A waste recycling method, system, device and storage medium

Abstract

The present application provides a waste recycling method, system, device and storable medium. The method obtains a positive electrode test image sequence and a negative electrode test image sequence by acquisition; extracts the electrode recovery area in the positive electrode test image sequence and the negative electrode test image sequence and performs recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode; when the feature difference is higher than a preset threshold, extracts the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence, respectively, and determines the positive and negative electrode difference model in the battery electrode recovery process based on all the contrast quantization coefficients; performs machine learning recovery prediction based on the positive and negative electrode difference model to obtain a predicted temperature value, and controls the battery electrode recovery process according to the predicted temperature value, thereby reducing the influence of temperature on the battery electrode waste recovery and improving the battery electrode waste recovery efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of battery waste recycling, and more specifically, to a waste recycling method, system, device and storable medium, such as terminal equipment, chips, computer storage media, etc. Background Art

[0002] The internal structure of lithium-ion batteries is complex and tight, and the efficiency of directly recovering valuable metals from battery electrodes is very low. In addition, other components of lithium-ion batteries contain a variety of toxic substances, which are highly corrosive and polluting. If they are not effectively treated before recycling, the substances in the battery will enter the environment and cause heavy metal nickel, cobalt pollution, fluorine pollution, organic pollution, dust and acid-base pollution. Among them, the recycling of waste metal foil in battery electrodes has great economic value.

[0003] When recycling lithium-ion batteries, the recycling temperature has a great influence on the recycling efficiency and recycling quality. Therefore, in order to improve the recycling rate of battery waste such as aluminum foil and copper foil, it is often necessary to set up a control group to conduct long-term experiments to detect the optimal recycling temperature of the battery. This method is inefficient and has a long result cycle, which is difficult to meet the needs of modern battery waste recycling. As a result, when recycling battery pole pieces, the recycling rate of waste is very low, which greatly restricts the development of battery pole piece industrial recycling. Summary of the invention

[0004] The present application provides a waste recycling method, system, equipment and storable medium, which can reduce the influence of temperature on the recycling of battery pole piece waste and improve the recycling efficiency of battery pole piece waste.

[0005] In a first aspect, the present application provides a waste recycling method, which can be executed by a network device, or can also be executed by a chip configured in the network device, and the present application does not limit this.

[0006] Specifically, the method includes:

[0007] After performing a temperature rise test on the positive battery test electrode piece and the negative battery test electrode piece, thermal imaging images of the positive battery test electrode piece and the negative battery test electrode piece are respectively collected within a preset period to obtain a positive electrode piece test image sequence and a negative electrode piece test image sequence;

[0008] Extracting the electrode recovery areas in the positive electrode test image sequence and the negative electrode test image sequence and performing recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode;

[0009] When the characteristic difference is higher than a preset threshold, the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence are extracted, and the positive and negative electrode difference model in the battery electrode recycling process is determined based on all the contrast quantization coefficients;

[0010] The recycling prediction of the positive and negative electrode difference model is performed based on machine learning to obtain a predicted temperature value, and the recycling process of the battery electrode is controlled according to the predicted temperature value.

[0011] In conjunction with the first aspect, in certain implementations of the first aspect, performing a temperature rise test on a positive battery test electrode piece and a negative battery test electrode piece specifically includes:

[0012] According to a preset reference temperature, a low temperature test is performed on the positive battery test electrode in the first test area;

[0013] According to the preset reference temperature and temperature range, a temperature rise test is performed on the negative battery test electrode in the second test area.

[0014] In combination with the first aspect, in certain implementations of the first aspect, extracting the electrode recovery area in the positive electrode test image sequence and the negative electrode test image sequence and performing recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode specifically includes:

[0015] Performing background separation and enhancement processing on the positive electrode sheet test image sequence to obtain a positive electrode sheet enhanced image;

[0016] Performing background separation on the negative electrode sheet test image sequence to obtain a negative electrode sheet enhanced image set;

[0017] performing differential processing on each negative electrode enhanced image in the negative electrode enhanced image set according to the positive electrode enhanced image to obtain a difference image set;

[0018] Determine the feature smoothing coefficients corresponding to the respective difference images in the difference image set;

[0019] The feature difference degree is determined according to the feature smoothing coefficients corresponding to each difference image.

