Method and apparatus for monitoring abnormality of conductive line, storage medium, program product
Patent Information
- Application Number
- CN202611265356.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-20
- Publication Date
- 2026-10-16
AI Technical Summary
然而，这需要温度传感器紧贴导电线路设置，且其准确性受到紧贴程度影响，从而导致异常监测的稳定性不佳
[0014] In another aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aforementioned method.
Smart Images
Figure CN122922579A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of conductive line monitoring, and in particular to a method and apparatus, storage medium, and program product for abnormal monitoring of conductive lines. Background Technology
[0002] In the electronics manufacturing industry, conductive pastes are widely used in products such as photovoltaic modules, touch screens, automotive window heating wires, and flexible circuits because they can form conductive lines on a substrate through processes such as printing and sintering, exhibiting high plasticity. During operation, conductive lines are prone to aging due to various factors such as current thermal effects, environmental corrosion, and mechanical stress, leading to abnormal states such as localized overheating and breakage, ultimately causing product damage. To address this, related technologies typically use temperature sensors attached to different locations on the conductive lines to detect abnormal conditions. However, this requires the temperature sensors to be tightly attached to the conductive lines, and its accuracy is affected by the degree of contact, resulting in poor stability in anomaly monitoring. Summary of the Invention
[0003] Based on this, this application provides an abnormal monitoring method for conductive circuits, which can identify abnormal states of conductive circuits in a non-contact manner, thereby improving the stability of the abnormal monitoring method.
[0004] On one hand, this application provides a method for abnormal monitoring of a conductive circuit, wherein the conductive circuit is formed by a conductive paste; the conductive paste is doped with fluorescent particles; the fluorescent particles are configured to generate light signals in response to a preset external light field; the method includes: applying a preset external light field to the conductive circuit and detecting the light signals generated by the conductive circuit at multiple preset locations; calculating temperature detection values of the conductive circuit at multiple preset locations based on the light signals; identifying abnormal states of the conductive circuit at multiple preset locations based on the temperature detection values at multiple preset locations and a preset healthy temperature baseline; the preset healthy temperature baseline is used to characterize the temperature of the conductive circuit at multiple preset locations in a healthy state.
[0005] Optionally, the step of identifying the abnormal state of the conductive line at multiple preset locations based on the temperature detection values at multiple preset locations and a preset healthy temperature baseline includes: when the deviation between the temperature detection value at a single preset location and the corresponding healthy temperature value within the preset healthy temperature baseline is greater than or equal to a preset deviation threshold, it is determined that the conductive line is in an abnormal state at that preset location.
[0006] Optionally, the conductive paste is also doped with magnetic particles; the magnetic particles are configured to generate a magnetic response signal in response to a preset external magnetic field; the magnetic response signal includes magnetic permeability; the method further includes: applying a preset external magnetic field to the conductive line and obtaining magnetic permeability detection values generated at multiple preset locations of the conductive line; based on the magnetic permeability detection values at the multiple preset locations and a preset healthy magnetic permeability baseline, identifying abnormal states of the conductive line at the multiple preset locations; the preset healthy magnetic permeability baseline is used to characterize the magnetic permeability of the conductive line at the multiple preset locations in a healthy state.
[0007] Optionally, the method further includes: obtaining historical temperature detection values of the conductive line; the historical temperature detection values include temperature detection values of the conductive line at multiple preset locations within several consecutive detection cycles in the past; determining a risk probability value based on the historical temperature detection values and a preset healthy temperature baseline; the risk probability value is used to characterize the probability of the conductive line entering an abnormal state; and sending a health warning command when the risk probability value is greater than or equal to a preset probability threshold.
[0008] Optionally, the step of determining the risk probability value of the current detection cycle based on historical temperature detection values and a preset healthy temperature baseline includes: obtaining the maximum deviation between the temperature detection value of the conductive line and the corresponding healthy temperature value within the preset healthy temperature baseline in each detection cycle; and inputting the time series consisting of the maximum deviation value and the corresponding detection time into a preset probability prediction model to obtain the risk probability value.
