CN116975567B - Method, system, equipment and storage medium for testing radiation interference resistance of server - Google Patents

Method, system, equipment and storage medium for testing radiation interference resistance of server Download PDF

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CN116975567B
CN116975567B CN202310937484.1A CN202310937484A CN116975567B CN 116975567 B CN116975567 B CN 116975567B CN 202310937484 A CN202310937484 A CN 202310937484A CN 116975567 B CN116975567 B CN 116975567B
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server
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state evaluation
evaluation index
value
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CN116975567A (en
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王蓉
马晓艺
戴凤
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Unilab Shanghai Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/001—Measuring interference from external sources to, or emission from, the device under test, e.g. EMC, EMI, EMP or ESD testing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Abstract

The invention belongs to the technical field of server testing, and discloses a method, a system, equipment and a storage medium for testing radiation interference resistance of a server, which comprise the following steps: generating a strain state evaluation index according to the access data of each server corresponding to each test period; generating a test state evaluation index according to the test environment information of each server corresponding to each test period; comprehensively analyzing based on the strain state evaluation index and the test state evaluation index to obtain an operation state evaluation index; comparing and analyzing the running state evaluation index with a running state threshold value to obtain a first target server of the current test period; and extracting the running state evaluation indexes of the first target server corresponding to the test period from the database, sequencing the running state evaluation indexes according to the height, and taking the server corresponding to the maximum running state evaluation index as the preferred server of the radiation interference resistance.

Description

Method, system, equipment and storage medium for testing radiation interference resistance of server
Technical Field
The invention relates to the technical field of server testing, in particular to a method, a system, equipment and a storage medium for testing radiation interference resistance of a server.
Background
Along with the continuous acceleration of the progress of the informatization age, various IT products are continuously updated and developed. Especially, the cloud era comes, and the performance requirements on the server are higher and higher. With the continuous improvement of server performance, high-power consumption and high-power servers gradually become the main stream of the server market, and the servers need to work in a stable electromagnetic environment, so that the servers can be ensured to stably operate and provide reliable computing and storage services.
As disclosed in chinese patent application publication No. CN114545120a, a method, an apparatus, a device, and a medium for testing radiation immunity of a server are disclosed, in which the scheme directly uses a radiation emission signal of the server to analyze, and the test frequency is selected during radiation resistance test, so that a more efficient and accurate selection of the test frequency of radiation immunity of the server is realized, the server is ensured to meet the requirement of radiation immunity, and the server is ensured to operate normally without being affected by electromagnetic radiation in complex electromagnetic environments such as a machine room;
however, the above-mentioned prior art ignores the detection of the strain state of the server itself and the detection of the test environment, and cannot monitor the performance and stability of the server from multiple aspects, so that potential problems of the server, such as excessive load, delayed response time, etc., cannot be found, and it is difficult to identify potential faults or abnormal conditions, and the difficulty is increased in predicting the accuracy of the anti-interference degree of the server.
In view of the above, the present invention provides a method, a system, a device and a storage medium for testing radiation interference resistance of a server.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method, a system, equipment and a storage medium for testing the radiation interference resistance of a server.
According to one aspect of the invention, a method for testing radiation interference resistance of a server is provided, which comprises the following steps of;
generating a strain state evaluation index according to the access data of each server corresponding to each test period;
generating a test state evaluation index according to the test environment information of each server corresponding to each test period;
comprehensively analyzing based on the strain state evaluation index and the test state evaluation index to obtain an operation state evaluation index;
comparing and analyzing the running state evaluation index with a running state threshold value to obtain a first target server of the current test period;
and extracting the running state evaluation indexes of the first target server corresponding to the test period from the database, sequencing the running state evaluation indexes according to the height, and taking the server corresponding to the maximum running state evaluation index as the preferred server of the radiation interference resistance.
Further, the obtaining logic for obtaining the strain state evaluation index of each server corresponding to each test period includes:
Obtaining access data of each server corresponding to each test period, wherein the access data comprises I access data parameters;
carrying out standardization processing on the I access data parameters to obtain I access data parameter standardization values, wherein the value range of each access data parameter standardization value is 0, 1; and assigning a corresponding weight factor to each access data parameter;
multiplying the I access data parameter standardized values by corresponding weight factors to obtain weighted values of the I access data parameter standardized values;
accumulating weighted values of the normalized values of the I access data parameters to obtain a strain state evaluation index P y 。
Further, the obtaining logic for obtaining the test state evaluation index of each server corresponding to each test period includes:
the test environment information comprises electromagnetic field intensity, air pressure value, temperature value and humidity value of the test space, and the electromagnetic field intensity, air pressure value, temperature value and humidity value are integrated to generate a corresponding test state evaluation index P h The specific formula is as follows:
wherein, P is more than or equal to 0 h ≤1，0≤α 1 ≤1，0≤α 2 ≤1，0≤α 3 ≤1，0≤α 4 ≤1，α 1 、α 2 、α 3 And alpha 4 Respectively the electromagnetic field strengthDegree B n Air pressure value F n Temperature value W n And a humidity value C n Corresponding weight, B Z For optimum electromagnetic field strength, F Z For optimum air pressure value, W Z For optimum temperature value, C Z And R is a constant correction coefficient, and the optimal electromagnetic field strength, the optimal air pressure value, the optimal temperature value and the optimal humidity value are respectively electromagnetic field strength values, air pressure values, temperature values and humidity values under the condition that the strain state evaluation index corresponding to the test period is 1.
Further, the running state evaluation index P corresponding to each test period is evaluated for each server G The acquisition logic of (1) comprises:
the specific analysis formula is as follows: p (P) G ＝β 1 ×P y -β 2 ×P h Wherein beta is 1 、β 2 Respectively representing the set strain state evaluation index and the weight factors corresponding to the test state evaluation index.
Further, the logic for comparing the operating state evaluation index with the operating state threshold value is:
extracting running state evaluation indexes of each server corresponding to the test period from a database, and comparing the running state evaluation indexes with a preset running state threshold YP G Comparing;
if P G ＞YP G The running state evaluation index P G The method has the advantages that the corresponding server has strong radiation anti-interference capability, and the corresponding server is marked as a first target server;
if P G ≤YP G The running state evaluation index P G And (3) the corresponding server has poor radiation anti-interference capability, and is marked as a second target server.
Further, the second target server is analyzed:
extracting an operation state evaluation index P corresponding to the second target server G And an operating state evaluation index P G Corresponding strain state evaluation index P y And a test state evaluation index P h ；
Will strain state evaluation index P y Analyzing the strain threshold value to obtain the strain state grade of the server; the server strain state level comprises a first-level strain level and a second-level strain level;
if the strain state evaluates the index P y If the strain state of the current server is greater than or equal to the strain threshold, indicating that the strain state of the current server is good, and marking the corresponding strain state level of the server as a first-level strain level;
if the strain state evaluates the index P y If the strain state of the current server is smaller than the strain threshold, indicating that the strain state of the current server is poor, and marking the corresponding strain state level of the server as a second-level strain level;
test state evaluation index P h Performing environment threshold analysis to obtain a test environment state level; the testing environment state level comprises a primary testing level and a secondary testing level;
if the test state evaluates the index P h If the environmental state is greater than or equal to the environmental threshold, indicating that the current test environment state is good, and marking the corresponding test environment state level as a first-level test level;
If the test state evaluates the index P h And if the test environment state is smaller than the environment threshold value, indicating that the current test environment state is poor, and marking the corresponding test environment state level as a secondary test level.
