Edge computing-based hanging system monitoring method, system, device and medium

Abstract

The application provides a kind of based on edge computing's hanging system monitoring method, system, equipment and medium, the method comprises: obtaining current sampling signal in motor drive loop of hanging system;The current sampling signal is respectively transmitted to hardware protection module and MCU software protection module;In response to the current sampling signal is higher than the first reference voltage that the hardware protection module dynamically sets, the switch tube of motor power supply main loop is generated off signal;In response to the current sampling signal is higher than the second reference voltage that the MCU software protection module sets, and the current sampling signal continues integral value exceeds safety threshold, by PWM regulation instruction execution software frequency reduction or soft stop;According to hardware off or software regulation trigger interrupt service procedure, overload event data is packaged and stored to carry out edge computing diagnosis.The application provides a kind of based on edge computing's hanging system intelligent overload protection and health early warning technical scheme.

Description

Technical Field

[0001] This application belongs to the technical field of industrial automation and intelligent manufacturing, and relates to a monitoring method, system, equipment and medium for a hanging system based on edge computing. Background Technology

[0002] Garment hanging systems are widely used in garment production lines, where multiple motors drive hangers to rotate on guide rails. In actual operation, due to factors such as excessive garment weight, dust accumulation on the guide rails, damaged hanger bearings, or fabric jamming into the mechanical structure, motor overload or even stalling can easily occur. Existing hanging system control systems have the following shortcomings in handling overload faults:

[0003] (1) Traditional protection is slow to respond and cannot be recovered. Existing systems mostly use thermal relays or fuses, which operate based on thermal effects and cannot cope with microsecond-level short circuits or severe stall impacts. This can easily lead to the burnout of the drive board, and once triggered, manual replacement is required, which affects the efficiency of the production line.

[0004] (2) Single software protection has blind spots. Some systems use MCU (Microcontroller Unit) to sample current through ADC (Analog-to-Digital Converter) for software judgment. However, due to the limitations of sampling period and interrupt delay (usually in milliseconds), it cannot cut off sudden large currents at the microsecond level, which poses a risk of burning out power devices.

[0005] (3) Protection and diagnosis are disconnected and lack predictive capabilities. Once the existing system triggers overload protection, it usually cuts off the power directly, and the characteristic data of the fault moment (such as transient peak current and fault location) are lost. For gradual mechanical jamming (which is only manifested as slight and brief current increase in the early stage), the traditional protection scheme with dead zone threshold cannot detect it, which leads to "minor problems becoming major problems", and eventually causes unplanned shutdowns, reducing the OEE (Overall Equipment Effectiveness) of the production line. Summary of the Invention

[0006] This application provides a monitoring method, system, device, and medium for a suspended system based on edge computing, which addresses the problems of slow response, unrecoverable nature, and inability to capture fault characteristics for mechanical health prediction in existing overload protection systems.

[0007] In a first aspect, this application provides a monitoring method for a hanging system based on edge computing, comprising: acquiring a current sampling signal in the motor drive circuit of the hanging system; transmitting the current sampling signal to a hardware protection module and an MCU software protection module respectively; generating a switch-off signal for the main power supply circuit of the motor in response to the current sampling signal being higher than a first reference voltage dynamically set by the hardware protection module; executing software frequency reduction or soft shutdown through a PWM adjustment command in response to the current sampling signal being higher than a second reference voltage set by the MCU software protection module and the continuous integral value of the current sampling signal exceeding a safety threshold; wherein the second reference voltage is lower than the first reference voltage; triggering an interrupt service routine according to the switch-off signal or the PWM adjustment command to package, record and store overload event data; extracting the stored overload event data, performing edge computing diagnosis, and distinguishing between normal inrush current, abnormal overload position guide rail or mechanical jamming of the hanger according to the overload event data, as well as performing early jamming warning judgment of the overload position guide rail.

[0008] In one implementation of the first aspect, the step of generating a switch-off signal for the main power supply circuit of the motor in response to the current sampling signal being higher than the first reference voltage dynamically set by the hardware protection module includes: outputting a dynamically changing first reference voltage according to the current operating state of the motor during the starting phase, steady-state operation phase, or deceleration phase; generating a switch-off signal for the main power supply circuit of the motor in response to the current sampling signal at the non-inverting input terminal of the comparator being higher than the first reference voltage at the inverting input terminal of the comparator, wherein the switch-off signal is used to pull down the gate voltage of the switch in the main power supply circuit of the motor to perform a rapid physical disconnection of the main power supply circuit of the motor.

[0009] In one implementation of the first aspect, the step of executing software frequency reduction or soft shutdown via a PWM adjustment command in response to the current sampling signal being higher than the second reference voltage set by the MCU software protection module and the continuous integral value of the current sampling signal exceeding a safety threshold includes: converting the current sampling signal into a digital current value according to a preset frequency; and, in response to the digital current value being higher than the second reference voltage set by the MCU software protection module, using... The integral algorithm and the sliding window delay algorithm calculate the continuous integral value. If the continuous integral value of the current sampling signal exceeds the safety threshold, it is determined to be a mild or stall overload. The software frequency reduction or soft shutdown is executed through the PWM adjustment command.

