CN-120864372-B - Container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion

CN120864372BCN 120864372 BCN120864372 BCN 120864372BCN-120864372-B

Abstract

The application provides a container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion, which can be used in the technical field of lifting equipment. The method is applied to a control system of lifting equipment, the control system is in communication connection with a plurality of measuring units, the measuring units are arranged on lifting appliances of the lifting equipment, the measuring units correspond to lifting point positions of the lifting appliances one by one, measurement data at the lifting points are obtained in real time based on the measuring units in the process that the lifting appliances are lifted to a preset height, the measurement data comprise tension data, acceleration lag data and time lag data, the measurement data are input into a preset fuzzy logic algorithm to obtain unlocking risk values of F-TR locks corresponding to the lifting points, and the unlocking state of the F-TR locks is determined based on the unlocking risk values. The method improves the identification accuracy of detecting whether the F-TR lock is unlocked or not, and effectively reduces false alarm and false hanging.

Inventors

KANG JULONG
ZHAO GANG
ZENG BING
LIU LIN
LIU XUANHAO
DU RUN
CHENG WENMING

Assignees

中铁联合国际集装箱有限公司

Dates

Publication Date: 20260505
Application Date: 20250814

Claims (6)

1. The utility model provides a container F-TR lock unblock detection method based on multi-point inertial measurement and hoisting point weight integration which characterized in that is applied to the control system of hoisting equipment, control system and a plurality of measuring unit communication connection, measuring unit set up in on the hoist of hoisting equipment, measuring unit with the hoisting point position one-to-one of hoist, the method includes: acquiring measurement data at the lifting point in real time based on the measurement unit in the process that the lifting appliance lifts to a preset height, wherein the measurement data comprises tension data, acceleration lag data and time lag data; Inputting the measurement data into a preset fuzzy logic algorithm to obtain an unlocking risk value of the F-TR lock corresponding to the lifting point; Determining an unlocking state of the F-TR lock based on the unlocking risk value; inputting the measurement data into a preset fuzzy logic algorithm to obtain an unlocking risk value of the F-TR lock corresponding to the lifting point, wherein the unlocking risk value comprises the following steps: The measurement data is subjected to fuzzification processing to obtain membership data, wherein the membership data comprises a gravity overrun ratio membership, an acceleration hysteresis membership and a time hysteresis membership; Fuzzy reasoning is carried out on the membership data based on a preset fuzzy rule base, and unlocking risk level membership is obtained; deblurring the unlocking risk level membership to obtain the unlocking risk value; the gravity overrun ratio membership is obtained based on the following method: constructing a first fuzzy set corresponding to the gravity overrun ratio and a first membership function corresponding to the first fuzzy set; Based on the tension data and the theoretical average tension, obtaining a gravity overrun ratio; obtaining the gravity overrun ratio membership based on the gravity overrun ratio and the first membership function; the acceleration hysteresis membership is obtained based on the following method: Constructing a second fuzzy set corresponding to the acceleration hysteresis data and a second membership function corresponding to the second fuzzy set; acquiring the acceleration hysteresis membership based on the acceleration hysteresis data and the second membership function; The time lag membership is obtained based on the following method: Constructing a third fuzzy set corresponding to the time lag data and a third membership function corresponding to the third fuzzy set; And obtaining the time lag membership based on the time lag data and the third fuzzy set.
2. The method of claim 1, wherein determining an unlock state of the F-TR lock based on the unlock risk value comprises: If the unlocking risk value is smaller than a first threshold value, determining that the unlocking state of the F-TR lock is unlocked; if the unlocking risk value is not smaller than the first threshold value and smaller than the second threshold value, determining the unlocking state of the F-TR lock to be confirmed; and if the unlocking risk value is not smaller than a second threshold value, determining that the unlocking state of the F-TR lock is not unlocked.
3. The method according to claim 2, wherein the method further comprises: if the unlocking states of the F-TR locks corresponding to the lifting points are unlocked, controlling the lifting appliance to continue lifting; if the unlocking state of the F-TR lock corresponding to the hanging point is to be confirmed, controlling the hanging tool to suspend hanging, and sending out an early warning prompt; and if the unlocking state of the F-TR lock corresponding to the hanging point is not unlocked, controlling the lifting appliance to stop lifting, and sending out an alarm prompt.
4. A detection device for implementing the container F-TR lock unlocking detection method based on multipoint inertia measurement and lifting point weight fusion according to any one of claims 1-3, characterized in that the detection device is applied to a control system of a lifting device, the control system is in communication connection with a plurality of measurement units, the measurement units are arranged on a lifting appliance of the lifting device, the measurement units are in one-to-one correspondence with lifting point positions of the lifting appliance, and the device comprises: The data acquisition module is used for acquiring measurement data at the lifting point in real time based on the measurement unit in the process of lifting the lifting appliance to a preset height, wherein the measurement data comprise tension data, acceleration lag data and time lag data; the risk calculation module is used for inputting the measurement data into a preset fuzzy logic algorithm to obtain an unlocking risk value of the F-TR lock corresponding to the lifting point; and the unlocking detection module is used for determining the unlocking state of the F-TR lock based on the unlocking risk value.
5. An electronic device comprising a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; The processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 3.
6. A computer readable storage medium having stored therein computer executable instructions which when executed are adapted to carry out the method of any one of claims 1 to 3.