Further, the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are obtained by a machine learning means to obtain a more accurate test state evaluation index, which comprises the following steps:
integrating the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space to generate an environment feature vector, and taking the environment feature vector as an input neuron of a machine learning model;
establishing a server anti-interference matrix based on the radiation anti-interference degree, wherein elements of the environment feature vector correspond to the radiation anti-interference degree; establishing an association relation between an anti-interference matrix of a server and output layer neurons of a machine learning model, wherein [ m1, m2, m3 and m4] is taken as the influence degree of radiation anti-interference degree of the corresponding output layer neurons, and m1 represents the influence degree of corresponding electromagnetic field intensity on the radiation anti-interference degree; m2 represents the influence degree of the corresponding air pressure value on the radiation anti-interference degree, and m3 represents the influence degree of the corresponding temperature value on the radiation anti-interference degree; m4 represents the influence degree of the corresponding humidity value on the radiation interference resistance;
Based on the influence degree of electromagnetic field intensity, air pressure value, temperature value and humidity value on radiation interference resistance, generating a corresponding test state evaluation index P through a formula h The specific formula is as follows:
P h ＝α m1 .m1+α m2 .m2+α m3 .m3+α m4 .m4+F；
wherein, P is more than or equal to 0 h ≤1，0≤α m1 ≤1，0≤α m2 ≤1，0≤α m3 ≤1，0≤α m4 ≤1，α m1 Weight, alpha, corresponding to the influence degree of electromagnetic field intensity on radiation interference resistance m2 Weight, alpha corresponding to influence degree of air pressure value on radiation interference resistance m3 Weight sum alpha corresponding to influence degree of temperature value on radiation interference resistance m4 The weight corresponding to the influence degree of the humidity value on the radiation interference resistance is given; f is a constant correction coefficient.
Further, the construction process of the machine learning model comprises the following steps:
obtaining a standard training sample set and a test sample set, and obtaining a standard training sample test result corresponding to the standard training sample set and a test sample test result corresponding to the test sample set based on a machine learning model; the standard training sample set comprises an electromagnetic field strength anti-interference degree sample, an air pressure value anti-interference degree sample, a temperature value anti-interference degree sample, a humidity value anti-interference degree sample and a normal standard sample; the normal standard sample is a standard sample with electromagnetic field intensity, air pressure value, temperature value and humidity value not influencing radiation interference resistance; the test sample set is a sample set of actually measured electromagnetic field intensity samples, actually measured air pressure value samples, actually measured temperature value samples and actually measured humidity value sample sets;
The standard training sample set is learned through a machine learning model to obtain a standard training sample test result, wherein the standard training sample test result comprises a standard electromagnetic field intensity range, a standard air pressure value range, a standard temperature value range and a standard humidity value range which have no influence on the radiation anti-interference degree;
the test sample set is learned by a machine learning model to obtain a test result of the test sample, wherein the test result of the test sample comprises an actually measured electromagnetic field intensity, an actually measured air pressure value, an actually measured temperature value and an actually measured humidity value which are measured in real time;
comparing the standard training sample test result with the test sample test result to obtain a comparison result;
and obtaining the influence degree of the corresponding environment feature vector on the radiation anti-interference degree of the server based on the comparison result.
Further, the comparison result obtaining logic includes:
comparing the measured electromagnetic field intensity, the measured air pressure value, the measured temperature value and the measured humidity value corresponding to the test result of the test sample with the standard electromagnetic field intensity range, the standard air pressure value range, the standard temperature value range and the standard humidity value range corresponding to the test result of the standard training sample in sequence;
M1=0 if the measured electromagnetic field strength is within the standard electromagnetic field strength range, m1=1 if the measured electromagnetic field strength is not within the standard electromagnetic field strength range;
m2=0 if the measured air pressure value is within the standard air pressure value range, and m2=1 if the measured air pressure value is not within the standard air pressure value range;
m3=0 if the measured temperature value is within the standard temperature value range, and m3=1 if the measured temperature value is not within the standard temperature value range;
m4=0 if the measured humidity value is within the standard humidity value range, m4=1 if the measured humidity value is not within the standard humidity value range;
if the comparison result is zero, the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are not influenced on the radiation interference resistance;
if the comparison result has a non-zero value, the electromagnetic field intensity, the air pressure value, the temperature value or the humidity value of the test space are indicated to influence the radiation interference resistance.
According to another aspect of the present invention, there is provided a server radiation tamper resistance testing system, comprising:
the acquisition module acquires access data parameters of each server corresponding to each test period and acquires test environment information of each server corresponding to each test period;
The analysis module is used for obtaining a strain state evaluation index of each server corresponding to each test period according to the access data parameters of each server corresponding to each test period;
according to the test environment information of each server corresponding to each test period, obtaining a test state evaluation index of each server corresponding to each test period;
the comprehensive analysis module is used for carrying out comprehensive analysis based on the strain state evaluation index and the test state evaluation index to obtain an operation state evaluation index;
the acquisition module is used for comparing and analyzing the running state evaluation index with the running state threshold value to acquire a first target server of the current test period;
the sequencing module extracts the running state evaluation indexes of the first target server corresponding to the test period from the database, sequences the running state evaluation indexes according to the height, and takes the server corresponding to the maximum running state evaluation index as the preferred server of the radiation interference resistance.
According to still another aspect of the present invention, there is provided an electronic apparatus including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the server radiation anti-interference degree testing method by calling the computer program stored in the memory.
According to yet another aspect of the present invention, there is provided a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described server radiation tamper resistance testing method.
By means of the technical scheme, the method, the system, the equipment and the storage medium for testing the radiation anti-interference degree of the server have the technical effects and advantages that:
the method based on data acquisition and analysis can improve the stability, reliability and performance of the server, reduce fault risk, improve the efficiency and response capability of the system, help an administrator evaluate the running state and the anti-interference capability of the server, and select the optimal server to improve the stability and reliability of the system.
The invention is helpful to find out abnormal conditions or potential problems of the server in time by analyzing the access data parameters and the test environment information of the server so as to repair or optimize, improve the reliability and performance of the server, predict the anti-interference capability of the server by analyzing the running state evaluation index of the server, and is helpful to determine which servers are more suitable for running in the interference environment and provide more reliable protection for critical tasks or sensitive data.
The invention sorts the running state evaluation indexes of the servers, and can select the server with the highest running state evaluation index as the optimal selection. This helps to distribute tasks and loads reasonably, improving overall performance and efficiency of the system.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a schematic diagram of a system for testing radiation interference resistance of a server according to the present invention;
FIG. 2 is a flow chart of a method for testing radiation interference resistance of a server according to the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the present embodiment provides a server radiation anti-interference testing system, which includes an acquisition module 100, an analysis module 200, an integrated analysis module 300, an acquisition module 400, and a sequencing module 500; the modules are connected by wire and/or wireless.
