Intelligent package and dynamic scheduling method for whole process of aluminum tapping

Abstract

The application discloses an intelligent package and dynamic scheduling method for the whole process of aluminum tapping, and belongs to the technical field of automatic control, which comprises aluminum liquid initial thermal state acquisition, ladle thermal characteristic query, candidate transportation path segmentation, road section thermal loss coefficient calculation and temperature recursion, trace heat risk exposure calculation, aluminum liquid thermal-transport coupling state quantity construction, scheduling decision and instruction issuing, solves the technical problem of constructing aluminum liquid thermal-transport coupling state quantity, accurately depicting the risk mutation characteristics of aluminum liquid approaching irreversible thermal scrap boundary, and accurately obtaining vehicle scheduling decision, avoids the thermal margin misjudgment caused by the wrong assumption that "the same total time means the same thermal state", can accurately depict the risk mutation characteristics of aluminum liquid approaching irreversible thermal scrap boundary, and eliminates the occurrence conditions of aluminum liquid irreversible thermal scrap from the scheduling decision level.

Description

Technical Field

[0001] This invention belongs to the field of automation control technology, and in particular relates to an intelligent packaging and dynamic scheduling method for the entire process of aluminum electrolysis. Background Technology

[0002] After the molten aluminum exits the electrolytic cell, it is collected by a vacuum ladle and transported by vehicles along the factory roads to the foundry workshop. During transportation, the molten aluminum continuously dissipates heat into the environment, causing its temperature to drop. The vacuum ladle is an indispensable piece of equipment for transferring high-temperature molten aluminum in electrolytic aluminum production enterprises. However, with prolonged use, problems such as decreased heat retention performance gradually emerge in the ladle. The casting process has specific technological requirements for the temperature of the molten aluminum; the temperature of the molten aluminum before entering the casting machine typically needs to be controlled between 690℃ and 710℃.

[0003] In the existing technologies, one approach is to schedule overhead cranes by estimating the heat loss of molten metal during transportation. This approach uses a thermal model to estimate the heat loss during ladle lifting based on molten metal parameters, ladle properties, and transportation time. Another approach is to predict the arrival temperature of molten aluminum using a temperature decay model and combine this with path efficiency for vehicle scheduling. A third approach addresses the issues of electrolytic cell allocation and aluminum tapping scheduling in the electrolytic aluminum production process by optimizing the loading of the aluminum tapping path.

[0004] However, the common drawback of the aforementioned prior art is that: Treating the entire transportation route as a whole, scheduling decisions are made solely based on total transportation time or expected arrival temperature. However, in actual transportation, vehicle speeds and ambient temperatures vary across different road sections, directly impacting the heat loss rate of the molten aluminum. Current technology cannot reflect the differentiated heat loss of vehicles under varying motion conditions, making it difficult to accurately distinguish candidate routes with similar total times but different actual thermal safety states during the scheduling phase. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent packaging and dynamic scheduling method for the entire process of aluminum electrolysis, which solves the technical problem of constructing thermal-transport coupled state variables of molten aluminum, accurately characterizing the risk mutation characteristics of molten aluminum when it approaches the irreversible thermal scrap boundary, and accurately obtaining vehicle scheduling decisions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for intelligent batching and dynamic scheduling of the entire aluminum electrolysis process includes the following steps: Step 1: The aluminum liquid status acquisition module acquires the initial temperature, loading mass, ladle number, and lowest temperature of the current aluminum liquid. The initial dataset is constructed and transferred to the thermal state calculation module; Step 2: The thermal state calculation module queries the aging state data of the lifting package using the lifting package number as an index, calculates the aging correction coefficient and the current reference heat loss coefficient of the lifting package, and outputs it to the path segmentation and calculation module. Step 3: The route segmentation module retrieves candidate transportation routes from the vehicle dispatching system, divides the candidate transportation routes into multiple continuous road segments according to the segmentation criteria, constructs a candidate route set, and sends it to the road segment calculation module; Step 4: The road segment calculation module performs road segment heat loss coefficient calculation and temperature recursion on each candidate path in the candidate path set to obtain the final arrival temperature and aluminum liquid temperature sequence of each candidate path. Step 5: The risk calculation module retrieves the results obtained from the road segment calculation module, accumulates the thermal risk exposure contribution of each road segment along any candidate path, obtains the trajectory thermal risk exposure of the candidate path, and calculates the trajectory thermal risk exposure of all candidate paths. Step 6: The state quantity construction module is based on the lowest temperature of the molten aluminum. The final arrival temperature and trajectory thermal risk exposure of each candidate path are used to construct the thermal-transport coupling state variables of the aluminum liquid for each candidate path. Step 7: The scheduling decision module selects the execution path based on the aluminum liquid thermal-transport coupling state of each candidate path and sends the result to the corresponding vehicle terminal or crane control system.

