Data analysis method and system capable of realizing crane data perspective

Abstract

The invention discloses a data analysis method and system capable of achieving crane data perspective. The method comprises the steps that structured and unstructured data of crane operation, environment, personnel operation and the like are collected; after preprocessing, dividing into multiple dimensions; analyzing by using a multi-algorithm and multi-mode collaborative perspective mechanism, and outputting a conclusion in combination with a risk and efficiency model; and generating and adjusting an action scheme based on the working condition, the analysis conclusion and the action scheme rule base, and performing visual display. The system comprises a data acquisition module, a preprocessing module, a dimension division module, a perspective analysis module, an intelligent decision generation module and a visualization module. According to the method, deep perspective of crane data is realized, timely fault discovery is assisted, operation is optimized, and crane operation safety and efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of crane data processing technology, specifically to a data analysis method and system that enables crane data visualization. Background Technology

[0002] Cranes are key equipment in engineering construction, port transportation, and other fields. Their operation generates massive amounts of data, including workload, operating speed, location information, and fault records. This data contains crucial information about the crane's operating status, performance changes, and potential failure risks, which is of great significance for the safe operation, maintenance, and improvement of operational efficiency. However, current technologies for processing crane data are relatively simple, mostly involving only basic storage and statistical analysis, failing to enable in-depth data mining and multi-dimensional analysis. This makes it difficult for managers to fully and accurately grasp the crane's operating status, to promptly identify potential faults, and to develop scientific and reasonable maintenance plans and operational scheduling schemes, seriously affecting the crane's operational safety and efficiency. Therefore, we propose a data analysis method and system that enables crane data visualization. Summary of the Invention

[0003] The purpose of this invention is to provide a data analysis method and system that enables crane data visualization, thereby solving the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a data analysis method and system capable of realizing crane data perspective.

[0005] As a preferred embodiment of the present invention As a preferred embodiment of the present invention As a preferred embodiment of the present invention As a preferred embodiment of the present invention As a preferred embodiment of the present invention As a preferred embodiment of the present invention As a preferred embodiment of the present invention As a preferred embodiment of the present invention Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a comprehensive and accurate reflection of the crane's operating status, performance level, failure risks, and operational efficiency through multi-dimensional and in-depth analysis of crane data, combined with the collaborative processing of structured and unstructured data. Simultaneously, an intelligent decision-making generation mechanism transforms the analysis conclusions into customized action plans, achieving a closed loop from data analysis to operational execution. Managers can promptly identify potential faults based on the analysis results and decision plans, formulate scientific and reasonable maintenance plans, and optimize operational scheduling schemes, thereby improving the crane's operational safety and efficiency while reducing operating costs. Furthermore, the visualization of analysis results and decision plans allows managers to intuitively view and understand the data and suggestions, providing strong support for decision-making. Attached Figure Description

[0006] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a data analysis method for realizing crane data perspective according to the present invention; Figure 2 This is a block diagram of a data analysis system that enables crane data visualization according to the present invention. Detailed Implementation

[0007] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0008] like Figure 1-2 As shown, a data analysis method and system for realizing crane data perspective analysis is presented. The system includes a data acquisition module, a data preprocessing module, a data dimension segmentation module, a data perspective analysis module, an intelligent decision generation module, and a visualization display module. The specific settings and functions of each module are as follows: Data acquisition module This module consists of various types of sensors, camera equipment, voice recording equipment, and a data interface. Sensors are installed on key parts of the crane, such as the boom, hook, slewing mechanism, and luffing mechanism, to collect operational data including workload, speed, position, vibration, temperature, and fault records. Environmental sensors are installed around the crane's operating area and in the operator's cab to collect data on ambient temperature, humidity, wind speed, and rainfall. Motion sensors are installed on the control levers in the operator's cab to collect operator behavior data, and the operator is also equipped with status monitoring equipment to collect their status data. Camera equipment is installed at the top of the boom, near the hook, and in key locations within the operating area to collect video of the crane's operation. The voice recording equipment is installed in the operator's cab to collect operator commands and equipment operation sounds. The data interface connects to the port company's maintenance management system via a network to obtain unstructured text data such as crane fault repair reports and maintenance logs. This module transmits all collected data to the data preprocessing module in real time.

