Multi-dimensional sensing fusion-based quay crane container hanging identification and hierarchical pressure relief protection method

Abstract

The present application belongs to the technical field of port quay crane hydraulic control, and particularly relates to a multi-dimensional sensing fusion quay crane cabin recognition and graded pressure relief protection method, which is applied to the heavy load operation scene of the quay crane cabin single lifting hydraulic circuit, and characterized by the following steps: S1: multi-dimensional sensing data acquisition and preprocessing; S2: multi-feature fusion cabin recognition; S3: graded pressure relief protection execution; S4: lifting circuit cooperative steady load control; S5: system state monitoring and fault tolerance reset. The present application takes pressure as the core, supplements with multi-dimensional sensing fusion, matches with the "1 pressure core + 2 auxiliary features" determination rule, and the cabin recognition accuracy is greater than or equal to 99.9%, which completely solves the problems of lag and misjudgment of the traditional method.

Description

Technical Field

[0001] This invention belongs to the field of port quay crane hydraulic control technology, specifically relating to a method for multi-dimensional sensor fusion for quay crane hull identification and graded pressure relief protection. Background Technology

[0002] As the core equipment for container loading and unloading in ports, the safety of quay cranes during cargo loading operations directly determines the efficiency of port operations and the safety of personnel and equipment. In scenarios involving the lifting of overweight containers or large cargo weighing 65 tons or more, uneven load distribution, strong port vibrations, and electromagnetic interference can easily lead to cargo loading problems such as total load overload and single-angle unbalanced loading. If protection is not timely or appropriate, it can easily cause major safety accidents such as hydraulic pipeline rupture, valve jamming, and cargo falling.

[0003] Existing quay crane hull protection technologies generally suffer from three major defects: First, the protection logic is simplistic, with most relying on a single overflow valve for passive pressure relief and judging overload solely based on the lifting circuit pressure signal. This results in a response lag time of 0.3-0.5 seconds and an inability to identify complex hull scenarios such as off-center loading, leading to a high false alarm rate. Second, data reliability is insufficient. Some improved solutions only introduce a single weight sensor to detect off-center loading, without integrating multi-dimensional signals such as displacement and pressure. Once the sensor fails, the protection system will be paralyzed. Third, the protection strategy is crude, lacking graded pressure relief control. The pressure relief rate is uncontrollable and can easily cause secondary hull swaying due to excessively rapid pressure relief, exacerbating safety risks.

[0004] While some improvements have been attempted within the industry to address the aforementioned shortcomings, none have established a closed-loop system encompassing "multi-source data fusion - accurate identification - hierarchical protection - fault-tolerant assurance," thus failing to meet the high safety and reliability requirements of heavy-load quay crane operations. Therefore, a method for identifying and depressurizing cargo bays that balances accuracy, controllability, and fault tolerance is urgently needed to resolve the core pain points of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-dimensional sensor fusion method for identifying and classifying pressure relief protection of quay crane pods, so as to solve the problems existing in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-dimensional sensor fusion method for identifying and classifying pressure relief protection of quay crane pods, applied to heavy-load operation scenarios of single-lifting hydraulic circuits for quay crane pods, comprising the following steps: S1: Multi-dimensional sensor data acquisition and preprocessing: Through multiple weight sensors, cylinder displacement sensors and hydraulic pressure sensors deployed in the lifting system, raw data of load, mechanical deformation and circuit pressure are collected simultaneously; the raw data are sequentially subjected to moving average filtering, range verification and mutation verification to eliminate working condition interference; and a redundancy compensation algorithm is executed for single sensor failure scenarios to ensure data continuity and effectiveness. S2: Multi-feature fusion cabin identification: Based on preprocessed data, real-time calculation and extraction of pressure signal as the core, combined with total load, off-center load deviation, and displacement rate as auxiliary features; adopting the fusion judgment rule of "pressure core feature triggering and at least two auxiliary features verification" to classify suspected cabin status and confirmed cabin status. S3: Graded depressurization protection execution: Based on the cabin identification result, match and execute the early warning level or protection level depressurization mode; the early warning level mode intervenes by adjusting the lifting speed and triggering an alarm, while the protection level mode links the original overflow valve and the newly added rapid depressurization branch to achieve gradient depressurization through closed-loop control; S4: Hoisting circuit coordinated load stabilization control: After the pressure relief action is triggered, the hoisting circuit operation status is adjusted based on the preset timing control logic, and the deceleration and shutdown operations are executed in sequence to suppress secondary load swaying; S5: System status monitoring and fault-tolerant reset: Real-time monitoring of loop pressure and load data; when the preset composite overload release conditions are met, the system is reset sequentially; sensor and actuator fault diagnosis is performed simultaneously, and a graded alarm is triggered and the system switches to the backup identification and control logic when a fault occurs.

