A temperature monitoring method and system suitable for a counterbalanced AGV fork truck

Abstract

The application provides a temperature monitoring method and system suitable for a counterbalance AGV forklift, in which multiple-source operation data of key components are synchronously collected during operation of the counterbalance AGV forklift; the multiple-source operation data are preprocessed to obtain equipment state data containing real-time temperature values, temperature rise rates and historical temperature data; double fault detection is performed on a temperature sensor of a target component, and if the temperature sensor of the target component has no fault, state judgment is performed, wherein the double fault detection comprises self feature judgment and associated feature judgment, the self feature judgment is based on the equipment state data of the target component, and the associated feature judgment is based on temperature of an associated component to judge the equipment state data of the current target component; and state judgment is performed based on a working condition dynamic threshold and a real-time temperature value of the target component.

Description

Technical Field

[0001] This invention relates to the field of forklift temperature monitoring, and in particular to a temperature monitoring method and system suitable for counterbalanced AGV forklifts. Background Technology

[0002] With the explosive growth in demand for heavy-duty handling in industrial logistics, counterbalanced AGV forklifts have become core equipment in scenarios such as automobile manufacturing, machinery processing, and port terminals. Their operating characteristics dictate that the internal battery, drive motor, steering motor, driver, and hydraulic system (hydraulic oil, hydraulic valves, hydraulic pump motor) need to operate under high load for extended periods. Overheating failures account for more than 60% of all failures, with hydraulic system overheating being the primary cause of operational interruptions. Therefore, temperature monitoring of counterbalanced AGV forklifts is necessary.

[0003] Currently, technical solutions for monitoring the operational status of AGV equipment typically employ temperature sensors to collect temperatures from key components and determine whether any abnormalities exist based on preset temperature thresholds. For example, detecting the temperature of the power battery assesses battery thermal risk, detecting the temperature of the drive motor assesses the motor's operating status, or detecting the temperature of the driver unit indicates any abnormalities in power devices. However, existing temperature monitoring solutions are primarily designed for ordinary AGV vehicles and still have certain shortcomings when applied to heavy-duty equipment such as counterbalanced AGV forklifts. First, the coverage of temperature monitoring in existing technologies is insufficient. Current solutions typically focus on electrical components such as power batteries, motors, and drives, while lacking effective monitoring of the hydraulic actuators unique to counterbalanced AGV forklifts. Because the hydraulic system is responsible for lifting, lowering, and loading / unloading goods for extended periods, its hydraulic pumps, hydraulic oil, and hydraulic valve assemblies generate significant heat during high-frequency operation. When the hydraulic oil temperature rises abnormally, hydraulic valves malfunction, or the hydraulic pump is continuously overloaded, existing temperature monitoring systems struggle to detect these anomalies in a timely manner. This can easily lead to problems such as degraded hydraulic oil performance, aging of seals, valve jamming, and even hydraulic system failure.

[0004] Secondly, existing technologies typically rely on a single temperature parameter for anomaly detection, lacking multi-dimensional operational data fusion analysis. Equipment temperature increases can be caused by various factors, such as excessive load leading to increased current, abnormal heat dissipation structures preventing heat release, or increased friction due to mechanical component wear. Relying solely on temperature data makes it difficult to accurately determine the cause of the temperature rise. Therefore, existing solutions, upon detecting anomalies, usually only offer measures such as reducing operating capacity or direct shutdown, failing to address specific fault causes and impacting equipment operating efficiency.

[0005] Furthermore, existing temperature monitoring systems typically lack effective diagnostics for the condition of the temperature sensors themselves. In long-term industrial operating environments, temperature sensors may experience data drift, fixed value output, or abnormal fluctuations due to factors such as circuit abnormalities, communication failures, or component aging. If the system cannot identify the sensor's own faults, it may misjudge erroneous data as equipment failure, resulting in invalid alarms or equipment shutdowns; or, due to sensor failure, the true temperature rise may not be detected, leading to equipment operation with a fault, increasing safety risks.

[0006] Furthermore, existing technologies typically treat different devices as independent monitoring objects, judging solely based on the temperature thresholds of each component, without considering the heat transfer relationships and operational status correlations between devices. For example, there is a clear correlation between drive motor temperature, actuator temperature, and current load; similarly, there are corresponding patterns in the changes of hydraulic pump motor temperature, hydraulic oil temperature, and hydraulic valve temperature. Due to the lack of an inter-device temperature correlation model, existing systems cannot utilize data from multiple devices for cross-validation and struggle to detect potential faults in a timely manner.

[0007] Furthermore, existing temperature anomaly handling strategies typically employ fixed thresholds and fixed response methods. For example, when the temperature of a component exceeds a preset value, an alarm is directly triggered or the entire vehicle is shut down. However, the actual operating temperature of equipment is affected by various factors such as load conditions, ambient temperature, equipment aging, heating rate, and mechanical vibration. Fixed temperature thresholds can easily lead to false alarms under high load and high temperature environments, or fail to provide timely warnings when equipment performance deteriorates. Simultaneously, directly stopping the entire vehicle for minor local anomalies can disrupt production logistics and reduce the operating efficiency of automated production lines.

[0008] Therefore, given the numerous problems with existing counterbalanced AGV forklift temperature monitoring systems, it is necessary to propose a temperature monitoring system specifically designed for counterbalanced AGV forklifts. Summary of the Invention

[0009] The purpose of this invention is to provide a temperature monitoring method and system suitable for counterbalanced AGV forklifts. By collecting multi-dimensional operating data of multiple components, including the hydraulic system, and combining dual sensor self-testing, multi-system temperature correlation cross-verification, multi-factor dynamic threshold determination, and hierarchical flexible task scheduling, this invention solves the problems of missed hydraulic faults, false alarms from sensors, poor adaptation of fixed thresholds, and large downtime losses due to temperature rise in traditional monitoring, while taking into account both equipment thermal safety and continuous production line operation.

[0010] To achieve the above objectives, this technical solution provides a temperature monitoring method suitable for counterbalanced AGV forklifts, comprising the following steps: S1. During the operation of the counterbalanced AGV forklift, multi-source operating data of key components are collected synchronously. The key components include at least the power battery, drive motor, steering motor, driver, and hydraulic oil, main hydraulic valve, and hydraulic pump motor of the entire hydraulic system. The multi-source operating data includes at least the temperature data of each key component and the operating status data related to temperature changes. S2. Preprocess the multi-source operating data to obtain equipment status data that includes real-time temperature values, temperature rise rate and historical temperature data. S3: Perform dual fault detection on the temperature sensor of the target component. If the temperature sensor of the target component is not faulty, proceed to step S4. The dual fault detection includes self-feature detection and associated feature detection. Self-feature detection is based on the equipment status data of the target component. Associated feature detection is based on the temperature of associated components to determine the equipment status data of the current target component. S4: Determine the status based on the dynamic threshold of the operating condition and the real-time temperature value of the target component.