[0010] In one implementation of the first aspect, the step of packaging, recording, and storing overload event data by triggering an interrupt service routine based on the switch-off signal or the PWM adjustment instruction includes: configuring the MCU's external interrupt to the highest priority; triggering an edge-triggered interrupt based on the switch-off signal or the PWM adjustment instruction and entering the interrupt service routine; in the interrupt service routine, reading the transient current peak value in the analog-to-digital converter holding register, and simultaneously reading the system timestamp in the first timer, the current pulse position value of the quadrature encoder, and the currently driven hanger RFID number, packaging all the read data into an overload event data entry, and pushing it into a first-in-first-out memory.

[0011] In one implementation of the first aspect, the steps of extracting stored overload event data, performing edge computing diagnosis, and distinguishing between normal inrush flow, overload position rail abnormality, or clothes hanger mechanical jamming based on the overload event data, as well as performing early jamming warning judgment of overload position rail, include: extracting stored overload event data, determining whether its timestamp is within a preset inrush flow window period after motor startup, and if so, marking the event corresponding to the overload event data as "normal inrush flow" and removing it.

[0012] In one implementation of the first aspect, the steps of extracting stored overload event data, performing edge computing diagnosis, and distinguishing between normal inrush current, overload position rail abnormality, or hanger mechanical jamming based on the overload event data, as well as performing early jamming warning judgment for overload position rails, include: establishing a hash table with the position identification code of the rail section and the hanger RFID number as the primary key; if the cumulative overload frequency of the same position identification code exceeds a first frequency threshold, it is determined that the position rail is abnormal; if the same hanger RFID number triggers overload at different position identification codes, it is determined that the hanger is mechanically jammed; wherein, the position identification code is determined based on the current pulse position value of the quadrature encoder; for each overload event of the same position identification code, its transient current peak value is extracted and least squares linear fitting is performed; if the peak current shows an increasing slope over time, and the slope is greater than a preset deterioration rate, it is determined that the mechanical resistance at that position is continuously deteriorating, and an early jamming warning signal is output.

[0013] In one implementation of the first aspect, the method further includes: in response to the generation of a switch turn-off signal, triggering hardware cutoff, starting a second timer, using the second timer to delay a preset cooling time, and then tentatively turning on the switch in the main power supply circuit of the motor; if the current in the motor drive circuit does not exceed the second reference voltage after the tentative turn-on, it is determined that the intermittent interference has been eliminated, and the motor drive system is restored to normal operation and the fault state is reset; if the hardware cutoff is triggered again after the tentative turn-on, and the continuous retry failure exceeds a preset number, the motor drive system is locked and a fault alarm is triggered.

[0014] Secondly, this application provides a monitoring system for a hanging system based on edge computing, comprising: a current sampling module configured to acquire current sampling signals in the motor drive circuit of the hanging system; and to transmit the current sampling signals to a hardware protection module and an MCU software protection module respectively; the hardware protection module configured to generate a switch-off signal for the main power supply circuit of the motor in response to the current sampling signal being higher than a first reference voltage dynamically set by the hardware protection module; the MCU software protection module configured to execute software frequency reduction or soft shutdown via a PWM adjustment command in response to the current sampling signal being higher than a second reference voltage set by the MCU software protection module, and the continuous integral value of the current sampling signal exceeding a safety threshold; wherein the second reference voltage is lower than the first reference voltage; an event latching and recording module configured to trigger an interrupt service routine according to the switch-off signal or the PWM adjustment command to package, record, and store overload event data; and an edge computing diagnostic module configured to extract the stored overload event data, perform edge computing diagnostics, and distinguish between normal inrush current, abnormal overload position rail, or mechanical jamming of the hanger based on the overload event data, as well as to perform early jamming warning judgment of the overload position rail.

[0015] Thirdly, this application provides an electronic device, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method described thereon.

[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon that, when executed, implements the method described thereon.

[0017] As described above, the edge computing-based monitoring method, system, device, and medium for suspended systems described in this application have the following beneficial effects:

[0018] This application achieves hardware-software synergy and microsecond-level safe cutoff. The hardware comparator directly controls the MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor), bypassing the MCU software delay to achieve microsecond-level physical cutoff and eliminate the risk of board burn-out. The MCU software handles minor overloads to achieve fine and flexible control.

[0019] This application addresses the issue of lossless fault data capture by utilizing a hardware interrupt latching mechanism to retain the most authentic fault characteristic data at the moment the current is cut off, thus solving the problem of data loss at the fault site after traditional protection actions.

[0020] This application can shift from passive protection to proactive prediction, using overload protection events as probes of mechanical health. By using edge computing to uncover the spatiotemporal distribution patterns of minor overloads, it can provide early warnings before serious mechanical jamming occurs, transforming unplanned downtime into planned maintenance.

[0021] This application incorporates an intelligent self-recovery mechanism. After the hardware protection action is activated, a trial soft restart is used to determine whether the fault has been eliminated, which greatly reduces production line downtime caused by occasional interference. Attached Figure Description

[0022] Figure 1 The diagram shows an application scenario of the edge computing-based monitoring method for suspended systems described in this application.