[0007] Preferably, the lowest temperature of the molten aluminum is... The irreversible thermal scrapping boundary represents the physical hard constraint boundary that the molten aluminum must not fall below.

[0008] Preferably, when performing step 2, the thermal state calculation module specifically performs the following steps: The aging status data of a bale can be retrieved by indexing its bale number. This aging data includes at least the bale's manufacturing material, wall thickness, and factory-calibrated baseline heat loss coefficient. Total number of uses Time since the last major overhaul ; Based on cumulative usage and the time since the last major overhaul Calculate the aging correction factor : ; in, and These are all system-preset aging parameters, and their values ​​are pre-calibrated based on the actual aging statistics of the bales in this factory area. Calculate the current reference heat loss coefficient for: .

[0009] Preferably, when performing step 3, the road segment is characterized by its expected duration, expected average vehicle speed, and ambient temperature; The estimated duration of each road segment is determined by the segment length and the estimated average speed of the segment. The estimated average speed of each road segment is estimated from traffic condition data, and the ambient temperature of each road segment is provided by a sensor network.

[0010] Preferably, when performing step 4, the heat loss coefficient calculation and temperature recursion processing performed by the road segment calculation module are as follows: The heat loss coefficient of the road section The calculation formula is: ; in, Let be the heat loss coefficient of the i-th road segment; The reference heat loss coefficient for lifting the package; Let be the expected average speed of the i-th road segment; The temperature of the molten aluminum at the starting point of the i-th segment; Let i be the ambient temperature of the i-th road segment; and All are preset vehicle speed-convection coupling parameters; The method for temperature recursion processing is shown in the following formula: ; ; in, Let be the temperature of the molten aluminum at the starting point of segment i. Let be the temperature of the molten aluminum at the end of the i-th segment. Let be the estimated duration of the i-th segment; To obtain the final arrival temperature of the candidate path after recursively extending to the Nth segment; The road segment calculation module is based on Construct a temperature sequence for molten aluminum.

[0011] Preferably, step 5 includes the following steps: Step 5-1: The risk calculation module retrieves the final arrival temperature and aluminum liquid temperature sequence corresponding to all candidate road segments in the candidate path set obtained in Step 4; For candidate paths For the i-th road segment, the risk calculation module first uses the vehicle speed risk weight function to calculate the risk weight corresponding to the vehicle speed in that road segment. The specific formula for calculating the vehicle speed risk weight function is as follows: ; in, Let be the expected average speed of the i-th road segment; The reference speed is preset by the system based on the speed limits of the factory roads; This is a low-speed risk amplification factor. ; The decay rate coefficient, j represents the index of the candidate path in the candidate path set; Step 5-2: The risk calculation module calculates the current aluminum liquid temperature of the road section. Minimum allowable temperature Vehicle speed risk weight and the duration of the road segment Calculate the contribution of this road segment to the total thermal risk exposure of the entire trajectory. : ;in, The system's preset zero-prevention constant, ; Step 5-3: The risk calculation module accumulates the risk exposure contribution of each segment along the entire path: ; Step 5-4: The risk calculation module uses the methods from Steps 5-1 to 5-3 to perform the above-mentioned cumulative calculation on each candidate path to obtain the trajectory thermal risk exposure of each candidate path in the candidate path set. And send it to the state quantity construction module.

[0012] Preferably, during step 6, the state variable construction module retrieves the final arrival temperature of each candidate path. and trajectory thermal risk exposure Subsequently, the thermal-mechanical coupling state variables of molten aluminum were constructed. The specific construction method is as follows: First, for each candidate path j, the state variable construction module first calculates its final temperature margin. : ; in, The final reached temperature; Final temperature margin Used to indicate the degree of margin of aluminum liquid temperature relative to the irreversible thermal scrap boundary when arriving at the destination after transporting according to candidate path j; Subsequently, the state quantity construction module is based on the final temperature margin. and trajectory thermal risk exposure Constructing the thermal-physical coupling state variables of molten aluminum : ; in, The system has a preset risk penalty coefficient. .

[0013] The state variable construction module performs the above construction method on each candidate path in the candidate path set to obtain the aluminum liquid thermal-mechanical coupling state variables for each candidate path. Preferably, during step 7, the scheduling decision module retrieves the thermal-physical coupling state of the molten aluminum for each candidate path. Then, choose to make Maximize and Candidate paths This serves as the actual execution path for the current aluminum molten material transportation task.

