Battery heating and curing tunnel furnace temperature early warning method, device, storage medium and system

Abstract

The application discloses a temperature early warning method, device, storage medium and system of a battery heating and curing tunnel furnace, relates to the field of power batteries, and comprises the following steps: obtaining temperature time series data of at least one furnace area in the battery heating and curing tunnel furnace, wherein the temperature time series data comprises temperature data and corresponding time stamps; then the temperature time series data can be input into a pre-trained temperature early warning model for processing to generate an early warning signal, wherein the early warning signal is used to represent a trend that the temperature of a target furnace area in the at least one furnace area deviates from a target range; finally, the early warning signal can be used to generate temperature early warning information corresponding to the target furnace area, so that the temperature of the target furnace area can be adjusted in advance according to the temperature early warning information or a technician can maintain the battery heating and curing tunnel furnace. The temperature exceeding the upper limit value can be avoided to affect the quality of battery products, and the shutdown problem caused by the failure of the battery heating and curing tunnel furnace can be avoided.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and in particular to a temperature warning method, device, storage medium and system for a battery heating and curing tunnel oven. Background Technology

[0002] In the production of power batteries, they need to be heated in a battery heating and curing tunnel oven. This heating and curing process is a critical step, with the structural adhesive requiring high-temperature curing to rapidly develop structural strength and ensure battery reliability. This process demands extremely high temperature control. The precision of temperature control during heating and curing directly determines the consistency, safety, and final performance of the battery product. Excessively high or low temperatures, or excessive temperature fluctuations, can lead to defects in the battery product and even pose safety hazards.

[0003] In existing technology, the PLC collects data from the temperature sensors in each furnace zone, forming a temperature time series data, and sets upper and lower limits for the temperature specifications in the program. When the temperature data of a certain furnace zone exceeds the set range, the PLC sends a command to adjust the power of the heater in the corresponding zone, gradually correcting the temperature back to the specified range over several cycles.

[0004] Because temperature adjustment requires multiple cycles, the temperature in the target furnace area remains above specifications during the delay period, exposing the battery products to a poor processing environment. This temperature fluctuation can cause insufficient or excessive curing of the structural adhesive, affecting battery life and safety and leading to potential quality issues. Furthermore, the lag in the adjustment mechanism can easily cause unplanned downtime, impacting the production efficiency of the power batteries. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a temperature early warning method, device, storage medium, and system for a battery-heated curing tunnel oven.

[0006] In a first aspect, embodiments of this application provide a temperature warning method for a battery-heated curing tunnel oven, the method comprising: Temperature time-series data of at least one zone in a battery heating and curing tunnel oven is obtained. This data includes temperature values ​​and corresponding timestamps. The temperature time-series data is then input into a pre-trained temperature warning model for processing, generating a warning signal. This warning signal indicates a trend of temperature deviation from the target range in at least one zone. Finally, the warning signal is used to issue a temperature warning for the target zone, allowing for advance temperature adjustments or enabling technicians to perform maintenance on the battery heating and curing tunnel oven. This prevents temperatures from exceeding the upper limit, which could impact battery product quality, and avoids downtime caused by oven malfunctions.

[0007] Secondly, embodiments of this application provide a temperature warning device for a battery-heated curing tunnel oven, comprising: The acquisition unit is used to acquire temperature time series data of at least one furnace zone in the battery heating and curing tunnel furnace, wherein the temperature time series data includes temperature data and corresponding timestamps. The first generation unit is used to input the temperature time series data into a pre-trained temperature early warning model for processing and to generate an early warning signal. The early warning signal is used to characterize the trend of the temperature or temperature rise rate of the target furnace area in the at least one furnace area deviating from the target range. The second generation unit is used to generate temperature warning information corresponding to the target furnace area using the warning signal.

[0008] Thirdly, embodiments of this application provide a computing device, including: Memory; Processor; and Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in the first aspect.

[0009] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method as described in the first aspect.

[0010] Fifthly, embodiments of this application provide a vehicle, including: Memory; Processor; and Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in the first aspect.

[0011] In a sixth aspect, embodiments of this application also provide a computer program product, which includes a computer program or instructions that, when executed by a processor, implement the temperature warning method for a battery-heated curing tunnel oven as described above.