[0023] Figure 2 The diagram shown illustrates the principle flowchart of the edge computing-based monitoring method for suspended systems described in this application embodiment.

[0024] Figure 3 The diagram shown is an overall flowchart of the edge computing-based monitoring method for a suspended system as described in an embodiment of this application.

[0025] Figure 4 The diagram shown is a flowchart of the hardware and software collaborative determination process of the edge computing-based suspended system monitoring method described in this application embodiment.

[0026] Figure 5 The diagram shown is a flowchart of the edge computing judgment process for the monitoring method of a suspended system based on edge computing as described in an embodiment of this application.

[0027] Figure 6 The diagram shown illustrates the self-recovery process of the edge computing-based suspended system monitoring method described in this application embodiment.

[0028] Figure 7 The diagram shown is a structural schematic of the edge computing-based suspended system monitoring system described in this application embodiment.

[0029] Figure 8 The diagram shown is a structural connection diagram of the electronic device described in an embodiment of this application.

[0030] Component designation explanation

[0031] 7 Edge computing-based monitoring system for suspended systems 71 Current sampling module 72 Hardware protection module 73 MCU software protection module 74 Event latching and logging module 75 Edge computing diagnostic module 8 electronic devices 81 processor 82 memory 83 Communication interface 84 System bus S21~S26 step Detailed Implementation

[0032] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0033] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0034] The following embodiments of this application provide a method, system, device and medium for monitoring a suspended system based on edge computing, including but not limited to applications in a suspended system. The following description will use this application scenario as an example.

[0035] Please see Figure 1 The image shows a schematic diagram illustrating an application scenario of the edge computing-based monitoring method for suspended systems described in this application. Figure 1 As shown in the embodiments of this application, the edge computing-based hanging system monitoring method relates to an intelligent hanging production line in the garment and textile industry. It is applied to a hanging system production line for sewing garments. Specifically, it mainly includes a motor drive circuit and a main control MCU. The current sampling signal generated by the motor drive circuit is transmitted to the main control MCU, and the main control MCU performs system monitoring operations such as intelligent overload protection and health warning.

[0036] The garment hanging system is a key piece of equipment for achieving automated, flexible, and intelligent production in the sewing industry. Its core lies in realizing the "non-landing" flow of cut pieces between various processes through track transmission, intelligent scheduling, and information integration. The basic operating principle includes: (1) Cut piece hanging and information entry: The cut pieces of the whole garment are hung on a special hanger (or intelligent carrier). Each hanger is equipped with an RFID (Radio Frequency Identification) tag or barcode to store information such as order number, style, and process. The system enters the preset process route of the cut piece through the host computer or terminal. (2) Automatic transmission and path planning: The hanger is placed on a circular or linear guide rail and driven by a motor to run in a cycle. The system dynamically allocates the target workstation for the next process through an intelligent scheduling algorithm based on the preset process and real-time workstation status (such as busy / idle, work-in-process backlog). It supports cross-floor and long-distance transportation and can connect multiple links such as cutting, sewing, and ironing.

[0037] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0038] Please see Figure 2 The diagram shows the principle flowchart of the edge computing-based monitoring method for suspended systems described in this application embodiment. Figure 2 As shown, this embodiment provides a monitoring method for a suspended system based on edge computing, specifically including the following steps:

[0039] S21, acquire the current sampling signal in the motor drive circuit of the suspension system.

[0040] Please see Figure 3 The diagram shows the overall flowchart of the edge computing-based monitoring method for suspended systems described in this application embodiment. Figure 3 As shown, the analog current signal V_sense of the motor drive circuit is acquired in real time; at the same time, the MCU dynamically calculates and outputs the first reference threshold V_ref1 to the high-speed comparator on the hardware protection side through the DAC based on the current motor speed feedback and PWM duty cycle, as well as the second reference threshold V_ref2 configured for MCU software protection judgment.

[0041] S22, the current sampling signal is transmitted to the hardware protection module and the MCU software protection module respectively.

[0042] S23, in response to the current sampling signal being higher than the first reference voltage dynamically set by the hardware protection module, a switch turn-off signal is generated for the main power supply circuit of the motor.

[0043] In one embodiment, the step of generating a switch turn-off signal for the main power supply circuit of the motor in response to the current sampling signal being higher than a first reference voltage dynamically set by the hardware protection module includes:

[0044] (1) Output a dynamically changing first reference voltage based on the current operating state of the motor during the starting stage, steady-state operation stage or deceleration stage.

[0045] (2) In response to the current sampling signal at the non-inverting input of the comparator being higher than the first reference voltage at the inverting input of the comparator, a switch turn-off signal for the main power supply circuit of the motor is generated. The switch turn-off signal is used to pull down the gate voltage of the switch in the main power supply circuit of the motor to perform a fast physical cut-off of the main power supply circuit of the motor.

[0046] Specifically, the high-speed hardware comparator flips, and the flip signal turns off the MOSFET switch of the motor drive circuit through the logic drive unit in microseconds.

