A matrix controllable switch fault self-detection and life prediction system and working method

Abstract

The present invention discloses a matrix-controlled switch fault self-test and life prediction system and operating method, belonging to the field of non-invasive matrix switch testing. The system comprises a test module consisting of a matrix switch unit, a circuit component to be tested, and a functional test unit, a non-invasive matrix self-test module, and a life prediction and data recording module. The matrix switch unit comprises #imgabs0# matrix switches, where #imgabs1# represents the total number of test points of the circuit component to be tested, #imgabs2# represents the total number of test points connected to the functional test unit, and #imgabs3# represents the number of shared column lines. The matrix switch unit is connected to the non-invasive matrix self-test module, which is connected to the life prediction and data recording module. The matrix-controlled switch fault self-test and life prediction system and operating method achieve efficient, safe, and intelligent circuit component testing through non-invasive self-testing, matrix optimization, intelligent prediction models, and precise fault location.

Description

Technical Field

[0001] The present invention relates to the technical field of non-invasive testing of matrix switches, and in particular to a matrix controllable switch fault self-detection and life prediction system and a working method. Background Art

[0002] In automated circuit component testing systems, fault detection of matrix controllable switches primarily relies on real-time monitoring of on-resistance values, using resistance changes to determine the switch's health. However, existing detection methods have the following significant limitations in complex systems:

[0003] 1. Testing efficiency bottleneck: Traditional testing solutions (such as CN202410529278.1) use a serial testing mode that activates each channel and performs resistance measurement. A single measurement takes several seconds and requires manual intervention to switch test channels. For large-scale matrix systems containing switch nodes, completing a full test can take hours or even longer, severely limiting production testing efficiency.

[0004] 2. Operational safety hazards: Although CN202110588376.9 proposes a parallel detection architecture, it still requires physically opening the matrix switch cabinet and establishing a hard-wired connection between the relay and the non-intrusive matrix self-test module. This physical connection method not only significantly increases operational complexity but also introduces safety hazards in high-voltage environments.

[0005] 3. Lack of predictive capabilities: Existing technologies can only achieve post-fault detection, and lack the ability to predict the remaining life of the switch and the early warning mechanism for potential failures. Summary of the Invention

[0006] The purpose of the present invention is to provide a matrix controllable switch fault self-detection and life prediction system and working method to solve the above technical problems.

[0007] To achieve the above-mentioned object, the present invention provides a matrix controllable switch fault self-test and life prediction system, comprising a test module consisting of a matrix switch unit, a circuit component to be tested and a functional test unit, a non-intrusive matrix self-test module and a life prediction and data recording module; wherein the matrix switch unit comprises A matrix switch, is the total number of test points of the circuit component to be tested, The total number of test points accessed by the functional test unit, is the number of shared column lines;

[0008] The matrix switch unit is connected to a non-intrusive matrix self-test module for real-time measurement of the on-resistance of paired matrix switches;

[0009] The non-intrusive matrix self-test module is connected to the life prediction and data recording module, and is used to store timestamps and corresponding on-resistance data, and output fault warnings and life prediction results.

[0010] Preferably, the matrix switch unit is composed of Root lines and A controllable switch network structure consisting of cross-connected common column lines, wherein the common column lines are connected to the circuit components to be tested, and the row lines are adjacent to the functional test units;

[0011] The non-intrusive matrix self-test module includes two leads, which are respectively connected to an upper column line and a lower column line of a common column line;

[0012] When the matrix switch unit includes an even number of common column lines, two leads are connected to each other. When the matrix switch unit includes an odd number of shared column lines, two leads are connected to each as well as Root common column lines.

[0013] Preferably, the life prediction and data recording module includes a time series database, a life prediction model and an early warning model;

[0014] The time series database is used to store the on-resistance value, measurement timestamp and corresponding switch number of each matrix switch;

[0015] Life prediction models include life prediction models based on resistance change rate and state prediction models based on deep learning , used to predict the remaining life and usage status of matrix switch units;

[0016] The early warning model is used to trigger a dual early warning signal based on the output results of the life prediction model.

[0017] Preferably, the state prediction model For deep learning models.

