Logistics forklift intelligent maintenance method and system based on Internet of Things

Abstract

The invention discloses a logistics forklift intelligent maintenance method and system based on the Internet of Things, and relates to the technical field of intelligent maintenance. According to the method, hydraulic working condition data of a forklift are collected in real time through sensors arranged at all positions of a hydraulic system, a power loop and a hoisting mechanism, data processing is conducted through a vehicle-mounted IoT gateway, and the data are stored in a database; the method comprises the following steps of: acquiring a hydraulic energy efficiency index Lcx, encrypting and transmitting the hydraulic energy efficiency index Lcx to a cloud maintenance platform through MQTT in a second-level period, performing structured storage and integrity verification by using a time sequence database, performing pressure attenuation rate, hydraulic energy efficiency and energy efficiency degradation analysis, constructing a comprehensive steady-state offset index Lcx to perform state stability evaluation, automatically generating maintenance information of different priorities, and performing maintenance management on the hydraulic energy efficiency index Lcx. Triggering maintenance work order scheduling and emergency disposal; after maintenance is completed, a maintenance log is generated and stored in the knowledge base. According to the method, closed-loop management of the hydraulic system from real-time monitoring, anomaly recognition and comprehensive evaluation to maintenance execution and knowledge accumulation is achieved, and the safety, maintenance efficiency and reliability of the forklift hydraulic system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent maintenance, in particular to a logistics forklift intelligent maintenance method and system based on the Internet of Things. BACKGROUND

[0002] With the wide application of the Internet of Things technology in industrial equipment, real-time sensing and remote monitoring of equipment operating state gradually become an important means to improve the operation and maintenance efficiency. As the key equipment in the warehouse and transportation link, the hydraulic system of the logistics forklift undertakes the core functions of cargo lifting, lowering and adjusting, and the stability of the hydraulic system directly determines the operation efficiency and safety of the forklift. However, the hydraulic system is prone to problems such as pressure fluctuation, abnormal oil temperature, flow pulsation and energy efficiency decline due to long-term operation in high-strength and high-frequency working conditions. The traditional manual inspection and regular maintenance mode is not easy to master the real-time state of the hydraulic system, which leads to the difficulty in timely exposure of the hydraulic hidden faults and restricts the intelligent level of the logistics forklift maintenance.

[0003] In the prior art, the maintenance of the hydraulic system mainly depends on fixed period maintenance and after-service, and its mode has obvious limitations. On the one hand, the hidden dangers such as pressure attenuation, valve gap increase and internal leakage trend of the hydraulic system have no obvious external symptoms in the early stage, and manual detection is not easy to form early warning; on the other hand, the fixed period maintenance mode ignores the differences in load strength, operation frequency and working environment of different forklifts, resulting in that some equipment is over-maintained when the state is normal, while some equipment with hidden defects cannot be timely processed. This not only increases the operation and maintenance workload and wastes maintenance resources, but also makes the hydraulic system abnormalities exposed in high load scenarios, increasing the operation risk. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a logistics forklift intelligent maintenance method and system based on the Internet of Things, which solves the problems in the background art.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a logistics forklift intelligent maintenance method based on the Internet of Things, comprising the following steps: S1, according to the sensor group arranged at each position of the forklift, real-time collection of the hydraulic working condition data of the forklift, and transmission of the hydraulic working condition data to the vehicle-mounted IoT gateway of the forklift through the CAN bus for data processing to obtain the pressure data group, the operation performance data group and the hydraulic health data group; S2, the vehicle-mounted IoT gateway packages the pressure data group, the operation performance data group and the hydraulic health data group with a second-level period, and transmits them to the cloud maintenance platform through MQTT encryption and packaging, and the platform decrypts, decompresses and verifies to convert them into a time series database structured record; S3, pressure decay rate analysis of the forklift hydraulic system is performed according to the pressure data set, and a pressure stability evaluation is generated, and S4 is triggered when the pressure is abnormal; S4, hydraulic energy efficiency analysis and energy efficiency degradation analysis of the forklift with abnormal hydraulic system pressure are performed according to the working performance data set, and the analysis results of the pressure decay rate and the hydraulic health data set are combined to perform comprehensive state analysis of the forklift hydraulic system, and a state stability evaluation and maintenance information are generated; S5, a maintenance work order is generated according to the maintenance information, a maintenance time window is automatically selected, and a maintenance log is generated and stored in a maintenance knowledge base.

[0006] Preferably, the S1 comprises S11; S11, a sensor group is arranged on the hydraulic system, power circuit and lifting mechanism to collect hydraulic working condition data of the forklift in real time; The sensor group comprises a pressure sensor, an oil temperature sensor, a micro flowmeter, a current sensor, a load sensor and a displacement sensor; The pressure sensor is used to be installed between the main hydraulic oil inlet pipeline and the distribution valve to collect the pressure value P of the hydraulic system; The oil temperature sensor is used to be fixed at the outlet pipeline of the hydraulic system oil tank to collect the hydraulic oil temperature To; The micro flowmeter is used to be installed on the oil return branch of the hydraulic system to capture the hydraulic oil flow pulse QΔ returned to the oil tank, and to calculate the peak-to-valley difference of the flow pulse QΔ in each cycle to output the flow fluctuation amplitude Af; The current sensor is used to be fixed on the main circuit of the forklift battery to monitor the operation energy consumption Eo of the forklift; The load sensor is used to be installed at the fixed point of the fork hanger to measure the load Wl of the goods in real time, indicating the actual operation load level of the forklift; The displacement sensor is used to be fixed at the slide rail stroke position of the forklift lifting mechanism to measure the displacement h of the fork in real time.

[0007] Preferably, the S1 further comprises S12; S12, the hydraulic working condition data collected by the sensor group is transmitted to the vehicle-mounted IoT gateway of the forklift through the CAN bus for data processing to obtain the pressure data set, the working performance data set and the hydraulic health data set; The data processing is used to denoise, time stamp alignment, missing value filling and normalization processing of the hydraulic working condition data, and then effective load analysis and variance analysis are performed; The denoising process uses wavelet denoising technology to preserve the transient structural features of hydraulic operating condition data and remove high-frequency noise from the hydraulic operating condition data. The timestamp alignment is based on the timestamp of the sensor acquisition and uses a streaming processing framework to align hydraulic operating condition data with the same timestamp online. Missing value filling is performed by missing value interpolation technology to complete the hydraulic operating condition data due to sampling interruption and communication packet loss. The normalization process is performed by the Max-Min method to remove the dimensional influence of the hydraulic operating condition data. Effective load analysis is used to calculate the lifting speed vh of each forklift based on the lifting time s recorded by the forklift control system and the displacement h of each forklift. The effective load power Pl is then obtained by multiplying the lifting speed vh of each forklift by the load Wl. Analysis of variance (ANOVA) is used to calculate the variance of hydraulic oil temperature To using a sliding window, and to obtain the oil temperature fluctuation variance σTo. Specifically: Where n represents the number of sampling points, To i This represents the hydraulic oil temperature sampled at time i. This indicates the average hydraulic oil temperature within the window; The pressure data set includes the pressure value P; The operational performance data set includes effective load power P1 and operational energy consumption Eo; The hydraulic health data set includes the flow fluctuation amplitude Af and the oil temperature fluctuation variance σTo.