[0047] S24, in response to the current sampling signal being higher than the second reference voltage set by the MCU software protection module, and the continuous integral value of the current sampling signal exceeding the safety threshold, software frequency reduction or soft shutdown is executed through PWM adjustment command; wherein, the second reference voltage is lower than the first reference voltage.

[0048] In one embodiment, in response to the current sampling signal being higher than the second reference voltage set by the MCU software protection module, and the continuous integral value of the current sampling signal exceeding a safety threshold, the step of executing software frequency reduction or soft shutdown via a PWM adjustment command includes:

[0049] (1) Convert the current sampling signal into a digital current value according to a preset frequency.

[0050] (2) In response to the digital current value being higher than the second reference voltage set by the MCU software protection module, the following measures are taken: The integral algorithm and the sliding window delay algorithm calculate the continuous integral value. If the continuous integral value of the current sampling signal exceeds the safety threshold, it is determined to be a mild or stall overload. The software frequency reduction or soft shutdown is executed through the PWM adjustment command.

[0051] Specifically, the MCU outputs a reduced PWM signal to reduce the frequency and speed of the motor or to soft stop it, avoiding the impact of hard cut-off on the mechanical mechanism; at the same time, it records the current current value, position code, timestamp and object ID to generate a minor overload event record and proceeds to step S25.

[0052] Please see Figure 4 The diagram shows the hardware-software collaborative decision-making process of the edge computing-based suspended system monitoring method described in this application embodiment. Figure 4As shown, according to the motor start command, during the start-up phase, the MCU controls the DAC output to raise V_ref1, waits for the inrush current, and determines whether the window period has ended. If not, it continues to wait and determines the end of the window period; if so, it enters the steady-state operation phase. The MCU controls the DAC output V_ref1 to acquire V_sense in real time and performs a simulation comparison between V_sense and V_ref1 in a high-speed comparator on the hardware side. If V_sense exceeds V_ref1, the MOSFET is turned off in microseconds, triggering a hardware interrupt to latch the data and proceeding to subsequent edge computing diagnostics. If V_sense does not exceed V_ref1, the MCU compares the sampled digital current value with V_ref2 and integrates it. If the integrated value exceeds the V_ref2 safety threshold, i.e., exceeds the safety integration threshold set by using V_ref2 as the boundary for integration calculation, software frequency reduction or soft shutdown is executed, software event recording is triggered, and subsequent edge computing diagnostics are proceeded.

[0053] S25, trigger the interrupt service routine according to the switch turn-off signal or the PWM adjustment command, and package, record and store the overload event data.

[0054] In one embodiment, the step of packaging, recording, and storing overload event data by triggering an interrupt service routine based on the switch turn-off signal or the PWM adjustment command includes:

[0055] (1) Configure the external interrupt of the MCU to the highest priority; trigger an edge interrupt according to the switch turn-off signal or the PWM adjustment instruction, and enter the interrupt service routine.

[0056] (2) In the interrupt service routine, the transient current peak value in the analog-to-digital converter holding register is read, and the system timestamp, the current pulse position value of the quadrature encoder, and the RFID number of the currently driven clothes hanger in the first timer are read. All the read data are packaged into an overload event data and pushed into the first-in-first-out memory.

[0057] Specifically, this application employs microsecond-level hardware cutoff and fault transient latching. The hardware comparator flips to turn off the MOSFETs in the main circuit of the motor in microseconds. Simultaneously, the high-priority interrupt of the MCU is triggered by the transition edge of the high-speed comparator output. The MCU responds to the interrupt, latches the absolute value of the current transient current, the position code (the current pulse position value of the quadrature encoder), the timestamp, and the object ID, and generates a severe overload event record.

[0058] Specifically, this application also employs millisecond-level software frequency reduction and event logging. The MCU outputs a reduced PWM signal to execute motor frequency reduction and speed reduction or soft shutdown, avoiding the impact of hard cut-off on the mechanical mechanism. At the same time, it records the current transient current value, position code, timestamp, and object ID to generate a minor overload event record.

[0059] S26, extract the stored overload event data, perform edge computing diagnosis, and distinguish between normal inrush, overload position rail abnormality or hanger mechanical jamming based on the overload event data, and make early jamming warning judgment for overload position rail.

[0060] In one embodiment, the steps of extracting stored overload event data, performing edge computing diagnosis, and distinguishing between normal inrush flow, overload position rail abnormality, or clothes hanger mechanical jamming based on the overload event data, as well as performing early jamming warning judgment for the overload position rail, include:

[0061] Extract the stored overload event data, determine whether its timestamp is within the preset surge window period after the motor starts, and if so, mark the event corresponding to the overload event data as "normal surge" and remove it.

[0062] Please see Figure 5 The diagram shows the edge computing judgment flowchart of the hanging system monitoring method based on edge computing described in the embodiments of this application. Figure 5 As shown, the edge computing node receives overload event records and performs the following filtering and parsing:

[0063] First, a blind spot filtering is performed, comparing the event timestamp with the motor's most recent start time. If the difference is less than a preset surge window period, the event is discarded. Then, object decoupling and classification are performed, clustering the event records according to two dimensions: "location ID" and "hanger ID".

