Hoisting state analysis method and system of container power supply system, and storage medium

Abstract

The application relates to the technical field of hoisting control, and discloses a hoisting state analysis method and system of a container-type power supply system and a storage medium. The method is based on a hoisting system equipped with a hydraulic damping member, and attitude and vibration sensors are arranged on the container-type power supply system. After a hoisting start instruction is acquired, two types of sensor data are collected in real time, attitude percentages and vibration percentages are obtained through quantitative calculation, and the attitude percentages and the vibration percentages are fused into a state percentage. According to different reference ranges of the state percentage, a differentiated adjustment strategy is adopted to link control the damping coefficient of the hydraulic damping member and the moving speed of a hoisting cable. The method effectively offsets impact and shaking, avoids hidden damage to precise components in the container, improves hoisting safety and reliability, has strong adaptability, and reduces operation and maintenance costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hoisting control, in particular to a hoisting state analysis method and system for a container-type power system and a storage medium. BACKGROUND

[0002] Driven by the demand for emission reduction in the shipping industry, new energy ships have become the core direction of the green transformation of the shipping industry and are ushering in an opportunity for large-scale development. China has introduced a number of policies to support the research and development and application of new energy ships. New energy ships effectively solve the problems of carbon emissions and environmental pollution of traditional fuel ships with the advantages of clean and low-carbon, low noise, and controllable operating costs, and are increasingly widely used in inland shipping and offshore transportation.

[0003] The container-type power system, as the core power support unit of the new energy ship, is crucial to the endurance and running stability of the ship. It has the advantages of modular and standardized design, can realize rapid deployment and power supply, and is suitable for different power demands of different routes. For example, the ten-thousand-ton electric ship "Gezhouba" relies on the container-type lithium battery system to provide power. The system integrates high-precision batteries and power distribution components and is the core support for new energy ships to achieve zero-emission navigation. Its reliable operation directly determines the safety of ship operation and environmental benefits, and has become a key supporting technology for the large-scale promotion of new energy ships.

[0004] The hoisting of the container-type power system is a key link in shipbuilding and operation and maintenance, and the safety of the equipment needs to be strictly guaranteed. However, the traditional hoisting relies on manual operation of large cranes, which has low control accuracy and is easily affected by environmental wind load and operation coordination deviation, resulting in impact and shaking during the hoisting process. Such operation can easily cause hidden damage to the precise electrical components in the container, reducing the reliability of the power system, and even causing safety hazards. Therefore, the traditional hoisting method cannot meet the high-precision protection requirements. SUMMARY

[0005] In order to improve the safety of the container-type power system during hoisting, the present application provides a hoisting state analysis method and system for a container-type power system and a storage medium.

[0006] In the first aspect, the present application provides a hoisting state analysis method and system for a container-type power system and a storage medium, which adopts the following technical solution:

[0007] A hoisting state analysis method for a container-type power system, based on a hoisting system provided with a hoisting cable, the end of the hoisting cable being connected with a hydraulic damping piece; during hoisting, an attitude sensor and a vibration sensor are provided on the container-type power system; the method comprises the following steps:

[0008] The system receives the hoisting start command, acquires attitude data generated by the attitude sensor in real time, acquires vibration data generated by the vibration sensor in real time, and controls the hoisting cable to move at a preset first speed.

[0009] Calculate the difference between the attitude data and the preset first reference data, take the absolute value of the difference as the attitude difference, and calculate the percentage value as the attitude percentage based on the attitude difference and the preset attitude standard value; calculate the difference between the vibration data and the preset second vibration data, take the absolute value of the difference as the vibration difference, and calculate the percentage value as the vibration percentage based on the vibration difference and the preset vibration standard value.

[0010] The state percentage is calculated by fusing the attitude percentage and vibration percentage. If the state percentage is within the preset first reference range, the damping coefficient of the hydraulic damping component is adjusted negatively correlated with the first adjustment parameter according to the state percentage.

[0011] If the state percentage is within the preset second reference range, the damping coefficient of the hydraulic damper is adjusted positively relative to the second adjustment parameter according to the state percentage; the first speed is adjusted negatively relative to the state percentage; wherein the value in the second reference range is greater than the value in the first reference range, and the second adjustment parameter is less than the first adjustment parameter.

[0012] By adopting the above technical solution, attitude and vibration data during the hoisting process are collected in real time by attitude sensors and vibration sensors. The attitude percentage and vibration percentage are obtained through quantitative calculation and fused into a state percentage. Based on the different reference ranges of the state percentage, a differentiated adjustment strategy is adopted to control the damping coefficient of the hydraulic damping components and the moving speed of the hoisting cable. In the low-risk range, the damping coefficient is sensitively fine-tuned to ensure hoisting stability. In the high-risk range, the damping coefficient is adjusted smoothly and the hoisting speed is reduced to form a dual protection, effectively offsetting the impact and sway caused by environmental wind load and operational coordination deviations. This avoids hidden damage to the high-precision batteries and power distribution components inside the container. It not only improves the safety and reliability of the containerized power system hoisting, but also has the flexibility to adapt to different specifications of equipment without large-scale modification of the existing hoisting system. At the same time, it shortens the hoisting operation adjustment time and reduces the later operation and maintenance costs.

[0013] Optionally, the step of acquiring attitude data generated by the attitude sensor in real time also includes the following sub-steps:

[0014] The containerized power system is equipped with a level to acquire the level data generated by the level.

[0015] Calculate the average of the most recent multiple attitude data as the attitude average data;

[0016] Calculate the data deviation between the average attitude data and the horizontal data. If the data deviation is less than the preset reference deviation, calculate the attitude fusion data based on the attitude data and the horizontal data, and use the attitude fusion data to update the attitude data.