[0007] Preferably, in step S1, the moving average filtering adopts a 5-point moving average algorithm; the range verification is to mark data exceeding ±10% of the sensor's calibrated range as invalid, and to complete it by the average of the previous 3 valid data; the mutation verification is to determine that a mutation has occurred when the weight difference, pressure difference, or displacement difference between adjacent sampling points is greater than 10%, and to temporarily store the data and continuously monitor it for at least 2 sampling cycles to confirm whether it is an interference signal; the redundancy compensation algorithm is specifically as follows: when only one of the multiple weight sensors fails, the average of the remaining valid sensors is used for compensation, and the compensation formula for calculating the total load is: F_total compensation = (W1 + W2 + … + W_m) / m ×n, where n is the total number of sensors, m is the number of valid sensors, and W_i is the collected value of the i-th valid sensor.

[0008] Preferably, in step S2, the triggering condition for the pressure core feature is: the lifting circuit pressure value is greater than or equal to 18MPa; the verification conditions for the auxiliary features include any two or more of the following: total load is greater than or equal to 95% of the rated load, off-center load deviation is greater than 8%, displacement rate is greater than 5mm / s, and displacement amount is greater than 3mm.

[0009] Preferably, in step S2, the formula for calculating the off-center load deviation δ is: δ = [max(W1, W2, …, W nThe formula is: ) -F_total / n] / (F_total / n) × 100%, where F_total is the sum of all weight sensor values, W_i is the value collected by the i-th weight sensor, and n is the number of weight sensors; the displacement rate v_S is calculated as: v_S = (S n - S n-1 The pressure change rate v_P is calculated as follows: v_P = (P / Δt) / Δt. n - P n-1 ) / Δt; where Δt is the fixed operating period of the controller, S n and P n These are the displacement and pressure values ​​collected during the nth cycle.

[0010] Preferably, in step S3, the condition for executing the early warning level pressure relief mode is: the pressure core feature is triggered, but there are fewer than two auxiliary feature verification items; in this mode, hydraulic pressure relief is not initiated, but the lifting speed is reduced to below 80% of the rated speed, and an audible and visual alarm is triggered; the condition for executing the protection level pressure relief mode is: the pressure core feature is triggered and the auxiliary feature verification items reach or exceed two; in this mode, the original overflow valve and the rapid pressure relief branch are opened simultaneously, and the rate of decrease of the circuit pressure is controlled within 2MPa / s through a PID closed-loop control algorithm.

[0011] Preferably, in step S5, the composite overload release conditions include: the circuit pressure is less than 16MPa, the total load is less than 90% of the rated load, and the off-center load deviation is less than 5%, and this state lasts for at least 3 consecutive PLC control cycles; after the release conditions are met, the reset is performed in the order of "first shutting down the fast pressure relief branch, then restoring the lifting circuit", and the completion time of the entire reset process is less than or equal to 1 second.

[0012] Preferably, in step S5, the fault diagnosis and backup logic includes: when the number of sensors that have failed reaches or exceeds two, a high-level alarm is triggered, and the identification logic is switched to a backup dual-signal fusion judgment mode based on pressure change rate and displacement rate; when the feedback timeout of the pressure relief valve group is detected to be greater than 1 second, the control command is repeatedly sent at least 2 times, and if the abnormality continues, an emergency shutdown is performed and the hydraulic circuit is locked.

[0013] Preferably, in step S1, the sensor data is transmitted to the industrial PLC controller via a 4-20mA analog signal; the sensors are deployed as follows: the weight sensor is installed on the hoisting wire rope, the displacement sensor is installed at the end of the piston rod of the hoisting hydraulic cylinder, and the pressure sensor is installed in the main hoisting oil circuit.

[0014] Preferably, the heavy-duty operation scenario is suitable for lifting loads of 65 tons or more, the rated working pressure of the hydraulic circuit is 14MPa, and the protection level of all sensors is not lower than IP67.

[0015] Preferably, in step S4, the timing control logic is as follows: within the same control cycle when an emergency protection state is determined, the output signal reduces the lifting speed to 80% of the rated speed; after 50ms, the rapid pressure relief branch is activated; after 100ms, the lifting circuit is completely stopped.

