An electrolytic crane self-adaptive intelligent lubrication method suitable for high-temperature heavy-load working conditions

Abstract

The application discloses an electrolytic crane self-adaptive intelligent lubricating method suitable for high-temperature heavy-load working conditions, adopts a partition type self-adaptive lubricating architecture, divides three types of working conditions according to a lubricating point temperature gradient and a load size, constructs a temperature-load coupling lubricating model, a temperature-vibration coupling hardware calibration model and a simple quantitative model for fault grade determination and system stability evaluation, corrects coefficients, grease supply flow and lubricating interval parameters through a differentiated configuration model, and matches gradient working condition requirements of normal-temperature light load, high-temperature medium load and high-temperature heavy load. Meanwhile, the application is provided with PLC double-redundancy control logic and a hierarchical fault early warning mechanism, and system integration debugging and multi-condition field trial operation verification are completed. The application can realize dynamic self-adaptive regulation and control of electrolytic crane lubricating parameters, effectively improve lubricating precision and system operation stability, avoid risks of manual high-altitude operation, can cover all types of production working conditions of electrolytic cranes, and is suitable for metallurgical electrolytic equipment batch intelligent lubricating transformation.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology for metallurgical electrolysis equipment, specifically to an adaptive intelligent lubrication method for electrolysis overhead cranes adapted to high-temperature and heavy-load operating conditions. Background Technology

[0002] The electrolytic multi-functional overhead crane is a core heavy-duty operating equipment in the electrolytic aluminum production workshop. It is in a harsh working environment with high temperature, high vibration and high dust for a long time. The core moving parts such as bearings and pins of the equipment need to be continuously lubricated to ensure operational stability.

[0003] Currently, the industry generally adopts a manual, periodic, and fixed-point lubrication mode for electrolytic overhead crane lubrication operations. This mode has many technical drawbacks: First, lubrication points are mostly located at high or suspended positions, posing a high safety risk to manual operations. Second, lubrication operations are highly dependent on manual operating experience and are greatly affected by human factors, easily leading to problems such as lubrication leakage, insufficient lubrication, and over-lubrication. Third, the frequency and amount of manual lubrication cannot be adapted to the dynamic load and temperature changes of the overhead crane. Under high-temperature conditions, the lubricating medium is prone to rapid oxidation and failure, and accelerated wear, which can easily cause equipment wear, jamming, and downtime, significantly increasing equipment maintenance costs and production downtime losses. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the existing defects and provide an adaptive intelligent lubrication method for electrolytic cranes that is suitable for high-temperature and heavy-load working conditions. Through multi-dimensional working condition survey, precise hardware calibration, quantitative model adaptive control, graded fault early warning and batch stability verification, the method realizes unmanned operation, precise parameters and dynamic adaptation to working conditions of electrolytic cranes, thereby improving the lubrication accuracy and operational stability of the equipment.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an adaptive intelligent lubrication method for electrolytic overhead cranes adapted to high-temperature and heavy-load operating conditions, comprising the following steps:

[0006] S1: Working condition point survey and customized lubrication system solution design: For the high temperature, high vibration and high-altitude operation conditions of electrolytic crane, on-site working condition surveys are carried out on all core lubrication points of the equipment to obtain the load characteristics, operating temperature range and single lubrication demand of each lubrication point. Based on the survey results, the lubrication points are zoned and the layout is planned to construct a zoned intelligent independent lubrication circuit.

[0007] S2: Multi-parameter sensing hardware selection and working condition adaptation reliability verification: Temperature sensing elements and displacement clearance sensing elements are deployed at each core lubrication point to collect state parameters such as the working temperature of the lubrication part and the change in the clearance of moving parts.

[0008] Matching working conditions with a high-pressure power lubrication pump and a precise quantitative distribution mechanism, and establishing an explosion-proof and high-temperature resistant data transmission link;

[0009] For the high-temperature and continuous vibration operating environment of the electrolysis crane, we conducted hardware condition adaptability and reliability tests, and calibrated the response performance of the power components and the acquisition accuracy of the sensing elements.