[0017] By adopting the above technical solution, macroscopic level data is obtained by adding a level, forming a complementary monitoring system with the real-time attitude data of the attitude sensor. At the same time, the instantaneous fluctuation error is weakened by calculating the average of multiple recent attitude data, and the effective data is screened by the data deviation threshold to ensure the rationality of the fusion. When the deviation is less than the reference deviation, the attitude data and level data are merged and updated. This not only makes up for the measurement blind spots that may exist in a single sensor, but also improves the stability and accuracy of the attitude data. It provides a more reliable data foundation for the subsequent calculation of attitude percentage and state percentage, thereby making the adjustment of hydraulic damping components and lifting speed more targeted, further reducing the adjustment deviation caused by data error, strengthening the protection of precision components in the containerized power system, and improving the safety and stability of the lifting process.

[0018] Optionally, the step of calculating the attitude fusion data based on the attitude data and the level data further includes the following sub-steps:

[0019] The algorithm used to calculate the attitude fusion data is a weighted average algorithm.

[0020] Calculate the absolute value of the difference between the most recent multiple attitude data and the average attitude data to obtain multiple attitude difference data, and calculate the sum of the multiple attitude difference data to obtain the fluctuation sum data;

[0021] The ratio of the fluctuation sum data to the attitude mean data is used as a reference ratio, and the weight of the level data is adjusted according to the positive correlation of the reference ratio.

[0022] By adopting the above technical solution, the fluctuation of attitude difference data is calculated and compared with the reference value to quantitatively reflect the degree of fluctuation of recent attitude data. The weight of horizontal data is adjusted according to the positive correlation of the reference ratio: when the attitude data fluctuates greatly (high reference ratio), the weight of horizontal data as a macroscopic stable benchmark is increased to weaken the interference of fluctuating data on the fusion result; when the attitude data fluctuates little (low reference ratio), the weight of horizontal data is reduced to highlight the microscopic dynamic response advantage of attitude data.

[0023] Optionally, the step of calculating the state percentage based on the fusion of attitude percentage and vibration percentage also includes the following sub-steps:

[0024] The algorithm for calculating the percentage of states by fusion is a weighted average algorithm;

[0025] Acquire multiple recent vibration data points, and calculate the fluctuation value of the multiple vibration data points as vibration fluctuation data based on a preset fluctuation algorithm;

[0026] If the vibration fluctuation data is greater than the preset fluctuation warning data, the ratio of the vibration fluctuation data to the fluctuation warning data is calculated as the vibration comparison data, and the weight of the attitude data is adjusted according to the positive correlation of the vibration comparison data.

[0027] By adopting the above technical solution, a weighted average algorithm is used in conjunction with dynamic adjustment of vibration fluctuation weights. The stability of vibration data is judged by calculating vibration fluctuation data. When the vibration fluctuation exceeds the warning level, the weight of attitude data is increased according to the positive correlation of vibration comparison data, which weakens the interference of fluctuation vibration data, makes the state percentage calculation more in line with the actual hoisting risk, and improves the accuracy of state assessment.

[0028] Optionally, the step of calculating the state percentage based on the fusion of attitude percentage and vibration percentage also includes the following sub-steps:

[0029] Acquire multiple recent vibration data points, and calculate the fluctuation value of the multiple vibration data points as vibration fluctuation data based on a preset fluctuation algorithm;

[0030] Acquire multiple recent attitude data points, and calculate the fluctuation value of the multiple attitude data points as attitude fluctuation data based on the fluctuation algorithm;

[0031] Obtain multiple recent horizontal data points, and calculate the fluctuation value of these multiple horizontal data points as horizontal fluctuation data based on a fluctuation algorithm;

[0032] If the vibration fluctuation data is greater than the preset fluctuation warning data, the attitude fluctuation data is greater than the preset attitude warning data, and the horizontal fluctuation data is greater than the preset horizontal warning data, then a sensor alarm will be triggered.

[0033] In response to sensor alarm prompts, the damping coefficient of the hydraulic damping components is kept constant with the initial speed of the hoisting cable until the hoisting is completed.

[0034] By adopting the above technical solution, and by simultaneously monitoring the fluctuation values ​​of three types of data—vibration, attitude, and level—and setting up a collaborative early warning mechanism, when the fluctuations of all three exceed the preset warning, the sensor alarm is triggered. Furthermore, the hydraulic damping coefficient and hoisting speed are locked before the hoisting is completed. This effectively avoids the risk of secondary impact caused by erroneous adjustment operations due to abnormal multi-source data, and can also promptly prompt staff to check for sensor malfunctions or abnormal hoisting conditions, further ensuring the safety and stability of the hoisting process.

[0035] Optionally, the method further includes the following steps:

[0036] Select multiple attitude percentages and multiple vibration percentages corresponding to the most recent successful hoisting processes;

[0037] Calculate the average of multiple attitude percentages as the attitude reference value, and calculate the average of multiple vibration percentages as the vibration reference value;

[0038] The ratio of the current attitude percentage to the attitude reference value is used as the attitude comparison value, and the ratio of the current vibration percentage to the vibration reference value is used as the vibration comparison value.

[0039] The ratio between the attitude contrast value and the vibration contrast value is calculated as the relative ratio, where relative ratio = attitude contrast value / vibration contrast value;

[0040] If the relative ratio is less than the preset first comparison value or greater than the preset second comparison value, a hoisting abnormality alarm will be issued and hoisting will be stopped; wherein, the first comparison value is less than the second comparison value.

[0041] By adopting the above technical solution, using the average value of the posture and vibration percentage of the most recent successful hoisting operations as a reliable benchmark, and by calculating the relative ratio of the current data to the benchmark and setting dual threshold judgments, when the relative ratio exceeds the preset range, an alarm for hoisting abnormality is immediately issued and hoisting is stopped. This can accurately identify serious deviations between the current hoisting status and the standard working conditions, promptly cut off risky operation processes, effectively avoid hidden equipment damage or safety accidents caused by abnormal parameters, and further enhance the risk prevention and control capabilities of hoisting operations.

[0042] Optionally, the method further includes the following steps:

[0043] The containerized power system is equipped with a wind sensor to acquire the latest wind data generated by the wind sensor.

[0044] The average of multiple wind force data points is the wind force average data.

[0045] If the average wind force data is greater than the preset wind force reference data, the hoisting is stopped; otherwise, the ratio of the average wind force data to the wind force reference data is calculated as the wind force calculation parameter, and the first comparison value is adjusted according to the negative correlation of the wind force calculation parameter, and / or the second comparison value is adjusted according to the positive correlation of the wind force calculation parameter.

