated outbreaks of fault clusters, periodic calibrations, and concentrated expiration of remaining lifespan. When multiple trigger conditions are met simultaneously, the adjustment range is determined according to preset priorities, with severe weather warnings having absolute priority. The specific rules of this adaptive weight adjustment mechanism are as follows: Rule 1: Automatic weight adjustment based on severe weather warnings (environment-driven) (1) Triggering conditions: The system receives severe weather warning information such as typhoon and storm through the meteorological API interface (wind speed exceeds 12m / s).

[0052] (2) Automatic adjustment of actions: Adjust the weight of collision avoidance warning The weighting was temporarily increased from 10% to 25%-30%; the weighting of operational monitoring was simultaneously reduced. and status monitoring weights (Keep the sum of the three to 100%).

[0053] (3) Recovery mechanism: The system will automatically restore to the original configuration 24 hours after the warning is lifted.

[0054] (4) Technical principle: Under severe weather conditions, the impact of wind load on cranes increases significantly. The flexible leg side laser anti-collision system is more likely to generate false alarms or real collision risks due to structural sway. At this stage, anti-collision related indicators should be monitored first.

[0055] Rule 2: Automatic weight adjustment based on the concentrated outbreak of fault clusters (event-driven) (1) Triggering conditions: The system detects a concentrated outbreak of the same type of fault / alarm event within the group within the rolling time window (nearly 7 days), for example: ≥2 equipment in the same plant area have an alarm for "abnormal stress of drum shaft", or ≥3 equipment have an alarm for "overspeed of hoisting mechanism".

[0056] (2) Automatic adjustment action: For related plant areas or equipment groups, automatically increase the weight of scoring dimensions related to the type of failure (e.g., increase the weight of status monitoring for drum shaft abnormalities). The sub-dimension weights in the formula are specifically reflected in the correction factor. The coefficient was increased by 20%-30%; the threshold of the correlation correction factor was lowered by 10% (i.e., more sensitive).

[0057] (3) Adjustment scope: It only applies to the plant area or equipment group where the fault cluster occurred, and is not a global adjustment.

[0058] (4) Technical principle: When the same type of failure occurs in multiple devices in a short period of time, it often reflects a certain systemic risk (such as the quality defects of the same batch of wire ropes or the common hidden dangers of a certain type of operation process). In this case, this dimension should be given higher attention weight.

[0059] Rule 3: Automatic weight fine-tuning based on periodic calibration (data-driven) (1) Triggering condition: The system automatically performs periodic calibration on the 1st of each month.

[0060] (2) Automatic adjustment action: The system counts the total number of alarms, alarm type distribution, number of high-risk devices and other indicators of the group's total number of devices in the previous month; compares them with historical data (such as the same period last year, the average of the first three months); if the month-on-month growth rate of a certain type of alarm exceeds 30%, the weight of the corresponding scoring dimension will be increased by 5-10 percentage points; if the month-on-month decrease of a certain type of alarm exceeds 50%, the weight of the corresponding scoring dimension will be automatically adjusted back to the baseline value.

[0061] (3) Technical principle: Based on historical data-driven adaptive calibration, the evaluation model can automatically follow the changes in the risk characteristics of the equipment group, maintaining the timeliness and accuracy of the model.

[0062] Rule 4: Automatic weight adjustment based on concentrated expiration of remaining lifetime (lifecycle driven) (1) Triggering condition: The system detects that ≥30% of the equipment in a certain factory area has a remaining service life of less than 20% of the design life.

[0063] (2) Automatic adjustment action: Automatically adjust the service life-fatigue coupling correction factor of the plant area. The coefficient was increased by 20%; at the same time, the plant area was marked as a "key area of ​​concentrated life end" on the cockpit interface.

[0064] (3) Technical principle: When multiple pieces of equipment are close to the end of their service life at the same time, the overall structural safety risk of the plant area is non-linearly amplified, and it is necessary to increase the scoring weight of fatigue damage-related dimensions.

[0065] The priority and conflict prevention mechanisms of the above rules are shown in the table below: When multiple rules are triggered simultaneously, the principle of "taking the larger adjustment value but not stacking" is followed, and the severe weather warning rule has absolute priority to ensure that the safety management needs under extreme working conditions are not diluted by other rules.

[0066] The aforementioned adaptive weight adjustment mechanism enables the risk assessment model of this invention to dynamically optimize weight allocation based on changes in the external environment and internal state, avoiding the subjectivity and lag of manual settings, and significantly improving the assessment model's adaptability to complex working conditions and sudden risks.

