CN111614504A - Power grid regulation and control data center service characteristic fault positioning method and system based on time sequence and fault tree analysis - Google Patents
Power grid regulation and control data center service characteristic fault positioning method and system based on time sequence and fault tree analysis Download PDFInfo
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Abstract
The invention provides a method and a system for locating a service characteristic fault of a power grid regulation and control data center based on a time sequence and fault tree analysis, wherein the method comprises the following steps: establishing a fault tree model; acquiring a group of variable data corresponding to each device in the power regulation and control data center, and performing maximum and minimum normalization processing on the variable data; predicting the future state of the acquired variable data through a MARA self-adaptive prediction model, and storing the predicted data into a database; comparing the threshold value of the predicted data, and outputting the predicted data after binary conversion when the predicted data meets the threshold value; receiving binary prediction data, and performing availability analysis through a fault tree model; receiving fault signals sent by fault nodes which may exist in the future, marking the fault signals, organizing and executing migration operation; the method has the advantages of being fast and accurate in positioning, and is suitable for the field of data center fault positioning.
Description
Technical Field
The invention relates to the technical field of fault location, in particular to a method and a system for locating a service characteristic fault of a power grid regulation and control data center based on time series and fault tree analysis.
Background
In the power system, accurate fault positioning is carried out on equipment in a power regulation and control data center machine room, so that operation and maintenance personnel and managers can be helped to improve the working efficiency and the control capability, and finally a support effect is provided for actual operation and maintenance operation and maintenance management.
Grid regulation data centers have a large number of servers and components, and the devices are interconnected into a complex structure, so that the failure rate is usually high, and in addition, long-running applications and heavy workload are common in these facilities. The performance of the system depends on the availability of the machine, and if the fault cannot be properly handled, the entire regulatory data center is vulnerable.
Disclosure of Invention
Aiming at the defects in the related technology, the technical problem to be solved by the invention is as follows: the method and the system for locating the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis can be used for quickly and accurately locating the fault.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the method for locating the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis comprises the following steps: establishing a fault tree model; acquiring a group of variable data corresponding to each device in the power regulation and control data center, and performing maximum and minimum normalization processing on the variable data; predicting the future state of the acquired variable data through a MARA self-adaptive prediction model, and storing the predicted data into a database; comparing the threshold value of the predicted data, and outputting the predicted data after binary conversion when the predicted data meets the threshold value; receiving binary prediction data, and performing availability analysis through a fault tree model; and receiving fault signals sent by fault nodes which possibly exist in the future, marking, organizing and executing the migration operation.
Preferably, the predicting future state of the acquired variable data by the MARA adaptive prediction model, and storing the predicted data in the database specifically includes:
creating a MARA model; establishing a multi-step MARA model, and training in a self-adaptive mode to obtain a MARA self-adaptive prediction model based on variable data; and predicting the future state of the acquired variable data, and storing the predicted data value into a database when the MARA self-adaptive prediction model acquires the predicted data value at the future moment.
Preferably, the establishing a fault tree model specifically includes: creating a top service for representing a system state, splitting the top service to obtain a lower terminal service, splitting the lower terminal service step by step, and ending the splitting when the maximum value of the interpretable resolution is reached; connecting the lower sub-service with the top service in a Boolean logic gate index mode, and establishing Boolean expressions which correspond to the sub-services one by one and the Boolean expressions which correspond to the top service;
the receiving binary prediction data and performing availability analysis through a fault tree model specifically include: the fault tree model decomposes the binary prediction data to simplify the binary prediction data into a Boolean value in the top service; and analyzing the top service, and when the top service can be carried out, taking the value of the output variable in the fault tree model as 1, or taking the value as 0.
Preferably, the threshold comparison of the prediction data is performed, and when the prediction data meets the threshold, the prediction data is output after binary conversion, specifically including: setting a plurality of thresholds for indicating the degree of severity; and comparing the predicted data with a plurality of threshold values, and outputting the predicted data after performing corresponding binary conversion when the predicted data meets a certain threshold value.