[0020] In combination with the first aspect, in certain implementations of the first aspect, background separation and enhancement processing are performed on the positive electrode sheet test image sequence to obtain the positive electrode sheet enhanced image, which specifically includes:

[0021] Performing edge detection on the positive electrode thermal imaging image in the positive electrode sheet test image sequence to obtain a plurality of electrode sheet separation images;

[0022] The plurality of pole piece separation images are aligned according to the image features, and the aligned plurality of pole piece separation images are enhanced to obtain a positive pole piece enhanced image.

[0023] In combination with the first aspect, in some implementations of the first aspect, extracting the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence, respectively, and determining the positive and negative electrode difference model in the battery electrode recycling process based on all the contrast quantization coefficients specifically includes:

[0024] Extracting the contrast quantization coefficient and the corresponding temperature label value respectively corresponding to each positive electrode thermal imaging image in the positive electrode sheet test image sequence;

[0025] Extracting the contrast quantization coefficient and the corresponding temperature label value respectively corresponding to each negative electrode thermal imaging image in the negative electrode sheet test image sequence;

[0026] According to the contrast quantization coefficients corresponding to each positive electrode thermal imaging image and the contrast quantization coefficients corresponding to each negative electrode thermal imaging image and the temperature label value, a positive and negative electrode difference model is obtained.

[0027] In combination with the first aspect, in certain implementations of the first aspect, performing machine learning recovery prediction based on the positive and negative electrode difference model to obtain the predicted temperature value specifically includes:

[0028] Obtain the positive and negative pole difference model, extract the comparison quantization coefficient difference values ​​at different temperatures in the positive and negative pole difference model for autoregressive prediction, and obtain multiple prediction deviations and their corresponding temperature labels;

[0029] A temperature label corresponding to the maximum prediction deviation is determined as the predicted temperature value.

[0030] In combination with the first aspect, in certain implementations of the first aspect, when the characteristic difference is lower than a preset threshold, the battery pole pieces are recycled as waste according to a preset reference temperature.

[0031] In a second aspect, the present application provides a waste recycling system, the waste recycling system comprising:

[0032] An acquisition module is used to acquire thermal imaging images of the positive battery test electrode piece and the negative battery test electrode piece respectively within a preset period after performing a temperature rise test on the positive battery test electrode piece and the negative battery test electrode piece to obtain a positive electrode piece test image sequence and a negative electrode piece test image sequence;

[0033] A processing module, used for extracting the electrode recovery area in the positive electrode test image sequence and the negative electrode test image sequence and performing recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode;

[0034] The processing module is further configured to extract the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence respectively when the feature difference is higher than a preset threshold, and determine the positive and negative electrode difference model in the battery electrode recycling process based on all contrast quantization coefficients;

[0035] The execution module is used to perform machine learning recycling prediction on the positive and negative electrode difference model to obtain a predicted temperature value, and control the recycling process of the battery electrode according to the predicted temperature value.

[0036] In a third aspect, the present application provides a computer terminal device, comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned waste recycling method.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned waste recycling method.

[0038] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0039] In a waste recycling method, system, equipment and storable medium provided by the present application, first, after a temperature test is performed on a positive battery test electrode and a negative battery test electrode, thermal imaging images of the positive battery test electrode and the negative battery test electrode are respectively collected within a preset period to obtain a positive electrode test image sequence and a negative electrode test image sequence; the electrode recovery areas in the positive electrode test image sequence and the negative electrode test image sequence are extracted and recycling area feature comparison is performed to obtain the feature difference between the positive battery test electrode and the negative battery test electrode; when the feature difference is higher than a preset threshold, the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence are extracted, and the positive and negative electrode difference model in the battery electrode recycling process is determined based on all the contrast quantization coefficients; machine learning recycling prediction is performed based on the positive and negative electrode difference model to obtain a predicted temperature value, and the battery electrode recycling process is controlled according to the predicted temperature value.

[0040] Therefore, it can be seen that the present application collects thermal imaging image sequences of positive and negative electrodes and extracts recycling area features, compares feature differences, and realizes accurate quantification of waste separation behaviors in positive and negative electrodes under temperature conditions. When the feature difference is higher than a preset threshold, the positive and negative electrode difference model in the battery electrode recycling process is determined based on the comparison quantification coefficient. The model analyzes the comparison quantization coefficients of the positive and negative electrodes at different temperatures to capture the nonlinear effect of temperature changes on recycling performance, and reveals the overall effect of different temperatures on the characteristics of the positive and negative electrodes during the waste separation process, avoiding inefficient recycling caused by misjudgment of a single temperature point. Based on the positive and negative electrode difference model, a machine learning method is used to predict the optimal recycling temperature value, so that the system can dynamically adapt to different temperature conditions, avoid low separation efficiency caused by inappropriate temperature conditions, and reduce the interference of temperature changes on the separation process of adhesives and waste foils, thereby improving the waste recycling efficiency of battery electrodes.