[0011] Secondly, this solution provides a temperature monitoring system suitable for counterbalanced AGV forklifts, including the following: The data acquisition layer is used to synchronously collect multi-source operating data of key components during the operation of the counterbalanced AGV forklift. The key components include at least the power battery, drive motor, steering motor, driver, and hydraulic oil, main hydraulic valve, and hydraulic pump motor of the entire hydraulic system. The multi-source operating data includes at least the temperature data of each key component and the operating status data related to temperature changes. The data processing layer is used to preprocess multi-source operating data to obtain equipment status data that includes real-time temperature values, temperature rise rates, and historical temperature data. The temperature sensor detection layer is used to perform dual fault judgment on the temperature sensor of the target component. If the temperature sensor of the target component is not faulty, the fault detection layer is triggered. The dual fault detection includes self-feature judgment and related feature judgment. Self-feature judgment is based on the equipment status data of the target component, and related feature judgment is based on the temperature of related components to judge the equipment status data of the current target component. The fault detection layer is used to determine the status based on dynamic thresholds of operating conditions and the real-time temperature value of the target component.

[0012] Compared with existing technologies, this technical solution has the following characteristics and beneficial effects: 1. This solution breaks through the limitations of traditional AGV monitoring, which only covers electrical components such as batteries, motors, and drivers. For the first time, it incorporates the entire hydraulic system, including hydraulic pump motors, hydraulic oil, and main hydraulic valves, into the temperature monitoring system. It fully covers all high-heat-generating core components in the power battery, drive / steering motors, power drivers, and the entire hydraulic chain. Simultaneously, it collects multi-dimensional operating data such as temperature, current, voltage, internal resistance, speed, three-axis vibration, and ambient temperature and humidity, constructing a multi-modal data fusion acquisition architecture. This fully restores the heating conditions of the entire forklift system, completely solving the industry problem of missed detection of hydraulic system overheating under heavy load conditions and the inability to locate the root cause of temperature rise by relying on a single temperature parameter. It avoids hydraulic oil deterioration, valve sticking, seal failure, and hydraulic system shutdown failures from the source.

[0013] 2. This solution employs a dual sensor fault detection logic of "self-feature judgment + cross-component correlation verification": On one hand, it identifies sensor anomalies through temperature time-series curve features, directly determining sensor failure based on fault data such as prolonged constant temperature or short-term sudden temperature drift; on the other hand, it constructs a temperature correlation model based on the heating coupling law of the equipment, deriving the theoretical temperature of the target component from the measured temperature of the correlated component, and performing cross-verification by comparing the deviation of the measured values. These two mechanisms work together to accurately identify various sensor faults such as circuit breaks, component aging, data jumps, and numerical freezes. The system can automatically switch to temporary monitoring of theoretical temperature and simultaneously report sensor faults, effectively avoiding the dual risks of false alarms and unexplained equipment shutdowns caused by sensor anomalies, or thermal damage to equipment caused by sensor failure failing to detect the actual high temperature, significantly improving the long-term operational reliability in industrial scenarios.

[0014] 3. This solution establishes dedicated temperature correlation models for the three major heat-generating systems of forklifts: the models integrate load, speed, and duty cycle correction coefficients to accurately calculate the theoretical temperature of each component. During operation, the temperature data of multiple components are cross-validated, which is not only used for sensor self-testing, but also for capturing hidden anomalies where the temperature of a single component does not exceed the standard, but the temperature coupling relationship of multiple components deviates from the normal pattern. Early warning is completed when the temperature rise is not obvious in the early stage of the fault, realizing early fault identification and reducing the probability of major equipment damage.

[0015] 4. Abandoning the traditional fixed temperature threshold judgment logic, this solution comprehensively considers five influencing factors—load rate, equipment aging degree, ambient temperature, temperature rise rate, and vibration intensity—to adjust multi-level temperature thresholds in real time. Under high load and high temperature environments, the threshold is automatically raised to avoid false alarms due to normal overheating at full load; when equipment ages, temperature rises rapidly, or vibration intensifies, the threshold is automatically lowered to provide early warning. The dynamic threshold can adaptively adjust in real time according to forklift operating conditions, equipment lifespan, and external environment, perfectly adapting to complex on-site conditions such as heavy-duty handling, high and low temperature factory areas, and equipment aging, significantly reducing the frequency of false alarms while improving the sensitivity of high-temperature risk warnings.

[0016] 5. This solution deeply integrates the temperature monitoring system with AGV task scheduling, classifying operating states into four levels based on the severity of temperature anomalies. Differentiated flexible handling strategies are designed for different abnormal components and fault levels: Level 1 minor anomalies involve speed and load limits and intensified monitoring; Level 2 subsystem anomalies specifically restrict high-load tasks while preserving basic planar handling capabilities; Level 3 severe overheating results in automatic return to the station for cooling after completing the current task; Level 4 emergency faults, such as battery thermal runaway and hydraulic oil overheating, require immediate emergency shutdown and power cutoff. Unlike traditional solutions that brutally shut down the entire vehicle upon temperature rise, this solution maximizes the preservation of basic operational capabilities while ensuring equipment safety and mitigating the risk of thermal runaway, significantly reducing logistics interruptions on automated production lines and balancing equipment safety with factory production efficiency.