[0012] Seventhly, this embodiment provides a temperature warning system for a battery heating and curing tunnel oven, the system comprising: a programmable logic controller and a host computer; The programmable logic controller is used to collect temperature time-series data of at least one furnace zone in the battery heating and curing tunnel furnace; The host computer is used to execute any of the methods described in the first aspect.

[0013] This application has the following advantages over the prior art: This application can obtain temperature time-series data of at least one furnace zone in a battery heating and curing tunnel oven. The temperature time-series data includes temperature data and corresponding timestamps. This data can then be input into a pre-trained temperature early warning model for processing, generating an early warning signal. This signal indicates that the temperature of a target furnace zone in at least one furnace zone is deviating from a target range. Finally, the early warning signal can be used to issue a temperature warning for the target furnace zone, allowing for advance temperature adjustments or enabling technicians to perform maintenance on the battery heating and curing tunnel oven. This avoids the impact of temperatures exceeding the upper limit on battery product quality and prevents downtime caused by furnace malfunctions. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This application provides a flowchart of a temperature early warning method for a battery-heated curing tunnel oven. Figure 2 A flowchart of another temperature warning method for a battery-heated curing tunnel oven is provided for embodiments of this application; Figure 3 A schematic diagram of temperature time series data curves for the first type of battery-heated curing tunnel oven provided in this application embodiment; Figure 4 This is a schematic diagram of the temperature time series data curve of the second type of battery-heated curing tunnel oven provided in the embodiments of this application; Figure 5 A schematic diagram of the temperature time series data curve of the third type of battery-heated curing tunnel oven provided in the embodiments of this application; Figure 6 A schematic diagram of the temperature time series data curve of the fourth type of battery heating and curing tunnel oven provided in the embodiments of this application; Figure 7 This is a schematic diagram of the temperature warning device for the battery heating and curing tunnel oven provided in an embodiment of this application; Figure 8 A schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0017] To better understand the above-mentioned objectives, features, and advantages of this application, the solution of this application will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of this application, but this application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of this application, and not all embodiments.

[0019] The following is a description of the technical terms that may be used in this application: A battery heating and curing tunnel oven is a key heat treatment equipment used in the production and manufacturing of power batteries. Its core function is to heat and maintain the temperature of batteries (such as cells and electrodes) to complete key processes such as drying, curing coatings, and activating material properties. For example, after the battery electrodes are coated, the solvent in the slurry needs to be evaporated and cured.

[0020] A furnace zone refers to a physical and logical division of the long tunnel oven into multiple independent temperature control sections to achieve precise control of the temperature profile. A complete battery heating and curing process typically includes multiple stages, such as heating, holding, and cooling. The tunnel oven is correspondingly divided into multiple furnace zones, each of which can be independently set and maintained at different target temperatures. As the battery moves on the conveyor belt, passing through different furnace zones in sequence, it undergoes a pre-set, continuous temperature change process.

[0021] A Programmable Logic Controller (PLC) is responsible for directly connecting to and reading data from temperature sensors (such as thermocouples or resistance temperature detectors) distributed throughout the furnace zones. It can collect temperature time-series data, forming the foundational data source for early warning analysis. Once the host computer makes a decision, the PLC receives control commands and directly drives the execution devices (such as adjusting heater power, opening or closing dampers, etc.) to adjust the temperature of the target furnace zone in real time.

[0022] In the production of power batteries, they need to be heated in a battery heating and curing tunnel oven. This heating and curing process is a critical step, with the structural adhesive requiring high-temperature curing to rapidly develop structural strength and ensure battery reliability. This process demands extremely high temperature control. The precision of temperature control during heating and curing directly determines the consistency, safety, and final performance of the battery product. Excessively high or low temperatures, or excessive temperature fluctuations, can lead to defects in the battery product and even pose safety hazards.

[0023] In existing technologies, temperature detection in battery heating and curing tunnel ovens mainly relies on the collaborative work of a PLC and a host computer.

[0024] Specifically, the PLC collects data from the temperature sensors in each furnace zone, forming a temperature time series data, and sets upper and lower limits for the temperature specifications in the program. When the temperature data of a certain furnace zone exceeds the set range, the PLC sends a command to adjust the power of the heater in the corresponding zone, gradually correcting the temperature back to the specified range over several cycles.

