A method and system for controlling the disassembly of mobile phone patches

Abstract

This application proposes a method and system for controlling the disassembly of mobile phone surface mount components, relating to the field of mobile phone surface mount component disassembly control technology. The method includes acquiring surface thermal radiation characteristic parameters of a target surface mount component; heating the target surface mount component according to a preset initial heating strategy, and monitoring the surface temperature of the target surface mount component during the heating process to obtain a monitored surface temperature; monitoring the acoustic and electrical characteristic changes of the target surface mount component caused by solder phase transition during the heating process to obtain monitored acoustic and electrical characteristic changes; correcting the monitored surface temperature based on the surface thermal radiation characteristic parameters to obtain a corrected surface temperature; analyzing the corrected surface temperature, monitored acoustic characteristic changes, and monitored electrical characteristic changes to obtain the melting state of the target surface mount component; and peeling off the target surface mount component when it is fully melted. This application can improve the success rate and non-destructive nature of disassembly.

Description

Technical Field

[0001] This application relates to the field of mobile phone patch disassembly control technology, and in particular to a mobile phone patch disassembly control method and system. Background Technology

[0002] In the field of electronic product recycling and remanufacturing, the efficient and non-destructive disassembly of surface-mount components on discarded mobile phone motherboards is a key industry focus, but current technologies face multiple challenges. Traditional automated disassembly systems employ experience-based open-loop control strategies. While preset heating parameters are highly efficient for new motherboards or standard solder, they become ineffective when dealing with discarded motherboards from diverse sources and with varying usage histories. This is because third-party repairs may use solder with different melting points, leading to component damage or disassembly failure. Existing technologies introduce closed-loop control mechanisms with real-time temperature feedback. By monitoring the component surface temperature using infrared temperature sensors and dynamically adjusting the heating power, adaptability to different solder melting points is improved to some extent. However, when disassembling package types with obscured solder joints (such as BGA and QFN) or components with high heat capacity, there is a "thermal gradient" and "thermal hysteresis" effect between the component surface temperature and the actual solder layer temperature. When the infrared sensor detects that the surface temperature has reached the set value, the solder may not have melted. Increasing the target temperature or extending the heating time may damage the internal structure of the component or the motherboard pads. Meanwhile, the surface conditions of discarded motherboard components vary greatly. Differences in packaging materials, coating colors, and textures affect surface emissivity. Infrared sensors rely on preset emissivity parameters, and discrepancies between actual and preset parameters can lead to temperature reading errors. Furthermore, dust, oxides, and other contaminants adhering to the component surface can alter local emissivity and form a heat-insulating layer, causing the sensor to measure the temperature of the adhering material rather than the component's true temperature. This further exacerbates inaccurate readings, making it difficult for the control system to determine the solder melting state, potentially causing thermal damage to the component, surrounding components, and the motherboard, or even leading to disassembly failure. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method and system for controlling the disassembly of mobile phone patches, aiming to improve the success rate and non-destructive nature of the disassembly process.

[0004] In a first aspect, embodiments of this application provide a method for controlling the disassembly of mobile phone patches, including:

[0005] Obtain the surface thermal radiation characteristics parameters of the target patch element;

[0006] The target patch element is heated according to a preset initial heating strategy, and the surface temperature of the target patch element is monitored during the heating process to obtain the monitored surface temperature.

[0007] During the heating process, the acoustic and electrical properties of the target patch element are monitored due to the solder phase transition, and the changes in acoustic and electrical properties are monitored.

[0008] Based on the surface thermal radiation characteristic parameters, the monitored surface temperature is corrected to obtain the corrected surface temperature;

[0009] The melting state of the target patch element is obtained by analyzing the modified surface temperature, the changes in the monitored acoustic properties, and the changes in the monitored electrical properties.

[0010] When the molten state is fully molten, the target patch element is peeled off.

[0011] According to some embodiments of this application, before heating the target patch element according to a preset initial heating strategy, the process includes:

[0012] An acoustic pulse is applied to the solder joint area of ​​the target surface mount component, and the acoustic response signal of the solder joint area is acquired.

[0013] The attenuation characteristics and energy distribution of the acoustic response signal are analyzed to assess the risk of voids in the weld area, and the void presence risk assessment results are obtained.

[0014] A multi-frequency AC signal is applied to the solder joint area, and the electrical impedance spectrum of the solder joint area is measured.

[0015] The nonlinearity or resonance peaks of the electrical impedance spectrum are analyzed to assess the risk of the presence of an intermetallic compound layer in the solder joint area, and the risk assessment result of the presence of the intermetallic compound layer is obtained.

[0016] Based on the risk assessment results of the voids and the intermetallic compound layer, the risk level of microstructural anomalies in the solder joint area is assessed to obtain the microstructural anomaly risk level.

[0017] According to some embodiments of this application, the step of analyzing the modified surface temperature, monitoring changes in acoustic properties, and monitoring changes in electrical properties to obtain the melting state of the target patch element includes:

[0018] The target confidence threshold is set according to the microstructural anomaly risk level, and the phase transition characteristic judgment criteria for the monitored acoustic characteristic changes and the monitored electrical characteristic changes are adjusted.

[0019] Based on the monitored surface temperature, the changes in the monitored acoustic characteristics, and the changes in the monitored electrical characteristics, the multimodal signal characteristics are obtained;

[0020] The confidence level of the solder melting is dynamically updated based on the multimodal signal characteristics.

[0021] When the confidence level of the solder melting reaches or exceeds the target confidence level threshold, the melting state of the target surface mount component is obtained as complete melting.

[0022] According to some embodiments of this application, the step of setting a target confidence threshold based on the microstructural anomaly risk level and adjusting the phase transition characteristic judgment criteria for monitoring changes in acoustic properties and electrical properties includes:

[0023] The presence of micro-voids and intermetallic compound layers in the solder joint area is detected, and the detection results are obtained.

[0024] Based on the detection results, the existence and distribution characteristics of the micro-voids and the thickness and distribution characteristics of the intermetallic compound layer are identified.

[0025] Based on the presence and distribution characteristics of the microvoids and the thickness and distribution characteristics of the intermetallic compound layer, the risk level of the microstructure anomaly is divided into different types, resulting in multiple types of microstructure anomalies.

[0026] For each type of microstructural anomaly and the risk level of the microstructural anomaly, a target confidence threshold and a phase transition characteristic judgment standard for the changes in the monitored acoustic properties and the changes in the monitored electrical properties are set.

[0027] According to some embodiments of this application, obtaining multimodal signal characteristics based on the monitored surface temperature, the monitored acoustic characteristic changes, and the monitored electrical characteristic changes includes:

[0028] Spatiotemporal filtering is performed on the monitored surface temperature, the changes in the monitored acoustic characteristics, and the changes in the monitored electrical characteristics to obtain a multimodal signal after interference elimination;

[0029] Feature extraction is performed on the multimodal signal after interference elimination to obtain multimodal signal features.

