A safety supervision method for operating equipment

Abstract

This invention discloses a safety monitoring method for operating equipment, applied to an automated warehouse system, which includes a Warehouse Management System (WMS), Automated Guided Vehicles (AGVs), and a guidance and positioning device. The method includes: collecting data on AGV operating status, warehouse environment, WMS tasks, and the mechanical status of the guidance and positioning device; constructing a standard path model based on a historical trajectory database and calculating real-time trajectory deviation; constructing a multi-dimensional risk assessment model to comprehensively calculate the risks of the AGV itself, the environment, the task, and the guidance and positioning device; classifying risk levels according to the comprehensive risk value, implementing differentiated early warning and response measures, and dynamically adjusting AGV task allocation in conjunction with the WMS system. This invention also integrates an automatic monitoring function for near-expiry goods, enabling near-expiry identification, AGV dispatching, and automatic sorting and disposal. This invention achieves comprehensive, three-dimensional monitoring from equipment safety to cargo safety, improving the safety and intelligence level of warehouse operations.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehousing and logistics automation technology, specifically a safety monitoring method for operating equipment in automated warehouses. Background Technology

[0002] With the rapid development of intelligent manufacturing and logistics automation, automated warehouses combined with AGVs (Automated Guided Vehicles) have become the mainstream configuration for modern warehousing. The WMS (Warehouse Management System) is responsible for scheduling and recording cargo information, the AGVs are responsible for the automatic handling of goods, and the guiding and positioning devices ensure that the AGVs operate stably on the tracks.

[0003] However, existing automated warehouses have the following technical problems during operation:

[0004] Safety supervision is limited in scope: Existing technologies focus primarily on collision detection or emergency stop alarms for individual AGVs, lacking a comprehensive risk assessment that considers the AGV's operating trajectory, environmental conditions, task load, and the mechanical condition of the guiding device.

[0005] The early warning mechanism is lagging behind: it mostly provides alarms after the fact (such as stopping after a collision), and relies on a collision avoidance device for AGV unmanned transport vehicles, such as CN218578828U, for protection. It lacks the ability to provide early warning based on trajectory anomaly prediction.

[0006] Disconnected from WMS system: The safety monitoring system is independent of WMS and cannot dynamically adjust task allocation according to the risk status of equipment, which leads to high-risk equipment continuing to operate and is prone to safety accidents.

[0007] Lack of status monitoring for guide and positioning devices: For example, the authorization announcement number CN113060465B discloses a warehouse guide and positioning device based on a WMS system, which solves the problem of guide wheel spacing adjustment. However, mechanical faults such as guide wheel wear, slider displacement deviation, and cleaning card swiping lag cannot be monitored in real time, affecting the stability of AGV operation.

[0008] Cargo safety and equipment safety are disconnected: the identification and handling of goods nearing expiration rely on manual labor and cannot be linked with AGV scheduling, leading to losses due to expiration.

[0009] Some automated warehouse material outbound management systems lack further comprehensive monitoring methods for equipment operation safety. In view of this, in-depth research was conducted to address the above issues, leading to this case. Summary of the Invention

[0010] The purpose of this invention is to provide a safety supervision method for operating equipment, in order to solve the problems mentioned in the background art, such as single safety supervision dimension, delayed early warning, disconnection from WMS, missing status of guidance device, and separation of cargo and equipment safety.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a method for safety monitoring of operating equipment, applied to an automated warehouse system, wherein the automated warehouse system includes a WMS warehouse management system, several AGV unmanned transport vehicles, and a guiding and positioning device, and the specific method includes the following steps:

[0012] Step S1: Multi-source data acquisition

[0013] Collect AGV operating status data, warehouse environment data, WMS task data, and mechanical status data of the guide and positioning device; wherein, the mechanical status data of the guide and positioning device includes slider displacement, guide wheel wear, and cleaning brush working status;

[0014] Step S2: Trajectory Anomaly Detection

[0015] Based on the historical operation trajectory database, a standard path model is constructed for each AGV. The real-time collected AGV position trajectory is compared with the standard path model, and the trajectory deviation D is calculated.

[0016] Step S3: Multi-dimensional risk assessment

[0017] Construct a risk assessment model based on at least four dimensions: AGV inherent risk, environmental risk, task risk, and guidance and positioning device risk, and calculate the comprehensive risk value R. total ;

[0018] Step S4: Tiered Early Warning and Coordinated Response

[0019] Based on the comprehensive risk value R total Risk levels are classified, and differentiated early warning and response measures are taken for different risk levels. The AGV task allocation is dynamically adjusted in conjunction with the WMS system.