[0009] Optionally, the method further includes: obtaining the rate of change of the maximum deviation value through linear fitting based on the maximum deviation value corresponding to several consecutive detection cycles; calculating the remaining service life of the conductive circuit based on the maximum deviation value and the rate of change of the current detection cycle; the remaining service life is the time required for the maximum deviation value to increase to be greater than or equal to a preset deviation threshold.
[0010] Optionally, the concentration of fluorescent particles doped in the conductive paste corresponding to each conductive line is different; the method further includes: during the fabrication of the conductive line, assigning a corresponding identification code to the conductive line based on the concentration value of the fluorescent particles doped in the conductive paste used to fabricate the conductive line; after the conductive line is fabricated, obtaining the identification code corresponding to the conductive line and determining the preset healthy temperature baseline corresponding to the conductive line.
[0011] On the other hand, this application also provides an abnormality monitoring device for a conductive circuit, wherein the conductive circuit is formed by a conductive paste; the conductive paste is doped with fluorescent particles; the fluorescent particles are configured to generate light signals in response to a preset external light field; the abnormality monitoring device includes: a detection module for applying a preset external light field to the conductive circuit and detecting the light signals generated by the conductive circuit at multiple preset locations; a calculation module for calculating temperature detection values of the conductive circuit at multiple preset locations based on the light signals; and an identification module for identifying abnormal states of the conductive circuit at multiple preset locations based on the temperature detection values at multiple preset locations and a preset healthy temperature baseline; the preset healthy temperature baseline is used to characterize the temperature of the conductive circuit at multiple preset locations in a healthy state.
[0012] In another aspect, this application also provides another abnormality monitoring device for conductive lines, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the aforementioned method.
[0013] In another aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method.
[0014] In another aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aforementioned method.
[0015] The embodiments provided in this application involve doping a conductive paste used to fabricate conductive circuits with fluorescent particles capable of generating light signals in response to a preset external light field. After the conductive paste forms the conductive circuits, the preset external light field is applied to the conductive circuits to cause the fluorescent particles to generate light signals. The light signals at multiple preset locations are then detected, and temperature detection values at these locations are calculated based on the mapping relationship between the luminescence characteristics of the fluorescent particles and temperature. Finally, by comparing the temperature detection values with a preset healthy temperature baseline, abnormal states of the conductive circuits at these multiple preset locations are identified. This allows the monitoring method to identify abnormal states of conductive circuits in a non-contact manner, improving the stability of the anomaly monitoring method. Attached Figure Description
[0016] Figure 1 This is a flowchart illustrating an abnormality monitoring method for conductive lines according to an embodiment of this application.
[0017] Figure 2 This is a flowchart illustrating an abnormality monitoring method for conductive lines according to another embodiment of this application;
[0018] Figure 3 This is a structural block diagram of an abnormality monitoring device for conductive lines according to an embodiment of this application;
[0019] Figure 4 This is a structural block diagram of an abnormality monitoring device for conductive lines according to another embodiment of this application. Detailed Implementation
[0020] To make the technical solution and beneficial effects of this application more apparent and understandable, a detailed description is provided below by listing specific embodiments. Unless otherwise defined, the technical and scientific terms used in this application have the same meaning as those in the technical field to which this application pertains. The terminology used in the specification of this application is for the purpose of describing specific embodiments only and is not intended to limit this application.
[0021] See Figure 1 This application provides a method for anomaly monitoring of a conductive circuit in some embodiments. The conductive circuit is formed from a conductive paste. The conductive paste is doped with fluorescent particles. The fluorescent particles are configured to generate a light signal in response to a preset external light field. The method includes:
[0022] S101, apply a preset external light field to the conductive line and detect the light signals generated by the conductive line at multiple preset positions.