[0030] According to some embodiments of this application, the step of extracting features from the interference-free multimodal signal to obtain multimodal signal features includes:

[0031] Based on the model information of the target surface mount component, obtain the theoretical melting point range and typical phase transition characteristic curve of the solder alloy;

[0032] During the heating process, the frequency band and time window of interest for feature extraction are dynamically adjusted;

[0033] The frequency band of interest and the time window are compared with the typical phase transition characteristic curve to obtain the comparison results;

[0034] Based on the comparison results, the multimodal signal features are obtained.

[0035] According to some embodiments of this application, the step of dynamically adjusting the focus band and time window of the feature extraction during the heating process includes:

[0036] To obtain the rate of temperature change and heating power of the solder surface;

[0037] Based on the rate of change of the solder surface temperature and the heating power, predict the key time points of the solder phase transition;

[0038] Before the critical time point of the predicted solder phase transition, the frequency band of interest for the acoustic signal is narrowed and the acquisition time window of the acoustic signal is extended to capture the weak acoustic features in the early stage of the solder phase transition.

[0039] After the critical time point for predicting the solder phase transition, the focus band of the electrical signal is expanded and the acquisition time window of the electrical signal is shortened to capture the changes in electrical characteristics after the solder phase transition is completed.

[0040] When a change is detected in the acoustic signal or the electrical signal, the target frequency band is obtained to dynamically adjust the time window.

[0041] According to some embodiments of this application, the step of obtaining the target frequency band to dynamically adjust the time window when a change in the acoustic signal or the electrical signal is detected includes:

[0042] Monitor changes in the acoustic or electrical signals;

[0043] Time-frequency analysis is performed on the changing signal to obtain the identified frequency bands and their changing trends;

[0044] Based on the corrected surface temperature, determine whether the corrected surface temperature is within the theoretical melting point range of the solder, and obtain the determination result;

[0045] If the judgment result shows that the corrected surface temperature is within the theoretical melting point range of the solder, then the identified frequency band is compared with the pre-stored typical frequency band characteristics of solder melting to obtain the frequency band comparison result;

[0046] The target frequency band is obtained based on the frequency band comparison results, and the time window is dynamically adjusted.

[0047] According to some embodiments of this application, when the confidence level of the solder melting reaches or exceeds the target confidence level threshold, the step further includes:

[0048] Start the preset verification cycle;

[0049] The stability of the acoustic and electrical signals of the molten solder is continuously monitored, and the surface temperature change rate of the molten solder is monitored simultaneously to obtain the stability of the monitoring signals and the rate of temperature change.

[0050] Within the preset verification period, if the stability of the monitoring signal shows that the acoustic signal or the electrical signal has recovered solid-state characteristics, or if the rate of change of the monitored temperature decreases significantly, it is determined that there is local re-solidification or insufficient fluidity, and heating is restarted until it reaches a completely melted state again.

[0051] Secondly, embodiments of this application provide a mobile phone patch disassembly and control system, comprising:

[0052] The acquisition module is used to acquire the surface thermal radiation characteristic parameters of the target patch element;

[0053] A heating module is used to heat the target patch element according to a preset initial heating strategy, and monitor the surface temperature of the target patch element during the heating process to obtain the monitored surface temperature.

[0054] The monitoring module is used to monitor the changes in acoustic and electrical properties of the target patch element caused by the solder phase transition during the heating process, and to obtain the monitored acoustic and electrical property changes.

[0055] The correction module is used to correct the monitored surface temperature based on the surface thermal radiation characteristic parameters to obtain the corrected surface temperature.

[0056] The analysis state module is used to analyze the modified surface temperature, the monitored acoustic characteristic changes, and the monitored electrical characteristic changes to obtain the melting state of the target patch element;

[0057] A stripping module is used to strip the target patch element when the melting state is fully melted.

[0058] The technical solution according to the embodiments of this application has at least the following beneficial effects: The mobile phone patch disassembly control method disclosed in this application obtains the surface thermal radiation characteristic parameters of the target patch component and corrects the monitored surface temperature accordingly, effectively overcoming the problem of inaccurate temperature readings caused by differences in surface emissivity and contaminants in traditional infrared thermometry. Simultaneously, during the heating process, this method monitors in real time the changes in acoustic and electrical characteristics caused by the solder phase transition, and combines this with the corrected surface temperature to comprehensively analyze and accurately determine the melting state of the solder. This multimodal, multi-dimensional data fusion and analysis mechanism can effectively solve the problem of inconsistency between the actual solder temperature and the surface temperature caused by the "thermal gradient" and "thermal hysteresis" effects in the prior art, avoiding disassembly failure due to incomplete solder melting or damage to the internal structure of the component or the motherboard pads due to overheating. Finally, peeling is performed when the solder is completely melted, significantly improving the success rate and non-destructive nature of disassembly, providing efficient and reliable technical support for the recycling and remanufacturing of waste mobile phone motherboards.

[0059] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0060] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0061] Figure 1 This is a flowchart illustrating a mobile phone patch disassembly control method provided in one embodiment of this application;

[0062] Figure 2 This is a schematic diagram of a mobile phone patch disassembly control system provided in one embodiment of this application. Detailed Implementation

[0063] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0064] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0065] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: the existence of a alone, the existence of b alone, the existence of c alone, the simultaneous existence of a and b, the simultaneous existence of a and c, the simultaneous existence of b and c, or the simultaneous existence of a, b, and c, where a, b, and c can be single or multiple.

[0066] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0067] Based on the above, this application proposes a mobile phone patch disassembly control method and system, aiming to improve the success rate and non-destructive nature of disassembly.

[0068] The mobile phone patch disassembly control method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application that implements the mobile phone patch disassembly control method, but is not limited to the above forms.

[0069] This application can be applied to numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via communication networks. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices. It should be noted that in various specific embodiments of this invention, when processing is required based on data related to the characteristics of an object (e.g., user attributes or sets of attribute information), permission or consent from the corresponding object is obtained first, and the collection, use, and processing of this data comply with relevant laws and standards. Furthermore, when the embodiments of the present invention need to obtain the attribute information of an object, they will obtain the separate permission or separate consent of the corresponding object through pop-up windows or redirection to a confirmation page. After obtaining the separate permission or separate consent of the corresponding object, they will then obtain the relevant data of the object necessary for the embodiments of the present invention to operate normally.

[0070] See Figure 1 , Figure 1 This is a flowchart illustrating a mobile phone patch removal control method according to an embodiment of this application. The mobile phone patch removal control method provided in this embodiment includes, but is not limited to, steps S110 to S160, which will be described in detail below.

[0071] Step S110: Obtain the surface thermal radiation characteristic parameters of the target patch element;

[0072] Step S120: Heat the target patch element according to the preset initial heating strategy, and monitor the surface temperature of the target patch element during the heating process to obtain the monitored surface temperature;

[0073] Step S130: During the heating process, monitor the changes in acoustic and electrical properties of the target surface mount component caused by the solder phase transition, and obtain the monitored acoustic and electrical property changes.