Preferably, the receiving and marking a fault signal sent by a fault node which may exist in the future, organizing and executing a migration operation specifically includes: receiving fault signals sent by fault nodes which may exist in the future and marking the fault signals, wherein: the fault node comprises: the time of failure that may occur; establishing a migration operation corresponding to each fault node and a target address corresponding to each migration operation; determining a migration sequence according to the fault time and the migration required time; and executing the migration operation according to the migration sequence.
Correspondingly, the power grid regulation and control data center service characteristic fault positioning system based on the time sequence and fault tree analysis comprises: the building unit is used for building a fault tree model; the acquisition unit is used for acquiring a group of variable data corresponding to each equipment in the electric power regulation and control data center and carrying out maximum and minimum normalization processing on the variable data; the fault prediction unit is used for predicting the future state of the acquired variable data through the MARA self-adaptive prediction model and storing the predicted data into a database; the threshold comparison unit is used for performing threshold comparison on the prediction data, and outputting the prediction data after binary conversion when the prediction data accords with a threshold; the availability analysis unit is used for receiving binary prediction data and carrying out availability analysis through the fault tree model; and the management and control unit is used for receiving fault signals sent by fault nodes which may exist in the future, marking the fault signals, organizing and executing migration operation.
Preferably, the failure prediction unit includes: a creating unit that creates a MARA model; the training unit is used for creating a multi-step MARA model and training in a self-adaptive mode to obtain a MARA self-adaptive prediction model based on variable data; and the prediction unit is used for predicting the future state of the acquired variable data, and storing the predicted data value into the database when the MARA self-adaptive prediction model acquires the predicted data value at the future moment.
Preferably, the establishing unit includes: a service establishing unit for establishing a top service for representing the system state, splitting the top service to obtain a lower sub-service, splitting the lower sub-service step by step, and ending the splitting when the maximum value of the interpretable resolution is reached; the Boolean expression establishing unit is used for connecting the lower sub-service with the top service in a Boolean logic gate index mode, and establishing Boolean expressions which are in one-to-one correspondence with the sub-services and the Boolean expressions which are in correspondence with the top service;
the usability analyzing unit includes: the decomposition unit is used for decomposing the binary prediction data into a Boolean value in the top service;
and the output unit is used for analyzing the top service, when the top service can be carried out, the value of the variable output in the fault tree model is 1, and otherwise, the value is 0.
Preferably, the threshold comparing unit includes: a setting unit configured to set a plurality of thresholds representing degrees of severity; and the comparison unit is used for comparing the prediction data with a plurality of threshold values, and outputting the prediction data after performing corresponding binary conversion when the prediction data meets a certain threshold value.
Preferably, the management unit includes: a receiving unit, configured to receive and mark a fault signal sent by a fault node that may exist in the future, where: the fault node comprises: the time of failure that may occur; the system comprises a formulating unit, a judging unit and a judging unit, wherein the formulating unit is used for establishing a migration operation corresponding to each fault node and a target address corresponding to each migration operation; determining a migration sequence according to the fault time and the time length required by migration; and the execution unit is used for executing the migration operation according to the migration sequence formulated by the formulation unit.
The invention has the beneficial technical effects that:
1. the method and the system for locating the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis are simple to use, easy to implement, high in prediction precision and capable of achieving the purpose of quickly and accurately locating the fault of the power grid regulation and control data center.
2. According to the method, specific contents of a group of variable data are defined aiming at the diversified characteristics of power regulation data center equipment, wherein the specific contents comprise the running state of a CPU (Central processing Unit), the running state of a memory, the throughput of input and output equipment and the like, the negative influence possibly caused by the difference between different types of equipment on the detection result can be eliminated through a unified data collection standard, meanwhile, the maximum and minimum normalization processing is carried out on the obtained variable data, the negative influence caused by the dimensions of different variables on the result can be eliminated, and the data processing accuracy in subsequent prediction and analysis is improved.
3. In the invention, a group of variable data of each device in the force regulation data center can be monitored, after the monitored data is obtained, the future state prediction of the obtained variable data is carried out through the MARA self-adaptive prediction model, and the availability analysis is carried out through the fault tree model, if the prediction result is as follows: if the risk of failure exists at a certain time in the future, it is found that a failure node (equipment) possibly exists in the future, a corresponding failure signal is sent to the management and control unit, and the management and control unit carries out migration operation after marking the failure node; wherein: when a plurality of nodes have the risk of simultaneous failures, the migration sequence is determined according to the failure time (FLT) and the time length required for migration operation migration, and the management and control unit assigns a target address to each migration operation, wherein the nodes with lower failure probability are easier to assign.
