Disaster recovery sensor determination method and device, equipment and medium

Abstract

The embodiment of the invention provides a disaster recovery sensor determination method and device, equipment and a medium, and is used for solving the problems of relatively large workload, relatively low efficiency and relatively poor reliability of manually selecting a disaster recovery sensor in the prior art. The method comprises the following steps: acquiring a first test temperature of each sensor at each preset moment; for each sensor, determining the temperature change rate of the sensor at each preset moment according to the first test temperature of the sensor at each preset moment; determining correlation coefficients of the sensor and any other sensor according to the temperature change rate of the sensor and any other sensor at each preset moment; and determining a target disaster recovery sensor of the sensor according to the correlation coefficient of the sensor and any other sensor. According to the method, automatic selection of the disaster recovery sensors is achieved, the workload of heat dissipation engineers is reduced, the sensor with the strongest correlation with the sensor serves as the target disaster recovery sensor, and the reliability and rationality of disaster recovery sensor selection are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, equipment and medium for determining a disaster recovery sensor. Background Art

[0002] At present, the selection of disaster recovery sensors is usually done by heat dissipation engineers based on their previous design experience. For example, heat dissipation engineers usually tend to select sensors around failed sensors or upstream of the air duct as disaster recovery sensors. Due to the complexity of the internal environment of the server, this selection method is sometimes not the optimal solution. Moreover, for each sensor, the heat dissipation engineer needs to manually select its disaster recovery sensor, which is labor-intensive, inefficient, and unreliable. Summary of the invention

[0003] The embodiments of the present application provide a method, device, equipment and medium for determining a disaster recovery sensor, so as to solve the problem in the prior art of manually selecting a disaster recovery sensor, which has a large workload, low efficiency and poor reliability.

[0004] In a first aspect, an embodiment of the present application provides a method for determining a disaster recovery sensor, the method being applied to an electronic device, the method comprising:

[0005] Obtaining a first test temperature of each sensor at each preset time;

[0006] For each of the sensors, the temperature change rate of the sensor at each preset moment is determined based on the first test temperature of the sensor at each preset moment; the correlation coefficient between the sensor and any other sensor is determined based on the temperature change rate of the sensor and any other sensor at each preset moment; the target disaster recovery sensor of the sensor is determined based on the correlation coefficient between the sensor and any other sensor.

[0007] Through the above method, the automatic selection of disaster recovery sensors is realized, and the workload of heat dissipation engineers is reduced. In addition, for each sensor, the sensor with the strongest correlation with the sensor is used as the target disaster recovery sensor in this application, rather than selecting only based on past experience or sensor location information, thereby improving the reliability and rationality of disaster recovery sensor selection.

[0008] In a possible implementation manner, determining the correlation coefficient between the sensor and any other sensor according to the temperature change rate of the sensor and any other sensor at each preset moment includes:

[0009] For any other sensor, the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor is determined according to the temperature change rate of the sensor and the other sensor at each preset moment; the variances corresponding to the temperature change rates of the sensor and the other sensor are determined according to the temperature change rate of the sensor at each preset moment and the temperature change rate of the other sensor at each preset moment; and the correlation coefficient between the sensor and the other sensor is determined according to each variance and the covariance.

[0010] In the above manner, the correlation coefficient between the sensor and the other sensors is accurately determined through the covariance between the temperature change rate of the sensor and the temperature change rates of other sensors, the first variance corresponding to the temperature change rate of the sensor, and the second variance corresponding to the temperature change rates of other sensors, so as to facilitate the subsequent determination of the target disaster recovery sensor of the sensor according to the correlation coefficient, reduce the workload of heat dissipation engineers, and further improve the reliability and rationality of disaster recovery sensor selection.

[0011] In a possible implementation manner, determining the target disaster recovery sensor of the sensor according to the correlation coefficient between the sensor and any other sensor includes:

[0012] Other sensors with the largest absolute value of correlation coefficient with the sensor are determined as target disaster recovery sensors of the sensor.

[0013] Through the above method, other sensors with the largest absolute value of correlation coefficient with the sensor are directly determined as target disaster recovery sensors, which improves the efficiency of determining the target disaster recovery sensors, and can ensure that other sensors with the strongest correlation with the sensor are used as target disaster recovery sensors, reducing the workload of heat dissipation engineers, and further improving the reliability and rationality of disaster recovery sensor selection.

[0014] In a possible implementation manner, determining the target disaster recovery sensor of the sensor according to the correlation coefficient between the sensor and any other sensor includes:

[0015] Selecting a first preset number of other sensors having the highest absolute values ​​of correlation coefficients with the sensor as candidate disaster recovery sensors for the sensor;

[0016] When the sensor failure is identified, the disaster recovery sensor of the sensor determined in the last time interval is saved as the target disaster recovery sensor, wherein the real-time correlation coefficient between the sensor and any candidate disaster recovery sensor is determined based on the real-time first test temperature of the sensor and any candidate disaster recovery sensor at each moment in the last time interval; the candidate disaster recovery sensor having the largest absolute value of the real-time correlation coefficient with the sensor is determined as the disaster recovery sensor of the sensor in the last time interval.

[0017] Through the above method, a first preset number of candidate disaster recovery sensors with the highest absolute value of the correlation coefficient with the sensor are determined, and during the real-time operation of the electronic device, the real-time correlation coefficient between the sensor and each candidate disaster recovery sensor is determined at every preset time interval, and the candidate disaster recovery sensor with the largest absolute value of the real-time correlation coefficient with the sensor is used as the target disaster recovery sensor, that is, the candidate disaster recovery sensor that best matches the sensor within the preset time interval is used as the target disaster recovery sensor, thereby reducing the workload of heat dissipation engineers and further improving the reliability and rationality of disaster recovery sensor selection.