[0023] It is understandable that fluorescent particles refer to fluorescent materials doped into conductive pastes. For example, fluorescent particles can be sodium yttrium fluoride doped with ytterbium or erbium (YT). The doping concentration can be set to 100ppm-2000ppm, and the fluorescent particle size can be set to 20nm-100nm. After being excited by a preset external light field (such as near-infrared laser or other lasers with specific wavelengths), the fluorescent particles emit light through upconversion, generating a temperature-dependent light signal. Furthermore, the fluorescent particles in the above example also exhibit high-temperature resistance, withstanding high-temperature sintering below 850℃ without any attenuation in luminescence performance, thus enabling anomaly monitoring after the conductive circuit is formed. Preset positions refer to multiple monitoring points pre-set along the length of the conductive circuit, which can be specifically set according to actual monitoring needs. For example, multiple preset positions can be set at equal intervals. Alternatively, key structural nodes of the conductive circuit (such as bends, connections, and cross-sectional changes) can also be set as preset positions.
[0024] It should be noted that the type, doping concentration, and particle size of the fluorescent particles described above are exemplary. In other embodiments, other types and concentrations of fluorescent materials with luminescent properties can also be used as fluorescent particles.
[0025] S102 calculates the temperature detection values of the conductive line at multiple preset locations based on the optical signal.
[0026] Specifically, due to the characteristics of fluorescent particles, a certain physical parameter (such as fluorescence intensity or fluorescence lifetime) of the light signal generated after the fluorescent particles are excited by a preset external light field has a preset mapping relationship (such as a linear relationship or mapping table) with the temperature at the location of the fluorescent particles. This mapping relationship can be determined through pre-experimental calibration. After detecting the light signals of the conductive circuit at multiple preset locations, the temperature detection values of the conductive circuit at multiple preset locations can be calculated based on the above preset relationship.
[0027] S103, based on temperature detection values at multiple preset locations and a preset healthy temperature baseline, identifies abnormal states of the conductive circuit at multiple preset locations. The preset healthy temperature baseline is used to characterize the temperature of the conductive circuit at multiple preset locations under healthy conditions.
[0028] It is understood that the preset healthy temperature baseline refers to the temperature collected at various preset locations under normal operating conditions of the conductive circuit. The preset healthy temperature baseline can be obtained through testing and calibration in an experimental environment or under the initial operating conditions of the conductive circuit. An abnormal state refers to at least one monitored physical parameter of the conductive circuit (such as temperature, stress, silver migration level, etc.) deviating from the operating condition corresponding to its preset healthy temperature baseline. For example, in this embodiment, when the temperature detection value at a certain preset location deviates significantly from the preset healthy temperature baseline or for a long period, the conductive circuit can be considered to be in an abnormal state at that preset location.
[0029] The aforementioned anomaly monitoring method involves doping the conductive paste used to fabricate the conductive circuit with fluorescent particles capable of generating light signals in response to a preset external light field. After the conductive paste forms the conductive circuit, the preset external light field is applied to the conductive circuit to induce the fluorescent particles to generate light signals. The light signals at multiple preset locations are then detected, and based on the mapping relationship between the luminescence characteristics of the fluorescent particles and temperature, the temperature detection values at these multiple preset locations are calculated. Finally, by comparing the temperature detection values with a preset healthy temperature baseline, the abnormal state of the conductive circuit at these multiple preset locations is identified. This allows the monitoring method to identify abnormal states of the conductive circuit in a non-contact manner, improving the stability of the anomaly monitoring method.
[0030] Furthermore, in some anomaly detection methods that use temperature sensors to monitor the temperature of conductive lines, temperature sensors need to be placed near each preset location to ensure that the anomaly detection function covers all preset locations on the conductive line. This results in complex wiring and power supply lines for the temperature sensors, leading to high hardware costs for the anomaly detection method. In contrast, the aforementioned anomaly detection method can identify abnormal states of conductive lines non-contactly, and the fluorescent particles do not require a power supply, thus eliminating the need for complex wiring and power supply lines and reducing hardware costs.
[0031] In some embodiments, the step of identifying abnormal states of a conductive line at multiple preset locations based on temperature detection values at multiple preset locations and a preset healthy temperature baseline includes: determining that the conductive line is in an abnormal state at a preset location when the deviation between the temperature detection value at a single preset location and the corresponding healthy temperature value within the preset healthy temperature baseline is greater than or equal to a preset deviation threshold. Thus, the anomaly monitoring method achieves the function of identifying abnormal states of conductive lines based on temperature detection values and a preset healthy temperature baseline.