[0046] By adopting the above technical solution, average wind force data is collected and calculated using wind sensors. When the wind force exceeds the preset reference value, the hoisting is stopped directly, thus avoiding the risk of hoisting impact and swaying caused by strong winds from the source. Within the safe wind range, the dual thresholds for judging hoisting anomalies are dynamically adjusted according to the wind force calculation parameters, making the anomaly alarm standards more in line with real-time environmental conditions. This effectively avoids the problem of false alarms or missed alarms caused by fixed thresholds under different wind conditions, further improving the accuracy and environmental adaptability of hoisting risk judgment.

[0047] Optionally, the method further includes the following steps:

[0048] Wind force curves are fitted based on multiple wind force data, and vibration curves are fitted based on multiple vibration data.

[0049] Calculate the graphical similarity between the wind force curve and the vibration curve;

[0050] If the graphic similarity is greater than the preset reference similarity, then the graphic calculation parameters are calculated based on the graphic similarity and the reference similarity, and the weight of the vibration percentage is adjusted according to the negative correlation of the graphic calculation parameters.

[0051] By adopting the above technical solution, the wind force curve and vibration curve are overfitted and their graphical similarity is calculated to accurately determine the dominant cause of vibration. When the similarity is higher than the preset reference value, it indicates that the vibration is mainly caused by wind. At this time, the weight of the vibration percentage is adjusted negatively, which can effectively weaken the excessive interference of vibration data on the state percentage calculation under the dominance of wind, making the state assessment more consistent with the actual operating conditions of the hoisting system, improving the pertinence and rationality of subsequent adjustment strategies, and further ensuring the stability and safety of the hoisting process.

[0052] Secondly, this application provides a hoisting status analysis system for a containerized power system, employing the following technical solution:

[0053] A hoisting status analysis system for a containerized power system includes a processor, wherein the processor executes the steps of the hoisting status analysis method for a containerized power system as described in any of the preceding claims.

[0054] Thirdly, this application provides a storage medium, which adopts the following technical solution:

[0055] A storage medium storing a program, which, when executed by a processor, implements the steps of the hoisting status analysis method for the containerized power system described in any one of the preceding claims.

[0056] In summary, this application includes at least one of the following beneficial technical effects:

[0057] It enables precise quantitative assessment and dynamic adjustment of the hoisting status. By collecting data in real time through attitude sensors and vibration sensors and fusing it into a status percentage, the damping coefficient of the hydraulic damping component and the hoisting speed are adjusted in linkage based on different risk ranges. This effectively counteracts the impact and sway caused by environmental wind loads and operational deviations, avoids hidden damage to precision components in the containerized power system, and significantly improves the stability and safety of the hoisting process.

[0058] A multi-source data fusion and dynamic weight adjustment mechanism is constructed. Combined with level instruments to supplement macroscopic benchmark data, the calculation of attitude fusion data and state percentage is optimized through weighted average algorithm and dynamic weight allocation strategy. At the same time, the weights are adjusted according to vibration fluctuations and the correlation between wind and vibration, which effectively weakens the interference of instantaneous data fluctuations and environmental factors, improves data accuracy and the rationality of state assessment, and provides reliable support for adjustment strategies.

[0059] Establish a multi-level, comprehensive risk warning and prevention system, and set up a collaborative alarm mechanism for vibration, attitude, and level data to avoid erroneous adjustments when multiple data sources are abnormal; establish a reference benchmark based on historical successful hoisting data, judge hoisting anomalies through relative ratios and stop operations in a timely manner; combine wind sensors to achieve rigid prevention and control of direct work stoppage in strong winds, further ensuring the safety of hoisting operations and effectively avoiding equipment damage and safety accidents.

[0060] This method enhances the environmental adaptability and operational condition suitability of hoisting solutions. It dynamically adjusts the threshold range for hoisting anomaly judgment based on real-time average wind data. By fitting the similarity between wind curves and vibration curves and adjusting the vibration percentage weight, the condition assessment and adjustment strategies are made more consistent with actual environmental conditions. This avoids false alarms and missed alarms caused by fixed parameters under different conditions. At the same time, it does not require large-scale modification of existing hoisting systems and has strong versatility and scalability.

[0061] This indirectly reduces the construction and maintenance costs of new energy ships by improving the lifting safety of containerized power systems, extending their service life and operational reliability, and reducing subsequent maintenance and replacement costs; at the same time, it improves lifting operation efficiency and reduces reliance on manual operation experience. Attached Figure Description

[0062] Figure 1 This is a step diagram of a method for analyzing the hoisting status of a containerized power system.

[0063] Figure 2 This is a diagram of the sub-steps for acquiring attitude data generated by the attitude sensor in real time. Detailed Implementation

[0064] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0065] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0066] This application discloses a method for analyzing the lifting status of a containerized power system. This method is based on a dedicated lifting system for lifting operations. The lifting system includes at least a crane body, a lifting cable, and a hydraulic damping component. One end of the lifting cable is connected to the lifting mechanism of the crane body, and the other end is fixedly connected to the upper end of the hydraulic damping component. The lower end of the hydraulic damping component is detachably connected to a pre-set lifting lug on the top of the containerized power system. The function of the hydraulic damping component is to buffer the impact and shaking caused by environmental wind load and operational coordination deviation during the lifting process. To achieve real-time, high-precision monitoring of the hoisting status, an attitude sensor and a vibration sensor are fixedly installed on a sensor mounting base pre-set on the top of the containerized power system. In this embodiment, the attitude sensor is preferably a MEMS inertial measurement unit, which can collect core attitude parameters such as pitch angle and roll angle of the containerized power system in real time, with a collection frequency of not less than 100Hz. The vibration sensor is preferably a piezoelectric triaxial vibration sensor, which can collect vibration acceleration data of the containerized power system in the X, Y, and Z directions in real time, with the collection frequency consistent with that of the attitude sensor to ensure data synchronization and continuity.