The acquisition module 100 comprises a first acquisition unit 101 and a second acquisition unit 102; the analysis module 200 includes a first analysis unit 201 and a second analysis unit 202;
the first acquisition unit 101 acquires access data parameters of each server corresponding to each test period, and sends the access data parameters to the first analysis unit 201;
the second acquisition unit 102 acquires the test environment information of each server corresponding to each test period, and sends the test environment information to the second analysis unit 202;
a first analysis unit 201 that generates a strain state evaluation index from access data of each server for each test period; and transmits the strain state evaluation index to the comprehensive analysis module 300;
the obtaining logic for obtaining the strain state evaluation index of each server corresponding to each test period comprises the following steps:
obtaining access data of each server corresponding to each test period, wherein the access data comprises I access data parameters;
What needs to be explained here is: the server may simulate the situation when a large number of users access the server at the same time during the immunity test period to determine its performance and stability under load, which may be achieved by using a server stress test tool, such as Apache JMeter, gatling or LoadRunner.
Firstly, the throughput, response time, concurrent connection number and other access data parameters of the server are explicitly tested, and corresponding application scenes are defined through the throughput, response time, concurrent connection number and other access data parameters, namely, different numbers of users are simulated to access different types of pages or interfaces.
The pressure test tool is started to simulate that a plurality of users request the server at the same time, access data parameters such as response time, throughput and the like of the server are recorded, and the access data parameters such as the number of concurrent users, the request frequency and the like can be adjusted according to the needs.
The performance and stability of the server under load are determined by analyzing the test results, including the throughput, response time, number of concurrent connections, etc. access data parameters. Corresponding optimization measures are adopted according to the test result, such as adding server hardware, optimizing software configuration and adjusting network bandwidth; the access data parameters include one or more of the following information: throughput, response time, and number of concurrent connections. The number and the type of the access data parameters which are specifically collected need to analyze the radiation interference resistance of the server, and the parameters which are determined to have influence on the radiation interference resistance of the server are defined as the access data parameters, and the specific analysis mode is as follows:
Carrying out standardization processing on the I access data parameters to obtain I access data parameter standardization values, wherein the value range of each access data parameter standardization value is 0, 1; and assigning a corresponding weight factor to each access data parameter;
what needs to be explained here is: the normalization method may subtract the minimum value of the access data parameter during the test period from the access data parameter, and then divide the access data parameter by the difference between the maximum value and the minimum value of the access data parameter during the test period.
The specific formula is as follows:
where I is the number of access data parameters, i=1, 2, I total access data parameters,is the ithAccessing data parameter normalization values, Z i For the ith access data parameter, +.>For the ith access data parameter minimum,maximum value of the data parameter is accessed for the ith;
corresponding weight factors are assigned to the different access data parameters to reflect the importance of the different access data parameters to the server load. The method can be specifically determined according to the characteristics and requirements of the application program, for example, the current server is used in laboratory management, and the number of concurrent connections is more important, so that a higher weight coefficient can be allocated to the number of concurrent connections, and other cases can be similar, and will not be described in detail herein.
Multiplying the I access data parameter standardized values by corresponding weight factors to obtain weighted values of the I access data parameter standardized values;
the formula according here is:
wherein P is i b The weighting value for the normalized value of the ith access data parameter,normalizing values, w, for the ith access data parameter i The weighting factor of the normalized value for the ith access data parameter.
What needs to be explained here is: a weighted value for each access data parameter normalization value may be obtained, reflecting the degree of contribution of the respective access data parameter in the strain state estimation index, which weighted values may be summed in the calculation to obtain the strain state estimation index.
Accumulating weighted values of the normalized values of the I access data parameters to obtain a strain state evaluation index P y 。
According to thisThe formula is:
wherein P is 1 b Weighting value for normalized value of 1 st access data parameter, P i b The weighting value for the normalized value of the ith access data parameter,weighting values for normalized values of the I-th access data parameter, P y The strain state evaluation index is the sum of weighted value accumulation of I access data parameter standardization values;
it is specifically understood that if the access data parameters are throughput, response time and concurrent connection number, a total of 3 access data parameters correspond to the normalized value of the first access data parameter as The first access data parameter normalized value has a weight factor w 1 The method comprises the steps of carrying out a first treatment on the surface of the The second access data parameter normalized value is +.>The second access data parameter normalized value has a weight factor w 2 The method comprises the steps of carrying out a first treatment on the surface of the The third access data parameter normalized value is +.>The third access data parameter normalized value has a weight factor of w 3 。
Corresponding strain state evaluation index
It should be noted that, the size of the weight factor is preset by a technician according to the corresponding access data parameter and the influence degree of the radiation interference resistance of the server, the strain state evaluation index is obtained through the weight factor and the standardized value of the corresponding access data parameter, and the pre-evaluation value of the current running state of the server is obtained through the strain state evaluation index.
A second analysis unit 202 for generating a test state evaluation index according to the test environment information of each server corresponding to each test period; and transmits the test state evaluation index to the comprehensive analysis module 300;
the obtaining logic for obtaining the test state evaluation index of each server corresponding to each test period comprises the following steps:
the test environment information of each server corresponding to each test period comprises electromagnetic field intensity, air pressure value, temperature value and humidity value of a test space, and the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are normalized;
Integrating the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value to generate a corresponding test state evaluation index P h The specific formula is as follows:
wherein, P is more than or equal to 0 h ≤1，0≤α 1 ≤1，0≤α 2 ≤1，0≤α 3 ≤1，0≤α 4 ≤1，α 1 、α 2 、α 3 And alpha 4 Respectively the electromagnetic field intensity B n Air pressure value F n Temperature value W n And a humidity value C n Corresponding weight, B Z For optimum electromagnetic field strength, F Z For optimum air pressure value, W Z For optimum temperature value, C Z For optimal humidity values, R is a constant correction factor that can be set by user adjustment or generated by fitting an analytical function.
What needs to be explained here is: the electromagnetic field strength, air pressure value, temperature value and humidity value are normalized and converted into standardized values within the same magnitude range to eliminate the difference between different magnitude levels. And weighting and summing the normalized electromagnetic field intensity, the air pressure value, the temperature value and the humidity value by using the weight coefficient. The weight coefficient reflects the importance of each test environment information to the test state evaluation index.
And taking the weighted sum result as a test state evaluation index, wherein specific values of a constant correction coefficient and a weight coefficient in the formula can be set according to actual conditions. The user can determine the values according to the own requirements and the fitting result of the analysis function, so that the test state evaluation index reflects the influence degree of the test environment information on the server state more accurately.