[0067] Example 2 As attached Figure 2As shown, this embodiment provides a gantry crane full lifecycle risk assessment and management system to implement the method of Embodiment 1. The system is deployed on a group-side server, located on the subsystems of each subordinate unit, and interacts with the crane monitoring platform, health assessment system, and operation and maintenance management platform of each subordinate unit through data interfaces. The system includes a data acquisition and governance module 1, a risk assessment engine module 2, an early warning and ranking module 3, a file management module 4, a cockpit display module 5, and a report generation module.

[0068] The data acquisition and governance module 1 includes a data interface unit, a data cleaning unit, and a data standardization unit.

[0069] The data interface unit establishes data interfaces with the crane monitoring platforms of each subsidiary, collecting multi-source heterogeneous data according to the group's unified "Crane Data Dictionary". The data interface unit supports multiple communication protocols, including OPC UA, MQTT, and HTTP RESTful API, and is compatible with data formats from different vendors' systems in each subsidiary. The collected data types include real-time operating data, condition monitoring and evaluation results data, operating condition statistics data, and full lifecycle archive data. Real-time operating data includes online or offline status, real-time alarm information, and alarm levels; condition monitoring and evaluation results data includes health scores, stress spectrum coefficients, cumulative fatigue damage values, and remaining service life obtained from the health assessment system; operating condition statistics data includes monthly operating hours, cumulative work cycles, and load spectrum coefficients.

[0070] The data cleaning unit performs quality checks and cleaning on the collected raw data, specifically including format validation, missing value handling, and outlier detection. Format validation checks whether each data field conforms to the format requirements defined in the unified data dictionary, and marks data that does not meet the requirements. Missing value handling involves filling in temporarily missing data using linear interpolation, and generating a missing value report and notifying the user to fill in permanently missing data. Outlier detection identifies and marks abnormal data that exceeds a reasonable range.

[0071] The data standardization unit performs dimensional and format standardization on the cleaned data. Dimensional and format standardization specifically include: mapping alarm levels from different subordinate units to the group's standard "Critical," "General," and "Alert" levels; converting different units of measurement used by different units into standard units; and converting different timestamp formats into the group's standard time format.

[0072] After the above processing, the data acquisition and governance module 1 associates the processed data with the equipment identification information of the gantry crane, establishes a full lifecycle data view, and stores the data in the group-level data warehouse.

[0073] The risk assessment engine module 2 includes a parameter configuration unit, a correction factor calculation unit, and a comprehensive scoring unit.

[0074] The parameter configuration unit is used to receive and manage various parameters required for risk assessment. These parameters include the weighting coefficients for the basic risk score. , , and the weighting coefficients of each correction factor. , The parameter configuration unit has a built-in adaptive weight adjustment subunit. This subunit connects to the meteorological API interface, alarm event database, historical statistics database, and equipment lifespan database. It is used to execute the four adaptive weight adjustment rules described in step S7 of Embodiment 1 (severe weather warning, concentrated outbreak of fault clusters, periodic calibration, and concentrated expiration of remaining lifespan). It also includes a priority arbitrator. When multiple rules are triggered simultaneously, the final adjustment scheme is determined according to the preset priority. The parameter configuration unit provides a management interface, allowing administrators to dynamically adjust the values ​​of the above weight coefficients according to the group's safety management priorities at different times. When the administrator inputs and submits the adjustment command through the management interface, the parameter configuration unit verifies the legality of the parameter value. After confirming its legality, it stores the new parameter value in the configuration database and notifies the correction factor calculation unit to update the calculation.

[0075] The correction factor calculation unit obtains the full life cycle correction factor parameters for each gantry crane based on the full life cycle data view established by the data acquisition and management module 1, and calculates the service life-fatigue coupling correction factor according to the formula in step S4 of Example 1. Operational intensity correction factor Maintenance quality correction factor and spare parts health correction factor Among them, the cumulative fatigue damage value Calculated based on Miner's linear cumulative damage theory. When the damage exceeds the preset warning value or the remaining service life of the gantry crane is lower than the preset service life warning value, the correction factor calculation unit automatically triggers the service life warning signal and notifies the warning and ranking module 3 to generate a service life warning message.