[0017] 6. The entire solution is designed specifically for the heavy-duty, high-frequency hydraulic operation and multi-system coupled heat generation characteristics of counterbalanced AGV forklifts. Data acquisition, sensor self-testing, correlation model calculation, dynamic threshold determination, and task scheduling control are all integrated into the on-board controller. It achieves millisecond-level synchronous data acquisition through the CAN bus, eliminating the need for numerous additional peripherals. The model parameters can be factory-calibrated according to the forklift's rated parameters, adapting to counterbalanced AGV forklifts of different tonnages and hydraulic configurations. It has strong versatility and engineering application value, and can be widely used in heavy-duty logistics scenarios such as automobile manufacturing, ports, and machinery processing. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of a temperature monitoring method for counterbalanced AGV forklifts according to an embodiment of this application; Figure 2 This is a framework diagram of a temperature monitoring system for a counterbalanced AGV forklift according to one embodiment of this application; Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0020] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0021] Example 1 This solution provides a temperature monitoring method suitable for counterbalanced AGV forklifts, including the following steps: S1. During the operation of the counterbalanced AGV forklift, multi-source operating data of key components are collected synchronously. The key components include at least the power battery, drive motor, steering motor, driver, and hydraulic oil, main hydraulic valve, and hydraulic pump motor of the entire hydraulic system. The multi-source operating data includes at least the temperature data of each key component and the operating status data related to temperature changes. S2. Preprocess the multi-source operating data to obtain equipment status data that includes real-time temperature values, temperature rise rate and historical temperature data. S3: Perform dual fault detection on the temperature sensor of the target component. If the temperature sensor of the target component is not faulty, proceed to step S4. The dual fault detection includes self-feature detection and associated feature detection. Self-feature detection is based on the equipment status data of the target component. Associated feature detection is based on the temperature of associated components to determine the equipment status data of the current target component. S4: Determine the status based on the dynamic threshold of the operating condition and the real-time temperature value of the target component.

[0022] Specifically, regarding step S1: This solution automatically scans all sensors and actuators during the operation of the counterbalanced AGV forklift and simultaneously collects multi-source operating data of each key component.

[0023] In some embodiments, the multi-source operating data includes temperature data, voltage data, current data, and internal resistance data of the power battery; temperature data, speed data, and current data of the drive motor and steering motor; temperature data and output current data of each actuator; temperature data of the hydraulic oil, temperature data of the main hydraulic valve, and temperature data of the hydraulic pump motor; and triaxial vibration data of the drive motor, steering motor, and hydraulic pump.

[0024] In some embodiments, step S1 also records the ambient temperature of the environment in which the counterbalanced AGV forklift is currently located.

[0025] In this embodiment of the solution, multi-source operating data of each key component is collected synchronously at a frequency of 1Hz, and the multi-source operating data is transmitted to the AGV main controller via the CAN bus, with a control time synchronization accuracy of ≤10ms.

[0026] It should be noted that the steering motor, travel motor, and oil pump motor each have their own drivers, namely, steering driver, travel driver, and oil pump driver.

[0027] Regarding step S2: This solution uses a moving average filter to remove random noise from multi-source operating data. Then, it differentiates the temperature data of each key component to obtain the temperature rise rate and buffers historical temperature data within a preset time period to obtain equipment status data. The advantage of this approach is that the moving average filter removes random noise from the original multi-source operating data to ensure data reliability. Simultaneously, it extracts temperature rise rate features and caches historical temperature data, providing standardized feature data and historical comparison support for subsequent sensor health verification, temperature correlation modeling, dynamic threshold calculation, and fault diagnosis.

[0028] In some embodiments, the first derivative of the temperature data of each key component is obtained by taking the first derivative as the heating rate, and the second derivative is obtained by taking the second derivative as the temperature change acceleration, wherein both the temperature change acceleration and the heating rate are temperature rise rates.

[0029] Regarding step S3: Before diagnosing temperature faults, this solution first performs a self-test on the temperature sensors of each target component. The self-testing methods for the temperature sensors of the target components include self-feature judgment and correlation feature judgment. Self-feature judgment is based on the temperature feature data of the current target component itself, while correlation feature judgment is based on the relationship between the related components and the current target component.

[0030] Specifically, the rules for judging self-features are as follows: The judgment is made based on the historical temperature data in the current target component's device status data. If the temperature value of the current component remains unchanged within a continuous preset time period or changes significantly within a short period of time, it is determined that the temperature sensor of the current target component is faulty.

[0031] In some embodiments, if the temperature value of the current component remains unchanged for 5 consecutive minutes or the temperature value changes by more than 10°C within 1 second, it is determined that the temperature sensor of the current target component is faulty.

[0032] Specifically, the rules for determining association features are as follows: Establish a core temperature correlation model for operating conditions, and use the core temperature correlation model to calculate the theoretical temperature value of the target component under the current operating conditions. If the deviation between the real-time temperature value and the theoretical temperature value of the target component is greater than the deviation threshold within a preset time period, it is determined that the temperature sensor of the current target component is faulty.

[0033] It should be noted that the deviation thresholds for the temperature sensors of each key component in this solution are determined by combining the temperature error of the sensor itself and the physical deviation of the key component model. The temperature sensors used in the key components of the forklift typically have an allowable error of ±0.3 ℃, and approximately ±0.8 ℃ near 100 ℃. Considering the fluctuations introduced by the signal conditioning circuit, CAN bus transmission quantization error, and electromagnetic interference, the overall measurement uncertainty of the sensor channel is estimated to be approximately ±1.0~±1.5 ℃ in engineering practice. This is the inherent temperature error of the sensor itself. Furthermore, different key components have different heat capacities and heat dissipation boundary conditions of their heat conduction paths, resulting in different physical deviations of the model under normal operating conditions.

[0034] In other words, this solution combines the temperature error of the sensor itself and the temperature deviation of the key components to determine the deviation threshold of each key component (the deviation threshold of each component = sensor error (approximately ±1~1.5℃) + the physical deviation of the model of that component, and rounded up).

[0035] In addition, since the deviation threshold set in this solution is for deviations under extreme conditions, it can be adapted to forklifts of different tonnages and working conditions.

[0036] In one specific embodiment of this solution, when the deviation between the real-time temperature value of the driver and the theoretical temperature value exceeds ±3℃ and continues for a preset period of time, it is determined that the temperature sensor of the current driver is abnormal. There is a thermal resistance gradient between the junction temperature of the IGBT / MOSFET in the driver (power device) and the surface temperature of the heat sink, and the change in the speed of the cooling fan will affect the convective heat transfer coefficient. The physical deviation of the model is about ±1.5~±2℃, so the total deviation threshold is set to ±3℃.

[0037] If the real-time temperature value of the steering motor deviates from the theoretical temperature value by more than ±2℃ and continues for a preset period of time, the temperature sensor of the steering motor is judged to be abnormal. The physical deviation of the steering motor model is very small and can usually be controlled within ±1℃. Therefore, the allowable deviation threshold for the steering motor is the sum of the upper limit of sensor accuracy and the physical deviation, which is approximately ±2℃.

