A chip data self-checking system and method based on artificial intelligence

Abstract

The present invention discloses a chip data self-checking system and method based on artificial intelligence, which belongs to the field of electronic digital data processing. The present invention substitutes the output data of each output interface of a chip to be detected into an output abnormality evaluation strategy to perform output abnormality evaluation, obtains the surface temperature data of the chip during operation and imports it into a temperature abnormality analysis strategy to perform chip temperature abnormality analysis, imports the output abnormality evaluation result and the chip temperature abnormality analysis result into a data self-checking evaluation strategy to perform data self-checking evaluation, performs chip detection and judgment according to the data self-checking evaluation result, and performs chip abnormality warning according to the chip detection and judgment result, and comprehensively analyzes the output data and surface temperature data of each output interface of the chip to be detected, thereby improving the chip data detection accuracy and intelligence level.

Description

Technical Field

[0001] The present invention belongs to the field of electronic digital data processing, and specifically relates to a chip data self-checking system and method based on artificial intelligence. Background Art

[0002] Artificial intelligence-based chip data self-checking usually involves using specialized algorithms and hardware to analyze and verify the data on the chip to ensure the accuracy and completeness of the data. This process can include data collection, analysis, identification of potential problems and errors, and providing solutions. Artificial intelligence-based chips can monitor and maintain their own status in real time during operation to improve the reliability and stability of the system. This is particularly important for applications that require high reliability, such as in aerospace, automotive, and medical fields. The existing technology is unable to conduct a comprehensive analysis of the output data and surface temperature data of each output interface of the chip to be tested during the chip data self-checking process, resulting in low chip data detection accuracy and intelligence level. The above problems exist in the existing technology.

[0003] In order to solve these problems, the present application designs a chip data self-checking system and method based on artificial intelligence. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention proposes a chip data self-checking system and method based on artificial intelligence.

[0005] To achieve the above object, the present invention provides the following technical solution: a chip data self-checking method based on artificial intelligence, which comprises the following specific steps:

[0006] Install the chip on the testing equipment, input the specified signal data to the corresponding interface of the chip, obtain the output data of each output interface of the chip to be tested, and obtain the chip surface temperature data at the same time;

[0007] Substituting the obtained output data of each output interface of the chip to be detected into the output anomaly assessment strategy to perform output anomaly assessment;

[0008] Obtain the chip surface temperature data during operation and import it into the temperature anomaly analysis strategy to perform chip temperature anomaly analysis;

[0009] Import the output abnormality evaluation results and the chip temperature abnormality analysis results into the data self-check evaluation strategy for data self-check evaluation;

[0010] The chip detection and judgment are carried out according to the data self-test evaluation results, and the chip abnormality warning is issued according to the chip detection and judgment results.

[0011] It should be noted that the specific steps of mounting the chip on the testing device, inputting designated signal data to the corresponding interface of the chip, obtaining output data of each output interface of the chip to be tested, and obtaining the chip surface temperature data are as follows:

[0012] S11, the chip to be tested is installed on the testing device, the signal sending component inputs the specified signal data to the corresponding input interface of the chip, obtains the standard output data of each output interface after the specified signal data is input, and simultaneously obtains the output data of each output interface in real time and stores it in the first storage module;

[0013] S12, evenly dividing the chip surface into regions equal to the number of interfaces according to the number of interfaces, acquiring temperature data of each region, and storing the data in a second storage module.

[0014] It should be specifically explained here that the step of substituting the acquired output data of each output interface of the chip to be detected into the output anomaly evaluation strategy for output anomaly evaluation includes the following specific steps:

[0015] S21, obtaining output data of each output interface of the chip to be tested within the test time and standard output data of each output interface after inputting designated signal data;

[0016] S22, importing the obtained output data of each output interface of the chip to be tested within the test time and the standard output data of each output interface after inputting the specified signal data into the output abnormality evaluation value calculation formula to calculate the output abnormality evaluation value, wherein the output abnormality evaluation value calculation formula is: , where T is the test time, n is the number of output interfaces, ci is the number of transistors connected to the i-th output interface, Jti is the output data of the i-th output interface at time t, Jtim is the standard output data of the i-th output interface at time t, and dt is the time integral;

[0017] S23. Obtain the calculated output anomaly assessment value.