[0010] The temperature-vibration coupling accuracy calibration model is used to correct the sensor data. The calibration model formula is: S real =S collect -γ·△T s -δ·V;

[0011] Where Sreal represents the calibrated actual operating parameters, S collect The data represents the raw data acquired by the sensor, where γ is the temperature drift coefficient, ranging from 0.01 to 0.05, and ΔT... s δ represents the dynamic temperature difference between the real-time temperature measured by the sensor and the standard operating temperature of the equipment; δ is the vibration compensation coefficient, ranging from 0.02 to 0.06; and V is the vibration frequency of the overhead crane operation.

[0012] S3: PLC Intelligent Adaptive Linkage Control and Anomaly Early Warning: Based on the PLC control system, an integrated intelligent lubrication control core is built, and an adaptive lubrication regulation logic with time triggering and load linkage is constructed to adjust the lubrication working interval and grease supply parameters according to the equipment operating conditions.

[0013] The system monitors the temperature at lubrication points and the working pressure parameters of the lubrication system in real time. It incorporates a multi-level anomaly monitoring and rapid alarm response mechanism. The system quantifies the fault level using a pressure-temperature linkage fault determination model. The simplified calculation formula for fault determination is: G = a·T real +b·P real ;

[0014] Where G is the fault level determination value, and T real For real-time temperature, P real The system's real-time pressure is represented by a and b, which are constant weighting coefficients.

[0015] When abnormal operating conditions occur, an early warning is triggered and abnormal data is uploaded to the central control management platform; a visual human-machine monitoring interface is built, and remote parameter control, historical operation data storage and traceability, and equipment fault record query functions are configured.

[0016] S4: System integration and debugging and batch deployment verification of multiple devices: Complete the integrated installation and software and hardware joint debugging of lubrication pipeline circuit, sensing and monitoring hardware, and PLC control module; simulate equipment no-load, full-load, and high-temperature extreme conditions; and optimize system lubrication parameters and intelligent control logic.

[0017] Pilot equipment was selected for on-site trial operation. The system's operating status was verified using a system stability evaluation model. The simplified calculation formula for stability evaluation is: η=1-A·|△Q|-B·Nerr ;

[0018] Where η is the system operating stability, ΔQ is the fat supply deviation, and N err The number of periodic failures is represented by A and B, which are preset correction coefficients.

[0019] The system's lubrication supply accuracy, early warning alarm reliability, and continuous operation stability were verified. In addition, the adaptation scheme was optimized based on the different chemical conditions and lubrication requirements of multiple electrolytic overhead cranes, and the batch implementation and acceptance of the entire intelligent lubrication system were completed.

[0020] As a preferred technical solution of the present invention, the partitioned intelligent lubrication circuit in step S1 is divided into partitions according to the temperature gradient of the lubrication point and the load size, and a temperature-load coupled lubrication quantitative calculation model is constructed. The grease supply flow rate and lubrication interval parameters corresponding to each partition are matched through the calculation model.

[0021] Collect real-time operating temperature data and real-time load data of each lubrication point, and classify all lubrication points by dividing them into temperature ranges and load ranges.

[0022] The temperature-load coupled lubrication calculation model adopts a simplified linear correction formula, which is as follows:

[0023] Q = Q0·(1+α·△T+β·K) F ), t=t0·(1-α·△T+β·K),t=t0·(1-α·△T+β·K) F );

[0024] Where Q is the actual single-time grease supply to the zone, Q0 is the standard grease supply at room temperature under light load, ΔT is the zone temperature difference between the real-time temperature of the lubrication point and the standard room temperature, and K... F t is the load ratio coefficient of real-time load relative to rated load, t is the actual lubrication interval of the zone, t0 is the reference lubrication interval, α and β are the temperature correction coefficient and load correction coefficient, respectively, and their values ​​range from 0.1 to 0.3.

[0025] Based on the temperature gradient range and load range, the lubrication points are divided into normal temperature light load zone, high temperature medium load zone, and high temperature heavy load zone. Through iterative calculation using the above calculation model, the grease supply flow rate parameters and lubrication interval parameters corresponding to each zone are set respectively.