[0014] This invention presents an intelligent packaging and dynamic scheduling method for the entire aluminum electrolysis process. It solves the technical problem of constructing a coupled thermal-transportation state variable for molten aluminum, accurately characterizing the risk mutation characteristics of molten aluminum approaching the irreversible thermal scrap boundary, and accurately obtaining vehicle scheduling decisions. This invention segments candidate transport paths according to their motion state, calculates the heat loss coefficient for each segment, and performs temperature recursion. This enables the scheduling system to distinguish the differentiated heat loss between "high-speed driving" and "low-speed / waiting" states, avoiding misjudgments of heat margin due to the erroneous assumption that "the same total time means the same thermal state." This invention constructs a trajectory thermal risk exposure quantity, which can non-linearly amplify the risk when molten aluminum approaches the minimum allowable temperature. By using a vehicle speed weighting function to additionally penalize risk exposure in low-speed states, it can accurately characterize the risk mutation characteristics of molten aluminum approaching the irreversible thermal scrap boundary. This invention constructs a coupled thermal-transportation state variable for molten aluminum, asymmetrically coupling the endpoint heat margin with the trajectory thermal risk exposure quantity. Even if two candidate paths arrive at the same temperature, differences in their internal velocity distribution and ambient temperature sequence can lead to different thermal risk exposures, enabling the system to distinguish candidate schemes with the same "endpoint state" but different "trajectory thermal safety states." This invention uses the minimum allowable temperature of molten aluminum as the boundary for irreversible thermal failure, transforming it into a hard constraint for scheduling decisions through feasibility assessment. This allows for the screening of candidate schemes, eliminating the conditions for irreversible thermal failure of molten aluminum at the scheduling decision level. The product coupling of conduction, convection, and radiation factors in the segment heat loss coefficient of this invention, and the asymmetric coupling of the endpoint margin and trajectory risk exposure in the molten aluminum thermal-transport coupling state quantity, are not simply the sum of the individual effects of the aforementioned physical factors or mathematical operations. Their combined effect enables the molten aluminum thermal-transport coupling state quantity to identify candidate schemes with the same endpoint temperature but different thermal safety trajectories. This identification capability cannot be achieved independently by any single physical factor. Attached Figure Description

[0015] Figure 1 This is the main flowchart of the present invention; Figure 2 This is a flowchart of the process for querying the thermal characteristics of the lifting package according to the present invention; Figure 3 This is a flowchart of the recursive relationship between the road section heat loss coefficient and temperature according to the present invention; Figure 4 This is a flowchart of the trajectory thermal risk exposure calculation method of the present invention; Figure 5 This is a flowchart of the aluminum liquid thermal-transport coupling state quantity construction and scheduling decision of the present invention. Detailed Implementation

[0016] Depend on Figures 1-5 The method for intelligent batching and dynamic scheduling of the entire aluminum electrolysis process, as shown, includes the following steps: Step 1: After the scheduling system starts, the aluminum liquid status acquisition module performs the initial thermal status acquisition task of the aluminum liquid, which specifically includes: reading the current aluminum liquid temperature through the thermocouple temperature sensor set at the ladle inlet. The mass of the molten aluminum currently being lifted and loaded is obtained by using the weighbridge at the aluminum outlet. The baggage number is automatically identified by the RFID reader at the baggage entrance. It also reads the minimum allowable temperature of molten aluminum for downstream casting processes from the system database. Minimum temperature of molten aluminum It is preset in the system database.

[0017] In this embodiment, the lowest temperature of the molten aluminum is... It is regarded as an irreversible thermal scrap boundary, that is, a physical hard constraint that the temperature of the aluminum liquid must not be lower than this value, rather than a soft constraint that the scheduling system can use to make trade-offs. The specific value is determined by the process specifications of the downstream casting process, and is generally assumed to be 700℃-750℃. For example, aluminum-silicon alloys are usually 680-740℃, and aluminum-copper alloys are usually 700-750℃. The maximum value in the range can be taken, such as 740℃ or 750℃.

[0018] After the data acquisition is completed, the aluminum liquid state acquisition module combines the acquired data into an initial dataset. It is then transmitted to the thermal state calculation module.

[0019] Step 2: After receiving the initial dataset, the thermal state calculation module executes the task of querying the thermal characteristics of the lifting package, specifically by lifting package number. The index is used to access the aging status data of the package history stored in the system database.

[0020] The resume information should at least include: the manufacturing material of the bail, the wall thickness, and the factory-specified baseline heat loss coefficient. Total number of uses Time since the last major overhaul The thermal state calculation module calculates based on the cumulative number of uses. and the time since the last major overhaul Calculate the aging correction factor : ; in, and These are all system-preset aging parameters, and their values ​​are pre-calibrated based on the actual aging statistics of the ballast in the factory area.