The optimal electromagnetic field strength is a preferable electromagnetic field strength value under the condition that the strain state evaluation index corresponding to the test period is 1, and the difference value between the current electromagnetic field strength and the optimal electromagnetic field strength can reflect the electromagnetic radiation deviation degree of the electromagnetic field, namely the deviation degree of electromagnetic radiation in the test environment where the server is located, so that the influence of the current electromagnetic field strength on the anti-interference degree of the server is analyzed. If the difference is large, it means that the actual electromagnetic field strength deviates from the electromagnetic field strength in the ideal state, and the electromagnetic radiation in the test environment may be too high or too low. The high electromagnetic radiation environment may cause interference to the server, and the interference signal may cause unstable operation of the device, communication failure or data transmission error. The low electromagnetic radiation environment may result in weak or insufficient signals, affecting the quality of communication and proper operation of the device. If the difference value is smaller, the actual electromagnetic field intensity is close to the electromagnetic field intensity in an ideal state, and the electromagnetic radiation change in the test environment is smaller. This is advantageous in maintaining stable operation of the server and the device, reducing problems due to electromagnetic interference;
similarly, the optimal air pressure value is a preferable air pressure value under the condition that the strain state evaluation index corresponding to the test period is 1, the difference value between the current air pressure value and the optimal air pressure value can reflect the air pressure deviation degree in the test environment, if the difference value is larger, the actual air pressure deviates from the air pressure in the ideal state, and the air pressure in the test environment can be too high or too low. The high air pressure environment may increase the difficulty of heat dissipation, affect the heat dissipation effect of the server, and cause the temperature of the server to rise, thereby affecting the performance and reliability. The low air pressure environment may result in rarefaction of air, affect heat dissipation or cause other air pressure related problems. Conversely, if the difference is smaller, it means that the actual air pressure is close to the air pressure in the ideal state, and the air pressure change in the test environment is smaller. This is advantageous in maintaining stable operation and performance of the server.
The optimal temperature value is a preferable temperature value under the condition that the strain state evaluation index corresponding to the test period is 1, and the difference value between the current temperature value and the optimal temperature value can reflect the temperature deviation degree in the test environment. If the difference is large, indicating that the actual temperature deviates from the ideal temperature, the temperature in the test environment may be too high or too low. The high temperature environment can increase heat accumulation of the server, influence the heat dissipation effect, and cause the performance of the server to be reduced or even to be failed. The low temperature environment may result in insufficient cooling of the server, affecting proper operation. Conversely, if the difference is smaller, it means that the actual temperature is close to the temperature in the ideal state, and the temperature change in the test environment is smaller. This is advantageous in maintaining stable operation and performance of the server.
The optimal humidity value is a humidity value which is preferable under the condition that the strain state evaluation index of the corresponding test period is 1, and the difference value between the current humidity value and the optimal humidity value can reflect the humidity deviation degree in the test environment. If the difference is large, indicating that the actual humidity deviates from the ideal humidity, the humidity in the test environment may be too high or too low. The high humidity environment may cause problems such as equipment wetting, corrosion or short circuit, and the like, which negatively affects the performance and stability of the server. Low humidity environments may cause static electricity to accumulate, increasing the risk of equipment failure. Conversely, if the difference is smaller, it means that the actual humidity is close to the ideal humidity, and the humidity change in the test environment is smaller. This is advantageous in maintaining stable operation of the server and the device.
In summary, the larger the test state evaluation index is, the larger the deviation degree between the test environment information and the optimal electromagnetic field intensity, the optimal air pressure value, the optimal temperature value and the optimal humidity value in the test environment is, the larger the influence on the radiation anti-interference degree is, otherwise, the smaller the test state evaluation index is, the smaller the deviation degree between the test environment information and the optimal electromagnetic field intensity, the optimal air pressure value, the optimal temperature value and the optimal humidity value is, and the smaller the influence on the radiation anti-interference degree is.
The comprehensive analysis module 300 is used for analyzing the strain state evaluation indexes and the test state evaluation indexes of the servers corresponding to the test periods to obtain the running state evaluation indexes of the servers corresponding to the test periods;
the running state evaluation index P corresponding to each test period for each server G The acquisition logic of (1) comprises:
the specific analysis formula is as follows: p (P) G ＝β 1 ×P y -β 2 ×P h Wherein beta is 1 、β 2 Respectively representing the set strain state evaluation index and the weight factors corresponding to the test state evaluation index.
What needs to be explained here is: the operation state evaluation index obtained through calculation can reflect the overall operation condition of each server in different test periods. A higher running state evaluation index indicates that the running state of the server is better, and a lower running state evaluation index indicates that there may be running problems or anomalies.
And setting a proper weight factor corresponding to the strain state evaluation index and the test state evaluation index according to specific requirements and actual conditions so as to accurately evaluate the running state of the server.
The obtaining module 400 compares and analyzes the operation state evaluation index with the operation state threshold value to obtain a first target server of the current test period;
the logic for comparing the operating state evaluation index with the operating state threshold value is:
extracting running state evaluation indexes of each server corresponding to the test period from a database, and comparing the running state evaluation indexes with a preset running state threshold YP G Comparing;
if P G ＞YP G The running state evaluation index P G The method has the advantages that the corresponding server has strong radiation anti-interference capability, and the corresponding server is marked as a first target server;
if P G ≤YP G The running state evaluation index P G And (3) the corresponding server has poor radiation anti-interference capability, and is marked as a second target server.
What needs to be explained here is: preset operating state threshold YP G For the operation state evaluation index value corresponding to the normal operation of the server, if the operation state evaluation index is greater than the preset operation state threshold value YP G The method comprises the steps that a corresponding server is indicated to have strong radiation anti-interference capability, and the corresponding server is marked as a first target server; the first target server is a server passing the anti-interference test; whereas the running state evaluation index is greater than the preset running state threshold YP G The method comprises the steps of marking a corresponding server as a second target server, wherein the corresponding server is small in radiation anti-interference capability; the reason why the second target server cannot pass the anti-interference test needs to be analyzed, namely, the strain state evaluation index and the test state evaluation index corresponding to the operation state evaluation index need to be analyzed.
The second target server is further analyzed:
extracting an operation state evaluation index P corresponding to the second target server G And an operating state evaluation index P G Corresponding strain state evaluation index P y And a test state evaluation index P h ；
Will strain state evaluation index P y Analyzing the strain threshold value to obtain the strain state grade of the server; the server strain state level comprises a first-level strain level and a second-level strain level;
if the strain state evaluates the index P y If the strain state of the current server is greater than or equal to the strain threshold, indicating that the strain state of the current server is good, and marking the corresponding strain state level of the server as a first-level strain level;
if the strain state evaluates the index P y If the strain state of the current server is smaller than the strain threshold, indicating that the strain state of the current server is poor, and marking the corresponding strain state level of the server as a second-level strain level;
what needs to be explained here is: threshold analysis is carried out on the strain state evaluation indexes, the strain state evaluation indexes are divided into different grades, and in the embodiment, the strain state grades of the servers are divided into a first-level strain grade and a second-level strain grade, wherein the first-level strain grade corresponds to a server with good strain state; the second-level strain level corresponds to a server with a poor strain state, and the server is indicated to have a larger influence degree of radiation interference resistance under the current operation.
Preferably, in order to have more accurate judgment, the strain threshold value is set in a gradient mode, the strain state of the server is further evaluated, and the performance of the server under different levels is determined, so that the strain state level of the server is refined; the degree of influence on the radiation immunity of the current server can be known from the refinement server strain state level.