[0009] Data preprocessing module This module comprises a data processing unit, an image recognition unit, and a text processing unit. The data processing unit receives structured data from the data acquisition module, first performing data cleaning by removing noisy, missing, and abnormal data through pre-defined logical checks; then performing data conversion to uniformly convert structured data of different formats into a pre-defined standard data format; finally, performing data standardization to bring the data to a uniform scale. The image recognition unit processes the acquired operational videos, extracting key feature information such as hook status and cargo position. The text processing unit analyzes unstructured textual data such as fault repair reports and maintenance logs, extracting keywords such as fault type and repair measures, and converting the unstructured data into structured data. After processing, this module transmits all structured data to the data dimension partitioning module.

[0010] Data Pivot Analysis Module This module comprises a time-series analysis unit, a statistical analysis unit, a machine learning unit, an efficiency evaluation unit, and a multimodal collaborative analysis unit. The time-series analysis unit analyzes data from the operational status dimension, using time-series analysis algorithms to trace the crane's operational status changes over different time periods and identify abnormal operating states. The statistical analysis unit uses performance indicator data, employing statistical analysis algorithms to calculate relevant statistical parameters for various performance indicators and evaluate the crane's performance level. The machine learning unit utilizes fault diagnosis data, using machine learning algorithms to build a fault diagnosis model, predict potential crane fault risks, and locate the location and cause of faults. The efficiency evaluation unit processes operational efficiency data, using efficiency evaluation algorithms to calculate indicators such as crane operating efficiency and energy consumption efficiency, and analyzes factors affecting operational efficiency. The multimodal collaborative analysis unit correlates the structured data from each dimension with the transformed feature information from unstructured data, constructing a dual-quantitative analysis model for risk and efficiency. Combined with the identification of the crane's current operating conditions, it outputs precise insightful conclusions and transmits these conclusions to the intelligent decision generation module.

[0011] Intelligent decision generation module This module includes a rule base unit, a solution generation unit, and a dynamic adjustment unit. The rule base unit stores rules corresponding to "operating conditions, analysis conclusions, and action plans" built upon industry standards related to port crane operations, historical crane failure cases, and port operational needs. The solution generation unit receives the perspective conclusions transmitted from the data perspective analysis module, combines them with the identified current crane operating conditions, and calls the corresponding rules from the rule base unit to generate customized action plans. The dynamic adjustment unit receives updated perspective conclusions from the data perspective analysis module in real time, dynamically adjusts the generated action plans, and pushes the finalized action plans to the crane control system, port maintenance management system, and dispatch system, while also transmitting the action plans to the visualization module.

[0012] Visualization module This module connects to the data perspective analysis module and the intelligent decision generation module respectively. After receiving the analysis results and customized action plans from various dimensions, it uses charts such as line charts, bar charts, pie charts, and heat maps to visualize the analysis results and action plans, making it convenient for port managers to view and understand the relevant information intuitively.