[0021] Aging correction factor This indicates that the lining material of the ladle gradually deteriorates after repeated exposure to high-temperature molten aluminum thermal shock, and its thermal insulation performance decreases with increasing usage. The larger, The larger the lining, the better; at the same time, the insulation performance is restored after each major overhaul and lining replacement. The larger, The smaller the value, the more the cumulative aging effect is suppressed. In this embodiment, the aging correction factor... This is used to incorporate the physical degradation laws of the ballast as a thermal container into thermal state calculations.

[0022] Subsequently, the thermal state calculation module calculates the current reference heat loss coefficient. and will Output to the path segmentation and calculation module.

[0023] Current benchmark heat loss coefficient The calculation formula is as follows: .

[0024] Step 3: After obtaining the location and status information of all available transport vehicles in the current factory area through the vehicle dispatching system, the route segmentation module executes the candidate transport route segmentation task. Specifically, the vehicle dispatching system extracts the candidate route from the current location to the destination for each candidate vehicle from the factory area GIS map and sends the candidate route to the route segmentation module. The route segmentation module segments each candidate route according to the segmentation criteria.

[0025] In this embodiment, the destination is the unloading port of the foundry workshop. The vehicle dispatching system uses the road network constructed from the factory's GIS map as a directed topology graph, and uses either Dijkstra's algorithm or A* algorithm to calculate several candidate paths for each candidate vehicle from its current location to its destination, and then sends the candidate paths to the path segmentation module.

[0026] The segmentation is based on changes in motion state and external conditions. Motion state includes changes in road attributes (such as turning from a main road to a side road) and points of change in vehicle speed (such as intersections, speed bumps, and speed-limited areas); changes in external conditions include changes in ambient temperature (such as moving from an outdoor road to an indoor workshop passage).

[0027] In this embodiment, the candidate path is divided into several continuous road segments with the aforementioned change points as boundaries, and the motion state and external conditions within each road segment remain relatively consistent.

[0028] In this embodiment, the path segmentation module divides a complete transportation route into N consecutive segments. This completes the segmentation of each candidate path. After segmentation, each candidate path... Represented as: ; in: The expected duration of the i-th road segment is determined by the segment length and the expected average speed of the segment. The expected average speed of the i-th road segment is estimated from real-time traffic data provided by the factory traffic management system. The ambient temperature collected by the a-th environmental sensor for the i-th segment is provided by the plant's temperature sensor network (such as several temperature sensors spaced along the transportation path). The index represents the candidate path; i represents the index of the segment of the candidate path, and i takes values ​​from 1 to N.

[0029] The path segmentation module combines the path data of all candidate paths into a candidate path set. The data is then transmitted to the road segment calculation module, where m is the total number of candidate paths and j ranges from 1 to m.

[0030] Step 4: After receiving the candidate path set, the road segment calculation module performs calculations on each candidate path. For each segment, the heat loss coefficient of the segment and the aluminum liquid temperature are calculated and updated sequentially, as follows: The initial condition is: the first road segment The starting temperature is the current temperature of the molten aluminum collected in step 1, i.e. For the i-th road segment, the road segment calculation module first calculates its corresponding heat loss coefficient. The calculation formula is as follows: ; in: The temperature of the molten aluminum at the starting point of the i-th segment; The current reference heat loss coefficient for lifting the package is calculated in step 2; and All parameters are preset vehicle speed-convection coupling parameters.

[0031] In this embodiment, and All calibrations were performed on-site based on the ladle's shape and aerodynamic characteristics. The specific calibration method involved conducting forced ventilation cooling experiments on a ladle loaded with a standard amount of molten aluminum at different vehicle speeds (v) under controlled experimental conditions. The rate of temperature drop of the molten aluminum at each wind speed (in this embodiment, calibration was performed directly on-site at different vehicle speeds under a fixed ventilation wind speed) was recorded, yielding multiple sets of data points. ,in, This represents the rate of temperature drop of the molten aluminum at vehicle speed v. right and By performing linear regression, the values ​​can be simultaneously labeled. (The index of the intercept) and (Slope). This calibration process is completed once before system deployment, and the calibration results are stored in the system parameter library for subsequent scheduling. For different types of payloads, the above calibration process is executed separately to obtain their respective... (The index of the intercept) and (Slope).

[0032] Calculate the heat loss coefficient of the road section , This represents the inherent conductive heat dissipation characteristics of the packaging. This represents the contribution of vehicle speed to forced convection heat transfer. The higher the vehicle speed, the stronger the forced convection and the faster the heat dissipation. This represents the contribution of radiative heat dissipation.

