A container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion.

Abstract

The application provides a container F-TR lock unlocking detection method based on multi-point inertial measurement and fusion of lifting point weight, which can be used in the technical field of hoisting equipment. The method is applied to a control system of the hoisting equipment, the control system is in communication connection with a plurality of measurement units, the measurement units are arranged on a lifting appliance of the hoisting equipment, the measurement units correspond to lifting point positions of the lifting appliance one by one, and the method comprises the following steps: in the process that the lifting appliance is lifted to a preset height, measurement data at the lifting point is obtained in real time based on the measurement units; wherein the measurement data comprises tension data, acceleration lag data and time lag data; the measurement data is input into a preset fuzzy logic algorithm to obtain an unlocking risk value of the F-TR lock corresponding to the lifting point; and based on the unlocking risk value, the unlocking state of the F-TR lock is determined. The method provided in the application improves the recognition accuracy of detecting whether the F-TR lock is unlocked, and effectively reduces false positives and false lifting.

Description

Technical Field

[0001] This application relates to the field of lifting equipment technology, and in particular to a container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight. Background Technology

[0002] The F-TR lock is a special locking device used in railway container transportation to secure containers to flatcars. It is called the "eagle beak lock" because its shape resembles an eagle's beak. This lock is used to prevent containers from tipping over and jumping during transportation.

[0003] Currently, railway container flatcars use F-TR locks (eagle head locks) to secure containers to the car body. During container unloading, the eagle head part of the F-TR lock can easily get caught in the container's locking hole. Lifting a container before the F-TR lock is fully disengaged from the locking hole can cause a "double-lift" accident. Currently, single-sensor discrimination systems based on tensile or gravity, displacement, or machine vision detection are available.

[0004] However, existing F-TR lock unlocking detection methods have low accuracy, which can easily lead to false alarms and false lifting. Summary of the Invention

[0005] This application provides a container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight, in order to solve the technical problems of low detection accuracy and false alarms and false lifting in existing F-TR lock unlocking detection methods.

[0006] According to the first aspect disclosed in this application, this application provides a container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight, applied to the control system of lifting equipment. The control system is communicatively connected to multiple measurement units, which are mounted on the lifting device's spreader. Each measurement unit corresponds one-to-one with a lifting point position on the spreader. The method includes:

[0007] During the process of lifting the lifting device to a preset height, the measurement unit acquires measurement data at the lifting point in real time; wherein, the measurement data includes tension data, acceleration lag data, and time lag data;

[0008] The measurement data is input into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point;

[0009] Based on the unlocking risk value, the unlocking status of the F-TR lock is determined.

[0010] In one feasible implementation, the measurement data is input into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point, including:

[0011] The measurement data is fuzzified to obtain membership data; wherein, the membership data includes gravity over-limit ratio membership, acceleration lag membership, and time lag membership.

[0012] Based on a preset fuzzy rule base, fuzzy reasoning is performed on the membership data to obtain the membership degree of the unlock risk level;

[0013] The membership degree of the unlock risk level is defuzzified to obtain the unlock risk value.

[0014] In one feasible implementation, the gravity exceedance ratio membership degree is obtained based on the following method:

[0015] Construct the first fuzzy set corresponding to the gravity exceedance ratio, and the first membership function corresponding to the first fuzzy set;

[0016] Based on the tensile data and the theoretical average tensile force, the gravity exceedance ratio is obtained;

[0017] The gravity exceedance ratio membership degree is obtained based on the gravity exceedance ratio and the first membership function.

[0018] In one feasible implementation, the acceleration hysteresis membership degree is obtained based on the following method:

[0019] Construct a second fuzzy set corresponding to the acceleration hysteresis data, and a second membership function corresponding to the second fuzzy set;

[0020] The acceleration lag membership is obtained based on the acceleration lag data and the second membership function.

[0021] In one feasible implementation, the time lag membership degree is obtained based on the following method:

[0022] Construct a third fuzzy set corresponding to the time-lag data, and a third membership function corresponding to the third fuzzy set;

[0023] Based on the time-lag data and the third fuzzy set, the time-lag membership degree is obtained.

