CN-122022321-A - AGV feeding scheduling system and method for multi-tensile testing machine based on dynamic priority

CN122022321ACN 122022321 ACN122022321 ACN 122022321ACN-122022321-A

Abstract

The invention provides a multi-tensile-testing-machine AGV feeding scheduling system and method based on dynamic priority, belongs to the field of industrial automation, and aims at the problems of equipment idling, high sensor cost and interface sealing caused by static strategies in multi-tensile-testing-machine AGV scheduling, wherein the system comprises a hardware sensing layer (an infrared correlation sensor and a charging tray pre-drilling), a data acquisition layer (a residual sample amount is reversely calculated by LIMS), and an intelligent decision layer (a dynamic priority algorithm). And the algorithm instructs the AGV to dynamically feed according to the real-time progress difference value and the new sample batch. After implementation, the utilization rate of the equipment exceeds 96%, the daily sample treatment efficiency is increased by more than 50%, the cost is reduced by 60% compared with a wireless/weighing scheme, and the equipment is suitable for multiple brands of testing machines.

Inventors

CHEN YONG
SONG XIAOYONG
GUO FENG
LIU ZHIJUN
WAN CHUNHUA
RUI QI
WANG YANYANG
JIANG JIN

Assignees

马鞍山钢铁有限公司

Dates

Publication Date: 20260512
Application Date: 20260128

Claims (4)

1. Many tensile testing machine AGV material loading dispatch system based on dynamic priority, its characterized in that includes: the hardware perception layer comprises a fixed tray emptying detection device arranged on a tray parking station base of each tensile testing machine, wherein the detection device is a pair of infrared correlation photoelectric sensors, the sensor light paths pass through pre-drilled through holes at the bottom of the tray after being calibrated, and the sensors are connected with a system master control PLC; The data acquisition layer comprises a residual amount calculation module based on external informatization, wherein the module records the total number S1 of samples sent to each testing machine through a system main control computer, monitors a test completion signal uploaded to a database by the testing machine computer to count the number S2 of the completed samples, and calculates the real-time residual sample amount of each testing machine through a formula of 'residual amount=S1-S2'; The intelligent decision layer comprises an AGV dynamic scheduling algorithm running in a main control computer or an advanced PLC and an AGV communicated with the algorithm; And the tensile testing machines are communicated with the hardware sensing layer, the data acquisition layer and the intelligent decision layer, receive samples conveyed by the AGV and execute testing tasks.
2. A control method based on the system of claim 1, comprising the steps of: S1, drilling a through round hole on a material tray, mounting an infrared correlation photoelectric sensor on a station base of the material tray of each tensile testing machine, and connecting the sensor into a system master control PLC; s2, after the processing center finishes sample preparation, the main control computer records the total number S1 of samples and instructs the AGV to convey to a designated testing machine, meanwhile, a data monitoring service is started, and a database is monitored to count the number S2 of the finished samples; s3, when the sensor detects that the last sample of the material tray is taken away, sending a signal of 'the material tray is about to be emptied' to the PLC, activating an intelligent decision layer algorithm, calculating the residual sample quantity of each testing machine by the algorithm through 'residual quantity=S1-S2', and calculating an absolute value difference value |delta N| of the residual quantities of any two testing machines; And S4, intelligent decision and AGV dispatching, wherein if a new sample batch (total quantity Q) is to be distributed, the algorithm decides the feeding quantity according to the magnitude relation between Q and |delta N|: If Q is not less than |delta N|, instructing the AGV to convey the samples with the quantity of |delta N| to a testing machine with small residual quantity, and evenly distributing the residual samples to two new trays; if Q < |delta N|, instructing the AGV to convey all Q samples to a testing machine with less residual quantity; And S5, instruction execution and system feedback, namely, AGV executes a feeding instruction, so that the testing machine can receive new samples seamlessly after finishing the current test, and S3-S4 are executed circularly to realize dynamic scheduling.
3. The method according to claim 2, wherein in step S3, when the sensor detects that the last sample of the tray is removed, the state is changed from "blocked" to "unblocked", and after the PLC detects the state transition signal, an interrupt event is sent to the host computer to activate the scheduling algorithm.
4. The method of claim 2, wherein in step S4, when all of the plurality of testers are about to finish, the AGV preferentially performs the synchronous feeding task, so as to ensure that the plurality of testers finish the stage test synchronously.