[0025] Because temperature adjustment requires multiple cycles, the temperature in the target furnace area remains above specifications during the lag period, exposing battery products to a poor processing environment. This temperature fluctuation can lead to insufficient or excessive curing of the structural adhesive, potentially impacting battery life, safety, and consistency. Furthermore, the lag in the adjustment mechanism makes it impossible to predict equipment failures or abnormal temperature trends in advance, easily causing unplanned downtime and affecting the production efficiency and cost control of power batteries.

[0026] In view of this, this application provides a temperature early warning method for a battery heating and curing tunnel oven. The method includes obtaining temperature time-series data of at least one furnace zone within the oven, wherein the temperature time-series data includes temperature data and corresponding timestamps. The temperature time-series data is then input into a pre-trained temperature early warning model for processing to generate an early warning signal. This early warning signal characterizes a trend where the temperature of a target furnace zone deviates from a target range. This allows for the issuance of temperature early warning information for the target furnace zone, enabling timely temperature adjustments or maintenance by technicians. This method avoids the impact of temperatures exceeding upper limits on battery product quality and prevents downtime caused by oven malfunctions, thereby improving the production efficiency of power batteries and reducing their production cost.

[0027] This application provides a temperature early warning method for a battery-heated curing tunnel oven. The method will be described below with reference to specific embodiments.

[0028] Figure 1 This is a flowchart illustrating a temperature warning method for a battery-heated curing tunnel oven provided in an embodiment of this application. This method can be applied to a host computer.

[0029] It is understood that the temperature warning method for the battery heating and curing tunnel oven provided in this application embodiment can also be applied in other scenarios.

[0030] The following is about Figure 1 The temperature warning method for the battery heating and curing tunnel oven is described below. The method is provided as an example of execution in a computing device, which can be referred to as Example 1. The method provided in Example 1 may include the following steps: S101: Obtain temperature time series data for at least one furnace zone in a battery-heated curing tunnel oven.

[0031] The host computer can acquire temperature time-series data from at least one zone in the battery heating and curing tunnel oven via the PLC. This means it can acquire temperature time-series data for a single zone, a subset of zones, or even the individual temperature time-series data for each zone. Those skilled in the art can configure this according to actual needs; no limitations are imposed here. The temperature time-series data may include temperature data and a corresponding timestamp. For example, a timestamp of 30 minutes of heating corresponds to a temperature of 45°C. This is merely an example and not a limitation.

[0032] It should be noted that the host computer can obtain the temperature time series data of at least one furnace zone in the battery heating and curing tunnel oven in real time, or it can obtain the temperature time series data once every preset period, such as 5 minutes or 10 minutes. Those skilled in the art can also set it according to actual needs, which is not limited here.

[0033] S102: Input the temperature time series data into the pre-trained temperature early warning model for processing and generate an early warning signal.

[0034] After obtaining the temperature time series data, the host computer can input the temperature time series data into a pre-trained temperature early warning model for processing and generate an early warning signal. The early warning signal can be used to characterize that the temperature of the target furnace area in at least one furnace area is deviating from the target range, that is, it indicates that the predicted temperature data or temperature rise rate of the target furnace area is abnormal. The temperature early warning model can be a pre-trained neural network model.

[0035] S103: Generate temperature warning information corresponding to the target furnace area using the warning signal.

[0036] The host computer can use the warning signal to issue temperature warning information corresponding to the target furnace area.

[0037] This embodiment can obtain temperature time-series data of at least one furnace zone in a battery heating and curing tunnel oven. The temperature time-series data includes temperature data and corresponding timestamps. This data can then be input into a pre-trained temperature early warning model for processing, generating an early warning signal. This signal characterizes a trend where the temperature of a target furnace zone deviates from the target range. Finally, the early warning signal can be used to generate temperature early warning information for the target furnace zone. This allows for early warning of the temperature or temperature rise rate of the target furnace zone, enabling timely temperature adjustments or maintenance of the battery heating and curing tunnel oven by technicians. This avoids the impact of temperatures exceeding the upper limit on battery product quality and prevents downtime caused by furnace malfunctions.

[0038] This application also provides another method for temperature early warning of a battery-heated curing tunnel oven, such as... Figure 2 As shown, Figure 2 A flowchart of another temperature warning method for a battery-heated curing tunnel oven provided in this application is shown below. Figure 2 As shown, the method includes the following steps: S201: Obtain temperature time series data for at least one furnace zone in a battery-heated curing tunnel oven.