[0038] If the real-time temperature value of the hydraulic pump motor deviates from the theoretical temperature value by more than ±4℃ for a preset period of time, the temperature sensor of the hydraulic pump motor is judged to be abnormal. The hydraulic pump motor simultaneously has winding copper loss heating, iron core eddy current heating and hydraulic oil conduction heat dissipation. The thermal coupling path is complex and the physical deviation of the model can reach ±2.5~±3℃. Therefore, the total deviation threshold is set to ±4℃.

[0039] If the real-time temperature of the hydraulic oil deviates from the theoretical temperature by more than ±2℃ for a preset period of time, the temperature sensor of the hydraulic oil is considered to be malfunctioning. Hydraulic oil has the characteristics of large heat capacity, uniform heat conduction, and balanced temperature distribution under forced circulation in the pump body. Therefore, the physical deviation of the model is very small and can usually be controlled within ±1℃. Thus, the allowable deviation threshold for hydraulic oil is the sum of the upper limit of sensor accuracy and the physical deviation, which is approximately ±2℃.

[0040] If the real-time temperature value of the drive motor deviates from the theoretical temperature value by more than ±5℃ for a preset time period, the temperature sensor of the current drive motor is considered abnormal. Similarly, if the real-time temperature value of the main hydraulic valve deviates from the theoretical temperature value by more than ±5℃ for a preset time period (manually set), the temperature sensor of the current main hydraulic valve is considered abnormal. The drive motor / main hydraulic valve is affected by forced air cooling, and the hydraulic valve is affected by irregular oil flow, resulting in relatively larger physical deviations in their models, approximately ±3~±4℃. Therefore, the overall deviation threshold is relaxed to ±5℃.

[0041] In some embodiments, the preset time period is remotely set by the AGV upper system or modified through the AGV forklift software. This preset time period can be adjusted according to the customer's sensitivity requirements for temperature monitoring, and it also filters and reduces sensor erroneous data. This solution sets the default preset time period to 10 seconds, because fluctuations in sensor communication or electromagnetic interference often last only tens of milliseconds, and a setting of 10 seconds is sufficient.

[0042] In some embodiments, the working condition core temperature correlation model records the temperature relationship between the associated components and the current component. It is a temperature theoretical value calculation system that covers the three core heating systems of the counterbalanced AGV forklift, based on the physical heating law of the equipment. The core is to derive the theoretical temperature of the target component through the measured temperature of the associated components, which is used for sensor health cross-verification and hidden fault identification.

[0043] In some embodiments, the theoretical temperature value of the current target component is calculated using the operating condition adaptive core temperature correlation model and the real-time temperature value of the correlated components.

[0044] In the embodiments of this solution, the working condition adaptive core temperature correlation model includes a drive system temperature correlation model, a steering system temperature correlation model, and a hydraulic system full-link temperature correlation model. The drive system temperature correlation model records the temperature correlation between the drive motor and the driver under rated working conditions. The steering system temperature correlation model records the temperature correlation between the steering motor and the steering driver under rated working conditions. The hydraulic system full-link temperature correlation model records the temperature correlation between the hydraulic pump motor, hydraulic oil, and the main hydraulic valve under rated working conditions.

[0045] In other words, for drive motors and actuators, the theoretical temperature value of another target component is calculated based on the real-time temperature value of any associated component using the drive system temperature correlation model; for steering motors and steering actuators, the theoretical temperature value of another target component is calculated based on the real-time temperature value of any associated component using the steering system temperature correlation model; for hydraulic pump motors, hydraulic oil, and main hydraulic valves, the theoretical temperature value of another target component is calculated based on the real-time temperature values ​​of any two associated components using the full-link temperature correlation model of the hydraulic system.

[0046] Specifically, regarding the temperature correlation model of the drive system: The physical logic between the drive motor and the driver is as follows: Under low-speed, high-load conditions, the driver outputs a large current and generates more heat, increasing the temperature difference between the driver and the drive motor; under high-speed, light-load conditions, the motor's air-cooling efficiency is improved, reducing the temperature difference between the driver and the drive motor.

[0047] Correspondingly, the temperature correlation model of the drive system is expressed as: T_drive_cal = T_motor + T_drive_base K_v K_l; K_v = 1.2 - 0.3 v / v_rated; K_l = 0.7 + 0.5 I_motor / I_motor_rated; Where T_drive_cal is the temperature value of the driver, T_motor is the temperature value of the drive motor, T_drive_base is the rated operating condition reference temperature difference (rated speed, rated load under calibration, taken as 4℃), K_v is the speed correction coefficient, v is the real-time travel speed, v_rated is the rated travel speed; K_l is the load correction coefficient, I_motor is the real-time operating current of the motor, and I_motor_rated is the rated current of the motor.

[0048] It should be noted that the lower the speed, the larger the corresponding speed correction factor, and the higher the temperature difference between the drive motor and the driver; the higher the load, the larger the corresponding load correction factor, and the higher the temperature difference between the drive motor and the driver.

[0049] Regarding the temperature correlation model of the steering system: The physical logic between the steering motor and the steering drive is as follows: the frequency of steering directly affects the heat generation of the drive; the higher the steering duty cycle, the greater the temperature difference between the steering motor and the steering drive.

[0050] Correspondingly, the temperature correlation model of the steering system is expressed as: T_steer_drive_cal = T_steer_motor +T_steer_base K_d; K_d = 0.8 + 0.4 D_steer; Where T_steer_base is the reference temperature difference under rated steering conditions, K_d is the steering duty cycle correction coefficient, D_steer is the percentage of steering action time in the past 5 minutes (0~1), T_steer_motor is the temperature value of the steering motor, and T_steer_drive_cal is the temperature value of the steering drive.

[0051] Regarding the end-to-end temperature correlation model for hydraulic systems: The physical logic between the hydraulic pump motor, hydraulic oil, and main hydraulic valve is as follows: the heating of the hydraulic system is directly related to the frequency of lifting / loading operations. During frequent operations, the hydraulic pump works continuously, and the temperature difference between the hydraulic oil and the pump body and valve group changes synchronously.

[0052] Correspondingly, the temperature correlation model for the entire hydraulic system is as follows: T_oil_cal = T_pump_motor 0.92 - 3℃ K_hyd_d; T_valve_cal = T_oil_cal + 2℃ × K_hyd_d; K_hyd_d = 0.7 + 0.5 D_hyd; Where T_oil_cal is the temperature value of the hydraulic oil, T_pump_motor is the temperature value of the hydraulic pump motor, K_hyd_d is the hydraulic action duty cycle correction coefficient, D_hyd is the time percentage of hydraulic lifting / loading / unloading actions in the past 10 minutes (0~1), and T_valve_cal is the temperature value of the main hydraulic valve.