[0013] The implementation steps of the method are as follows: (a) Data collection steps The data acquisition module activates sensors, cameras, and voice recording devices. Sensors collect real-time data on crane operation, environment, and personnel operation. Cameras capture crane operation videos, and voice recording devices record operator commands and equipment operation sounds. Unstructured text data, such as crane fault repair reports and maintenance logs, are obtained from the port maintenance management system via a data interface. All collected data is then transmitted in real-time to the data preprocessing module. (II) Data Preprocessing Steps After receiving the data, the data preprocessing module cleans, transforms, and standardizes the structured data in sequence; the image recognition unit analyzes the operation video and extracts key feature information; the text processing unit processes the unstructured text data, extracts keyword information, and transforms the unstructured data into structured data. Then, all the processed structured data is transmitted to the data dimension partitioning module. (III) Steps for dividing data dimensions The data dimension segmentation module divides the preprocessed structured data into operating status dimension, performance index dimension, fault diagnosis dimension, operational efficiency dimension, environmental impact dimension, and personnel operation dimension based on the operating characteristics and analysis requirements of the port gantry crane. After the segmentation is completed, the data of each dimension is transmitted to the data pivot analysis module. (iv) Steps for data pivot analysis Each unit of the data perspective analysis module analyzes the data in its corresponding dimension: the time series analysis unit analyzes the operational status dimension data to obtain the trend of operational status changes and abnormal states; the statistical analysis unit processes the performance index dimension data, calculates statistical parameters and evaluates performance; the machine learning unit uses the fault diagnosis dimension data to build models, predict fault risks and locate faults; the efficiency evaluation unit analyzes the operational efficiency dimension data, calculates efficiency indicators and identifies influencing factors; and the multimodal collaborative analysis unit associates multi-dimensional data and unstructured data feature information, combines risk and efficiency models and operating condition identification, outputs perspective conclusions, and transmits them to the intelligent decision generation module. (V) Intelligent Decision Generation Steps The intelligent decision generation module's solution generation unit generates customized action plans by calling rules from the rule base based on the perspective conclusions and current working conditions; the dynamic adjustment unit adjusts the plan according to the updated perspective conclusions, pushes the final plan to the relevant systems, and transmits it to the visualization display module. (vi) Visualization steps The visualization module displays the received analysis results and action plans in various chart formats for managers to view.

[0014] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0015] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A data analysis method for realizing crane data perspective, characterized in that: The methods and steps include the following: Step 1: Data Acquisition: Collect crane operation data, environmental data, personnel operation data, operation videos, operation sounds, operator instructions, and unstructured text data through sensors, camera equipment, voice recording equipment, and data interface interfaces. Step 2, Data Preprocessing: The collected structured data is cleaned, transformed, and standardized. For unstructured data, key feature information is extracted using image recognition technology and keyword information is extracted using text processing technology, thus transforming unstructured data into structured data. Step 3: Data Dimension Segmentation: Divide the preprocessed data into operational status dimension, performance index dimension, fault diagnosis dimension, operational efficiency dimension, environmental impact dimension, and personnel operation dimension. Step 4: Data Perspective Analysis: Time series analysis, statistical analysis, machine learning, and efficiency evaluation algorithms are used to analyze each dimension. At the same time, a multimodal data collaborative perspective mechanism and a dual-quantitative analysis model of risk and efficiency are constructed, and perspective conclusions are output in combination with working condition identification. Step 5: Intelligent Decision Generation: Based on the perspective conclusions and working conditions, analysis conclusions and action plan rule base, generate customized action plans and dynamically adjust them, and push them to relevant systems; Step Six: Visualization: Present the analysis results and decision-making solutions in the form of charts and graphs.

2. The data analysis method for realizing crane data perspective according to claim 1, characterized in that: The operational data mentioned in step one includes workload data, operating speed data, location information data, vibration data, temperature data, and fault record data; the environmental data includes ambient temperature and humidity, wind speed, and rainfall data; the personnel operation data includes operator operation behavior and status data; and the text-based unstructured data includes fault repair reports and maintenance log data.

3. The data analysis method for realizing crane data perspective according to claim 1, characterized in that: In step two, data cleaning is used to remove noisy, missing, and outlier data; data transformation is used to convert data in different formats into a unified format; and data standardization is used to convert data to the same magnitude.