[0018] It should be noted that the specific steps of obtaining the chip surface temperature data during operation and importing it into the temperature anomaly analysis strategy to perform chip temperature anomaly analysis are as follows:

[0019] The average temperature data of each corresponding interface position of the chip is obtained, and the average temperature data of each corresponding interface position of the chip is imported into the temperature anomaly analysis value calculation formula to calculate the temperature anomaly analysis value, wherein the temperature anomaly analysis value calculation formula is: , where Wit is the temperature value of the i-th interface at time t, Wm is the median value of the interface temperature safety range, Wmax is the maximum value of the interface temperature safety range, and Wmin is the minimum value of the interface temperature safety range.

[0020] It should be noted that the importing of the output abnormality evaluation result and the chip temperature abnormality analysis result into the data self-check evaluation strategy for data self-check evaluation includes the following specific steps: S41, obtaining the calculated output abnormality evaluation value and temperature abnormality analysis value, substituting the obtained output abnormality evaluation value and temperature abnormality analysis value into the data self-check evaluation value calculation formula to calculate the data self-check evaluation value, wherein the data self-check evaluation value calculation formula is: ,in, Output abnormal evaluation value ratio coefficient;

[0021] S42. Compare the data self-check evaluation value with the set data self-check evaluation threshold. If the obtained data self-check evaluation value is greater than or equal to the set data self-check evaluation threshold, the evaluation chip is in a healthy state. If the obtained data self-check evaluation value is less than the set data self-check evaluation threshold, the evaluation chip is in an abnormal state and sends an abnormal warning signal.

[0022] A chip data self-checking system based on artificial intelligence is implemented based on the above-mentioned chip data self-checking method based on artificial intelligence, and specifically includes a data acquisition module, an output anomaly assessment module, a chip temperature anomaly analysis module, a data self-checking assessment module, a chip anomaly warning module and a general control module, wherein the data acquisition module is used to install the chip on a detection device, input specified signal data to the corresponding interface of the chip, obtain the output data of each output interface of the chip to be detected, and simultaneously obtain the chip surface temperature data, and the output anomaly assessment module is used to substitute the obtained output data of each output interface of the chip to be detected into the output anomaly assessment strategy for output anomaly assessment.

[0023] It should be specifically explained here that the chip temperature anomaly analysis module is used to obtain the chip surface temperature data during operation and import it into the temperature anomaly analysis strategy to perform chip temperature anomaly analysis; the data self-check evaluation module is used to import the output abnormality evaluation results and the chip temperature anomaly analysis results into the data self-check evaluation strategy to perform data self-check evaluation; the chip abnormality warning module is used to perform chip detection and judgment based on the data self-check evaluation results, and to perform chip abnormality warning based on the chip detection and judgment results.

[0024] It should be specifically explained here that the master control module is used for controlling the operation of the data acquisition module, the output abnormality assessment module, the chip temperature abnormality analysis module, the data self-check assessment module and the chip abnormality warning module.

[0025] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0026] The processor executes the above-mentioned chip data self-checking method based on artificial intelligence by calling the computer program stored in the memory.

[0027] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned chip data self-checking method based on artificial intelligence.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention installs a chip on a detection device, inputs specified signal data to a corresponding interface of the chip, obtains output data of each output interface of the chip to be detected, and simultaneously obtains chip surface temperature data, substitutes the obtained output data of each output interface of the chip to be detected into an output anomaly evaluation strategy to perform output anomaly evaluation, obtains chip surface temperature data during operation and imports it into a temperature anomaly analysis strategy to perform chip temperature anomaly analysis, imports the output anomaly evaluation result and the chip temperature anomaly analysis result into a data self-check evaluation strategy to perform data self-check evaluation, performs chip detection judgment according to the data self-check evaluation result, and performs chip anomaly warning according to the chip detection judgment result, performs comprehensive analysis on the output data and surface temperature data of each output interface of the chip to be detected, and improves the accuracy rate and intelligence level of chip data detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a schematic diagram of the overall process of a chip data self-checking method based on artificial intelligence of the present invention;

[0031] Figure 2 The figure is a schematic diagram of the overall framework of a chip data self-checking system based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0033] Example 1