[0067] Reference Figure 1 The method for analyzing the hoisting status of the containerized power system in this embodiment specifically includes the following steps:

[0068] The operator issues a lifting start command via the lifting control console. Upon receiving this command, the control unit of the lifting system immediately activates the attitude sensor and vibration sensor, putting them into real-time data acquisition mode. Simultaneously, it outputs control signals to the hoisting mechanism of the crane body, driving the lifting cable to move at a preset first speed. In this embodiment, the preset first speed is an exemplary parameter and can be preset according to the tonnage of the containerized power system, lifting height, and other working conditions. A preferred value is 0.5 m / s, but those skilled in the art can adjust it according to actual needs, and it is not limited to this. Throughout the lifting operation, the control unit continuously receives real-time attitude data generated by the attitude sensor and real-time vibration data generated by the vibration sensor, and temporarily stores them in a built-in cache module.

[0069] The control unit retrieves real-time attitude data from the cache module and calculates the difference between it and a preset first reference data. The first reference data represents the ideal attitude parameters for the hoisting operation of the containerized power system. In this embodiment, it is exemplarily set to a pitch angle of 0° and a roll angle of 0°. After the calculation, the absolute value of the difference is taken as the attitude difference. The attitude difference is then compared with a preset attitude standard value, and the result is multiplied by 100% to obtain the attitude percentage. The attitude standard value is the maximum allowable attitude deviation during the hoisting of the containerized power system. In this embodiment, it is exemplarily set to a pitch angle of 3° and a roll angle of 3°. The magnitude of the attitude percentage is positively correlated with the degree of hoisting danger; that is, the larger the attitude percentage, the more severe the attitude deviation of the containerized power system, and the higher the risk of the hoisting operation. Similarly, the control unit retrieves real-time vibration data and calculates the difference between it and a preset second reference data. The second reference data is a baseline value under vibration-free conditions, which is set to 0 m / s² in this embodiment. After the calculation, the absolute value of the difference is taken as the vibration difference. The vibration difference is then compared with a preset vibration standard value, and the result is multiplied by 100% to obtain the vibration percentage. The vibration standard value is the maximum permissible vibration acceleration value during the hoisting of the containerized power system, which is set to 5 m / s² in this embodiment. The magnitude of the vibration percentage is also positively correlated with the degree of danger during hoisting.

[0070] The control unit fuses the calculated attitude percentage and vibration percentage to obtain a state percentage that comprehensively reflects the overall risk level of the hoisting operation. In this embodiment, a weighted average algorithm is preferably used for the fusion calculation. The default weights for both attitude percentage and vibration percentage are set to 0.5. Those skilled in the art can adjust the weight allocation according to actual hoisting needs and are not limited to this. The control unit pre-stores two independent reference ranges: a first reference range and a second reference range. All values ​​in the second reference range are greater than those in the first reference range. In this embodiment, the first reference range is set to 0-30%, representing a low-risk hoisting range, and the second reference range is set to 31%-100%, representing a high-risk hoisting range. Simultaneously, the control unit also pre-stores two adjustment parameters: a first adjustment parameter and a second adjustment parameter. The value of the second adjustment parameter is less than the value of the first adjustment parameter. In this embodiment, the first adjustment parameter is set to 0.1 and the second adjustment parameter to 0.05.

[0071] If the control unit determines that the currently calculated state percentage is within the preset first reference range, it indicates that the current hoisting operation is in a low-risk range. At this time, the control unit adjusts the damping coefficient of the hydraulic damping component according to the state percentage with a negative correlation to the first adjustment parameter. That is, the larger the state percentage value, the smaller the damping coefficient of the hydraulic damping component. In this embodiment, the adjustment formula is given as an example: Damping coefficient = Initial damping coefficient - State percentage × First adjustment parameter, where the initial damping coefficient is set to 500 N·s / m for example. Through this sensitive fine-tuning adjustment method, slight impacts and swaying can be offset in real time without affecting the hoisting operation efficiency, ensuring the stability of the hoisting process. If the control unit determines that the currently calculated state percentage is within the preset second reference range, it indicates that the current hoisting operation is in a high-risk zone. At this time, the control unit executes a dual protection adjustment strategy: On the one hand, it adjusts the damping coefficient of the hydraulic damping component positively correlated with the second adjustment parameter according to the state percentage, that is, the larger the value of the state percentage, the larger the damping coefficient of the hydraulic damping component; in this embodiment, the adjustment formula is given as follows: Damping coefficient = Initial damping coefficient + State percentage × Second adjustment parameter. By increasing the damping coefficient, the large sway of the containerized power system is quickly suppressed, and the impact load is reduced. On the other hand, it adjusts the first speed of the hoisting cable negatively correlated with the state percentage, that is, the larger the value of the state percentage, the slower the moving speed of the hoisting cable; in this embodiment, the adjustment formula is given as follows: First speed = Initial first speed - State percentage × Speed ​​adjustment parameter, where the speed adjustment parameter is set to 0.01 for example. By reducing the hoisting speed, sufficient response time is provided for the adjustment of the damping coefficient, further improving the stability of the hoisting process.

[0072] Reference Figure 2 To further improve the accuracy and stability of attitude data, this application also provides an optional technical solution, which further includes the following sub-steps in the above-mentioned step of acquiring attitude data generated by the attitude sensor in real time:

[0073] On the top of the containerized power system, adjacent to the attitude sensor, a dual-axis electronic level is additionally fixed. While collecting attitude data, the control unit simultaneously acquires macroscopic level data generated by the level. The control unit retrieves multiple recent attitude data points from the cache module; in this embodiment, the five most recent attitude data points are exemplarily set, but those skilled in the art can preset it to 3-10 based on actual needs. The arithmetic mean of these data points is calculated as the attitude average data, effectively mitigating instantaneous fluctuation errors in the attitude data. The control unit calculates the data deviation between the attitude average data and the level data, using the absolute value of this deviation as a judgment criterion. If this deviation is less than a preset reference deviation (exemplarily set to 0.5° in this embodiment), it indicates a high degree of consistency between the current attitude sensor and the level measurement data. At this point, the control unit calculates attitude fusion data based on the real-time attitude data and level data, and uses this attitude fusion data to update the real-time attitude data in the cache module. This optional technical solution enables the macroscopic level data from the level instrument and the microscopic dynamic attitude data from the attitude sensor to form a complementary monitoring system. This not only compensates for the measurement blind spots that may exist in a single sensor, but also improves the stability and accuracy of the attitude data. As a result, the adjustment of hydraulic damping components and hoisting speed can be more targeted, further reducing the adjustment deviation caused by data errors.

