Sensor state detection method, apparatus, and readable storage medium

Abstract

Embodiments of the present application provide a sensor state detection method and device, and a readable storage medium, wherein the method comprises: obtaining state data collected by a target sensor in a server; in the case that the state data is abnormal, determining a target verification rule corresponding to the abnormal state data; obtaining target verification data according to the target verification rule to judge the credibility of the abnormal state data; in the case that the abnormal state data is determined to be untrustworthy, sending a first alarm information, wherein the first alarm information is used to indicate that the target sensor is faulty. Through the present application, the problem that the baseboard management controller obtains incorrect sensor readings due to interference of the sensor in the server, and thus the baseboard management controller malfunctions, is solved, and the effect of ensuring the accuracy of server monitoring is achieved.

Description

Technical Field

[0001] This application relates to the field of data verification technology, and more specifically, to a sensor state detection method, apparatus, and readable storage medium. Background Technology

[0002] In the information age, servers are crucial as core devices supporting enterprise operations, storing vast amounts of important data. Therefore, ensuring stable server operation is paramount. The Baseboard Management Controller (BMC), as a system for managing and monitoring server status, plays a vital role in ensuring normal server operation.

[0003] The baseboard management controller (BMC) monitors the server by acquiring information such as temperature, voltage, fan status, and power status from internal sensors. When anomalies are detected, it takes appropriate adjustments to ensure the system remains healthy. Most BMCs in related technologies acquire sensor readings directly from third-party chips. However, relying solely on chip-based readings is prone to errors. Interference in communication between the BMC and the chip, or malfunctions in the chip itself, often leads to inaccurate sensor readings acquired by the BMC. The BMC may fail to detect these errors, resulting in malfunctions and affecting the accuracy of server monitoring.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a sensor status detection method, apparatus, and readable storage medium to at least solve the problem that when monitoring a server through a baseboard management controller, interference with the sensors in the server causes erroneous sensor readings, leading to malfunctions of the baseboard management controller.

[0006] According to one embodiment of this application, a sensor state detection method is provided, comprising: acquiring state data collected by a target sensor within a server, wherein the state data is used to indicate the operating state of the server; determining a target verification rule corresponding to the abnormal state data when the state data is abnormal; acquiring target verification data to determine the credibility of the abnormal state data based on the target verification rule, wherein the target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, voltage data inside the server collected by the target sensor; and sending a first alarm message when the abnormal state data is determined to be unreliable, wherein the first alarm message is used to characterize a fault in the target sensor.

[0007] In an exemplary embodiment, the process of obtaining target verification data to determine the credibility of abnormal state data according to the target verification rule includes: determining the target verification data and target verification formula corresponding to the target verification rule; collecting the target verification data corresponding to the target verification rule from the server through an analog-to-digital converter sampler; calculating the target theoretical value corresponding to the state data based on the target verification formula and the target verification data; calculating the target error value based on the state data and the target theoretical value; and determining whether the state data is credible by comparing the target error value with a preset error threshold.

[0008] In an exemplary embodiment, the calculation of the target theoretical value corresponding to the state data based on the target verification formula and the target verification data further includes: sampling the server a preset number of times to obtain a preset number of target verification data; calculating based on the target verification formula and the preset number of target verification data to obtain a preset number of target candidate values, wherein the target verification data and the target candidate values ​​correspond one-to-one; and calculating the preset number of target candidate values ​​based on preset data processing rules to obtain the target theoretical value, wherein the preset data processing rules include at least one of the following: taking the arithmetic mean of the preset number of target candidate values, taking the weighted average of the preset number of target candidate values, and taking the median of the preset number of target candidate values.

[0009] In one exemplary embodiment, after determining whether the status data is reliable by comparing the target error value and a preset error threshold, the method further includes: if the status data with an anomaly is determined to be reliable, sending a second alarm message, wherein the second alarm message is used to characterize the presence of an anomaly in the server.

[0010] In an exemplary embodiment, the status data includes at least one of the following: temperature data collected by an internal temperature sensor of the server, and power consumption data collected by an internal power consumption sensor of the server. Determining the target verification rule corresponding to the abnormal status data includes: obtaining the identifier of the target sensor corresponding to the abnormal status data, wherein the identifier is used to identify a uniquely determined target sensor; and searching in a preset verification rule database based on the identifier of the target sensor to obtain the target verification rule.