[0034] The technical problem solved by this embodiment is that the prior art cannot comprehensively analyze the output data and surface temperature data of each output interface of the chip to be detected during the chip data self-detection process, resulting in low chip data detection accuracy and intelligence level;

[0035] In order to solve the above problems, the present invention provides a preferred embodiment: Figure 1 As shown, a chip data self-checking method based on artificial intelligence includes the following specific steps:

[0036] Install the chip on the testing equipment, input the specified signal data to the corresponding interface of the chip, obtain the output data of each output interface of the chip to be tested, and obtain the chip surface temperature data at the same time;

[0037] In this embodiment, the chip is mounted on the detection device, the specified signal data is input to the corresponding interface of the chip, the output data of each output interface of the chip to be detected is obtained, and the specific steps of obtaining the chip surface temperature data are:

[0038] S11, the chip to be tested is installed on the testing device, the signal sending component inputs the specified signal data to the corresponding input interface of the chip, obtains the standard output data of each output interface after the specified signal data is input, and simultaneously obtains the output data of each output interface in real time and stores it in the first storage module;

[0039] Here is the source code to implement this step:

[0040] # Assume there is a hardware interface class HardwareInterface, which can communicate with the chip

[0041] class HardwareInterface:

[0042] def initialize(self):

[0043] # Initialize the hardware interface

[0044] pass

[0045] def send_signal(self, input_pin, signal):

[0046] # Send a signal to the chip's input interface

[0047] pass

[0048] def read_output_data(self, output_pins):

[0049] # Read the data of the chip output interface

[0050] return [] # Returns an empty list to indicate no data

[0051] #Signal sending component, which knows how to send signals and get standard output data

[0052] class SignalSender:

[0053] def send_signal(self, chip, input_pin, signal):

[0054] chip.send_signal(input_pin, signal)

[0055] standard_output_data = self.get_standard_output_data(signal)

[0056] return standard_output_data

[0057] def get_standard_output_data(self, input_signal):

[0058] # Calculate standard output data based on input signal

[0059] return [1, 2, 3] # Hypothetical output data

[0060] # Data storage module, used to store real-time output data and standard output data

[0061] class DataStorage:

[0062] def store_data(self, real_output_data, standard_output_data):

[0063] # Store the data in the first storage module

[0064] pass

[0065] # Hardware interface initialization

[0066] hardware_interface = HardwareInterface()

[0067] hardware_interface.initialize()

[0068] # Signal sending component instantiation

[0069] signal_sender = SignalSender()

[0070] # Data storage module instantiation

[0071] data_storage = DataStorage()

[0072] # Send specified signal data

[0073] input_signal = [1, 2, 3] # Assumed input signal data

[0074] standard_output_data = signal_sender.send_signal(hardware_interface,input_signal)

[0075] # Get the output data of each output interface in real time

[0076] real_output_data = hardware_interface.read_output_data([0, 1, 2])

[0077] # Store the actual output data and standard output data

[0078] data_storage.store_data(real_output_data, standard_output_data)

[0079] print("Standard output data:", standard_output_data)

[0080] print("Real-time output data:", real_output_data)

[0081] S12, evenly dividing the chip surface into regions equal to the number of interfaces according to the number of interfaces, acquiring temperature data of each region, and storing the data in a second storage module;

[0082] Substituting the obtained output data of each output interface of the chip to be detected into the output anomaly assessment strategy to perform output anomaly assessment;

[0083] In this embodiment, substituting the acquired output data of each output interface of the chip to be detected into the output anomaly evaluation strategy to perform output anomaly evaluation includes the following specific steps:

[0084] S21, obtaining output data of each output interface of the chip to be tested within the test time and standard output data of each output interface after inputting designated signal data;

[0085] To obtain the output data of each output interface of the chip to be tested within the test time, as well as the standard output data of each output interface after inputting the specified signal data, the following steps are usually required:

[0086] S211. Test preparation:

[0087] Ensure that the test environment meets the test requirements, including stable power supply and calibration of test equipment;

[0088] Prepare test equipment such as signal generators, oscilloscopes, logic analyzers, etc. for testing; confirm the model, pin definition, data sheet and test procedures of the test chip.