Test state evaluation index P h Performing environment threshold analysis to obtain a test environment state level; the testing environment state level comprises a primary testing level and a secondary testing level;
if the test state evaluates the index P h If the environmental state is greater than or equal to the environmental threshold, indicating that the current test environment state is good, and marking the corresponding test environment state level as a first-level test level;
if the test state evaluates the index P h If the test environment state is smaller than the environment threshold value, indicating that the current test environment state is poor, and marking the corresponding test environment state level as a secondary test level;
based on the second-level test grade, the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are further analyzed, and the influence degree of the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space on the radiation anti-interference degree is judged.
What needs to be explained here is: and carrying out threshold analysis on the test state evaluation index to obtain a test environment state grade, and dividing the test state evaluation index into different grades, such as a primary test grade and a secondary test grade, according to the set environment threshold value, similar to the processing of the strain state grade. This helps to further evaluate the state of the test environment and determine the performance of the server at different test environment state levels. The first-level test grade indicates that the current test state evaluation index is larger than or equal to the environmental threshold value, and indicates that the current test state evaluation index is a normal value; the second-level test grade indicates that the current test state evaluation index is smaller than the environmental threshold value, and indicates that the current strain state evaluation index is an abnormal value;
preferably, in order to have more accurate judgment, setting the environmental threshold value in a gradient way, further evaluating the test environmental state of the server and determining the performance of the server under different grades, thereby refining the grade of the test environmental state of the server; the degree of influence on the radiation interference resistance of the current server can be known according to the test environment state level of the refinement server.
The ranking module 500 extracts the running state evaluation indexes of the first target server corresponding to the test period from the database, ranks the running state evaluation indexes according to the height, and uses the server corresponding to the maximum running state evaluation index as the preferred server of the radiation interference resistance.
What can be stated here is: the server with the best performance corresponding to the test period is selected from the first target server to be used as a preferred server, and the server is considered to have higher anti-interference degree and can be preferentially used in the scene needing to keep stable operation and lower interference.
Example 2
The embodiment provides a server radiation anti-interference testing system, which is different from embodiment 1 in that the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of a testing space are obtained by a machine learning means, so that a more accurate testing state evaluation index is obtained, and the accuracy is higher.
The logic for obtaining more accurate test state evaluation index through the means of machine learning by the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space further comprises:
integrating the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space to generate an environment feature vector, and taking the environment feature vector as an input neuron of a machine learning model;
establishing a server anti-interference matrix based on the radiation anti-interference degree, wherein elements of the environment feature vector correspond to the radiation anti-interference degree; establishing an association relation between an anti-interference matrix of a server and output layer neurons of a machine learning model, wherein [ m1, m2, m3 and m4] is taken as the influence degree of radiation anti-interference degree of the corresponding output layer neurons, and m1 represents the influence degree of corresponding electromagnetic field intensity on the radiation anti-interference degree; m2 represents the influence degree of the corresponding air pressure value on the radiation anti-interference degree, and m3 represents the influence degree of the corresponding temperature value on the radiation anti-interference degree; m4 represents the influence degree of the corresponding humidity value on the radiation interference resistance;
Generating a corresponding test state evaluation index P through a formula based on the influence degree of electromagnetic field intensity, air pressure value, temperature value and humidity value on radiation interference resistance h The specific formula is as follows:
P h ＝α m1 .m1+α m2 .m2+α m3 .m3+α m4 .m4+F；
wherein, P is more than or equal to 0 h ≤1，0≤α m1 ≤1，0≤α m2 ≤1，0≤α m3 ≤1，0≤α m4 ≤1，α m1 Weight, alpha, corresponding to the influence degree of electromagnetic field intensity on radiation interference resistance m2 Weight, alpha corresponding to influence degree of air pressure value on radiation interference resistance m3 Weight sum alpha corresponding to influence degree of temperature value on radiation interference resistance m4 The weight corresponding to the influence degree of the humidity value on the radiation interference resistance is given; f is a constant correction coefficient.
What needs to be explained here is: the influence degree of the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value in the test environment on the radiation anti-interference degree is directly obtained through a machine learning model, the obtained numerical value is more accurate, the obtained test state evaluation indexes are more accurate through weighting and summing corresponding weights, the larger the test state evaluation indexes are, the larger the influence degree of the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value in the test environment on the radiation anti-interference degree is indicated, and on the contrary, the smaller the test state evaluation indexes are, the smaller the influence degree of the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value in the test environment on the radiation anti-interference degree is indicated.
The construction process of the machine learning model comprises the following steps:
obtaining a standard training sample set and a test sample set, and obtaining a standard training sample test result corresponding to the standard training sample set and a test sample test result corresponding to the test sample set based on a machine learning model; the standard training sample set comprises an electromagnetic field strength anti-interference degree sample, an air pressure value anti-interference degree sample, a temperature value anti-interference degree sample, a humidity value anti-interference degree sample and a normal standard sample; the normal standard sample is a standard sample with electromagnetic field intensity, air pressure value, temperature value and humidity value not influencing radiation interference resistance; the test sample set is a sample set of actually measured electromagnetic field intensity samples, actually measured air pressure value samples, actually measured temperature value samples and actually measured humidity value sample sets;
the standard training sample set is learned through a machine learning model to obtain a standard training sample test result, wherein the standard training sample test result comprises a standard electromagnetic field intensity range, a standard air pressure value range, a standard temperature value range and a standard humidity value range which have no influence on the radiation anti-interference degree;
the test sample set is learned by a machine learning model to obtain a test result of the test sample, wherein the test result of the test sample comprises an actually measured electromagnetic field intensity, an actually measured air pressure value, an actually measured temperature value and an actually measured humidity value which are measured in real time;
Comparing the standard training sample test result with the test sample test result to obtain a comparison result;
and obtaining the influence degree of the corresponding environment feature vector on the radiation anti-interference degree of the server based on the comparison result.
The comparison result obtaining logic includes:
comparing the measured electromagnetic field intensity, the measured air pressure value, the measured temperature value and the measured humidity value corresponding to the test result of the test sample with the standard electromagnetic field intensity range, the standard air pressure value range, the standard temperature value range and the standard humidity value range corresponding to the test result of the standard training sample in sequence;
m1=0 if the measured electromagnetic field strength is within the standard electromagnetic field strength range, m1=1 if the measured electromagnetic field strength is not within the standard electromagnetic field strength range;
m2=0 if the measured air pressure value is within the standard air pressure value range, and m2=1 if the measured air pressure value is not within the standard air pressure value range;
m3=0 if the measured temperature value is within the standard temperature value range, and m3=1 if the measured temperature value is not within the standard temperature value range;
m4=0 if the measured humidity value is within the standard humidity value range, m4=1 if the measured humidity value is not within the standard humidity value range;
If the comparison result is zero, the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are not influenced on the radiation interference resistance;
if the comparison result has a non-zero value, the electromagnetic field intensity, the air pressure value, the temperature value or the humidity value of the test space are indicated to influence the radiation interference resistance.
What can be stated here is: in this process, the anti-interference mode of the server is used to describe the radiation anti-interference degree of the corresponding environment feature vector of the server. By training the machine learning model, the model can learn the relation between different environment feature vectors and the anti-interference mode of the server.
In order to evaluate the radiation tamper resistance of the server, a standard training sample set and a test sample set need to be obtained. And testing the standard training sample set by using a machine learning model, and obtaining a corresponding standard training sample test result. Similarly, testing the test sample set to obtain a test result of the test sample; finally, the radiation interference resistance of the server can be obtained by comparing the test result of the training sample with the test sample result.