[0039] The host computer can obtain temperature time series data of at least one furnace zone in the battery heating and curing tunnel oven. This step is similar in principle to step S101 and will not be described in detail here.

[0040] S202: Input the temperature time series data into the pre-trained temperature early warning model for processing, and use the temperature early warning model to determine whether the temperature or temperature rise rate of the target furnace area deviates from the target range.

[0041] The host computer can input temperature time series data into a pre-trained temperature early warning model for processing. The model determines whether the temperature or temperature rise rate in the target furnace area deviates from the target range. Here, the temperature rise rate refers to the rate at which the temperature in the target furnace area of ​​the battery heating and curing tunnel furnace changes over time, i.e., the amount of temperature change per unit time. This is used to detect whether the temperature or temperature rise rate deviates from the target range through the temperature early warning model. Figure 4 The slope of the temperature time series data curve is shown below. The conditions for using a temperature early warning model to make judgments and generate early warning signals during the warming phase are described below.

[0042] In some possible implementations, temperature time-series data is input into a pre-trained temperature early warning model for processing. An early warning signal is generated if, during the first heating phase, the temperature data at at least two consecutive time points in the target furnace area exceed the corresponding target temperature range, or if, during the second heating phase, the temperature data at at least one time point in the target furnace area exceeds the corresponding target temperature range. This can be referred to as Example 2.

[0043] For example, to improve the timeliness and accuracy of early warnings and reduce false alarms, the warming phase can be divided into multiple sub-phases (e.g., 0-10 minutes as the third warming phase, 11-45 minutes as the first warming phase, and 46-60 minutes as the second warming phase) and differentiated judgments can be made.

[0044] In the third heating stage, the battery heating and curing tunnel oven has just started heating, such as Figure 3 As shown, the temperature time series data curve (the horizontal axis represents the timestamp of heating time, in minutes, for example, and the vertical axis represents the temperature data of the furnace area, in degrees Celsius, for example) is in a rapid rise phase. The temperature in the battery heating and curing tunnel furnace exhibits normal fluctuations and inertia during this stage. Therefore, the temperature warning model can be set not to detect anomalies during this phase. The purpose is to give the battery heating and curing tunnel furnace sufficient stabilization time, avoiding misjudging normal start-up fluctuations as abnormal trends, thereby preventing false alarms at the source and ensuring continuous and stable production. It should be noted that... Figure 3The upper limit of the temperature specification refers to the maximum temperature that the furnace zone in the battery heating and curing tunnel oven (called the high temperature curing oven in the attached diagram) is allowed to reach, for example, 60°C. The lower limit of the temperature specification refers to the minimum temperature that the furnace zone in the battery heating and curing tunnel oven is allowed to heat, for example, 50°C. That is, during the heat preservation stage, the temperature of the furnace zone needs to be maintained between the upper limit and the lower limit of the temperature specification.

[0045] After passing through the initial unstable phase, the temperature change should tend to rise steadily. Entering the first heating phase, a strict anomaly detection rule is employed for temperature warnings. That is, a warning signal is only generated when the temperature data at at least two consecutive time points within the target furnace area exceeds the corresponding target temperature range. It should be noted that those skilled in the art can set different target temperature ranges for data at different time points, thereby using the temperature warning model to determine whether a warning signal is generated only when the temperature data at at least two consecutive time points in the target furnace area exceeds the corresponding target temperature range. Here, exceeding the target temperature range means that the temperature is greater than the upper limit of the target temperature range or less than the lower limit of the target temperature range, i.e., the temperature is not within the corresponding temperature range.

[0046] For example, such as Figure 3 As shown, for example, taking the temperature data corresponding to the heating time of the target furnace area as 16, 21, 26, 31 and 36 minutes as an example for judgment, an early warning signal is generated only if the temperature data corresponding to each time stamp exceeds the corresponding target temperature range. It should be noted that the target temperature range corresponding to different time stamps can be different. For example, the target temperature range corresponding to 16 minutes can be 40-45℃, and the target temperature range corresponding to 26 minutes can be 45-50℃. Those skilled in the art can set it in advance according to actual needs, and it is not limited here.

[0047] This rule aims to confirm trends. It requires anomalies to be persistent, thereby effectively filtering out occasional, transient disturbances and ensuring that the captured anomalies represent real and stable trends, thus improving the accuracy of early warnings.