[0076] The comprehensive scoring unit first calculates the basic risk score for each type of gantry crane based on the full lifecycle data view and the formula in step S2 of Example 1. Then, based on the basic risk score... The final risk score for each gantry crane is calculated using the four full life-cycle correction factors output by the correction factor calculation unit, according to the formula in step S5 of Example 1. The comprehensive scoring unit will calculate the final risk score for each type of gantry crane. The values ​​of each correction factor, as well as the remaining service life information obtained from the full life cycle data view, are output to the early warning and ranking module 3 and the cockpit display module 5.

[0077] The early warning and ranking module 3 is used to calculate the final risk score output by the comprehensive scoring unit. The risk levels of each gantry crane were determined and ranked. The risk level classification criteria were consistent with those in step S6 of Example 1.

[0078] When a certain gantry crane When the preset safety threshold is exceeded, the early warning and ranking module 3 immediately generates a risk warning message. The warning message includes equipment identification information, current risk score, risk level, ranking position, and a description of the main risk factors. The warning message is pushed to the person in charge of the plant area where the equipment belongs, the crane operator, and the group's safety management department through the message push service.

[0079] The early warning and ranking module 3 is also used to calculate the plant-level risk score of each plant area according to the aggregation formula in step S6 of Example 1. Based on the calculations of each factory area The system generates a risk ranking for each factory area and pushes the ranking results to the cockpit display module 5. When a certain factory area... When the value rises continuously beyond the preset trend threshold, the early warning and ranking module 3 determines that the risk of the plant area is showing a continuous deterioration trend and automatically pushes the plant area risk trend early warning message to the group's safety management department.

[0080] The document management module 4 is used to store and manage the document data of the gantry crane throughout its entire lifecycle in a structured manner, based on the equipment identification information of the gantry crane. The document data includes design documents, manufacturing and installation documents, operation and maintenance documents, and decommissioning management data.

[0081] The document management module 4 established a unified coding rule for gantry cranes across the group, namely a unified coding system. The unified code includes the equipment type code, the factory area code, the equipment serial number, and the year of commissioning, serving as a unique identifier throughout the entire lifecycle of the gantry crane. This coding is applied to all stages of crane design, manufacturing, installation, operation and maintenance, and scrapping, ensuring that data is searchable, manageable, and traceable.

[0082] The Archives Management Module 4 organizes archive data in a structured manner according to different stages of the crane's lifecycle. The Design Data area stores crane design drawings, calculation sheets, design load spectrum coefficients, design stress spectrum coefficients, design working level, rated load, design life, and other technical parameters. The Manufacturing and Installation area stores factory certificates of conformity, installation and commissioning records, initial state parameters, and acceptance reports. The Operation and Maintenance Archives area automatically synchronizes crane operating data, alarm records, health assessment reports, fault downtime records, maintenance and repair records, and spare parts usage ledgers from various subsystems. The Assessment Report area stores automatically generated risk assessment reports, health assessment reports, and remaining life prediction reports. The Decommissioning Management area stores crane scrapping applications, decommissioning assessment reports, and scrapping dates.

[0083] The document management module 4 is also used for automatic association and intelligent retrieval of full lifecycle document data based on a unified code. Through the unified equipment code, the document management module 4 automatically associates real-time operational data, evaluation data, and document data. Users can perform intelligent searches using criteria such as equipment number, equipment name, factory area, and time range to quickly obtain complete information about the crane's entire lifecycle.

[0084] The cockpit display module 5 is used to visually display the safety risk status and full life cycle information of each gantry crane to the group's management personnel.

[0085] The cockpit display module 5 generates an integrated safety and health summary interface for a single gantry crane, essentially a holographic profile of the crane's operation. This interface includes the cumulative number of alarms for the month and a comprehensive risk score. The crane's health status, monthly operating hours, cumulative work cycles, remaining service life, and values ​​of each correction factor are as follows: , , , The scoring details of operation monitoring, status monitoring, and collision avoidance warning are displayed visually in the form of radar charts or bar charts.

[0086] Users can switch to the full lifecycle archive view in the cockpit interface, which is a visualization of the full lifecycle archive. This view displays key events of the crane from design to its current status in a timeline format, including design parameters, installation and commissioning, each major maintenance, parts replacement, health assessment results, and risk score change trends.

[0087] The cockpit display module 5 is also used to display the currently effective weight configuration and the triggering reason in real time when the adaptive weight adjustment mechanism is triggered. When the system triggers the "concentrated lifespan expiration" adjustment according to rule four of step S7, the cockpit interface automatically marks the corresponding plant area as a "key area for concentrated lifespan expiration" and displays the remaining lifespan distribution map of the plant area in a prominent manner. Coefficient adjustment status.