[0020] Preferably, in step S1:

[0021] The AGV operating status data includes at least: real-time position coordinates, operating speed, acceleration, battery level, and number of emergency stop signals;

[0022] The warehouse environmental data includes at least: temperature and humidity, light intensity, and smoke concentration;

[0023] The WMS task data includes at least: current task type, target cargo location coordinates, task priority, and estimated completion time;

[0024] The mechanical status data of the guide positioning device is collected by sensors installed on the housing base of the guide positioning device. The sensors include at least a displacement sensor, a torque sensor, and a current detection module.

[0025] Preferably, the formula for calculating the trajectory deviation D in step S2 is: D = α × ΔP + β × ΔV + γ × ΔT in: ΔP is the spatial position deviation between the real-time trajectory and the standard path, calculated using Euclidean distance or dynamic time warping algorithms; ΔV represents the deviation between the real-time speed and the standard speed curve; ΔT represents the deviation between the task execution time and the standard task time; α, β, and γ are weighting coefficients, and α+β+γ=1, which can be dynamically adjusted according to the warehouse scenario; when D is greater than the preset threshold T1, it is judged as a mild trajectory anomaly; when D is greater than the preset threshold T2, it is judged as a severe trajectory anomaly, where T2 > T1.

[0026] Preferably, in step S3, the comprehensive risk value R total The calculation formula is: R total =w1×R agv +w2×R env +w3×R task +w4×R guide in: R agv The risk value of the AGV itself is calculated based at least on battery level, emergency stop frequency, and trajectory deviation D.

[0027] R env The environmental risk value is calculated based at least on temperature and humidity, smoke concentration, and light intensity.

[0028] R task The task risk value is calculated based at least on task priority, the degree of congestion in the work area, and the status of the shelves.

[0029] R guide The risk value of the guide positioning device is calculated based at least on the wear of the guide wheel, the displacement deviation of the slider, and the sluggish state of the cleaning swipe.

[0030] w1, w2, w3, and w4 are weighting coefficients, and w1+w2+w3+w4=1.

[0031] Preferably, the risk value R of the guiding and positioning device guide The calculation methods include:

[0032] Obtain the actual distance between the guide wheels on both sides of the slider and the guide rail in the guiding and positioning device, compare it with the standard distance, and calculate the distance deviation value ΔG;

[0033] Obtain the cumulative number of rotations or running time of the guide wheel, and calculate the wear degree W of the guide wheel based on the preset wear model;

[0034] The current value of the drive motor of the cleaning brush is obtained. When the current value exceeds the preset threshold, it is determined that the cleaning brush is stuck.

[0035] Rguide is calculated based on the spacing deviation value ΔG, wear degree W, and cleaning card swiping status.

[0036] Preferably, the risk level classification in step S4 is as follows:

[0037] When 0≤R total When the value is less than 0.3, it is considered low risk, and the system only logs the information without triggering an alert.

[0038] When 0.3≤R total When the value is less than 0.6, it is judged as medium risk. The system will display a prompt on the monitoring screen, and the AGV will automatically slow down and expand the avoidance range.

[0039] When 0.6≤R total When the value is less than 0.8, it is considered a high-risk situation. The system will trigger an audible and visual alarm and send a message to the management personnel. The AGV will immediately stop running and wait for instructions.

[0040] When R total When the value is ≥0.8, it is determined to be a critical risk. The system triggers a full-warehouse alarm, the AGV stops urgently and the power supply is cut off. At the same time, the area is sealed off and personnel are notified to intervene.

[0041] Preferably, the dynamic adjustment of AGV task allocation by the linkage WMS system includes:

[0042] When the risk level is determined to be medium, the WMS system adjusts the priority of subsequent tasks for the AGV, prioritizing low-risk tasks or charging tasks.

[0043] When a task is deemed high-risk, the WMS system suspends the assignment of new tasks to that AGV and schedules other AGVs to take over the unfinished tasks.

[0044] When a critical risk is identified, the WMS system replans the paths of all AGVs in the area to avoid the restricted area and records the location of the malfunctioning AGV for maintenance personnel to handle.

[0045] Preferably, the method further includes a joint supervision step for near-expiry goods:

[0046] Step S5: Obtain the production date and shelf life information of the goods from the WMS system, and calculate the near-expiration value based on the near-expiration judgment formula (MN) / Q-0.7, where M is the current date, N is the production date, and Q is the shelf life in days;

[0047] Step S6: When the near-expiry value exceeds the preset threshold, the goods are determined to be near-expiry, a near-expiry warning message is generated and highlighted on the monitoring screen;

[0048] Step S7: Dispatch the AGV to the storage location of the goods that are about to expire and automatically pick them up, and transport the goods to the sorting module;

[0049] Step S8: The sorting module automatically sorts the near-expiry goods to the corresponding disposal area according to their category and degree of near expiry, and updates the inventory status in the WMS system.