[0024] In one feasible implementation, determining the unlocking status of the F-TR lock based on the unlocking risk value includes:

[0025] If the unlocking risk value is less than the first threshold, then the unlocking status of the F-TR lock is determined to be unlocked;

[0026] If the unlocking risk value is not less than the first threshold and less than the second threshold, then the unlocking status of the F-TR lock is determined to be pending confirmation.

[0027] If the unlocking risk value is not less than the second threshold, then the unlocking status of the F-TR lock is determined to be unlocked.

[0028] In one feasible implementation, the method further includes:

[0029] If the unlocked status of the F-TR locks corresponding to each lifting point is unlocked, then control the lifting device to continue lifting;

[0030] If the unlocking status of the F-TR lock corresponding to a lifting point is pending confirmation, the lifting device will be controlled to pause lifting and a warning will be issued.

[0031] If the F-TR lock corresponding to a lifting point is locked, the lifting device will be controlled to stop lifting and an alarm will be issued.

[0032] According to a second aspect disclosed in this application, this application provides a container F-TR lock unlocking detection device based on the fusion of multi-point inertial measurement and lifting point weight, applied to the control system of lifting equipment. The control system is communicatively connected to multiple measurement units, which are mounted on the lifting device's spreader. Each measurement unit corresponds one-to-one with a lifting point position on the spreader. The device includes:

[0033] The data acquisition module is used to acquire measurement data at the lifting point in real time based on the measurement unit during the process of lifting the spreader to a preset height; wherein, the measurement data includes tension data, acceleration lag data and time lag data;

[0034] The risk calculation module is used to input the measurement data into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point;

[0035] The unlock detection module is used to determine the unlock status of the F-TR lock based on the unlock risk value.

[0036] According to a third aspect disclosed in this application, this application provides an electronic device, including a processor and a memory communicatively connected to the processor;

[0037] The memory stores computer-executed instructions;

[0038] The processor executes computer execution instructions stored in the memory to implement the method described in any one of the first aspects.

[0039] According to the fourth aspect disclosed in this application, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the method described in any one of the first aspects.

[0040] According to the fifth aspect disclosed in this application, this application provides a computer program product, including a computer program, which, when executed, is used to implement the method described in any one of the first aspects.

[0041] Compared with the prior art, this application has the following advantages:

[0042] This application provides a container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight. By using fuzzy logic algorithms, it can accurately simulate the natural thinking of humans in complex and uncertain environments, thereby flexibly quantifying the measurement data and transforming fuzzy information into results that are closer to the actual situation and more robust. This is used to accurately determine whether the F-TR lock is unlocked, reduce deviations caused by data uncertainty, improve the accuracy of detecting whether the F-TR lock is unlocked, and effectively reduce false alarms and false lifting. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] Figure 1 A schematic diagram of the initial state of container hoisting provided in an embodiment of this application;

[0045] Figure 2 This application provides a schematic diagram of the lifting state of a container hoisting according to an embodiment of the present application.

[0046] Figure 3 This application provides a schematic diagram of the lifting state of a container during hoisting, as illustrated in an embodiment of the present application.

[0047] Figure 4 This application provides a schematic diagram illustrating the state changes during the container lifting process.

[0048] Figure 5 A flowchart illustrating a container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion, provided for embodiments of this application;

[0049] Figure 6 A flowchart illustrating another container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight, provided for embodiments of this application;

[0050] Figure 7 A schematic diagram of a container F-TR lock unlocking detection device based on multi-point inertial measurement and lifting point weight fusion provided for embodiments of this application;

[0051] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0052] Figure label:

[0053] 1-IMU inertial measurement sensor;

[0054] 2-Turnlock;

[0055] 3-Lifting gear;

[0056] 4- Gravity measurement sensor;

[0057] 5-Container;

[0058] 6-Flatcar;

[0059] 7-F-TR lock.

[0060] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0061] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0062] The F-TR lock is a specialized locking device used in railway container transportation to secure containers to flatcars. It is nicknamed the "eagle beak lock" because of its beak-like shape. The lock uses an integral frame structure to connect eight lock bodies (four fixed to the four corners of the car body and four flip-mounted in the middle of the vehicle) to the flatcar. Utilizing three guiding surfaces—the upper guide ramp for lowering the container, the lower guide ramp for exiting the container, and the lower guide ramp for lowering the container—along with the eagle head structure, it hooks onto the corner fittings of the container, thus preventing the container from tipping over and jumping during transportation.