Drawings
Fig. 1 is a schematic flowchart of a method for locating a fault of a service characteristic of a power grid regulation and control data center based on a time sequence and fault tree analysis according to an embodiment of the present invention;
fig. 2 is a schematic circuit structure diagram of a fault location system for service characteristics of a power grid regulation and control data center based on time series and fault tree analysis according to an embodiment of the present invention;
fig. 3 is a schematic circuit structure diagram of a power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis according to a second embodiment of the present invention;
fig. 4 is a schematic circuit structure diagram of a power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis according to a third embodiment of the present invention;
fig. 5 is a schematic circuit structure diagram of a power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis according to a fourth embodiment of the present invention;
fig. 6 is a schematic circuit structure diagram of a power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis according to a fifth embodiment of the present invention;
FIG. 7 is a fault tree structure constructed by the present invention;
FIG. 8 is a time relationship line in the online fault prediction of the present invention;
FIG. 9 is an error curve for variable mean prediction using the adaptive MARA model of the present invention;
FIG. 10 is a graph showing the relationship between sensitivity and specificity of failure prediction in the present invention;
in the figure: the method comprises the following steps that 10 is an establishing unit, 20 is an obtaining unit, 30 is a fault prediction unit, 40 is a threshold value comparison unit, 50 is an availability analysis unit, and 60 is a management and control unit;
101 is a service establishing unit, 102 is a Boolean expression establishing unit;
301 is a creating unit, 302 is a training unit, and 303 is a predicting unit;
401 is a setting unit, 402 is a comparing unit;
501 is a decomposition unit, and 502 is an output unit;
601 is a receiving unit, 602 is a formulation unit, and 603 is an execution unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention; all other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Next, the present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially according to the general scale for convenience of illustration when describing the embodiments of the present invention, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
The following describes in detail specific embodiments of the method and system for locating a fault of a service characteristic of a power grid regulation and control data center based on time series and fault tree analysis with reference to the accompanying drawings.
Example one
Fig. 1 is a schematic flowchart of a method for locating a service characteristic fault of a power grid regulation and control data center based on a time sequence and fault tree analysis according to an embodiment of the present invention, as shown in fig. 1, the method for locating a service characteristic fault of a power grid regulation and control data center based on a time sequence and fault tree analysis according to the embodiment may include:
establishing a fault tree model; acquiring a group of variable data corresponding to each device in the power regulation and control data center, and performing maximum and minimum normalization processing on the variable data; predicting the future state of the acquired variable data through a MARA self-adaptive prediction model, and storing the predicted data into a database; comparing the threshold value of the predicted data, and outputting the predicted data after binary conversion when the predicted data meets the threshold value; receiving binary prediction data, and performing availability analysis through a fault tree model; and receiving fault signals sent by fault nodes which possibly exist in the future, marking, organizing and executing the migration operation.
Fig. 2 is a schematic circuit structure diagram of a power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis according to an embodiment of the present invention, and as shown in fig. 2, the power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis may include: the building unit 10 is used for building a fault tree model; the acquiring unit 20 is configured to acquire a set of variable data corresponding to each device in the power regulation and control data center, and perform maximum and minimum normalization processing on the variable data; the fault prediction unit 30 is used for predicting the future state of the acquired variable data through the MARA self-adaptive prediction model and storing the predicted data into a database; a threshold comparison unit 40, configured to perform threshold comparison on the prediction data, and output the prediction data after binary conversion when the prediction data meets a threshold; an availability analysis unit 50 for receiving the binary prediction data and performing availability analysis through the fault tree model; and the management and control unit 60 is configured to receive fault signals sent by fault nodes which may exist in the future, mark the fault signals, organize the fault signals, and perform migration operations.