[0011] In an exemplary embodiment, determining the target verification rule corresponding to the abnormal state data further includes: obtaining a rule adjustment instruction, wherein the rule adjustment instruction is an instruction set by the target object in the front-end interactive interface; and adjusting the target verification rule according to the rule adjustment instruction.

[0012] In one exemplary embodiment, after acquiring the status data collected by the target sensor within the server, the method further includes: determining a preset sampling threshold range corresponding to the status data; and determining whether the status data is abnormal if the status data exceeds the preset sampling threshold range.

[0013] According to another embodiment of this application, a sensor state detection device is provided, comprising: a sampling value monitoring module, configured to acquire state data collected by a target sensor within a server, wherein the state data is used to indicate the operating state of the server; a verification rule determination module, configured to determine a target verification rule corresponding to the abnormal state data when the state data is abnormal; a credibility judgment module, configured to perform credibility judgment on the abnormal state data based on the target verification rule and acquiring target verification data, wherein the target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, voltage data inside the server collected by the target sensor; and an alarm sending module, configured to send a first alarm message when the abnormal state data is determined to be unreliable, wherein the first alarm message is used to characterize a fault in the target sensor.

[0014] According to another embodiment of this application, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of the sensor state detection method in any of the above method embodiments when running.

[0015] According to yet another embodiment of this application, an electronic device is also provided, 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 steps of the sensor state detection method in any of the above method embodiments.

[0016] The proposed solution employs a data reliability assessment method based on analog-to-digital (AD) sampling. By adding an AD sampling circuit to key sensors, when abnormal readings occur in the server's key sensors, the sensor values ​​are calculated using the AD sampling values. By comparing the difference between the readings and the calculated values, the reliability of the readings is determined, helping to determine whether a server malfunction has occurred. This solves the problem that interference with sensors in the server can lead to incorrect sensor readings obtained by the baseboard management controller, causing malfunctions in the controller. Therefore, this approach ensures accurate server monitoring, improves server robustness, and guarantees stable server operation. Attached Figure Description

[0017] Figure 1 This is a flowchart of a sensor state detection method according to an embodiment of this application;

[0018] Figure 2 This is a hardware structure block diagram of a mobile terminal according to an embodiment of the present application for a sensor state detection method;

[0019] Figure 3 This is a flowchart of a method for determining the reliability of data based on AD sampling, according to an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of a sensor state detection device provided according to an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application will be described in detail below with reference to the accompanying drawings and examples.

[0022] To facilitate a better understanding of the embodiments of this application by those skilled in the art, some technical terms or nouns involved in the embodiments of this application are explained as follows:

[0023] Baseboard Management Controller (BMC): This is a remote management controller that performs operations such as firmware upgrades and monitoring of the machine even when it is not powered on.

[0024] Analog-to-digital conversion (AD) sampling: Through sampling and successive approximation, analog quantities such as voltage and current are converted into digital quantities that can be calculated and compared.

[0025] Sensor: A device used to convert measured parameters of a server, such as temperature and power consumption, into numerical values.

[0026] IPMI (Intelligent Platform Management Interface): At its core is a dedicated chip / controller (BMC), independent of the operating system, BIOS, and processor. Therefore, it is an out-of-band management device. At the same time, IPMI is a set of computer interface specifications defined for autonomous computer subsystems, used to provide management and monitoring functions for hardware and software such as CPU, firmware (BIOS or UEFI), and operating system independent of the host system. Developers can interact with the BMC through IPMI OEM CMD.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0028] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 2 This is a hardware structure block diagram of a mobile terminal for a sensor state detection method according to an embodiment of this application. Figure 2 As shown, a mobile terminal may include one or more ( Figure 2 Only one is shown in the diagram. A processor 202 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 204 for storing data are also shown. The mobile terminal may further include a transmission device 206 for communication functions and an input / output device 208. Those skilled in the art will understand that... Figure 2 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 2 The more or fewer components shown, or having the same Figure 2 The different configurations shown.