[0018] A method for operating a matrix controllable switch fault self-detection and life prediction system includes the following steps:

[0019] S1. Build a test module and connect the upper column line and the lower column line of the common column line of the matrix switch unit through two leads;

[0020] S2. Measure the on-resistance of the matrix switch according to a preset period and record the test timestamp;

[0021] S3, periodically triggering self-test: determining whether the number of on-resistance measurements reaches a set threshold number of times; if not, returning to step S2; otherwise, triggering monitoring of the on-resistance change rate of the paired matrix switches to perform self-test;

[0022] During the self-test process, when the on-resistance change rate of the paired matrix switches exceeds the threshold, a fault is marked; otherwise, the life prediction result and usage status are output through the continuously recorded on-resistance and test timestamp;

[0023] S4. Issue early warning based on life prediction results and usage status.

[0024] Preferably, the set number threshold in step S3 is 1000 times;

[0025] On-resistance change rate The calculation formula is as follows:

[0026] (1);

[0027] Where, 、 The number of tests is and The on-resistance value when .

[0028] Preferably, in step S3, the life prediction model is used Output life prediction results, and life prediction model The expression is as follows:

[0029] (2);

[0030] in,

[0031] (3);

[0032] Where, A prediction model for on-resistance values ​​of paired matrix switches; 、 、 、 All are regression coefficients; 、 、 and All are power exponents; is the initial resistance value; 、 、 are the growth coefficients for the initial, middle and late stages respectively; 、 They are the end time of the initial stage and the end time of the middle stage respectively; The acceleration degree of resistance increase.

[0033] Preferably, in step S3, the state prediction model is used Output usage status and status prediction model The construction steps are as follows:

[0034] Step 1: Data labeling: Label the usage status corresponding to each pair of matrix switch on-resistance values ​​to obtain a training set, where the usage status includes normal and potential faults.

[0035] The second step is to input the training set into the deep learning model, and use the deep learning model to learn the relationship between the on-resistance value and the usage status until convergence, and obtain the status prediction model. .

[0036] Preferably, in step S4, when the state prediction model Output potential failure or life prediction model When the output life prediction result is lower than the set life threshold, an early warning signal is output.

[0037] Therefore, the present invention adopts the above-mentioned matrix controllable switch fault self-detection and life prediction system and working method, which has the following beneficial effects:

[0038] 1. Using a non-intrusive self-test method, measurements can be performed without opening the matrix switch cabinet, avoiding physical operation of the switch cabinet and ensuring the efficiency and safety of the test process;

[0039] 2. By optimizing the test objects and access methods, two matrix switches can be connected for self-test at a time, making the matrix switch self-test process more efficient, reducing manual intervention and operation, and significantly improving self-test efficiency;

[0040] 3. Establish a matrix switch life prediction model based on historical data and state prediction models It can intelligently predict the remaining life and status changes of the switch, and identify potential faults in advance through a dual early warning mechanism so that potential faults can be discovered in time and early warning prompts can be issued to avoid downtime or test errors caused by faults.

[0041] In summary, the present invention can not only realize the self-test of the matrix controllable switch, but also accurately predict the remaining service life of the switch, thereby significantly improving the self-test efficiency and reducing maintenance costs.

[0042] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a principle block diagram of a matrix controllable switch fault self-detection and life prediction system according to the present invention;

[0044] Figure 2A graph showing the change in on-resistance of a matrix switch over time in a matrix controllable switch fault self-detection and life prediction system according to the present invention;

[0045] Figure 3 This is a flow chart of the working method of the matrix controllable switch fault self-detection and life prediction system according to the present invention;

[0046] Figure 4 This is a connection diagram of Test 1 of Example 1 of the present invention;

[0047] Figure 5 This is a connection diagram of Test 2 of Example 1 of the present invention;

[0048] Figure 6 This is a connection diagram of Test 3 of Example 1 of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purposes, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar numbers throughout represent the same or similar elements or elements with the same or similar functions.