4. The data analysis method for realizing crane data perspective according to claim 1, characterized in that: Step four, the data pivot analysis, specifically includes: Operational status dimension: Time series analysis algorithms are used to analyze the trend of operational status changes and identify abnormal operational statuses; Performance metrics dimension: Statistical analysis algorithms are used to calculate the average, maximum, minimum, and standard deviation of performance metrics to assess performance levels; Fault diagnosis dimension: A fault diagnosis model is established using machine learning algorithms to predict fault risks and locate the fault location and cause; Operational efficiency dimension: Efficiency evaluation algorithms are used to calculate operational efficiency and energy efficiency, and factors affecting operational efficiency are analyzed; Multimodal data collaborative perspective: It combines the feature information of structured and unstructured data after transformation, and outputs perspective conclusions by combining risk and efficiency dual-quantitative analysis models and working condition identification.

5. The data analysis method for realizing crane data perspective according to claim 1, characterized in that: In step five, the rule base for operating conditions, analysis conclusions, and action plans is built based on industry standards, historical failure cases, and enterprise operational needs; dynamic adjustments are achieved by receiving real-time data updates; related systems include the crane control system, maintenance management system, and dispatching system.

6. A data analysis system capable of realizing crane data perspective, applicable to the data analysis method capable of realizing crane data perspective as described in any one of claims 1-5, characterized in that: It includes a data acquisition module, a data preprocessing module, a data dimension segmentation module, a data perspective analysis module, an intelligent decision generation module, and a visualization display module; Data acquisition module: used to collect crane operation data, environmental data, personnel operation data, operation video, operation sound, operator instructions and unstructured text data, and transmit them to the data preprocessing module; Data preprocessing module: Used to process the collected data and transmit the processed data to the data dimension partitioning module; Data Dimension Segmentation Module: Used to segment data dimensions and transmit the segmented data to the data pivot analysis module; Data Pivot Analysis Module: Used to perform pivot analysis on data and output pivot conclusions to the intelligent decision generation module; Intelligent decision generation module: used to generate and dynamically adjust action plans, push them to relevant systems, and transmit the plans to the visualization module; Visualization module: Used to display analysis results and action plans.

7. A data analysis system capable of realizing crane data perspective according to claim 6, characterized in that: The data acquisition module consists of multiple sensors, cameras, voice recording devices, and data interface interfaces; the sensors are installed in key parts of the crane; the cameras and voice recording devices are installed in key areas of crane operation; and the data interface interfaces are used to connect to the enterprise's maintenance management system.

8. A data analysis system capable of realizing crane data perspective according to claim 6, characterized in that: The data preprocessing module includes an image recognition unit and a text processing unit; the image recognition unit is used to extract key feature information from the work video; the text processing unit is used to extract keyword information from unstructured text data.

9. A data analysis system capable of realizing crane data perspective according to claim 6, characterized in that: The data perspective analysis module includes a time series analysis unit, a statistical analysis unit, a machine learning unit, an efficiency evaluation unit, and a multimodal collaborative analysis unit; Time series analysis unit: used to analyze operational status dimension data to obtain operational status change trends and abnormal operational status; Statistical Analysis Unit: Used to analyze performance indicator dimension data and obtain statistical parameters of performance indicators; Machine learning unit: used to build fault diagnosis models, predict fault risks, and locate fault locations and causes; Efficiency Assessment Unit: Used to analyze operational efficiency data to obtain operational efficiency indicators and influencing factors; Multimodal collaborative analysis unit: used to correlate multi-dimensional data, build a dual-quantitative analysis model of risk and efficiency, and output insightful conclusions by combining working condition identification.

10. A data analysis system capable of realizing crane data perspective according to claim 6, characterized in that: The intelligent decision generation module includes a rule base unit, a scheme generation unit, and a dynamic adjustment unit. Rule base unit: Used to store the rules corresponding to operating conditions, analysis conclusions and action plans; Solution generation unit: used to invoke rules to generate customized action plans; Dynamic adjustment unit: Used to receive data updates in real time, adjust action plans, and push them to relevant systems.

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