[0032] It is understandable that the deviation between the temperature detection value and the corresponding healthy temperature value within the preset healthy temperature baseline is an unsigned scalar value used to reflect the degree of deviation between the temperature detection value and the healthy temperature value.
[0033] It should be noted that the abnormal state identification method in the above embodiments is exemplary. In other embodiments, other methods can also be used to identify the abnormal state of the conductive line at multiple preset locations. For example, the integral value of the deviation between the temperature detection value of the conductive line at a single preset location and a preset healthy temperature baseline over time can be calculated. Within a preset time window, if the integral value is greater than or equal to a preset deviation integral threshold, it is determined that the conductive line is in an abnormal state at that preset location. The advantage of this method is that it can avoid misjudgments caused by instantaneous temperature fluctuations, thereby improving the accuracy of abnormal monitoring.
[0034] In some embodiments, the conductive paste is further doped with magnetic particles. The magnetic particles are configured to generate a magnetic response signal in response to a preset external magnetic field. The magnetic response signal includes magnetic permeability. The method further includes:
[0035] S201, apply a preset external magnetic field to the conductive line and obtain the magnetic permeability detection values generated at multiple preset positions of the conductive line.
[0036] It is understandable that magnetic particles refer to magnetic nanocrystals doped in conductive pastes. For example, magnetic particles can be iron(III) oxide (Fe3O4), with a doping concentration of 80ppm-2000ppm and a particle size of 50nm-1000nm. Magnetic response signal refers to the detectable signal generated by a conductive circuit under the excitation of a preset external magnetic field. Magnetic permeability is one of the specific parameters representing the magnetic response signal, and it can be detected by setting up a magnetic sensing coil, etc.
[0037] S202, based on the permeability detection values at multiple preset locations and a preset healthy permeability baseline, identifies abnormal states of the conductive circuit at multiple preset locations. The preset healthy permeability baseline is used to characterize the permeability of the conductive circuit at multiple preset locations in a healthy state.
[0038] It is understandable that the preset healthy permeability baseline refers to the permeability of a conductive circuit measured at multiple preset locations under normal operating conditions and after a preset external magnetic field has been applied. The preset healthy permeability baseline can be obtained through testing and calibration in an experimental environment or under the environment during the initial operation of the conductive circuit.
[0039] Specifically, when magnetic particles are doped into a conductive circuit, these particles generate specific magnetic response signals under the excitation of a preset external magnetic field, causing the permeability of the conductive circuit at different preset locations to exhibit specific values. Simultaneously, the permeability of the conductive circuit at different preset locations is affected by various factors, such as the mechanical stress at that location, the corrosion level of the conductive circuit, and the silver migration level. These factors all lead to changes in the distribution of magnetic particles doped in the conductive paste, the surrounding medium environment, or the magnetic properties of the particles themselves, thus collectively causing variations in the permeability of the conductive circuit at different locations.
[0040] More specifically, since magnetic particles (such as Fe3O4) are magnetic materials and have a piezomagnetic effect, when a predetermined position of a conductive circuit is subjected to external stress parallel to the direction of the magnetic field, the compressive stress usually leads to a decrease in the permeability at that predetermined position, while the tensile stress leads to an increase in the permeability.
[0041] Silver migration refers to the phenomenon where silver ions migrate along the surface of a conductive circuit and form dendritic crystals under the influence of an electric field and ambient humidity when the circuit contains silver. A higher level of silver migration indicates a more severe silver migration phenomenon in the conductive circuit. Since the dendritic crystals formed by silver migration are conductive, when a high-frequency alternating magnetic field is applied to the conductive circuit, eddy currents are generated inside the dendritic crystals, which in turn generate a reverse magnetic field, partially canceling out the magnetic field and causing a decrease in the permeability of the conductive circuit.