[0074] Step S140: Based on the surface thermal radiation characteristic parameters, correct the monitored surface temperature to obtain the corrected surface temperature;

[0075] Step S150: Analyze and correct the surface temperature, monitor changes in acoustic properties and electrical properties to obtain the melting state of the target patch element;

[0076] Step S160: When the target chip element is fully melted, peel it off.

[0077] It should be noted that target surface-mount components refer to electronic components that need to be removed from the mobile phone motherboard, such as resistors, capacitors, and integrated circuit chips. These components are typically fixed to the motherboard by solder. Surface thermal radiation characteristics refer to the ability of a component's surface to radiate heat during heating, mainly including emissivity and absorptivity. These parameters affect the measurement accuracy of infrared temperature sensors. Solder phase transition refers to the process of solder changing from a solid to a liquid or vice versa, accompanied by energy absorption or release, and causing significant changes in the material's physical properties, such as acoustic and electrical properties. The molten state refers to the physical state of solder during heating, including unmelted, partially melted, and fully melted states.

[0078] In one embodiment, the mobile phone surface mount component disassembly control method first requires obtaining the surface thermal radiation characteristic parameters of the target surface mount component. These parameters are crucial for accurately measuring the component's surface temperature. This can be achieved by scanning the component surface with a spectrometer or infrared thermometer before heating to obtain its emissivity data at different wavelengths. Alternatively, the corresponding surface thermal radiation characteristic parameters can be retrieved from a preset database based on the component's model, packaging material, and surface treatment process. Next, the target surface mount component is heated according to a preset initial heating strategy. The initial heating strategy can be set based on the component's type, size, and the common melting point range of solder; for example, setting an initial heating power and heating time curve. During the heating process, the surface temperature of the target surface mount component needs to be monitored in real time, which can be achieved using a non-contact infrared temperature sensor. The infrared temperature sensor converts the infrared energy radiated from the component surface into an electrical signal, thereby calculating the surface temperature. Simultaneously, during the heating process, it is also necessary to monitor the changes in acoustic and electrical characteristics of the target surface mount component caused by the solder phase transition. Changes in acoustic properties can be captured using high-sensitivity microphones or piezoelectric sensors. For example, the melting of solder may produce tiny bubbles bursting or sound wave attenuation caused by changes in internal structure. Changes in electrical properties can be detected by applying a weak alternating current signal to the solder joint area and measuring parameters such as impedance and conductivity. The transition of solder from a solid to a liquid state significantly alters its electrical properties. Since the surface thermal radiation characteristics of components may be inaccurate, the monitored surface temperature needs to be corrected based on the acquired surface thermal radiation characteristics. For example, if the measured surface emissivity of the component is lower than a preset value, the temperature reading measured by the infrared sensor needs to be increased to obtain a corrected surface temperature that is closer to the true temperature. The corrected surface temperature more accurately reflects the actual temperature of the component surface. Subsequently, the corrected surface temperature, acoustic property changes, and electrical property changes are analyzed to determine the melting state of the target surface mount component. For example, when the corrected surface temperature reaches the theoretical melting point range of the solder, and simultaneously the acoustic signal shows significant attenuation or frequency drift, and the electrical impedance drops sharply, the coordinated changes in these multimodal signals can serve as strong evidence of solder melting. By comprehensively analyzing these multimodal signals, it can be determined whether the solder is in an unmelted, partially melted, or fully melted state. Finally, if the solder is fully melted, the target surface-mount component is peeled off. When the system determines that the solder is fully melted, a robotic arm or suction device can be activated to peel the component from the motherboard with minimal force. This peeling operation based on precise melting state judgment effectively avoids component damage or pad tearing caused by incomplete solder melting, while also preventing thermal damage to components or the motherboard due to overheating.

[0079] In one embodiment, before heating the target patch element according to a preset initial heating strategy, the process includes: applying an acoustic pulse to the solder joint area of ​​the target patch element and acquiring the acoustic response signal of the solder joint area; analyzing the attenuation characteristics and energy distribution of the acoustic response signal to assess the risk of voids within the solder joint area and obtaining a void presence risk assessment result; applying a multi-frequency AC signal to the solder joint area and measuring the electrical impedance spectrum of the solder joint area; analyzing the nonlinearity or resonance peaks of the electrical impedance spectrum to assess the risk of intermetallic compound layer presence in the solder joint area and obtaining an intermetallic compound layer presence risk assessment result; and assessing the microstructural anomaly risk level of the solder joint area based on the void presence risk assessment result and the intermetallic compound layer presence risk assessment result, thereby obtaining a microstructural anomaly risk level.

[0080] It should be noted that applying an acoustic pulse to the solder joint area of ​​the target surface mount component refers to emitting a short-duration, high-energy sound wave into the solder joint area using an ultrasonic sensor or other acoustic excitation device. The propagation characteristics of this acoustic pulse within the solder are affected by the solder's microstructure. Acquiring the acoustic response signal from the solder joint area involves capturing the sound wave signals reflected, transmitted, or scattered from the solder joint area using a receiving sensor. These signals carry information about the internal structure of the solder joint. Furthermore, the attenuation characteristics and energy distribution of the acoustic response signal are analyzed to assess the risk of voids within the solder joint area, resulting in a void presence risk assessment. Specifically, voids within the solder joint scatter and absorb sound waves, causing accelerated attenuation of the acoustic response signal amplitude and changes in its energy distribution across different frequencies. By performing time-domain and frequency-domain analysis on the received acoustic signal, such as calculating the signal attenuation coefficient and energy spectral density, characteristics associated with voids can be identified, thereby assessing the presence of voids and their risk level. Applying multi-frequency alternating current signals to the solder joint area refers to injecting a series of alternating current signals of different frequencies into the solder joint area through electrodes. Solder and the intermetallic compound layer formed between it and the substrate and component leads have different electrical properties. Measuring the electrical impedance spectrum of the solder joint area involves simultaneously applying alternating current signals of different frequencies, measuring the voltage and current responses of the solder joint area, and calculating the complex impedance values ​​at different frequencies to form an electrical impedance spectrum.

[0081] It should be noted that by conducting a microstructural anomaly risk assessment of the solder joint area of ​​the target surface mount component before heating, the problem of inaccurate melting state judgment caused by unknown internal structure of the solder joint in the basic solution can be effectively solved. Specifically, by applying acoustic pulses and analyzing acoustic response signals, the presence of voids inside the solder joint can be detected, as voids scatter and attenuate sound waves, thereby changing the attenuation characteristics and energy distribution of the acoustic response signal. Simultaneously, by applying multi-frequency AC signals and measuring the electrical impedance spectrum, the presence risk of intermetallic compound layers can be assessed, as intermetallic compound layers have different electrical properties than solder and exhibit specific nonlinearities or resonance peaks in the electrical impedance spectrum. Therefore, combining the results of these two assessments, the microstructural anomaly risk level of the solder joint area can be obtained. Obtaining this risk level allows subsequent heating strategies and melting state judgments to fully consider the actual situation inside the solder joint. For example, for solder joints with high-risk voids, the heating power distribution can be adjusted to avoid localized overheating; for solder joints with thick intermetallic compound layers, the heating time can be appropriately extended or the temperature profile adjusted to ensure complete melting. This significantly improves the intelligence and adaptability of the entire disassembly control method.