[0074] To further optimize the computational effect of attitude fusion data, this application provides a more preferred optional sub-step in the above-mentioned step of calculating attitude fusion data based on attitude data and level data:

[0075] The algorithm for fusing and calculating the attitude fusion data is a weighted average algorithm, ensuring the simplicity and feasibility of the calculation process. The control unit retrieves multiple recent attitude data from the cache module, calculates the absolute value of the difference between each attitude data and the average attitude data, obtaining multiple attitude difference data. These multiple attitude difference data are then summed to obtain the fluctuation sum data, which can intuitively reflect the degree of fluctuation of the recent attitude data. The control unit calculates the ratio of the fluctuation sum data to the average attitude data to obtain a reference ratio, and adjusts the weight of the horizontal data in the weighted average algorithm according to the positive correlation of the reference ratio. Specifically, when the degree of fluctuation of the attitude data is large, that is, when the value of the reference ratio is high, the control unit automatically increases the weight of the horizontal data, which is a macroscopically stable benchmark, to weaken the interference of the large-scale attitude data on the fusion result; when the degree of fluctuation of the attitude data is small, that is, when the value of the reference ratio is low, the control unit automatically decreases the weight of the horizontal data to highlight the advantages of the microscopic dynamic response of the attitude data. Through this optional sub-step, the limitations of the fixed-weight fusion algorithm can be effectively avoided, and the accuracy and adaptability of the attitude fusion data can be further improved.

[0076] To further improve the accuracy of the state percentage calculation and make it more closely reflect the actual risk situation of hoisting operations, this application also provides an optional sub-step in the above-mentioned step of calculating the state percentage based on the fusion of attitude percentage and vibration percentage:

[0077] The algorithm for calculating the state percentage is a weighted average algorithm. The control unit retrieves multiple recent vibration data points from the cache module; in this embodiment, the five most recent vibration data points are exemplarily set. Based on a preset fluctuation algorithm, the fluctuation value of these multiple vibration data points is calculated to obtain vibration fluctuation data. The preset fluctuation algorithm can preferably be an average difference algorithm or a variance algorithm; those skilled in the art can choose according to actual needs and are not limited to this. The control unit compares the calculated vibration fluctuation data with preset fluctuation warning data. If the vibration fluctuation data is greater than the preset fluctuation warning data, it indicates that the current vibration data has poor stability. At this time, the control unit calculates the ratio of the vibration fluctuation data to the fluctuation warning data to obtain vibration comparison data, and adjusts the weight of attitude data in the weighted average algorithm based on the positive correlation of this vibration comparison data. Specifically, the control unit automatically increases the weight of attitude data and correspondingly decreases the weight of vibration data, weakening the interference of unstable vibration data on the state percentage calculation result, making the state percentage calculation result more consistent with the actual risk situation of hoisting operations, thereby improving the accuracy of state assessment.

[0078] In another embodiment, this application provides a different technical solution for the step of calculating the state percentage based on the fusion of attitude percentage and vibration percentage, in order to further improve the safety and stability of hoisting operations. Specifically, it includes the following sub-steps:

[0079] The control unit retrieves multiple recent vibration data points from the cache module and calculates their fluctuation values ​​based on a preset fluctuation algorithm, such as the mean difference algorithm or variance algorithm, to obtain vibration fluctuation data. The control unit also retrieves multiple recent attitude data points from the cache module and calculates their fluctuation values ​​based on the same fluctuation algorithm, to obtain attitude fluctuation data. Finally, the control unit retrieves multiple recent horizontal data points from the cache module and calculates their fluctuation values ​​based on the same fluctuation algorithm, to obtain horizontal fluctuation data. The control unit compares the vibration fluctuation data, attitude fluctuation data, and horizontal fluctuation data with preset fluctuation warning data, attitude warning data, and horizontal warning data, respectively. If the vibration fluctuation data is greater than the preset fluctuation warning data, the attitude fluctuation data is greater than the preset attitude warning data, and the horizontal fluctuation data is greater than the preset horizontal warning data, it indicates that all multi-source monitoring data are abnormal, potentially indicating a sensor safety issue. In the event of faults such as loose mounting base, damaged sensors, or abnormal tilting of the containerized power system, the control unit immediately triggers a sensor alarm. This alarm is preferably an audible and visual alarm on the hoisting control console, which simultaneously displays possible causes of the fault on the console's display interface for quick troubleshooting by operators. Responding to the sensor alarm, the control unit maintains the damping coefficient of the hydraulic damping components and the initial speed of the hoisting cable at their current values ​​until the hoisting operation is completed. To ensure the safety of the hoisting operation, the current damping coefficient should be within a relatively large range (800~1200 N·s / m), and the current initial speed should be within a relatively small range (0.1~0.3 m / s). This effectively avoids erroneous adjustments caused by abnormal multi-source data, thereby preventing the risk of secondary impacts. It also promptly alerts personnel to check for sensor malfunctions or abnormal hoisting conditions, further ensuring the safety and stability of the hoisting process.