[0064] In one embodiment, the steps of extracting stored overload event data, performing edge computing diagnosis, and distinguishing between normal inrush flow, overload position rail abnormality, or clothes hanger mechanical jamming based on the overload event data, as well as performing early jamming warning judgment for the overload position rail, include:

[0065] A hash table is established using the position identification code of the guide rail section and the RFID number of the hanger as the primary key. If the cumulative overload frequency of the same position identification code exceeds a first frequency threshold, it is determined that there is an anomaly in the guide rail at that position. If the same hanger RFID number triggers overload at different position identification codes, it is determined that the hanger is mechanically jammed. The position identification code is determined based on the current pulse position value of the quadrature encoder. In practical applications, quadrature encoders (e.g., incremental encoders) only provide relative displacement and direction information; therefore, it is necessary to combine a "zero reference point" (such as a Z-phase pulse or physical limit switch) to establish an absolute position mapping, and then generate the position identification code through a preset guide rail section position division logic.

[0066] Specifically, such as Figure 5 As shown, this application uses spatial clustering to determine the spatial dimension. If the number of overload events (N_pos) clustered within a unit time window for a single "location ID" exceeds the frequency threshold (N_th1), then the health score H_pos for that location is calculated.

[0067]

[0068] Where H_max is the best historical health score, These are preset parameter values. When H_pos ≥ H_warn, a jamming warning is output for that guide rail section. This is the alarm threshold.

[0069] This application also determines the object dimension through object clustering, such as Figure 5 As shown, if the number of overload events (N_carrier) triggered by a single "clothes hanger ID" exceeds the frequency threshold (N_th2) and is distributed in different locations, a mechanical fault warning for that specific clothes hanger will be output.

[0070] For each overload event of the identification code at the same location, the peak value of the transient current is extracted and linearly fitted using the least squares method. If the peak current shows an increasing slope over time and the slope is greater than the preset deterioration rate, it is determined that the mechanical resistance at that location is continuously deteriorating, and an early jamming warning signal is output.

[0071] Specifically, such as Figure 5 As shown, this application performs trend dimension determination, performs linear regression on the recent overload current peak sequence of a specific "location ID" to obtain the overload current alarm threshold I_max. If the calculated value I_max is greater than 0 and continues to increase, an early warning of wear deterioration at that location is output.

[0072] In one embodiment, the edge computing-based monitoring method for the suspended system further includes:

[0073] In response to the generation of the switch turn-off signal, the hardware is triggered to cut off and start the second timer. After a preset cooling time is delayed by the second timer, the switch of the main power supply circuit of the motor is tentatively turned on. If the current in the motor drive circuit does not exceed the second reference voltage after the tentative turn-on, it is determined that the intermittent interference has been eliminated, and the motor drive system is restored to normal operation and the fault state is reset. If the hardware is triggered to cut off again after the tentative turn-on, and the retry fails more than a preset number of times, the motor drive system is locked and a fault alarm is triggered.

[0074] Please see Figure 6 The diagram shows a self-recovery process of the edge computing-based suspended system monitoring method described in this application embodiment. Figure 6 As shown, after the hardware overload protection triggers hardware cutoff in step S23, the MOSFET is turned off, the system is suspended, the MCU starts a timer, delays for a preset cooling time (e.g., 3 seconds), and then attempts intelligent self-recovery. The retry counter Count=0, the MCU controls the DAC to output an extremely low test threshold, i.e., the start threshold (V_soft), and slowly turns on the MOSFET with an extremely low duty cycle of 5% to test; it monitors whether the current exceeds V_ref2 again. If the current does not exceed the second reference threshold, If the intermittent interference is determined to have been eliminated, the system resumes normal operation and resets the fault state; if the hardware MOSFET is triggered to shut down again, the retry counter is incremented. If the number of consecutive retry failures does not exceed the preset number, wait for 3 seconds or other reasonable time to cool down; if the number of consecutive retry failures exceeds the preset number, such as 3 times, it is determined to be a stubborn mechanical jam, the system is locked and restart is prohibited, a serious fault alarm is reported, and manual intervention is required.

[0075] Therefore, this application utilizes hardware-software synergy and dynamic threshold configuration technology to construct a dual-layer defense of "hardware limit stoppage + software flexible frequency reduction." It also breaks through the limitations of traditional fixed thresholds, with the MCU dynamically outputting the reference voltage of the hardware comparator via the DAC based on the motor's start / stop status (tolerating inrush current during startup and strictly controlling limits during operation), thus balancing anti-false triggering and ultimate protection. This application provides edge computing diagnostic technology based on spatiotemporal clustering, transforming overload events from simple "electrical faults" into "mechanical health probes." Through pseudo-feature filtering (eliminating startup inrush current) and object decoupling classification (separating guide rail position factors from hanger-specific factors), a spatiotemporal health decay model is established, achieving a leap from "post-event power-off protection" to "pre-event prediction and early warning." This application sets up intelligent self-recovery technology based on current trend probing, replacing the one-time failure of traditional fuses or the blind reset of thermal relays. After the hardware protection action is activated, the MCU performs a "trial soft start" with extremely low PWM and extremely low threshold. Based on the current feedback, it intelligently determines whether it is an occasional interference (automatic recovery) or a stubborn jam (lock alarm), which greatly reduces the rate of unplanned downtime.