[0082] In one embodiment, assuming a batch of mobile phones needs to be disassembled, some of the surface-mount components may have micro-voids or thicker intermetallic compound layers at their solder joints due to production batches or material differences. When disassembling using the method of this application, an acoustic pulse is first applied to the solder joint area of ​​the target surface-mount component. For example, an ultrasonic sensor emits a 5MHz pulse and receives the acoustic response signal after reflection and transmission within the solder joint. By analyzing the attenuation curve and spectral energy distribution of this signal, the presence of internal voids can be identified. If the signal attenuation is significant and the high-frequency energy loss is large, it indicates the presence of voids, and a void presence risk assessment result is obtained. Simultaneously, multi-frequency AC signals from 1kHz to 1MHz are applied to the same solder joint area, and its electrical impedance spectrum is measured. By analyzing the nonlinear response or resonance peaks at specific frequencies in the impedance spectrum, the thickness or distribution of the intermetallic compound layer can be determined. If a significant impedance peak appears in a specific frequency range, a thicker intermetallic compound layer may exist, and an intermetallic compound layer presence risk assessment result is obtained. Based on these assessment results, the system can comprehensively determine the risk level of microstructural anomalies in the solder joint area and mark it as "medium risk". During subsequent heating, the system can adjust the heating power curve according to this "medium risk" level, for example, by using a slower heating rate and more strictly monitoring acoustic and electrical phase transition signals, to ensure that the solder melts completely under optimal conditions, thereby avoiding damage to components or substrates during the stripping process.

[0083] In one embodiment, analyzing the modified surface temperature, the monitored acoustic characteristic changes, and the monitored electrical characteristic changes to obtain the melting state of the target surface mount element includes: setting a target confidence threshold based on the microstructure anomaly risk level, and adjusting the phase transition characteristic judgment criteria for the monitored acoustic characteristic changes and the monitored electrical characteristic changes; obtaining multimodal signal characteristics based on the monitored surface temperature, the monitored acoustic characteristic changes, and the monitored electrical characteristic changes; dynamically updating the confidence level of the solder melting based on the multimodal signal characteristics; and determining that the melting state of the target surface mount element is completely melted when the confidence level of the solder melting reaches or exceeds the target confidence threshold.

[0084] In one embodiment, setting the target confidence threshold based on the microstructure anomaly risk level involves assessing the microstructure anomaly risk level of the solder joint area by applying acoustic pulses and multi-frequency AC signals to the solder joint area before heating the target surface mount component. The risk level reflects the risk of voids and intermetallic compound layers within the solder joint. Multimodal signal characteristics are obtained by monitoring surface temperature, acoustic property changes, and electrical property changes during heating. These characteristics are obtained by continuously monitoring the surface temperature of the target surface mount component, changes in acoustic properties caused by solder phase transitions, and changes in electrical properties. These different modal signals reflect changes in the physical state of the solder from different dimensions. The confidence level of solder melting is dynamically updated based on the multimodal signal characteristics. This is achieved by using the extracted multimodal signal characteristics to assess the probability that the solder is in a molten state in real time. Confidence level is a quantitative indicator representing the probability or certainty that the solder is currently in a fully molten state. This confidence level is continuously and dynamically updated as heating progresses and the multimodal signal characteristics change. For example, the confidence level of solder melting gradually increases when the surface temperature approaches the theoretical melting point, the acoustic signal exhibits a specific attenuation mode, and the electrical impedance drops sharply. Once the dynamically updated confidence level of solder melting reaches or exceeds the target confidence level threshold set according to the risk level of microstructure anomalies, the system can determine that the solder of the target surface mount component has reached a fully molten state. At this point, the solder has sufficient fluidity to allow for safe stripping.

[0085] In one embodiment, a solder joint area of ​​a target surface mount component is assessed as having a moderately high risk level of microstructural anomalies before heating, based on detection using acoustic pulses and multi-frequency AC signals. Specifically, the detection results show a small number of microvoids within the solder joint, and the intermetallic compound layer is slightly thicker than the standard value. Based on this moderately high risk level of microstructural anomalies, the system sets the target confidence threshold to 0.85 (higher than the standard solder joint's 0.75). During heating, the system continuously monitors the solder's surface temperature, acoustic properties, and electrical properties. For example, when the surface temperature reaches the theoretical melting point of the solder, the acoustic sensor detects a specific acoustic emission signal attenuation mode, while the electrical impedance measurement shows a significant decrease. These multimodal signals are acquired in real time and feature extracted to form multimodal signal features. Subsequently, these features are input into a pre-trained model to dynamically calculate and update the confidence level of solder melting. As heating continues, the solder gradually melts, and its melting confidence level gradually increases from 0.3 to 0.6, 0.7, and 0.8. When the confidence level reaches 0.85, the system determines that the solder has completely melted. At this point, by considering the microstructural anomalies within the solder joint and accordingly raising the judgment standard, it ensures that even with minor defects, the solder has fully melted, allowing for safe stripping. This adaptive judgment mechanism avoids stripping when the solder is not fully melted, effectively reducing the risk of component damage.

[0086] In one embodiment, a target confidence threshold is set based on the risk level of microstructural anomalies, and the phase transition characteristic judgment criteria for monitoring changes in acoustic properties and electrical properties are adjusted. This includes: detecting whether microvoids and intermetallic compound layers exist in the solder joint area, obtaining detection results; identifying the presence and distribution characteristics of the microvoids and the thickness and distribution characteristics of the intermetallic compound layer based on the detection results; classifying the risk level of microstructural anomalies into different types based on the presence and distribution characteristics of the microvoids and the thickness and distribution characteristics of the intermetallic compound layer, resulting in multiple microstructural anomaly types; and setting a target confidence threshold and the phase transition characteristic judgment criteria for monitoring changes in acoustic properties and electrical properties for each microstructural anomaly type and risk level.

[0087] It should be noted that detecting the presence of microvoids and intermetallic compound layers in the solder joint area, and obtaining the test results, refers to a detailed internal structural investigation of the solder joint area using non-destructive testing techniques, such as ultrasonic scanning, X-ray computed tomography (CT), or scanning acoustic microscopy (SAM). The purpose is to obtain preliminary information about the presence of microvoids and the intermetallic compound layer (IMC layer) within the solder joint. Identifying the presence and distribution characteristics of microvoids and the thickness and distribution characteristics of the intermetallic compound layer based on the detection results can be understood as performing image processing and analysis on the raw data. For example, through image segmentation and feature extraction algorithms, the size, number, location, and aggregation degree of microvoids, as well as the average thickness, maximum thickness, uniformity, and distribution of the intermetallic compound layer within the solder joint, can be quantified. The aim is to obtain detailed quantitative information about microstructural anomalies, providing a basis for subsequent classification and judgment. In practical applications, based on the presence and distribution characteristics of the identified micro-voids and the thickness and distribution characteristics of the intermetallic compound layer, the microstructure anomaly level is divided into different types, resulting in multiple microstructure anomaly types. Specifically, based on the above-mentioned quantitative characteristics, clustering algorithms or predefined rules are used to classify solder joints with similar microstructure anomaly characteristics into different anomaly types.