[0018] In a possible implementation manner, determining the temperature change rate of the sensor at each preset moment according to the first test temperature of the sensor at each preset moment includes:

[0019] For any preset moment, according to the first test temperature of the sensor at the preset moment and a set number of other preset moments adjacent to the preset moment, determine the average value corresponding to the first test temperature within the preset time period, and determine the average value as the second test temperature corresponding to the preset moment;

[0020] According to the difference between the second test temperatures at any two adjacent preset moments and the corresponding time interval, the temperature change rate of the sensor at each corresponding preset moment is determined.

[0021] Through the above method, the first test temperature is mean filtered and gradient calculated to accurately obtain the temperature change rate of the sensor at each preset time, so as to facilitate more accurate determination of the target disaster recovery sensor in the subsequent process and further improve the reliability and rationality of the disaster recovery sensor selection.

[0022] In a possible implementation manner, obtaining the first test temperature of each sensor at each preset time includes:

[0023] Obtaining the original test temperature of each sensor at each preset time;

[0024] The original test temperature of each sensor is filtered to obtain each first test temperature of each sensor.

[0025] Through the above method, the original test temperature is cleaned and filtered to obtain the normal first test temperature, and the subsequent process of determining the target disaster recovery sensor is carried out based on the normal first test temperature, thereby avoiding the influence of abnormal data, improving the accuracy of determining the target disaster recovery sensor, and further improving the reliability and rationality of disaster recovery sensor selection.

[0026] In a second aspect, the embodiment of the present application further provides a disaster recovery sensor determination device, which has the function of implementing the behavior of the device in the method embodiment of the first aspect above, and the beneficial effects can be referred to the description of the first aspect and will not be repeated here. The disaster recovery sensor determination device includes:

[0027] An acquisition module, used for acquiring a first test temperature of each sensor at each preset moment;

[0028] A determination module is used to determine, for each sensor, the temperature change rate of the sensor at each preset moment based on the first test temperature of the sensor at each preset moment; determine the correlation coefficient between the sensor and any other sensor based on the temperature change rate of the sensor and any other sensor at each preset moment; and determine the target disaster recovery sensor of the sensor based on the correlation coefficient between the sensor and any other sensor.

[0029] In a third aspect, an embodiment of the present application further provides an electronic device, which includes at least a processor and a memory, and the processor is used to implement the steps of the disaster recovery sensor determination method as described in any one of the above items when executing a computer program stored in the memory.

[0030] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the method for determining a disaster recovery sensor as described in any one of the above items are implemented.

[0031] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the steps of the disaster recovery sensor determination method as described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A schematic diagram of a disaster recovery sensor determination process provided in an embodiment of the present application;

[0034] Figure 2 A schematic diagram of a fan heat dissipation control process provided in an embodiment of the present application;

[0035] Figure 3A schematic diagram of a disaster recovery sensor determination process provided in an embodiment of the present application;

[0036] Figure 4 A schematic diagram of the structure of a disaster recovery sensor determination device provided in an embodiment of the present application;

[0037] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose and implementation method of the present application clearer, the exemplary implementation method of the present application will be clearly and completely described below in conjunction with the drawings in the exemplary embodiments of the present application. Obviously, the described exemplary embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0039] It should be noted that the brief description of terms in this application is only for the convenience of understanding the embodiments described below, and is not intended to limit the embodiments of this application. Unless otherwise specified, these terms should be understood according to their common and usual meanings.

[0040] The terms "first", "second", "third", etc. in the specification and claims of this application and the above drawings are used to distinguish similar or similar objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise noted. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances.

[0041] The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device comprising a list of components is not necessarily limited to all the components expressly listed but may include other components not expressly listed or inherent to such product or device.

[0042] The term "module" refers to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0044] For the convenience of explanation, the above description has been made in conjunction with specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or limit the embodiments to the specific forms disclosed above. Based on the above teachings, various modifications and variations can be obtained. The selection and description of the above embodiments are to better explain the principles and practical applications, so that those skilled in the art can better use the embodiments and various different variations of the embodiments suitable for specific use considerations.

[0045] The embodiment of the present application provides a method, device, equipment and medium for determining a disaster recovery sensor. In the method, the first test temperature of each sensor at each preset moment is obtained; for each sensor, the temperature change rate corresponding to the sensor at each preset moment is determined based on the first test temperature of the sensor at each preset moment; the correlation coefficient between the sensor and any other sensor at each preset moment is determined based on the temperature change rate between the sensor and any other sensor; and the target disaster recovery sensor of the sensor is determined based on the correlation coefficient between the sensor and any other sensor. Automatic selection of disaster recovery sensors is achieved to reduce the workload of heat dissipation engineers. In addition, for each sensor in the present application, the sensor with the strongest correlation with the sensor is selected as the target disaster recovery sensor, rather than selecting based solely on past experience or sensor location information, thereby improving the reliability and rationality of disaster recovery sensor selection.

[0046] Embodiment 1:

[0047] Figure 1 A schematic diagram of a disaster recovery sensor determination process provided in an embodiment of the present application, the process includes:

[0048] S101: Obtain a first test temperature of each sensor at each preset time.

[0049] The disaster recovery sensor determination method provided in the embodiment of the present application is applied to an electronic device, which may be a computer (Personal Computer, PC), a server, etc.