[0063] Currently, railway container flatcars use F-TR locks (eagle head locks) to secure containers to the car body. During container unloading, the eagle head part of the F-TR lock can easily get caught in the container's locking holes. In actual loading and unloading operations, crane operators must concentrate, carefully observe, and require ground personnel to check whether the F-TR lock is completely disengaged from the container's locking holes. Lifting a container with the F-TR lock not completely disengaged can cause a "double-lift" accident. While single-sensor discrimination systems based on tension or gravity, displacement, or machine vision detection exist, displacement alone is insufficient to determine whether the F-TR lock is fully disengaged under stable lifting conditions when anti-sway spreaders are in operation. In machine vision, laser sensors are easily damaged in such vibrating environments due to their precision, and often result in invalid measurements. Image detection in machine vision is also affected by weather and lighting conditions, leading to poor recognition accuracy.

[0064] Therefore, existing F-TR lock unlocking detection methods have low detection accuracy, which can easily lead to false alarms and false lifting.

[0065] To address the aforementioned technical issues, this application proposes a container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight, which improves the accuracy of detecting whether the F-TR lock is unlocked and effectively reduces false alarms and false lifting.

[0066] The technical solution of the container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight provided in this application will be described in detail below through specific embodiments. It should be noted that the following embodiments may exist alone or in combination with each other, and the same or similar content may not be described again in different embodiments.

[0067] It should be noted that the container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion provided in this application embodiment is executed by the control system of the lifting equipment. Correspondingly, the container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion is also set in the control system of the lifting equipment.

[0068] See Figure 1 Specifically, the lifting equipment includes a control system and a lifting device 3. An IMU inertial measurement sensor 1 is installed above each of the four corner lifting points of the lifting device 3. The IMU inertial measurement sensor 1 is used to collect the acceleration and attitude changes of each lifting point at the initial stage of lifting. A rotary lock 2 is installed below each of the four corner lifting points of the lifting device 3. A gravity measurement sensor 4 is installed on the rotary lock 2. The gravity measurement sensor 4 is used to detect the gravity changes during the lifting process.

[0069] Container 5 is placed on flatcar 6 and secured with F-TR lock 7. To avoid simultaneous lifting, flatcar 6 and container 5 are lifted together. When lifting with spreader 3, it is necessary to check whether F-TR lock 7 has been unlocked.

[0070] See Figure 2 and Figure 3 During the lifting process of container 5, inertial measurement data at the lifting point is collected by IMU inertial measurement sensor 1, and gravity measurement data at the lifting point is collected by gravity measurement sensor 4.

[0071] Specifically, inertial measurement data includes the acceleration lag value at a certain suspension point. The time lag value of a significant initial lifting response at a certain lifting point And the displacement of a certain lifting point.

[0072] Specifically, gravity measurement data includes the tension force at a certain suspension point. .

[0073] Among them, tension Satisfy the following formula:

[0074]

[0075] in, This indicates the stiffness of the steel rope at suspension point i (N / m). This represents the displacement of suspension point i (obtained from the IMU displacement sensor, in meters). The first derivative of displacement (velocity, m / s). The second derivative of displacement (acceleration) ), This represents the damping coefficient (Ns / m). This indicates the total mass of the spreader and container (kg), where g is the acceleration due to gravity. ).

[0076] In the initial stage of lifting by spreader 3, it is assumed that the F-TR lock 7 of one lifting point is not unlocked, while the F-TR lock 7 of three lifting points is normally unlocked.

[0077] Then the normal unlocking lifting point is subjected to force ( It satisfies the following formula:

[0078]

[0079] The unlocked lifting point is subjected to force ( It satisfies the following formula:

[0080]

[0081] in, This indicates the mass of the flatcar.

[0082] By solving the force formula of the unlocked lifting point, refer to... Figure 4 As shown, when a certain lifting point is not unlocked, the following phenomenon will occur:

[0083] 1. Displacement Growth slowed;

[0084] 2. Increase speed Decrease, then increase, and finally match the normal unlocking point speed;

[0085] 3. First, a downward acceleration is generated. Then it generates an upward acceleration, and finally the acceleration tends to 0;

[0086] 4. Gravity sensor 4 detects tension. rise.