[0088] In one embodiment, assuming that before disassembling the solder joint area of ​​a target surface mount component, ultrasonic scanning and X-ray CT scanning reveal the presence of microvoids and intermetallic compound (IMC) layers in the solder joint area. Further, the system analyzes the detection results, identifying that the microvoids in the solder joint area are mainly concentrated in the center and are relatively large, while the IMC layer shows localized thickening at the edge of the solder joint. Based on these identified characteristics, the system classifies the microstructural anomaly of the solder joint as "large central void with edge IMC thickening," combined with its pre-assessed "medium microstructural anomaly risk level." The system dynamically adjusts the melting judgment parameters for this specific anomaly type. Since large voids may lead to increased acoustic signal attenuation, the system may adjust the acoustic signal phase transition characteristic judgment criteria to focus more on low-frequency energy changes and appropriately relax the acoustic signal amplitude change threshold. Meanwhile, considering that edge IMC thickening may affect the uniformity of electrical signals, the criteria for judging the phase transition characteristics of electrical signals may be adjusted to focus more on the nonlinear changes of the impedance spectrum in a specific high-frequency band, and the weight of the electrical melting confidence level will be increased. The system will set a target confidence threshold that matches the anomaly type and risk level to ensure that the confidence level of solder melting under these complex microstructure conditions can accurately reflect its true melting state, thereby guiding subsequent stripping operations.

[0089] In one embodiment, multimodal signal features are obtained based on monitored surface temperature, monitored acoustic characteristic changes, and monitored electrical characteristic changes, including: performing spatiotemporal filtering on the monitored surface temperature, monitored acoustic characteristic changes, and monitored electrical characteristic changes to obtain a multimodal signal after interference elimination; and performing feature extraction on the multimodal signal after interference elimination to obtain multimodal signal features.

[0090] It should be noted that spatiotemporal filtering refers to the joint temporal and spatial processing of data collected by different sensors (e.g., infrared sensors for monitoring surface temperature, microphones or piezoelectric sensors for monitoring acoustic properties, and electrodes for monitoring electrical properties). Its purpose is to eliminate environmental noise, sensor drift, and other interference signals caused by non-phase transitions, thereby improving the signal-to-noise ratio and accuracy. Kalman filtering, wavelet denoising, or moving average algorithms can be used to process time-series data, while spatial interpolation or regional averaging methods can be combined to process spatially distributed temperature data. Furthermore, feature extraction from the interference-free multimodal signals involves identifying and quantifying key information closely related to the solder phase transition from these purified signals. These features may include, but are not limited to: inflection points in the rate of temperature change, frequency shifts or energy abrupt changes in acoustic signals, sharp drops in electrical impedance, or significant changes in phase angle. By extracting these features, the original, high-dimensional sensor data can be transformed into more representative and interpretable low-dimensional feature vectors for subsequent melt state determination.

[0091] In one embodiment, feature extraction is performed on the multimodal signal after interference elimination to obtain multimodal signal features, including: obtaining the theoretical melting point range and typical phase transition characteristic curve of the solder alloy based on the model information of the target patch element; dynamically adjusting the focus frequency band and time window of feature extraction during the heating process; comparing the focus frequency band and time window with the typical phase transition characteristic curve to obtain the comparison result; and obtaining the multimodal signal features based on the comparison result.

[0092] In one embodiment, before feature extraction, the system retrieves the theoretical melting point range and typical phase transition characteristic curves of the solder alloy corresponding to the model of the target surface mount component to be disassembled from a preset database. The model information may include the component manufacturer, batch number, package type, etc., which helps to accurately match the type of solder alloy. The theoretical melting point range of the solder alloy refers to the temperature range from the solid state to the fully liquid state, while the typical phase transition characteristic curve describes the changes in acoustic signals, electrical signals, and temperature changes during the melting process of the solder under standard heating conditions. These curves can serve as a benchmark for subsequent dynamic adjustment and comparison. During the heating process, the frequency band and time window for feature extraction are dynamically adjusted. The frequency band of interest refers to the frequency range that is of primary focus in the analysis of acoustic and electrical signals; for example, specific frequencies of sound waves or electrical responses may be generated when the solder melts. The time window refers to the length of time for signal acquisition and analysis. Dynamic adjustment means that these parameters are not fixed but are optimized in real time based on real-time monitoring data and a preset phase transition model. In the early stages of the phase transition, a wider frequency band and a longer time window may be needed to capture the weak initial melting signal; while in the later stages of the phase transition, a narrower frequency band and a shorter time window may be necessary to accurately determine the complete melting point. Subsequently, the real-time signal features acquired within the dynamically adjusted focus frequency band and time window are compared with pre-acquired typical phase transition characteristic curves to obtain comparison results. This comparison can employ various signal processing and pattern recognition techniques, such as correlation analysis, feature matching, and machine learning algorithms, to quantify the similarity or difference between the real-time signal and the typical curve. Finally, based on the above comparison results, multimodal signal features are obtained. These features are optimized and refined to more accurately reflect the actual melting state of the solder.

[0093] In one embodiment, suppose a mobile phone surface mount component with model number "XYZ" needs to be disassembled. First, the system will query its internal database based on the model information "XYZ" to find that the solder alloy used in the component is SnAgCu-based, with a theoretical melting point range of 217℃-227℃. It will then acquire typical phase transition characteristic curves of the alloy's acoustic signals (e.g., vibration decay curves at specific frequencies) and electrical signals (e.g., resistivity change curves) under standard heating conditions. During the actual heating process, as the surface temperature of the target surface mount component gradually increases and approaches 200℃, the system will, according to a preset strategy, set the frequency band of interest for the acoustic signals to 5kHz-20kHz and the time window to 500 milliseconds to capture the weak acoustic characteristics generated when the solder begins to soften and the micro-grains melt. As the temperature rises further and enters the theoretical melting point range, the system dynamically adjusts. For example, it expands the focus frequency band of the electrical signal to 1MHz-10MHz and shortens the time window to 100 milliseconds to more accurately monitor the sharp drop in solder resistivity, which usually indicates that a large amount of solder has entered the liquid state. The real-time acquired acoustic and electrical signals undergo time-frequency analysis to extract their characteristics within the current focus frequency band and time window, such as acoustic energy peaks and the phase angle change rate of electrical impedance. These real-time characteristics are then compared with the pre-acquired typical phase transition characteristic curves of SnAgCu solder alloy. If the attenuation mode of the real-time acoustic signal closely matches the typical curve in the early stage of melting, and the decreasing trend of electrical impedance matches the liquefaction stage characteristics of the typical curve, the system calculates a high-confidence multimodal signal feature based on the comparison results, indicating that the solder has reached a fully melted state. Through this dynamic and targeted feature extraction and comparison, accurate judgment of the solder's melting state is ensured.