[0050] In a possible implementation, multiple sensors, a heat dissipation control system, hardware components, and fans for heat dissipation are deployed in the electronic device. Specifically, the sensors in the electronic device are used to collect temperature data of the hardware components and transmit the collected temperature data to the heat dissipation control system. The heat dissipation control system is used to calculate and control the fan speed adjustment according to requirements to achieve heat dissipation of the hardware components in the electronic device. Specifically, Figure 2The schematic diagram of the fan heat dissipation control process is shown. In the electronic device, the sensor collects the temperature data of the hardware components and transmits the collected temperature data to the heat dissipation control system. After obtaining the temperature data of the hardware components, the heat dissipation control system calculates the fan adjustment speed as required according to the temperature data, and enables the sensor to use the speed control strategy to control the fan to adjust the speed to the adjusted speed, thereby realizing the fan heat dissipation for the hardware components. Among them, the speed control strategy includes but is not limited to open-loop speed control, proportional-integral-differential (ProportionalIntegralDerivative, PID) closed-loop speed control, etc. Open-loop speed control and PID closed-loop speed control are both existing technologies and will not be repeated here.

[0051] In an embodiment of the present application, each sensor of the electronic device collects the first test temperature of the hardware component at each preset time and transmits it to the heat dissipation control system in the electronic device, and the heat dissipation control system obtains the first test temperature of the hardware component collected by each sensor at each preset time. In a possible implementation, the time intervals corresponding to any two adjacent preset times may be the same.

[0052] In another possible implementation, sensors, hardware components, and fans for heat dissipation are deployed in other devices, and the heat dissipation control system is deployed in the electronic device. Specifically, the sensors in the other devices are used to collect temperature data of the hardware components and transmit the collected temperature data to the heat dissipation control system in the electronic device. The heat dissipation control system in the electronic device obtains the first test temperature of the hardware components collected at each preset time and sent by each sensor. And the heat dissipation control system in the electronic device calculates and sends a regulation signal for controlling the fan speed to the other device as required, so as to achieve heat dissipation of the hardware components in the other devices.

[0053] Among them, the pressure conditions at different preset moments can be the same or different, but in order to determine the disaster recovery sensor that best matches the sensor under different pressure conditions, the pressure conditions at different preset moments are usually set differently, so as to obtain the first test temperature of each sensor under different pressure conditions at each preset moment.

[0054] It can be understood that the sensors involved in the embodiments of the present application may be speed sensors, temperature sensors, etc., and no specific limitations are made here.

[0055] S102: For each sensor, determine the temperature change rate of the sensor at each preset moment according to a first test temperature of the sensor at each preset moment.

[0056] For each sensor, the electronic device calculates a gradient at each preset moment according to the first test temperature of the sensor at each preset moment, and the gradient is the temperature change rate corresponding to the sensor at each preset moment. The temperature change rate can be positive, negative or zero, and is not limited here.

[0057] For example, the temperature change rate of the sensor at each corresponding preset moment is determined according to the difference between the first test temperatures of the sensor at any two adjacent preset moments and the time interval between the two adjacent preset moments.

[0058] S103: For each sensor, determine a correlation coefficient between the sensor and any other sensor according to the temperature change rate of the sensor and any other sensor at each preset time; and determine a target disaster recovery sensor for the sensor according to the correlation coefficient between the sensor and any other sensor.

[0059] For each sensor, after determining the temperature change rate of the sensor at each preset time, a temperature change rate curve corresponding to the sensor can be determined with time as the x-axis and the temperature change rate as the y-axis.

[0060] For each sensor, according to the temperature change rate of the sensor and any other sensor at each preset moment, that is, according to the temperature change rate curve corresponding to the sensor and the temperature change rate curve corresponding to any other sensor, a correlation algorithm is used to determine the correlation coefficient between the temperature change rate curve corresponding to the sensor and the temperature change rate curve corresponding to the other sensor, and the correlation coefficient is determined as the correlation coefficient between the sensor and the other sensor. Among them, the correlation coefficient of any two sensors can represent the similarity of the gradient change trend of the temperature measured by the two sensors, and serve as an indicator for disaster recovery sensor screening.

[0061] The correlation algorithm includes but is not limited to the Pearson correlation coefficient algorithm, the Spearman correlation coefficient algorithm, etc., and is not specifically limited here.

[0062] After obtaining the correlation coefficient between any two sensors, for each sensor, the similarity between the sensor and any other sensor is determined based on the correlation coefficient between the sensor and any other sensor. Based on the similarity between the sensor and any other sensor, other sensors are screened to determine the target disaster recovery sensor of the sensor.

[0063] If the absolute value of the correlation coefficient between the sensor and any other sensor is closer to 1, it is determined that the degree of similarity between the sensor and the other sensor is higher.

[0064] It can be understood that the target disaster recovery sensor is the backup sensor of the sensor. In a possible implementation, the sensor can be used as the main sensor. When it is identified that the main sensor fails or fails, the main sensor is disabled, and the target disaster recovery sensor (i.e., the backup sensor) corresponding to the main sensor is enabled to control the open-loop speed regulation strategy of the target disaster recovery sensor to take effect, and control the fan to dissipate heat for the hardware component monitored by the main sensor. Among them, since multiple sensors may be configured to monitor a hardware component (such as a hard disk, a central processing unit (CPU), etc.), the temperature collected by the sensor used to monitor the same hardware component is usually similar, while the temperature collected by the sensor used to collect different hardware components may deviate greatly. Therefore, the target disaster recovery sensor determined by the correlation coefficient is usually a sensor used to monitor the same hardware component as the main sensor.