[0088] The report generation module is used to respond to the user's one-click operation command, retrieve data from the database within a specified time range, and automatically generate an evaluation report according to a preset template.

[0089] After the user clicks the "One-Click Export Report" button through the interface of the cockpit display module 5 or the report generation module, the report generation module performs the following operations. First, based on the user-selected time range, such as this month, this quarter, or a custom time period, it retrieves the risk scores, plant area scores, ranking data, and detailed values ​​of each correction factor for each crane within that time range from the data warehouse. Then, it automatically populates the text content, data tables, and trend charts according to the group's preset report template. The generated report includes a comparison of the rankings of each plant area, details of high-risk cranes, a distribution map of the remaining service life of each crane, and a summary of key events throughout the entire life cycle. Finally, the report is output in a standard format such as PDF or DOCX.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for risk assessment and management throughout the entire life cycle of a gantry crane, characterized in that: Includes the following steps: S1. Collect multi-source heterogeneous data of gantry cranes. The multi-source heterogeneous data includes real-time operation data, condition monitoring and evaluation results data, working condition statistics data and full life cycle archive data. Perform format verification, missing value processing and unit unification on the multi-source heterogeneous data, and associate the processed data with the equipment identification information of the gantry crane to establish a full life cycle data view. S2. Calculate the basic risk score for the gantry crane based on the full lifecycle data view. Basic risk score Based on operation monitoring score Status monitoring score Collision avoidance warning score Weighted summation yields: ； in, , , These are the weighting coefficients for the operation monitoring score, condition monitoring score, and collision avoidance warning score, respectively. S3. Obtain the full life cycle correction factor parameters of the gantry crane. The full life cycle correction factor parameters include: service life. Design lifespan Cumulative fatigue damage value Damage limit value When the station is in operation this month Average monthly operating hours of similar cranes Actual load spectral coefficients Design load spectral coefficients Number of preventative maintenance Total number of maintenance times Mean Time To Repair (MTBT) Average fault repair time for similar cranes Total number of replacements of key components ; S4. Calculate the full lifecycle correction factor, including: Service life-fatigue coupling correction factor: ; Operational intensity correction factor: ; Maintenance quality correction factor: ; Spare parts health correction factor: ; in, These are the weighting coefficients; S5. Based on the basic risk score The final risk score of the gantry crane is calculated using the life-cycle correction factor. : ； S6. Based on the final risk score Risk levels of gantry cranes are assessed and ranked, and the final risk score is determined. When the preset safety threshold is exceeded, a risk warning message is generated and pushed. S7. The system monitors environmental data, equipment alarm events, historical statistical trends, and remaining lifespan distribution in real time, and automatically adjusts the weighting coefficients when preset trigger conditions are met. , , , The preset triggering conditions include severe weather warnings, concentrated outbreaks of fault clusters, periodic calibrations, and concentrated expiration of remaining lifespan. When multiple triggering conditions are met simultaneously, the adjustment range is determined according to preset priorities, with severe weather warnings having absolute priority.

2. The method according to claim 1, characterized in that: In step S1, the real-time operating data includes online / offline status, real-time alarm information, and alarm level; Condition monitoring and assessment results include health scores, stress spectrum coefficients, cumulative fatigue damage values, and remaining service life obtained from the health assessment system; Operating statistics include monthly operating hours, cumulative number of work cycles, and load spectrum coefficients; The full lifecycle archive data includes data from the design and manufacturing stage, the installation and commissioning stage, the operation and maintenance stage, and the scrapping and decommissioning stage; The unification of dimensions includes mapping all alarm levels to a unified three-level system: "Critical", "General", and "Alert".

3. The method according to claim 1, characterized in that: In step S2, the monitoring score is run. The calculation is based on the number of alarms and alarm levels for overspeed alarms, overload alarms, and over-limit alarms, with different severity penalty coefficients corresponding to different alarm levels; Status monitoring score The crane health index is mapped from the output of the lower-level health assessment system; Collision avoidance warning score Calculation of trigger counts based on the soft-leg laser collision avoidance warning system.

4. The method according to claim 1, characterized in that: In step S4, the service life-fatigue coupling correction factor In the middle, the cumulative fatigue damage value Calculated based on Miner's linear cumulative damage theory; When the cumulative fatigue damage value When the damage exceeds the preset warning value or the remaining service life of the gantry crane is lower than the preset service life warning value, a life warning message will be automatically triggered.