[0087] Based on the above principles, an F-TR lock unlocking detection method integrating inertial measurement and weight fusion is constructed.

[0088] Figure 5 A flowchart illustrating a container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion, provided in this application embodiment, is shown below. Figure 5 In some embodiments, the container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion is applied to the control system of the lifting equipment. The control system is communicatively connected to multiple measurement units, which are installed on the lifting device's spreader. The measurement units correspond one-to-one with the lifting point positions of the spreader. The method includes the following steps:

[0089] S501, during the process of lifting the spreader to the preset height, the measurement unit acquires measurement data at the lifting point in real time; the measurement data includes tension data, acceleration lag data and time lag data.

[0090] The spreader slowly lifts the container to a preset height under the control of the lifting equipment's control system. During this process, the measurement unit transmits measurement data from each lifting point in real time.

[0091] Specifically, the preset height is 2 to 5 centimeters.

[0092] Specifically, the measurement units are an IMU inertial measurement sensor and a gravity measurement sensor.

[0093] S502 inputs the measurement data into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point.

[0094] Specifically, the collected measurement data is processed using a fuzzy algorithm to determine whether the F-TR lock at the lifting point is in an unlocked state through fuzzy control.

[0095] S503 determines the unlock status of the F-TR lock based on the unlock risk value.

[0096] Specifically, the unlocking status of the F-TR lock is determined based on its unlocking risk value.

[0097] In this embodiment, the fuzzy logic algorithm can accurately simulate the natural thinking of humans in complex and uncertain environments, thereby flexibly quantifying the measurement data and transforming fuzzy information into results that are closer to the actual situation and more robust, so as to accurately determine whether the F-TR lock is unlocked, reduce the deviation caused by data uncertainty, improve the recognition accuracy of whether the F-TR lock is unlocked, and effectively reduce false alarms and false lifting.

[0098] Specifically, the detection accuracy rate is ≥95%.

[0099] exist Figure 5 Based on the embodiments shown, the following is combined with Figure 6 This paper further introduces the technical solution of the container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight.

[0100] Figure 6 A flowchart illustrating another container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion provided in this application embodiment is shown below. Figure 6 In some embodiments, the process of the container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion includes the following steps:

[0101] S601, during the process of lifting the spreader to the preset height, the measurement unit acquires measurement data at the lifting point in real time; the measurement data includes tension data, acceleration lag data and time lag data.

[0102] S602, perform fuzzification processing on the measurement data to obtain membership data; among which, the membership data includes gravity over-limit ratio membership, acceleration lag membership, and time lag membership.

[0103] Optionally, the gravity exceedance ratio membership is obtained based on the following method:

[0104] Step 1: Construct the first fuzzy set corresponding to the gravity exceedance ratio, and the first membership function corresponding to the first fuzzy set.

[0105] For example, the first fuzzy set is divided into:

[0106] Normal: Membership functions are trigonometric membership functions, vertex (1,1), left boundary right boundary .

[0107] Among these, the heavier the container, the narrower the normal range becomes, depending on its weight.

[0108] High: The membership function is a trigonometric membership function, and the vertex ( ,1), left boundary right boundary .

[0109] Among them, the threshold center point With container weight Increases and decreases.

[0110] Extreme: The membership function is an ascending semi-trapezoidal membership function, starting at... .

[0111] The starting point decreases as the weight increases.

[0112] Step 2: Based on the tensile data and the theoretical average tensile force, obtain the gravity over-limit ratio.

[0113] The gravity exceedance ratio is the ratio of the actual measured tensile force to the theoretical average tensile force, and it satisfies the following formula:

[0114]

[0115] Where G represents the gravity over-limit ratio.

[0116] Step 3: Obtain the gravity over-limit ratio membership degree based on the gravity over-limit ratio and the first membership function.

[0117] The gravity exceedance ratio is substituted into the above membership function to obtain the gravity exceedance ratio membership degree.

[0118] Optionally, the acceleration hysteresis membership is obtained based on the following method:

[0119] Step 1: Construct the second fuzzy set corresponding to the acceleration lag data, and the second membership function corresponding to the second fuzzy set.