[0026] As a preferred technical solution of the present invention, in step S1, the lubrication pipeline and valve group structure are custom-processed using high-temperature resistant and corrosion-resistant metal materials, the overall pipeline layout path is optimized, direct contact with high-temperature heat sources of equipment is avoided, and a high-performance lubricating medium for high-temperature working conditions is matched.

[0027] As a preferred technical solution of the present invention, the hardware operating condition adaptability and reliability test in step S2 includes high temperature steady-state test and vibration fatigue test.

[0028] The high-temperature steady-state test is: running continuously for 72 hours under 120℃ conditions, while controlling the sensor data acquisition error to be within a preset accuracy threshold range;

[0029] The vibration fatigue test was conducted by simulating the vibration frequency of an electrolytic crane operation for 96 hours to test the installation status of pipelines, valve groups, sensors, and data transmission status.

[0030] As a preferred technical solution of the present invention, the adaptive lubrication control logic in step S3 adopts a dual-redundant control mechanism of time-triggered and load-linked control, pre-configures a fixed basic lubrication time, and corrects the lubrication working interval and grease supply parameters in combination with real-time load data of the equipment.

[0031] As a preferred technical solution of the present invention, in step S3, a visual human-machine monitoring interface is built, and remote parameter control, historical operation data storage and traceability, and equipment fault record query functions are configured.

[0032] As a preferred technical solution of the present invention, the abnormal monitoring and alarm logic in step S3 is set in a hierarchical manner;

[0033] Setting the temperature of the lubrication point exceeding the threshold and the pressure of the lubrication system under the level 1 fault will trigger a continuous audible and visual alarm and lock the current lubrication process.

[0034] Data transmission anomalies and excessive fat supply deviations are set as level 2 faults, triggering intermittent alarms and generating fault logs.

[0035] As a preferred technical solution of the present invention, in the on-site trial operation stage of step S4, the grease supply, operating temperature and system pressure data of each lubrication point are collected at regular intervals to generate an operating condition report. The grease supply accuracy of the quantitative distributor and the operating parameters of the control program are corrected based on the operating condition report data.

[0036] Compared with the prior art, the beneficial effects of the present invention are: (1) The present invention abandons the traditional manual lubrication mode and the general fixed parameter lubrication mode, and realizes the differential adaptive lubrication of lubrication points through temperature-load coupling quantitative model. It can dynamically match the lubrication loss requirements of different points and different working conditions of the electrolytic crane, completely solve the problem of insufficient lubrication or redundancy caused by fixed parameter lubrication, and greatly improve the lubrication accuracy; (2) The present invention adds a temperature-vibration coupling hardware calibration model to solve the problem of sensor data drift and insufficient acquisition accuracy under high temperature vibration and harsh working conditions, ensure the authenticity and stability of working condition data acquisition, and provide accurate data support for adaptive lubrication control; (3) The present invention adopts a graded fault judgment and early warning mechanism, judges the fault level through quantitative model, realizes fault graded handling, and is equipped with dual redundant lubrication control logic to improve the reliability of system operation and avoid the risk of equipment lubrication failure; (4) The present invention realizes the adaptation of different working conditions of multiple cranes through multi-working condition simulation debugging and on-site trial operation stability verification, which can be applied in batches. No manual high-altitude operation is required throughout the process, eliminating the safety hazards of manual operation, while reducing equipment maintenance costs and downtime losses, and is suitable for industrial batch promotion and application. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0038] 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.

[0039] Example 1: Adaptive Intelligent Lubrication Method for Complete Electrolytic Crane under Normal Temperature and Light Load Conditions

[0040] like Figure 1 This embodiment is adapted to the normal operating conditions in an electrolysis workshop. The specific operating conditions are: ambient temperature 40-60℃, temperature difference ΔT between the lubrication point and the ambient temperature reference < 30℃, and the ratio of the real-time load of the equipment to the rated load K. F <50%, the overall condition is light-load operation at room temperature. The complete lubrication method and steps are as follows:

[0041] S1: Working condition point survey and customized lubrication system design: On-site survey of 28 core lubrication points of the electrolytic crane was carried out to clarify that the single lubrication requirement of each point is 0.5 to 2 mL / time, and all points were uniformly divided into the normal temperature light load lubrication zone.