[0008] Preferably, S2 includes S21 and S22; S21. Pack the pressure data group, the operation performance data group, and the hydraulic health data group into data frames at a time interval of seconds. Each data frame is accompanied by a sensor number, timestamp, and data integrity identifier. After being encrypted and uniformly formatted and packaged according to the MQTT protocol of the vehicle IoT gateway, it is transmitted to the cloud maintenance platform. S22. The cloud-based maintenance platform receives data packets pushed by the vehicle IoT gateway through MQTTBroker, performs AES-256 decryption on the received data packets, performs Gzip decompression, completes multi-source data alignment through a timestamp synchronization mechanism, and confirms data integrity based on hash verification. Then, it converts the data into structured records in a time-series database format. Each record includes a timestamp, sensor identifier, parameter name and value, thus restoring the original data stream collected by the sensor.

[0009] Preferably, S3 includes S31; S31, the cloud-based maintenance platform performs pressure analysis on the forklift hydraulic system based on the pressure data set and constructs the pressure decay rate Rps, specifically: The pressure decay rate of the hydraulic system under static holding conditions is analyzed, where Δt represents the sampling time interval, ln represents the logarithmic function, and P(t) and P(t+Δt) represent the pressure values ​​at time t and t+Δt, respectively.

[0010] Preferably, S3 further includes S32; S32. Collect the pressure decay rate Rps of all stable pressures within the past six months, and calculate the 95th percentile value as the standard decay threshold TR based on the percentile method. Then, compare it with the real-time pressure decay rate Rps to evaluate the pressure stability. The specific evaluation scheme is as follows. When the pressure decay rate Rps < standard decay threshold TR, it indicates that the pressure of the forklift hydraulic system is stable, and normal monitoring should be maintained at this time. When the pressure decay rate Rps is greater than or equal to the standard decay threshold TR, it indicates that the pressure of the forklift hydraulic system is abnormal and there is a risk of hydraulic oil leakage. In this case, hydraulic energy efficiency analysis should be performed.

[0011] Preferably, S4 includes S41 and S42; S41. When the cloud-based maintenance platform determines that the forklift hydraulic system pressure is abnormal during the pressure stability assessment, it performs hydraulic energy efficiency analysis on the forklift hydraulic system based on the operational performance data group and calculates the hydraulic efficiency nh, specifically: , represents the proportion of input energy converted into effective output power by the forklift hydraulic system during the work cycle, where Δt represents the data collection time interval; S42. Based on the reference efficiency nh of the hydraulic system when the forklift is operating under standard conditions. ref The energy efficiency degradation Dn of the forklift hydraulic system is calculated as follows: Dn = nh ref -nh is used to analyze the efficiency decline of hydraulic systems during actual operation, reflecting the actual impact of abnormal pressure on the energy efficiency of hydraulic systems.

[0012] Preferably, S4 further includes S43 and S44; S43. The cloud-based maintenance platform performs a comprehensive state analysis of the hydraulic system based on the pressure decay rate Rps, energy efficiency degradation Dn, and hydraulic health data sets, and constructs a comprehensive steady-state deviation index Lcx, specifically: This indicates the overall health status of the hydraulic system under combined action; S44. Collect the comprehensive steady-state deviation index Lcx for all historical abnormal and unstable states of hydraulic systems, and set the 10th percentile value as the critical threshold Yl for hydraulic abnormality and the 80th percentile value as the threshold Yl for hydraulic abnormal instability using the percentile method. Then, evaluate the state stability by comparing it with the real-time acquired comprehensive steady-state deviation index Lcx. The specific evaluation scheme is as follows. When the comprehensive steady-state deviation index Lcx < the hydraulic abnormality critical threshold Yl, it indicates that the hydraulic system is in a normal state, and the pressure stability assessment is an intermittent pressure false detection abnormality. At this time, normal maintenance information is generated to maintain the normal maintenance plan. When the critical threshold of hydraulic abnormality Yl ≤ comprehensive steady-state deviation index Lcx ≤ hydraulic abnormal instability threshold Yl, it indicates that there is an abnormality in the hydraulic system. At this time, the first priority maintenance information is generated, the current forklift is listed as the first priority maintenance object, and maintenance personnel are notified through the cloud maintenance platform. When the comprehensive steady-state deviation index Lcx is greater than the hydraulic abnormal instability threshold Yl, it indicates that the hydraulic system is in a leaky and unstable state. At this time, emergency maintenance information is generated, the operation of the forklift is forcibly cut off, an emergency maintenance command is immediately triggered, and the maintenance personnel are notified to carry out maintenance immediately through the cloud maintenance platform.

[0013] Preferably, S5 includes S51 and S52; S51, the cloud-based maintenance platform generates maintenance work orders based on the maintenance information generated by S4. The maintenance work order includes maintenance content, estimated start time, required tools and parts, and priority. It also automatically selects the maintenance time window based on the forklift's maintenance priority, work task, current location, battery level, and maintenance station location. S52. After the maintenance task is completed, the cloud-based maintenance platform automatically records the maintenance process and results, including the replaced parts, the inspections and adjustments performed, and generates a maintenance log which is uploaded and stored in the maintenance knowledge base.

[0014] An IoT-based intelligent maintenance system for logistics forklifts includes a working condition acquisition module, a data synchronization module, an energy efficiency assessment module, a stability analysis module, and a maintenance management module. The working condition acquisition module is used to collect hydraulic working condition data of the forklift in real time based on the sensor group arranged at various positions of the forklift, and transmit the hydraulic working condition data to the vehicle IoT gateway of the forklift via CAN bus for data processing to obtain pressure data group, working performance data group and hydraulic health data group. The data synchronization module is used by the vehicle-mounted IoT gateway to package pressure data groups, operational performance data groups, and hydraulic health data groups in seconds, encrypt and encapsulate them via MQTT, and transmit them to the cloud maintenance platform. After the platform decrypts, decompresses, and verifies them, it converts them into time-series database structured records. The energy efficiency assessment module is used to analyze the pressure decay rate of the forklift hydraulic system based on the pressure data set and generate a pressure stability assessment. It triggers the stability analysis module when the pressure is abnormal. The stability analysis module is used to perform hydraulic energy efficiency analysis and energy efficiency degradation analysis on forklifts with abnormal hydraulic system pressure based on the operation performance data set. Then, it combines the analysis results of pressure decay rate and hydraulic health data set to perform comprehensive state analysis of the forklift hydraulic system and generate state stability assessment and maintenance information. The maintenance management module is used to generate maintenance work orders based on maintenance information, automatically select maintenance time windows, and generate maintenance logs to be stored in the maintenance knowledge base.