[0089] S212. Build the test circuit:

[0090] Build the circuit according to the pin definition of the chip to ensure signal integrity and power supply stability; for analog signals, you may need to use a signal generator and corresponding probes for measurement; for digital signals, use a logic analyzer or oscilloscope probe to connect to the output pins of the chip.

[0091] S213. Start the test procedure:

[0092] If the chip is part of an embedded system, the corresponding test software or firmware needs to be started; if the chip has built-in test modes or test interfaces, enable these functions.

[0093] S214. Collect output data:

[0094] During the test time, use an oscilloscope, logic analyzer and other equipment to collect the output data of each output interface in real time; for digital signals, record the high and low states of the output level; for analog signals, record the voltage value;

[0095] S215. Input the specified signal data:

[0096] Use a signal generator to generate the specified test signal and input it to the input interface of the chip; ensure that the signal parameters such as frequency, amplitude, shape, etc. meet the test requirements.

[0097] S216. Get standard output data:

[0098] According to the chip's data sheet and specification, determine the theoretical output data of each output interface after inputting the specified signal; these data usually include the output level, waveform shape, frequency, etc.

[0099] Please note that the above steps are a generalized process and the specific test steps may vary depending on the type, characteristics and test requirements of the chip to be tested.

[0100] S22, importing the obtained output data of each output interface of the chip to be tested within the test time and the standard output data of each output interface after inputting the specified signal data into the output abnormality evaluation value calculation formula to calculate the output abnormality evaluation value, wherein the output abnormality evaluation value calculation formula is: , where T is the test time, n is the number of output interfaces, ci is the number of transistors connected to the i-th output interface, Jti is the output data of the i-th output interface at time t, Jtim is the standard output data of the i-th output interface at time t, and dt is the time integral;

[0101] S23. Obtain the calculated output anomaly assessment value.

[0102] Obtain the chip surface temperature data during operation and import it into the temperature anomaly analysis strategy to perform chip temperature anomaly analysis;

[0103] In this embodiment, the specific steps of obtaining the chip surface temperature data during operation and importing it into the temperature anomaly analysis strategy to perform chip temperature anomaly analysis are as follows:

[0104] The average temperature data of each corresponding interface position of the chip is obtained, and the average temperature data of each corresponding interface position of the chip is imported into the temperature anomaly analysis value calculation formula to calculate the temperature anomaly analysis value, wherein the temperature anomaly analysis value calculation formula is: , where Wit is the temperature value of the i-th interface at time t, Wm is the median value of the interface temperature safety range, Wmax is the maximum value of the interface temperature safety range, and Wmin is the minimum value of the interface temperature safety range.

[0105] To obtain the average temperature data of each corresponding interface position of the chip, you can follow the steps below:

[0106] 1. Temperature data collection: Use temperature sensors or thermal imagers to monitor the temperature of each interface of the chip;

[0107] 2. Data preprocessing: De-noise the collected temperature data to eliminate random errors; normalize the data to facilitate subsequent calculations and analysis.

[0108] Import the output abnormality evaluation results and the chip temperature abnormality analysis results into the data self-check evaluation strategy for data self-check evaluation;

[0109] In this embodiment, the output abnormality evaluation result and the chip temperature abnormality analysis result are imported into the data self-check evaluation strategy for data self-check evaluation, which includes the following specific steps: S41, obtaining the calculated output abnormality evaluation value and temperature abnormality analysis value, and substituting the obtained output abnormality evaluation value and temperature abnormality analysis value into the data self-check evaluation value calculation formula to calculate the data self-check evaluation value, wherein the data self-check evaluation value calculation formula is: ,in, Output abnormal evaluation value ratio coefficient;

[0110] S42. Compare the data self-check evaluation value with the set data self-check evaluation threshold. If the obtained data self-check evaluation value is greater than or equal to the set data self-check evaluation threshold, the evaluation chip is in a healthy state. If the obtained data self-check evaluation value is less than the set data self-check evaluation threshold, the evaluation chip is in an abnormal state and sends an abnormal warning signal.