[0041] In summary, the present application performs machine learning recycling prediction based on the positive and negative electrode difference model to obtain the predicted temperature value, and controls the recycling process of the battery electrode according to the predicted temperature value, thereby reducing the impact of temperature on the recycling of battery electrode waste and improving the recycling efficiency of battery electrode waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is an exemplary flow chart of a waste recycling method according to some embodiments of the present application;

[0043] Figure 2 is an exemplary flow chart for determining a positive and negative electrode difference model in some embodiments of the present application;

[0044] Figure 3 is a schematic diagram of a waste recycling system according to some embodiments of the present application;

[0045] Figure 4 It is a structural schematic diagram of a computer terminal device for implementing a waste recycling method according to some embodiments of the present application. DETAILED DESCRIPTION

[0046] The present application first performs a temperature test on a positive battery test electrode and a negative battery test electrode, and then collects thermal imaging images of the positive battery test electrode and the negative battery test electrode respectively within a preset period to obtain a positive electrode test image sequence and a negative electrode test image sequence; extracts the electrode recovery areas in the positive electrode test image sequence and the negative electrode test image sequence and performs a recovery area feature comparison to obtain a feature difference between the positive battery test electrode and the negative battery test electrode; when the feature difference is higher than a preset threshold, extracts the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence, respectively, and determines the positive and negative electrode difference model in the battery electrode recovery process based on all the contrast quantization coefficients; performs machine learning recovery prediction based on the positive and negative electrode difference model to obtain a predicted temperature value, and controls the battery electrode recovery process according to the predicted temperature value, thereby reducing the influence of temperature on the recovery of battery electrode waste and improving the battery electrode waste recovery efficiency.

[0047] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 , which is an exemplary flow chart of a waste recycling method according to some embodiments of the present application, the waste recycling method 100 mainly includes the following steps:

[0048] In step S101, after the positive battery test electrode and the negative battery test electrode are subjected to a temperature rise test, thermal imaging images of the positive battery test electrode and the negative battery test electrode are respectively collected within a preset period to obtain a positive electrode test image sequence and a negative electrode test image sequence.

[0049] In some embodiments, performing a temperature rise test on a positive battery test electrode sheet and a negative battery test electrode sheet specifically includes:

[0050] According to a preset reference temperature, a low temperature test is performed on the positive battery test electrode in the first test area;

[0051] According to the preset reference temperature and temperature range, a temperature rise test is performed on the negative battery test electrode in the second test area.

[0052] In specific implementation, performing a low-temperature test on the positive battery test electrode in the first test area specifically includes: controlling the temperature of the first test area where the positive battery test electrode is located to be a reference temperature within a preset time period; performing a temperature increase test on the negative battery test electrode in the second test area based on the preset reference temperature and variable temperature range specifically includes: controlling the temperature of the second test area where the negative battery test electrode is located to change from the reference temperature to the upper limit of the temperature of the variable temperature range within a preset time period.

[0053] It should be noted that the recycling of waste battery pole pieces is mainly carried out by roasting the battery pole pieces so that the adhesive between the active material of the battery and the surface waste melts, thereby completing the separation between the active material of the battery and the surface waste. By testing under slow heating conditions, compared with the heat conduction absorption of the battery area under slow heating conditions, the large amount of heat that the adhesive needs to absorb due to melting can be captured by thermal imaging. Therefore, it can be tested under low temperature conditions and the temperature change characteristics of the battery pole piece area can be captured by thermal imaging to investigate the recycling effect of the battery pole piece. In this application, a heating test is performed based on the different heat absorption capacities of the adhesive and the waste. The negative battery test pole piece is tested at a low temperature according to the reference temperature and the variable temperature interval within a fixed time, and the thermal imaging image is compared with the heating test of the positive battery test pole piece of the same battery, so as to determine the influence of different temperatures on the temperature absorption of the adhesive. For example, the greater the temperature difference between the adhesive area and the non-adhesive area at this temperature, the stronger the heat absorption capacity of the adhesive at this temperature, and the better the recycling effect of the waste. Optionally, in some embodiments, the upper and lower limits of the reference temperature and the variable temperature interval are calibrated as constants based on historical experience.