[0015] This invention provides an intelligent maintenance method and system for logistics forklifts based on the Internet of Things (IoT). It has the following beneficial effects: (1) This method collects hydraulic condition data of the forklift in real time through sensor groups arranged in the hydraulic system, power circuit and lifting mechanism. After data processing, the collected hydraulic condition data is used to obtain pressure data group, operation performance data group and hydraulic health data group. The transient characteristics of the hydraulic system are preserved and the difference in dimensions is eliminated to ensure the integrity and comparability of the data. Subsequently, the vehicle-mounted IoT gateway encrypts and encapsulates the pressure data group, operation performance data group and hydraulic health data group at the second level and transmits them securely to the cloud maintenance platform. It is then converted into a structured time-series database through decryption, decompression and hash verification, realizing a fully automated closed loop from the underlying sensor data to the high-quality data stream. Compared with the existing technology that mainly relies on manual inspection and single-point collection, this step significantly improves the collection dimensions and transmission efficiency, and provides reliable data guarantee for subsequent status analysis.

[0016] (2) This method obtains the pressure decay rate Rps through exponential decay fitting, identifies early signs of hydraulic oil leakage, and performs pressure stability assessment with the standard decay threshold TR. When the pressure stability assessment determines that the pressure of the forklift hydraulic system is stable, the system is automatically determined to be in a stable state. When the pressure of the forklift hydraulic system is determined to be abnormal, the hydraulic efficiency nh and energy efficiency degradation Dn are calculated through the operation performance data group to represent the degree of energy conversion efficiency decline. Then, the comprehensive steady-state deviation index Lcx is constructed in combination with the hydraulic health data group to form a panoramic analysis of the health status of the hydraulic system. The state stability is assessed with the hydraulic abnormality critical threshold Yl and the hydraulic abnormal instability threshold Yl. When the comprehensive steady-state deviation index Lcx is between the hydraulic abnormality critical threshold Yl and the hydraulic abnormal instability threshold Yl, the first priority maintenance information is generated. If the comprehensive steady-state deviation index Lcx exceeds the hydraulic abnormal instability threshold Yl, the forklift operation task is forcibly cut off and an emergency maintenance command is issued. This hierarchical threshold judgment and multi-indicator fusion analysis mechanism breaks through the limitations of traditional single parameter monitoring, and realizes accurate identification and dynamic intervention of hydraulic systems in stages from "mild anomaly" to "severe instability".

[0017] (3) Based on maintenance information generated in the cloud, this method automatically generates maintenance work orders containing maintenance content, required tools and accessories, time windows, and priorities. It also intelligently selects the optimal maintenance time window by combining the forklift's operational tasks, battery level, geographical location, and maintenance station distribution, minimizing interference with logistics operations. After maintenance is completed, the system automatically records the maintenance process and results, generates a maintenance log, and writes it into a knowledge base, establishing reusable experience accumulation. Compared with existing technologies that rely primarily on manual experience and lack data accumulation, this method, through closed-loop management of the entire process of monitoring, evaluation, decision-making, execution, and feedback, not only improves the scientific nature and response speed of maintenance decisions but also achieves continuous knowledge accumulation and iterative optimization. Overall, this method effectively reduces the risk of sudden failures and downtime, improves the energy efficiency and reliability of the hydraulic system, and thus enhances the continuity of logistics forklift operations and the level of intelligent operation and maintenance management. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the steps of an intelligent maintenance method for logistics forklifts based on the Internet of Things according to the present invention. Figure 2 This is a schematic diagram of the process of an intelligent maintenance system for logistics forklifts based on the Internet of Things according to the present invention; Figure 3 This invention provides a schematic diagram of the variation of the comprehensive steady-state offset index Lcx. Detailed Implementation

[0019] 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. Example 1

[0020] Please see Figure 1 This invention provides an intelligent maintenance method for logistics forklifts based on the Internet of Things. To achieve the above objectives, this invention is implemented through the following technical solution, including the following steps: S1. Based on the sensor groups arranged at various locations on the forklift, the hydraulic condition data of the forklift is collected in real time, and the hydraulic condition data is transmitted to the vehicle IoT gateway of the forklift via CAN bus for data processing to obtain pressure data group, operation performance data group and hydraulic health data group. S2. The vehicle-mounted IoT gateway packages the pressure data group, operation performance data group and hydraulic health data group in seconds, and transmits them to the cloud maintenance platform after being encrypted and encapsulated by MQTT. The platform decrypts, decompresses and verifies the data, and converts it into a time-series database structured record. S3. Analyze the pressure decay rate of the forklift hydraulic system based on the pressure data set and generate a pressure stability assessment. Trigger S4 when the pressure is abnormal. S4. Based on the operational performance data set, perform hydraulic energy efficiency analysis and energy efficiency degradation analysis on forklifts with abnormal hydraulic system pressure. Then, combine the analysis results of pressure decay rate and hydraulic health data set to conduct a comprehensive status analysis of the forklift hydraulic system and generate status stability assessment and maintenance information. S5. Generate maintenance work orders based on maintenance information, automatically select maintenance time windows, and generate maintenance logs to store in the maintenance knowledge base.