[0050] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0051] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] like Figure 1 and Figure 2 As shown, a matrix controllable switch fault self-test and life prediction system includes a test module composed of a matrix switch unit, a circuit component to be tested and a functional test unit, a non-intrusive matrix self-test module and a life prediction and data recording module; wherein the matrix switch unit includes A matrix switch, is the total number of test points of the circuit component to be tested, The total number of test points accessed by the functional test unit, is the number of shared column lines, since usually The value is small, so Compared with traditional The matrix switch can save a large number of matrix switches, reduce hardware requirements and test time; the matrix switch unit is connected to the non-intrusive matrix self-test module, which is used to measure the on-resistance of paired matrix switches in real time and convert the resistance value signal into a voltage signal output; the non-intrusive matrix self-test module is connected to the life prediction and data recording module, which is used to store timestamps and corresponding on-resistance data, and output fault warnings and life prediction results.

[0053] Among them, the matrix switch unit is composed of Root lines and The controllable switch network structure is composed of cross-connected common column lines. The common column lines are connected to the circuit components to be tested, and the row lines are adjacent to the functional test units. The non-intrusive matrix self-test module includes two leads, and the two leads (SC1 and SC2) are respectively connected to the upper column line and the lower column line of the common column lines. When the matrix switch unit includes an even number of common column lines, the two leads are each connected to When the matrix switch unit includes an odd number of shared column lines, two leads are connected to each as well as Shared column lines. By dividing the matrix switch unit into two parts through two leads (SC1 and SC2), the non-intrusive matrix self-test module can connect to two matrix switches on different column lines for testing each time, avoiding interference and conflicts caused by shared column line connections during the test process, thereby improving test efficiency. By optimizing the connection method of the matrix unit under test, only two leads, SC1 and SC2, are used for connection, which not only avoids the bottleneck of a single test channel, but also effectively reduces repeated testing and circuit load, improving the overall test capability and accuracy of the system.

[0054] The life prediction and data recording module includes a time series database, a life prediction model and an early warning model; the time series database is used to store the on-resistance value, measurement timestamp and corresponding switch number of each matrix switch; the life prediction model includes a life prediction model based on the resistance change rate and state prediction models based on deep learning , used to predict the remaining life and usage status of the matrix switch unit; the early warning model is used to trigger a dual early warning signal according to the output results of the life prediction model.

[0055] State prediction model For deep learning models.

[0056] like Figure 3 As shown, a working method of a matrix controllable switch fault self-detection and life prediction system includes the following steps:

[0057] S1. Build a test module and connect the upper column line and the lower column line of the common column line of the matrix switch unit through two leads;

[0058] S2. Measure the on-resistance of the matrix switch according to a preset period and record the test timestamp;

[0059] S3, periodically triggering self-test: determining whether the number of on-resistance measurements reaches a set threshold number of times; if not, returning to step S2; otherwise, triggering monitoring of the on-resistance change rate of the paired matrix switches to perform self-test;

[0060] During the self-test process, when the on-resistance change rate of a pair of matrix switches exceeds a threshold, a fault is marked and an alarm is issued; otherwise, the life prediction result and usage status are output through the continuously recorded on-resistance and test timestamp;

[0061] The setting number threshold in step S3 is 1000 times;

[0062] On-resistance change rate The calculation formula is as follows:

[0063] (1);

[0064] Where, 、 The number of tests is (represents the use cycle of the matrix switch (unit: times)) and The on-resistance value when .

[0065] In step S3, the lifespan prediction model is used Output the life prediction result (i.e. establish a life prediction model based on the sudden on-resistance signal) ), and the life prediction model The expression is as follows:

[0066] (2);

[0067] in,

[0068] (3);