[0050] A safety monitoring system for operating equipment, used to implement the method described in any one of the above, comprising:

[0051] The data acquisition module, located in the automated warehouse, includes:

[0052] The first sensor group installed on the AGV is used to collect AGV operating status data.

[0053] The second sensor group, installed inside the warehouse, is used to collect environmental data.

[0054] The third sensor group installed on the guide and positioning device is used to collect mechanical status data of the guide and positioning device.

[0055] An interface module that communicates with the WMS system to acquire task data;

[0056] The trajectory anomaly detection module, connected to the data acquisition module, is used to calculate the trajectory deviation D based on the historical trajectory database;

[0057] The risk assessment module, connected to the trajectory anomaly detection module, is used to calculate the comprehensive risk value R based on multi-dimensional risk factors. total ;

[0058] The tiered early warning module, connected to the risk assessment module, is used to trigger differentiated early warning responses based on the risk level.

[0059] The WMS linkage module connects with the hierarchical early warning module and the WMS system to dynamically adjust AGV task allocation.

[0060] The storage module is used to store historical data, standard path models, and risk threshold parameters.

[0061] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a security monitoring method for an operating device as described in any of the preceding claims.

[0062] Compared with existing technologies, the beneficial effects of this method for safety monitoring of operating equipment include: 1. Multi-dimensional comprehensive risk assessment: This invention integrates four dimensions for assessment: AGV inherent risk, environmental risk, task risk, and guidance and positioning device risk. This overcomes the limitations of single-point detection in existing technologies and improves the accuracy and comprehensiveness of risk identification. 2. Track anomaly prediction capability: By constructing a standard path model and calculating the trajectory deviation in real time, it can predict anomalies 1-3 seconds before the AGV collides or gets stuck, achieving early warning rather than post-event alarm; 3. Tiered early warning and differentiated response: Based on the comprehensive risk value, four risk levels are divided, and differentiated response measures are taken from log recording to emergency shutdown. This avoids false alarms that may interfere with normal operations, while ensuring timely handling of high-risk situations. 4. Deep integration with WMS system: Risk status and WMS task scheduling are linked in a closed loop. Medium and high risk AGVs automatically adjust task priorities or suspend new tasks to avoid causing greater failures by operating with defects. 5. Status monitoring of the guide positioning device: Real-time monitoring of mechanical conditions such as guide wheel wear, slider displacement deviation, and cleaning sluggishness fills the gap in status monitoring and risk warning in existing guide device patents (such as CN113060465B); 6. Integration of cargo safety and equipment safety: Linking the identification of near-expiry goods with AGV scheduling enables the automatic outbound and sorting of near-expiry goods, which not only reduces losses due to expiration but also expands the scope of safety supervision; 7. High system integration: It can seamlessly connect with existing WMS systems, AGV scheduling systems and guidance and positioning devices without replacing hardware, resulting in low transformation costs and easy promotion and implementation. Attached Figure Description

[0063] Figure 1 This is a schematic flowchart of the method of the present invention;

[0064] Figure 2 This is a schematic diagram of the risk assessment model in this invention;

[0065] Figure 3 This is a schematic diagram illustrating the risk level classification rules of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Please see Figure 1-3 The present invention provides the following technical solutions:

[0068] Example 1: System Overall Architecture

[0069] This embodiment provides a safety monitoring system for operating equipment, including:

[0070] The data acquisition module, located in the automated warehouse, specifically includes:

[0071] The first sensor group installed on the AGV is used to collect AGV operating status data, including but not limited to: GPS / UWB positioning sensor (to obtain real-time position coordinates), encoder (to obtain running speed and acceleration), power monitoring module (to obtain battery power), and emergency stop signal acquisition module (to record the number of emergency stops).

[0072] The second sensor group installed in the warehouse is used to collect environmental data, including but not limited to: temperature and humidity sensors, light intensity sensors, and smoke concentration sensors.

[0073] The third sensor group installed on the guide and positioning device is used to collect mechanical status data of the guide and positioning device, including but not limited to: displacement sensor (detecting the displacement of the slider), torque sensor (detecting the force state of the guide wheel), and current detection module (detecting the operating current of the cleaning brush drive motor).

[0074] The interface module that communicates with the WMS system is used to acquire task data in real time, including task type, target cargo location coordinates, task priority, and estimated completion time.