[0029] The memory 204 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the sensor state detection method in this embodiment. The processor 202 executes various functional applications and data processing by running the computer program stored in the memory 204, thus implementing the aforementioned method. The memory 204 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 204 may further include memory remotely located relative to the processor 202, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0030] The transmission device 206 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 206 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 206 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0031] Servers are crucial as core devices carrying enterprise business operations, storing vast amounts of important data. Therefore, ensuring stable server operation is paramount. The Baseboard Management Controller (BMC) acts as the server's steward, managing and monitoring its operational status. It determines server health by monitoring readings such as temperature, voltage, and power consumption. Upon detecting anomalies, such as excessively high temperature or power consumption, it takes appropriate adjustments to maintain system health. However, to prevent erroneous readings from the BMC and subsequent false alarms, alternative methods are needed to verify the reliability of abnormal sensor readings and determine whether further action is required.

[0032] This embodiment provides a method that runs on the aforementioned mobile terminal. Figure 1 This is a flowchart of a sensor state detection method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0033] Step S102: Obtain status data collected by the target sensor inside the server, wherein the status data is used to indicate the operating status of the server;

[0034] In this embodiment, the aforementioned status data is data directly read from sensors inside the server to determine whether the server is operating normally. The status data includes, but is not limited to, temperature data collected by the server's internal temperature sensor and power consumption data collected by the server's internal power consumption sensor.

[0035] In some embodiments of this application, after obtaining the status data collected by the target sensor in the server, the following steps are further included: determining the preset sampling threshold range corresponding to the status data; and determining whether the status data is abnormal if the status data exceeds the preset sampling threshold range.

[0036] For example, if the temperature data collected by the internal temperature sensor of the server is 120°C, and the temperature data threshold corresponding to the temperature sensor is 100°C, then if the temperature data collected by the internal temperature sensor of the server exceeds the preset sampling threshold, it is determined that the temperature data collected by the internal temperature sensor of the server is abnormal.

[0037] Step S104: If there is an anomaly in the state data, determine the target verification rule corresponding to the anomaly state data.

[0038] In some embodiments of this application, the status data includes at least one of the following: temperature data collected by an internal temperature sensor of the server, power consumption data collected by an internal power consumption sensor of the server, and determining the target verification rule corresponding to the abnormal status data includes the following steps: obtaining the identifier of the target sensor corresponding to the abnormal status data, wherein the identifier is used to identify a uniquely determined target sensor; and searching in a preset verification rule database based on the identifier of the target sensor to obtain the target verification rule.

[0039] In this embodiment, each state data corresponds to a different target verification rule. For example, for abnormal temperature data, the current value flowing inside the server can be obtained from the analog-to-digital converter sampler (AD sampler). Then, based on the current value, the resistance value of the target resistor inside the server can be calculated, and the current temperature inside the server can be further deduced from the resistance value. The resistance value of the target resistor is related to the temperature inside the server.

[0040] To enhance the flexibility of verifying abnormal state data, determining the target verification rule corresponding to the abnormal state data also includes the following steps: obtaining rule adjustment instructions, wherein the rule adjustment instructions are instructions set by the target object in the front-end interactive interface; and adjusting the target verification rule according to the rule adjustment instructions.

[0041] Specifically, the target can perform a series of operations such as adding, deleting, modifying, and viewing the pre-stored target verification rules in the system on the front-end interactive interface according to actual needs, so as to meet different needs in different situations in actual production and life.

[0042] Step S106: According to the target verification rules, obtain target verification data to determine the credibility of abnormal state data. The target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, and voltage data inside the server collected by the target sensor.

[0043] To assess the credibility of abnormal state data, when sensor readings are abnormal, the sensor values ​​are calculated using AD sampling values ​​(i.e., the aforementioned target verification data). Specifically, based on the target verification rules, the credibility assessment of abnormal state data involves the following steps: determining the target verification data and target verification formula corresponding to the target verification rules; acquiring the target verification data corresponding to the target verification rules from the server using an analog-to-digital converter sampler; calculating the target theoretical value corresponding to the state data based on the target verification formula and target verification data; calculating the target error value based on the state data and target theoretical value; and determining the credibility of the state data by comparing the target error value with a preset error threshold.