[0016] The beneficial effects of the present invention are: (1) accurate and efficient identification: with pressure as the core and multi-dimensional sensing fusion as a supplement, and with the "1 pressure core + 2 auxiliary features" judgment rule, the accuracy of the hangar identification is ≥99.9%, which completely solves the problems of lag and misjudgment in traditional methods; (2) Safety and controllability: The two-stage pressure relief mode combined with PID rate control ensures a stable pressure drop rate of ≤2MPa / s, avoiding hydraulic pipeline impact; the multi-loop timing coordination effectively suppresses secondary swaying, ensuring a safe and controllable protection process; (3) Excellent compatibility and economy: No need to modify the original hydraulic circuit core structure of the quay crane, it can be achieved by simply adding sensors and upgrading the PLC control program. The modification cost is low, it is compatible with heavy-duty quay cranes of 65t class and above, and has strong versatility. (4) Excellent fault tolerance and reliability: The triple fault tolerance design of redundancy compensation, fault diagnosis and backup logic can cope with fault scenarios such as single sensor failure and valve group action timeout. The average fault-free working time of the system is significantly improved, meeting the requirements of continuous port operation. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0018] In the description of this disclosure, it should be understood that the terms “center,” “upper,” “lower,” “front,” “rear,” “left,” “right,” “vertical,” “horizontal,” “top,” “bottom,” “inner,” and “outer,” etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this disclosure and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this disclosure, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0021] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings and preferred embodiments.

[0022] The following describes the implementation steps of the present invention in detail, taking into account the actual operation scenario of a 65t-class quay crane hydraulic circuit (working pressure 14MPa), so that those skilled in the art can completely reproduce the method based on this description.

[0023] Step 1: Multi-dimensional sensor data acquisition and preprocessing Sensor deployment and selection: Four weight sensors with a range of 0~80t (suitable for 65t heavy load) are installed on the hoisting wire rope; four magnetostrictive displacement sensors with a range of 0~500mm are installed at the end of the piston rod of the hoisting hydraulic cylinder; and six industrial-grade pressure sensors with a range of 0~30MPa are installed in the main hoisting oil circuit. All sensors have a protection rating of ≥IP67 and are suitable for the high humidity and strong electromagnetic interference environment of the port.

[0024] Data Acquisition: All sensor data are synchronously acquired at a frequency of 100Hz via the PLC controller. Weight, displacement, and pressure signals are 4-20mA analog quantities, which are converted into digital quantities by the PLC analog input module to ensure real-time data transmission.

[0025] Data preprocessing: ① Filtering: Perform a 5-point moving average filter on the analog signal to control the signal fluctuation amplitude within ±0.5% and filter out port vibration and electromagnetic interference; ② Anomaly verification: Range verification removes data exceeding ±10% of the range and completes it with the average of the first 3 valid values; Abrupt change verification identifies abnormal changes in weight, pressure, and displacement, temporarily stores them, and then continuously monitors for 2 cycles for confirmation; ③ Redundancy compensation: When a single weight sensor fails, the three-sensor average compensation algorithm is activated to calculate the total load, ensuring continuous and valid load data.

[0026] Step 2: Multi-feature fusion and pod identification Feature Extraction: The PLC controller calculates four core features in real time with a 10ms operation cycle. The specific calculation methods are as follows: ① Total load F_total = W1 + W2 + W3 + W4, and the off-center load deviation is calculated using the formula δ = [max(W1,W2,W3,W4) - F_total / 4] / (F_total / 4) × 100%; ② Displacement rate is calculated using the formula v_S = (S_total / W4) / (W1,W2,W3,W4) - F_total / 4] / (F_total / 4) × 100%; n - S n-1 ) / 10ms, reflecting the deformation rate of the hydraulic cylinder; ③ Pressure change rate is calculated according to the formula v_P = (P n - P n-1 The calculation is performed in 10ms to capture sudden changes in circuit pressure; all features are output after smoothing to eliminate the effects of instantaneous jitter.

[0027] Cabin attachment determination: The classification determination is performed according to the preset rules, with the pressure signal as the core benchmark: ① Suspected cabin attachment: Only the core pressure feature is triggered (loop pressure ≥ 18MPa), but the two auxiliary feature verifications are not met; ② Emergency protection required: The core pressure feature is triggered and two or more auxiliary features are met (such as loop pressure ≥ 18MPa, total load ≥ 95% of rated load, displacement ≥ 3mm), and it is directly determined that emergency protection is required.