[0075] The trajectory anomaly detection module is electrically connected to the data acquisition module 1. It has a pre-set historical operation trajectory library, which is used to build a standard path model for each AGV based on historical data, and compare the real-time acquired AGV position trajectory with the standard path model to calculate the trajectory deviation D.

[0076] The risk assessment module is electrically connected to the trajectory anomaly detection module 2. It has a pre-set risk assessment model to calculate the comprehensive risk value R based on four dimensions: AGV's own risk, environmental risk, task risk, and guidance and positioning device risk. total .

[0077] The graded early warning module is electrically connected to the risk assessment module 3. It has preset risk level thresholds and is used to determine the risk level based on the comprehensive risk value R. total Classify risk levels and trigger differentiated early warning responses.

[0078] The WMS linkage module is bidirectionally connected to the graded early warning module 4 and the WMS system. It is used to dynamically adjust the AGV task allocation according to the risk level and feed back the task adjustment results to the graded early warning module 4.

[0079] The storage module, electrically connected to the above modules, is used to store historical data, standard path models, risk threshold parameters, and near-expiration judgment formula parameters, etc.

[0080] Example 2: Integrated Application of Guiding and Positioning Devices

[0081] In this embodiment, the automated warehouse system further includes a guiding and positioning device, which can refer to the structure described in the applicant's previously granted patent CN113060465B, "A Warehouse Guiding and Positioning Device Based on WMS System".

[0082] This guiding and positioning device is installed at a corresponding position on the AGV's running track. Its main function is to ensure the AGV runs stably along the track and to provide data support for safety supervision by monitoring its own mechanical status in real time. Specifically:

[0083] The basic structure of the guiding and positioning device includes:

[0084] A single unit, installed on one side of the track or on the AGV body;

[0085] The sliders are slidably mounted on both sides of the base, and each slider has a guide wheel that is adapted to the track at its lower end;

[0086] An adjustment mechanism is used to drive two sliders to move synchronously in opposite directions, thereby adjusting the distance between the guide wheel and the track;

[0087] A cleaning mechanism, installed on the front side of the seat, includes a swingable lever and a rotary-driven cleaning brush for automatically cleaning the track surface.

[0088] This guiding and positioning device can achieve rapid and synchronous adjustment of the guide wheel spacing through the adjustment mechanism, ensuring that the guide wheels maintain uniform contact with both sides of the track; the cleaning mechanism can automatically remove dust and debris from the track surface, reducing abnormal wear of the guide wheels.

[0089] Application in this embodiment:

[0090] In step S1, the mechanical status data of the guiding and positioning device is collected by a sensor group installed thereon, including:

[0091] A displacement sensor is used to detect the displacement of the slider, and then calculate the actual distance between the guide wheel and the track.

[0092] Torque sensor or current detection module is used to monitor the force state and rotational resistance of the guide wheel;

[0093] The current detection module is used to monitor the operating current of the cleaning brush drive motor and determine whether there is any jamming.

[0094] The above data serves as an important input for the risk dimension of the guidance and positioning device, and is used in the subsequent step S3 for R. guide The calculation is as follows. When the system detects excessive deviation in guide wheel spacing, abnormal increase in guide wheel rotation resistance, or sluggish cleaning card swiping, it will correspondingly increase R. guide The value affects the overall risk assessment results and triggers differentiated early warning responses.

[0095] Example 3: Data Acquisition and Preprocessing

[0096] In this embodiment, the data acquisition module 1 continuously acquires various types of data at a preset frequency (e.g., 100ms / time) and preprocesses the data, including:

[0097] Data cleaning: Remove obviously abnormal sensor data, such as values ​​that are out of range or null values ​​when communication is interrupted;

[0098] Data alignment: Timestamps are aligned to ensure that data such as AGV position, environmental parameters, and task status at the same time can be matched;

[0099] Data storage: The preprocessed data is classified and stored in the storage module. The historical AGV operation trajectory data is indexed by AGV number, date and task type for subsequent standard path model training.

[0100] Example 4: Trajectory Anomaly Detection

[0101] In this embodiment, the trajectory anomaly detection module performs the following steps:

[0102] Standard path model construction:

[0103] For each AGV, historical trajectory data under normal operating conditions is collected and categorized by task type (e.g., inbound, outbound, and relocation) and target storage area. Dynamic Time Warping (DTW) algorithm is used to align and average multiple trajectories of the same category, generating a standard path model for that task. The standard path model includes three dimensions: position sequence, speed curve, and time reference.

[0104] Real-time trajectory deviation calculation:

[0105] When the AGV performs a task, its real-time position trajectory is compared with the standard path model for the corresponding task type, and the trajectory deviation D is calculated according to the formula: D = α × ΔP + β × ΔV + γ × ΔT

[0106] in:

[0107] ΔP is the spatial position deviation between the real-time trajectory and the standard path. The position deviation of each sampling point is calculated using Euclidean distance and then the average value is taken.