[0111] It should be noted that the output abnormality evaluation value ratio coefficient and the data self-check evaluation threshold are obtained as follows: at least 500 sets of output data of each output interface of the chip to be detected are obtained, and the chip surface temperature data is obtained at the same time. 50 experts in this field evaluate whether the chip is in an abnormal state. The obtained output data of each output interface of the chip to be detected and the chip surface temperature data are substituted into the data self-check evaluation value calculation formula to calculate the data self-check evaluation value. The calculated data self-check evaluation value and the judgment result of whether the chip is in an abnormal state are imported into the fitting software, and the output abnormality evaluation value ratio coefficient and the data self-check evaluation threshold that meet the highest judgment accuracy are output;

[0112] The chip detection and judgment are carried out according to the data self-test evaluation results, and the chip abnormality warning is issued according to the chip detection and judgment results.

[0113] It should be noted in this embodiment that the advantages of this embodiment over the prior art are: the chip is installed on the detection equipment, the specified signal data is input to the corresponding interface of the chip, the output data of each output interface of the chip to be detected is obtained, and the chip surface temperature data is obtained at the same time, and the output data of each output interface of the chip to be detected is substituted into the output abnormality evaluation strategy for output abnormality evaluation, and the chip surface temperature data during operation is obtained and imported into the temperature abnormality analysis strategy for chip temperature abnormality analysis, and the output abnormality evaluation results and the chip temperature abnormality analysis results are imported into the data self-check evaluation strategy for data self-check evaluation, and chip detection judgment is performed according to the data self-check evaluation results, and chip abnormality warning is performed according to the chip detection judgment results, and the output data of each output interface of the chip to be detected and the surface temperature data are comprehensively analyzed, thereby improving the accuracy and intelligence level of chip data detection.

[0114] Example 2

[0115] like Figure 2 As shown, a chip data self-inspection system based on artificial intelligence is implemented based on the above-mentioned chip data self-inspection method based on artificial intelligence, and specifically includes a data acquisition module, an output abnormality evaluation module, a chip temperature abnormality analysis module, a data self-inspection evaluation module, a chip abnormality warning module and a general control module, wherein the data acquisition module is used to install the chip on the detection device, input the specified signal data to the corresponding interface of the chip, obtain the output data of each output interface of the chip to be detected, and obtain the chip surface temperature data at the same time, the output abnormality evaluation module is used to substitute the obtained output data of each output interface of the chip to be detected into the output abnormality evaluation strategy for output abnormality evaluation; the chip temperature abnormality analysis module is used to obtain the chip surface temperature data during operation and import it into the temperature abnormality analysis strategy for chip temperature abnormality analysis, the data self-inspection evaluation module is used to import the output abnormality evaluation result and the chip temperature abnormality analysis result into the data self-inspection evaluation strategy for data self-inspection evaluation, the chip abnormality warning module is used to perform chip detection judgment according to the data self-inspection evaluation result, and to perform chip abnormality warning according to the chip detection judgment result; the general control module is used to control the operation of the data acquisition module, the output abnormality evaluation module, the chip temperature abnormality analysis module, the data self-inspection evaluation module and the chip abnormality warning module.

[0116] Example 3

[0117] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0118] The processor executes the above-mentioned chip data self-checking method based on artificial intelligence by calling the computer program stored in the memory.

[0119] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a chip data self-checking method based on artificial intelligence provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0120] Example 4

[0121] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0122] When the computer program runs on a computer device, the computer device executes the above-mentioned chip data self-checking method based on artificial intelligence.