[0094] In one embodiment, during the heating process, dynamically adjusting the frequency band and time window for feature extraction includes: acquiring the rate of change of solder surface temperature and heating power; predicting the critical time point of the solder phase transition based on the rate of change of solder surface temperature and the heating power; before predicting the critical time point of the solder phase transition, narrowing the frequency band of interest for the acoustic signal and extending the acquisition time window of the acoustic signal to capture the weak acoustic features in the early stage of the solder phase transition; after predicting the critical time point of the solder phase transition, expanding the frequency band of interest for the electrical signal and shortening the acquisition time window of the electrical signal to capture the changes in electrical characteristics after the solder phase transition is completed; and obtaining the target frequency band to dynamically adjust the time window when a change in the acoustic signal or the electrical signal is detected.

[0095] It should be noted that during the heating process of the target surface mount component, the rate of change of solder surface temperature and the heating power are first acquired. The rate of change of solder surface temperature can be obtained by continuously monitoring the surface temperature and calculating its rate of change over time, while the heating power can be directly read from the heating equipment. These parameters are crucial for predicting the solder phase transition process. Furthermore, based on the acquired rate of change of solder surface temperature and heating power, the critical time points of the solder phase transition can be predicted. For example, a pre-established thermodynamic model or machine learning model, combined with the current heating state, can be used to predict the possible time points of key stages such as the start of melting and complete melting of the solder. This prediction mechanism provides forward-looking guidance for subsequent dynamic adjustments. Before the predicted critical time points, in order to more sensitively capture the weak acoustic characteristics of the early stage of the phase transition, the frequency band of interest for the acoustic signal is narrowed, and the acquisition time window of the acoustic signal is extended. For example, focusing the attention band on specific low-frequency or ultrasonic bands related to changes in the internal microstructure of solder (such as grain boundary sliding and microbubble formation), and increasing signal accumulation by extending the acquisition time window, thereby improving the probability of detecting weak acoustic events. After the predicted critical time point, to efficiently capture changes in electrical properties after the phase transition, the attention band for the electrical signal is expanded, and the acquisition time window is shortened. For example, the attention band is expanded to a wide range encompassing changes in the liquid resistivity and conductivity of the solder, and the real-time responsiveness is improved by shortening the acquisition time window, enabling rapid identification of significant changes in electrical parameters during the solder's transition from solid to liquid state.

[0096] In one embodiment, when a change in an acoustic or electrical signal is detected, a target frequency band is obtained to dynamically adjust the time window. This includes: monitoring the change in the acoustic or electrical signal; performing time-frequency analysis on the changed signal to obtain the identified frequency band and its trend; determining whether the corrected surface temperature is within the theoretical melting point range of solder, based on the corrected surface temperature, and obtaining a determination result; if the determination result shows that the corrected surface temperature is within the theoretical melting point range of solder, comparing the identified frequency band with pre-stored typical solder melting frequency band characteristics to obtain a frequency band comparison result; and obtaining the target frequency band based on the frequency band comparison result to dynamically adjust the time window.

[0097] It should be noted that when changes in acoustic or electrical signals are detected, time-frequency analysis should be performed on the changed signals first. Time-frequency analysis can employ methods such as Short-Time Fourier Transform (STFT), wavelet transform, or Hilberbert-Huang transform to reveal the frequency components and intensity changes of the signal at different time points, thereby identifying the frequency bands and trends of change. For example, changes in acoustic signals may manifest as energy enhancement or attenuation within a specific frequency range, while changes in electrical signals may be reflected as abrupt changes in impedance or admittance at a specific frequency. After identifying the frequency bands and trends of change, further judgment needs to be made in conjunction with the corrected surface temperature. The corrected surface temperature is the temperature after correction using surface thermal radiation characteristic parameters, which can more accurately reflect the actual temperature of the solder. By determining whether the corrected surface temperature is within the theoretical melting point range of the solder, the validity of the signal change can be preliminarily verified. If the temperature is not within the theoretical melting point range, the signal change may be caused by other interference rather than a solder phase transition. The theoretical melting point range of solder can be obtained from a preset database based on the type of solder alloy (e.g., Sn-Pb, Sn-Ag-Cu, etc.). If the judgment result shows that the corrected surface temperature is within the theoretical melting point range of solder, the identified frequency band is compared with the pre-stored typical frequency band characteristics of solder melting. These pre-stored typical frequency band characteristics of solder melting are formed based on a large amount of experimental data and accumulated experience, representing the unique acoustic and electrical response frequency bands of different solder alloys during the melting process. Through comparison, it can be confirmed whether the currently monitored signal changes match the characteristics of the solder melting process, thus obtaining the frequency band comparison result. Therefore, based on the frequency band comparison result, the target frequency band can be obtained to dynamically adjust the time window. For example, if the comparison result shows that the current signal change highly matches the characteristics of the initial stage of solder melting, the focus frequency band of the acoustic signal can be further narrowed and the acquisition time window extended to capture the initial phase transition information more precisely; if it matches the characteristics of the completed melting stage, the focus frequency band of the electrical signal can be expanded and the acquisition time window shortened to confirm the completion of the phase transition. This dynamic adjustment mechanism based on multi-dimensional information (time-frequency analysis, temperature verification, feature comparison) can significantly improve the accuracy and robustness of feature extraction.

[0098] In one embodiment, assuming that during the heating of a target surface mount component, the system detects an abnormal change in the acoustic signal of its solder area at a certain moment. At this time, the system immediately performs time-frequency analysis on the changing acoustic signal, for example using wavelet transform, to identify its main frequency components and energy distribution characteristics, resulting in an identified frequency band of 50kHz-70kHz with a significant energy enhancement trend. Simultaneously, the system obtains the current corrected surface temperature of the solder area, assuming it is 185°C. The system determines whether 185°C is within the theoretical melting point range of the solder alloy (e.g., Sn63Pb37) (e.g., 183°C-190°C). Since 185°C is within this range, the system further compares the identified 50kHz-70kHz frequency band with pre-stored typical acoustic frequency band characteristics of Sn63Pb37 solder during the initial melting stage (e.g., energy enhancement at 55kHz-65kHz). If the comparison results show a high degree of match, the system will confirm that frequency band as the target band and dynamically adjust the acoustic signal acquisition time window accordingly, for example, extending it to 500ms to more precisely capture the weak acoustic features in the early stages of melting. Conversely, if a change in the electrical signal is detected, and the corrected surface temperature is within the melting point range, and the comparison results show a match with the typical electrical characteristics of the completed melting stage, the system will expand the frequency band of interest for the electrical signal and shorten the acquisition time window to quickly confirm the completion of the phase transition. This multi-dimensional intelligent judgment and adjustment ensures accurate perception of the solder melting state.