[0044] In this embodiment, the analog-to-digital converter sampler is an AD sampling circuit, and the target verification data is the AD sampling value collected by the AD sampling circuit. The AD sampling value includes at least: the current data flowing through the server and the voltage data inside the server.

[0045] For example, for abnormal temperature data, the current value flowing through the server's interior can be obtained from the AD sampling circuit. Based on this current value, the resistance of the target resistor inside the server can be calculated, and the current internal temperature of the server can be further deduced from this resistance value. The resistance value of the target resistor is correlated with the internal temperature of the server. Further analysis is conducted to determine whether the error between the deduced internal temperature and the temperature status data collected by the temperature sensor exceeds an error threshold. Similarly, for abnormal power consumption data, the current value flowing through the server's interior and the voltage value inside the server can be obtained from the AD sampling circuit. Then, based on the power consumption calculation formula, the theoretical power consumption value is calculated using these current and voltage values. Further analysis is conducted to determine whether the error between the calculated theoretical power consumption value and the power consumption status data collected by the power consumption sensor exceeds an error threshold.

[0046] As an optional implementation, the error between the calculated value (i.e., the theoretical value mentioned above) and the reading value (i.e., the state data mentioned above) of the sensor is calculated. When the error is within 10%, the reading value is considered reliable; if it exceeds 10%, the reading value is considered unreliable.

[0047] To improve the accuracy of the target theoretical value calculated based on the target verification data, the calculation of the target theoretical value corresponding to the state data based on the target verification formula and the target verification data further includes the following steps: sampling the server a preset number of times to obtain a preset number of target verification data; calculating based on the target verification formula and the preset number of target verification data to obtain a preset number of target candidate values, wherein the target verification data and the target candidate values ​​correspond one-to-one; and calculating the preset number of target candidate values ​​according to preset data processing rules to obtain the target theoretical value, wherein the preset data processing rules include at least one of the following: taking the arithmetic mean of the preset number of target candidate values, taking the weighted average of the preset number of target candidate values, or taking the median of the preset number of target candidate values.

[0048] Specifically, multiple sampling can be performed using an AD sampling circuit, and multiple candidate theoretical values ​​can be calculated based on the target verification data obtained from each sampling. The multiple candidate theoretical values ​​are then processed according to preset data processing rules to obtain the target theoretical value. The preset data processing rules include, but are not limited to, averaging the multiple candidate theoretical values ​​and aggregating the multiple candidate theoretical values.

[0049] In some embodiments of this application, after determining whether the status data is reliable by comparing the target error value and a preset error threshold, the following steps are also included: if the status data with an abnormality is determined to be reliable, a second alarm message is sent, wherein the second alarm message is used to indicate that there is an anomaly in the server.

[0050] Specifically, if the abnormal status data is determined to be reliable, it means that the sensor inside the server is in normal condition and the sensor reading is accurate. Therefore, the abnormal status data is the server's real data. For example, if the abnormal temperature status data collected by the temperature sensor is determined to be reliable, an alarm message is sent to indicate that there is an abnormal situation of excessively high temperature inside the target server.

[0051] Step S108: If it is determined that the abnormal state data is unreliable, a first alarm message is sent, wherein the first alarm message is used to indicate that the target sensor has a fault.

[0052] Specifically, if the abnormal status data is determined to be unreliable, it indicates that the sensor inside the server is in an abnormal state and the sensor reading is inaccurate. Therefore, the abnormal status data is not the server's real data, but rather a sensor reading error. For example, if the abnormal temperature status data collected by the temperature sensor is determined to be unreliable, an alarm message is sent to indicate that the temperature sensor reading inside the target server is abnormal.

[0053] The sensor state detection method in steps S102 to S108 of the embodiments of this application will be further described below.

[0054] Most existing BMCs acquire sensor readings by directly reading data from a third-party chip (sensor). However, relying solely on readings from the chip as the sensor's readings can lead to malfunctions. If communication between the BMC and the chip is interfered with, or if the chip itself malfunctions, the BMC may fail to detect incorrect readings, resulting in false triggering.