[0021] In this embodiment, multiple sensors arranged in the forklift's hydraulic system, power circuit, and lifting mechanism collect real-time hydraulic condition data. This data is then processed by an onboard IoT gateway to construct pressure data sets, operational performance data sets, and hydraulic health data sets, ensuring data continuity, comparability, and reliability. The data is then transmitted to the gateway via a CAN bus and packaged in MQTT encryption at second-level intervals before being transmitted to the cloud-based maintenance platform. The cloud-based maintenance platform decrypts, decompresses, and verifies the data, transforming it into structured records in a time-series database, achieving automated processing from raw signals to high-quality data streams. Compared to existing methods relying on manual inspections and periodic patrols, this method generates a high-frequency, multi-dimensional, and comprehensive hydraulic system operation dataset, improving the coverage and real-time performance of data acquisition. In the condition analysis and evaluation stage, this method uses exponential decay fitting to the pressure data sets to obtain the pressure decay rate Rps for pressure stability assessment, identifying hydraulic oil leakage risks in advance. When the forklift hydraulic system experiences abnormal pressure, the hydraulic efficiency (nh) and energy efficiency degradation (Dn) are further calculated based on the operational performance data set. The pressure decay rate (Rps), energy efficiency degradation (Dn), and hydraulic health data set are combined to construct a comprehensive steady-state deviation index, which quantitatively analyzes the overall health status of the hydraulic system. Based on the hydraulic abnormality critical threshold (Yl) and hydraulic abnormal instability threshold (Yl), a graded judgment is made, achieving accurate identification and dynamic classification of normal, abnormal, and unstable states. Unlike most current techniques that rely on single indicators or manual experience, this method achieves quantitative analysis based on multi-indicator fusion, possessing higher sensitivity and accuracy. It can identify potential problems in the early stages and trigger emergency maintenance mechanisms in severe stages, avoiding serious downtime and economic losses due to delayed repairs. In the maintenance execution and closed-loop management phase, this method automatically generates maintenance work orders based on the comprehensive status assessment results. The work orders clearly include maintenance content, required tools and parts, priority, and estimated start time. Combining the forklift's current task queue, battery level, geographical location, and maintenance station distribution, the optimal maintenance time window is intelligently selected, reducing maintenance interference with logistics operations. After maintenance is completed, the cloud platform automatically generates a maintenance log, recording the inspected and replaced parts, the adjustments made, and the maintenance results. The log is then stored in a knowledge base, enabling long-term accumulation and continuous optimization of maintenance experience. Through this closed-loop management model of "collection-transmission-analysis-decision-execution-feedback," this method not only solves the problems of information fragmentation and delayed response in traditional manual maintenance but also achieves standardization and intelligentization of operational experience through knowledge reuse. Overall, this method improves the energy efficiency, reliability, and operational continuity of forklift hydraulic systems, reduces the rate of sudden failures and downtime, and achieves dynamic prediction and proactive intervention. Example 2

[0022] Please refer to Figure 1Specifically: S1 includes S11; S11. Arrange sensor groups in the hydraulic system, power circuit and lifting mechanism to collect hydraulic condition data of the forklift in real time; The sensor group includes a pressure sensor, an oil temperature sensor, a micro-flow meter, a current sensor, a load sensor, and a displacement sensor; The pressure sensor is installed between the main hydraulic inlet line and the distribution valve to collect the pressure value P of the hydraulic system and reflect the dynamic process of pressure change of the hydraulic system over time during operation. The oil temperature sensor is fixed at the outlet pipeline of the hydraulic system oil tank to collect the hydraulic oil temperature To, reflecting the thermal stability of the hydraulic system. The micro-flow meter is installed in the return oil branch of the hydraulic system to capture the hydraulic oil flow pulse QΔ returning to the oil tank, and calculates the peak-to-valley difference of the flow pulse QΔ in each cycle to output the flow fluctuation amplitude Af. Irregular pulse signals are usually related to internal leakage and increased valve body clearance, serving as a direct characterization of early leakage. The current sensor is fixed to the main circuit of the forklift battery to monitor the forklift's operating energy consumption Eo and reflect the forklift's power consumption. The load sensor is installed at a fixed point on the forklift to measure the cargo load Wl in real time, representing the actual operating load level of the forklift. The displacement sensor is fixed at the slide rail travel position of the forklift lifting mechanism to measure the fork displacement h in real time.

[0023] S1 further includes S12; S12. The hydraulic condition data collected by the sensor group is transmitted to the forklift’s on-board IoT gateway via the CAN bus for data processing to obtain pressure data group, operation performance data group and hydraulic health data group. The data processing is used to denoise, align timestamps, fill in missing values ​​and normalize the hydraulic operating data before performing effective load analysis and variance analysis. The denoising process uses wavelet denoising technology to preserve the transient structural features of hydraulic operating condition data and remove high-frequency noise from the hydraulic operating condition data. The timestamp alignment is based on the timestamp of the sensor acquisition and uses a streaming processing framework to align hydraulic operating condition data with the same timestamp online. Missing value filling is performed by missing value interpolation technology to complete the hydraulic operating condition data due to sampling interruption and communication packet loss. The normalization process is performed by the Max-Min method to remove the dimensional influence of the hydraulic operating condition data. Effective load analysis is used to calculate the lifting speed vh of each forklift based on the lifting time s recorded by the forklift control system and the displacement h of each forklift. The effective load power Pl is then multiplied with the load Wl to obtain the effective load power, which represents the actual useful output power of the hydraulic system. Analysis of variance (ANOVA) is used to calculate the variance of hydraulic oil temperature To using a sliding window, and to obtain the oil temperature fluctuation variance σTo. Specifically: Where n represents the number of sampling points, To i This represents the hydraulic oil temperature sampled at time i. This indicates the average hydraulic oil temperature within the window; The pressure data set includes the pressure value P; The operational performance data set includes effective load power P1 and operational energy consumption Eo; The hydraulic health data set includes the flow fluctuation amplitude Af and the oil temperature fluctuation variance σTo.

[0024] In this embodiment, pressure sensors, oil temperature sensors, micro-flow meters, current sensors, load sensors, and displacement sensors are arranged in the forklift hydraulic system, power circuit, and lifting mechanism to acquire pressure value P, hydraulic oil temperature To, flow fluctuation amplitude Af, operating energy consumption Eo, cargo load Wl, and fork displacement h in real time. This data is transmitted via CAN bus to the onboard IoT gateway for unified processing. The gateway uses wavelet denoising, timestamp alignment, missing value interpolation, and normalization methods to ensure data continuity and accuracy. Furthermore, it performs effective load analysis and oil temperature variance analysis to calculate the effective load power P1 and oil temperature fluctuation variance σTo, thereby converting the raw signals into pressure data sets, operating performance data sets, and hydraulic health data sets. This implementation enables full-scenario, multi-dimensional dynamic monitoring of the forklift's hydraulic conditions, identifying early signs of internal leakage and thermal instability factors, and accurately reflecting the useful output power and energy efficiency level of the hydraulic system. Compared with traditional methods that rely on manual experience or single-point testing, this method achieves a systematic and quantitative characterization of hydraulic operating conditions, improves the sensitivity of diagnosis and the reliability of data, and achieves the beneficial effects of providing high-quality data support for subsequent maintenance analysis, reducing potential faults, extending the service life of hydraulic systems, and improving the continuity of logistics operations. Example 3

[0025] Please refer to Figure 1 Specifically: S2 includes S21 and S22; S21. Pack the pressure data group, the operation performance data group, and the hydraulic health data group into data frames at a time interval of seconds. Each data frame is accompanied by a sensor number, timestamp, and data integrity identifier. After being encrypted and uniformly formatted and packaged according to the MQTT protocol of the vehicle IoT gateway, it is transmitted to the cloud maintenance platform. S22. The cloud-based maintenance platform receives data packets pushed by the vehicle IoT gateway through MQTTBroker, performs AES-256 decryption on the received data packets, performs Gzip decompression, completes multi-source data alignment through a timestamp synchronization mechanism, and confirms data integrity based on hash verification. Then, it converts the data into structured records in a time-series database format. Each record includes a timestamp, sensor identifier, parameter name and value, thus restoring the original data stream collected by the sensor.