[0099] In one embodiment, when the confidence level of solder melting reaches or exceeds the target confidence level threshold, the method further includes: initiating a preset verification cycle; continuously monitoring the stability of the acoustic and electrical signals of the solder melting, and simultaneously monitoring the surface temperature change rate of the solder melting to obtain the monitoring signal stability and the monitoring temperature change rate; within the preset verification cycle, if the monitoring signal stability shows that the acoustic or electrical signals exhibit solid-state characteristic recovery, or the monitoring temperature change rate decreases significantly, it is determined to be local resolidification or insufficient fluidity, and heating is restarted until the complete melting state is reached again.

[0100] It should be noted that the preset verification period refers to a time window set by the system to ensure the stability and continuity of the molten state after the solder melt confidence level reaches the complete melting standard. The length of this period can be preset according to various factors such as solder type, component size, heating power, and ambient temperature, and can be set to several seconds to tens of seconds. Its main purpose is to provide a buffer time to further confirm whether the solder is truly and stably in a molten state, rather than a brief and unstable melting. During the verification period, the stability of the acoustic and electrical signals of the aforementioned solder area is continuously monitored. This means that the system will continuously collect the acoustic and electrical signals of the solder area. The stability monitoring of acoustic signals can focus on changes in their frequency, amplitude, or attenuation characteristics. For example, the acoustic damping of solder in a molten state is usually low, and the signal propagation characteristics will change significantly. The stability monitoring of electrical signals can focus on changes in their resistance, conductivity, or impedance spectrum. For example, the conductivity of solder in a molten state usually increases, and the impedance decreases. The stability of these signals refers to the fact that, in the molten state, their characteristic parameters should remain within a relatively stable range, without showing a tendency to transform into solid-state characteristics. Simultaneous monitoring of the surface temperature change rate of the solder area means that, in addition to acoustic and electrical signals, the system will also monitor the surface temperature change rate of the solder area synchronously. In a stable molten state, if the heating power is constant, the surface temperature change rate should tend to be gradual or remain at a low level. If the temperature change rate decreases significantly, it may indicate insufficient heat input or excessive heat loss, causing the solder to tend to re-solidify.

[0101] It's important to note that solid-state characteristic recovery refers to the acoustic and electrical signals of solder exhibiting characteristics consistent with solid solder as it transitions from a molten state to a solid state. For example, the acoustic signal might show higher attenuation or a different resonant frequency, while the electrical signal might show increased resistivity or a change in impedance spectrum. This "solid-state characteristic recovery" means that the monitored signal characteristics begin to deviate from the typical characteristics of the molten state and approach solid-state characteristics. Local resolidification or insufficient fluidity refers to the solder resolidifying in certain areas due to uneven heat dissipation or temperature fluctuations, resulting in an incomplete or unstable overall molten state. "Insufficient fluidity" means that although the solder is in a molten state, its viscosity is too high to achieve a smooth peeling operation, which may be due to the temperature not reaching the optimal melting temperature or uneven alloy composition. When local resolidification or insufficient fluidity is detected, the system immediately takes corrective measures, i.e., restarting or increasing heating, to ensure that the solder reaches and maintains a stable fully molten state again. This process continues until the criteria for a fully molten state are met again, including reaching a confidence threshold and passing the verification cycle.

[0102] In one embodiment, when disassembling a BGA-packaged CPU, the system, based on the above method, analyzes and corrects surface temperature, monitors changes in acoustic characteristics, and monitors changes in electrical characteristics. It calculates that the confidence level of solder melting has reached 95%, exceeding the preset target confidence threshold of 90%, and preliminarily determines that the solder has completely melted. At this point, the system does not immediately activate the stripping module but instead initiates a preset 5-second verification cycle. During this verification cycle, the system continuously monitors the output signals of the acoustic and electrical sensors in the solder area. For example, if a sudden increase in acoustic signal attenuation is detected, and the resistance value of the electrical signal begins to rise, while the infrared temperature sensor shows that the solder surface temperature change rate decreases from 0.5℃ / s to 0.1℃ / s, these signs collectively indicate that the solder may be undergoing localized resolidification. The system immediately determines this as localized resolidification and restarts the heating module, increasing the heating power by 20% until the acoustic and electrical signals stabilize again within the characteristic range of the molten state, the surface temperature change rate returns to normal, and the 5-second verification cycle is passed again. The stripping module is activated to strip the CPU only when the solder remains in a stable, fully molten state throughout the verification cycle. This ensures the solder is in optimal molten condition before stripping, even in complex thermal environments, thus preventing component damage or stripping failure due to localized solder resolidification.

[0103] See Figure 2 , Figure 2 This is a schematic diagram of a mobile phone patch disassembly control system provided in one embodiment of this application. The mobile phone patch disassembly control system 200 includes:

[0104] The acquisition module 210 is used to acquire the surface thermal radiation characteristic parameters of the target patch element;

[0105] The heating module 220 is used to heat the target patch element according to a preset initial heating strategy, and monitor the surface temperature of the target patch element during the heating process to obtain the monitored surface temperature.

[0106] The monitoring module 230 is used to monitor the changes in acoustic and electrical properties of the target patch element caused by the solder phase change during the heating process, and to obtain the monitored acoustic and electrical property changes.

[0107] The correction module 240 is used to correct the monitored surface temperature based on the surface thermal radiation characteristic parameters to obtain the corrected surface temperature.

[0108] The analysis status module 250 is used to analyze and correct the surface temperature, monitor changes in acoustic properties and electrical properties, and obtain the melting state of the target patch element.

[0109] The stripping module 260 is used to strip the target patch element when it is fully melted in the molten state.

[0110] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0111] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0112] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A mobile phone patch disassembly control method, characterized in that, include: Obtain the surface thermal radiation characteristics parameters of the target patch element; The target patch element is heated according to a preset initial heating strategy, and the surface temperature of the target patch element is monitored during the heating process to obtain the monitored surface temperature. During the heating process, the acoustic and electrical properties of the target patch element are monitored due to the solder phase transition, and the changes in acoustic and electrical properties are monitored. Based on the surface thermal radiation characteristic parameters, the monitored surface temperature is corrected to obtain the corrected surface temperature; The melting state of the target patch element is obtained by analyzing the modified surface temperature, the changes in the monitored acoustic properties, and the changes in the monitored electrical properties. When the molten state is fully molten, the target patch element is peeled off; Before heating the target patch element according to the preset initial heating strategy, the following steps are included: An acoustic pulse is applied to the solder joint area of ​​the target surface mount component, and the acoustic response signal of the solder joint area is acquired. The attenuation characteristics and energy distribution of the acoustic response signal are analyzed to assess the risk of voids in the weld area, and the void presence risk assessment results are obtained. A multi-frequency AC signal is applied to the solder joint area, and the electrical impedance spectrum of the solder joint area is measured. The nonlinearity or resonance peaks of the electrical impedance spectrum are analyzed to assess the risk of the presence of an intermetallic compound layer in the solder joint area, and the risk assessment result of the presence of the intermetallic compound layer is obtained. Based on the risk assessment results of the voids and the intermetallic compound layer, the risk level of microstructural anomalies in the solder joint area is assessed to obtain the microstructural anomaly risk level.