[0053] As described above, step S3 of this solution performs fault detection on the temperature sensor of the target component. If the temperature sensor of the target component fails, it automatically switches to the theoretical temperature value and performs temporary monitoring, while uploading fault information to the host computer.

[0054] Regarding step S4: Unlike traditional solutions that use fixed thresholds to determine the status of target components, this solution uses dynamic thresholds that are updated in real time according to operating conditions, environment, and equipment status to determine the real-time temperature value of the target component. This fundamentally solves the problem of misjudgment caused by fixed thresholds in heavy load, high temperature, and aging scenarios.

[0055] In addition, this solution sets three levels of dynamic thresholds and designs four operating states accordingly. The operating condition dynamic thresholds include normal threshold, alarm threshold, and danger threshold. When the real-time temperature value of the target component is less than the normal dynamic threshold, the operating state is normal operation. When the real-time temperature value of the target component is greater than the normal dynamic threshold but less than the alarm threshold, the operating state is Level 1 abnormal. When the real-time temperature value of the target component is greater than the alarm threshold but less than the danger threshold, the operating state is Level 2 abnormal. When the real-time temperature value of the target component is greater than the danger threshold, or when the operating states of multiple key components are Level 2 abnormal, the operating state is Level 3 abnormal.

[0056] Furthermore, this plan adopts different decisions for different operating states.

[0057] In some embodiments, when the operating status is normal, the system periodically samples key operating data of the equipment, including but not limited to motor speed, battery voltage, load weight, and operating position. The sampling frequency is set to once every 5 seconds, and the sampled data is reported to the central monitoring system in real time so that monitoring personnel can promptly grasp the operating status of the equipment. When the operating status is Level 1 abnormal, such as when the motor temperature is slightly higher than the normal threshold or the battery voltage fluctuates slightly, the system will automatically encrypt the sampled data, using the AES-256 encryption algorithm to ensure data security. At the same time, the operating speed of the equipment will be reduced to 70% of the normal speed, the load will be reduced to 80% of the rated load, and a warning signal will be issued to the on-site operator through an audible and visual alarm. Simultaneously, the abnormal information will be pushed to the monitoring personnel's mobile APP. When the status is at Level 2 anomaly, such as sensor failure or local circuit short circuit, the system will restrict subsystem tasks, suspend the execution of non-core tasks, prioritize the operation of critical tasks, and activate a flexible scheduling strategy to dynamically adjust task allocation and running paths based on the real-time status of the equipment and task priorities to ensure that the equipment can continue to complete important tasks. When the operating status is at Level 3 anomaly, such as serious situations like motor overheating or battery smoke, the system will accelerate the currently executing task, optimize the running algorithm, and shorten the task execution time as much as possible while ensuring safety. After the task is completed, the system will immediately initiate the return-to-station cooling process. The equipment will return to the charging station according to the preset optimal path. Upon arrival at the charging station, the system will automatically activate the cooling system to cool down the equipment and send an emergency alarm message to the monitoring center to notify maintenance personnel to carry out timely repairs.

[0058] More specifically, when the operating status is Level 1 abnormal (i.e., the temperature of a single critical component exceeds the normal threshold range, but has not yet reached the alarm threshold. The dynamic threshold is a reasonable temperature range calculated in real time based on the equipment's operating conditions), the sampling frequency of the target component is increased to 2Hz to more accurately monitor equipment temperature changes; the maximum operating speed of the AGV is reduced by 20%, and the maximum load is limited to 80% to reduce equipment heat generation, and an early warning information, including the equipment number, current temperature, and abnormality level, is reported to the host computer every 10 seconds.

[0059] When the operating status is at level two abnormal (the temperature of a single critical component is close to the alarm threshold), tasks are rationally scheduled according to the type of abnormal component: Hydraulic system malfunction: Tasks requiring frequent use of the hydraulic system, such as stacking and loading / unloading, are prohibited. Only surface handling tasks are permitted to reduce the workload on the hydraulic system.

[0060] Steering system malfunction: Disallow tasks requiring frequent steering; only allow long-distance straight-line driving tasks to reduce the operational stress on the steering system.

[0061] Drive system malfunction: Heavy-load tasks are prohibited; only light-load handling tasks are allowed to reduce the burden on the drive system.

[0062] Reduce the maximum operating speed of the AGV by 50% to further reduce the heat generated by the equipment.

[0063] When the operating status is Level 3 abnormal (severe overheating): the temperature of a single critical component exceeds the alarm threshold or multiple critical components reach Level 2 abnormality at the same time, the system will immediately stop receiving new tasks to prevent the fault from escalating further. After completing the current task, the system will automatically plan the optimal path to return to the nearest charging station or designated safe area, activate the cooling system to cool down, and report the severe fault information to the host computer, including the specific location of the faulty equipment, the fault type, and the severity.

[0064] When the operating status is Level 4 Abnormal (Emergency Danger): If signs of impending battery thermal runaway are detected (such as a sharp rise in battery temperature, abnormal voltage fluctuations, etc.) or the hydraulic oil temperature exceeds 85°C, immediately press the emergency stop button to cut off the AGV's main power supply to prevent further danger. Trigger the audible and visual alarm, emitting a high-frequency alarm sound and flashing warning lights to alert surrounding personnel. At the same time, report the emergency fault information to the host computer, clarify the urgency of the fault, and recommend immediate implementation of appropriate emergency measures.

[0065] It is worth mentioning that if the temperature of the hydraulic oil exceeds 95°C or there are signs of impending battery thermal runaway, the breakpoint and audible and visual alarms will be triggered independently.

[0066] In some embodiments, the operating condition dynamic threshold is obtained by real-time correction based on the equipment baseline threshold combined with the load rate correction factor, aging correction factor, ambient temperature correction value, calculated temperature rise correction value, and vibration correction value.

[0067] The dynamic thresholds for operating conditions, including normal thresholds, alarm thresholds, and danger thresholds, are represented as follows; T_normal = T_base_normal × K_load × K_aging + T_env + T_trend + T_vib T_danger = T_base_danger × K_load × K_aging + T_env + T_trend + T_vib T_warn = T_base_warn × K_load × K_aging + T_env + T_trend + T_vib; Where T_base_normal, T_base_danger, and T_base_warn are the equipment baseline thresholds, which refer to the factory calibration values ​​of brand-new equipment under a standard environment of 25℃ and rated load (such as drive motor baseline normal 60℃, warning 70℃, danger 80℃), and are fixed constants; K_load is the load rate correction factor, K_aging is the aging correction factor, T_env is the ambient temperature correction value, T_trend is the calculated temperature rise correction value, and T_vib is the vibration correction value.