[0065] Preferably, in order to ensure that the hardware components do not overheat, after the target disaster recovery sensor is enabled, the heat dissipation control system can control the fan speed to be higher than the fan speed controlled when the main sensor is used.

[0066] The embodiment of the present application realizes automatic selection of disaster recovery sensors, reduces the workload of heat dissipation engineers, and uses the sensor with the strongest correlation with the sensor as the target disaster recovery sensor, rather than selecting based solely on past experience or sensor location information, thereby improving the reliability and rationality of disaster recovery sensor selection.

[0067] Embodiment 2:

[0068] In order to further improve the reliability and rationality of the disaster recovery sensor selection, based on the above embodiment, in the embodiment of the present application, the correlation coefficient between the sensor and any other sensor is determined according to the temperature change rate of the sensor and any other sensor at each preset moment, including:

[0069] For any other sensor, the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor is determined according to the temperature change rate of the sensor and the other sensor at each preset moment; the variances corresponding to the temperature change rates of the sensor and the other sensor are determined according to the temperature change rate of the sensor at each preset moment and the temperature change rate of the other sensor at each preset moment; and the correlation coefficient between the sensor and the other sensor is determined according to each variance and the covariance.

[0070] In an embodiment of the present application, when calculating the correlation coefficient between the sensor and any other sensor, for any other sensor, the average value of the first temperature change rate corresponding to the sensor is determined based on the temperature change rate of the sensor at each preset moment; the average value of the second temperature change rate corresponding to the other sensor is determined based on the temperature change rate of the other sensor at each preset moment; based on the temperature change rates of the sensor and the other sensor at each preset moment, the average value of the first temperature change rate corresponding to the sensor, and the average value of the second temperature change rate corresponding to the other sensor, the covariance calculation formula is used to determine the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor.

[0071] Specifically, the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensors may satisfy the following formula:

[0072]

[0073] Wherein, Cov(X, Y) represents the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensors; i represents each preset moment; n represents the total number of preset moments; X i represents the temperature change rate of the sensor at the preset time i; μ X represents the average value of the first temperature change rate corresponding to the sensor; Y i represents the temperature change rate of the other sensor at the preset time i; μ Y Indicates the average value of the second temperature change rate corresponding to the other sensors.

[0074] Furthermore, according to the temperature change rate of the sensor at each preset moment, a variance calculation formula is used to determine a first variance corresponding to the temperature change rate of the sensor; according to the temperature change rate of the other sensor at each preset moment, a variance calculation formula is used to determine a second variance corresponding to the temperature change rate of the other sensor. The process of determining the variance value by using the variance calculation formula is a prior art and will not be described in detail here.

[0075] After obtaining the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor, the first variance corresponding to the temperature change rate of the sensor, and the second variance corresponding to the temperature change rate of the other sensor, according to the first variance, the second variance and the covariance, adopt A correlation coefficient between the sensor and the other sensor is determined.

[0076] Among them, ρ XY represents the correlation coefficient between the sensor and the other sensor, D(X) represents the first variance corresponding to the temperature change rate of the sensor, and D(Y) represents the second variance corresponding to the temperature change rate of the other sensor.

[0077] In the embodiment of the present application, the correlation coefficient between the sensor and the other sensors is accurately determined through the covariance between the temperature change rate of the sensor and the temperature change rates of other sensors, the first variance corresponding to the temperature change rate of the sensor, and the second variance corresponding to the temperature change rates of other sensors, so as to facilitate the subsequent determination of the target disaster recovery sensor of the sensor according to the correlation coefficient, reduce the workload of heat dissipation engineers, and further improve the reliability and rationality of disaster recovery sensor selection.

[0078] Embodiment 3:

[0079] In order to further improve the reliability and rationality of the disaster recovery sensor selection, based on the above embodiments, in the embodiment of the present application, the target disaster recovery sensor of the sensor is determined according to the correlation coefficient between the sensor and any other sensor, including:

[0080] Other sensors with the largest absolute value of correlation coefficient with the sensor are determined as target disaster recovery sensors of the sensor.

[0081] Since the value range of the correlation coefficient between the sensor and any other sensor is within the interval [-1, 1], when the correlation coefficient is a positive number, it means that the sensor is positively correlated with the other sensors, and the closer the correlation coefficient is to 1, the stronger the positive correlation between the sensor and the other sensors is; when the correlation coefficient is a negative number, it means that the sensor is negatively correlated with any other sensor, and the closer the correlation coefficient is to -1, the stronger the negative correlation between the sensor and the other sensors is. In other words, when the absolute value of the correlation coefficient between the sensor and the other sensors is larger (that is, closer to 1), the stronger the correlation between the sensor and the other sensors is.

[0082] Therefore, in the embodiment of the present application, according to the correlation coefficient between the sensor and any other sensor, the absolute value of the correlation coefficient between the sensor and any other sensor is determined, and the other sensor with the largest absolute value of the correlation coefficient with the sensor is determined, that is, the other sensor with the absolute value of the correlation coefficient with the sensor closest to 1 is determined, and the other sensor is determined to have the strongest correlation with the sensor, and the other sensor is determined as the target disaster recovery sensor. If there are multiple other sensors with the largest absolute value of the correlation coefficient with the sensor, one of the other sensors can be selected as the target disaster recovery sensor.

[0083] In the embodiment of the present application, other sensors with the largest absolute value of correlation coefficient with the sensor are directly determined as target disaster recovery sensors, which improves the efficiency of determining the target disaster recovery sensors, and can ensure that other sensors with the strongest correlation with the sensor are used as target disaster recovery sensors, reducing the workload of heat dissipation engineers, and further improving the reliability and rationality of disaster recovery sensor selection.