[0028] Step 3: Execution of graded pressure relief protection Early warning level protection: In case of suspected cabin malfunction, the depressurization action will not be initiated. Instead, the PLC outputs a control signal to adjust the current of the proportional valve in the hoisting circuit, reducing the hoisting speed from 0.5m / s (rated speed) to 0.4m / s (80% of rated speed). At the same time, the audible and visual alarm will be triggered to flash at a frequency of 2Hz, reminding the operator to pay attention to the load status and avoid over-protection that could affect work efficiency.

[0029] Protection level: For emergency protection situations, the PLC synchronously outputs control signals to open the original relief valve and the solenoid directional valve of the quick pressure relief branch. The opening degree of the pressure relief branch is adjusted through the PID closed-loop control algorithm to precisely control the pressure drop rate at 1.5MPa / s (example: it takes 5.3s to drop from 18MPa to 10MPa), so as to avoid impact damage to the hydraulic pipeline due to sudden pressure changes.

[0030] Step 4: Multi-loop cooperative attitude stabilization The PLC controls the hoisting circuit according to a preset timing sequence to achieve synchronous control of "pressure relief-load stabilization" and suppress secondary swaying of the hull: at 0ms, the current of the proportional valve of the hoisting circuit is adjusted, and the hoisting speed is synchronously reduced to 80% of the rated speed; at 50ms, the electromagnetic reversing valve of the rapid pressure relief branch is energized and opened to continuously monitor the pressure change of the hoisting circuit; at 100ms, the hoisting circuit is completely stopped, prohibiting any hoisting action to ensure load stability.

[0031] Step 5: System Status Monitoring and Fault-Tolerant Reset Status monitoring: The PLC collects circuit pressure and load data in real time to continuously determine whether the overload has been relieved, and diagnoses equipment faults through sensor signal feedback and valve group action feedback.

[0032] Overload release and reset: When the circuit pressure is detected to be <16MPa, the total load is 85% of the rated load, the off-center load deviation is 3%, and this state lasts for 100ms (10 PLC cycles), it is determined to be overload release; the reset is performed in the order of "quick pressure relief branch closed → hoisting circuit restored", and the entire reset cycle is controlled within 0.8s to quickly restore operation.

[0033] Fault tolerance handling: ① Sensor failure: When two or more sensors fail, the central control emergency alarm is triggered, and the system automatically switches to the "pressure-displacement dual signal" backup logic. Based on the pressure change rate and displacement rate, basic cabin identification is achieved to ensure core protection functions; ② Valve group failure: When the pressure relief valve group has an action timeout of >1s without feedback, the PLC repeatedly outputs the control signal twice. If there is still no feedback, the system will execute the shutdown protection, lock the hydraulic circuit to prevent the fault from escalating, and issue a fault location alarm to facilitate troubleshooting by maintenance personnel.

[0034] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-dimensional sensor fusion method for identifying and classifying pressure relief protection of quay crane pods, applied to heavy-load operation scenarios of single-lifting hydraulic circuits for quay crane pods, characterized in that, Includes the following steps: S1: Multi-dimensional sensor data acquisition and preprocessing: Through multiple weight sensors, cylinder displacement sensors and hydraulic pressure sensors deployed in the lifting system, raw data of load, mechanical deformation and circuit pressure are collected simultaneously; the raw data are then subjected to moving average filtering, range verification and mutation verification in sequence to eliminate working condition interference; Furthermore, a redundancy compensation algorithm is executed for single sensor failure scenarios to ensure data continuity and effectiveness; S2: Multi-feature fusion cabin identification: Based on preprocessed data, real-time calculation and extraction of pressure signal as the core, combined with total load, off-center load deviation, and displacement rate as auxiliary features; adopting the fusion judgment rule of "pressure core feature triggering and at least two auxiliary features verification" to classify suspected cabin status and confirmed cabin status. S3: Graded depressurization protection execution: Based on the cabin identification result, match and execute the early warning level or protection level depressurization mode; the early warning level mode intervenes by adjusting the lifting speed and triggering an alarm, while the protection level mode links the original overflow valve and the newly added rapid depressurization branch to achieve gradient depressurization through closed-loop control; S4: Hoisting circuit coordinated load stabilization control: After the pressure relief action is triggered, the hoisting circuit operation status is adjusted based on the preset timing control logic, and the deceleration and shutdown operations are executed in sequence to suppress secondary load swaying; S5: System status monitoring and fault-tolerant reset: Real-time monitoring of loop pressure and load data; when the preset composite overload release conditions are met, the system is reset sequentially; sensor and actuator fault diagnosis is performed simultaneously, and a graded alarm is triggered and the system switches to the backup identification and control logic when a fault occurs.