[0048] In the final stage of the heating process, the second heating stage, the temperature is very close to the upper limit of the set target temperature range, reaching a critical point of risk. At this point, any slight deviation could quickly lead to actual exceeding of the standard, directly impacting the quality of the battery product.

[0049] Therefore, the temperature warning model can be highly sensitive at this stage, that is, as long as the temperature data of at least one timestamp exceeds the corresponding target temperature range, a warning signal will be generated immediately.

[0050] In some possible ways of implementation, such as Figure 4 As shown, this application can also use the rate of temperature rise to determine whether a warning signal needs to be issued.

[0051] For example, temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate for at least two consecutive time intervals during the first heating stage, or if the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate for at least one time interval during the second heating stage, an early warning signal is generated. This can be referred to as Example 3.

[0052] For example, with T1 (e.g.) Figure 4 (Taking 11 minutes as an example) to T3 (for example) Figure 4 (Taking 28 minutes as an example) and T3 to T2 (for example) Figure 4 (Taking 46 minutes as an example) If the temperature rise rate of both time intervals exceeds or is less than the corresponding target temperature rise rate, an early warning signal is generated.

[0053] Compared to judgments based on static temperature ranges, the core advantage of using temperature rise rate for early warning lies in its ability to detect potential performance degradation of equipment earlier and more directly. Temperature ranges detect the result of temperature operation, while temperature rise rate detects the state of the heating process. The latter can more sensitively reflect temperature change trends, that is, more sensitively reflect essential issues such as whether the heater power output is normal and whether heat exchange efficiency has decreased. For example, when heater performance begins to degrade, the temperature value may still be within the target range, but the temperature rise rate will show a slowing trend first. This allows for the earlier identification of the nascent stage of equipment failure before it has a real impact on battery product quality, thus eliminating the fault before it occurs.

[0054] The following describes the conditions for using a temperature early warning model to make judgments and generate early warning signals during the heat preservation stage.

[0055] In some possible implementations, the host computer can input temperature time series data into a pre-trained temperature early warning model for processing, such as... Figure 5 As shown, an early warning signal is generated when the temperature data of the target furnace area exceeds the corresponding target temperature range for at least two consecutive time stamps during the heat preservation stage. The core advantage here is that by effectively filtering out false alarms caused by single-point fluctuations due to brief disturbances (such as furnace door opening and closing) during the heat preservation stage through continuous conditions, the accuracy of the early warning is significantly improved. This ensures that the alarm is only triggered when the anomaly is confirmed as a continuous trend, ultimately achieving early prediction of the risk of temperature exceeding specifications and avoiding quality hazards caused by the curing of battery products at excessively high or low temperatures. This can be referred to as Example 4.

[0056] In some possible implementations, the host computer can input temperature time series data into a pre-trained temperature early warning model for processing, such as... Figure 6 As shown, Figure 6China and Israel T1 (e.g.) Figure 4 (Taking 71 minutes as an example) to T3 (for example) Figure 4 (Taking 81 minutes as an example) and T3 to T2 (for example) Figure 4 Taking 91 minutes as an example, and two consecutive time intervals, if the temperature rise rate of the target furnace area exceeds or falls below the corresponding target temperature rise rate in at least two consecutive time intervals during the heat preservation stage, an early warning signal is generated. Compared to simply detecting temperature, the core advantage of the temperature rise rate-based early warning mechanism is its ability to reveal the potential degradation of the heating performance of the battery-heated curing tunnel furnace earlier. During the heat preservation stage, the ideal temperature rise rate should approach zero to maintain temperature stability. If the temperature rise rate (i.e., the slope of temperature change) in at least two consecutive time intervals within the target furnace area exceeds or falls below the corresponding target temperature rise rate, it directly indicates that the power output of the heating device has experienced a sustained abnormality. This detection method can keenly capture minute slope changes (such as upward or downward trends) in the heat preservation curve; these changes are leading indicators that the temperature is about to run out of control. By judging the rate trend through the temperature early warning model, an early warning can be issued before the temperature value exceeds the specified range, thereby significantly improving the accuracy of the early warning. This can be referred to as Example 5.