[0055] To solve the above problems, Figure 3 This is a flowchart of a method for determining the reliability of data based on AD sampling, provided in an embodiment of this application. Figure 3 As shown, the method includes the following steps:

[0056] Step S302: Add an AD sampling circuit to the key sensor;

[0057] Specifically, add AD sampling circuits to key sensors in the server, such as temperature sensors and power consumption sensors;

[0058] Step S304: Configure the strategy to be executed in the BMC when the sensor reading is unreliable via IPMI command;

[0059] For example, no action is taken if the read value is reliable; an alarm is issued if the read value is unreliable, and then the action is taken according to the existing settings.

[0060] Step S306: When the sensor reading exceeds the normal range, start AD sampling and calculate the value;

[0061] Specifically, when the BMC detects that the sensor reading exceeds the normal range, it initiates AD sampling for the corresponding sensor. Based on the AD sampling value and the sensor's sampling principle, it calculates the sensor value. For example, for a temperature sensor, temperature and voltage can be calculated using a mathematical expression, so the temperature can be calculated from the collected voltage value. For a power consumption sensor, power can be calculated from the collected voltage and current values. In this embodiment, multiple AD samplings can be performed and the average of the multiple calculated values ​​can be taken.

[0062] Step S308: Determine whether the error between the calculated value and the read value exceeds 10%.

[0063] In this embodiment, when the error is within 10%, the reading is considered reliable; if it exceeds 10%, the reading is considered unreliable, and the BMC will act according to the pre-set strategy.

[0064] It should be noted that this embodiment uses a temperature sensor and a power consumption sensor for illustration, but basically all sensors used in BMC convert the measured quantity into electrical quantities such as voltage and current output. As long as the conversion relationship is determined, any sensor can use AD sampling to calculate the sensor reading. Therefore, the sensor state detection method in this application can be extended to determine the reliability of any sensor reading.

[0065] The sensor status detection method in this embodiment adds an AD sampling circuit to key sensors. When a sensor reading is abnormal, the sensor value is calculated using the AD sampling value. By comparing the difference between the reading and the calculated value, the reliability of the reading is determined, helping to determine whether a server malfunction has occurred. This improves the robustness of the BMC management server and further ensures the stable operation of services. Calculating sensor values ​​through AD sampling avoids BMC malfunctions caused by communication interference or third-party chip abnormalities affecting sensor readings, thus protecting the server for safer and more stable operation.

[0066] The above steps solve the problem that when monitoring servers through the baseboard management controller, interference with the sensors in the server causes erroneous sensor readings, leading to malfunctions in the baseboard management controller. This achieves the goal of ensuring accurate server monitoring, improving server robustness, and guaranteeing stable server operation.

[0067] The entity performing the above steps can be a controller, a terminal, etc., but is not limited to these.

[0068] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0069] This embodiment also provides a sensor state detection device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0070] Figure 4 This is a schematic diagram of a sensor state detection device according to an embodiment of this application, as shown below. Figure 4 As shown, the sensor state detection device includes:

[0071] The sampling value monitoring module 40 is used to acquire status data collected by the target sensor inside the server, wherein the status data is used to indicate the operating status of the server;

[0072] In this embodiment, the aforementioned status data is data directly read from sensors inside the server to determine whether the server is operating normally. The status data includes, but is not limited to, temperature data collected by the server's internal temperature sensor and power consumption data collected by the server's internal power consumption sensor.

[0073] In some embodiments of this application, after obtaining the status data collected by the target sensor in the server, the following steps are further included: determining the preset sampling threshold range corresponding to the status data; and determining whether the status data is abnormal if the status data exceeds the preset sampling threshold range.

[0074] For example, if the temperature data collected by the internal temperature sensor of the server is 120°C, and the temperature data threshold corresponding to the temperature sensor is 100°C, then if the temperature data collected by the internal temperature sensor of the server exceeds the preset sampling threshold, it is determined that the temperature data collected by the internal temperature sensor of the server is abnormal.

[0075] The verification rule determination module 42 is used to determine the target verification rule corresponding to the abnormal state data when there is an anomaly in the state data.