[0120] For example, the second fuzzy set is partitioned as follows:

[0121] Severe: The membership function is an ascending semi-trapezoidal membership function, when... When the membership degree is 0, Membership increases linearly when The membership degree is 1.

[0122] Among them, the following changes with weight: complete membership points With container weight Increase and decrease

[0123] Not_Severe: Defined as the complement of the severe set, with its membership function satisfying the following formula:

[0124]

[0125] in, Describes the complement function. This indicates that the fuzzy set is a severe membership function.

[0126] Step 2: Obtain the acceleration lag membership degree based on the acceleration lag data and the second membership function.

[0127] The acceleration lag data is then input into the membership function to obtain the acceleration lag membership degree.

[0128] Optionally, the time-lag membership degree is obtained based on the following method:

[0129] Step 1: Construct the third fuzzy set corresponding to the time-lag data, and the third membership function corresponding to the third fuzzy set.

[0130] For example, the third fuzzy set is partitioned as follows:

[0131] Severe: The membership function is an ascending semi-trapezoidal membership function, when... When the membership degree is 0, The time increases linearly, when Membership degree is 1.

[0132] Fixed range: Does not change with weight.

[0133] Not_Severe: Defined as the complement of the severe set, with its membership function satisfying the following formula:

[0134]

[0135] in, Describes the complement function. This indicates that the fuzzy set is a severe membership function.

[0136] Fixed range: Does not change with weight.

[0137] Step 2: Obtain the time-lag membership degree based on the time-lag data and the third fuzzy set.

[0138] Specifically, by substituting the time lag data into the aforementioned membership function, the acceleration lag membership degree is obtained.

[0139] S603 performs fuzzy reasoning on membership data based on a preset fuzzy rule base to obtain the membership degree of the unlock risk level.

[0140] For example, a fuzzy rule base containing the following 6 core rules is established:

[0141] 1. If G is Extreme and A is Severe, then R is Extreme. If the gravity exceedance ratio membership G is extremely high and the acceleration lag membership A is severe, then the unlock risk level membership R is extremely high.

[0142] 2. If A is Severe and T is Severe, then R is High. If the acceleration lag membership degree A is severe and the time lag membership degree T is severe, then the unlock risk level membership degree R is high.

[0143] 3. If A is Severe AND (G is High OR G is Extreme) THEN R is High, then the unlock risk level membership R is High.

[0144] 4. If G is High AND A is Not_Severe AND T is Not_Severe THEN R is Medium, then the membership degree of unlock risk level R is medium.

[0145] 5. If G is Normal and T is Severe, then R is Medium.

[0146] 6. If G is Normal AND A is Not_Severe AND T is Not_Severe THEN R isLow, then if the gravity over-limit membership G is normal, the acceleration lag membership A is not severe, and the time lag membership A is not severe, then the unlock risk level membership R is low.

[0147] For example, the domain of the membership degree R of the unlock risk level is [0,1], and its fuzzy set is partitioned as follows:

[0148] Low: Triangular membership function, vertex (0.15,1), left boundary (0.0,0), right boundary (0.3,0).

[0149] Medium: Triangular membership function, vertex (0.45,1), left boundary (0.2,0), right boundary (0.7,0).

[0150] High: Triangle membership function, vertex (0.7,1), left boundary (0.5,0), right boundary (0.9,0).

[0151] Extreme: Triangular membership function, vertex (1.0,1), left boundary (0.8,0), right boundary (1.2,0) (intervals exceeding 1 are ignored in actual calculation).

[0152] S604 defuzzifies the membership degree of the unlock risk level to obtain the unlock risk value.

[0153] Optionally, the membership degree of the unlocking risk level is defuzzified based on the centroid method, and the unlocking risk value satisfies the following formula:

[0154]

[0155] in, This indicates that the risk value has been unlocked. This represents the membership function for unlocking risk levels.

[0156] S605, if the unlocking risk value is less than the first threshold, then the unlocking status of the F-TR lock is determined to be unlocked.

[0157] If the unlocking risk value is less than the first threshold, it indicates that the F-TR lock has a low risk of not being unlocked.

[0158] For example, the first threshold can be set to 0.4.

[0159] S606, if the unlocking risk value is not less than the first threshold and less than the second threshold, then the unlocking status of the F-TR lock is determined to be pending confirmation.