[0033] Obtain the heat loss coefficient of the road section Then, the road segment calculation module calculates the temperature drop of the molten aluminum caused by the current road segment based on the heat loss coefficient and the duration of the road segment. : ; in, The estimated duration of the i-th segment is determined in step 3; Then, the road segment calculation module updates the aluminum melt temperature to obtain the aluminum melt temperature at the end of the road segment. : ; The road segment calculation module will calculate the results. Use this as the starting temperature for the next segment, and repeat the above steps until all N segments of the path have been calculated. Construct an aluminum liquid temperature sequence based on the calculation results.

[0034] When path After all road segments have been calculated, the final arrival temperature of this route is... for: ; The road segment calculation module performs the complete recursive calculation process described above for each candidate path to obtain the final arrival temperature of each candidate path.

[0035] Step 5: After retrieving the segment parameters and corresponding molten aluminum temperature sequences for each candidate path obtained from the segment calculation module, the risk calculation module performs the trajectory thermal risk exposure calculation, which includes the following steps: Step 5-1: For candidate paths For the i-th road segment, the risk calculation module first uses the vehicle speed risk weight function to calculate the risk weight corresponding to the vehicle speed in that road segment. The specific formula for calculating the vehicle speed risk weight function is as follows: ; in, Let be the expected average speed of the i-th road segment; The reference speed is preset by the system based on the speed limits of the factory roads; This is a low-speed risk amplification factor. ; The decay rate coefficient, .

[0036] In this embodiment, The default speed limit for factory roads (e.g., 30 km / h) is taken, which is directly determined by the factory's traffic management regulations. and Historical transportation data analysis was used to determine the specific data, which involved extracting actual data from several complete transportation missions from the factory's historical transportation records. This included vehicle speed, duration, ambient temperature, and the actual temperature of the molten aluminum upon arrival at each route. For each complete transportation mission, different... , The predicted arrival temperature is calculated by combining the results, and the temperature that minimizes the prediction error (root mean square error) is selected. , The parameters are combined as system parameters; this process is completed before system deployment and can be updated periodically (monthly or daily) as historical data accumulates.

[0037] When the vehicle is traveling at low speed or is waiting ( (gradually decelerating to 0), the weight function approaches This means that the risk exposure weight has been amplified. This is because at low speeds or while waiting, forced convection weakens, and although radiative heat dissipation decreases, the duration is longer, thus increasing the overall risk of heat loss; when the vehicle is traveling at high speed ( The weighting function approaches 1, meaning the risk exposure weight reverts to the baseline level. Step 5-2: The risk calculation module calculates the risk based on the current aluminum molten temperature of the road section. Minimum allowable temperature Vehicle speed risk weight and the duration of the road segment Calculate the contribution of this road segment to the total thermal risk exposure of the entire trajectory. : ; in, The system's preset zero-prevention constant, .

[0038] In this embodiment, when calculating the contribution At that time, its reciprocal term It can accurately depict the boundary of molten aluminum approaching irreversible thermal failure (i.e., the lowest temperature of molten aluminum). Risk mutation characteristics at that time: When the temperature of the molten aluminum is much higher than Time (e.g.) The value of the reciprocal term is approximately 0.01, indicating a low thermal risk. When the temperature of the aluminum melt is close to Time (e.g.) The value of the reciprocal term increases sharply to about 1.0, indicating that the thermal risk has increased rapidly by nearly two orders of magnitude.

[0039] This nonlinear abrupt change characteristic originates directly from the aluminum melt temperature being lower than... This refers to the physical constraint of irreversible scrapping; the closer to the boundary, the greater the risk increment represented by each degree of temperature loss. Vehicle speed is a weighted factor. Risk exposure at low speeds is amplified further because low speed or waiting states mean the molten aluminum will remain at high temperatures for a longer period, with continuous radiative heat dissipation and a risk of prolonged arrival time. Step 5-3: The risk calculation module accumulates the risk exposure contribution of each segment along the entire path: ; Step 5-4: The risk calculation module uses the methods from Steps 5-1 to 5-3 to perform the above cumulative calculation on each candidate path to obtain the trajectory thermal risk exposure of each candidate path. .

[0040] Step 6: The state variable construction module retrieves the final arrival temperature of each candidate path. and trajectory thermal risk exposure Subsequently, the thermal-mechanical coupling state variables of molten aluminum were constructed. The specific construction method is as follows: First, for each candidate path j, the state variable construction module first calculates its final temperature margin. : ; Final temperature margin This is used to indicate the degree of excess temperature of the molten aluminum relative to the irreversible thermal scrap boundary when it arrives at the destination after transporting along candidate path j.