[0084] Embodiment 4:

[0085] In order to further improve the reliability and rationality of the disaster recovery sensor selection, based on the above embodiments, in the embodiment of the present application, the target disaster recovery sensor of the sensor is determined according to the correlation coefficient between the sensor and any other sensor, including:

[0086] Selecting a first preset number of other sensors having the highest absolute values ​​of correlation coefficients with the sensor as candidate disaster recovery sensors for the sensor;

[0087] When the sensor failure is identified, the disaster recovery sensor of the sensor determined in the last time interval is saved as the target disaster recovery sensor, wherein the real-time correlation coefficient between the sensor and any candidate disaster recovery sensor is determined based on the real-time first test temperature of the sensor and any candidate disaster recovery sensor at each moment in the last time interval; the candidate disaster recovery sensor having the largest absolute value of the real-time correlation coefficient with the sensor is determined as the disaster recovery sensor of the sensor in the last time interval.

[0088] In an embodiment of the present application, based on the correlation coefficient between the sensor and any other sensor, the absolute value of the correlation coefficient between the sensor and each other sensor is determined, and the other sensors are arranged in descending order according to the absolute value of the correlation coefficient with the sensor; a first preset number of other sensors having the highest absolute value of the correlation coefficient with the sensor are determined, and the first preset number of other sensors are used as candidate disaster recovery sensors to obtain a candidate disaster recovery sensor cluster.

[0089] The first preset number is a positive integer greater than 1.

[0090] After determining the candidate disaster recovery sensor cluster, during the operation of the electronic device, at every preset time interval, the real-time first test temperature of the sensor and each candidate disaster recovery sensor at each moment in the preset time interval is obtained. In which, each moment can be determined according to the saved set time length within the preset time interval according to the set time length.

[0091] According to the real-time first test temperature of the sensor at each moment in the preset time interval, the real-time temperature change rate corresponding to each moment in the preset time interval of the sensor is determined; according to the real-time first test temperature of any candidate disaster recovery sensor at each moment in the preset time interval, the real-time temperature change rate corresponding to each moment in the preset time interval of the candidate disaster recovery sensor is determined. The process of determining the real-time temperature change rate is consistent with the process of determining the temperature change rate in the above embodiment, and will not be repeated here.

[0092] For any candidate disaster recovery sensor, a real-time correlation coefficient between the sensor and the candidate disaster recovery sensor is determined using a correlation algorithm according to the real-time temperature change rate corresponding to the sensor and the candidate disaster recovery sensor at each moment within a preset time interval. The process of determining the real-time correlation coefficient is consistent with the process of determining the correlation coefficient in the above embodiment, and will not be repeated here.

[0093] According to the real-time correlation coefficient between the sensor and any candidate disaster recovery sensor, the absolute value of the real-time correlation coefficient between the sensor and each candidate disaster recovery sensor is determined, and the candidate disaster recovery sensor with the largest absolute value of the real-time correlation coefficient with the sensor is determined, that is, the candidate disaster recovery sensor with the absolute value of the real-time correlation coefficient with the sensor closest to 1 is determined, and it is determined that the candidate disaster recovery sensor has the strongest correlation with the sensor, and the candidate disaster recovery sensor is determined as the target disaster recovery sensor corresponding to the sensor within the next preset time interval of the current moment and saved, thereby ensuring that if the sensor fails within the next preset time interval of the current moment, it can be quickly switched to the target disaster recovery sensor.

[0094] Specifically, when the sensor failure is identified, the disaster recovery sensor of the sensor determined in the last time interval is saved as the target disaster recovery sensor.

[0095] In an embodiment of the present application, a first preset number of candidate disaster recovery sensors having the highest absolute value of the correlation coefficient with the sensor are determined, and during the real-time operation of the electronic device, the real-time correlation coefficient between the sensor and each candidate disaster recovery sensor is determined at every preset time interval, and the candidate disaster recovery sensor having the largest absolute value of the real-time correlation coefficient with the sensor is used as the target disaster recovery sensor, that is, the candidate disaster recovery sensor that best matches the sensor within the preset time interval is used as the target disaster recovery sensor, thereby reducing the workload of heat dissipation engineers and further improving the reliability and rationality of disaster recovery sensor selection.

[0096] Embodiment 5:

[0097] In order to further improve the reliability and rationality of the disaster recovery sensor selection, on the basis of the above embodiments, in the embodiment of the present application, the temperature change rate corresponding to the sensor at each preset time is determined according to the first test temperature of the sensor at each preset time, including:

[0098] For any preset moment, according to the first test temperature of the sensor at the preset moment and a set number of other preset moments adjacent to the preset moment, determine the average value corresponding to the first test temperature within the preset time period, and determine the average value as the second test temperature corresponding to the preset moment;

[0099] According to the difference between the second test temperatures at any two adjacent preset moments and the corresponding time interval, the temperature change rate of the sensor at each corresponding preset moment is determined.

[0100] Since each sensor has a corresponding first test temperature at each preset moment, in the embodiment of the present application, the temperature curve corresponding to each sensor that changes with time can be determined with time as the x-axis and the test temperature as the y-axis, and each temperature curve can also be smoothed by mean filtering.

[0101] Specifically, for any sensor and any preset moment, based on the first test temperature of the sensor at the preset moment and a set number of other preset moments adjacent to the preset moment, and the number of preset moments, determine the average value corresponding to the first test temperature in the preset time period, and determine the average value as the second test temperature corresponding to the sensor at the preset moment.