[0160] If the unlocking risk value is not less than the first threshold and less than the second threshold, it indicates that the unlocking risk of the F-TR lock is medium risk.

[0161] For example, the second threshold can be set to 0.7.

[0162] S607, if the unlocking risk value is not less than the second threshold, then the unlocking status of the F-TR lock is determined to be unlocked.

[0163] If the unlocking risk value is not less than the second threshold, it indicates that the F-TR lock has a high risk of not being unlocked.

[0164] S608, if the unlocked status of the F-TR locks corresponding to each lifting point is unlocked, then control the lifting device to continue lifting.

[0165] If all F-TR locks at each lifting point show as unlocked (e.g., the lock tongue switch is triggered, the corner component gravity sensor does not exceed the threshold, etc.), and the tension distribution at the four corners of the lifting device is normal, then it is determined that the locks are safely unlocked and lifting can continue (only a normal indication is given).

[0166] Once all locks are confirmed to be disengaged, the spreader accelerates to lift the container off the flatcar.

[0167] S609 If the unlocking status of the F-TR lock corresponding to a lifting point is pending confirmation, the lifting device will be controlled to pause lifting and a warning will be issued.

[0168] If the F-TR lock at any lifting point is uncertain to be unlocked, the lifting will be paused and a warning will be issued (a yellow indicator light will illuminate) to remind the operator to check and confirm whether the F-TR lock is disengaged.

[0169] S610 If the F-TR lock corresponding to a lifting point is locked, the lifting device will be stopped and an alarm will be issued.

[0170] If any F-TR lock on a lifting point remains locked, a stop lifting command is immediately issued, accompanied by a strong alarm (flashing red light and buzzer). The system locks the spreader and prevents further lifting, immediately triggering the spreader controller or PLC to output a "lifting prohibited" control signal. Simultaneously, an audible and visual alarm and a display in the cab indicate which lock is still locked. The driver then lowers the container back onto the flatcar. Ground personnel must use tools to remove any stuck locks, inspect the mechanical parts of the locks, and then reset the process to try again. Because the spreader speed and travel are limited before unhooking is confirmed, serious accidents involving the lifted vehicle are prevented, thus improving lifting safety.

[0171] Optionally, once all locks are confirmed to be disengaged, the spreader accelerates the lifting of the container away from the flatcar. The system stores the data of this unlocking process (signals from each corner, judgment results, time consumption, etc.) in the data recording module. Using the accumulated data, an AI model can be trained offline to continuously optimize fuzzy rules and decision algorithm parameters, improving the accuracy of future judgments. This also provides a basis for operation and maintenance (for example, identifying a lock that frequently jams allows for scheduling maintenance).

[0172] In this embodiment, while improving the accuracy of F-TR lock unlocking detection, it also features autonomous judgment and automatic interlocking with lifting equipment, reducing reliance on manual intervention and making it particularly suitable for identification in anti-sway lifting environments. Furthermore, the F-TR lock unlocking detection method in this embodiment is not only applicable to traditional lifting environments but can also be extended to multi-layer stacking and unmanned lifting automatic identification scenarios.

[0173] Figure 7 This is a schematic diagram of a container F-TR lock unlocking detection device based on multi-point inertial measurement and lifting point weight fusion, provided in an embodiment of this application. (See attached diagram.) Figure 7 The container F-TR lock unlocking detection device based on multi-point inertial measurement and lifting point weight fusion includes various functional modules for realizing the aforementioned container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion. Any functional module can be implemented by software and / or hardware.

[0174] In some embodiments, the container F-TR lock unlocking detection device 700 based on multi-point inertial measurement and lifting point weight fusion is applied to the control system of the lifting equipment. The control system is communicatively connected to multiple measurement units, which are mounted on the lifting device's spreader. Each measurement unit corresponds one-to-one with a lifting point on the spreader. The device includes a data acquisition module 701, a risk calculation module 702, and an unlocking detection module 703. Wherein:

[0175] The data acquisition module 701 is used to acquire measurement data at the lifting point in real time based on the measurement unit during the process of lifting the spreader to the preset height; the measurement data includes tension data, acceleration lag data and time lag data;

[0176] The risk calculation module 702 is used to input the measurement data into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point;

[0177] The unlock detection module 703 is used to determine the unlock status of the F-TR lock based on the unlock risk value.