2. The method according to claim 1, characterized in that, In step S1, the moving average filtering uses a 5-point moving average algorithm; the range verification involves marking data exceeding ±10% of the sensor's calibrated range as invalid and supplementing it with the average of the previous 3 valid data; the mutation verification involves determining that a data mutation has occurred when the weight difference between adjacent sampling points is greater than 10% of the sensor's full range, or the pressure difference is greater than 10% of the sensor's full range, or the displacement difference is greater than 10% of the sensor's full range, and temporarily storing the data while continuously monitoring it for at least 2 sampling cycles to confirm whether it is an interference signal; the redundancy compensation algorithm specifically involves using the average of the remaining valid sensors for compensation when only one of the multiple weight sensors fails, and calculating the total load compensation formula as: F_total_compensation = (W1 + W2 + … + W_m) / m × n, where n is the total number of sensors, m is the number of valid sensors, and W_i is the collected value of the i-th valid sensor.

3. The method according to claim 1, characterized in that, In step S2, the triggering condition for the pressure core feature is: the lifting circuit pressure value is greater than or equal to 18MPa; the verification conditions for the auxiliary features include any two or more of the following: total load is greater than or equal to 95% of the rated load, off-center load deviation is greater than 8%, displacement rate is greater than 5mm / s, and displacement amount is greater than 3mm.

4. The method according to claim 1 or 3, characterized in that, In step S2, the formula for calculating the off-center load deviation δ is: δ = [max(W1, W2, …, W n The formula is: ) - F_total / n] / (F_total / n) × 100%, where F_total is the sum of all weight sensor readings, W_i is the reading of the i-th weight sensor, and n is the number of weight sensors; the displacement rate v_S is calculated as: v_S = (S n - S n-1 The pressure change rate v_P is calculated as follows: v_P = (P / Δt) / Δt. n -P n-1 ) / Δt; where Δt is the fixed operating period of the controller, S n and P n These are the displacement and pressure values ​​collected during the nth cycle.

5. The method according to claim 1, characterized in that, In step S3, the conditions for executing the early warning level pressure relief mode are: the pressure core feature is triggered, but there are fewer than two auxiliary feature verification items; in this mode, hydraulic pressure relief is not initiated, but the lifting speed is reduced to below 80% of the rated speed, and an audible and visual alarm is triggered; the conditions for executing the protection level pressure relief mode are: the pressure core feature is triggered and the auxiliary feature verification items reach or exceed two; in this mode, the original overflow valve and the rapid pressure relief branch are opened simultaneously, and the rate of decrease of the circuit pressure is controlled within 2MPa / s through a PID closed-loop control algorithm.

6. The method according to claim 1, characterized in that, In step S5, the combined overload release conditions include: the circuit pressure is less than 16MPa, the total load is less than 90% of the rated load, and the off-center load deviation is less than 5%, and this state lasts for at least 3 consecutive PLC control cycles; after the release conditions are met, the reset is performed in the order of "first shutting down the fast pressure relief branch, then restoring the lifting circuit", and the completion time of the entire reset process is less than or equal to 1 second.

7. The method according to claim 1, characterized in that, In step S5, the fault diagnosis and backup logic includes: when the number of sensors that have failed reaches or exceeds two, a high-level alarm is triggered, and the identification logic is switched to the backup dual-signal fusion judgment mode based on the pressure change rate and displacement rate; when the feedback timeout of the pressure relief valve group is detected to be greater than 1 second, the control command is repeatedly sent at least 2 times, and if the abnormality continues, an emergency shutdown is performed and the hydraulic circuit is locked.

8. The method according to claim 1, characterized in that, In step S1, the sensor data is transmitted to the industrial PLC controller via a 4-20mA analog signal; the two most recent sampling data points are processed each cycle to ensure real-time performance; the sensors are deployed as follows: the weight sensor is installed on the lifting wire rope of the front beam, the displacement sensor is installed at the end of the piston rod of the lifting hydraulic cylinder, and the pressure sensor is installed in the main lifting oil circuit.

9. The method according to claim 1, characterized in that, The heavy-duty operation scenario is applicable to lifting loads of 65 tons and above. The rated working pressure of the hydraulic circuit is 14MPa, and the protection level of all sensors is not lower than IP67.

10. The method according to claim 1, characterized in that, In step S4, the timing control logic is as follows: within the same control cycle when an emergency protection state is determined, the output signal reduces the lifting speed to 80% of the rated speed; after 50ms, the rapid pressure relief branch is activated; after 100ms, the lifting circuit is completely stopped.

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