[0042] The lubrication pipelines and valve groups are custom-made using 316L stainless steel, and the pipeline routing is optimized to avoid direct contact with the high-temperature heat source of the equipment.

[0043] Select a high-temperature synthetic grease with a dropping point ≥200℃ and oxidation stability ≥1000h;

[0044] A temperature-load coupled lubrication linear correction calculation model is adopted:

[0045] Q = Q0·(1+α·△T+β·K) F ), t=t0·(1-α·△T+β·K),t=t0·(1-α·△T+β·K) F );

[0046] Where Q is the actual single-time grease supply to the zone, Q0 is the standard grease supply at room temperature under light load, ΔT is the zone temperature difference between the real-time temperature of the lubrication point and the standard room temperature, and K... F t is the load ratio coefficient of real-time load relative to rated load, t is the actual lubrication interval of the zone, t0 is the reference lubrication interval, α and β are the temperature correction coefficient and load correction coefficient, respectively, and their values ​​range from 0.1 to 0.3.

[0047] The baseline grease supply rate Q0 is set to 1 mL / cycle, the baseline lubrication interval t0 is set to 2 h, the temperature correction factor α is set to 0.15, and the load correction factor β is set to 0.15.

[0048] Based on the calculation of the low temperature difference and low load operating parameters in this embodiment, the fixed operating parameters for this condition are determined as follows: single grease supply Q = 1 mL / time, lubrication interval t = 2 h.

[0049] S2: Multi-parameter sensing hardware selection and working condition adaptation reliability verification: Temperature sensing elements with a measurement range of -20 to 200℃ and an accuracy of ±0.5℃, as well as displacement clearance sensing elements with a detection accuracy of ±0.01mm, are deployed at each core lubrication point.

[0050] Select a high-pressure power lubrication pump with a working pressure of 10MPa and a matching quantitative distribution mechanism, and build a high-temperature resistant and explosion-proof data transmission link;

[0051] Hardware reliability testing was conducted, including a 120℃, 72-hour high-temperature steady-state test and a simulated operational vibration, 96-hour vibration fatigue test, to ensure the stability of the hardware structure and data transmission.

[0052] Temperature and vibration coupling accuracy calibration model S real =S collect -γ·△T s -δ·V is used for data correction;

[0053] Among them, S real To obtain the actual operating parameters after calibration, S collectThe data represents the raw data acquired by the sensor, where γ is the temperature drift coefficient, ranging from 0.01 to 0.05, and ΔT... s The real-time temperature difference between the sensor and the standard operating temperature of the equipment is δ, which is the vibration compensation coefficient, ranging from 0.02 to 0.06. V is the vibration frequency of the overhead crane operation. In this working condition, the temperature drift coefficient γ is set to 0.02 and the vibration compensation coefficient δ is set to 0.03. After calibration, the hardware start-stop response delay is ≤0.5s.

[0054] S3: PLC Intelligent Adaptive Linkage Control and Anomaly Early Warning: Based on the PLC control system, a dual-redundant control logic of time-triggered and load-linked control is built to perform automated lubrication operations according to fixed parameters for this working condition; a simplified formula for fault judgment based on pressure-temperature linkage is used: G=a·T. real +b·P real Quantify the fault level, where G is the fault level determination value, and T is the fault level determination value. real For real-time temperature, P real For the real-time system pressure, a and b are constant weighting coefficients, set to a=0.5 and b=0.5;

[0055] Implement a graded alarm mechanism: If the point temperature is ≥110℃ or the system pressure is <8MPa, it is determined as a first-level fault, triggering a continuous audible and visual alarm and locking the lubrication process.

[0056] Abnormal data transmission or a deviation in oil supply > ±5% is classified as a Level 2 fault, triggering an intermittent alarm and generating a fault log.