Specifically, according to the invention, specific contents of a group of variable data are defined aiming at the diversification characteristics of the power regulation data center equipment, including the running state of a CPU (central processing unit), the running state of a memory, the throughput of input and output equipment and the like, negative influences of differences among different types of equipment on detection results can be eliminated through a unified data collection standard, meanwhile, the maximum and minimum normalization processing is carried out on the obtained variable data, the negative influences of dimensions of different variables on the results can be eliminated, and the accuracy of data processing in subsequent prediction and analysis is improved.
The method and the system for locating the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis are simple to use, easy to implement, high in prediction precision and capable of achieving the purpose of quickly and accurately locating the fault of the power grid regulation and control data center.
Example two
On the basis of the first embodiment, the method for quickly locating the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis provided by the second embodiment predicts the future state of the acquired variable data through the MARA adaptive prediction model, and stores the predicted data in the database, and specifically may include:
creating a MARA model; establishing a multi-step MARA model, and training in a self-adaptive mode to obtain a MARA self-adaptive prediction model based on variable data; and predicting the future state of the acquired variable data, and storing the predicted data value into a database when the MARA self-adaptive prediction model acquires the predicted data value at the future moment.
Fig. 3 is a schematic circuit structure diagram of a fault location system for regulating and controlling service characteristics of a data center of a power grid based on a time sequence and fault tree analysis according to a second embodiment of the present invention, and as shown in fig. 3, the fault prediction unit 30 includes: a creating unit 301 that creates a MARA model; a training unit 302, configured to create a multi-step MARA model, and train in a self-adaptive manner to obtain a variable data-based MARA adaptive prediction model; and the prediction unit 303 is configured to perform future state prediction on the acquired variable data, and store the predicted data value into the database when the MARA adaptive prediction model obtains the predicted data value at a future time.
Specifically, the MARA model is composed of an MA adaptive regression model and an RA sliding mean model, and the MARA model is constructed on the basis of a p-th order MA model and a q-th order RA model, wherein: p represents a time point sequence, and q represents the number of historical mispredictions;
the MARA model is expressed by the following formula (3-1):
wherein:
representing the predicted value, x, calculated at a future time tt-iRepresenting the value of the variable obtained at time t-i,
et-jrepresenting a series of errors previously observed at time t-j, etThe values of the above variables are optimal variable values obtained by adaptation in the process of training the prediction model by using the collected historical time series data, and when the MARA model obtains each variable value at a future time, the values are permanently placed in a database constructed in advance;
further, the MARA adaptive prediction model is expressed by the following formula (3-2):
wherein: k represents the number of steps that need to be taken to make a future value prediction.
EXAMPLE III
On the basis of the first embodiment, the method for quickly locating the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis provided by the third embodiment specifically includes:
creating a top service for representing a system state, splitting the top service to obtain a lower terminal service, splitting the lower terminal service step by step, and ending the splitting when the maximum value of the interpretable resolution is reached;
connecting the lower sub-service with the top service in a Boolean logic gate index mode, and establishing Boolean expressions which correspond to the sub-services one by one and the Boolean expressions which correspond to the top service;
the receiving binary prediction data and performing availability analysis through a fault tree model may specifically include:
the fault tree model decomposes the binary prediction data to simplify the binary prediction data into a Boolean value in the top service;
and analyzing the top service, and when the top service can be carried out, taking the value of the output variable in the fault tree model as 1, or taking the value as 0.
Fig. 4 is a schematic circuit structure diagram of a fault location system for service characteristics of a power grid regulation and control data center based on a time sequence and fault tree analysis according to a third embodiment of the present invention, and as shown in fig. 4, the establishing unit 10 may include: a service establishing unit 101, configured to establish a top service for representing a system state, split the top service to obtain a lower sub-service, split the lower sub-service step by step, and terminate the splitting when a maximum value of interpretable resolution is reached; a boolean expression establishing unit 102, configured to connect the lower end sub-service with the top end service in a boolean logic gate index manner, and establish boolean expressions corresponding to the respective sub-services one to one and the top end service;
the usability analyzing unit 50 may include: a decomposition unit 501, configured to decompose the binary prediction data into a boolean value in the top service; an output unit 502, configured to analyze the top service, where when the top service can be performed, a value of a variable output in the fault tree model is 1, and otherwise, the value is 0.