[0178] In some embodiments, the risk calculation module 702 is specifically used for:

[0179] The measurement data is fuzzified to obtain membership data; the membership data includes gravity over-limit ratio membership, acceleration lag membership, and time lag membership.

[0180] Based on a pre-defined fuzzy rule base, fuzzy reasoning is performed on the membership data to obtain the membership degree of the unlock risk level;

[0181] The membership degree of the unlock risk level is defuzzified to obtain the unlock risk value.

[0182] In some embodiments, the risk calculation module 702 is further configured to:

[0183] Construct the first fuzzy set corresponding to the gravity exceedance ratio, and the first membership function corresponding to the first fuzzy set;

[0184] Based on tensile data and theoretical average tensile force, the gravity exceedance ratio is obtained;

[0185] The gravity exceedance ratio and the first membership function are used to obtain the gravity exceedance ratio membership degree.

[0186] In some embodiments, the risk calculation module 702 is further configured to:

[0187] Construct the second fuzzy set corresponding to the acceleration lag data, and the second membership function corresponding to the second fuzzy set;

[0188] Acceleration lag membership is obtained based on acceleration lag data and the second membership function.

[0189] In some embodiments, the risk calculation module 702 is further configured to:

[0190] Construct the third fuzzy set corresponding to the time-lag data, and the third membership function corresponding to the third fuzzy set;

[0191] Based on time-lag data and the third fuzzy set, the time-lag membership degree is obtained.

[0192] In some embodiments, the unlock detection module 703 is specifically used for:

[0193] If the unlocking risk value is less than the first threshold, the F-TR lock is determined to be unlocked.

[0194] If the unlocking risk value is not less than the first threshold and less than the second threshold, then the unlocking status of the F-TR lock is determined to be pending confirmation.

[0195] If the unlocking risk value is not less than the second threshold, then the unlocking status of the F-TR lock is determined to be locked.

[0196] In some embodiments, the device 700 further includes a hoisting control module 704, which is specifically used for:

[0197] If the unlocked status of the F-TR locks corresponding to each lifting point is unlocked, then control the lifting device to continue lifting;

[0198] If the unlocking status of the F-TR lock corresponding to a lifting point is pending confirmation, the lifting device will be controlled to pause lifting and a warning will be issued.

[0199] If the F-TR lock corresponding to a lifting point is locked, the lifting device will be stopped and an alarm will be issued.

[0200] The container F-TR lock unlocking detection device 700 based on multi-point inertial measurement and lifting point weight fusion provided in this application embodiment is used to execute the technical solution provided in the aforementioned container F-TR lock unlocking detection method embodiment based on multi-point inertial measurement and lifting point weight fusion. Its implementation principle and technical effect are similar to those in the aforementioned method embodiment, and will not be repeated here.

[0201] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing elements, entirely in hardware, or partially in software via processing elements and partially in hardware. For example, the data acquisition module 701 can be a separate processing element, or it can be integrated into a chip in the above device. Alternatively, it can be stored as program code in the memory of the above device, and its functions can be called and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0202] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (See attached diagram.) Figure 8 The electronic device 800 includes a processor 801 and a memory 02 communicatively connected to the processor 801;

[0203] The 802 memory stores instructions executed by the computer;

[0204] The processor 801 executes the computer execution instructions stored in the memory 802 to implement the aforementioned technical solution of the container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight.

[0205] In the aforementioned electronic device 800, the memory 802 and processor 801 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as bus connections. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be classified as address buses, data buses, control buses, etc., but this does not mean that there is only one bus or one type of bus. The memory 802 stores computer execution instructions for implementing the aforementioned container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion, including at least one software function module that can be stored in the memory 802 in the form of software or firmware. The processor 801 executes various functional applications and data processing by running the software program and module stored in the memory 802.

[0206] The memory 802 includes at least one type of readable storage medium, not limited to Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 802 stores programs, which are executed by the processor 801 upon receiving execution instructions. Furthermore, the software programs and modules within the memory 802 may also include an operating system, which may include various software components and / or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components.

[0207] Processor 801 can be an integrated circuit chip with signal processing capabilities. The aforementioned processor 801 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or processor 801 can be any conventional processor.