5. The method according to claim 1, characterized in that: In step S7, the preset triggering conditions include: when a severe weather warning with wind speeds exceeding 12 m / s is received, the collision avoidance warning weight is increased. Increase to 25%-30%, and return to normal 24 hours after the warning is lifted; When the same type of fault alarm occurs in the same factory area within a rolling 7-day time window, the weight of the scoring dimension associated with the fault type is automatically increased, and the threshold of the associated correction factor is simultaneously decreased by 10%. Periodic calibration is performed on the 1st of each month. If the month-on-month growth rate of a certain type of alarm exceeds 30%, the weight of the corresponding scoring dimension will be increased by 5-10 percentage points. If the month-on-month decrease exceeds 50%, it will be reverted to the baseline value. When ≥30% of the equipment in the plant area has a remaining service life less than 20% of its design life, the service life-fatigue coupling correction factor for that plant area will be automatically adjusted. The coefficient was increased by 20%, and the area was marked as a key area of ​​concern due to concentrated lifespan expiration. The preset priorities are: severe weather warning > concentrated outbreak of fault clusters > concentrated expiration of remaining lifespan > periodic calibration, with the adjustment range taking the larger value but not superimposed.

6. The method according to claim 1, characterized in that: In step S6, the risk level determination also includes performing aggregate calculations on multiple gantry cranes within the same factory area to obtain a factory-level risk score. : ； in, This represents the total number of gantry cranes within the factory area. For the first The final risk score for the gantry crane. For the first Number of days the gantry crane was offline in the current month. The total number of days in the month. For the first Number of serious alarms for gantry cranes in the current month These are the weighting coefficients.

7. The method according to claim 4, characterized in that: The lifespan warning message displays remaining service life information and uses a service life-fatigue coupling correction factor to amplify the effect of remaining service life on risk score. The cumulative fatigue damage term is realized.

8. A gantry crane full life cycle risk assessment and management system, used to implement the method described in any one of claims 1-7, characterized in that: include: The data acquisition and governance module (1) includes a data interface unit, a data cleaning unit and a data standardization unit, which are used to collect multi-source heterogeneous data of gantry cranes, perform format verification, missing value processing and dimension unification on the multi-source heterogeneous data, and associate the processed data with the equipment identification information of gantry cranes to establish a full life cycle data view. The risk assessment engine module (2) includes a parameter configuration unit, a correction factor calculation unit, and a comprehensive scoring unit, which are used to calculate the basic risk score based on the full life cycle data view. And obtain the full life cycle correction factor parameters, and calculate the service life-fatigue coupling correction factor. Operational intensity correction factor Maintenance quality correction factor and spare parts health correction factor Then according to the formula Calculate the final risk score for the gantry crane; The early warning and ranking module (3) is used to determine the risk score based on the final risk rating. Risk levels of gantry cranes are assessed and ranked, and the final risk score is determined. A risk warning message will be generated and pushed when the preset safety threshold is exceeded; The file management module (4) is used to store and manage the file data of the gantry crane throughout its entire life cycle in a structured manner according to the equipment identification information of the gantry crane. The file data includes design data, manufacturing and installation data, operation and maintenance files and decommissioning management data. The cockpit display module (5) is used to display the final risk score, correction factor values, remaining service life and key information of the gantry crane throughout its entire life cycle.

9. The system according to claim 8, characterized in that: The data acquisition and management module (1) establishes a data interface with the crane monitoring platform of each subordinate unit and collects data according to a unified data dictionary; The equipment identification information is a unified code, which includes the equipment type code, the factory area code, the equipment serial number, and the year of commissioning, serving as a unique identifier for the gantry crane throughout its entire life cycle; The archive management module (4) is also used to automatically associate and intelligently retrieve archive data throughout its entire lifecycle based on a unified code.

10. The system according to claim 8, characterized in that: The early warning and ranking module (3) is also used to calculate the plant-level risk score according to the aggregation formula. And generate a risk ranking for the factory area; When factory-level risk assessment When the risk level rises continuously and exceeds the preset trend threshold, a risk trend warning message for the factory area will be pushed. The system also includes a report generation module, which responds to the user's one-click operation command, retrieves data within a specified time range from the database, and automatically generates an assessment report containing risk scores, rankings, and key information throughout the entire life cycle of the gantry crane according to a preset template.

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