[0026] In this embodiment, in S21, the vehicle-mounted IoT gateway packages the pressure data group, operational performance data group, and hydraulic health data group into a unified data frame at a second-level cycle. Each data frame includes a sensor number, timestamp, and data integrity identifier. Simultaneously, it encrypts and formats the data using the MQTT protocol before transmitting it to the cloud-based maintenance platform, ensuring the security and consistency of the transmission process. In S22, the cloud platform receives the data packets using MQTTBroker, performs AES-256 decryption and Gzip decompression, and achieves precise alignment of multi-source data through a timestamp synchronization mechanism. A hash verification mechanism is then used to confirm data integrity. Finally, the decoded data is transformed into structured records in a time-series database, ensuring that each record retains a timestamp, sensor identifier, and parameter value, thus restoring the original data stream consistent with the sensor acquisition end. This step not only achieves secure transmission and reliable storage of hydraulic operating condition data under high-frequency conditions but also guarantees the integrity and traceability of the data during the cloud analysis phase. Compared with existing solutions that rely on low-frequency uploads or lack encryption mechanisms, this method improves the real-time performance, security, and consistency of data transmission, providing a high-quality data foundation for subsequent pressure decay analysis, energy efficiency degradation analysis, and comprehensive condition assessment, thereby improving the accuracy, response speed, and reliability of forklift hydraulic system maintenance. Example 4

[0027] Please refer to Figure 1 Specifically: S3 includes S31; S31, the cloud-based maintenance platform performs pressure analysis on the forklift hydraulic system based on the pressure data set and constructs the pressure decay rate Rps, specifically: The pressure decay rate of the hydraulic system under static holding conditions is analyzed, where Δt represents the data acquisition time interval, ln represents the logarithmic function, and P(t) and P(t+Δt) represent the pressure values ​​at time t and t+Δt, respectively. This represents the relative attenuation of pressure within the interval Δt. Taking the logarithm transforms the nonlinear decrease into a linear measure.

[0028] S3 further includes S32; S32. Collect the pressure decay rate Rps of all stable pressures within the past six months, and calculate the 95th percentile value as the standard decay threshold TR based on the percentile method. Then, compare it with the real-time pressure decay rate Rps to evaluate the pressure stability. The specific evaluation scheme is as follows. When the pressure decay rate Rps < standard decay threshold TR, it indicates that the pressure of the forklift hydraulic system is stable, and normal monitoring should be maintained at this time. When the pressure decay rate Rps is greater than or equal to the standard decay threshold TR, it indicates that the pressure of the forklift hydraulic system is abnormal and there is a risk of hydraulic oil leakage. In this case, hydraulic energy efficiency analysis should be performed.

[0029] In this embodiment, the cloud-based maintenance platform first analyzes the pressure changes of the forklift hydraulic system based on pressure data sets. It then uses an exponential decay fitting method to calculate the pressure decay rate Rps, thereby quantifying the pressure loss of the hydraulic system under static holding conditions. This method transforms the originally nonlinear decay trend into a linearly measurable parameter by comparing pressure values ​​at different time points and taking the logarithm, effectively enhancing the stability and comparability of the analysis.

[0030] The derivation of this formula is based on fluid mechanics and mathematical models of pressure decay. In classical fluid mechanics models, the decay process of pressure over time is often described as an exponential decay process, which conforms to the second law of thermodynamics and the principle of energy conservation. The logarithmic function in the formula... This is used to convert a nonlinear decay process into a linear metric. In classical physics and mathematics, the exponential decay process is represented by the equation P(t) = P0 × e^(-t / t). −λt The description is as follows: P(t) is the pressure at time t, P0 is the initial pressure, and λ represents the decay rate of the hydraulic system. The decay constant λ is defined as the pressure decay rate Rps. In discrete sampling and digital signal processing, the exponential decay equation needs to be discretized and linearized, which is achieved through logarithmic transformation. The formula, based on the definition of pressure decay rate, uses a logarithmic function to discretize the original exponential decay model to obtain the pressure decay rate Rps. Furthermore, historical pressure decay rate samples from the past six months when the hydraulic system was in a stable state were collected, and the 95th percentile value was extracted using the percentile method as the standard decay threshold TR, serving as a benchmark for long-term stable operation. The real-time pressure decay rate Rps is compared with the standard decay threshold TR. If it is less than the standard decay threshold TR, the hydraulic system is in a stable state, requiring only routine monitoring; if it is greater than or equal to the standard decay threshold TR, it is automatically determined that the hydraulic system has a leakage risk, immediately triggering hydraulic efficiency analysis. This step not only enables rapid identification and dynamic assessment of early-stage hidden dangers in the hydraulic system but also avoids the lag and limitations of traditional manual detection, thereby improving the timeliness and accuracy of pressure anomaly detection. This step can issue early warnings when the problem is still under control, reducing sudden failures, extending the service life of the hydraulic system, and improving the overall operational reliability and continuity of logistics forklifts. Example 5

[0031] Please refer to Figure 1 and Figure 3 Specifically: S4 includes S41 and S42; S41. When the cloud-based maintenance platform determines that the forklift hydraulic system pressure is abnormal during the pressure stability assessment, it performs hydraulic energy efficiency analysis on the forklift hydraulic system based on the operational performance data group and calculates the hydraulic efficiency nh, specifically: , represents the proportion of input energy converted into effective output power by the forklift hydraulic system during the work cycle, where Δt represents the data collection time interval; S42. Based on the reference efficiency nh of the hydraulic system when the forklift is operating under standard conditions. ref The energy efficiency degradation Dn of the forklift hydraulic system is calculated as follows: Dn = nh ref -nh is used to analyze the efficiency decline of hydraulic systems during actual operation, reflecting the actual impact of abnormal pressure on the energy efficiency of hydraulic systems.