[0102] Among them, the set number of other preset moments adjacent to the preset moment may include a second preset number of preset moments before the preset moment, and a second preset number of preset moments after the preset moment. Among them, the second preset number is a positive integer. In a possible implementation, if the number of preset moments before the preset moment is less than the second preset number or the number of preset moments after the preset moment is less than the second preset number, then all the preset moments before the preset moment and the second preset number of preset moments after the preset moment can be used as the set number of other preset moments adjacent to the preset moment; or the second preset number of preset moments before the preset moment and all the preset moments after the preset moment can be used as the set number of other preset moments adjacent to the preset moment.

[0103] After obtaining the second test temperature of the sensor at each preset moment after mean filtering processing, for each preset moment, determine the test temperature change data between the preset moment and the adjacent preset moment according to the second test temperature at the preset moment and the second test temperature at the adjacent preset moment adjacent to the preset moment; determine the ratio of the test temperature change data to the time interval according to the test temperature change data between the preset moment and the adjacent preset moment, and the time interval corresponding to the preset moment and the adjacent preset moment, and determine the ratio as the temperature change rate of the sensor corresponding to the preset moment.

[0104] Among them, the adjacent preset moment adjacent to the preset moment is the adjacent preset moment adjacent to the preset moment in a preset direction, wherein the preset direction may be before the preset moment or after the preset moment, and no specific limitation is made here.

[0105] In an embodiment of the present application, mean filtering and gradient calculation are performed on the first test temperature to accurately obtain the temperature change rate of the sensor corresponding to each preset time, thereby facilitating more accurate determination of the target disaster recovery sensor in the subsequent process and further improving the reliability and rationality of the disaster recovery sensor selection.

[0106] Embodiment 6:

[0107] In order to further improve the reliability and rationality of the disaster recovery sensor selection, based on the above embodiments, in the embodiment of the present application, the obtaining of the first test temperature of each sensor at each preset time includes:

[0108] Obtaining the original test temperature of each sensor at each preset time;

[0109] The original test temperature of each sensor is filtered to obtain each first test temperature of each sensor.

[0110] In an embodiment of the present application, the electronic device obtains the original test temperature of the hardware component collected by each sensor deployed by the electronic device at each preset time.

[0111] For example, the electronic device may be placed under different pressure conditions, and at any preset time, the electronic device obtains the first test temperature of the hardware component of each sensor under the pressure condition corresponding to the preset time.

[0112] For the original test temperature of each sensor, determine whether the original test temperature is a null value. If so, determine that the original test temperature with a null value is abnormal data, and filter the null value, that is, remove the original test temperature; if not, determine whether the original test temperature is within a preset range. If not, determine that the original test temperature is an abnormal value outside the preset range, determine that the original test temperature with an abnormal value is abnormal data, and filter it with the original test temperature, that is, remove the original test temperature.

[0113] In a possible implementation, in order to ensure that all sensors have normal test temperatures at each preset moment, if the original test temperature of any sensor is determined to be abnormal data at a preset moment, the original test temperatures of all sensors at the preset moment can be removed.

[0114] After removing abnormal data in the original test temperature of each sensor, the first test temperature of each sensor is obtained.

[0115] In an embodiment of the present application, the original test temperature is cleaned and filtered to obtain a normal first test temperature, and the subsequent process of determining the target disaster recovery sensor is performed based on the normal first test temperature, thereby avoiding the influence of abnormal data, improving the accuracy of determining the target disaster recovery sensor, and further improving the reliability and rationality of disaster recovery sensor selection.

[0116] Embodiment 7:

[0117] Based on the above embodiments, the present application embodiment provides a schematic diagram of a disaster recovery sensor determination process, such as Figure 3 As shown, the process includes:

[0118] Step 1: Obtain original data: Obtain the original test temperature of each sensor under different pressure conditions at each preset time.

[0119] Step 2: Clear abnormal values: Clean and filter the null values ​​in the original test temperature of each sensor and the abnormal values ​​outside the preset range to obtain the first test temperature of each sensor at each preset time.

[0120] Step 3: mean filtering: For any sensor and any preset moment, according to the first test temperature of the sensor in the preset time period including the preset moment, determine the mean value corresponding to the first test temperature in the preset time period, and determine the mean value as the second test temperature corresponding to the preset moment.

[0121] Step 4: Gradient conversion. For any sensor, according to the second test temperatures at any two adjacent preset moments, determine the test temperature change data corresponding to the second test temperatures at the two adjacent preset moments, and according to the test temperature change data and the time interval corresponding to the two adjacent preset moments, determine the temperature change rate of the sensor at each corresponding preset moment.

[0122] Step 5: Calculate the correlation coefficient. For any sensor, according to the temperature change rate of the sensor and any other sensor at each preset time, a correlation algorithm is used to determine the correlation coefficient between the sensor and the other sensor.

[0123] The correlation algorithm includes but is not limited to the Pearson correlation coefficient algorithm, the Spearman correlation coefficient algorithm, etc., and is not specifically limited here.

[0124] Step 6: Select the target disaster recovery sensor. For any sensor, determine the target disaster recovery sensor based on the correlation coefficient between the sensor and any other sensor.

[0125] For example, according to the correlation coefficient between the sensor and any other sensor, the other sensor having the largest absolute value of the correlation coefficient with the sensor is determined as the target disaster recovery sensor.