[0068] Correspondingly, in the step of "judging the status based on the dynamic threshold of the working condition and the real-time temperature value of the target component", the load rate of the current counterbalance AGV forklift is obtained to calculate the load rate correction factor, the operating status of the current target component is obtained to calculate the aging correction factor, the ambient temperature of the current counterbalance AGV forklift is obtained to calculate the ambient temperature correction value, the temperature rise rate of the current target component is obtained to calculate the temperature rise correction value, and the measured three-axis vibration acceleration of the current counterbalance AGV forklift is obtained to calculate the vibration correction value.

[0069] Regarding the load factor correction: If the load rate of the current counterbalanced AGV forklift is higher, the corresponding heat generation temperature of each target component will be higher. Therefore, the corresponding dynamic threshold of the working condition needs to be reasonably increased to avoid misjudging normal heat generation under full load as abnormal.

[0070] In some embodiments, the average operating current and rated operating current of the current counterbalanced AGV forklift over a preset time period are obtained, and the load rate correction factor is calculated based on the average operating current and rated operating current, as shown in the following formula: K_load = 0.85 + 0.15 I_avg / I_rated; Where I_avg is the average operating current of the counterbalanced AGV forklift over a preset time period (5 minutes for example), and I_rated is the rated operating current.

[0071] In some embodiments, the load factor correction factor ranges from 0.85 to 1.15. If the calculated value is greater than this range, the endpoint of the range is used as the load factor correction factor.

[0072] Regarding aging correction factors: It is generally believed that the insulation and heat dissipation performance of counterbalanced AGV forklifts deteriorates after equipment aging, resulting in higher risks at the same temperature. Therefore, the corresponding dynamic threshold for operating conditions needs to be adjusted downward according to the degree of aging to provide early warning.

[0073] In some embodiments, for target components including drive motors, steering motors, hydraulic pump motors, drivers, and hydraulic valves, an aging correction factor is calculated based on the cumulative operating time and rated lifespan of the target components; for target components including power batteries, an aging correction factor is calculated based on the number of charge-discharge cycles of the target components and the rated cycle life of the battery.

[0074] That is, the calculation formula for the aging correction factor for target components including drive motors, steering motors, hydraulic pump motors, actuators, and hydraulic valves is as follows: K_aging = 1.0 - 0.15 H / H_rated_life; Where H is the cumulative runtime and H_rated_life is the rated lifespan (uniformly set to 20,000 hours).

[0075] In some embodiments, the aging correction factor is in the range of 0.85 to 1.0. If the calculated value is greater than this range, the endpoint of the range is used as the aging correction factor.

[0076] The formula for calculating the aging correction factor for power batteries is as follows: K_aging = 1.0 - 0.2 C / C_rated; Where C represents the cumulative number of charge-discharge cycles of the battery, and C_rated represents the rated cycle life of the battery.

[0077] In some embodiments, the aging correction factor is in the range of 0.8 to 1.0. If the calculated value is greater than this range, the endpoint of the range is used as the aging correction factor.

[0078] Regarding the ambient temperature correction value: The higher the ambient temperature, the lower the heat dissipation efficiency of the equipment, and the corresponding normal operating temperature rises accordingly. Therefore, the dynamic threshold of the corresponding threshold operating condition needs to be linearly compensated with the ambient temperature.

[0079] The formula for calculating the ambient temperature correction value is as follows: T_env = (T_ambient - 25℃) 0.6 Where T_ambient is the ambient temperature of the current counterbalanced AGV forklift.

[0080] In some embodiments, the ambient temperature correction value ranges from -5°C to +8°C. Additionally, if the ambient temperature of the current counterbalanced AGV forklift is below 0°C or above 40°C, the extreme values ​​of the range are used as the ambient temperature correction value.

[0081] Regarding the temperature rise correction value: The faster the temperature of critical components rises, the higher the risk of thermal runaway. Therefore, the corresponding dynamic value of the threshold operating condition needs to be actively lowered to achieve early warning.

[0082] The formula for calculating the temperature rise correction value is as follows: T_trend = -2 dT / dt; Where dT / dt is the rate of temperature rise.

[0083] In some embodiments, the temperature rise correction value ranges from -10°C to 0°C. Additionally, if the temperature rise rate of the target component is greater than 5°C / min, then -10°C is used as the temperature rise correction value.

[0084] Regarding vibration correction values: T_vib = -3 × (a / a0); Where a is the measured triaxial vibration acceleration, and a0 is the reference vibration value under rated operating conditions.

[0085] In some embodiments, the vibration correction value ranges from -5℃ to 0℃.

[0086] In some embodiments, this solution can also perform multimodal fault diagnosis based on multiple key components.

[0087] Specifically, the multimodal fault diagnosis rules are as follows: If the real-time temperature of the drive motor increases, and the motor current increases synchronously while the vibration is normal, the fault of the current counterbalanced AGV forklift is determined to be motor overload. If the real-time temperature of the drive motor increases, and the motor current and vibration are normal, the fault of the current counterbalanced AGV forklift is determined to be a cooling system fault. If the real-time temperature of the drive motor increases, and the motor current is normal while the vibration increases abnormally, the fault of the current counterbalanced AGV forklift is determined to be bearing wear or rotor imbalance. If the real-time temperature of the power battery increases, and the battery internal resistance suddenly increases while the voltage fluctuates, the fault of the current counterbalanced AGV forklift is determined to be a micro-short circuit inside the battery, a precursor to thermal runaway. If the real-time temperature of the hydraulic oil increases, and the hydraulic pump current and ambient temperature are normal, the fault of the current counterbalanced AGV forklift is determined to be hydraulic oil contamination or oil circuit blockage.