[0032] S43. The cloud-based maintenance platform performs a comprehensive state analysis of the hydraulic system based on the pressure decay rate Rps, energy efficiency degradation Dn, and hydraulic health data sets, and constructs a comprehensive steady-state deviation index Lcx, specifically: This indicates the overall health status of the hydraulic system under combined action; S44. Collect the comprehensive steady-state deviation index Lcx for all historical abnormal and unstable states of hydraulic systems, and set the 10th percentile value as the critical threshold Yl for hydraulic abnormality and the 80th percentile value as the threshold Yl for hydraulic abnormal instability using the percentile method. Then, evaluate the state stability by comparing it with the real-time acquired comprehensive steady-state deviation index Lcx. The specific evaluation scheme is as follows. When the comprehensive steady-state deviation index Lcx < the hydraulic abnormality critical threshold Yl, it indicates that the hydraulic system is in a normal state, and the pressure stability assessment is an intermittent pressure false detection abnormality. At this time, normal maintenance information is generated to maintain the normal maintenance plan. When the critical threshold of hydraulic abnormality Yl ≤ comprehensive steady-state deviation index Lcx ≤ hydraulic abnormal instability threshold Yl, it indicates that there is an abnormality in the hydraulic system. At this time, the first priority maintenance information is generated, the current forklift is listed as the first priority maintenance object, and maintenance personnel are notified through the cloud maintenance platform. When the comprehensive steady-state deviation index Lcx is greater than the hydraulic abnormal instability threshold Yl, it indicates that the hydraulic system is in a leaky and unstable state. At this time, emergency maintenance information is generated, the operation of the forklift is forcibly cut off, an emergency maintenance command is immediately triggered, and the maintenance personnel are notified to carry out maintenance immediately through the cloud maintenance platform.

[0033] In this embodiment, when the cloud-based maintenance platform determines that the forklift hydraulic system has an abnormal pressure through pressure stability assessment, it first conducts hydraulic energy efficiency analysis based on the operational performance data set. By calculating the hydraulic efficiency nh, the ratio of input energy to effective output power of the hydraulic system during the operating cycle is quantified, thus intuitively reflecting the energy conversion capability of the hydraulic system. The basic definition of efficiency comes from the law of conservation of energy. In dynamics and engineering, power is often used to represent the rate of change of energy; therefore, efficiency can also be expressed as... In a hydraulic system, the input energy mainly comes from the electrical and mechanical energy provided by the hydraulic pump drive motor, while the output energy is manifested as the mechanical work performed by actuators such as hydraulic cylinders and hydraulic motors. Therefore, the efficiency formula can be directly derived. The input energy of the hydraulic system within one sampling period Δt is the operating energy consumption Eo. Therefore, the input power is expressed as Eo / Δt. Combining this with the efficiency formula, the hydraulic efficiency nh is obtained, representing a comprehensive measure of the energy conversion level of the hydraulic system, that is, the proportion of input energy converted into useful output power within Δt. Subsequently, the hydraulic efficiency nh is compared with the reference efficiency nh under standard conditions. refA comparison is made to obtain the energy efficiency degradation amount Dn, which is used to assess the degree of performance degradation of the hydraulic system during actual operation. Based on the basic definition of efficiency difference, the concept of "performance difference" is directly applied to the efficiency index, which belongs to the differential evaluation method and is classically widely used in reliability analysis, fatigue life assessment, and energy efficiency comparison analysis. Through this process, not only can the direct impact of energy efficiency degradation be accurately revealed when pressure anomalies occur, but the loss of work output due to anomalies can also be quantified. Then, the cloud-based maintenance platform integrates the pressure decay rate Rps, energy efficiency degradation amount Dn, and hydraulic health data set to construct a comprehensive steady-state deviation index Lcx, which performs a multi-dimensional assessment of the overall health status of the hydraulic system. As a multiplicative correction factor, the oil temperature fluctuation variance and the flow pulse amplitude are used to construct a "thermal disturbance / fluid disturbance ratio" to amplify or weaken the impact of the basic attenuation. The (1+Dn) energy efficiency degradation correction directly reflects the cumulative amplification effect of the hydraulic efficiency decline on the attenuation trend. Using the pressure attenuation rate Rps as the basic leakage rate, and further using a disturbance factor... Dynamic corrections are made, and the energy efficiency degradation factor (1+Dn) is used to reflect the aggravated effect of energy transfer loss, forming a product structure of exponential decay model × steady-state disturbance correction × efficiency degradation correction. A comprehensive steady-state deviation index Lcx is constructed to express the overall health of the hydraulic system under the combined effects of pressure maintenance, thermal stability, fluid stability, and energy efficiency utilization. Finally, based on historical data statistics, a hydraulic anomaly critical threshold Yl and a hydraulic anomaly instability threshold Yl are set using the percentile method to achieve graded judgment of the hydraulic system state. When the comprehensive steady-state deviation index Lcx is lower than the hydraulic anomaly critical threshold Yl, normal maintenance information is generated and the original plan is maintained; when the comprehensive steady-state deviation index Lcx is between the hydraulic anomaly critical threshold Yl and the hydraulic anomaly instability threshold Yl, first-priority maintenance information is generated and maintenance personnel are actively notified; when the comprehensive steady-state deviation index Lcx exceeds the hydraulic anomaly instability threshold Yl, an emergency maintenance command is automatically triggered, forcibly cutting off forklift operations and issuing an emergency repair task. Through the above steps, this implementation method achieves closed-loop control from anomaly identification and energy efficiency degradation analysis to comprehensive health status assessment. This not only improves the accuracy of hydraulic system anomaly diagnosis and the rationality of graded responses but also effectively reduces the risk of failures caused by hydraulic leakage and instability. Compared with traditional manual inspections and single-parameter monitoring, this method can identify potential hazards in advance, optimize maintenance resource scheduling, and shorten fault response time, ultimately improving the operational reliability of the forklift hydraulic system, the level of intelligent maintenance, and the continuity of logistics operations. Example 6

[0034] Please refer to Figure 1 Specifically: S5 includes S51 and S52; S51, the cloud-based maintenance platform generates maintenance work orders based on the maintenance information generated by S4. The maintenance work order includes maintenance content, estimated start time, required tools and parts, and priority. It also automatically selects the maintenance time window based on the forklift's maintenance priority, work task, current location, battery level, and maintenance station location. S52. After the maintenance task is completed, the cloud-based maintenance platform automatically records the maintenance process and results, including the replaced parts, the inspections and adjustments performed, and generates a maintenance log which is uploaded and stored in the maintenance knowledge base.