[0080] To further enhance the risk prevention and control capabilities of hoisting operations and promptly identify and shut down risky operational processes, this application also provides an optional technical solution, which, based on the above method, further includes the following steps:

[0081] The control unit selects multiple attitude percentages and multiple vibration percentages corresponding to the most recent successful lifting operations from the historical database. The number of most recent operations can be preset according to actual needs; in this embodiment, it is set to the most recent 10 successful lifting operations, but those skilled in the art can adjust it to 10-20, and it is not limited to this. The control unit calculates the arithmetic mean of these multiple attitude percentages and uses it as an attitude reference value. Simultaneously, it calculates the arithmetic mean of these multiple vibration percentages and uses it as a vibration reference value. These attitude and vibration reference values ​​serve as standard operating condition benchmarks for lifting operations, possessing high reliability. The control unit calculates the ratio of the current lifting operation's attitude percentage to the attitude reference value to obtain an attitude comparison value, and simultaneously calculates the ratio of the current lifting operation's vibration percentage to the vibration parameter. The ratio of the attitude comparison value to the vibration comparison value is used to obtain the vibration comparison value. The control unit calculates the ratio between the attitude comparison value and the vibration comparison value to obtain the relative ratio value, which is calculated as: Relative Ratio Value = Attitude Comparison Value / Vibration Comparison Value. The control unit compares this relative ratio value with a preset first comparison value and a preset second comparison value, where the first comparison value is less than the second comparison value. In this embodiment, the first comparison value is set to 0.5 and the second comparison value to 2.0. If the relative ratio value is less than the preset first comparison value or greater than the preset second comparison value, it indicates that the current hoisting state deviates significantly from the standard operating conditions. At this time, the control unit immediately issues a hoisting abnormality alarm. This alarm can preferably be an audible and visual alarm on the hoisting control console, and the control unit stops or suspends the hoisting operation, waiting for the operator to investigate the cause of the fault. Through this optional technical solution, the significant deviation between the current hoisting state and the standard operating conditions can be accurately identified, and risky operation processes can be promptly cut off, effectively avoiding hidden equipment damage or safety accidents caused by abnormal parameters, and further strengthening the risk prevention and control capabilities of hoisting operations.

[0082] By adopting the above technical solutions, this application can achieve precise quantitative assessment and hierarchical dynamic adjustment of the hoisting status, effectively offsetting the impact and shaking caused by environmental wind loads and operational coordination deviations, and avoiding hidden damage to high-precision batteries and power distribution components inside the container. At the same time, through multi-source data fusion, dynamic weight adjustment, and multi-level risk early warning technologies, the accuracy of data, the rationality of status assessment, and the safety of hoisting operations are further improved. In addition, this method does not require large-scale modification of the existing hoisting system; it can be deployed simply by adding sensors and control modules. It has flexible adaptability to different specifications of containerized power systems, which can significantly shorten the adjustment time of hoisting operations, reduce the subsequent equipment maintenance costs, and improve the safety and reliability of containerized power system hoisting.

[0083] To further improve the environmental adaptability and risk assessment accuracy of hoisting operations, and to meet the hoisting safety requirements under different wind conditions, this application also provides a dynamic threshold adjustment technology solution combined with wind monitoring as an optional step of the above method, specifically including the following:

[0084] A wind sensor is additionally fixed in the sensor installation area pre-designed on the top of the containerized power system. This wind sensor is preferably an ultrasonic anemometer, with its sampling direction facing the prevailing wind direction of the hoisting operation. It can collect instantaneous wind speed data in the environment in real time, and the acquisition frequency is consistent with that of the attitude and vibration sensors (not less than 100Hz) to ensure the time synchronization of wind data with attitude and vibration data. During the hoisting operation, the control unit continuously receives real-time wind data generated by the wind sensor and temporarily stores it in a cache module. When this optional step needs to be executed, the control unit retrieves multiple recently generated wind data points from the cache module. In this embodiment, the most recent 20 wind data points are retrieved. Those skilled in the art can adjust the number of data points (e.g., 15-30) according to the rate of wind change to accurately reflect the current stable state of the environmental wind.

[0085] The control unit performs an arithmetic average calculation on multiple retrieved wind force data points to obtain average wind force data. This data effectively reduces the interference of instantaneous gusts on wind force judgment and more closely reflects the stable wind conditions of the actual hoisting environment. The control unit pre-stores wind force reference data, which is set based on the hoisting safety standards of the containerized power system. An example setting is 10.8 m / s (corresponding to level 6 wind). That is, when the ambient wind force reaches or exceeds this value, uncontrollable impacts and swaying are likely to occur during hoisting due to strong winds, exceeding the buffering capacity of the hydraulic damping components. The control unit compares the calculated average wind force data with the preset wind force reference data. If the average wind force data is greater than the preset wind force reference data, it indicates that the current ambient wind force has exceeded the safe hoisting threshold. The control unit immediately outputs a braking signal to the hoisting mechanism of the crane body to control the hoisting cable to stop moving. At the same time, it issues a strong wind warning alarm, such as a red audible and visual alarm on the hoisting control console, to avoid hoisting risks caused by strong winds from the source. If the average wind force data is less than or equal to the preset wind force reference data, it indicates that the current ambient wind force is within the safe hoisting range. At this time, the control unit calculates the ratio of the average wind force data to the wind force reference data to obtain the wind force calculation parameter, which has a value range of 0 to 1.

[0086] Based on the aforementioned wind force calculation parameters, the control unit dynamically adjusts the hoisting anomaly judgment threshold: On one hand, it adjusts the first comparison value according to the negative correlation of the wind force calculation parameters, that is, the larger the wind force calculation parameters, the closer the current wind force is to the safety threshold, and the larger the value of the first comparison value. In this embodiment, an exemplary adjustment formula is given: Adjusted first comparison value = Initial first comparison value × (1 + Wind force calculation parameter × 0.5), where the initial first comparison value is 0.5. If the wind force calculation parameter is 0.8, that is, the average wind force data is 8.64 m / s, then the adjusted first comparison value = 0.5 × (1 + 0.8 × 0.5) = 0.7, making the anomaly... The lower threshold for judgment is raised to reduce false alarms under conditions of low attitude deviation but high wind force. On the other hand, the second comparison value is adjusted according to the positive correlation with the wind force calculation parameter. That is, the larger the wind force calculation parameter, the smaller the value of the second comparison value. An exemplary adjustment formula is: adjusted second comparison value = initial second comparison value × (1 - wind force calculation parameter × 0.3), where the initial second comparison value is 2.0. If the wind force calculation parameter is 0.8, then the adjusted second comparison value = 2.0 × (1 - 0.8 × 0.3) = 1.52, which lowers the upper threshold for anomaly judgment, enhances the sensitivity to abnormal attitude / vibration under high wind force conditions, and avoids missed alarms. It should be noted that the coefficients (0.5, 0.3) in the above adjustment formula are exemplary settings. Those skilled in the art can adjust them according to the wind resistance of different specifications of containerized power systems and the buffer performance of hoisting systems. This application does not limit this. At the same time, the control unit can choose to adjust only the first comparison value, only the second comparison value, or both at the same time. The specific adjustment method can be preset through the parameter configuration interface of the hoisting system.