[0108] ΔV represents the deviation between the real-time speed and the standard speed curve, and the root mean square error of the speed curve is calculated.

[0109] ΔT is the deviation between the task execution time and the standard task time. It is calculated as the ratio of the time taken for the completed part to the standard time.

[0110] α, β, and γ are weighting coefficients. In this embodiment, α = 0.5, β = 0.3, and γ = 0.2.

[0111] Anomaly detection:

[0112] Two thresholds are set: T1=0.3 and T2=0.6. When D > 0.3, it is judged as a mild trajectory anomaly, and when D > 0.6, it is judged as a severe trajectory anomaly.

[0113] For example, when an AGV is performing an inbound task, if the spatial position of its real-time trajectory deviates significantly from the standard path, and D is calculated to be 0.45, it is determined to be a minor trajectory anomaly; if the deviation continues to increase and D reaches 0.65, it is upgraded to a severe trajectory anomaly.

[0114] Example 5: Multi-dimensional Risk Assessment

[0115] In this embodiment, the risk assessment module calculates risk values ​​for each dimension based on the collected data:

[0116] AGV's own risk value R agv calculate: R agv =k1×(1-Battery / 100)+k2×(E stop / E max )+k3×(D / D max )

[0117] in:

[0118] Battery represents the current battery percentage.

[0119] E stop E represents the number of emergency stops in the most recent hour. max The preset threshold for the number of emergency stops (5 times in this embodiment);

[0120] D represents the current trajectory deviation. max The maximum deviation threshold is set to 1.0 in this embodiment.

[0121] k1, k2, and k3 are weighting coefficients. In this embodiment, k1 = 0.3, k2 = 0.3, and k3 = 0.4.

[0122] Environmental risk value R env calculate: R env =m1×(TT opt / T max- T opt )+m2×(HH opt / H max- H opt )+m3×(S / S max )+m4×(L opt- L / L opt )

[0123] in:

[0124] T is the current temperature. opt For the optimal temperature (20℃), T max The maximum permissible temperature is 40℃.

[0125] H represents the current humidity. opt For optimal humidity (50%), H max Maximum permissible humidity (90%);

[0126] S represents the smoke concentration, S max The threshold for smoke concentration;

[0127] L represents the current light intensity. opt For optimal light intensity;

[0128] m1~m4 are weighting coefficients. In this embodiment, m1=0.3, m2=0.3, m3=0.2, and m4=0.2.

[0129] Task risk value R task calculate: R task =n1×(P / P max )+n2×C+n3×(S load / S max )

[0130] in: P represents the task priority (levels 1-10). max It has the highest priority of 10; C represents the congestion level of the work area, calculated based on the ratio of the number of AGVs in the area to the area's capacity. S load S represents the load status of the shelving (tilt angle, overload level). max Maximum allowable load; n1, n2, and n3 are weighting coefficients. In this embodiment, n1 = 0.4, n2 = 0.3, and n3 = 0.3.

[0131] Risk value R of guide positioning device guide calculate:

[0132] According to the guiding and positioning device in Embodiment 2, data is collected through the third sensor group, and the calculation is performed according to the following method:

[0133] First, obtain the actual distance between the guide wheels on both sides of the slider and the guide rail, compare it with the standard distance (preset value, such as 50mm), and calculate the distance deviation value ΔG (unit mm).

[0134] Secondly, obtain the cumulative number of rotations or running time of the guide wheel, and calculate the wear degree W of the guide wheel (between 0 and 1, where 1 represents complete wear) based on the preset wear model.

[0135] Next, the current value of the drive motor of the cleaning brush is obtained. When the current value exceeds a preset threshold (such as 1.5 times the rated current) and the duration exceeds 2 seconds, it is determined that the cleaning brush is stuck, and the stuck state is C. clean =1, otherwise C clean =0; Finally, calculate R according to the formula. guide : R guide =p1×(ΔG / ΔG max )+p2×W+p3×C clean

[0136] in: ΔG max The maximum allowable spacing deviation is 10mm in this embodiment. p1, p2, and p3 are weighting coefficients. In this embodiment, p1 = 0.4, p2 = 0.4, and p3 = 0.2.

[0137] Overall risk value calculation:

[0138] Calculate the comprehensive risk value R according to the formula. total : R total =w1×R agv +w2×R env +w3×R task +w4×R guide

[0139] In this embodiment, the weighting coefficients are w1=0.3, w2=0.2, w3=0.2, and w4=0.3, reflecting the importance attached to the AGV itself and the guiding device.