[0123] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0124] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0125] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0126] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. A chip data self-checking method based on artificial intelligence, characterized in that: It includes the following specific steps: Install the chip on the testing equipment, input the specified signal data to the corresponding interface of the chip, obtain the output data of each output interface of the chip to be tested, and obtain the chip surface temperature data at the same time; Substituting the obtained output data of each output interface of the chip to be detected into the output anomaly assessment strategy to perform output anomaly assessment; Obtain the chip surface temperature data during operation and import it into the temperature anomaly analysis strategy to perform chip temperature anomaly analysis; Import the output abnormality evaluation results and the chip temperature abnormality analysis results into the data self-check evaluation strategy for data self-check evaluation; Perform chip detection and judgment according to the data self-check evaluation result, and perform chip abnormality warning according to the chip detection and judgment result; the output data of each output interface of the chip to be detected is substituted into the output abnormality evaluation strategy for output abnormality evaluation, which includes the following specific steps: Obtain the output data of each output interface of the chip to be tested within the test time and the standard output data of each output interface after inputting the specified signal data; The output data of each output interface of the chip to be tested within the test time and the standard output data of each output interface after the specified signal data is input are imported into the output abnormality evaluation value calculation formula to calculate the output abnormality evaluation value, wherein the output abnormality evaluation value calculation formula is: , where T is the test time, n is the number of output interfaces, ci is the number of transistors connected to the i-th output interface, Jti is the output data of the i-th output interface at time t, Jtim is the standard output data of the i-th output interface at time t, and dt is the time integral; Obtain the calculated output anomaly evaluation value; the specific steps of obtaining the chip surface temperature data during operation and importing it into the temperature anomaly analysis strategy to perform chip temperature anomaly analysis are: The average temperature data of each corresponding interface position of the chip is obtained, and the average temperature data of each corresponding interface position of the chip is imported into the temperature anomaly analysis value calculation formula to calculate the temperature anomaly analysis value, wherein the temperature anomaly analysis value calculation formula is: , where Wit is the temperature value of the i-th interface at time t, Wm is the median value of the interface temperature safety range, Wmax is the maximum value of the interface temperature safety range, and Wmin is the minimum value of the interface temperature safety range; the step of importing the output abnormality evaluation result and the chip temperature abnormality analysis result into the data self-check evaluation strategy for data self-check evaluation includes the following specific steps: obtaining the calculated output abnormality evaluation value and temperature abnormality analysis value, substituting the obtained output abnormality evaluation value and temperature abnormality analysis value into the data self-check evaluation value calculation formula to calculate the data self-check evaluation value, where the data self-check evaluation value calculation formula is: ,in, Output abnormal evaluation value ratio coefficient; The data self-test evaluation value is compared with the set data self-test evaluation threshold. If the obtained data self-test evaluation value is greater than or equal to the set data self-test evaluation threshold, the evaluation chip is in a healthy state. If the obtained data self-test evaluation value is less than the set data self-test evaluation threshold, the evaluation chip is in an abnormal state and an abnormal warning signal is sent.

2. The chip data self-checking method based on artificial intelligence as claimed in claim 1, characterized in that: The specific steps of installing the chip on the detection device, inputting the specified signal data to the corresponding interface of the chip, obtaining the output data of each output interface of the chip to be detected, and obtaining the chip surface temperature data are as follows: The chip to be tested is installed on the testing device, and the signal sending component inputs the specified signal data to the corresponding input interface of the chip, obtains the standard output data of each output interface after the input of the specified signal data, and simultaneously obtains the output data of each output interface in real time and stores it in the first storage module; The chip surface is evenly divided into regions with the same number of interfaces according to the number of interfaces, and the temperature data of each region is obtained and stored in the second storage module.

3. A chip data self-checking system based on artificial intelligence, which is implemented based on the chip data self-checking method based on artificial intelligence as claimed in any one of claims 1-2, characterized in that: It specifically includes a data acquisition module, an output abnormality evaluation module, a chip temperature abnormality analysis module, a data self-check evaluation module, a chip abnormality warning module and a master control module, wherein the data acquisition module is used to install the chip on the detection device, input the specified signal data to the corresponding interface of the chip, obtain the output data of each output interface of the chip to be detected, and simultaneously obtain the chip surface temperature data; the output abnormality evaluation module is used to substitute the obtained output data of each output interface of the chip to be detected into the output abnormality evaluation strategy to perform output abnormality evaluation; the chip temperature abnormality analysis module is used to obtain the chip surface temperature data during operation and import it into the temperature abnormality analysis strategy to perform chip temperature abnormality analysis; the data self-check evaluation module is used to import the output abnormality evaluation result and the chip temperature abnormality analysis result into the data self-check evaluation strategy to perform data self-check evaluation; the chip abnormality warning module is used to perform chip detection judgment according to the data self-check evaluation result, and to perform chip abnormality warning according to the chip detection judgment result; the master control module is used to control the operation of the data acquisition module, the output abnormality evaluation module, the chip temperature abnormality analysis module, the data self-check evaluation module and the chip abnormality warning module.

4. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the chip data self-checking method based on artificial intelligence as described in any one of claims 1-2 by calling the computer program stored in the memory.

5. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes an artificial intelligence-based chip data self-checking method as described in any one of claims 1-2.

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