[0088] Example 2 Referring to Example 1, as follows Figure 2 As shown, this solution provides a temperature monitoring system suitable for counterbalanced AGV forklifts, including the following steps: The data acquisition layer is used to synchronously collect multi-source operating data of key components during the operation of the counterbalanced AGV forklift. The key components include at least the power battery, drive motor, steering motor, driver, and hydraulic oil, main hydraulic valve, and hydraulic pump motor of the entire hydraulic system. The multi-source operating data includes at least the temperature data of each key component and the operating status data related to temperature changes. The data processing layer is used to preprocess multi-source operating data to obtain equipment status data that includes real-time temperature values, temperature rise rates, and historical temperature data. The temperature sensor detection layer is used to perform dual fault judgment on the temperature sensor of the target component. If the temperature sensor of the target component is not faulty, the fault detection layer is triggered. The dual fault detection includes self-feature judgment and related feature judgment. Self-feature judgment is based on the equipment status data of the target component, and related feature judgment is based on the temperature of related components to judge the equipment status data of the current target component. The fault detection layer is used to determine the status based on dynamic thresholds of operating conditions and the real-time temperature value of the target component.

[0089] For the technical features similar to those in Embodiment 1, please refer to the above description, and they will not be repeated here.

[0090] Example 3 This embodiment also provides an electronic device, see reference. Figure 3 It includes a memory 402 and a processor 401, the memory 402 storing a computer program and the processor 401 being configured to run the computer program to perform the steps in any of the above embodiments of the temperature monitoring method applicable to counterbalance AGV forklifts.

[0091] Specifically, the processor 401 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0092] The memory 402 may include a large-capacity memory 402 for data or instructions. The memory 402 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 401.

[0093] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any of the temperature monitoring methods applicable to counterbalance AGV forklifts in the above embodiments.

[0094] Optionally, the electronic device may further include a transmission device 403 and an input / output device 404, wherein the transmission device 403 is connected to the processor 401 and the input / output device 404 is connected to the processor 401.

[0095] The transmission device 403 can be used to receive or send data via a network. Specific examples of the network described above may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 403 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0096] Input / output device 404 is used to input or output information. In this embodiment, the input information may be multi-source operating data, etc., and the output information may be fault detection information, etc.

[0097] Optionally, in this embodiment, the processor 401 can be configured to perform the following steps via a computer program: S1. During the operation of the counterbalanced AGV forklift, multi-source operating data of key components are collected synchronously. The key components include at least the power battery, drive motor, steering motor, driver, and hydraulic oil, main hydraulic valve, and hydraulic pump motor of the entire hydraulic system. The multi-source operating data includes at least the temperature data of each key component and the operating status data related to temperature changes. S2. Preprocess the multi-source operating data to obtain equipment status data that includes real-time temperature values, temperature rise rate and historical temperature data. S3: Perform dual fault detection on the temperature sensor of the target component. If the temperature sensor of the target component is not faulty, proceed to step S4. The dual fault detection includes self-feature detection and associated feature detection. Self-feature detection is based on the equipment status data of the target component. Associated feature detection is based on the temperature of associated components to determine the equipment status data of the current target component. S4: Determine the status based on the dynamic threshold of the operating condition and the real-time temperature value of the target component.

[0098] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0099] Generally, various embodiments can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention can be implemented in hardware, while others can be implemented by firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, these blocks, apparatuses, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0100] Embodiments of the present invention can be implemented by computer software, which may be executable by a data processor of a mobile device, such as a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products), including software routines, applets, and / or macros, can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer-executable components configured to perform embodiments when the program is run. One or more computer-executable components may be at least one piece of software code or a portion thereof. Additionally, it should be noted that any block in the logical flow of the figures may represent a program step, or interconnected logical circuitry, blocks and functions, or a combination of program steps and logical circuitry, blocks and functions. The software may be stored on physical media such as memory chips or blocks of storage implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. The physical medium is a non-transient medium.

[0101] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A temperature monitoring method suitable for counterbalanced AGV forklifts, characterized in that, Includes the following steps: S1. During the operation of the counterbalanced AGV forklift, multi-source operating data of key components are collected synchronously. The key components include at least the power battery, drive motor, steering motor, driver, and hydraulic oil, main hydraulic valve, and hydraulic pump motor of the entire hydraulic system. The multi-source operating data includes at least the temperature data of each key component and the operating status data related to temperature changes. S2. Preprocess the multi-source operating data to obtain equipment status data that includes real-time temperature values, temperature rise rate and historical temperature data. S3: Perform dual fault detection on the temperature sensor of the target component. If the temperature sensor of the target component is not faulty, proceed to step S4. The dual fault detection includes self-feature detection and associated feature detection. Self-feature detection is based on the equipment status data of the target component. Associated feature detection is based on the temperature of associated components to determine the equipment status data of the current target component. in The rules for determining the correlation features are as follows: Establish an adaptive core temperature correlation model for operating conditions, use the adaptive core temperature correlation model for operating conditions to calculate the theoretical temperature value of the target component under the current operating conditions, and if the deviation between the real-time temperature value and the theoretical temperature value of the target component is greater than the set threshold within a preset time period, then it is determined that the temperature sensor of the current target component has failed. The theoretical temperature value of the current target component is calculated by using the adaptive core temperature correlation model and the real-time temperature value of the related components. The adaptive core temperature correlation model includes the drive system temperature correlation model, the steering system temperature correlation model, and the hydraulic system full-link temperature correlation model. The drive system temperature correlation model records the temperature correlation between the drive motor and the driver under rated operating conditions. The steering system temperature correlation model records the temperature correlation between the steering motor and the steering driver under rated operating conditions. The hydraulic system full-link temperature correlation model records the temperature correlation between the hydraulic pump motor, hydraulic oil, and the main hydraulic valve under rated operating conditions. The temperature correlation model of the drive system is expressed as follows: T_drive_cal = T_motor + T_drive_base K_v K_l; K_v = 1.2 - 0.3 v / v_rated; K_l = 0.7+0.5 I_motor / I_motor_rated; Where T_drive_cal is the temperature value of the driver, T_motor is the temperature value of the drive motor, T_drive_base is the rated operating condition reference temperature difference, K_v is the speed correction coefficient, v is the real-time travel speed, v_rated is the rated travel speed; K_l is the load correction coefficient, I_motor is the real-time operating current of the motor, and I_motor_rated is the rated current of the motor. The temperature correlation model of the steering system is expressed as follows: T_steer_drive_cal = T_steer_motor +T_steer_base K_d; K_d = 0.8 + 0.4 D_steer; Where T_steer_base is the reference temperature difference under rated steering conditions, K_d is the steering duty cycle correction coefficient, D_steer is the percentage of time spent on steering actions in the past 5 minutes, T_steer_motor is the temperature value of the steering motor, and T_steer_drive_cal is the temperature value of the steering drive. The full-link temperature correlation model of the hydraulic system is as follows: T_oil_cal = T_pump_motor 0.92 - 3℃ K_hyd_d; T_valve_cal = T_oil_cal + 2℃ × K_hyd_d; K_length_d = 0.7 + 0.5 D_length; Where T_oil_cal is the temperature value of the hydraulic oil, T_pump_motor is the temperature value of the hydraulic pump motor, K_hyd_d is the hydraulic action duty cycle correction coefficient, D_hyd is the time percentage of hydraulic lifting / loading and unloading actions in the past 10 minutes, and T_valve_cal is the temperature value of the main hydraulic valve. S4: Determine the status based on the dynamic threshold of the operating condition and the real-time temperature value of the target component.