[0076] In some embodiments of this application, the status data includes at least one of the following: temperature data collected by an internal temperature sensor of the server, power consumption data collected by an internal power consumption sensor of the server, and determining the target verification rule corresponding to the abnormal status data includes the following steps: obtaining the identifier of the target sensor corresponding to the abnormal status data, wherein the identifier is used to identify a uniquely determined target sensor; and searching in a preset verification rule database based on the identifier of the target sensor to obtain the target verification rule.

[0077] In this embodiment, each state data corresponds to a different target verification rule. For example, for abnormal temperature data, the current value flowing inside the server can be obtained from the analog-to-digital converter sampler (AD sampler). Then, based on the current value, the resistance value of the target resistor inside the server can be calculated, and the current temperature inside the server can be further deduced from the resistance value. The resistance value of the target resistor is related to the temperature inside the server.

[0078] To enhance the flexibility of verifying abnormal state data, determining the target verification rule corresponding to the abnormal state data also includes the following steps: obtaining rule adjustment instructions, wherein the rule adjustment instructions are instructions set by the target object in the front-end interactive interface; and adjusting the target verification rule according to the rule adjustment instructions.

[0079] Specifically, the target can perform a series of operations such as adding, deleting, modifying, and viewing the pre-stored target verification rules in the system on the front-end interactive interface according to actual needs, so as to meet different needs in different situations in actual production and life.

[0080] The credibility judgment module 44 is used to judge the credibility of abnormal state data by acquiring target verification data according to the target verification rules. The target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, and voltage data inside the server collected by the target sensor.

[0081] To assess the credibility of abnormal state data, when sensor readings are abnormal, the sensor values ​​are calculated using AD sampling values ​​(i.e., the aforementioned target verification data). Specifically, based on the target verification rules, the credibility assessment of abnormal state data involves the following steps: determining the target verification data and target verification formula corresponding to the target verification rules; acquiring the target verification data corresponding to the target verification rules from the server using an analog-to-digital converter sampler; calculating the target theoretical value corresponding to the state data based on the target verification formula and target verification data; calculating the target error value based on the state data and target theoretical value; and determining the credibility of the state data by comparing the target error value with a preset error threshold.

[0082] In this embodiment, the analog-to-digital converter sampler is an AD sampling circuit, and the target verification data is the AD sampling value collected by the AD sampling circuit. The AD sampling value includes at least: the current data flowing through the server and the voltage data inside the server.

[0083] For example, for abnormal temperature data, the current value flowing through the server's interior can be obtained from the AD sampling circuit. Based on this current value, the resistance of the target resistor inside the server can be calculated, and the current internal temperature of the server can be further deduced from this resistance value. The resistance value of the target resistor is correlated with the internal temperature of the server. Further analysis is conducted to determine whether the error between the deduced internal temperature and the temperature status data collected by the temperature sensor exceeds an error threshold. Similarly, for abnormal power consumption data, the current value flowing through the server's interior and the voltage value inside the server can be obtained from the AD sampling circuit. Then, based on the power consumption calculation formula, the theoretical power consumption value is calculated using these current and voltage values. Further analysis is conducted to determine whether the error between the calculated theoretical power consumption value and the power consumption status data collected by the power consumption sensor exceeds an error threshold.

[0084] As an optional implementation, the error between the calculated value (i.e., the theoretical value mentioned above) and the reading value (i.e., the state data mentioned above) of the sensor is calculated. When the error is within 10%, the reading value is considered reliable; if it exceeds 10%, the reading value is considered unreliable.

[0085] To improve the accuracy of the target theoretical value calculated based on the target verification data, the calculation of the target theoretical value corresponding to the state data based on the target verification formula and the target verification data further includes the following steps: sampling the server a preset number of times to obtain a preset number of target verification data; calculating based on the target verification formula and the preset number of target verification data to obtain a preset number of target candidate values, wherein the target verification data and the target candidate values ​​correspond one-to-one; and calculating the preset number of target candidate values ​​according to preset data processing rules to obtain the target theoretical value, wherein the preset data processing rules include at least one of the following: taking the arithmetic mean of the preset number of target candidate values, taking the weighted average of the preset number of target candidate values, or taking the median of the preset number of target candidate values.