[0076] The scope of protection of the edge computing-based hanging system monitoring method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0077] This application also provides an edge computing-based suspension system monitoring system. The edge computing-based suspension system monitoring system can implement the edge computing-based suspension system monitoring method described in this application. However, the implementation device of the edge computing-based suspension system monitoring method described in this application includes, but is not limited to, the structure of the edge computing-based suspension system monitoring system listed in this embodiment. All structural modifications and substitutions of the prior art made in accordance with the principles of this application are included within the protection scope of this application.

[0078] Please see Figure 7 The diagram shows the structural principle of the edge computing-based suspended system monitoring system described in this application embodiment. Figure 7 As shown, this embodiment provides a suspended system monitoring system 7 based on edge computing, specifically including:

[0079] The current sampling module 71 is configured to acquire the current sampling signal in the motor drive circuit of the suspension system; and transmit the current sampling signal to the hardware protection module and the MCU software protection module respectively.

[0080] like Figure 3 As shown, the current sampling module is connected in series in the motor drive circuit, and specifically uses a Hall current sensor for sampling. Its output terminal outputs an analog voltage sampling signal (V_sense) that is linearly proportional to the motor current. The V_sense is simultaneously sent to the comparator input terminal of the hardware fast protection module and the analog-to-digital converter (ADC) pin of the MCU.

[0081] The hardware protection module 72 is configured to generate a switch turn-off signal for the main power supply circuit of the motor in response to the current sampling signal being higher than a first reference voltage dynamically set by the hardware protection module.

[0082] like Figure 3 As shown, the hardware protection module 72 includes a dynamic threshold unit (MCU DAC), a high-speed comparator, a logic drive unit, and a MOSFET switch.

[0083] The dynamic threshold unit consists of a digital-to-analog converter (DAC) inside the MCU and an external filtering circuit. The MCU outputs a dynamically changing first reference threshold (V_ref1) according to the current operating state of the motor (such as the starting stage, steady-state operation stage, and deceleration stage). For example, V_ref1 floats up by 20% at the moment of starting to tolerate the inrush current, and V_ref1 returns to the rated limit value in steady state.

[0084] The high-speed comparator receives V_sense at its non-inverting input, V_ref1 at its inverting input, and its output is connected to a logic driver unit; when At that time, the comparator operates in the microsecond range (typically) The high-speed comparator is preferably a comparator with hysteresis function to prevent jitter caused by noise at the threshold critical point.

[0085] For the logic drive unit and MOSFET switch, the logic drive unit receives the toggle signal and immediately pulls down the gate voltage of the MOSFET, hard-cutting off the main power supply circuit of the motor, realizing microsecond-level physical cutoff; at the same time, the output of the high-speed comparator is also directly connected to the external interrupt pin (EXTI, External Interrupt) of the MCU.

[0086] The MCU software protection module 73 is configured to execute software frequency reduction or soft shutdown via PWM adjustment command in response to the current sampling signal being higher than the second reference voltage set by the MCU software protection module and the continuous integral value of the current sampling signal exceeding the safety threshold; wherein the second reference voltage is lower than the first reference voltage.

[0087] like Figure 3 As shown, the MCU software protection module includes an ADC sampling unit and an integration determination unit.

[0088] The ADC sampling unit continuously converts V_sense into a digital current value at a preset frequency (10kHz).

[0089] The integration determination unit incorporates a second reference threshold (V_ref2) (which is lower than the first reference threshold V_ref1) and adopts... The integral algorithm and sliding window delay algorithm determine a mild or stalled overload when the digital current value exceeds V_ref2 and the integral value continuously exceeds the safety threshold. The MCU then performs software frequency reduction or soft shutdown by lowering the PWM duty cycle, rather than directly physically cutting off the circuit.

[0090] The event latching and recording module 74 is configured to trigger an interrupt service routine based on the switch turn-off signal or the PWM adjustment instruction to package, record, and store overload event data.

[0091] The event latching and recording module is a bridge connecting physical protection and digital diagnostics, including a high-priority interrupt response unit and a data structured cache unit.

[0092] The high-priority interrupt response unit configures the MCU's external interrupt (EXTI) to the highest priority; when the hardware fast protection module is activated, the edge triggers the interrupt, and the MCU suspends the current task in microseconds and enters the interrupt service routine (ISR).

[0093] In the data structured cache unit of the ISR, the MCU immediately reads the transient current peak value in the ADC holding register, and at the same time reads the system timestamp in the timer, the current pulse position value of the quadrature encoder, and the RFID number of the currently driven clothes hanger. The above data is packaged into an "overload event data structure" and pushed into the first-in-first-out (FIFO) memory. For minor overloads triggered by the software protection module, the above packaging and recording operation is also performed.

[0094] The edge computing diagnostic module 75 is configured to extract stored overload event data, perform edge computing diagnostics, and distinguish between normal inrush flow, abnormal overload position guide rail, or mechanical jamming of the clothes hanger based on the overload event data, as well as to make early jamming warning judgments for overload position guide rail.