[0140] Example 6: Tiered Early Warning and Coordinated Response

[0141] In this embodiment, the graded early warning module is based on Figure 3 The rules shown classify risk levels and trigger responses;

[0142] For example, during the execution of a task, the battery level of an AGV drops to 15% (R). agv (rise), while entering a narrow passage area (R) task (increase), comprehensive calculation of R total =0.52, classified as medium risk. The AGV icon on the monitoring screen turned yellow and highlighted. The AGV automatically slowed down, and the WMS system automatically adjusted the AGV's subsequent task to return to the charging station for charging, and dispatched another AGV to take over its unfinished pickup task.

[0143] Example 7: Joint Supervision of Near-Expiry Goods

[0144] In this embodiment, the system also integrates a joint supervision function for near-expiry goods, and the specific workflow is as follows:

[0145] Step S5: Cargo Status Monitoring

[0146] Storage module 6 stores information on all goods, including item number, address, name, brand, production date, and shelf life in days. The monitoring module (integrated into risk assessment module 3 or set up independently) scans the production date and shelf life information of all goods daily and calculates the near-expiration value for each item according to the near-expiration judgment formula. V = (MN) / Q - 0.7

[0147] in:

[0148] M represents the current date (e.g., March 3, 2026);

[0149] N represents the production date (e.g., January 1, 2026);

[0150] Q represents the shelf life in days (e.g., 90 days).

[0151] Calculation example: A certain product was produced on January 1, 2026, with a shelf life of 90 days. The current date is March 3, 2026. Then (MN) = 61 days, 61 / 90 ≈ 0.678, 0.678 - 0.7 = -0.022, which is a negative value. It is determined that the product is not nearing its expiration date.

[0152] Step S6: Early Warning of Near-Expiration Risk

[0153] When V ≥ 0, the goods are determined to be near their expiration date. The system will highlight the storage location of the goods in orange on the warehouse floor plan on the monitoring screen and pop up a prompt box to display a list of near-expiration goods, including the name of the goods, storage location, and remaining shelf life.

[0154] Step S7: Automatic Outbound Linkage

[0155] For goods determined to be nearing their expiration date, the control module generates a retrieval task based on their storage location and dispatches an idle AGV to the designated storage location to retrieve the goods. Guided by the guiding and positioning device 7, the AGV accurately travels to the target storage location, unloads the goods, and places them on the conveyor module.

[0156] Step S8: Sorting and Processing

[0157] The conveying module transports near-expiry goods to the sorting module. The sorting module includes a barcode scanning module, a labeling module, and an execution module.

[0158] The barcode scanning module reads the barcode on the goods to obtain the goods number;

[0159] The labeling module automatically prints and affixes "near-expiry goods" labels based on the product number. The labels may contain information such as promotional prices and disposal suggestions.

[0160] The execution module sorts the goods to the corresponding areas based on their near-expiration status:

[0161] V between 0 and 0.2: Mildly near expiration, sort to the promotional area;

[0162] V between 0.2 and 0.5: moderately near expiration, sorted to the return area;

[0163] V > 0.5: Severely nearing expiration date, sorted to the scrap area.

[0164] After sorting is completed, the system automatically updates the inventory status in WMS, removes near-expiry goods from normal inventory, and records the disposal results.

[0165] Example 8: Environmental Anomaly Linkage

[0166] In this embodiment, the system also integrates an environmental anomaly linkage function:

[0167] The temperature and humidity detection module monitors the temperature, humidity, and smoke concentration in the warehouse in real time. When the temperature exceeds 35℃, the humidity exceeds 80%, or the smoke concentration exceeds the threshold, it is determined to be an environmental anomaly and feedback is sent to the control module.

[0168] The control module takes appropriate measures based on the type of exception:

[0169] Overheating: Activate ventilation equipment for forced ventilation;

[0170] High humidity: Turn on the dehumidifier to remove humidity;

[0171] Smoke alarm: Immediately triggers a fire alarm for the entire warehouse, dispatches all AGVs to stop operation and automatically travels to a safe area, and simultaneously notifies personnel to intervene and handle the situation.

[0172] Example 9: Inventory Early Warning Linkage

[0173] In this embodiment, the system also integrates an inventory early warning linkage function:

[0174] The control module sets upper and lower limits for the inventory of the same model of goods (e.g., upper limit 1000 units, lower limit 100 units). The monitoring module monitors the inventory of various types of goods in real time.