[0057] In some possible implementations, the temperature warning model can be trained in the following ways: First, the foundation of model training lies in acquiring a large amount of historical temperature time-series data. This data can be collected by a programmable logic controller (PLC) from various furnace zones of the tunnel furnace, containing continuous temperature data and its corresponding timestamps. This data is used for initial model training, enabling the model to learn the target temperature range that the normal temperature data of the target furnace zone should fall within during different process stages (such as the first heating stage, the second heating stage, and the holding stage), and the target temperature rise rate that the normal temperature rise rate should meet. The second crucial step, after the initial training of the temperature warning model is complete, involves continuous fault injection to verify and strengthen its warning capabilities. This stage inputs simulated abnormal data into the model. For example, simulating a situation where, during the first heating stage, at least two consecutive timestamps of temperature data exceed the corresponding target temperature range, or during the holding stage, at least two consecutive time intervals of temperature rise rates exceed or fall below the corresponding target temperature rise rate. Through this targeted abnormal data training, the temperature warning model can continuously optimize its internal parameters, ultimately becoming able to keenly detect trends in temperature or temperature rise rate deviating from the target range. This allows for the generation of accurate warning signals in advance during practical applications, achieving effective temperature warnings.

[0058] S203: If it is determined that the temperature or temperature rise rate of the target furnace area is deviating from the target range, the temperature warning information corresponding to the target furnace area is issued using the warning signal.

[0059] If the host computer determines that any of the above judgment conditions are met, it will determine that the temperature or temperature rise rate of the target furnace area is deviating from the target range. The temperature early warning model can generate an early warning signal, and then use the early warning signal to issue the corresponding temperature early warning information of the target furnace area, so that the alarm device can issue an alarm based on the temperature early warning information.

[0060] In some possible implementations, the host computer can also issue temperature regulation commands. These commands can be used to control the temperature of the target furnace area to decrease to a first target temperature if, during the heat preservation phase, the temperature data at at least two consecutive time points in the target furnace area exceeds the upper limit of the corresponding target temperature range, or if the temperature rise rate for at least two time intervals exceeds the target temperature rise rate. Alternatively, they can control the temperature of the target furnace area to increase to a second target temperature if, during the heat preservation phase, the temperature data at at least two consecutive time points in the target furnace area is less than the lower limit of the corresponding target temperature range, or if the temperature rise rate for at least two time intervals is less than the target temperature rise rate. Both the first and second target temperatures are located between the lower and upper limits of the temperature specifications. This autonomous regulation mechanism not only solves the quality problems caused by delays due to manual intervention but also avoids unplanned downtime caused by equipment failure. This can be referred to as Example 6.

[0061] In some possible implementations, after the host computer generates an early warning signal, it can present structured temperature early warning information and the corresponding early warning type to the operator on the display interface. For example, the specific target furnace area number where the anomaly occurred, the precise timestamp of the early warning trigger (e.g., 62 minutes after heating begins), and the stage (e.g., the heat preservation stage) can be clearly identified in the core early warning area of ​​the display interface. Subsequently, the specific nature of the abnormal parameters can be displayed in the parameter details area of ​​the display interface. For example, whether the temperature data exceeds the target temperature range or the temperature rise rate deviates from the target temperature rise rate, the degree of deviation between the actual value and the target range can be displayed in the form of numerical comparison. This significantly improves the relevant personnel's perception of the equipment status and greatly shortens the decision-making response time of the relevant personnel through accurate anomaly detection. This can be referred to as Example 7.

[0062] It should be noted that the communication methods involved in the above embodiments may include at least one of Embodiment 1, Embodiment 2, Embodiment 3, Embodiment 4, Embodiment 5, Embodiment 6, and Embodiment 7. For example, Embodiment 1 can be implemented as an independent embodiment, Embodiment 1+2 can be implemented as an independent embodiment, Embodiment 1+2+3 can be implemented as an independent embodiment, Embodiment 1+2+3+4 can be implemented as an independent embodiment, Embodiment 1+2+3+4+5 can be implemented as an independent embodiment, Embodiment 1+2+3+4+5+6 can be implemented as an independent embodiment, Embodiment 1+2+3+4+5+6+7 can be implemented as an independent embodiment, Embodiment 1+5 can be implemented as an independent embodiment, Embodiment 1+5+6 can be implemented as an independent embodiment, and Embodiment 1+5+6+7 can be implemented as an independent embodiment. Of course, this is just an example and is not limited to this, and will not be listed in detail here.