[0054] In specific implementation, the first test area and the second test area can be two independent internal spaces of high-temperature ovens. In some embodiments, the first test area and the second test area can also be the internal space of a rotary calcining kiln commonly used in the prior art for recycling battery pole pieces, or can be other devices or equipment that can realize temperature rise testing according to a preset temperature, which is not limited here.

[0055] It should be noted that the positive electrode test image sequence is a surface thermal imaging image of the positive battery test electrode during the low-temperature test process. In some embodiments, a thermal imaging camera located above the first waste recovery space can be used to capture the positive electrode test image sequence of the positive battery test electrode. In some other embodiments, other devices or equipment capable of thermal imaging image capture can also be used. There is no limitation here. It should be noted that each negative electrode thermal imaging image in the negative electrode test image sequence has a corresponding acquisition time and acquisition temperature.

[0056] In step S102, the electrode recovery areas in the positive electrode test image sequence and the negative electrode test image sequence are extracted and feature comparison of the recovery areas is performed to obtain feature differences between the positive battery test electrode and the negative battery test electrode.

[0057] It should be noted that during the separation process between the battery active material on the surface of the battery electrode and the surface waste, since the melting of the adhesive needs to absorb a large amount of heat locally, the temperature of different areas on the surface of the battery electrode can be monitored to indirectly detect the waste recovery situation in different areas on the surface of the battery electrode. The characteristic difference in this application is a quantitative value of the regional characteristic difference of waste recovery at different temperatures, which is used to reflect the influence of temperature on the separation of the waste surface.

[0058] Optionally, in some embodiments, extracting the electrode recovery area in the positive electrode test image sequence and the negative electrode test image sequence and performing recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode specifically includes:

[0059] Performing background separation and enhancement processing on the positive electrode sheet test image sequence to obtain a positive electrode sheet enhanced image;

[0060] Performing background separation on the negative electrode sheet test image sequence to obtain a negative electrode sheet enhanced image set;

[0061] performing differential processing on each negative electrode enhanced image in the negative electrode enhanced image set according to the positive electrode enhanced image to obtain a difference image set;

[0062] Determine the feature smoothing coefficients corresponding to the respective difference images in the difference image set;

[0063] The feature difference degree is determined according to the feature smoothing coefficients corresponding to each difference image.

[0064] It should be noted that, since the positive electrode thermal imaging image and the negative electrode thermal imaging image are battery electrode images in the same battery, the positive electrode electrode enhanced image and the negative electrode electrode enhanced image obtained after background separation of the positive electrode thermal imaging image and the negative electrode thermal imaging image have the same pixel size. Therefore, each negative electrode electrode enhanced image in the negative electrode electrode enhanced image set can be image-differentiated with the positive electrode electrode enhanced image to obtain difference images corresponding to each negative electrode electrode enhanced image, and the difference image set is composed in sequence, wherein the image difference between the negative electrode electrode enhanced image and the positive electrode electrode enhanced image specifically includes: performing differential processing on the grayscale values ​​of the same coordinate pixel points of the negative electrode electrode enhanced image and the positive electrode electrode enhanced image to obtain a reconstructed image as the difference image.

[0065] In a specific implementation, the standard deviation of the grayscale values ​​of the pixels in the difference image may be used as the characteristic smoothing coefficient of the difference image, and the mean of the characteristic smoothing coefficients corresponding to each difference image may be used as the characteristic difference degree.

[0066] Optionally, in some embodiments, performing background separation and enhancement processing on the positive electrode sheet test image sequence to obtain the positive electrode sheet enhanced image specifically includes:

[0067] Performing edge detection on the positive electrode thermal imaging image in the positive electrode sheet test image sequence to obtain a plurality of electrode sheet separation images;

[0068] The plurality of pole piece separation images are aligned according to the image features, and the aligned plurality of pole piece separation images are enhanced to obtain a positive pole piece enhanced image.

[0069] It should be noted that the pole piece separation image is an image of a partial area of ​​the positive pole piece image after the background is separated from the positive electrode thermal imaging image. In specific implementation, the SIFT image feature extraction algorithm can be used to extract feature points from each pole piece separation image, such as the edge points of the battery pole piece, so as to achieve image alignment and then enhance the image, which specifically includes: taking the mean of the grayscale values ​​of the pixels at the same position in each aligned pole piece separation image as the grayscale value of the pixels at that position in the reconstructed image to achieve enhancement, and the reconstructed image is the positive pole piece enhanced image.