2. The temperature monitoring method for counterbalanced AGV forklifts according to claim 1, characterized in that, The first derivative of the temperature data of each key component is obtained by taking the first derivative as the heating rate, and the second derivative is obtained by taking the second derivative as the temperature change acceleration. Both the temperature change acceleration and the heating rate are temperature rise rates.

3. The temperature monitoring method for counterbalanced AGV forklifts according to claim 1, characterized in that, The rule for judging its own characteristics is as follows: Based on the historical temperature data in the current target component's equipment status data, if the temperature value of the current component remains unchanged within a continuous preset time period or changes significantly within a short period of time, then it is determined that the temperature sensor of the current target component is faulty.

4. The temperature monitoring method for counterbalanced AGV forklifts according to claim 1, characterized in that, The dynamic thresholds for operating conditions include normal thresholds, alarm thresholds, and danger thresholds. When the real-time temperature value of the target component is less than the normal dynamic threshold, the operating status is normal operation. When the real-time temperature value of the target component is greater than the normal dynamic threshold but less than the alarm threshold, the operating status is Level 1 abnormal. When the real-time temperature value of the target component is greater than the alarm threshold but less than the danger threshold, the operating status is Level 2 abnormal. When the real-time temperature value of the target component is greater than the danger threshold, or when the operating status of multiple key components is Level 2 abnormal, the operating status is Level 3 abnormal.

5. The temperature monitoring method for counterbalanced AGV forklifts according to claim 1, characterized in that, The system obtains the current load rate of the counterbalanced AGV forklift to calculate the load rate correction factor, obtains the current operating status of the target component to calculate the aging correction factor, obtains the current ambient temperature of the counterbalanced AGV forklift to calculate the ambient temperature correction value, obtains the current temperature rise rate of the target component to calculate the temperature rise correction value, and obtains the measured triaxial vibration acceleration of the current counterbalanced AGV forklift to calculate the vibration correction value.

6. The temperature monitoring method for counterbalanced AGV forklifts according to claim 1, characterized in that, Multimodal fault diagnosis is performed based on multiple key components.

7. A temperature monitoring system suitable for counterbalanced AGV forklifts, characterized in that, Including the following: The data acquisition layer is used to synchronously collect multi-source operating data of key components during the operation of the counterbalanced AGV forklift. The key components include at least the power battery, drive motor, steering motor, driver, and hydraulic oil, main hydraulic valve, and hydraulic pump motor of the entire hydraulic system. The multi-source operating data includes at least the temperature data of each key component and the operating status data related to temperature changes. The data processing layer is used to preprocess multi-source operating data to obtain equipment status data that includes real-time temperature values, temperature rise rates, and historical temperature data. The temperature sensor detection layer is used to perform dual fault judgment on the temperature sensor of the target component. If the temperature sensor of the target component is not faulty, the fault detection layer is triggered. The dual fault detection includes self-feature judgment and related feature judgment. Self-feature judgment is based on the equipment status data of the target component, and related feature judgment is based on the temperature of related components to judge the equipment status data of the current target component. in The rules for determining the correlation features are as follows: Establish an adaptive core temperature correlation model for operating conditions, use the adaptive core temperature correlation model for operating conditions to calculate the theoretical temperature value of the target component under the current operating conditions, and if the deviation between the real-time temperature value and the theoretical temperature value of the target component is greater than the set threshold within a preset time period, then it is determined that the temperature sensor of the current target component has failed. The theoretical temperature value of the current target component is calculated by using the adaptive core temperature correlation model and the real-time temperature value of the related components. The adaptive core temperature correlation model includes the drive system temperature correlation model, the steering system temperature correlation model, and the hydraulic system full-link temperature correlation model. The drive system temperature correlation model records the temperature correlation between the drive motor and the driver under rated operating conditions. The steering system temperature correlation model records the temperature correlation between the steering motor and the steering driver under rated operating conditions. The hydraulic system full-link temperature correlation model records the temperature correlation between the hydraulic pump motor, hydraulic oil, and the main hydraulic valve under rated operating conditions. The temperature correlation model of the drive system is expressed as follows: T_drive_cal = T_motor + T_drive_base K_v K_l; K_v = 1.2 - 0.3 v / v_rated; K_l = 0.7+0.5 I_motor / I_motor_rated; Where T_drive_cal is the temperature value of the driver, T_motor is the temperature value of the drive motor, T_drive_base is the rated operating condition reference temperature difference, K_v is the speed correction coefficient, v is the real-time travel speed, v_rated is the rated travel speed; K_l is the load correction coefficient, I_motor is the real-time operating current of the motor, and I_motor_rated is the rated current of the motor. The temperature correlation model of the steering system is expressed as follows: T_steer_drive_cal = T_steer_motor +T_steer_base K_d; K_d = 0.8 + 0.4 D_steer; Where T_steer_base is the reference temperature difference under rated steering conditions, K_d is the steering duty cycle correction coefficient, D_steer is the percentage of time spent on steering actions in the past 5 minutes, T_steer_motor is the temperature value of the steering motor, and T_steer_drive_cal is the temperature value of the steering drive. The full-link temperature correlation model of the hydraulic system is as follows: T_oil_cal = T_pump_motor 0.92 - 3℃ K_hyd_d; T_valve_cal = T_oil_cal + 2℃ × K_hyd_d; K_length_d = 0.7 + 0.5 D_length; Where T_oil_cal is the temperature value of the hydraulic oil, T_pump_motor is the temperature value of the hydraulic pump motor, K_hyd_d is the hydraulic action duty cycle correction coefficient, D_hyd is the time percentage of hydraulic lifting / loading and unloading actions in the past 10 minutes, and T_valve_cal is the temperature value of the main hydraulic valve. The fault detection layer is used to determine the status based on dynamic thresholds of operating conditions and the real-time temperature value of the target component.

8. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the temperature monitoring method for counterbalanced AGV forklifts as described in any one of claims 1 to 6.

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