[0086] Specifically, multiple sampling can be performed using an AD sampling circuit, and multiple candidate theoretical values ​​can be calculated based on the target verification data obtained from each sampling. The multiple candidate theoretical values ​​are then processed according to preset data processing rules to obtain the target theoretical value. The preset data processing rules include, but are not limited to, averaging the multiple candidate theoretical values ​​and aggregating the multiple candidate theoretical values.

[0087] In some embodiments of this application, after determining whether the status data is reliable by comparing the target error value and a preset error threshold, the following steps are also included: if the status data with an abnormality is determined to be reliable, a second alarm message is sent, wherein the second alarm message is used to indicate that there is an anomaly in the server.

[0088] Specifically, if the abnormal status data is determined to be reliable, it means that the sensor inside the server is in normal condition and the sensor reading is accurate. Therefore, the abnormal status data is the server's real data. For example, if the abnormal temperature status data collected by the temperature sensor is determined to be reliable, an alarm message is sent to indicate that there is an abnormal situation of excessively high temperature inside the target server.

[0089] The alarm sending module 46 is used to send a first alarm message when it is determined that there is abnormal status data that is unreliable. The first alarm message is used to indicate that there is a fault in the target sensor.

[0090] Specifically, if the abnormal status data is determined to be unreliable, it indicates that the sensor inside the server is in an abnormal state and the sensor reading is inaccurate. Therefore, the abnormal status data is not the server's real data, but rather a sensor reading error. For example, if the abnormal temperature status data collected by the temperature sensor is determined to be unreliable, an alarm message is sent to indicate that the temperature sensor reading inside the target server is abnormal.

[0091] It should be noted that this embodiment uses a temperature sensor and a power consumption sensor for illustration, but basically all sensors used in BMC convert the measured quantity into electrical quantities such as voltage and current output. As long as the conversion relationship is determined, any sensor can use AD sampling to calculate the sensor reading. Therefore, the sensor state detection method in this application can be extended to determine the reliability of any sensor reading.

[0092] The sensor status detection method in this embodiment adds an AD sampling circuit to key sensors. When a sensor reading is abnormal, the sensor value is calculated using the AD sampling value. By comparing the difference between the reading and the calculated value, the reliability of the reading is determined, helping to determine whether a server malfunction has occurred. This improves the robustness of the BMC management server and further ensures the stable operation of services. Calculating sensor values ​​through AD sampling avoids BMC malfunctions caused by communication interference or third-party chip abnormalities affecting sensor readings, thus protecting the server for safer and more stable operation.

[0093] The above steps solve the problem that when monitoring servers through the baseboard management controller, interference with the sensors in the server causes erroneous sensor readings, leading to malfunctions in the baseboard management controller. This achieves the goal of ensuring accurate server monitoring, improving server robustness, and guaranteeing stable server operation.

[0094] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0095] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments at runtime: acquiring status data collected by a target sensor within a server, wherein the status data is used to indicate the operating status of the server; determining a target verification rule corresponding to the abnormal status data when the status data is abnormal; acquiring target verification data to determine the credibility of the abnormal status data according to the target verification rule, wherein the target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, voltage data inside the server collected by the target sensor; and sending a first alarm message when the abnormal status data is determined to be unreliable, wherein the first alarm message is used to characterize a fault in the target sensor.

[0096] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0097] Embodiments of this application also provide an electronic device, including a memory and a processor. The memory stores a computer program, and the computer terminal may include one or more processors (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.). The processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0098] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0099] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0100] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0101] Embodiments of this application also provide a controller for running a program, wherein the program executes the steps in any of the above method embodiments: acquiring status data collected by a target sensor within a server, wherein the status data is used to indicate the operating status of the server; determining a target verification rule corresponding to the abnormal status data when the status data is abnormal; acquiring target verification data to determine the credibility of the abnormal status data based on the target verification rule, wherein the target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, voltage data inside the server collected by the target sensor; and sending a first alarm message when the abnormal status data is determined to be unreliable, wherein the first alarm message is used to characterize a fault in the target sensor.

[0102] The above steps solve the problem that when monitoring servers through the baseboard management controller, interference with the sensors in the server causes erroneous sensor readings, leading to malfunctions in the baseboard management controller. This achieves the goal of ensuring accurate server monitoring, improving server robustness, and guaranteeing stable server operation.