[0070] In some embodiments, performing background separation on the negative electrode sheet test image sequence to obtain a negative electrode sheet enhanced image set specifically includes: performing edge detection on the positive electrode thermal imaging image in the positive electrode sheet test image sequence to obtain a negative electrode sheet enhanced image set.

[0071] It should be noted that the negative electrode enhanced image in the negative electrode enhanced image is an image of a partial area that only contains the negative electrode image after background separation. In specific implementation, the Canny edge detection algorithm commonly used in the prior art can be used to perform edge detection on the positive electrode test image sequence and the negative electrode test image sequence, thereby realizing separation of the background area and obtaining a regional sub-image that only contains the target object.

[0072] In step S103, when the feature difference is higher than a preset threshold, the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence are extracted, and the positive and negative electrode difference model in the battery electrode recycling process is determined based on all the contrast quantization coefficients.

[0073] The positive-negative electrode difference model in the present application is a difference function of the comparison quantization coefficients of the positive and negative electrode sheets at different temperatures. In specific implementation, the positive-negative electrode difference model can be a nonlinear function model, in which the independent variable of the function is the temperature value, and the dependent variable of the function is the difference value of the comparison quantization coefficients of the positive and negative electrode sheets at the temperature.

[0074] It should be noted that when the characteristic difference is higher than the preset threshold, it indicates that the temperature has a greater impact on the waste separation of the battery electrode. At this time, it is necessary to perform sensitive temperature extraction on the positive and negative electrode difference model of the battery, and control the waste recovery temperature of the battery electrode to be processed according to the predicted temperature value, so as to avoid the influence of excessively high or low temperature on the battery waste extraction process and improve the recycling rate of battery waste.

[0075] It should be noted that when the characteristic difference is lower than the preset threshold, it indicates that the temperature has little effect on the waste separation of the battery electrode. Therefore, in some embodiments, when the characteristic difference is lower than the preset threshold, the battery electrode can be recycled according to the preset reference temperature.

[0076] In some embodiments, reference Figure 2 As shown, this figure is an exemplary flow chart for determining the positive and negative electrode difference model in some embodiments of the present application, extracting the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence, and determining the positive and negative electrode difference model in the battery electrode recycling process based on all contrast quantization coefficients specifically includes:

[0077] In step S1031, the contrast quantization coefficient and the corresponding temperature label value corresponding to each positive electrode thermal imaging image in the positive electrode sheet test image sequence are extracted;

[0078] In step S1032, the contrast quantization coefficient and the corresponding temperature label value corresponding to each negative electrode thermal imaging image in the negative electrode sheet test image sequence are extracted;

[0079] In step S1033, fitting is performed according to the contrast quantization coefficients corresponding to each positive electrode thermal imaging image and the contrast quantization coefficients corresponding to each negative electrode thermal imaging image and the temperature label value to obtain a positive-negative electrode difference model.

[0080] In a specific implementation, the deviation of the image grayscale mean between the pole area part and the background part in the positive thermal imaging image can be used as the contrast quantization coefficient corresponding to the positive thermal imaging image, and the deviation of the image grayscale mean between the pole area part and the background part in the negative thermal imaging image can be used as the contrast quantization coefficient corresponding to the negative thermal imaging image. The deviation between the contrast quantization coefficient of the positive thermal imaging image and the contrast quantization coefficient of the negative thermal imaging image corresponding to the same moment is fitted according to the time series to obtain the positive-negative difference model.

[0081] It should be noted that the difference in the comparison quantization coefficients of the positive and negative electrodes in the positive and negative electrode difference model is used to characterize the recycling completion of the electrode when the waste is recycled under the temperature change. Generally speaking, the positive and negative electrode sheets are the same size, and the metal foil area recycled on the surface is equal. The larger the difference in the comparison quantization coefficients of the positive and negative electrode sheets, the higher the recycling completion of the negative electrode sheet under the corresponding temperature increase environment compared with the reference temperature. The present application characterizes the completion of waste recycling under temperature change conditions through the difference in the comparison quantization coefficients of the positive and negative electrode sheets. The larger the difference, the higher the recycling completion of the negative electrode sheet under the temperature condition. Assuming that the geometric dimensions and the recycled metal foil area of ​​the positive and negative electrode sheets are the same, the difference reflects the difference in recycling performance of the positive and negative electrode sheets under the corresponding temperature increase environment, thereby completing the reference of the degree of correlation between temperature and the recycling degree of the battery electrode sheets. Through quantitative analysis of thermal imaging images of the positive and negative electrode sheets, the present application can accurately evaluate the effect of waste recycling under different temperature environments, and reveal the characteristic differences in recycling performance of the positive and negative electrode sheets, providing a scientific basis for optimizing the recycling process.