[0057] A visual monitoring interface was built to enable remote parameter control, data storage and traceability for ≥3 months, and real-time uploading of abnormal data to the central control platform. Under this working condition, normal operation is guaranteed without fault triggering.

[0058] S4: System Integration, Debugging, and Batch Deployment Verification: Complete the integrated installation of lubrication pipelines, sensing hardware, and PLC control modules; simulate no-load and ambient temperature light-load conditions to complete software and hardware joint debugging and optimization; conduct a one-month on-site trial run, using the stability evaluation model η=1-A·|△Q|-B·N err Verify the system status, where η is the system operational stability, △Q is the fat supply deviation, and N... err The number of periodic failures is represented by A and B, which are preset correction coefficients. The correction coefficients are set to A=0.2 and B=0.1.

[0059] The acceptance criteria for this operating condition are: system stability η≥0.98 and grease supply deviation≤±5%;

[0060] During the trial operation, operating data was collected periodically to generate reports, the grease supply accuracy of the distributor and program parameters were corrected, and the batch deployment and acceptance of equipment under normal temperature and light load conditions was completed.

[0061] Example 2: Adaptive Intelligent Lubrication Method for Complete Electrolytic Crane under High Temperature and Medium Load Conditions

[0062] This embodiment is adapted to peak production conditions in an electrolysis workshop. Specific operating conditions are: ambient temperature 80–100℃, temperature difference between lubrication points and ambient temperature reference ΔT = 30–60℃, and real-time equipment load as a percentage of rated load K. F =50%~80%, the overall condition is high temperature and medium load, and the complete lubrication method steps are as follows:

[0063] S1: Working condition point survey and customized lubrication system solution design: The points corresponding to the high temperature and medium load areas among the 28 core lubrication points are divided into zones and classified. 316L stainless steel high temperature and corrosion resistant pipeline valve groups are used, and high temperature synthetic grease with a dropping point ≥200℃ is matched.

[0064] The unified reference parameters Q0=1mL / time and t0=2h are used.

[0065] The correction coefficients for this operating condition are set to α=0.20 and β=0.20. Substitute them into the temperature-load coupling model for calculation, and adaptive matching is used to obtain the operating parameters for this operating condition: single grease supply Q=1.3~1.5mL / time, lubrication interval t=1.7~1.8h.

[0066] S2: Multi-parameter sensing hardware selection and working condition adaptation reliability verification: The hardware equipment selection is consistent with Example 1, maintaining the same specifications for temperature sensor, displacement sensor, high-pressure lubrication pump and transmission link;

[0067] Completed 120℃ / 72h high-temperature steady-state test and 96h vibration fatigue test; adjusted calibration parameters for the high temperature and high vibration characteristics of this working condition, set temperature and vibration coupling model coefficients γ=0.03 and δ=0.04, corrected data drift caused by high temperature vibration in real time, ensured the accuracy of the collected data, and the hardware response delay was stable ≤0.5s.

[0068] S3: PLC intelligent adaptive linkage control and abnormal warning: The PLC system's dual redundant control logic remains unchanged, and lubrication operations are performed according to the differentiated grease supply and lubrication interval parameters calculated under this working condition.

[0069] The weighting coefficients of the fault determination model remain a=0.5 and b=0.5, and the first-level and second-level fault classification alarm logic is used.

[0070] The system monitors point temperature and pipeline pressure in real time, dynamically adapts to high temperature and medium load loss characteristics, and has no over-limit faults triggered during trial operation. The monitoring interface stores operating condition data in real time.

[0071] S4: System integration debugging and batch deployment verification: Simulate high temperature and medium load composite working conditions to complete software and hardware joint debugging and parameter iteration optimization;

[0072] After a one-month on-site trial run, the stability model correction coefficients A=0.2 and B=0.1. The acceptance criteria for this operating condition are system stability η≥0.96 and grease supply deviation ≤±4%.

[0073] During the trial operation, operating condition data was continuously collected to optimize the distributor's accuracy and control program. The system operated stably, meeting the long-term lubrication requirements under high temperature and medium load conditions, and completing the adaptation and implementation of operating conditions.