[0103] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A sensor state detection method, characterized in that, include: The status data collected by the target sensor inside the server is obtained through the baseboard management controller, wherein the status data is used to indicate the operating status of the server; In the event that the status data is abnormal, a target verification rule corresponding to the abnormal status data is determined; Based on the target verification rules, target verification data is obtained to determine the credibility of the abnormal state data. The target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, and voltage data inside the server collected by the target sensor. If the abnormal state data is determined to be unreliable, a first alarm message is sent, wherein the first alarm message is used to indicate that the target sensor has a fault; The process of obtaining target verification data to determine the credibility of the abnormal state data according to the target verification rule includes: determining the target verification data and target verification formula corresponding to the target verification rule; collecting the target verification data corresponding to the target verification rule from the server using an analog-to-digital converter sampler; calculating the target theoretical value corresponding to the state data based on the target verification formula and the target verification data; calculating the target error value based on the state data and the target theoretical value; and determining whether the state data is credible by comparing the target error value with a preset error threshold.

2. The method according to claim 1, characterized in that, The calculation based on the target verification formula and the target verification data to obtain the target theoretical value corresponding to the state data further includes: The server is sampled a preset number of times to obtain the preset number of target verification data. The target verification formula and the preset number of target verification data are used to calculate and obtain the preset number of target candidate values, wherein the target verification data and the target candidate values ​​correspond one-to-one. According to preset data processing rules, the preset number of target candidate values ​​are calculated to obtain the target theoretical value. The preset data processing rules include at least one of the following: taking the arithmetic mean of the preset number of target candidate values, taking the weighted average of the preset number of target candidate values, and taking the median of the preset number of target candidate values.

3. The method according to claim 1, characterized in that, After determining whether the state data is reliable by comparing the target error value with a preset error threshold, the process further includes: If the abnormal status data is determined to be reliable, a second alarm message is sent, wherein the second alarm message is used to indicate that there is an anomaly in the server.

4. The method according to claim 1, characterized in that, The status data includes at least one of the following: temperature data collected by the internal temperature sensor of the server, and power consumption data collected by the internal power consumption sensor of the server. The target verification rules for determining the status data that shows an anomaly include: Obtain the identifier number of the target sensor corresponding to the abnormal state data, wherein the identifier number is used to identify a unique target sensor; Based on the identifier of the target sensor, a search is performed in a preset verification rule database to obtain the target verification rule.

5. The method according to claim 4, characterized in that, The determination of the target verification rule corresponding to the abnormal state data also includes: Obtain rule adjustment instructions, wherein the rule adjustment instructions are instructions set by the target object in the front-end interactive interface; The target verification rules are adjusted according to the rule adjustment instructions.

6. The method according to claim 1, characterized in that, After obtaining the status data collected by the target sensor within the server, the following is also included: Determine the preset sampling threshold range corresponding to the state data; If the status data exceeds the preset sampling threshold range, it is determined whether the status data is abnormal.

7. A sensor state detection device, characterized in that, include: The sampling value monitoring module is used to acquire status data collected by the target sensor inside the server through the baseboard management controller, wherein the status data is used to indicate the operating status of the server; The verification rule determination module is used to determine the target verification rule corresponding to the abnormal state data when the state data is abnormal. The credibility judgment module is used to judge the credibility of the abnormal state data by acquiring target verification data according to the target verification rules, wherein the target verification data includes at least one of the following: current data flowing through the server collected by the target sensor, and voltage data inside the server collected by the target sensor. An alarm sending module is used to send a first alarm message when it is determined that the abnormal state data is unreliable, wherein the first alarm message is used to indicate that the target sensor has a fault; The credibility judgment module is further configured to: determine the target verification data and target verification formula corresponding to the target verification rule; collect the target verification data corresponding to the target verification rule from the server using an analog-to-digital converter sampler; calculate the target theoretical value corresponding to the state data based on the target verification formula and the target verification data; calculate the target error value based on the state data and the target theoretical value; and determine whether the state data is credible by comparing the target error value with a preset error threshold.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the sensor state detection method according to any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the sensor state detection method according to any one of claims 1 to 6.

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