[0175] When the inventory of a certain model of goods falls below the lower limit, the system automatically generates a stock replenishment warning, prompts "replenishment recommended" on the monitoring screen, and can automatically generate a purchase order in conjunction with the purchasing system;

[0176] When a certain model of goods has no shipment record in the past 30 days, it is determined to be a slow-moving product. The system generates a promotional warning and displays a "suggested promotion" message on the monitoring screen. It can also be linked with the sales system to automatically adjust the price or generate promotional activities.

[0177] In summary, the system of this invention collects multi-source data in real time, including AGV operating status, warehouse environment, WMS tasks, and the mechanical status of the guiding and positioning device, through a data acquisition module. The trajectory anomaly detection module calculates the deviation D of the current AGV trajectory based on a standard path model constructed from historical data, predicting the risk of trajectory anomalies. The risk assessment module comprehensively calculates the risk value R from four dimensions: the AGV itself, the environment, the task, and the guiding device. total This enables multi-dimensional quantitative assessment. The graded early warning module 4, based on R... total The system categorizes risks into four levels: low, medium, high, and critical, triggering differentiated responses from log recording to emergency shutdown. The WMS linkage module 5 dynamically adjusts AGV task allocation based on the risk level to prevent high-risk equipment from continuing operation.

[0178] Meanwhile, the system integrates a near-expiry goods monitoring function, automatically identifying near-expiry goods and dispatching AGVs for outbound disposal; it also integrates an environmental anomaly linkage function, automatically adjusting warehouse temperature and humidity or triggering a fire alarm response; and it integrates an inventory early warning linkage function, automatically prompting replenishment or promotions.

[0179] The guiding and positioning device synchronizes the distance between the two guide wheels and the track through the drive component, ensuring the stability of AGV operation; the cleaning component automatically cleans the track surface, reducing guide wheel wear and lowering R... guide Risk value.

[0180] This invention achieves comprehensive and multi-dimensional supervision from "equipment safety" to "cargo safety" and then to "environmental safety". Through multi-source data fusion, multi-dimensional risk assessment, hierarchical early warning response, and deep linkage with WMS, it significantly improves the operational safety and management intelligence level of automated warehouses.

[0181] Contents not described in detail in this specification are prior art known to those skilled in the art. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for safety supervision of operating equipment, characterized in that: This method is applied to an automated warehouse system, which includes a warehouse management system (WMS), several AGV (Automated Guided Vehicle) vehicles, and a guiding and positioning device. The specific method includes the following steps: Step S1: Multi-source data acquisition Collect AGV operating status data, warehouse environment data, WMS task data, and mechanical status data of the guide and positioning device; wherein, the mechanical status data of the guide and positioning device includes slider displacement, guide wheel wear, and cleaning brush working status; Step S2: Trajectory Anomaly Detection Based on the historical operation trajectory database, a standard path model is constructed for each AGV. The real-time collected AGV position trajectory is compared with the standard path model, and the trajectory deviation D is calculated. Step S3: Multi-dimensional risk assessment Construct a risk assessment model based on at least four dimensions: AGV inherent risk, environmental risk, task risk, and guidance and positioning device risk, and calculate the comprehensive risk value R. total ; Step S4: Tiered Early Warning and Coordinated Response Based on the comprehensive risk value R total Risk levels are classified, and differentiated early warning and response measures are taken for different risk levels. The AGV task allocation is dynamically adjusted in conjunction with the WMS system.

2. The method for safety supervision of operating equipment according to claim 1, characterized in that: In step S1: The AGV operating status data includes at least: real-time position coordinates, operating speed, acceleration, battery level, and number of emergency stop signals; The warehouse environmental data includes at least: temperature and humidity, light intensity, and smoke concentration; The WMS task data includes at least: current task type, target cargo location coordinates, task priority, and estimated completion time; The mechanical status data of the guide positioning device is collected by sensors installed on the housing base of the guide positioning device. The sensors include at least a displacement sensor, a torque sensor, and a current detection module.

3. The method for safety supervision of operating equipment according to claim 1, characterized in that: The formula for calculating the trajectory deviation D in step S2 is as follows: D = α × ΔP + β × ΔV + γ × ΔT in: ΔP is the spatial position deviation between the real-time trajectory and the standard path, calculated using Euclidean distance or dynamic time warping algorithms; ΔV represents the deviation between the real-time speed and the standard speed curve; ΔT represents the deviation between the task execution time and the standard task time; α, β, and γ are weighting coefficients, and α+β+γ=1, which can be dynamically adjusted according to the warehouse scenario; when D is greater than the preset threshold T1, it is judged as a mild trajectory anomaly; when D is greater than the preset threshold T2, it is judged as a severe trajectory anomaly, where T2 > T1.