[0082] In step S104, a machine learning recycling prediction is performed based on the positive and negative electrode difference model to obtain a predicted temperature value, and the recycling process of the battery electrode is controlled according to the predicted temperature value.

[0083] Optionally, in some embodiments, performing machine learning recovery prediction based on the positive and negative electrode difference model to obtain the predicted temperature value specifically includes:

[0084] Obtain the positive and negative pole difference model, extract the comparison quantization coefficient difference values ​​at different temperatures in the positive and negative pole difference model for autoregressive prediction, and obtain multiple prediction deviations and their corresponding temperature labels;

[0085] A temperature label corresponding to the maximum prediction deviation is determined as the predicted temperature value.

[0086] Optionally, in some embodiments, the difference values ​​of the comparison quantization coefficients of the positive and negative electrode sheets at each temperature in the positive and negative electrode difference model can be extracted to construct a moving average autoregressive model, and the difference values ​​of the comparison quantization coefficients of the positive and negative electrode sheets at subsequent temperatures can be predicted to obtain the prediction deviations at multiple subsequent temperatures, and the temperature value corresponding to the maximum prediction deviation is used as the predicted temperature value.

[0087] Optionally, in some embodiments, since the contrast quantization coefficient remains basically unchanged after entering the high temperature range that can be fully recovered, the temperature value corresponding to the minimum value of the first-order derivative in the positive and negative pole difference model can also be used as the predicted temperature value. In some other embodiments, the contrast quantization coefficient of the negative pole thermal imaging image corresponding to the temperature and the first-order derivative value of the positive and negative pole difference model at the temperature can be weighted averaged to obtain the predicted temperature value corresponding to the temperature, and the temperature value with the minimum predicted temperature value is used as the predicted temperature value. It should be noted that the predicted temperature value is the temperature value with the lowest sensitivity to changes in the recovery area feature comparison. Using the predicted temperature value to separate battery pole piece waste can minimize the impact of temperature changes on the separation of motor pole piece waste, thereby improving the battery pole piece separation rate.

[0088] Optionally, in some embodiments, controlling the recycling process of the battery electrodes according to the predicted temperature value specifically includes: controlling the recycling temperature of the battery electrodes in the rotary kiln according to the predicted temperature value, and recycling the waste of the battery electrodes to be processed according to the waste recovery temperature.

[0089] Optionally, in some embodiments, the deviation value between the current electrode temperature and the predicted temperature value can be used as input, and a temperature control signal can be output through a proportional-differential-integral (PID) controller to control the waste recovery temperature of the battery electrode to be processed. In specific implementation, the proportional coefficient, differential coefficient and integral coefficient in the proportional-differential-integral controller are calibrated as constants based on experience.

[0090] It should be noted that the waste recovery temperature is the recovery temperature when the battery pole pieces are recycled, and the temperature control signal is a control signal for controlling the waste recovery temperature. For example, when a rotary kiln is used to recycle the battery pole pieces, the air supply volume of the rotary kiln can be adjusted by the temperature control signal to achieve control of the waste recovery temperature.

[0091] In addition, in another aspect of the present application, in some embodiments, the present application provides a waste recycling system reference Figure 3 , which is a schematic diagram of exemplary hardware and / or software of a waste recycling system according to some embodiments of the present application, the waste recycling system 200 includes: a collection module 201, a processing module 202 and an execution module 203, which are described as follows:

[0092] The acquisition module 201 is used to acquire thermal imaging images of the positive battery test electrode piece and the negative battery test electrode piece respectively within a preset period after performing a temperature rise test on the positive battery test electrode piece and the negative battery test electrode piece to obtain a positive electrode piece test image sequence and a negative electrode piece test image sequence;

[0093] The processing module 202 is used to extract the electrode recovery area in the positive electrode test image sequence and the negative electrode test image sequence and perform recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode;

[0094] The processing module 202 is further configured to extract the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence respectively when the feature difference is higher than a preset threshold, and determine the positive and negative electrode difference model in the battery electrode recycling process based on all contrast quantization coefficients;

[0095] The execution module 203 is used to perform machine learning recycling prediction on the positive and negative electrode difference model to obtain a predicted temperature value, and control the recycling process of the battery electrode according to the predicted temperature value.