[0095] like Figure 3 As shown, the edge computing diagnostic module is deployed in the main control MCU of the hanging system and includes a pseudo-feature filtering submodule, a spatiotemporal clustering analysis submodule, and a trend decay assessment submodule.

[0096] The pseudo-feature filtering submodule extracts event data from the FIFO and determines whether its timestamp is within the preset inrush window period after the motor starts (e.g., within 0.5 seconds after power-on). If so, the event is marked as "normal inrush" and removed, and will not participate in subsequent diagnosis.

[0097] The spatiotemporal clustering analysis submodule establishes a hash table using the guide rail interval position ID and hanger ID as the primary keys. If the cumulative overload frequency of the same position ID exceeds the first frequency threshold, it is determined that there is an anomaly in the guide rail at that position. If the same hanger ID triggers overload at different positions, it is determined that the hanger is mechanically stuck.

[0098] The trend decay assessment submodule extracts the transient current peak value for each overload event at the same location ID and performs a least squares linear fit. If the peak current shows an increasing slope over time and the slope is greater than the preset deterioration rate, it is determined that the mechanical resistance at that location is continuously deteriorating, and an early jamming warning signal is output.

[0099] Therefore, this application establishes a hardware interrupt-driven "power-off while latching" mechanism. While the hardware comparator cuts off the main circuit in microseconds, a switching signal directly triggers the MCU's highest-priority interrupt, losslessly latching fault characteristic data at the moment of power failure, transforming passive protection actions into a data source for active diagnosis. Based on electromechanical decoupling and spatiotemporal clustering diagnosis of "protection events," this application filters out pseudo-features such as inrush current and decouples and clusters overload records according to "guide rail position ID" and "clothes hanger object ID" in a two-dimensional manner, accurately identifying mechanical fault sources and achieving a leap from electrical over-limit alarms to mechanical jamming prediction. This application can achieve dynamically configurable threshold-based dual-track hardware and software graded defense. The MCU dynamically adjusts the hardware comparator's limit threshold (tolerating inrush current / tightening the operational defense line) according to the operating conditions via the DAC, and combines this with a software integral algorithm to handle minor overloads, forming a collaborative defense line of "hard-speed loss prevention + soft-flexible frequency reduction." The intelligent self-recovery mechanism based on microcurrent probing: After the hardware protection action, it does not blindly reset, but performs soft start probing with an extremely low PWM duty cycle and an extremely low current threshold, intelligently distinguishing between occasional interference and stubborn jamming, taking into account both equipment safety and continuous production line operation.

[0100] In the embodiments provided in this application, it should be understood that the disclosed systems or methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or modules or units, and may be electrical, mechanical, or other forms.

[0101] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0102] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0103] This application provides an electronic device, including: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method described thereon.

[0104] Please see Figure 8 The diagram shows the structural connections of the electronic device described in the embodiments of this application. Figure 8 As shown, the electronic device 8 of this application includes: a processor 81, a memory 82, a communication interface 83 and / or a system bus 84. The memory 82 and the communication interface 83 are connected to the processor 81 through the system bus 84 and communicate with each other. The memory 82 is used to store computer programs, the communication interface 83 is used to communicate with other devices, and the processor 81 is used to run the computer program, causing the electronic device 8 to execute the various steps of the method.

[0105] The processor 81 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0106] The aforementioned memory 82 may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0107] The system bus 84 mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus 84 can be divided into an address bus, a data bus, a control bus, etc. The communication interface is used to enable communication between the database access device and other devices (such as clients, read-write databases, and read-only databases).

[0108] In practical applications, the electronic device can be the MCU (Microcontroller Unit) of the hanging system or a monitoring terminal such as a smartphone, tablet, or laptop. In other embodiments, the electronic device can also be a server. The server can be deployed on one or more physical servers according to various factors such as function and load, or it can be a cloud server composed of distributed or centralized server clusters. This embodiment does not limit this. When the electronic device is a server, the server can transmit the monitoring and analysis results of intelligent overload protection and health warning to the MCU of the hanging system, so that the MCU can further generate motor drive commands based on the monitoring and analysis results.

[0109] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described thereon.

[0110] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0111] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0112] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A monitoring method for a suspended system based on edge computing, characterized in that, include: Acquire the current sampling signal in the motor drive circuit of the suspension system; The current sampling signal is transmitted to the hardware protection module and the MCU software protection module, respectively. In response to the current sampling signal being higher than the first reference voltage dynamically set by the hardware protection module, a switch turn-off signal is generated for the main circuit power supply of the motor. In response to the current sampling signal being higher than the second reference voltage set by the MCU software protection module, and the continuous integral value of the current sampling signal exceeding a safety threshold, software frequency reduction or soft shutdown is executed via PWM adjustment command; wherein, the second reference voltage is lower than the first reference voltage; The interrupt service routine is triggered according to the switch turn-off signal or the PWM adjustment command to package, record and store the overload event data; Extract the stored overload event data, perform edge computing diagnosis, and distinguish between normal inrush, overload position rail abnormality, or clothes hanger mechanical jamming based on the overload event data, and make early jamming warning judgments for overload position rails.