Summarizing, the radiation immunity of the server is inferred by a machine learning model and a training sample set. The method can provide more accurate evaluation of the radiation interference resistance for server selection by learning the relation between the environment characteristic vector and the radiation interference resistance.
Example 3
Referring to fig. 2, the embodiment is not described in detail in embodiments 1 and 2, and the embodiment provides a method for testing radiation interference resistance of a server, which includes the following steps;
generating a strain state evaluation index according to the access data of each server corresponding to each test period;
generating a test state evaluation index according to the test environment information of each server corresponding to each test period;
comprehensively analyzing based on the strain state evaluation index and the test state evaluation index to obtain an operation state evaluation index;
comparing and analyzing the running state evaluation index with a running state threshold value to obtain a first target server of the current test period;
and extracting the running state evaluation indexes of the first target server corresponding to the test period from the database, sequencing the running state evaluation indexes according to the height, and taking the server corresponding to the maximum running state evaluation index as the preferred server of the radiation interference resistance.
The obtaining logic for obtaining the strain state evaluation index of each server corresponding to each test period comprises the following steps:
obtaining access data of each server corresponding to each test period, wherein the access data comprises I access data parameters;
Carrying out standardization processing on the I access data parameters to obtain I access data parameter standardization values, wherein the value range of each access data parameter standardization value is 0, 1; and assigning a corresponding weight factor to each access data parameter;
multiplying the I access data parameter standardized values by corresponding weight factors to obtain weighted values of the I access data parameter standardized values;
accumulating weighted values of the normalized values of the I access data parameters to obtain a strain state evaluation index P y 。
The obtaining logic for obtaining the test state evaluation index of each server corresponding to each test period comprises the following steps:
the test environment information comprises electromagnetic field intensity, air pressure value, temperature value and humidity value of the test space, and the electromagnetic field intensity, air pressure value, temperature value and humidity value are integrated to generate a corresponding test state evaluation index P h The specific formula is as follows:
wherein, P is more than or equal to 0 h ≤1，0≤α 1 ≤1，0≤α 2 ≤1，0≤α 3 ≤1，0≤α 4 ≤1，α 1 、α 2 、α 3 And alpha 4 Respectively the electromagnetic field intensity B n Air pressure value F n Temperature value W n And a humidity value C n Corresponding weight, B Z For optimum electromagnetic field strength, F Z For optimum air pressure value, W Z For optimum temperature value, C Z For optimal humidity values, R is a constant correction factor that can be set by user adjustment or generated by fitting an analytical function.
The running state evaluation index P corresponding to each test period for each server G The acquisition logic of (1) comprises:
the specific analysis formula is as follows: p (P) G ＝β 1 ×P y -β 2 ×P h Wherein beta is 1 、β 2 Respectively representing the set strain state evaluation index and the weight factors corresponding to the test state evaluation index.
The logic for comparing the operating state evaluation index with the operating state threshold value is:
extracting running state evaluation indexes of each server corresponding to the test period from a database, and comparing the running state evaluation indexes with a preset running state threshold YP G Comparing;
if P G ＞YP G The running state evaluation index P G The method has the advantages that the corresponding server has strong radiation anti-interference capability, and the corresponding server is marked as a first target server;
if P G ≤YP G The running state evaluation index P G And (3) the corresponding server has poor radiation anti-interference capability, and is marked as a second target server.
The second target server is further analyzed:
extracting an operation state evaluation index P corresponding to the second target server G And an operating state evaluation index P G Corresponding strain state evaluation index P y And a test state evaluation index P h ；
Will strain state evaluation index P y Analyzing the strain threshold value to obtain the strain state grade of the server; the server strain state level comprises a first-level strain level and a second-level strain level;
If the strain state evaluates the index P y If the strain state of the current server is greater than or equal to the strain threshold, indicating that the strain state of the current server is good, and marking the corresponding strain state level of the server as a first-level strain level;
if the strain state evaluates the index P y If the strain state of the current server is smaller than the strain threshold, indicating that the strain state of the current server is poor, and marking the corresponding strain state level of the server as a second-level strain level;
further analyzing the access data parameters based on the secondary strain level;
test state evaluation index P h Performing environment threshold analysis to obtain a test environment state level; the testing environment state level comprises a primary testing level and a secondary testing level;
if the test state evaluates the index P h If the environmental state is greater than or equal to the environmental threshold, indicating that the current test environment state is good, and marking the corresponding test environment state level as a first-level test level;
if the test state evaluates the index P h If the test environment state is smaller than the environment threshold value, indicating that the current test environment state is poor, and marking the corresponding test environment state level as a secondary test level;
based on the second-level test grade, the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are further analyzed, and the influence degree of the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space on the radiation anti-interference degree is judged.
The electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are obtained by a machine learning means to obtain a more accurate test state evaluation index, comprising:
integrating the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space to generate an environment feature vector, and taking the environment feature vector as an input neuron of a machine learning model;
establishing a server anti-interference matrix based on the radiation anti-interference degree, wherein elements of the environment feature vector correspond to the radiation anti-interference degree; establishing an association relation between an anti-interference matrix of a server and output layer neurons of a machine learning model, wherein [ m1, m2, m3 and m4] is taken as the influence degree of radiation anti-interference degree of the corresponding output layer neurons, and m1 represents the influence degree of corresponding electromagnetic field intensity on the radiation anti-interference degree; m2 represents the influence degree of the corresponding air pressure value on the radiation anti-interference degree, and m3 represents the influence degree of the corresponding temperature value on the radiation anti-interference degree; m4 represents the influence degree of the corresponding humidity value on the radiation interference resistance.
The construction process of the machine learning model comprises the following steps:
obtaining a standard training sample set and a test sample set, and obtaining a standard training sample test result corresponding to the standard training sample set and a test sample test result corresponding to the test sample set based on a machine learning model; the standard training sample set comprises an electromagnetic field strength anti-interference degree sample, an air pressure value anti-interference degree sample, a temperature value anti-interference degree sample, a humidity value anti-interference degree sample and a normal standard sample; the normal standard sample is a standard sample with electromagnetic field intensity, air pressure value, temperature value and humidity value not influencing radiation interference resistance; the test sample set is a sample set of actually measured electromagnetic field intensity samples, actually measured air pressure value samples, actually measured temperature value samples and actually measured humidity value sample sets;
The standard training sample set is learned through a machine learning model to obtain a standard training sample test result, wherein the standard training sample test result comprises a standard electromagnetic field intensity range, a standard air pressure value range, a standard temperature value range and a standard humidity value range which have no influence on the radiation anti-interference degree;
the test sample set is learned by a machine learning model to obtain a test result of the test sample, wherein the test result of the test sample comprises an actually measured electromagnetic field intensity, an actually measured air pressure value, an actually measured temperature value and an actually measured humidity value which are measured in real time;
comparing the standard training sample test result with the test sample test result to obtain a comparison result;
and obtaining the influence degree of the corresponding environment feature vector on the radiation anti-interference degree of the server based on the comparison result.