[0063] Figure 7 This is a schematic diagram of the structure of a temperature warning device for a battery-heated curing tunnel oven provided in an embodiment of this application. The temperature warning device for the battery-heated curing tunnel oven can be a computing device as described in the above embodiment, or it can be a component or assembly within that computing device. The temperature warning device for the battery-heated curing tunnel oven provided in this embodiment can execute the processing flow provided in the embodiment of the temperature warning method for the battery-heated curing tunnel oven, such as... Figure 7 As shown: The device includes: The acquisition unit 7001 is used to acquire temperature time series data of at least one furnace zone in the battery heating and curing tunnel furnace, wherein the temperature time series data includes temperature data and corresponding timestamps. The first generation unit 7002 is used to input the temperature time series data into a pre-trained temperature early warning model for processing and to generate an early warning signal. The early warning signal is used to characterize the trend that the temperature or temperature rise rate of the target furnace area in the at least one furnace area deviates from the target range. The second generation unit 7003 is used to generate temperature warning information corresponding to the target furnace area using the warning signal.

[0064] Optionally, the first generating unit is specifically used for: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature data of at least two consecutive times in the target furnace area exceeds the corresponding target temperature range during the first heating stage, or if the temperature data of at least one times in the target furnace area exceeds the corresponding target temperature range during the second heating stage, the early warning signal is generated.

[0065] Optionally, the first generating unit is specifically used for: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate in at least two consecutive time intervals during the first heating stage, or if the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate in at least one time interval during the second heating stage, the early warning signal is generated.

[0066] Optionally, the first generating unit is specifically used for: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature data of at least two consecutive timestamps in the target furnace area during the heat preservation stage exceed the corresponding target temperature range, the early warning signal is generated.

[0067] Optionally, the first generating unit is specifically used for: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate in at least two consecutive time intervals during the heat preservation stage, the early warning signal is generated.

[0068] Optionally, the device further includes: The issuing unit is used to issue a temperature adjustment command, which is used to control the temperature of the target furnace area to decrease to a first target temperature when the temperature data of the target furnace area at least two consecutive timestamps during the heat preservation stage exceeds the upper limit of the corresponding target temperature range or the temperature rise rate of at least two time intervals exceeds the target temperature rise rate; or to control the temperature of the target furnace area to increase to a second target temperature when the temperature data of the target furnace area at least two consecutive timestamps during the heat preservation stage is less than the lower limit of the corresponding target temperature range or the temperature rise rate of at least two time intervals is less than the target temperature rise rate.

[0069] Optionally, the device further includes: The display unit is used to display the temperature warning information and the corresponding temperature warning type on the display interface.

[0070] Figure 7 The temperature warning device of the battery heating curing tunnel oven shown in the embodiment can be used to implement the technical solution of the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0071] The host computer of this application is described below. The host computer can be, for example, a computing device.

[0072] Figure 8 This is a schematic diagram of a computing device according to an embodiment of this application. See below for details. Figure 8It shows a schematic diagram of a structure suitable for implementing the computing device 700 in the embodiments of this application. Figure 8 The computing device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0073] like Figure 8 As shown, the computing device 700 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703 to implement the temperature warning method for a battery-heated curing tunnel oven as described in the embodiments of this application. The RAM 703 also stores various programs and data required for the operation of the computing device 700. The processing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0074] Typically, the following devices can be connected to the I / O interface 705: input devices 706 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 707 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 708 including, for example, magnetic tape, hard disk, etc.; and a temperature warning device 709 for the battery-heated curing tunnel oven. The temperature warning device 709 allows the computing device 700 to exchange data with other devices via wireless or wired communication regarding the temperature warning of the battery-heated curing tunnel oven. Although Figure 6 A computing device 700 with various devices is shown; however, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0075] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program including program code for performing the methods shown in the flowcharts, thereby implementing the temperature warning method for a battery-heated curing tunnel oven as described above. In such embodiments, the computer program can be downloaded and installed from a network, or installed from a storage device 708, or installed from a ROM 702 via the temperature warning device 709 for the battery-heated curing tunnel oven. When the computer program is executed by the processing device 701, it performs the functions defined in the methods of the embodiments of this application.

[0076] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium that includes or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code included on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0077] Additionally, this application also provides a vehicle, including: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the temperature warning method for the battery heating curing tunnel oven as described above.