[0096] The above describes in detail an example of a waste recycling method, system, device and storable medium provided in an embodiment of the present application. It can be understood that in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to executing each function.

[0097] Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiments disclosed in this document, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function in the application is executed in hardware or in a computer software-driven hardware manner depends on the specific application and design constraints of the technical solution. Therefore, professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0098] In addition, the present application also provides a computer terminal device, which includes a memory and a processor, wherein the memory stores a code, and the processor is configured to obtain the code and execute the above-mentioned waste recycling method.

[0099] In some embodiments, reference Figure 4 , which is a schematic diagram of the structure of a computer terminal device for implementing a waste recycling method according to some embodiments of the present application. A waste recycling method in the above embodiment can be Figure 4 The computer terminal device 300 shown in the figure is implemented, and the computer terminal device 300 includes at least one communication bus 301, a communication interface 302, a processor 303 and a memory 304.

[0100] The processor 303 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more processors for controlling the execution of a waste recycling method in the present application.

[0101] The communication bus 301 may include a pathway for transmitting information between the above-mentioned components.

[0102] The memory 304 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 304 may exist independently and be connected to the processor 303 via the communication bus 301. The memory 304 may also be integrated with the processor 303.

[0103] The memory 304 is used to store the program code for executing the solution of the present application, and the execution is controlled by the processor 303. The processor 303 is used to execute the program code stored in the memory 304. The program code may include one or more software modules. The determination of the predicted temperature value in the above embodiment can be implemented by the processor 303 and one or more software modules in the program code in the memory 304.

[0104] The communication interface 302 uses any transceiver or other device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0105] Optionally, the computer terminal device 300 may further include a power supply 305 for providing power to various devices or circuits in the real-time computer terminal device.

[0106] In a specific implementation, as an embodiment, a computer terminal device may include multiple processors, each of which may be a single-CPU processor or a multi-CPU processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0107] The above-mentioned computer terminal device may be a general-purpose computer terminal device or a dedicated computer terminal device. In a specific implementation, the computer terminal device may be a desktop computer, a portable computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device or an embedded device. The embodiment of the present application does not limit the type of computer terminal device.

[0108] In addition, in other aspects of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one computer program, and the computer program is loaded and executed by a processor to implement the operations performed by the above-mentioned waste recycling method.

[0109] In summary, in a waste recycling method, system, device and storable medium disclosed in an embodiment of the present application, first, after a temperature test is performed on the positive battery test electrode and the negative battery test electrode, thermal imaging images of the positive battery test electrode and the negative battery test electrode are respectively collected within a preset period to obtain a positive electrode test image sequence and a negative electrode test image sequence; the electrode recovery areas in the positive electrode test image sequence and the negative electrode test image sequence are extracted and the recovery area features are compared to obtain the feature difference between the positive battery test electrode and the negative battery test electrode; when the feature difference is higher than a preset threshold, the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence are extracted, and the positive and negative electrode difference model in the battery electrode recycling process is determined based on all the contrast quantization coefficients; machine learning recycling prediction is performed based on the positive and negative electrode difference model to obtain a predicted temperature value, and the recycling process of the battery electrode is controlled according to the predicted temperature value, thereby reducing the influence of temperature on the recycling of battery electrode waste and improving the waste recycling efficiency of the battery electrode.

[0110] The above is only an embodiment of the present application, and the common knowledge such as the specific technical scheme or characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical scheme of the present application, several modifications and improvements can be made, which should also be regarded as the scope of protection of the present application, and these will not affect the effect of the implementation of the present application and the practicality of the patent.

[0111] The scope of protection claimed by this application shall be based on the content of its claims. The specific implementation methods and other records in the specification can be used to interpret the content of the claims. Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include these modifications and variations.

Claims

1. A waste recycling method, characterized in that: include: After performing a temperature rise test on the positive battery test electrode piece and the negative battery test electrode piece, thermal imaging images of the positive battery test electrode piece and the negative battery test electrode piece are respectively collected within a preset period to obtain a positive electrode piece test image sequence and a negative electrode piece test image sequence; Extracting the electrode recovery areas in the positive electrode test image sequence and the negative electrode test image sequence and performing recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode; When the characteristic difference is higher than a preset threshold, the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence are extracted, and the positive and negative electrode difference model in the battery electrode recycling process is determined based on all the contrast quantization coefficients; Based on the positive and negative electrode difference model, machine learning recycling prediction is performed to obtain a predicted temperature value, and the recycling process of the battery electrode is controlled according to the predicted temperature value.