[0042] Similarly, corrosion can also corrode magnetic particles in conductive circuits, transforming them into weakly magnetic materials. This weakens the magnetic response signal of the magnetic particles, resulting in a negative correlation between the permeability of the conductive circuit and the level of corrosion.
[0043] Therefore, it can be seen that when the magnetic permeability of a conductive line changes drastically at a single preset position, the conductive line will usually have problems such as excessive line stress, high corrosion level, and high silver migration level.
[0044] Therefore, in the anomaly monitoring method of the above embodiments, a preset healthy magnetic permeability baseline of the conductive circuit is first determined through pre-calibration. During actual monitoring of the conductive circuit, a preset external magnetic field can be applied to the conductive circuit to detect the magnetic permeability generated at multiple preset locations, obtaining multiple magnetic permeability detection values. Subsequently, the magnetic permeability detection values are compared with the preset healthy magnetic permeability baseline to identify whether the conductive circuit is in an abnormal state. For example, when the magnetic permeability detection value of the conductive circuit at a single preset location deviates significantly from the preset healthy magnetic permeability baseline or when the deviation time is long, it can be determined that the conductive circuit is in an abnormal state at that preset location.
[0045] The advantage of the above-mentioned anomaly monitoring method is that by doping magnetic particles into the conductive paste, the anomaly monitoring method can identify the abnormal state of the conductive line at different preset positions by detecting the magnetic permeability of the conductive line. This makes the anomaly monitoring method cover a wider range of anomalies and has higher accuracy, resulting in a better monitoring effect.
[0046] Furthermore, since magnetic particles and fluorescent particles operate on different principles and their detection methods are independent of each other, in the above-mentioned anomaly monitoring method, once the conductive circuit is determined to be in an abnormal state, the permeability detection value and the circuit temperature can independently reflect the actual abnormal state of the conductive circuit. Therefore, they can be used as initial parameters for users to troubleshoot conductive circuit faults, thereby facilitating users to troubleshoot faults and improving the convenience of the anomaly monitoring method.
[0047] In some embodiments, the method further includes: acquiring historical temperature detection values of the conductive line. The historical temperature detection values include temperature detection values of the conductive line at multiple preset locations over several consecutive detection cycles. A risk probability value for the current detection cycle is determined based on the historical temperature detection values and a preset healthy temperature baseline. The risk probability value characterizes the probability that the conductive line will enter an abnormal state. When the risk probability value is greater than or equal to a preset probability threshold, a health warning command is sent.
[0048] Specifically, when a conductive circuit enters an abnormal state due to reasons such as excessive temperature, there is usually a gradual trend of change. Therefore, the trend of the temperature detection value of the conductive circuit can reflect the risk of the conductive circuit entering an abnormal temperature state.
[0049] Therefore, in the above-mentioned anomaly monitoring method, historical temperature detection values of the conductive line over several (e.g., 30) detection cycles are obtained. Then, based on the changing trends or distribution characteristics of the deviations between these historical temperature detection values and a preset healthy temperature baseline, the risk probability value for the current detection cycle is calculated. When the risk probability value is greater than or equal to a preset probability threshold (e.g., 0.8), a health warning command is sent to the user or the upper-level controller to prompt the user to handle the anomaly risk. This enables the anomaly monitoring method to predict abnormal states of the conductive line.
[0050] In some embodiments, the step of determining the risk probability value of the current detection cycle based on historical temperature detection values and a preset healthy temperature baseline includes: obtaining the maximum deviation between the temperature detection value of the conductive line and the corresponding healthy temperature value within the preset healthy temperature baseline in each detection cycle; and inputting the time series consisting of the maximum deviation value and its corresponding detection time into a preset probability prediction model to obtain the risk probability value.
[0051] Specifically, in each detection cycle, the temperature detection values of the conductive line at multiple preset locations will each produce a deviation value from the corresponding healthy temperature value within a preset healthy temperature baseline. The maximum deviation value is the largest of these multiple deviation values. The preset probability prediction model refers to a pre-set learning model used to output the risk probability value of the conductive line entering an abnormal state in the current detection cycle based on the input time series (including multiple sets of maximum deviation values and their corresponding detection times). By inputting the real-time maximum deviation value time series of the current conductive line into the model, the risk probability value for the current detection cycle can be obtained.