[0078] In some implementations, the client and server can utilize any currently known or future-developed network protocol, such as HTTP (Hypertext Transfer Protocol), for temperature warnings of the battery-heated curing tunnel oven, and can interconnect with digital data temperature warning systems for battery-heated curing tunnel ovens of any form or medium (e.g., a temperature warning network for the battery-heated curing tunnel oven). Examples of temperature warning networks for battery-heated curing tunnel ovens include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0079] The aforementioned computer-readable medium may be included in the aforementioned computing device; or it may exist independently and not assembled into the computing device.

[0080] The aforementioned computer-readable medium carries one or more programs that, when executed by the computing device, cause the computing device to: Obtain temperature time-series data for at least one furnace zone in a battery heating and curing tunnel oven. The temperature time-series data includes temperature data and corresponding timestamps. Temperature time series data is input into a pre-trained temperature early warning model for processing to generate an early warning signal. The early warning signal is used to characterize the trend of temperature or temperature rise rate in at least one furnace area deviating from the target range. The system uses early warning signals to issue temperature warnings for the target furnace area. Optionally, when one or more of the above-described programs are executed by the computing device, the computing device may also execute other steps described in the above embodiments.

[0081] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0083] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

[0084] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0085] In the context of this application, a machine-readable medium can be a tangible medium that may include or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include, based on an electrical connection of one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0086] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0087] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. Multitasking and parallel processing may be advantageous in certain environments. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in at least one embodiment.

[0088] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A temperature early warning method for a battery-heated curing tunnel oven, characterized in that, The method includes: Obtain temperature time-series data for at least one furnace zone in a battery heating and curing tunnel oven, wherein the temperature time-series data includes temperature data and corresponding timestamps; The temperature time series data is input into a pre-trained temperature early warning model for processing to generate an early warning signal. The early warning signal is used to characterize the trend of the temperature or temperature rise rate of the target furnace area in the at least one furnace area deviating from the target range. The warning signal is used to generate temperature warning information corresponding to the target furnace area.

2. The method according to claim 1, characterized in that, The temperature time series data is input into a pre-trained temperature early warning model for processing to generate an early warning signal, including: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature data of at least two consecutive timestamps in the target furnace area exceeds the corresponding target temperature range during the first heating stage, or if the temperature data of at least one timestamp in the target furnace area exceeds the corresponding target temperature range during the second heating stage, the early warning signal is generated.

3. The method according to claim 1, characterized in that, The temperature time series data is input into a pre-trained temperature early warning model for processing to generate an early warning signal, including: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate in at least two consecutive time intervals during the first heating stage, or if the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate in at least one time interval during the second heating stage, the early warning signal is generated.

4. The method according to claim 1, characterized in that, The temperature time series data is input into a pre-trained temperature early warning model for processing to generate an early warning signal, including: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature data of at least two consecutive timestamps in the target furnace area during the heat preservation stage exceed the corresponding target temperature range, the early warning signal is generated.

5. The method according to claim 1, characterized in that, The temperature time series data is input into a pre-trained temperature early warning model for processing to generate an early warning signal, including: The temperature time series data is input into a pre-trained temperature early warning model for processing. If the temperature rise rate of the target furnace area exceeds or is less than the corresponding target temperature rise rate in at least two consecutive time intervals during the heat preservation stage, the early warning signal is generated.

6. The method according to claim 4 or 5, characterized in that, The method further includes: Based on the temperature warning information, a temperature adjustment command is issued. The temperature adjustment command is used to control the temperature of the target furnace area to decrease to a first target temperature when the temperature data of the target furnace area at least two consecutive times during the heat preservation stage exceeds the upper limit of the corresponding target temperature range or the temperature rise rate of at least two time intervals exceeds the target temperature rise rate; or to control the temperature of the target furnace area to increase to a second target temperature when the temperature data of the target furnace area at least two consecutive times during the heat preservation stage is less than the lower limit of the corresponding target temperature range or the temperature rise rate of at least two time intervals is less than the target temperature rise rate.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: The temperature warning information and the corresponding temperature warning type are displayed on the display interface.

8. A computing device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-7.

10. A computer program product comprising a computer program or instructions that, when executed by a processor, implement the method as described in any one of claims 1-7.

11. A temperature early warning system for a battery-heated curing tunnel oven, characterized in that, The system includes: a programmable logic controller and a host computer; The programmable logic controller is used to collect temperature time-series data of at least one furnace zone in the battery heating and curing tunnel furnace; The host computer is used to execute the method as described in any one of claims 1-7.

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