[0041] Subsequently, the state quantity construction module is based on the final temperature margin. and trajectory thermal risk exposure Constructing the thermal-physical coupling state variables of molten aluminum : ; in, The system has a preset risk penalty coefficient. . This is used to balance the relative weights of the two dimensions, "endpoint temperature margin" and "trajectory thermal risk exposure," in scheduling decisions.

[0042] In this embodiment, The value of is determined as follows: Several complete transportation tasks are selected from the factory's historical transportation records, and for each task, its corresponding value is calculated. and In different The system performs retrospective scheduling simulations on historical tasks, selecting the option that minimizes the "false positive rate". The value is used as a system parameter.

[0043] The false positive rate is specifically defined as either a situation where thermal failure actually occurs but is deemed feasible, or a situation where thermal failure does not actually occur but is deemed infeasible.

[0044] The determination process is completed before system deployment and can be calibrated periodically based on actual operating data.

[0045] In this embodiment, Used to reflect the thermal safety margin at the endpoint of the candidate path. The trajectory thermal risk exposure penalty is used to reflect the path.

[0046] For two candidate paths that ultimately arrive at the same temperature, if one of the candidate paths is closer during transportation... (Irreversible thermal failure boundary) or longer in a low-speed state, its corresponding The value will be larger, thus The smaller the value, the more likely the candidate path will be treated differently in subsequent scheduling decisions. This embodiment ensures that the system can not only determine "whether it can be reached," but also "what thermally safe trajectory to take to reach it."

[0047] The state variable construction module performs the above construction method on each candidate path in the candidate path set to obtain the aluminum liquid thermal-mechanical coupling state variables for each candidate path. Step 7: The scheduling decision module retrieves the thermal-physical coupling state variables of the molten aluminum for each candidate path. After the value is determined, a thermal feasibility assessment and vehicle-route joint selection are performed, which includes the following steps: Step 7-1: The scheduling decision module first performs a feasibility assessment on all candidate paths; the rule for feasibility assessment is: if a candidate path... If a path is found to be feasible, it is included in the optional set; otherwise, it is forcibly excluded from the optional set.

[0048] In this embodiment, if a candidate path If the route has a short transit time or the vehicles are close together, then this option will be forcibly excluded from the set of options. mean This means that the thermal risk exposure penalty of this path has exceeded its endpoint thermal margin, and choosing this path will expose the molten aluminum to the risk of irreversible thermal scrapping. This embodiment will... As a rigid constraint for thermal safety in aluminum molten material transportation, this corresponds to the physical requirement that "the temperature of the aluminum molten material upon arrival at its destination must not be lower than the minimum permissible temperature." Due to... This is an irreversible thermal failure boundary, and this constraint allows for no compromise. Step 7-2: The scheduling decision module performs a maximum value calculation on all feasible paths in the optional set to obtain the optimal choice. The specific formula is as follows: ; That is, choose to make Maximizing candidate paths This serves as the actual execution path for the current aluminum molten material transportation task.

[0049] In this embodiment, the system requires not only that the candidate path be thermally feasible ( It also requires selecting the path with the optimal overall thermal safety status. maximum). Taking the maximum value indicates that the corresponding candidate path has the best overall performance in the two dimensions of "end point thermal margin" and "trajectory risk exposure". In this embodiment, it can ensure that the irreversible thermal scrapping boundary is not exceeded while minimizing the thermal risk exposure during transportation.

[0050] After selecting the execution path, the scheduling decision module will issue scheduling instructions to the corresponding vehicle terminal or crane control system.

[0051] In this embodiment, the scheduling instruction includes at least: the selected vehicle number, route planning information, estimated departure time, and estimated arrival time.

[0052] In this embodiment, the intelligent packaging and dynamic scheduling method for the entire process of aluminum electrolysis runs in a scheduling system.

[0053] The scheduling system includes an aluminum liquid status acquisition module, a thermal status calculation module, a path segmentation module, a route calculation module, a risk calculation module, a state quantity construction module, and a scheduling decision module, all deployed on a central server.

[0054] The central server is connected via industrial Ethernet to: thermocouple temperature sensors and RFID readers at the bag inlet, aluminum outlet weighbridge, plant area GIS map server, plant area traffic management system, temperature sensor network deployed along the transportation route, vehicle terminal and crane control system.

[0055] The aluminum liquid status acquisition module acquires data from the thermocouple temperature sensor, RFID reader, and weighbridge via the industrial Ethernet; the path segmentation module acquires path and traffic data from the GIS map server and traffic management system via the industrial Ethernet; the road segment calculation module acquires ambient temperature data from the temperature sensor network via the industrial Ethernet; and the scheduling decision module sends scheduling instructions to the vehicle terminal or overhead crane control system via the industrial Ethernet.