[0052] Understandably, various learning models with different structures can be used as the preset probability prediction model. For example, an LSTM (Long Short-Term Memory) model can be chosen as the preset probability prediction model. LSTM models have good time series data processing capabilities and are suitable for extracting degradation trend features from the time series of maximum deviation values, thereby improving the accuracy of the output risk probability values. LSTM models can be trained through different methods such as supervised training or reinforcement training. For example, the time series of maximum deviation values of conductive lines in each detection cycle can be used as training sample data, with normal or abnormal states as label data, allowing the LSTM model to learn the mapping relationship between the evolution trend of the maximum deviation value and the probability of the line entering an abnormal state.
[0053] It should be noted that the description of the risk probability value determination method in the above embodiments is exemplary. In other embodiments, other methods may also be used to determine the risk probability value. For example, a temporal convolutional network can be used to process the above time series to obtain the risk probability value of the current detection period.
[0054] In addition, the above-mentioned anomaly monitoring method also selects the maximum deviation value as the parameter of the preset probability prediction model. The advantage of this approach is that it makes the preset probability prediction model more sensitive to the temperature detection value fluctuation of the conductive line, thereby enabling the anomaly monitoring method to respond to the abnormal state of the conductive line more quickly.
[0055] It is understood that in the above embodiments, the maximum deviation value is selected as the parameter input to the preset probability prediction model, and this setting is exemplary. In other embodiments, other statistical values can also be selected as input to the preset probability prediction model. For example, the average deviation value of multiple deviation values in each detection cycle can be calculated, and then the average deviation value and its corresponding detection time can be combined to form a time series, which is then input into the preset probability prediction model to obtain the risk probability value.
[0056] In some embodiments, the method further includes: obtaining the rate of change of the maximum deviation value through linear fitting based on the maximum deviation value corresponding to several consecutive detection cycles; calculating the remaining service life of the conductive circuit based on the maximum deviation value and the rate of change of the current detection cycle; and determining the remaining service life as the time required for the maximum deviation value to increase to a level greater than or equal to a preset deviation threshold.
[0057] Specifically, during the use of conductive circuits, their aging level increases monotonically and is usually linearly related to the usage time. Simultaneously, the aging level also leads to an increase in the temperature rise rate of the conductive circuit, and the two show a monotonically related relationship. Therefore, the maximum deviation value corresponding to several consecutive testing cycles can be obtained. That is, the maximum deviation value among the deviations of the temperature detection value of the conductive circuit from the corresponding healthy temperature value within a preset healthy temperature baseline in each testing cycle. The obtained maximum deviation values are then used to calculate the rate of change of the maximum deviation value over time using linear fitting methods such as the least squares method. This rate of change can be used to reflect the aging level of the conductive circuit.
[0058] Subsequently, based on the maximum deviation value of the current detection cycle and the rate of change obtained through fitting, the duration for the maximum deviation value to increase to a level greater than or equal to a preset deviation threshold can be calculated. This duration is the remaining service life of the conductive circuit. This allows the anomaly monitoring method to estimate the remaining service life of the conductive circuit for user inquiry, improving the user experience.
[0059] In some embodiments, the concentration of fluorescent particles doped in the conductive paste corresponding to each conductive line is different. The method further includes: during the fabrication of the conductive line, assigning a unique identification code to each conductive line based on the concentration of fluorescent particles doped in the conductive paste. After the conductive line is fabricated, obtaining the unique identification code for each conductive line and determining a preset healthy temperature baseline for that conductive line.
[0060] Specifically, during the production of conductive paste, the concentration of fluorescent particles doped in different batches varies due to differences in the paste formulation. This causes fluctuations in the preset healthy temperature baseline corresponding to the fluorescent particles in the conductive circuit made from the conductive paste. Consequently, the anomaly monitoring method requires calibration of the preset healthy temperature baseline of the conductive circuit before use, making the process rather cumbersome.