[0074] Example 3: Adaptive Intelligent Lubrication Method for Complete Electrolytic Crane under High Temperature and Heavy Load Conditions

[0075] This embodiment is adapted to the high-intensity, extreme production conditions in an electrolysis workshop. Specific operating conditions are: ambient temperature 100–120℃, temperature difference ΔT between lubrication points and ambient temperature reference > 60℃, and real-time equipment load as a percentage of rated load K. F ≥80%, the overall condition is a high temperature and heavy load extreme condition. The complete lubrication method and steps are as follows:

[0076] S1: Working condition location survey and customized lubrication system solution design: Separate zones for core high-temperature and heavy-load locations, further optimize the 316L pipeline layout path to minimize heat source radiation and heavy-load compression losses.

[0077] Use the same high-temperature resistant synthetic grease; keep the baseline parameters Q0=1mL / use and t0=2h unchanged;

[0078] With extreme working condition correction coefficients set at α=0.25 and β=0.25, and substituted into the coupled model for calculation, the extreme working condition adaptation parameters are obtained: single grease supply Q=1.8~2.0mL / time, lubrication interval t=1.5h.

[0079] S2: Multi-parameter sensing hardware selection and working condition adaptation reliability verification: The main hardware selection remains unchanged, and data calibration is enhanced for extreme high temperature of 120℃ and high frequency vibration working conditions.

[0080] The coefficients γ=0.04 and δ=0.05 of the temperature-vibration coupling calibration model were adjusted to minimize the acquisition errors caused by high-temperature drift and mechanical vibration.

[0081] After high and low temperature steady-state and vibration fatigue tests, the hardware response speed is stable at ≤0.5s, and the data acquisition accuracy meets the requirements for use under extreme working conditions.

[0082] S3: PLC intelligent adaptive linkage control and abnormal warning: Activate the time-load dual redundancy highest priority control strategy, and strictly perform automated lubrication according to the 1.5h lubrication interval and 1.8~2.0mL / time grease supply.

[0083] The fault determination model and the hierarchical alarm logic parameters are kept consistent, and temperature and pressure fluctuations under extreme operating conditions are monitored in real time.

[0084] The lubrication replenishment amount is dynamically matched to address the rapid wear characteristics of heavy-duty grease, ensuring that instantaneous operating condition fluctuations will not trigger the fault threshold and that the system operates safely and stably.

[0085] S4: System integration, debugging, and batch deployment verification: Simulating extreme high-temperature and full-load conditions to complete full-dimensional software and hardware debugging and parameter optimization;

[0086] During the one-month on-site trial operation, the same stability evaluation model was used. The acceptance criteria for this operating condition were system stability η≥0.95 and grease supply deviation≤±5%.

[0087] The trial run was completed without any lubrication failures or system errors, demonstrating its stable compatibility with the extreme and harsh production conditions of the electrolysis crane. Ultimately, the system adaptation, debugging, and acceptance of all eight electrolysis cranes were successfully completed.

[0088] The above three sets of embodiments correspond to three typical operating conditions of electrolytic cranes: ambient temperature light load, high temperature medium load, and high temperature heavy load. The whole adopts a unified technical implementation process, hardware selection system, calculation model architecture, and fault judgment logic. Only based on the difference in operating temperature gradient and load ratio, the model correction coefficient, lubrication operation parameters, and acceptance threshold are set differently to form a gradient and full-coverage technical implementation system.

[0089] The hardware calibration coefficients in the three sets of embodiments show an increasing trend with the increase of operating temperature and vibration intensity. The temperature correction coefficient α increases from 0.15 to 0.25, the load correction coefficient β increases from 0.15 to 0.25, the temperature drift coefficient γ increases from 0.02 to 0.04, and the vibration compensation coefficient δ increases from 0.03 to 0.05. The corresponding single lubrication amount increases from 1 mL / time to 1.8-2.0 mL / time, and the lubrication interval decreases from 2 hours to 1.5 hours, realizing adaptive dynamic matching of lubrication parameters according to the severity of operating conditions.