2. The method according to claim 1, characterized in that, The analysis of the modified surface temperature, the monitoring of changes in acoustic properties, and the monitoring of changes in electrical properties to obtain the melting state of the target patch element includes: The target confidence threshold is set according to the microstructural anomaly risk level, and the phase transition characteristic judgment criteria for the monitored acoustic characteristic changes and the monitored electrical characteristic changes are adjusted. Based on the monitored surface temperature, the changes in the monitored acoustic characteristics, and the changes in the monitored electrical characteristics, the multimodal signal characteristics are obtained; The confidence level of the solder melting is dynamically updated based on the multimodal signal characteristics. When the confidence level of the solder melting reaches or exceeds the target confidence level threshold, the melting state of the target surface mount component is obtained as complete melting.

3. The method according to claim 2, characterized in that, The step of setting a target confidence threshold based on the risk level of the microstructural anomaly, and adjusting the phase transition characteristic judgment criteria for the monitored acoustic characteristic changes and the monitored electrical characteristic changes, includes: The presence of micro-voids and intermetallic compound layers in the solder joint area is detected, and the detection results are obtained. Based on the detection results, the existence and distribution characteristics of the micro-voids and the thickness and distribution characteristics of the intermetallic compound layer are identified. Based on the presence and distribution characteristics of the microvoids and the thickness and distribution characteristics of the intermetallic compound layer, the risk level of the microstructure anomaly is divided into different types, resulting in multiple types of microstructure anomalies. For each type of microstructural anomaly and the risk level of the microstructural anomaly, a target confidence threshold and a phase transition characteristic judgment standard for the changes in the monitored acoustic properties and the changes in the monitored electrical properties are set.

4. The method according to claim 2, characterized in that, The process of obtaining multimodal signal characteristics based on the monitored surface temperature, the monitored acoustic characteristic changes, and the monitored electrical characteristic changes includes: Spatiotemporal filtering is performed on the monitored surface temperature, the changes in the monitored acoustic characteristics, and the changes in the monitored electrical characteristics to obtain a multimodal signal after interference elimination; Feature extraction is performed on the multimodal signal after interference elimination to obtain multimodal signal features.

5. The method according to claim 4, characterized in that, The step of extracting features from the interference-free multimodal signal to obtain multimodal signal features includes: Based on the model information of the target surface mount component, obtain the theoretical melting point range and typical phase transition characteristic curve of the solder alloy; During the heating process, the frequency band and time window of interest for feature extraction are dynamically adjusted; The frequency band of interest and the time window are compared with the typical phase transition characteristic curve to obtain the comparison results; Based on the comparison results, the multimodal signal features are obtained.

6. The method according to claim 5, characterized in that, The step of dynamically adjusting the frequency band and time window of interest for feature extraction during the heating process includes: To obtain the rate of temperature change and heating power of the solder surface; Based on the rate of change of the solder surface temperature and the heating power, predict the key time points of the solder phase transition; Before the critical time point of the predicted solder phase transition, the frequency band of interest for the acoustic signal is narrowed and the acquisition time window of the acoustic signal is extended to capture the weak acoustic features in the early stage of the solder phase transition. After the critical time point for predicting the solder phase transition, the focus band of the electrical signal is expanded and the acquisition time window of the electrical signal is shortened to capture the changes in electrical characteristics after the solder phase transition is completed. When a change is detected in the acoustic signal or the electrical signal, the target frequency band is obtained to dynamically adjust the time window.

7. The method according to claim 6, characterized in that, The step of obtaining the target frequency band and dynamically adjusting the time window when a change in the acoustic signal or the electrical signal is detected includes: Monitor changes in the acoustic or electrical signals; Time-frequency analysis is performed on the changing signal to obtain the identified frequency bands and their changing trends; Based on the corrected surface temperature, determine whether the corrected surface temperature is within the theoretical melting point range of the solder, and obtain the determination result; If the judgment result shows that the corrected surface temperature is within the theoretical melting point range of the solder, then the identified frequency band is compared with the pre-stored typical frequency band characteristics of solder melting to obtain the frequency band comparison result; The target frequency band is obtained based on the frequency band comparison results, and the time window is dynamically adjusted.

8. The method according to claim 2, characterized in that, The step of when the confidence level of the solder melting reaches or exceeds the target confidence level threshold further includes: Start the preset verification cycle; The stability of the acoustic and electrical signals of the molten solder is continuously monitored, and the surface temperature change rate of the molten solder is monitored simultaneously to obtain the stability of the monitoring signals and the rate of temperature change. Within the preset verification period, if the stability of the monitoring signal shows that the acoustic signal or the electrical signal has recovered solid-state characteristics, or if the rate of change of the monitored temperature decreases significantly, it is determined that there is local re-solidification or insufficient fluidity, and heating is restarted until it reaches a completely melted state again.

9. A mobile phone patch disassembly and control system, characterized in that, include: The acquisition module is used to acquire the surface thermal radiation characteristic parameters of the target patch element; A heating module is used to heat the target patch element according to a preset initial heating strategy, and monitor the surface temperature of the target patch element during the heating process to obtain the monitored surface temperature. The monitoring module is used to monitor the changes in acoustic and electrical properties of the target patch element caused by the solder phase transition during the heating process, and to obtain the monitored acoustic and electrical property changes. The correction module is used to correct the monitored surface temperature based on the surface thermal radiation characteristic parameters to obtain the corrected surface temperature. The analysis state module is used to analyze the modified surface temperature, the monitored acoustic characteristic changes, and the monitored electrical characteristic changes to obtain the melting state of the target patch element; A stripping module is used to strip the target patch element when the melting state is fully melted; Before heating the target patch element according to the preset initial heating strategy, the following steps are included: An acoustic pulse is applied to the solder joint area of ​​the target surface mount component, and the acoustic response signal of the solder joint area is acquired. The attenuation characteristics and energy distribution of the acoustic response signal are analyzed to assess the risk of voids in the weld area, and the void presence risk assessment results are obtained. A multi-frequency AC signal is applied to the solder joint area, and the electrical impedance spectrum of the solder joint area is measured. The nonlinearity or resonance peaks of the electrical impedance spectrum are analyzed to assess the risk of the presence of an intermetallic compound layer in the solder joint area, and the risk assessment result of the presence of the intermetallic compound layer is obtained. Based on the risk assessment results of the voids and the intermetallic compound layer, the risk level of microstructural anomalies in the solder joint area is assessed to obtain the microstructural anomaly risk level.

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