[0069] Where, Prediction model for the on-resistance of paired matrix switches (Taking into account the different change patterns of resistance values ​​in different use stages of the switch (initial, mid-term, and late stages), the change of resistance values ​​is divided into three stages, and a nonlinear model is used to fit the resistance change law of each stage. In the initial use stage of the switch: slow growth. In the initial stage of the matrix switch being put into use, the change of resistance value is usually slow, showing a linear or slow growth trend. At this time, the change of resistance is mainly affected by factors such as initial wear and oxidation of the contact surface, and the growth is relatively stable, so In the middle stage of switch use: relatively balanced. In the middle stage, the change of resistance value is relatively stable, and the amplitude of resistance change is even smaller than that in the early stage. At this time, the resistance increase is no longer as significant as in the early stage. In this stage, the resistance change tends to be stable, and the change speed begins to slow down, so there is In the later stage of switch use: the resistance value increases rapidly. In the later stage of switch use, the resistance value increases rapidly, especially when the contact surface is severely worn, the contact is oxidized, ablated or the material is aged. The resistance change in this stage conforms to the law of accelerated growth, so there is ; 、 、 、 are regression coefficients, which are obtained by fitting historical data; 、 、 and Both are power exponents used to describe the nonlinear relationship between the number of tests, resistance value, and remaining usage. They are determined based on actual resistance data and switch type through regression analysis or machine learning methods. is the initial resistance value; 、 、 The growth coefficients for the initial, middle, and late stages are determined by regression analysis or machine learning methods based on actual resistance data and switch types; 、 They are the end time of the initial stage and the end time of the middle stage respectively; is the acceleration degree of resistance increase.

[0070] In step S3, the state prediction model is used Output usage status and status prediction model The construction steps are as follows:

[0071] Step 1: Data annotation: Label the usage status corresponding to the on-resistance value of each pair of matrix switches to obtain a training set. The usage status includes normal and potential faults. The labeled data can be actual fault records, maintenance records, or remaining life estimates.

[0072] The second step is to input the training set into the deep learning model, and use the deep learning model to learn the relationship between the on-resistance value and the usage status until convergence, and obtain the status prediction model. ;

[0073] Input the current resistance value of the matrix switch into the state prediction model The current usage status can be obtained, and when the usage status is a potential fault, an early warning signal is issued to prompt maintenance personnel to perform maintenance or replacement in time to avoid downtime or test errors caused by the fault.

[0074] S4. Issue early warning based on life prediction results and usage status.

[0075] In step S4, when the state prediction model Output potential failure or life prediction model When the output life prediction result is lower than the set life threshold, an early warning signal is output.

[0076] In order to further disclose the present invention, the first and second embodiments are supplemented, and in the above two embodiments, is the current in the circuit; is the basic current source; 10 10 times the basic current source; 20 It is 20 times the basic current source;

[0077] Sel0, Sel1, and Sel2 are the control switches of the basic current source, 10 times the basic current source, and 20 times the basic current source respectively;

[0078] SC1 is a lead between the non-intrusive matrix self-test module and the matrix switch unit M; SC2 is another lead between the non-intrusive matrix self-test module and the matrix switch unit M;

[0079] is the total number of test points of the circuit components to be tested of the test module; The total number of test points accessed for the functional test unit; is the number of shared column lines, It is also the maximum number of connections that need to be connected to the test module in the functional test unit; C0~C (c-1) Common column lines for matrix switches;

[0080] The resistance of the wire between the constant current source and the matrix switch unit M through the SC2 lead is a constant; The constant current source passes through the SC2 lead and the line resistance of the matrix switch unit M matrix switch, and its value is constant; are all resistance values ​​of the matrix switches in the matrix switch unit M, and their values ​​are constants; is the circuit resistance, which is a constant;

[0081] like If it is an even number is the resistance value of the matrix switch to be tested connected to SC1 in the test module, is the resistance value of the matrix switch to be tested connected to SC2 in the test module, The line resistance between the matrix switch of the matrix switch unit M connected to SC2 and the matrix switch of the test module, The line resistance between the matrix switch of the matrix switch unit M connected to SC1 and the matrix switch of the test module, To test the line resistance between the two matrix switches of the module, the above values ​​are all constants;

[0082] like If it is an odd number is the resistance value of the matrix switch to be tested connected to SC1 in the test module, is the resistance value of the matrix switch to be tested connected to SC2 in the test module, The line resistance between the matrix switch of the matrix switch unit M connected to SC2 and the matrix switch of the test module, The line resistance between the matrix switch of the matrix switch unit M connected to SC1 and the matrix switch of the test module, To test the line resistance between the two matrix switches of the module, the above values ​​are all constants;

[0083] It is the output voltage signal of the matrix non-intrusive matrix self-test module; GND represents the ground wire.