[0061] The aforementioned anomaly monitoring method assigns a unique identification code to each conductive circuit during its fabrication process, based on the concentration of fluorescent particles doped in the conductive paste. This code is then stored in a cloud or local database. After the conductive circuit is fabricated, the identification code is retrieved from the database, allowing for the determination of a preset healthy temperature baseline for anomaly monitoring.
[0062] The advantage of this approach is that, for conductive circuits made using different batches of conductive paste, calibration steps such as setting a preset healthy temperature baseline, mapping the luminescence characteristics of fluorescent particles to temperature, and setting a preset healthy magnetic permeability baseline can be performed directly after sintering. During actual monitoring, the identification code can be directly matched with the corresponding baseline, eliminating the need for repeated baseline calibration steps, thereby improving the automation and convenience of the anomaly monitoring method.
[0063] Some embodiments of this application provide an anomaly monitoring device for conductive circuits. The conductive circuits are formed from conductive paste. The conductive paste is doped with fluorescent particles. The fluorescent particles are configured to generate light signals in response to a preset external light field. (Reference) Figure 3 The anomaly monitoring device 100 includes a detection module 110, a calculation module 120, and an identification module 130. The detection module 110 applies a preset external light field to the conductive line and detects the light signals generated by the conductive line at multiple preset locations. The calculation module 120 calculates the temperature detection values of the conductive line at the multiple preset locations based on the light signals. The identification module 130 identifies the abnormal state of the conductive line at the multiple preset locations based on the temperature detection values at the multiple preset locations and a preset healthy temperature baseline. The preset healthy temperature baseline characterizes the temperature of the conductive line at the multiple preset locations under healthy conditions.
[0064] Each module in the aforementioned monitoring device 100 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of the monitoring device 100 as software, so that the processor can call and execute the operations corresponding to each module. It should be noted that the above module division is illustrative and represents only a logical functional division; in actual implementation, other division methods may be used.
[0065] Based on the embodiments described above for the abnormal monitoring method of conductive lines, in some other embodiments provided in this application, another abnormal monitoring device is provided, the internal structure of which can be shown in the figure below. Figure 4 As shown, the anomaly monitoring device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for monitoring anomalies in conductive circuits.
[0066] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the anomaly monitoring device to which the present application is applied. A specific anomaly monitoring device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0067] Based on the aforementioned embodiments of the abnormal monitoring method for conductive lines, in another embodiment provided in this application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps in the above-described method embodiments.
[0068] Based on the aforementioned embodiments of the abnormal monitoring method for conductive lines, in another embodiment provided in this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0069] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitation, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. For example, the use of terms such as "first," "second," etc., is to denote names and does not indicate any specific order.
[0070] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having,” etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. Meanwhile, in this specification, the term “and / or” includes any and all combinations of the associated listed items.
[0071] In the description of this application, the references to terms such as "some embodiments," "other embodiments," "ideal embodiments," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative descriptions of the above terms do not necessarily refer to the same embodiments or examples.
[0072] It is understood that the various embodiments of the methods described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. Relevant details can be found in the descriptions of other method embodiments.
[0073] It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations included in the claims. Various modifications and changes can be made to the above embodiments without departing from the scope of this application. Similarly, the various technical features of the above embodiments can be arbitrarily combined to form other embodiments of this application that may not be explicitly described. Therefore, the above embodiments only illustrate several implementations of this application and do not limit the scope of protection of this patent application.
Claims
1. A method for monitoring anomalies in conductive circuits, characterized in that, The conductive circuit is formed by a conductive paste; the conductive paste is doped with fluorescent particles. The fluorescent particles are configured to generate a light signal in response to a preset external light field; the method includes: The preset external light field is applied to the conductive line, and the light signals generated by the conductive line at multiple preset positions are detected; The temperature detection values of the conductive line at the multiple preset positions are calculated based on the optical signal. Based on the temperature detection values at the multiple preset locations and the preset healthy temperature baseline, the abnormal state of the conductive circuit at the multiple preset locations is identified; the preset healthy temperature baseline is used to characterize the temperature of the conductive circuit at the multiple preset locations in a healthy state.