[0126] In another example, according to the correlation coefficient between the sensor and any other sensor, a first preset number of other sensors having the highest absolute value of the correlation coefficient with the sensor are selected as candidate disaster recovery sensors;

[0127] At every preset time interval, the real-time correlation coefficient between the sensor and any candidate disaster recovery sensor is determined based on the real-time first test temperature of the sensor and any candidate disaster recovery sensor at each preset moment within the preset time interval; based on the real-time correlation coefficient between the sensor and any candidate disaster recovery sensor, the candidate disaster recovery sensor having the largest absolute value of the real-time correlation coefficient with the sensor is determined as the target disaster recovery sensor.

[0128] Embodiment 8:

[0129] Based on the same technical concept, on the basis of the above embodiments, the present application provides a disaster recovery sensor determination device, Figure 4 A schematic diagram of a disaster recovery sensor determination device provided in an embodiment of the present application is shown in FIG. Figure 4 As shown, the device comprises:

[0130] An acquisition module 401 is used to acquire a first test temperature of each sensor at each preset time;

[0131] Determination module 402 is used to determine, for each sensor, the temperature change rate of the sensor at each preset moment based on the first test temperature of the sensor at each preset moment; determine the correlation coefficient between the sensor and any other sensor based on the temperature change rate of the sensor and any other sensor at each preset moment; determine the target disaster recovery sensor of the sensor based on the correlation coefficient between the sensor and any other sensor.

[0132] In a possible implementation, the determination module 402 is specifically used to determine, for any other sensor, the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor at each preset moment; determine the variances corresponding to the temperature change rates of the sensor and the other sensor respectively according to the temperature change rate of the sensor at each preset moment and the temperature change rate of the other sensor at each preset moment; and determine the correlation coefficient between the sensor and the other sensor according to each variance and the covariance.

[0133] In a possible implementation, the determination module 402 is specifically configured to determine other sensors having the largest absolute value of correlation coefficient with the sensor as target disaster recovery sensors for the sensor.

[0134] In one possible implementation, the determination module 402 is specifically used to select a first preset number of other sensors having the highest absolute value of the correlation coefficient with the sensor as candidate disaster recovery sensors for the sensor; when the sensor failure is identified, the disaster recovery sensor of the sensor determined in the last time interval is saved as the target disaster recovery sensor, wherein the real-time correlation coefficient of the sensor with any candidate disaster recovery sensor is determined based on the real-time first test temperature of the sensor and any candidate disaster recovery sensor at each moment in the last time interval; the candidate disaster recovery sensor having the largest absolute value of the real-time correlation coefficient with the sensor is determined as the disaster recovery sensor of the sensor in the last time interval.

[0135] In one possible implementation, the determination module 402 is specifically used to determine, for any preset moment, based on the first test temperature of the sensor at the preset moment and a set number of other preset moments adjacent to the preset moment, an average value corresponding to the first test temperature within the preset time period, and determine the average value as the second test temperature corresponding to the preset moment; and determine the temperature change rate of the sensor at each corresponding preset moment based on the difference between the second test temperatures of any two adjacent preset moments and the corresponding time interval.

[0136] In a possible implementation, the acquisition module 401 is specifically configured to acquire the original test temperature of each sensor at each preset moment; and filter the original test temperature of each sensor to obtain each first test temperature of each sensor.

[0137] Embodiment 9:

[0138] Based on the same technical concept, the present application also provides an electronic device, Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, it includes: a processor 501, a communication interface 502, a memory 503 and a communication bus 504, wherein the processor 501, the communication interface 502, and the memory 503 communicate with each other through the communication bus 504;

[0139] The memory 503 stores a computer program. When the program is executed by the processor 501, the processor 501 performs the following steps:

[0140] Obtaining a first test temperature of each sensor at each preset time;

[0141] For each of the sensors, the temperature change rate of the sensor at each preset moment is determined based on the first test temperature of the sensor at each preset moment; the correlation coefficient between the sensor and any other sensor is determined based on the temperature change rate of the sensor and any other sensor at each preset moment; the target disaster recovery sensor of the sensor is determined based on the correlation coefficient between the sensor and any other sensor.

[0142] In one possible implementation, the processor 501 is specifically used to determine, for any other sensor, the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor at each preset moment; determine the variances corresponding to the temperature change rates of the sensor and the other sensor respectively according to the temperature change rate of the sensor at each preset moment and the temperature change rate of the other sensor at each preset moment; and determine the correlation coefficient between the sensor and the other sensor according to each variance and the covariance.

[0143] In a possible implementation manner, the processor 501 is specifically configured to determine another sensor having a maximum absolute value of a correlation coefficient with the sensor as a target disaster recovery sensor for the sensor.

[0144] In one possible implementation, the processor 501 is specifically used to select a first preset number of other sensors having the highest absolute value of the correlation coefficient with the sensor as candidate disaster recovery sensors for the sensor; when the sensor failure is identified, the disaster recovery sensor of the sensor determined in the last time interval is saved as the target disaster recovery sensor, wherein the real-time correlation coefficient of the sensor with any candidate disaster recovery sensor is determined based on the real-time first test temperature of the sensor and any candidate disaster recovery sensor at each moment in the last time interval; the candidate disaster recovery sensor having the largest absolute value of the real-time correlation coefficient with the sensor is determined as the disaster recovery sensor of the sensor in the last time interval.

[0145] In one possible implementation, the processor 501 is specifically used to determine, for any preset moment, based on the first test temperature of the sensor at the preset moment and a set number of other preset moments adjacent to the preset moment, an average value corresponding to the first test temperature within the preset time period, and determine the average value as the second test temperature corresponding to the preset moment; and determine the temperature change rate of the sensor at each corresponding preset moment based on the difference between the second test temperatures of any two adjacent preset moments and the corresponding time interval.