4. The safety monitoring method for operating equipment according to claim 1, characterized in that: The comprehensive risk value R in step S3 total The calculation formula is: R total =w1×R agv +w2×R env +w3×R task +w4×R guide in: R agv The risk value of the AGV itself is calculated based at least on battery level, emergency stop frequency, and trajectory deviation D. R env The environmental risk value is calculated based at least on temperature and humidity, smoke concentration, and light intensity. R task The task risk value is calculated based at least on task priority, the degree of congestion in the work area, and the status of the shelves. R guide The risk value of the guide positioning device is calculated based at least on the wear of the guide wheel, the displacement deviation of the slider, and the sluggish state of the cleaning swipe. w1, w2, w3, and w4 are weighting coefficients, and w1+w2+w3+w4=1.

5. The safety monitoring method for operating equipment according to claim 4, characterized in that: Risk value R of the guiding and positioning device guide The calculation methods include: Obtain the actual distance between the guide wheels on both sides of the slider and the guide rail in the guiding and positioning device, compare it with the standard distance, and calculate the distance deviation value ΔG; Obtain the cumulative number of rotations or running time of the guide wheel, and calculate the wear degree W of the guide wheel based on the preset wear model; The current value of the drive motor of the cleaning brush is obtained. When the current value exceeds the preset threshold, it is determined that the cleaning brush is stuck. Rguide is calculated based on the spacing deviation value ΔG, wear degree W, and cleaning card swiping status.

6. The safety monitoring method for operating equipment according to claim 1, characterized in that: The risk level classification in step S4 is as follows: When 0≤R total When the value is less than 0.3, it is considered low risk, and the system only logs the information without triggering an alert. When 0.3≤R total When the value is less than 0.6, it is judged as medium risk. The system will display a prompt on the monitoring screen, and the AGV will automatically slow down and expand the avoidance range. When 0.6≤R total When the value is less than 0.8, it is considered a high-risk situation. The system will trigger an audible and visual alarm and send a message to the management personnel. The AGV will immediately stop running and wait for instructions. When R total When the value is ≥0.8, it is determined to be a critical risk. The system triggers a full-warehouse alarm, the AGV stops urgently and the power supply is cut off. At the same time, the area is sealed off and personnel are notified to intervene.

7. The safety monitoring method for operating equipment according to claim 6, characterized in that: The dynamic adjustment of AGV task allocation by the linked WMS system includes: When the risk level is determined to be medium, the WMS system adjusts the priority of subsequent tasks for the AGV, prioritizing low-risk tasks or charging tasks. When a task is deemed high-risk, the WMS system suspends the assignment of new tasks to that AGV and schedules other AGVs to take over the unfinished tasks. When a critical risk is identified, the WMS system replans the paths of all AGVs in the area to avoid the restricted area and records the location of the malfunctioning AGV for maintenance personnel to handle.

8. The safety monitoring method for operating equipment according to claim 1, characterized in that: The method also includes a joint supervision step for near-expiry goods: Step S5: Obtain the production date and shelf life information of the goods from the WMS system, and calculate the near-expiration value based on the near-expiration judgment formula (MN) / Q-0.7, where M is the current date, N is the production date, and Q is the shelf life in days; Step S6: When the near-expiry value exceeds the preset threshold, the goods are determined to be near-expiry, a near-expiry warning message is generated and highlighted on the monitoring screen; Step S7: Dispatch the AGV to the storage location of the goods that are about to expire and automatically pick them up, and transport the goods to the sorting module; Step S8: The sorting module automatically sorts the near-expiry goods to the corresponding disposal area according to their category and degree of near expiry, and updates the inventory status in the WMS system.

9. A safety monitoring system for operating equipment, used to implement the method described in any one of claims 1-8, characterized in that, include: The data acquisition module, located in the automated warehouse, includes: The first sensor group installed on the AGV is used to collect AGV operating status data. The second sensor group, installed inside the warehouse, is used to collect environmental data. The third sensor group installed on the guide and positioning device is used to collect mechanical status data of the guide and positioning device. An interface module that communicates with the WMS system to acquire task data; The trajectory anomaly detection module, connected to the data acquisition module, is used to calculate the trajectory deviation D based on the historical trajectory database; The risk assessment module, connected to the trajectory anomaly detection module, is used to calculate the comprehensive risk value R based on multi-dimensional risk factors. total ; The tiered early warning module, connected to the risk assessment module, is used to trigger differentiated early warning responses based on the risk level. The WMS linkage module connects with the hierarchical early warning module and the WMS system to dynamically adjust AGV task allocation. The storage module is used to store historical data, standard path models, and risk threshold parameters.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for security monitoring of an operating device as described in any one of claims 1-8.

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