[0087] To accurately identify the dominant causes of vibration and avoid excessive interference from vibrations caused by environmental wind forces in the assessment of hoisting status, this application also provides a dynamic weight adjustment technique based on curve similarity analysis as another optional step of the above method, specifically including the following:

[0088] The control unit retrieves multiple wind and vibration data points stored in the above steps from the cache module. The timestamps of the wind and vibration data correspond one-to-one, ensuring consistency in the time dimension of the curve fitting. The control unit uses a preset curve fitting algorithm to fit the two types of data separately: for wind data, a time-wind curve (referred to as the wind curve) is fitted with time as the x-axis and wind speed as the y-axis; for vibration data, the vibration acceleration data in the horizontal direction (perpendicular to the hoisting cable axis) most relevant to the wind force is selected, and a time-vibration curve (referred to as the vibration curve) is fitted with time as the x-axis and vibration acceleration value as the y-axis. In this embodiment, the curve fitting algorithm is preferably the least squares method, and the fitted curve is a continuous and smooth polynomial curve. Those skilled in the art can also choose other conventional fitting algorithms such as spline interpolation or moving average, as long as they can reflect the trend of data change.

[0089] The control unit calculates the graphic similarity between the wind curve and the vibration curve based on a preset graphic similarity algorithm. This similarity is used to quantify the consistency of the changing trends of the two types of curves. In this embodiment, the graphic similarity algorithm is preferably the Dynamic Time Warping (DTW) algorithm. By aligning the time axes of the two curves, the minimum cumulative distance between the curves is calculated, and then normalized to a similarity value between 0 and 1. The smaller the minimum cumulative distance, the closer the similarity value is to 1, indicating that the changing trends of the curves are more consistent. Those skilled in the art can also choose cosine similarity algorithms, mean square error ratio algorithms, etc. For example, when using the cosine similarity algorithm, the discrete data points of the two curves are regarded as vectors, and the cosine value of the angle between the vectors is calculated as the graphic similarity. The closer the cosine value is to 1, the higher the similarity. The control unit pre-stores a reference similarity value, which is calibrated based on a large amount of hoisting test data. For example, it is set to 0.8, which means that when the consistency of the changing trends of the two curves reaches 80% or more, it can be determined that the vibration is mainly caused by the environmental wind.

[0090] If the graphic similarity calculated by the control unit is greater than the preset reference similarity, it indicates that the vibration of the current containerized power system is mainly affected by the ambient wind force, rather than by the operation deviation of the hoisting system itself or equipment failure. At this time, the control unit calculates the graphic calculation parameter, which is (graphic similarity - reference similarity) / (1 - reference similarity), with a value range of 0 to 1, and is used to quantify the degree of dominance of wind force on vibration. The control unit adjusts the weight of the vibration percentage in the state percentage fusion calculation based on the negative correlation of the graphic calculation parameters: the larger the graphic calculation parameters, the higher the degree of wind-dominated vibration, and the lower the weight of the vibration percentage. An example adjustment formula is given: the adjusted vibration weight = the initial vibration weight × (1 - graphic calculation parameters × 0.6), where the initial vibration weight is 0.5. If the graphic similarity is 0.9 (the reference similarity is 0.8), then the graphic calculation parameters = (0.9-0.8) / (1-0.8) = 0.5, the adjusted vibration weight = 0.5 × (1-0.5 × 0.6) = 0.35, and correspondingly, the weight of the attitude percentage is automatically adjusted to 1-0.35 = 0.65. This dynamic weight adjustment effectively reduces the excessive interference of vibration data under wind-dominated conditions on the state percentage calculation, avoids unnecessary damping adjustments or speed reduction operations by the hoisting system due to normal vibrations caused by wind, makes the state assessment more consistent with the actual operating conditions of the hoisting system, improves the pertinence and rationality of subsequent adjustment strategies, and further ensures the stability and safety of the hoisting process.

[0091] It should be noted that the sensor selection, data acquisition quantity, and preset parameter values ​​(such as wind reference data, reference similarity, adjustment parameters, etc.) in the above optional steps are all exemplary settings. Those skilled in the art can make adaptive adjustments based on the tonnage, size, wind resistance performance of the containerized power system, and the specific scenario of the lifting operation (inland waterway shipping, coastal shipping, etc.), as long as the technical effects described in this application can be achieved, and are not limited to the specific values ​​disclosed in this embodiment. At the same time, the above optional steps can be implemented individually or in combination with the basic method to form a multi-dimensional and comprehensive lifting safety protection system, further improving the safety, accuracy, and environmental adaptability of lifting operations, providing more comprehensive technical support for the reliable lifting of containerized power systems, and helping the large-scale application of new energy ships and the green transformation of the shipping industry.

[0092] This application also discloses a hoisting status analysis system for a containerized power system, including a processor, wherein the processor executes the steps of the hoisting status analysis method for a containerized power system as described in any of the above embodiments.

[0093] This application also discloses a storage medium storing a program, which, when executed by a processor, implements the steps of the hoisting status analysis method for the containerized power system described in any of the above embodiments.