[0146] In a possible implementation, the processor 501 is specifically configured to obtain an original test temperature of each sensor at each preset moment; and filter the original test temperature of each sensor to obtain each first test temperature of each sensor.

[0147] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0148] The communication interface 502 is used for communication between the electronic device and other devices.

[0149] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0150] The above-mentioned processor can be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP), etc.; it can also be a digital signal processing processor (Digital Signal Processing, DSP), an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc.

[0151] Embodiment 10:

[0152] Based on the same technical concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program that can be executed by an electronic device. When the program runs on the electronic device, the electronic device implements any of the above embodiments when executing.

[0153] The above-mentioned computer-readable storage medium can be any available medium or data storage device that can be accessed by the processor in the electronic device, including but not limited to magnetic storage such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc., optical storage such as CD, DVD, BD, HVD, etc., and semiconductor storage such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD), etc.

[0154] Based on the same technical concept, the embodiment of the present application also provides a computer program product, which includes: computer program code, when the computer program code is run on a computer, the computer executes any of the above embodiments. Since the principle of solving the problem by the above computer program product is similar to the method for determining the disaster recovery sensor, the implementation of the above computer program product can refer to the implementation of the method, and the repeated parts will not be repeated.

[0155] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0156] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0157] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0158] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining a disaster recovery sensor, characterized in that: The method comprises: Obtaining a first test temperature of each sensor at each preset time; For each of the sensors, the temperature change rate of the sensor at each preset moment is determined based on the first test temperature of the sensor at each preset moment; the correlation coefficient between the sensor and any other sensor is determined based on the temperature change rate of the sensor and any other sensor at each preset moment; the target disaster recovery sensor of the sensor is determined based on the correlation coefficient between the sensor and any other sensor.

2. The method according to claim 1, characterized in that Determining the correlation coefficient between the sensor and any other sensor according to the temperature change rate of the sensor and any other sensor at each preset moment includes: For any other sensor, the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor is determined according to the temperature change rate of the sensor and the other sensor at each preset moment; the variances corresponding to the temperature change rates of the sensor and the other sensor are determined according to the temperature change rate of the sensor at each preset moment and the temperature change rate of the other sensor at each preset moment; and the correlation coefficient between the sensor and the other sensor is determined according to each variance and the covariance.

3. The method according to claim 1, characterized in that The step of determining the target disaster recovery sensor of the sensor according to the correlation coefficient between the sensor and any other sensor includes: Other sensors with the largest absolute value of correlation coefficient with the sensor are determined as target disaster recovery sensors of the sensor.

4. The method according to claim 1, characterized in that: The step of determining the target disaster recovery sensor of the sensor according to the correlation coefficient between the sensor and any other sensor includes: Selecting a first preset number of other sensors having the highest absolute values ​​of correlation coefficients with the sensor as candidate disaster recovery sensors for the sensor; When the sensor failure is identified, the disaster recovery sensor of the sensor determined in the last time interval is saved as the target disaster recovery sensor, wherein the real-time correlation coefficient between the sensor and any candidate disaster recovery sensor is determined based on the real-time first test temperature of the sensor and any candidate disaster recovery sensor at each moment in the last time interval; the candidate disaster recovery sensor having the largest absolute value of the real-time correlation coefficient with the sensor is determined as the disaster recovery sensor of the sensor in the last time interval.

5. The method according to claim 1, characterized in that Determining the temperature change rate of the sensor at each preset moment according to the first test temperature of the sensor at each preset moment includes: For any preset moment, according to the first test temperature of the sensor at the preset moment and a set number of other preset moments adjacent to the preset moment, determine the average value corresponding to the first test temperature within the preset time period, and determine the average value as the second test temperature corresponding to the preset moment; According to the difference between the second test temperatures at any two adjacent preset moments and the corresponding time interval, the temperature change rate of the sensor at each corresponding preset moment is determined.

6. The method according to claim 1, characterized in that The obtaining of the first test temperature of each sensor at each preset time comprises: Obtaining the original test temperature of each sensor at each preset time; The original test temperature of each sensor is filtered to obtain each first test temperature of each sensor.

7. A disaster recovery sensor determination device, characterized in that: The device comprises: An acquisition module, used for acquiring a first test temperature of each sensor at each preset moment; A determination module is used to determine, for each sensor, the temperature change rate of the sensor at each preset moment based on the first test temperature of the sensor at each preset moment; determine the correlation coefficient between the sensor and any other sensor based on the temperature change rate of the sensor and any other sensor at each preset moment; and determine the target disaster recovery sensor of the sensor based on the correlation coefficient between the sensor and any other sensor.

8. The device according to claim 7, characterized in that The determination module is specifically used to determine, for any other sensor, the covariance between the temperature change rate of the sensor and the temperature change rate of the other sensor according to the temperature change rate of the sensor and the other sensor at each preset moment; determine the variances corresponding to the temperature change rates of the sensor and the other sensor respectively according to the temperature change rate of the sensor at each preset moment and the temperature change rate of the other sensor at each preset moment; and determine the correlation coefficient between the sensor and the other sensor according to each variance and the covariance.

9. An electronic device, characterized in that: The electronic device comprises at least a processor and a memory, and the processor is configured to implement the steps of the method for determining a disaster recovery sensor according to any one of claims 1 to 6 when executing a computer program stored in the memory.

10. A computer storage medium, characterized in that: It stores a computer program executable by an electronic device. When the program runs on the electronic device, the electronic device executes the steps of the disaster recovery sensor determination method according to any one of claims 1 to 6.

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