The comparison result obtaining logic includes:
comparing the measured electromagnetic field intensity, the measured air pressure value, the measured temperature value and the measured humidity value corresponding to the test result of the test sample with the standard electromagnetic field intensity range, the standard air pressure value range, the standard temperature value range and the standard humidity value range corresponding to the test result of the standard training sample in sequence;
M1=0 if the measured electromagnetic field strength is within the standard electromagnetic field strength range, m1=1 if the measured electromagnetic field strength is not within the standard electromagnetic field strength range;
m2=0 if the measured air pressure value is within the standard air pressure value range, and m2=1 if the measured air pressure value is not within the standard air pressure value range;
m3=0 if the measured temperature value is within the standard temperature value range, and m3=1 if the measured temperature value is not within the standard temperature value range;
m4=0 if the measured humidity value is within the standard humidity value range, m4=1 if the measured humidity value is not within the standard humidity value range;
if the comparison result is zero, the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are not influenced on the radiation interference resistance;
if the comparison result has a non-zero value, the electromagnetic field intensity, the air pressure value, the temperature value or the humidity value of the test space are indicated to influence the radiation interference resistance.
Example 4
An electronic device is shown according to an exemplary embodiment, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the server radiation anti-interference degree testing method by calling the computer program stored in the memory.
The method based on data acquisition and analysis can improve the stability, reliability and performance of the server, reduce fault risk, improve the efficiency and response capability of the system, help an administrator evaluate the running state and the anti-interference capability of the server, and select the optimal server to improve the stability and reliability of the system.
The invention is helpful to find out abnormal conditions or potential problems of the server in time by analyzing the access data parameters and the test environment information of the server so as to repair or optimize, improve the reliability and performance of the server, predict the anti-interference capability of the server by analyzing the running state evaluation index of the server, and is helpful to determine which servers are more suitable for running in the interference environment and provide more reliable protection for critical tasks or sensitive data.
The invention sorts the running state evaluation indexes of the servers, and can select the server with the highest running state evaluation index as the optimal selection. This helps to distribute tasks and loads reasonably, improving overall performance and efficiency of the system.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, where the electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the at least one computer program is loaded and executed by the processors to implement the method for testing radiation interference resistance of a server provided in each of the above method embodiments. The electronic device can also include other components for implementing device functions, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like for input-output. The embodiments of the present application are not described herein.
Example 5
A computer readable storage medium having stored thereon a computer program that is erasable according to an exemplary embodiment is shown;
when the computer program runs on the computer equipment, the computer equipment is caused to execute the server radiation anti-interference degree testing method.
The method based on data acquisition and analysis can improve the stability, reliability and performance of the server, reduce fault risk, improve the efficiency and response capability of the system, help an administrator evaluate the running state and the anti-interference capability of the server, and select the optimal server to improve the stability and reliability of the system.
The invention is helpful to find out abnormal conditions or potential problems of the server in time by analyzing the access data parameters and the test environment information of the server so as to repair or optimize, improve the reliability and performance of the server, predict the anti-interference capability of the server by analyzing the running state evaluation index of the server, and is helpful to determine which servers are more suitable for running in the interference environment and provide more reliable protection for critical tasks or sensitive data.
The invention sorts the running state evaluation indexes of the servers, and can select the server with the highest running state evaluation index as the optimal selection. This helps to distribute tasks and loads reasonably, improving overall performance and efficiency of the system.
In an exemplary embodiment, a computer readable storage medium is also provided, for example a memory comprising at least one computer program executable by a processor to perform the method of server radiation tamper resistance testing in the above embodiments. For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising one or more program codes, the one or more program codes being stored in a computer readable storage medium. The one or more processors of the electronic device are capable of reading the one or more program codes from the computer-readable storage medium, and executing the one or more program codes to enable the electronic device to perform the server radiation tamper resistance testing method described above.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above-described embodiments can be implemented by hardware, or can be implemented by a program instructing the relevant hardware, and the program can be stored in a computer readable storage medium, and the above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description is only of alternative embodiments of the present application and is not intended to limit the present application, but any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the present application are intended to be included within the scope of the present application.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (9)

1. The method for testing the radiation interference resistance of the server is characterized by comprising the following steps of;
generating a strain state evaluation index according to the access data of each server corresponding to each test period;
generating a test state evaluation index according to the test environment information of each server corresponding to each test period;
comprehensively analyzing based on the strain state evaluation index and the test state evaluation index to obtain an operation state evaluation index;
comparing and analyzing the running state evaluation index with a running state threshold value to obtain a first target server of the current test period;
extracting an operation state evaluation index of a first target server corresponding to a test period from a database, sequencing the operation state evaluation indexes according to the height, and taking a server corresponding to the maximum operation state evaluation index as a preferred server of radiation interference resistance;
the logic for obtaining the strain state evaluation index of each server corresponding to each test period comprises the following steps:
obtaining access data of each server corresponding to each test period, wherein the access data comprises I access data parameters;
carrying out standardization processing on the I access data parameters to obtain I access data parameter standardization values, wherein the value range of each access data parameter standardization value is 0, 1; and assigning a corresponding weight factor to each access data parameter;
Multiplying the I access data parameter standardized values by corresponding weight factors to obtain weighted values of the I access data parameter standardized values;
accumulating weighted values of the normalized values of the I access data parameters to obtain a strain state evaluation index P y ；
The obtaining logic for obtaining the test state evaluation index of each server corresponding to each test period comprises the following steps:
the test environment information comprises electromagnetic field intensity, air pressure value, temperature value and humidity value of the test space, and the electromagnetic field intensity, air pressure value, temperature value and humidity value are integrated to generate a corresponding test state evaluation index P h The specific formula is as follows:
wherein, P is more than or equal to 0 h ≤1，0≤α 1 ≤1，0≤α 2 ≤1，0≤α 3 ≤1，0≤α 4 ≤1，α 1 、α 2 、α 3 And alpha 4 Respectively the electromagnetic field intensity B n Air pressure value F n Temperature value W n And a humidity value C n Corresponding weight, B Z For optimum electromagnetic field strength, F Z For optimum air pressure value, W Z For optimum temperature value, C Z R is a constant correction coefficient, and the optimal electromagnetic field strength, the optimal air pressure value, the optimal temperature value and the optimal humidity value are respectively electromagnetic field strength value, air pressure value, temperature value and humidity value under the condition that the strain state evaluation index corresponding to the test period is 1;
the running state evaluation index P corresponding to each test period for each server G The acquisition logic of (1) comprises:
the specific analysis formula is as follows: p (P) G ＝β 1 ×P y -β 2 ×P h Wherein beta is 1 、β 2 Respectively representing the set strain state evaluation index and the weight factors corresponding to the test state evaluation index.
2. The method for testing radiation interference resistance of a server according to claim 1, wherein: the logic for comparing the operating state evaluation index with the operating state threshold value is:
extracting running state evaluation indexes of each server corresponding to the test period from a database, and comparing the running state evaluation indexes with a preset running state threshold YP G Comparing;
if P G ＞YP G The running state evaluation index P G The method has the advantages that the corresponding server has strong radiation anti-interference capability, and the corresponding server is marked as a first target server;
if P G ≤YP G The running state evaluation index P G Low, the corresponding server has poor radiation anti-interference capability, and the corresponding server is marked as a second targetAnd a server.