[0094] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for analyzing the hoisting status of a containerized power system, characterized in that, The lifting operation is carried out using a lifting system equipped with lifting cables, the ends of which are connected to hydraulic damping components. During lifting, the containerized power system is equipped with attitude sensors and vibration sensors. The method includes the following steps: The system receives the hoisting start command, acquires attitude data generated by the attitude sensor in real time, acquires vibration data generated by the vibration sensor in real time, and controls the hoisting cable to move at a preset first speed. Calculate the difference between the attitude data and the preset first reference data, take the absolute value of the difference as the attitude difference, and calculate the percentage value as the attitude percentage based on the attitude difference and the preset attitude standard value; calculate the difference between the vibration data and the preset second vibration data, take the absolute value of the difference as the vibration difference, and calculate the percentage value as the vibration percentage based on the vibration difference and the preset vibration standard value. The state percentage is calculated by fusing the attitude percentage and vibration percentage. If the state percentage is within the preset first reference range, the damping coefficient of the hydraulic damping component is adjusted negatively correlated with the first adjustment parameter according to the state percentage. If the state percentage is within the preset second reference range, the damping coefficient of the hydraulic damping component is adjusted in positive correlation with the second adjustment parameter according to the state percentage. The first speed is adjusted based on the negative correlation of the state percentage; wherein the value in the second reference range is greater than the value in the first reference range, and the second adjustment parameter is less than the first adjustment parameter.

2. The method for analyzing the hoisting status of a containerized power system according to claim 1, characterized in that, The process of acquiring attitude data generated by the attitude sensor in real time also includes the following sub-steps: The containerized power system is equipped with a level to acquire the level data generated by the level. Calculate the average of the most recent multiple attitude data as the attitude average data; Calculate the data deviation between the average attitude data and the horizontal data. If the data deviation is less than the preset reference deviation, calculate the attitude fusion data based on the attitude data and the horizontal data, and use the attitude fusion data to update the attitude data.

3. The method for analyzing the hoisting status of a containerized power system according to claim 2, characterized in that, The step of calculating the attitude fusion data based on the attitude data and the horizontal data also includes the following sub-steps: The algorithm used to calculate the attitude fusion data is a weighted average algorithm. Calculate the absolute value of the difference between the most recent multiple attitude data and the average attitude data to obtain multiple attitude difference data, and calculate the sum of the multiple attitude difference data to obtain the fluctuation sum data; The ratio of the fluctuation sum data to the attitude mean data is used as a reference ratio, and the weight of the level data is adjusted according to the positive correlation of the reference ratio.

4. The method for analyzing the hoisting status of a containerized power system according to claim 3, characterized in that, The step of calculating the state percentage based on the fusion of attitude percentage and vibration percentage also includes the following sub-steps: The algorithm for calculating the percentage of states by fusion is a weighted average algorithm; Acquire multiple recent vibration data points, and calculate the fluctuation value of the multiple vibration data points as vibration fluctuation data based on a preset fluctuation algorithm; If the vibration fluctuation data is greater than the preset fluctuation warning data, the ratio of the vibration fluctuation data to the fluctuation warning data is calculated as the vibration comparison data, and the weight of the attitude data is adjusted according to the positive correlation of the vibration comparison data.

5. The method for analyzing the hoisting status of a containerized power system according to claim 3, characterized in that, The step of calculating the state percentage based on the fusion of attitude percentage and vibration percentage also includes the following sub-steps: Acquire multiple recent vibration data points, and calculate the fluctuation value of the multiple vibration data points as vibration fluctuation data based on a preset fluctuation algorithm; Acquire multiple recent attitude data points, and calculate the fluctuation value of the multiple attitude data points as attitude fluctuation data based on the fluctuation algorithm; Obtain multiple recent horizontal data points, and calculate the fluctuation value of these multiple horizontal data points as horizontal fluctuation data based on a fluctuation algorithm; If the vibration fluctuation data is greater than the preset fluctuation warning data, the attitude fluctuation data is greater than the preset attitude warning data, and the horizontal fluctuation data is greater than the preset horizontal warning data, then a sensor alarm will be triggered. In response to sensor alarm prompts, the damping coefficient of the hydraulic damping components is kept constant with the initial speed of the hoisting cable until the hoisting is completed.

6. The method for analyzing the hoisting status of a containerized power system according to claim 1, characterized in that, The method also includes the following steps: Select multiple attitude percentages and multiple vibration percentages corresponding to the most recent successful hoisting processes; Calculate the average of multiple attitude percentages as the attitude reference value, and calculate the average of multiple vibration percentages as the vibration reference value; The ratio of the current attitude percentage to the attitude reference value is used as the attitude comparison value, and the ratio of the current vibration percentage to the vibration reference value is used as the vibration comparison value. The ratio between the attitude contrast value and the vibration contrast value is calculated as the relative ratio, where relative ratio = attitude contrast value / vibration contrast value; If the relative ratio is less than the preset first comparison value or greater than the preset second comparison value, a hoisting abnormality alarm will be issued and hoisting will be stopped; wherein, the first comparison value is less than the second comparison value.

7. The method for analyzing the lifting status of a containerized power system according to claim 6, characterized in that, The method also includes the following steps: The containerized power system is equipped with a wind sensor to acquire the latest wind data generated by the wind sensor. The average of multiple wind force data points is the wind force average data. If the average wind force data is greater than the preset wind force reference data, the hoisting is stopped; otherwise, the ratio of the average wind force data to the wind force reference data is calculated as the wind force calculation parameter, and the first comparison value is adjusted according to the negative correlation of the wind force calculation parameter, and / or the second comparison value is adjusted according to the positive correlation of the wind force calculation parameter.

8. The method for analyzing the hoisting status of a containerized power system according to claim 7, characterized in that, The method also includes the following steps: Wind force curves are fitted based on multiple wind force data, and vibration curves are fitted based on multiple vibration data. Calculate the graphical similarity between the wind force curve and the vibration curve; If the graphic similarity is greater than the preset reference similarity, then the graphic calculation parameters are calculated based on the graphic similarity and the reference similarity, and the weight of the vibration percentage is adjusted according to the negative correlation of the graphic calculation parameters.

9. A hoisting status analysis system for a containerized power system, characterized in that, Includes a processor, wherein the steps of the method for analyzing the hoisting status of a containerized power system as described in any one of claims 1-8 are executed.

10. A storage medium, characterized in that, The storage medium stores a program that, when executed by a processor, implements the steps of the hoisting status analysis method for the containerized power system according to any one of claims 1-8.

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