2. The anomaly monitoring method according to claim 1, characterized in that, The step of identifying the abnormal state of the conductive circuit at the multiple preset locations based on the temperature detection values at the multiple preset locations and the preset healthy temperature baseline includes: When the deviation between the temperature detection value at a single preset location and the corresponding healthy temperature value within the preset healthy temperature baseline is greater than or equal to a preset deviation threshold, the conductive circuit is determined to be in an abnormal state at that preset location.
3. The anomaly monitoring method according to claim 1, characterized in that, The conductive paste is also doped with magnetic particles; the magnetic particles are configured to generate a magnetic response signal in response to a preset external magnetic field; the magnetic response signal includes magnetic permeability; the method further includes: The preset external magnetic field is applied to the conductive line to obtain the magnetic permeability detection values generated at multiple preset positions of the conductive line; Based on the magnetic permeability detection values at the multiple preset locations and the preset healthy magnetic permeability baseline, the abnormal state of the conductive line at the multiple preset locations is identified; the preset healthy magnetic permeability baseline is used to characterize the magnetic permeability of the conductive line at the multiple preset locations in a healthy state.
4. The anomaly monitoring method according to any one of claims 1-3, characterized in that, The method further includes: Obtain historical temperature detection values of the conductive line; the historical temperature detection values include temperature detection values of the conductive line at multiple preset locations over several consecutive detection cycles in the past. Based on the historical temperature detection values and the preset healthy temperature baseline, the risk probability value for the current detection cycle is determined; the risk probability value is used to characterize the probability that the conductive circuit will enter the abnormal state. When the risk probability value is greater than or equal to a preset probability threshold, a health warning command is sent.
5. The anomaly monitoring method according to claim 4, characterized in that, The step of determining the risk probability value for the current detection period based on the historical temperature detection value and the preset healthy temperature baseline includes: Obtain the maximum deviation between the temperature detection value of the conductive circuit and the corresponding healthy temperature value within the preset healthy temperature baseline in each detection cycle; The time series consisting of the maximum deviation value and its corresponding detection time is input into a preset probability prediction model to obtain the risk probability value.
6. The anomaly monitoring method according to claim 5, characterized in that, The method further includes: Based on the maximum deviation value corresponding to the several consecutive detection cycles, the rate of change of the maximum deviation value is obtained by linear fitting; Based on the maximum deviation value of the current detection cycle and the rate of change, the remaining service life of the conductive circuit is calculated; the remaining service life is the time required for the maximum deviation value to increase to a level greater than or equal to a preset deviation threshold.
7. The anomaly monitoring method according to any one of claims 1-3, characterized in that, The concentration of fluorescent particles doped in the conductive paste corresponding to each conductive line is different; the method further includes: During the fabrication of the conductive circuit, a corresponding identification code is assigned to the conductive circuit based on the concentration value of the fluorescent particles doped in the conductive paste used to fabricate the conductive circuit. After the conductive circuit is fabricated, the identification code corresponding to the conductive circuit is obtained, and the preset healthy temperature baseline corresponding to the conductive circuit is determined.
8. An anomaly monitoring device for conductive circuits, characterized in that, The conductive circuit is formed by a conductive paste; the conductive paste is doped with fluorescent particles. The fluorescent particles are configured to generate a light signal in response to a preset external light field; the anomaly monitoring device includes: The detection module is used to apply the preset external light field to the conductive line and detect the light signals generated by the conductive line at multiple preset positions. The calculation module is used to calculate the temperature detection values of the conductive line at the multiple preset positions based on the optical signal; The identification module is used to identify the abnormal state of the conductive line at the multiple preset locations based on the temperature detection values at the multiple preset locations and the preset healthy temperature baseline; the preset healthy temperature baseline is used to characterize the temperature of the conductive line at the multiple preset locations in a healthy state.
9. An anomaly monitoring device for conductive circuits, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.
11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.