[0090] Meanwhile, the system acceptance stability threshold of the three sets of embodiments is appropriately reduced as the complexity of the working conditions increases, the grease supply deviation control range is adapted to the loss characteristics of each working condition, and the trial operation data of all embodiments meet the preset technical indicators, with no faults and no parameter abnormalities. This verifies the adaptability, stability and repeatability of the technical solution of the present invention in the full range of working conditions, and can cover the intelligent lubrication implementation needs of all operation scenarios of electrolytic cranes.

[0091] The parts of the invention not described in detail are prior art. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive intelligent lubrication method for electrolytic overhead cranes adapted to high-temperature and heavy-load operating conditions, characterized in that: Includes the following steps: S1: Working condition point survey and customized lubrication system solution design: For the high temperature, high vibration and high-altitude operation conditions of electrolytic crane, on-site working condition surveys are carried out on all core lubrication points of the equipment to obtain the load characteristics, operating temperature range and single lubrication demand of each lubrication point. Based on the survey results, the lubrication points are zoned and the layout is planned to construct a zoned intelligent independent lubrication circuit. S2: Multi-parameter sensing hardware selection and working condition adaptation reliability verification: Temperature sensing elements and displacement clearance sensing elements are deployed at each core lubrication point to collect state parameters such as the working temperature of the lubrication part and the change in the clearance of moving parts. A high-pressure power lubrication pump with matching working conditions and a precise quantitative distribution mechanism are used to build an explosion-proof and high-temperature resistant data transmission link. For the high-temperature and continuous vibration operating environment of the electrolysis crane, we conducted hardware condition adaptability and reliability tests, and calibrated the response performance of the power components and the acquisition accuracy of the sensing elements. The temperature-vibration coupling accuracy calibration model is used to correct the sensor data. The calibration model formula is: S real =S collect -γ·△T s -δ·V; Where Sreal represents the calibrated actual operating parameters, S collect The data represents the raw data acquired by the sensor, where γ is the temperature drift coefficient, ranging from 0.01 to 0.05, and ΔT... s δ represents the dynamic temperature difference between the sensor's real-time temperature measurement and the equipment's standard operating temperature; δ is the vibration compensation coefficient, ranging from 0.02 to 0.06; and V is the overhead crane's operating vibration frequency. S3: PLC Intelligent Adaptive Linkage Control and Anomaly Early Warning: Based on the PLC control system, an integrated intelligent lubrication control core is built, and an adaptive lubrication regulation logic with time triggering and load linkage is constructed to adjust the lubrication working interval and grease supply parameters according to the equipment operating conditions. The system monitors the temperature at lubrication points and the working pressure parameters of the lubrication system in real time. It incorporates a multi-level anomaly monitoring and rapid alarm response mechanism. The system quantifies the fault level using a pressure-temperature linkage fault determination model. The simplified calculation formula for fault determination is: G = a·T real +b·P real ; Where G is the fault level determination value, and T real For real-time temperature, P real The system's real-time pressure is represented by a and b, which are constant weighting coefficients. When abnormal operating conditions occur, an early warning is triggered and abnormal data is uploaded to the central control management platform; a visual human-machine monitoring interface is built, and remote parameter control, historical operation data storage and traceability, and equipment fault record query functions are configured. S4: System integration and debugging and batch deployment verification of multiple devices: Complete the integrated installation and software and hardware joint debugging of lubrication pipeline circuit, sensing and monitoring hardware, and PLC control module; simulate equipment no-load, full-load, and high-temperature extreme conditions; and optimize system lubrication parameters and intelligent control logic. Pilot equipment was selected for on-site trial operation. The system's operating status was verified using a system stability evaluation model. The simplified calculation formula for stability evaluation is: η=1-A·|△Q|-B·N err ; Where η is the system operating stability, ΔQ is the fat supply deviation, and N err The number of periodic failures is represented by A and B, which are preset correction coefficients. The system's lubrication supply accuracy, early warning alarm reliability, and continuous operation stability were verified. In addition, the adaptation scheme was optimized based on the different chemical conditions and lubrication requirements of multiple electrolytic overhead cranes, and the batch implementation and acceptance of the entire intelligent lubrication system were completed.