Description

AGV feeding scheduling system and method for multi-tensile testing machine based on dynamic priority Technical Field The invention relates to the technical field of industrial automation, in particular to an AGV feeding scheduling system and method of a multi-tensile testing machine based on dynamic priority. Background In modern materials testing laboratories, it has become a trend to integrate multiple tensile testing machines into one large automated system through an AGV and rail system. Currently, in a multi-tensile testing machine automation system, the AGV scheduling policy is mostly in a static or semi-static mode, for example, a fixed feeding priority is set for the AGV (for example, the AGV always serves 1 bit and then 3 bit), or a manual preset dispatch list is relied on. The fundamental drawback of these strategies is that they fail to sense and respond to real-time dynamic changes in the system. The test cycle time on each tester is fluctuating (typically 2-4 minutes/root) due to the differences in mechanical properties of the different samples, which results in unpredictable, constantly changing deviations in the completion schedule of each tester. The static scheduling strategy is inevitably invalid in the dynamic environment, and the common situation is that a high-efficiency test probability is finished first and then the test machine is idle, and the AGV is conveying trays to a test machine with a backward progress in a fixed sequence, so that high-efficiency equipment is idle for a long time, and the overall productivity of the system is seriously reduced. In the prior art, a sensor (such as weighing and RFID) is arranged on a charging tray to acquire a state, but the problems of high cost, large transformation, poor reliability and the like exist. More importantly, the fundamental problem of how to make decisions cannot be solved by simple hardware detection. On the other hand, while laboratory informatization systems (LIMS) can receive the final test results, they are not used for real-time, prospective AGV scheduling optimization. Therefore, developing an integrated dispatching system capable of responding to system state change in real time and dynamically commanding AGV actions so as to realize load balancing of multiple testers has become a technical problem to be solved urgently in the field. Disclosure of Invention The invention aims to provide an AGV feeding scheduling system and method for a multi-tensile testing machine based on dynamic priority, which are used for solving the problems in the background technology. In order to achieve the purpose, the invention provides the following technical scheme that the AGV feeding scheduling system of the multi-tensile testing machine based on dynamic priority comprises the following components: the hardware perception layer comprises a fixed tray emptying detection device arranged on a tray parking station base of each tensile testing machine, wherein the detection device is a pair of infrared correlation photoelectric sensors, the sensor light paths pass through pre-drilled through holes at the bottom of the tray after being calibrated, and the sensors are connected with a system master control PLC; The data acquisition layer comprises a residual amount calculation module based on external informatization, wherein the module records the total number S1 of samples sent to each testing machine through a system main control computer, monitors a test completion signal uploaded to a database by the testing machine computer to count the number S2 of the completed samples, and calculates the real-time residual sample amount of each testing machine through a formula of 'residual amount=S1-S2'; The intelligent decision layer comprises an AGV dynamic scheduling algorithm running in a main control computer or an advanced PLC and an AGV communicated with the algorithm; And the tensile testing machines are communicated with the hardware sensing layer, the data acquisition layer and the intelligent decision layer, receive samples conveyed by the AGV and execute testing tasks. Based on the system, the invention also provides a corresponding control method, which comprises the following steps: S1, drilling a through round hole on a material tray, mounting an infrared correlation photoelectric sensor on a station base of the material tray of each tensile testing machine, and connecting the sensor into a system master control PLC; s2, after the processing center finishes sample preparation, the main control computer records the total number S1 of samples and instructs the AGV to convey to a designated testing machine, meanwhile, a data monitoring service is started, and a database is monitored to count the number S2 of the finished samples; s3, when the sensor detects that the last sample of the material tray is taken away, sending a signal of 'the material tray is about to be emptied' to the PLC, activating an intelligent decision layer algorithm, calc