[0208] The electronic device 800 is used to execute the technical solution provided in the aforementioned embodiment of the container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight. Its implementation principle and technical effect are similar to those in the aforementioned method embodiment, and will not be repeated here.

[0209] This application also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are executed, they are used to implement the aforementioned technical solution of the container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight.

[0210] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0211] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the control unit of a container F-TR lock unlocking detection device based on multi-point inertial measurement and lifting point weight fusion.

[0212] This application also provides a computer program product, including a computer program that, when executed, implements the aforementioned technical solution for the container F-TR lock unlocking detection method based on the fusion of multi-point inertial measurement and lifting point weight.

[0213] In the above embodiments, those skilled in the art will understand that the above method embodiments can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless network, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0214] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0215] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the appended claims.

[0216] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion, characterized in that, A control system for lifting equipment, wherein the control system is communicatively connected to multiple measuring units, the measuring units being mounted on the lifting device's lifting splint, and each measuring unit corresponding one-to-one with a lifting point on the splint; the method includes: During the process of lifting the lifting device to a preset height, the measurement unit acquires measurement data at the lifting point in real time; wherein, the measurement data includes tension data, acceleration lag data, and time lag data; The measurement data is input into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point; Based on the unlocking risk value, the unlocking status of the F-TR lock is determined; The measurement data is input into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point, including: The measurement data is fuzzified to obtain membership data; wherein, the membership data includes gravity over-limit ratio membership, acceleration lag membership, and time lag membership. Based on a preset fuzzy rule base, fuzzy reasoning is performed on the membership data to obtain the membership degree of the unlock risk level; Defuzzify the membership degree of the unlock risk level to obtain the unlock risk value; The gravity exceedance ratio membership degree is obtained based on the following method: Construct the first fuzzy set corresponding to the gravity exceedance ratio, and the first membership function corresponding to the first fuzzy set; Based on the tensile data and the theoretical average tensile force, the gravity exceedance ratio is obtained; Based on the gravity exceedance ratio and the first membership function, the gravity exceedance ratio membership degree is obtained; The acceleration hysteresis membership degree is obtained based on the following method: Construct a second fuzzy set corresponding to the acceleration hysteresis data, and a second membership function corresponding to the second fuzzy set; The acceleration lag membership is obtained based on the acceleration lag data and the second membership function; The time-lag membership degree is obtained based on the following method: Construct a third fuzzy set corresponding to the time-lag data, and a third membership function corresponding to the third fuzzy set; Based on the time-lag data and the third fuzzy set, the time-lag membership degree is obtained.

2. The method according to claim 1, characterized in that, Based on the unlocking risk value, the unlocking status of the F-TR lock is determined, including: If the unlocking risk value is less than the first threshold, then the unlocking status of the F-TR lock is determined to be unlocked; If the unlocking risk value is not less than the first threshold and less than the second threshold, then the unlocking status of the F-TR lock is determined to be pending confirmation. If the unlocking risk value is not less than the second threshold, then the unlocking status of the F-TR lock is determined to be unlocked.

3. The method according to claim 2, characterized in that, The method further includes: If the unlocked status of the F-TR locks corresponding to each lifting point is unlocked, then control the lifting device to continue lifting; If the unlocking status of the F-TR lock corresponding to a lifting point is pending confirmation, the lifting device will be controlled to pause lifting and a warning will be issued. If the F-TR lock corresponding to a lifting point is locked, the lifting device will be controlled to stop lifting and an alarm will be issued.

4. A detection device for implementing the container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion as described in any one of claims 1-3, characterized in that, A control system for lifting equipment, wherein the control system is communicatively connected to multiple measuring units, each measuring unit being mounted on the lifting device's lifting splint, and each measuring unit corresponding one-to-one with a lifting point on the splint; the device includes: The data acquisition module is used to acquire measurement data at the lifting point in real time based on the measurement unit during the process of lifting the spreader to a preset height; wherein, the measurement data includes tension data, acceleration lag data and time lag data; The risk calculation module is used to input the measurement data into a preset fuzzy logic algorithm to obtain the unlocking risk value of the F-TR lock corresponding to the suspension point; The unlock detection module is used to determine the unlock status of the F-TR lock based on the unlock risk value.

5. An electronic device, characterized in that, Includes a processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1 to 3.

Images