[0056] The product coupling of the three factors of conduction, convection and radiation in the road section heat loss coefficient described in this embodiment, and the asymmetric coupling of the endpoint margin and trajectory risk exposure in the aluminum liquid heat-transport coupling state quantity, are not a simple superposition of the individual effects of the above physical factors or mathematical operations. The combined effect of the two enables the aluminum liquid heat-transport coupling state quantity to identify candidate schemes with the same endpoint temperature but different thermal safety trajectories. This identification capability cannot be achieved independently by any single physical factor.

[0057] This invention presents an intelligent packaging and dynamic scheduling method for the entire aluminum electrolysis process. It solves the technical problem of constructing a coupled thermal-transportation state variable for molten aluminum, accurately characterizing the risk mutation characteristics of molten aluminum approaching the irreversible thermal scrap boundary, and accurately obtaining vehicle scheduling decisions. This invention segments candidate transport paths according to their motion state, calculates the heat loss coefficient for each segment, and performs temperature recursion. This enables the scheduling system to distinguish the differentiated heat loss between "high-speed driving" and "low-speed / waiting" states, avoiding misjudgments of heat margin due to the erroneous assumption that "the same total time means the same thermal state." This invention constructs a trajectory thermal risk exposure quantity, which can non-linearly amplify the risk when molten aluminum approaches the minimum allowable temperature. By using a vehicle speed weighting function to additionally penalize risk exposure in low-speed states, it can accurately characterize the risk mutation characteristics of molten aluminum approaching the irreversible thermal scrap boundary. This invention constructs a coupled thermal-transportation state variable for molten aluminum, asymmetrically coupling the endpoint heat margin with the trajectory thermal risk exposure quantity. Even if two candidate paths reach the same final temperature, as long as their internal velocity distribution and ambient temperature sequence differ, resulting in different thermal risk exposures, the system can distinguish candidate schemes with the same "endpoint state" but different "trajectory thermal safety states." This invention uses the minimum allowable temperature of molten aluminum as the boundary for irreversible thermal scrapping, transforms it into a hard constraint for scheduling decisions through feasibility assessment, and screens candidate schemes, thus eliminating the conditions for irreversible thermal scrapping of molten aluminum from the scheduling decision level.

Claims

1. A method for intelligent batching and dynamic scheduling of the entire aluminum electrolysis process, characterized in that: Includes the following steps: Step 1: The aluminum liquid status acquisition module acquires the initial temperature, loading mass, ladle number, and lowest temperature of the current aluminum liquid. The initial dataset is constructed and transferred to the thermal state calculation module; Step 2: The thermal state calculation module queries the aging state data of the lifting package using the lifting package number as an index, calculates the aging correction coefficient and the current reference heat loss coefficient of the lifting package, and outputs it to the path segmentation and calculation module; Step 3: The route segmentation module retrieves candidate transportation routes from the vehicle dispatching system, divides the candidate transportation routes into multiple continuous road segments according to the segmentation criteria, constructs a candidate route set, and sends it to the road segment calculation module; Step 4: The road segment calculation module performs road segment heat loss coefficient calculation and temperature recursion on each candidate path in the candidate path set to obtain the final arrival temperature and aluminum liquid temperature sequence of each candidate path. Step 5: The risk calculation module retrieves the results obtained from the road segment calculation module, accumulates the thermal risk exposure contribution of each road segment along any of the candidate paths, obtains the trajectory thermal risk exposure amount of the candidate path, and calculates the trajectory thermal risk exposure amount of all the candidate paths. Step 6: The state quantity construction module is based on the lowest temperature of the molten aluminum. In addition to the final arrival temperature and trajectory thermal risk exposure of each candidate path, aluminum liquid thermal-transport coupling state quantities of each candidate path are constructed respectively. Step 7: The scheduling decision module selects the execution path based on the aluminum liquid thermal-transport coupling state of each candidate path and sends the result to the corresponding vehicle terminal or crane control system.

2. The intelligent batching and dynamic scheduling method for the entire aluminum electrolysis process as described in claim 1, characterized in that: The lowest temperature of the molten aluminum The irreversible thermal scrapping boundary represents the physical hard constraint boundary that the molten aluminum must not fall below.