[0084] Example 1

[0085] like Figure 4 As shown in (Test 1), this is the first self-test of the matrix switch. One of the switches is damaged and the life prediction model has not yet been established. and state prediction models ; The total number of test points of the circuit components to be tested is 200, the total number of functional test access test points is 8, and the number of shared column lines is , is an even number, the total number of switches is , at this time SC1 and SC2 can be connected to the upper and lower 、 Root column line.

[0086] The matrix non-intrusive matrix self-test module includes three configurable constant current sources selected by the Sel0, Sel1, and Sel2 switches. During the test, it provides a stable current signal to prevent the final output voltage from being too high due to damage to the matrix switches and increased switch resistance.

[0087] Since the on-resistance of the matrix switch is gradually increasing due to factors such as mechanical fatigue, material aging, contact oxidation, working environment temperature, excitation voltage and current, the resistance value of the matrix switch can be converted into a voltage signal output through the circuit. Because it is impossible to measure a switch alone, the non-intrusive matrix self-test module connects two matrix switches each time to perform self-test operations. The non-intrusive matrix self-test module test output According to Ohm's law, the constant current source and the resistance value in the circuit can be used to calculate:

[0088] ;

[0089] Where, 、 、 、 and are all constants, and in this embodiment, their sum is denoted as C. and Using the same controllable switches in the matrix, the non-intrusive matrix self-test module is set to perform one self-test every 1000 tests. Obviously, the number of times the controllable switches in the matrix switch unit M are used is only one thousandth of the controllable switches in the two matrices in the test module. It is reasonable to assume that the on-resistance of these switches is constant during the entire test life of the self-test device. Then 、 and are all constants. Therefore, the output voltage Vout is as follows:

[0090] ;

[0091] Where, 、 and are all constants, and their sum is recorded as D. These three resistance values ​​are only related to the wiring of the matrix non-intrusive matrix self-test module and will not change once the device is implemented.

[0092] ;

[0093] Right now, Only by and Sure.

[0094] It can be deduced that:

[0095] ;

[0096] If one of the two switches under test is damaged, the test output When the value is too large, the matrix non-intrusive matrix self-check module will immediately issue an alarm and mark a fault to avoid test errors caused by these problems.

[0097] Furthermore, according to its resistance value , using multiple measurement methods, such as Figure 5 As shown, disconnect the matrix switch connected to SC1 and close the other matrix switch (Test 2); Figure 6 As shown, the matrix switch connected to SC2 is opened and the other matrix switch is closed (Test 3). The on-resistance of each controllable switch is included in the two tests. Based on the three self-test test results, it can be obtained:

[0098] ;

[0099] ;

[0100] ;

[0101] In the three measurement results, and If only one of the two controllable switches suddenly changes, assuming the switch is damaged, A, under normal circumstances For B, Take the corresponding switch damage as an example,

[0102] ;

[0103] ;

[0104] ;

[0105] exist In both tests, the switch was damaged. Only with When the switch is damaged, it can be identified by only three self-tests. The corresponding switch is damaged.

[0106] Example 2

[0107] This embodiment is based on the first embodiment and adds a life prediction model and state prediction models , and measure the on-resistance value and test times of the matrix switch in the test module , matrix switch resistance value conduction change rate and other parameters to predict the state and life of the matrix switch.

[0108] After the matrix self-test unit is tested, the matrix switch resistance value at this time is input into the state prediction model In the state prediction model, the state of the matrix switch pair can be obtained as "normal" or "potential fault". If the switch status is predicted to be "normal", the system will continue to monitor the change of the on-resistance value and regularly update the life prediction model. , to ensure that the switch operates within the normal operating range. If the state prediction model If a switch is predicted to be in a "potential failure" state, the system will immediately issue a warning signal, prompting maintenance personnel to inspect and consider replacing the matrix switch pair. This early warning mechanism effectively avoids system downtime or testing errors caused by switch failures.

[0109] And after the state prediction is completed, the system will measure the on-resistance value and the current number of tests Input to life prediction model and using life prediction models Output the remaining usage times (service life) of each matrix switch, compare the output remaining usage times with the set times threshold to determine whether there is a potential fault, and output a warning signal if so.