Description

Container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion Technical Field The application relates to the technical field of lifting equipment, in particular to a container F-TR lock unlocking detection method based on multipoint inertia measurement and lifting point weight fusion. Background The F-TR lock is a special locking device for fixing a container and a flatcar in railway container transportation, and is called as a 'olecranon lock' because of its appearance, and the lock is used for realizing the anti-overturning and anti-bouncing functions of the container in the transportation process. At present, an F-TR lock (hawk head lock) is used for fixing a container on a vehicle body of a railway container flatcar, when the container is unloaded, the hawk head part of the F-TR lock is easy to hang on a container lock hole, and a 'continuous hanging' accident is caused by hanging the container under the condition that the F-TR lock is not completely separated from the container lock hole. Currently, single sensing discrimination systems based on tensile or gravitational force, displacement or machine vision detection are available. However, the existing F-TR lock unlocking detection method has the problems of low detection accuracy and easy false alarm and false hanging. Disclosure of Invention The application provides a container F-TR lock unlocking detection method based on multi-point inertial measurement and lifting point weight fusion, which is used for solving the technical problems that the existing F-TR lock unlocking detection method is low in detection accuracy and easy to cause false alarm and false lifting. According to a first aspect of the disclosure, the application provides a container F-TR lock unlocking detection method based on multipoint inertia measurement and lifting point weight fusion, which is applied to a control system of lifting equipment, wherein the control system is in communication connection with a plurality of measurement units, the measurement units are arranged on lifting appliances of the lifting equipment, the measurement units correspond to lifting point positions of the lifting appliances one by one, and the method comprises the following steps: acquiring measurement data at the lifting point in real time based on the measurement unit in the process that the lifting appliance lifts to a preset height, wherein the measurement data comprises tension data, acceleration lag data and time lag data; Inputting the measurement data into a preset fuzzy logic algorithm to obtain an unlocking risk value of the F-TR lock corresponding to the lifting point; and determining the unlocking state of the F-TR lock based on the unlocking risk value. In a possible implementation manner, inputting the measurement data into a preset fuzzy logic algorithm to obtain an unlocking risk value of the F-TR lock corresponding to the lifting point, where the unlocking risk value comprises: The measurement data is subjected to fuzzification processing to obtain membership data, wherein the membership data comprises a gravity overrun ratio membership, an acceleration hysteresis membership and a time hysteresis membership; Fuzzy reasoning is carried out on the membership data based on a preset fuzzy rule base, and unlocking risk level membership is obtained; and deblurring the membership of the unlocking risk level to obtain the unlocking risk value. In a possible embodiment, the gravity overrun membership is obtained based on the following method: constructing a first fuzzy set corresponding to the gravity overrun ratio and a first membership function corresponding to the first fuzzy set; Based on the tension data and the theoretical average tension, obtaining a gravity overrun ratio; and obtaining the gravity overrun ratio membership degree based on the gravity overrun ratio and the first membership function. In one possible embodiment, the acceleration hysteresis membership is obtained based on the following method: Constructing a second fuzzy set corresponding to the acceleration hysteresis data and a second membership function corresponding to the second fuzzy set; and obtaining the acceleration hysteresis membership based on the acceleration hysteresis data and the second membership function. In a possible embodiment, the time-lag membership is obtained based on the following method: Constructing a third fuzzy set corresponding to the time lag data and a third membership function corresponding to the third fuzzy set; And obtaining the time lag membership based on the time lag data and the third fuzzy set. In a possible embodiment, determining the unlock state of the F-TR lock based on the unlock risk value includes: If the unlocking risk value is smaller than a first threshold value, determining that the unlocking state of the F-TR lock is unlocked; if the unlocking risk value is not smaller than the first threshold value and smalle