2. The adaptive intelligent lubrication method for electrolytic cranes adapted to high-temperature and heavy-load conditions according to claim 1, characterized in that: In step S1, the partitioned intelligent lubrication circuit is divided into partitions according to the temperature gradient of the lubrication point and the load size, and a temperature-load coupled lubrication quantitative calculation model is constructed. The calculation model is used to match the grease supply flow rate and lubrication interval parameters corresponding to each partition. Collect real-time operating temperature data and real-time load data of each lubrication point, and classify all lubrication points by dividing them into temperature ranges and load ranges. The temperature-load coupled lubrication calculation model adopts a simplified linear correction formula, which is as follows: Q=Q0·(1+α·△T+β·K F ),t=t0·(1-α·△T+β·K F ); Where Q is the actual single-time grease supply to the zone, Q0 is the standard grease supply at room temperature under light load, ΔT is the zone temperature difference between the real-time temperature of the lubrication point and the standard room temperature, and K... F t is the load ratio coefficient of real-time load relative to rated load, t is the actual lubrication interval of the zone, t0 is the reference lubrication interval, α and β are the temperature correction coefficient and load correction coefficient, respectively, and their values ​​range from 0.1 to 0.

3. Based on the temperature gradient range and load range, the lubrication points are divided into normal temperature light load zone, high temperature medium load zone, and high temperature heavy load zone. Through iterative calculation using the above calculation model, the grease supply flow rate parameters and lubrication interval parameters corresponding to each zone are set respectively.

3. The adaptive intelligent lubrication method for electrolytic cranes adapted to high-temperature and heavy-load conditions according to claim 1, characterized in that: In step S1, lubrication pipelines and valve group structures are custom-processed using high-temperature and corrosion-resistant metal materials, the overall pipeline layout is optimized, direct contact with high-temperature heat sources of equipment is avoided, and high-performance lubricating media are matched for high-temperature working conditions.

4. The adaptive intelligent lubrication method for electrolytic cranes adapted to high-temperature and heavy-load conditions according to claim 1, characterized in that: The hardware operating condition adaptability and reliability test in step S2 includes high temperature steady-state test and vibration fatigue test. The high-temperature steady-state test is: running continuously for 72 hours under 120℃ conditions, while controlling the sensor data acquisition error to be within a preset accuracy threshold range; The vibration fatigue test was conducted by simulating the vibration frequency of an electrolytic crane operation for 96 hours to test the installation status of pipelines, valve groups, sensors, and data transmission status.

5. The adaptive intelligent lubrication method for electrolytic cranes adapted to high-temperature and heavy-load conditions according to claim 1, characterized in that: In step S3, the adaptive lubrication control logic adopts a dual-redundant control mechanism of time-triggered and load-linked control. It pre-configures a fixed basic lubrication duration and adjusts the lubrication working interval and grease supply parameters in combination with real-time load data of the equipment.

6. The adaptive intelligent lubrication method for electrolytic cranes adapted to high-temperature and heavy-load conditions according to claim 1, characterized in that: In step S3, a visual human-machine monitoring interface is built, and functions for remote parameter control, historical operation data storage and traceability, and equipment fault record query are configured.

7. The adaptive intelligent lubrication method for electrolytic cranes adapted to high-temperature and heavy-load conditions according to claim 6, characterized in that: The anomaly monitoring and alarm logic in step S3 is set in a hierarchical manner. Setting the temperature of the lubrication point exceeding the threshold and the pressure of the lubrication system under the level 1 fault will trigger a continuous audible and visual alarm and lock the current lubrication process. Data transmission anomalies and excessive fat supply deviations are set as level 2 faults, triggering intermittent alarms and generating fault logs.

8. The adaptive intelligent lubrication method for electrolytic cranes adapted to high-temperature and heavy-load conditions according to claim 1, characterized in that: In step S4, during the on-site trial operation phase, data on grease supply, operating temperature, and system pressure at each lubrication point are collected periodically to generate an operating condition report. Based on the data in the operating condition report, the grease supply accuracy of the quantitative distributor and the operating parameters of the control program are corrected.

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