[0110] In this embodiment, after each test, the system will set the new on-resistance value , number of tests The data such as resistance value change rate are added to the historical data, and the life prediction model is retrained and updated. and state prediction models By continuously updating the model, the system can adapt to changes in the switch under different working environments and improve the accuracy and reliability of predictions.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A matrix controllable switch fault self-detection and life prediction system, characterized by: It includes a test module consisting of a matrix switch unit, a circuit component to be tested and a functional test unit, a non-intrusive matrix self-test module and a life prediction and data recording module; wherein the matrix switch unit includes A matrix switch, is the total number of test points of the circuit component to be tested, The total number of test points accessed by the functional test unit, is the number of shared column lines; The matrix switch unit is connected to a non-intrusive matrix self-test module for real-time measurement of the on-resistance of paired matrix switches; The non-intrusive matrix self-test module is connected to the life prediction and data recording module to store timestamps and corresponding on-resistance data, and output fault warnings and life prediction results; Matrix switch unit is Root lines and A controllable switch network structure consisting of cross-connected common column lines, wherein the common column lines are connected to the circuit components to be tested, and the row lines are adjacent to the functional test units; The non-intrusive matrix self-test module includes two leads, which are respectively connected to an upper column line and a lower column line of a common column line; When the matrix switch unit includes an even number of common column lines, two leads are connected to each other. When the matrix switch unit includes an odd number of shared column lines, two leads are connected to each as well as Root common column line; The life prediction and data recording module includes a time series database, a life prediction model, and an early warning model; The time series database is used to store the on-resistance value, measurement timestamp and corresponding switch number of each matrix switch; Life prediction models include life prediction models based on resistance change rate and state prediction models based on deep learning , used to predict the remaining life and usage status of matrix switch units; The early warning model is used to trigger dual early warning signals based on the output of the life prediction model; Utilizing lifespan prediction models Output life prediction results, and life prediction model The expression is as follows: （2）； in, （3）； Where, A prediction model for on-resistance values ​​of paired matrix switches; 、 、 、 All are regression coefficients; 、 、 and All are power exponents; is the initial resistance value; 、 、 are the growth coefficients for the initial, middle and late stages respectively; 、 They are the end time of the initial stage and the end time of the middle stage respectively; is the degree of acceleration of the resistance increase; Indicates the rate of change of on-resistance.

2. A matrix controllable switch fault self-detection and life prediction system according to claim 1, characterized in that: State prediction model For deep learning models.

3. A method for operating a matrix controllable switch fault self-detection and life prediction system according to claim 2, characterized in that: The following steps are involved: S1. Build a test module and connect the upper column line and the lower column line of the common column line of the matrix switch unit through two leads; S2. Measure the on-resistance of the matrix switch according to a preset period and record the test timestamp; S3, periodically triggering self-test: determining whether the number of on-resistance measurements reaches a set threshold number of times; if not, returning to step S2; otherwise, triggering monitoring of the on-resistance change rate of the paired matrix switches to perform self-test; During the self-test process, when the rate of change of the on-resistance of the paired matrix switches is higher than a threshold, a fault is flagged; Otherwise, the life prediction results and usage status are output through the continuously recorded on-resistance and test timestamp; S4. Issue early warning based on life prediction results and usage status.

4. The operating method of the matrix controllable switch fault self-detection and life prediction system according to claim 3 is characterized by: The setting number threshold in step S3 is 1000 times; On-resistance change rate The calculation formula is as follows: （1）； Where, 、 The number of tests is and The on-resistance value when .

5. The operating method of the matrix controllable switch fault self-detection and life prediction system according to claim 4 is characterized in that: In step S3, the state prediction model is used Output usage status and status prediction model The construction steps are as follows: Step 1: Data labeling: Label the usage status corresponding to each pair of matrix switch on-resistance values ​​to obtain a training set, where the usage status includes normal and potential faults. The second step is to input the training set into the deep learning model, and use the deep learning model to learn the relationship between the on-resistance value and the usage status until convergence, and obtain the status prediction model. .

6. The operating method of the matrix controllable switch fault self-detection and life prediction system according to claim 5, characterized in that: In step S4, when the state prediction model Output potential failure or life prediction model When the output life prediction result is lower than the set life threshold, an early warning signal is output.

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