3. The intelligent batching and dynamic scheduling method for the entire aluminum electrolysis process as described in claim 1, characterized in that: When performing step 2, the thermal state calculation module specifically performs the following steps: The aging status data of a bale can be retrieved by indexing its bale number. This aging data includes at least the bale's manufacturing material, wall thickness, and factory-calibrated baseline heat loss coefficient. Total number of uses Time since the last major overhaul ; Based on cumulative usage and the time since the last major overhaul Calculate the aging correction factor : ； in, and These are all system-preset aging parameters, and their values ​​are pre-calibrated based on the actual aging statistics of the bales in this factory area. Calculate the current reference heat loss coefficient for: 。 4. The intelligent batching and dynamic scheduling method for the entire aluminum electrolysis process as described in claim 1, characterized in that: When performing step 3, the road segment is characterized by its expected duration, expected average vehicle speed, and ambient temperature. The estimated duration of each road segment is determined by the segment length and the estimated average speed of the segment. The estimated average speed of each road segment is estimated from traffic condition data, and the ambient temperature of each road segment is provided by a sensor network.

5. The intelligent packaging and dynamic scheduling method for the entire aluminum electrolysis process as described in claim 1, characterized in that: When performing step 4, the heat loss coefficient calculation and temperature recursion processing performed by the road segment calculation module are as follows: The heat loss coefficient of the road section The calculation formula is: ； in, Let be the heat loss coefficient of the i-th road segment; The reference heat loss coefficient for lifting the package; Let be the expected average speed of the i-th road segment; Let be the temperature of the molten aluminum at the starting point of the i-th segment; Let i be the ambient temperature of the i-th road segment; and All are preset vehicle speed-convection coupling parameters; The method for temperature recursion processing is shown in the following formula: ； ； in, Let be the temperature of the molten aluminum at the starting point of the i-th segment. Let be the temperature of the molten aluminum at the end of the i-th segment. Let be the estimated duration of the i-th segment; To obtain the final arrival temperature of the candidate path after recursively extending to the Nth segment; The road segment calculation module is based on Construct a temperature sequence for molten aluminum.

6. The intelligent batching and dynamic scheduling method for the entire aluminum electrolysis process as described in claim 1, characterized in that: Step 5 includes the following steps: Step 5-1: The risk calculation module retrieves the final arrival temperature and aluminum liquid temperature sequence corresponding to all candidate road segments in the candidate path set obtained in Step 4; For candidate paths For the i-th road segment, the risk calculation module first uses the vehicle speed risk weight function to calculate the risk weight corresponding to the vehicle speed in that road segment. The specific formula for calculating the vehicle speed risk weight function is as follows: ； in, Let be the expected average speed of the i-th road segment; The reference speed is preset by the system based on the speed limits of the factory roads; This is a low-speed risk amplification factor. ; The decay rate coefficient, j represents the index of the candidate path in the candidate path set; Step 5-2: The risk calculation module calculates the current aluminum liquid temperature of the road section. Minimum allowable temperature Vehicle speed risk weight and the duration of the road segment Calculate the contribution of this road segment to the total thermal risk exposure of the entire trajectory. : ;in, The system's preset zero-removal constant, ; Step 5-3: The risk calculation module accumulates the risk exposure contribution of each segment along the entire path: ； Step 5-4: The risk calculation module uses the methods from Steps 5-1 to 5-3 to perform the above-mentioned cumulative calculation on each candidate path to obtain the trajectory thermal risk exposure of each candidate path in the candidate path set. And send it to the state quantity construction module.

7. The intelligent batching and dynamic scheduling method for the entire aluminum electrolysis process as described in claim 1, characterized in that: During step 6, the state variable construction module retrieves the final arrival temperature of each candidate path. and trajectory thermal risk exposure Subsequently, the thermal-mechanical coupling state variables of molten aluminum were constructed. The specific construction method is as follows: First, for each candidate path j, the state variable construction module first calculates its final temperature margin. : ； in, The final reached temperature; Final temperature margin Used to indicate the degree of margin of aluminum liquid temperature relative to the irreversible thermal scrap boundary when arriving at the destination after transporting according to candidate path j; Subsequently, the state quantity construction module is based on the final temperature margin. and trajectory thermal risk exposure Constructing the thermal-physical coupling state variables of molten aluminum : ； in, The system has a preset risk penalty coefficient. ; The state variable construction module performs the above construction method on each candidate path in the candidate path set to obtain the aluminum liquid thermal-mechanical coupling state variables for each candidate path. .

8. The intelligent batching and dynamic scheduling method for the entire aluminum electrolysis process as described in claim 1, characterized in that: During step 7, the scheduling decision module retrieves the thermal-transport coupling state of the molten aluminum for each candidate path. Then, choose to make Maximize and Candidate paths This serves as the actual execution path for the current aluminum molten material transportation task.

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