[0035] In this embodiment, S5 uses a cloud-based maintenance platform to generate maintenance work orders and accumulate maintenance knowledge based on the comprehensive status assessment results of the hydraulic system. On one hand, the cloud-based maintenance platform automatically generates maintenance work orders based on the maintenance information output by S4, combined with multi-dimensional parameters such as forklift maintenance priority, task details, battery level, geographical location, and maintenance station distribution. These work orders include maintenance content, estimated start time, required tools and parts, and priority. The platform also selects the optimal maintenance time window, ensuring timely maintenance while minimizing disruption to logistics operations. On the other hand, after the maintenance task is completed, the platform records the entire maintenance process and results, including component replacements, performed checks and adjustments, generating a maintenance log and storing it in the maintenance knowledge base. This forms a traceable and reusable accumulation of experience. This implementation not only automates the generation and scheduling of maintenance tasks, improving the scientific nature and flexibility of operation and maintenance management, but also constructs a complete "assessment-decision-execution-feedback" closed-loop mechanism through continuous accumulation and iterative optimization of the knowledge base. Compared with existing methods that rely on manual scheduling and decentralized recording, this method reduces the risk of maintenance omissions and response delays, improves the operational reliability and energy efficiency of the forklift hydraulic system, and thus enhances the continuity of logistics operations and the overall efficiency of operation and maintenance management. Example 7

[0036] Please refer to Figure 2 An intelligent maintenance system for logistics forklifts based on the Internet of Things includes a working condition acquisition module, a data synchronization module, an energy efficiency assessment module, a stability analysis module, and a maintenance management module. The working condition acquisition module is used to collect hydraulic working condition data of the forklift in real time based on the sensor group arranged at various positions of the forklift, and transmit the hydraulic working condition data to the vehicle IoT gateway of the forklift via CAN bus for data processing to obtain pressure data group, working performance data group and hydraulic health data group. The data synchronization module is used by the vehicle-mounted IoT gateway to package pressure data groups, operational performance data groups, and hydraulic health data groups in seconds, encrypt and encapsulate them via MQTT, and transmit them to the cloud maintenance platform. After the platform decrypts, decompresses, and verifies them, it converts them into time-series database structured records. The energy efficiency assessment module is used to analyze the pressure decay rate of the forklift hydraulic system based on the pressure data set and generate a pressure stability assessment. It triggers the stability analysis module when the pressure is abnormal. The stability analysis module is used to perform hydraulic energy efficiency analysis and energy efficiency degradation analysis on forklifts with abnormal hydraulic system pressure based on the operation performance data set. Then, it combines the analysis results of pressure decay rate and hydraulic health data set to perform comprehensive state analysis of the forklift hydraulic system and generate state stability assessment and maintenance information. The maintenance management module is used to generate maintenance work orders based on maintenance information, automatically select maintenance time windows, and generate maintenance logs to be stored in the maintenance knowledge base.

[0037] 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 alterations 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 smart maintenance method for logistics forklifts based on the Internet of Things, characterized in that: Includes the following steps: S1. Based on the sensor groups arranged at various locations on the forklift, the hydraulic condition data of the forklift is collected in real time, and the hydraulic condition data is transmitted to the vehicle IoT gateway of the forklift via CAN bus for data processing to obtain pressure data group, operation performance data group and hydraulic health data group. S2. The vehicle-mounted IoT gateway packages the pressure data group, operation performance data group and hydraulic health data group in seconds, and transmits them to the cloud maintenance platform after being encrypted and encapsulated by MQTT. The platform decrypts, decompresses and verifies the data, and then converts it into a time-series database structured record. S3. Analyze the pressure decay rate of the forklift hydraulic system based on the pressure data set and generate a pressure stability assessment. Trigger S4 when the pressure is abnormal. S4. Based on the operational performance data set, perform hydraulic energy efficiency analysis and energy efficiency degradation analysis on forklifts with abnormal hydraulic system pressure. Then, combine the analysis results of pressure decay rate and hydraulic health data set to conduct a comprehensive status analysis of the forklift hydraulic system and generate status stability assessment and maintenance information. S5. Generate maintenance work orders based on maintenance information, automatically select maintenance time windows, and generate maintenance logs to store in the maintenance knowledge base.

2. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 1, characterized in that: S1 includes S11; S11. Arrange sensor groups in the hydraulic system, power circuit and lifting mechanism to collect hydraulic condition data of the forklift in real time; The sensor group includes a pressure sensor, an oil temperature sensor, a micro-flow meter, a current sensor, a load sensor, and a displacement sensor; The pressure sensor is installed between the main hydraulic inlet line and the distribution valve to collect the pressure value P of the hydraulic system. The oil temperature sensor is fixed at the outlet pipeline of the hydraulic system oil tank to collect the hydraulic oil temperature To. The micro-flow meter is installed in the return oil branch of the hydraulic system to capture the hydraulic oil flow pulse QΔ returning to the oil tank, and calculates the peak-valley difference of the flow pulse QΔ in each cycle to output the flow fluctuation amplitude Af. The current sensor is used to be fixed in the main circuit of the forklift battery to monitor the forklift's operating energy consumption Eo. The load sensor is installed at a fixed point on the forklift to measure the cargo load Wl in real time, representing the actual operating load level of the forklift. The displacement sensor is fixed at the slide rail travel position of the forklift lifting mechanism to measure the fork displacement h in real time.

3. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 2, characterized in that: S1 further includes S12; S12. The hydraulic condition data collected by the sensor group is transmitted to the forklift’s on-board IoT gateway via the CAN bus for data processing to obtain pressure data group, operation performance data group and hydraulic health data group. The data processing is used to denoise, align timestamps, fill in missing values ​​and normalize the hydraulic operating data before performing effective load analysis and variance analysis. The denoising process uses wavelet denoising technology to preserve the transient structural features of hydraulic operating condition data and remove high-frequency noise from the hydraulic operating condition data. The timestamp alignment is based on the timestamp of the sensor acquisition and uses a streaming processing framework to align hydraulic operating condition data with the same timestamp online. Missing value filling is performed by missing value interpolation technology to complete the hydraulic operating condition data due to sampling interruption and communication packet loss. The normalization process is performed by the Max-Min method to remove the dimensional influence of the hydraulic operating condition data. Effective load analysis is used to calculate the lifting speed vh of each forklift based on the lifting time s recorded by the forklift control system and the displacement h of each forklift. The effective load power Pl is then obtained by multiplying the lifting speed vh of each forklift by the load Wl. Analysis of variance (ANOVA) is used to calculate the variance of hydraulic oil temperature To using a sliding window, and to obtain the oil temperature fluctuation variance σTo. Specifically: Where n represents the number of sampling points, To i This represents the hydraulic oil temperature sampled at time i. This indicates the average hydraulic oil temperature within the window; The pressure data set includes the pressure value P; The operational performance data set includes effective load power P1 and operational energy consumption Eo; The hydraulic health data set includes the flow fluctuation amplitude Af and the oil temperature fluctuation variance σTo.

4. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 3, characterized in that: S2 includes S21 and S22; S21. Pack the pressure data group, the operation performance data group, and the hydraulic health data group into data frames at a time interval of seconds. Each data frame is accompanied by a sensor number, timestamp, and data integrity identifier. After being encrypted and uniformly formatted and packaged according to the MQTT protocol of the vehicle IoT gateway, it is transmitted to the cloud maintenance platform. S22. The cloud-based maintenance platform receives data packets pushed by the vehicle IoT gateway through MQTTBroker, performs AES-256 decryption on the received data packets, performs Gzip decompression, completes multi-source data alignment through a timestamp synchronization mechanism, and confirms data integrity based on hash verification. Then, it converts the data into structured records in a time-series database format. Each record includes a timestamp, sensor identifier, parameter name and value, thus restoring the original data stream collected by the sensor.

5. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 4, characterized in that: S3 includes S31; S31, the cloud-based maintenance platform performs pressure analysis on the forklift hydraulic system based on the pressure data set and constructs the pressure decay rate Rps, specifically: The pressure decay rate of the hydraulic system under static holding conditions is analyzed, where Δt represents the sampling time interval, ln represents the logarithmic function, and P(t) and P(t+Δt) represent the pressure values ​​at time t and t+Δt, respectively.

6. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 5, characterized in that: S3 further includes S32; S32. Collect the pressure decay rate Rps of all stable pressures within the past six months, and calculate the 95th percentile value as the standard decay threshold TR based on the percentile method. Then, compare it with the real-time pressure decay rate Rps to evaluate the pressure stability. The specific evaluation scheme is as follows. When the pressure decay rate Rps < standard decay threshold TR, it indicates that the pressure of the forklift hydraulic system is stable, and normal monitoring should be maintained at this time. When the pressure decay rate Rps is greater than or equal to the standard decay threshold TR, it indicates that the pressure of the forklift hydraulic system is abnormal and there is a risk of hydraulic oil leakage. In this case, hydraulic energy efficiency analysis should be performed.

7. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 6, characterized in that: S4 includes S41 and S42; S41. When the cloud-based maintenance platform determines that the forklift hydraulic system pressure is abnormal during the pressure stability assessment, it performs hydraulic energy efficiency analysis on the forklift hydraulic system based on the operational performance data group and calculates the hydraulic efficiency nh, specifically: , represents the proportion of input energy converted into effective output power by the forklift hydraulic system during the work cycle, where Δt represents the data collection time interval; S42. Based on the reference efficiency nh of the hydraulic system when the forklift is operating under standard conditions. ref The energy efficiency degradation Dn of the forklift hydraulic system is calculated as follows: Dn = nh ref -nh is used to analyze the efficiency decline of hydraulic systems during actual operation, reflecting the actual impact of abnormal pressure on the energy efficiency of hydraulic systems.

8. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 7, characterized in that: S4 also includes S43 and S44; S43. The cloud-based maintenance platform performs a comprehensive state analysis of the hydraulic system based on the pressure decay rate Rps, energy efficiency degradation Dn, and hydraulic health data sets, and constructs a comprehensive steady-state deviation index Lcx, specifically: This indicates the overall health status of the hydraulic system under combined action; S44. Collect the comprehensive steady-state deviation index Lcx for all historical abnormal and unstable states of hydraulic systems, and set the 10th percentile value as the critical threshold Yl for hydraulic abnormality and the 80th percentile value as the threshold Yl for hydraulic abnormal instability using the percentile method. Then, evaluate the state stability by comparing it with the real-time acquired comprehensive steady-state deviation index Lcx. The specific evaluation scheme is as follows. When the comprehensive steady-state deviation index Lcx < the hydraulic abnormality critical threshold Yl, it indicates that the hydraulic system is in a normal state, and the pressure stability assessment is an intermittent pressure false detection abnormality. At this time, normal maintenance information is generated to maintain the normal maintenance plan. When the critical threshold of hydraulic abnormality Yl ≤ comprehensive steady-state deviation index Lcx ≤ hydraulic abnormal instability threshold Yl, it indicates that there is an abnormality in the hydraulic system. At this time, the first priority maintenance information is generated, the current forklift is listed as the first priority maintenance object, and maintenance personnel are notified through the cloud maintenance platform. When the comprehensive steady-state deviation index Lcx is greater than the hydraulic abnormal instability threshold Yl, it indicates that the hydraulic system is in a leaky and unstable state. At this time, emergency maintenance information is generated, the operation of the forklift is forcibly cut off, an emergency maintenance command is immediately triggered, and the maintenance personnel are notified to carry out maintenance immediately through the cloud maintenance platform.

9. The intelligent maintenance method for logistics forklifts based on the Internet of Things according to claim 1, characterized in that: S5 includes S51 and S52; S51, the cloud-based maintenance platform generates maintenance work orders based on the maintenance information generated by S4. The maintenance work order includes maintenance content, estimated start time, required tools and parts, and priority. It also automatically selects the maintenance time window based on the forklift's maintenance priority, work task, current location, battery level, and maintenance station location. S52. After the maintenance task is completed, the cloud-based maintenance platform automatically records the maintenance process and results, including the replaced parts, the inspections and adjustments performed, and generates a maintenance log which is uploaded and stored in the maintenance knowledge base.

10. An IoT-based intelligent maintenance system for logistics forklifts, comprising the IoT-based intelligent maintenance method for logistics forklifts as described in any one of claims 1-9, characterized in that: It includes a working condition acquisition module, a data synchronization module, an energy efficiency assessment module, a stability analysis module, and a maintenance management module; The working condition acquisition module is used to collect hydraulic working condition data of the forklift in real time based on the sensor group arranged at various positions of the forklift, and transmit the hydraulic working condition data to the vehicle IoT gateway of the forklift via CAN bus for data processing to obtain pressure data group, working performance data group and hydraulic health data group. The data synchronization module is used by the vehicle-mounted IoT gateway to package pressure data groups, operational performance data groups, and hydraulic health data groups in seconds, encrypt and encapsulate them via MQTT, and transmit them to the cloud maintenance platform. After the platform decrypts, decompresses, and verifies them, it converts them into time-series database structured records. The energy efficiency assessment module is used to analyze the pressure decay rate of the forklift hydraulic system based on the pressure data set and generate a pressure stability assessment. It triggers the stability analysis module when the pressure is abnormal. The stability analysis module is used to perform hydraulic energy efficiency analysis and energy efficiency degradation analysis on forklifts with abnormal hydraulic system pressure based on the operation performance data set. Then, it combines the analysis results of pressure decay rate and hydraulic health data set to perform comprehensive state analysis of the forklift hydraulic system and generate state stability assessment and maintenance information. The maintenance management module is used to generate maintenance work orders based on maintenance information, automatically select maintenance time windows, and generate maintenance logs to be stored in the maintenance knowledge base.

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