2. The method according to claim 1, characterized in that, The step of generating a switch turn-off signal for the main power supply circuit of the motor in response to the current sampling signal being higher than the first reference voltage dynamically set by the hardware protection module includes: The first reference voltage is output dynamically according to the current operating state of the motor during the starting phase, steady-state operation phase, or deceleration phase. In response to the current sampling signal at the non-inverting input of the comparator being higher than the first reference voltage at the inverting input of the comparator, a switch-off signal for the main power supply circuit of the motor is generated. The switch-off signal is used to pull down the gate voltage of the switch in the main power supply circuit of the motor to perform a rapid physical disconnection of the main power supply circuit of the motor.

3. The method according to claim 1, characterized in that, In response to the current sampling signal being higher than the second reference voltage set by the MCU software protection module, and the continuous integral value of the current sampling signal exceeding a safety threshold, the step of executing software frequency reduction or soft shutdown via PWM adjustment instructions includes: The current sampling signal is converted into a digital current value according to a preset frequency; In response to the digital current value being higher than the second reference voltage set by the MCU software protection module, the following measures are taken: The integral algorithm and the sliding window delay algorithm calculate the continuous integral value. If the continuous integral value of the current sampling signal exceeds the safety threshold, it is determined to be a mild or stall overload. The software frequency reduction or soft shutdown is executed through the PWM adjustment command.

4. The method according to claim 1, characterized in that, The steps of packaging, recording, and storing overload event data by triggering an interrupt service routine based on the switch turn-off signal or the PWM adjustment command include: Configure the MCU's external interrupt as the highest priority; trigger an edge-triggered interrupt based on the switch turn-off signal or the PWM adjustment instruction, and enter the interrupt service routine; In the interrupt service routine, the transient current peak value in the analog-to-digital converter holding register is read, and the system timestamp, the current pulse position value of the quadrature encoder, and the RFID number of the currently driven clothes hanger in the first timer are read. All the read data is packaged into an overload event data and pushed into the first-in-first-out memory.

5. The method according to claim 4, characterized in that, The steps of extracting stored overload event data, performing edge computing diagnostics, and differentiating between normal inrush flow, overloaded guide rail malfunction, or hanger mechanical jamming based on the overload event data, as well as performing early jamming warnings for overloaded guide rails, include: Extract the stored overload event data, determine whether its timestamp is within the preset surge window period after the motor starts, and if so, mark the event corresponding to the overload event data as "normal surge" and remove it.

6. The method according to claim 4, characterized in that, The steps of extracting stored overload event data, performing edge computing diagnostics, and differentiating between normal inrush flow, overloaded guide rail malfunction, or hanger mechanical jamming based on the overload event data, as well as performing early jamming warnings for overloaded guide rails, include: A hash table is established using the position identification code of the guide rail section and the RFID number of the hanger as the primary key; if the cumulative overload frequency of the same position identification code exceeds the first frequency threshold, it is determined that there is an abnormality in the guide rail at that position; if the same RFID number of the hanger triggers overload at different position identification codes, it is determined that the hanger is mechanically jammed; wherein, the position identification code is determined based on the current pulse position value of the quadrature encoder; For each overload event of the identification code at the same location, the peak value of the transient current is extracted and linearly fitted using the least squares method. If the peak current shows an increasing slope over time and the slope is greater than the preset deterioration rate, it is determined that the mechanical resistance at that location is continuously deteriorating, and an early jamming warning signal is output.

7. The method according to claim 1, characterized in that, Also includes: In response to the generation of the switch turn-off signal, the hardware is triggered to cut off and start the second timer. After delaying the preset cooling time using the second timer, the switch of the motor power supply main circuit is tentatively turned on. If the current in the motor drive circuit does not exceed the second reference voltage after the trial conduction, it is determined that the intermittent interference has been eliminated, and the motor drive system is allowed to resume normal operation and the fault state is reset. If the hardware is cut off again after a trial connection, and the failure to retry exceeds a preset number, the motor drive system will be locked and a fault alarm will be triggered.

8. A monitoring system for a suspended system based on edge computing, characterized in that, include: The current sampling module is configured to acquire the current sampling signal in the motor drive circuit of the suspension system; The current sampling signal is transmitted to the hardware protection module and the MCU software protection module, respectively. The hardware protection module is configured to generate a switch turn-off signal for the main power supply circuit of the motor in response to the current sampling signal being higher than a first reference voltage dynamically set by the hardware protection module. The MCU software protection module is configured to execute software frequency reduction or soft shutdown via PWM adjustment command in response to the current sampling signal being higher than a second reference voltage set by the MCU software protection module and the current sampling signal continuously exceeding a safety threshold; wherein the second reference voltage is lower than the first reference voltage; The event latching and recording module is configured to trigger an interrupt service routine based on the switch turn-off signal or the PWM adjustment instruction to package, record and store overload event data. The edge computing diagnostic module is configured to extract stored overload event data, perform edge computing diagnostics, and distinguish between normal inrush flow, abnormal overload position guide rail, or mechanical jamming of the clothes hanger based on the overload event data, as well as to make early jamming warning judgments for overload position guide rail.

9. An electronic device, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method described in any one of claims 1 to 7.

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