2. The method according to claim 1, characterized in that The temperature rise test of the positive battery test electrode and the negative battery test electrode specifically includes: According to a preset reference temperature, a low temperature test is performed on the positive battery test electrode in the first test area; According to the preset reference temperature and temperature range, a temperature rise test is performed on the negative battery test electrode in the second test area.

3. The method according to claim 1, characterized in that Extracting the electrode recovery area in the positive electrode test image sequence and the negative electrode test image sequence and performing recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode specifically includes: Performing background separation and enhancement processing on the positive electrode sheet test image sequence to obtain a positive electrode sheet enhanced image; Performing background separation on the negative electrode sheet test image sequence to obtain a negative electrode sheet enhanced image set; performing differential processing on each negative electrode enhanced image in the negative electrode enhanced image set according to the positive electrode enhanced image to obtain a difference image set; Determine the feature smoothing coefficients corresponding to the respective difference images in the difference image set; The feature difference degree is determined according to the feature smoothing coefficients corresponding to each difference image.

4. The method according to claim 3, characterized in that The background separation and enhancement processing of the positive electrode sheet test image sequence to obtain the positive electrode sheet enhanced image specifically includes: Performing edge detection on the positive electrode thermal imaging image in the positive electrode sheet test image sequence to obtain a plurality of electrode sheet separation images; The plurality of pole piece separation images are aligned according to the image features, and the aligned plurality of pole piece separation images are enhanced to obtain a positive pole piece enhanced image.

5. The method according to claim 1, characterized in that Extracting the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence, respectively, and determining the positive and negative electrode difference model in the battery electrode recycling process based on all the contrast quantization coefficients specifically includes: Extracting the contrast quantization coefficient and the corresponding temperature label value respectively corresponding to each positive electrode thermal imaging image in the positive electrode sheet test image sequence; Extracting the contrast quantization coefficient and the corresponding temperature label value respectively corresponding to each negative electrode thermal imaging image in the negative electrode sheet test image sequence; According to the contrast quantization coefficients corresponding to each positive electrode thermal imaging image and the contrast quantization coefficients corresponding to each negative electrode thermal imaging image and the temperature label value, a positive and negative electrode difference model is obtained.

6. The method according to claim 1, characterized in that Based on the positive and negative electrode difference model, machine learning recovery prediction is performed to obtain the predicted temperature value, which specifically includes: Obtain the positive and negative pole difference model, extract the comparison quantization coefficient difference values ​​at different temperatures in the positive and negative pole difference model for autoregressive prediction, and obtain multiple prediction deviations and their corresponding temperature labels; A temperature label corresponding to the maximum prediction deviation is determined as the predicted temperature value.

7. The method according to claim 1, characterized in that When the characteristic difference is lower than a preset threshold, the battery pole pieces are recycled according to a preset reference temperature.

8. A waste recycling system, characterized in that: The waste recycling system comprises: An acquisition module is used to acquire thermal imaging images of the positive battery test electrode piece and the negative battery test electrode piece respectively within a preset period after performing a temperature rise test on the positive battery test electrode piece and the negative battery test electrode piece to obtain a positive electrode piece test image sequence and a negative electrode piece test image sequence; A processing module, used for extracting the electrode recovery area in the positive electrode test image sequence and the negative electrode test image sequence and performing recovery area feature comparison to obtain the feature difference between the positive battery test electrode and the negative battery test electrode; The processing module is further used to extract the contrast quantization coefficients corresponding to the images in the positive electrode test image sequence and the negative electrode test image sequence respectively when the feature difference is higher than a preset threshold, and determine the positive and negative electrode difference model in the battery electrode recycling process based on all contrast quantization coefficients; The execution module is used to perform machine learning recycling prediction on the positive and negative electrode difference model to obtain a predicted temperature value, and control the recycling process of the battery electrode according to the predicted temperature value.

9. A computer terminal device, characterized in that: The computer terminal device comprises a memory and a processor, wherein the memory stores codes, and the processor is configured to obtain the codes and execute a waste recycling method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one computer program, characterized in that: The computer program is loaded and executed by a processor to implement the operations performed by a waste recycling method as claimed in any one of claims 1 to 7.

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