3. The method for testing radiation interference resistance of a server according to claim 2, wherein: analyzing the second target server:
extracting an operation state evaluation index P corresponding to the second target server G And an operating state evaluation index P G Corresponding strain state evaluation index P y And a test state evaluation index P h ；
Will strain state evaluation index P y Analyzing the strain threshold value to obtain the strain state grade of the server; the server strain state level comprises a first-level strain level and a second-level strain level;
if the strain state evaluates the index P y If the strain state of the current server is greater than or equal to the strain threshold, indicating that the strain state of the current server is good, and marking the corresponding strain state level of the server as a first-level strain level;
if the strain state evaluates the index P y If the strain state of the current server is smaller than the strain threshold, indicating that the strain state of the current server is poor, and marking the corresponding strain state level of the server as a second-level strain level;
test state evaluation index P h Performing environment threshold analysis to obtain a test environment state level; the testing environment state level comprises a primary testing level and a secondary testing level;
if the test state evaluates the index P h If the environmental state is greater than or equal to the environmental threshold, indicating that the current test environment state is good, and marking the corresponding test environment state level as a first-level test level;
if the test state evaluates the index P h And if the test environment state is smaller than the environment threshold value, indicating that the current test environment state is poor, and marking the corresponding test environment state level as a secondary test level.
4. The method for testing radiation interference resistance of a server according to claim 3, wherein: the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are obtained by a machine learning means to obtain a more accurate test state evaluation index, comprising:
integrating the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space to generate an environment feature vector, and taking the environment feature vector as an input neuron of a machine learning model;
establishing a server anti-interference matrix based on the radiation anti-interference degree, wherein elements of the environment feature vector correspond to the radiation anti-interference degree; establishing an association relation between an anti-interference matrix of a server and output layer neurons of a machine learning model, wherein [ m1, m2, m3 and m4] is taken as the influence degree of radiation anti-interference degree of the corresponding output layer neurons, and m1 represents the influence degree of corresponding electromagnetic field intensity on the radiation anti-interference degree; m2 represents the influence degree of the corresponding air pressure value on the radiation anti-interference degree, and m3 represents the influence degree of the corresponding temperature value on the radiation anti-interference degree; m4 represents the influence degree of the corresponding humidity value on the radiation interference resistance;
based on the influence degree of electromagnetic field intensity, air pressure value, temperature value and humidity value on radiation interference resistance, generating a corresponding test state evaluation index P through a formula h The specific formula is as follows:
P h ＝α m1 .m1+α m2 .m2+α m3 .m3+α m4 .m4+F；
wherein, P is more than or equal to 0 h ≤1，0≤α m1 ≤1，0≤α m2 ≤1，0≤α m3 ≤1，0≤α m4 ≤1，α m1 Weight, alpha, corresponding to the influence degree of electromagnetic field intensity on radiation interference resistance m2 Weight, alpha corresponding to influence degree of air pressure value on radiation interference resistance m3 Weight sum alpha corresponding to influence degree of temperature value on radiation interference resistance m4 The weight corresponding to the influence degree of the humidity value on the radiation interference resistance is given; f is a constant correction coefficient.
5. The method for testing radiation interference resistance of a server according to claim 4, wherein: the construction process of the machine learning model comprises the following steps:
obtaining a standard training sample set and a test sample set, and obtaining a standard training sample test result corresponding to the standard training sample set and a test sample test result corresponding to the test sample set based on a machine learning model; the standard training sample set comprises an electromagnetic field strength anti-interference degree sample, an air pressure value anti-interference degree sample, a temperature value anti-interference degree sample, a humidity value anti-interference degree sample and a normal standard sample; the normal standard sample is a standard sample with electromagnetic field intensity, air pressure value, temperature value and humidity value not influencing radiation interference resistance; the test sample set is a sample set of actually measured electromagnetic field intensity samples, actually measured air pressure value samples, actually measured temperature value samples and actually measured humidity value sample sets;
The standard training sample set is learned through a machine learning model to obtain a standard training sample test result, wherein the standard training sample test result comprises a standard electromagnetic field intensity range, a standard air pressure value range, a standard temperature value range and a standard humidity value range which have no influence on the radiation anti-interference degree;
the test sample set is learned by a machine learning model to obtain a test result of the test sample, wherein the test result of the test sample comprises an actually measured electromagnetic field intensity, an actually measured air pressure value, an actually measured temperature value and an actually measured humidity value which are measured in real time;
comparing the standard training sample test result with the test sample test result to obtain a comparison result;
and obtaining the influence degree of the corresponding environment feature vector on the radiation anti-interference degree of the server based on the comparison result.
6. The method for testing radiation interference resistance of a server according to claim 5, wherein: the comparison result obtaining logic includes:
comparing the measured electromagnetic field intensity, the measured air pressure value, the measured temperature value and the measured humidity value corresponding to the test result of the test sample with the standard electromagnetic field intensity range, the standard air pressure value range, the standard temperature value range and the standard humidity value range corresponding to the test result of the standard training sample in sequence;
M1=0 if the measured electromagnetic field strength is within the standard electromagnetic field strength range, m1=1 if the measured electromagnetic field strength is not within the standard electromagnetic field strength range;
m2=0 if the measured air pressure value is within the standard air pressure value range, and m2=1 if the measured air pressure value is not within the standard air pressure value range;
m3=0 if the measured temperature value is within the standard temperature value range, and m3=1 if the measured temperature value is not within the standard temperature value range;
m4=0 if the measured humidity value is within the standard humidity value range, m4=1 if the measured humidity value is not within the standard humidity value range;
if the comparison result is zero, the electromagnetic field intensity, the air pressure value, the temperature value and the humidity value of the test space are not influenced on the radiation interference resistance;
if the comparison result has a non-zero value, the electromagnetic field intensity, the air pressure value, the temperature value or the humidity value of the test space are indicated to influence the radiation interference resistance.
7. The server radiation anti-interference testing system is realized based on the server radiation anti-interference testing method according to any one of claims 1-6, and is characterized in that: comprising the following steps:
the acquisition module acquires access data parameters of each server corresponding to each test period and acquires test environment information of each server corresponding to each test period;
The analysis module is used for obtaining a strain state evaluation index of each server corresponding to each test period according to the access data parameters of each server corresponding to each test period;
according to the test environment information of each server corresponding to each test period, obtaining a test state evaluation index of each server corresponding to each test period;
the comprehensive analysis module is used for carrying out comprehensive analysis based on the strain state evaluation index and the test state evaluation index to obtain an operation state evaluation index;
the acquisition module is used for comparing and analyzing the running state evaluation index with the running state threshold value to acquire a first target server of the current test period;
the sequencing module extracts the running state evaluation indexes of the first target server corresponding to the test period from the database, sequences the running state evaluation indexes according to the height, and takes the server corresponding to the maximum running state evaluation index as the preferred server of the radiation interference resistance.
8. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs the server radiation tamper resistance testing method according to any of claims 1-6 by invoking a computer program stored in the memory.
9. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform the method of testing radiation tamper resistance of a server according to any one of claims 1 to 6.
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