In this embodiment, the relationship between the services is represented by boolean logic gates, and different organizational modes between the services are represented by different logic gates. The and, or, and NOT gates may be represented by respective boolean logic operations. Intersections between services are represented by and gates, unions between services are represented by or gates, and complements between services are represented by NOT gates.
The establishment of the fault tree comprises the following steps:
firstly, constructing a top service to express a system state;
then, the lower terminal service is connected with the top terminal service by means of Boolean logic gate index, wherein the lower terminal service is called as the initial contributor. The interrelationship between the top business and the lower sub-business is represented by corresponding logic gates, and the initial contributor is related to the contributor at the upper layer by a series of logic gates in an index manner; this step is repeated, ending when the maximum interpretable resolution is reached.
The basic service mainly comprises two parts: the resolution or the range of leaf nodes of the fault tree can be explained, the future value of the system variable is finally simplified into a Boolean value in the top service (root node) through the decomposition of the fault tree, when the service is found to be capable of being carried out, the variable input into the tree takes a value of 1, otherwise, the variable takes a value of 0, namely when the condition of the node is capable of being carried out, the variable state is 'true'; otherwise, it is false.
Specifically, the top service of the fault tree represents the probability of the system failure, and the top service represents a hardware cause service which has a very large probability of causing the system failure, and may also be referred to as a "main service", which mainly includes over-high temperature of a CPU, insufficient storage space (generally referred to as a memory), a disk sector failure, a power failure, and the like; and finally constructing a whole fault tree by analyzing the recorded fault data set samples.
Example four
On the basis of the first embodiment, the method for quickly locating a fault of a service characteristic of a power grid regulation and control data center based on a time sequence and fault tree analysis, provided by the fourth embodiment, includes the steps of comparing the predicted data with a threshold, and outputting the predicted data after binary conversion when the predicted data meets the threshold, and specifically includes:
setting a plurality of thresholds for indicating the degree of severity;
and comparing the predicted data with a plurality of threshold values, and outputting the predicted data after performing corresponding binary conversion when the predicted data meets a certain threshold value.
Fig. 5 is a schematic circuit structure diagram of a fault location system for service characteristics of a power grid regulation and control data center based on time series and fault tree analysis according to a fourth embodiment of the present invention, and as shown in fig. 5, the threshold comparison unit 40 may include:
a setting unit 401 for setting a plurality of thresholds representing degrees of severity;
a comparing unit 402 for comparing the predicted data with a plurality of threshold values, and outputting the predicted data after performing corresponding binary conversion when the predicted data meets a certain threshold value
Specifically, the severity includes: extremely high risk, higher risk and high risk;
the threshold comparison is expressed by the following formula:
wherein: equation (4-1) indicates that the threshold is set outside the conventional range when the value is varied
Is greater than a set threshold value ThrxWhen, it represents that the system has a very high risk;
equation (4-2) shows that the threshold is set below the normal range when the value is varied
Is less than a set threshold ThryTime, indicates that the system is at a higher risk.
Equation (4-3) indicates that the threshold is within a certain range if the value of the variable is variable
Is less than a set threshold Thrz1Or greater than a set threshold Thrz2It indicates that there is a high risk to the system.
EXAMPLE five
On the basis of the first embodiment, the method for quickly locating a fault of a service characteristic of a power grid regulation and control data center based on a time sequence and fault tree analysis, provided by the second embodiment, receives a fault signal sent by a fault node which may exist in the future, marks the fault signal, organizes and executes a migration operation, and specifically may include:
receiving fault signals sent by fault nodes which may exist in the future and marking the fault signals, wherein: the fault node comprises: the time of failure that may occur; establishing a migration operation corresponding to each fault node and a target address corresponding to each migration operation; determining a migration sequence according to the fault time and the time length required by migration; and executing the migration operation according to the migration sequence.
Fig. 6 is a schematic circuit structure diagram of a fault location system for regulating and controlling service characteristics of a data center of a power grid based on a time sequence and fault tree analysis according to a fifth embodiment of the present invention, as shown in fig. 6, on the basis of the first embodiment, the management and control unit 60 includes:
a receiving unit 601, configured to receive and mark a fault signal sent by a fault node that may exist in the future, where: the fault node comprises: the time of failure that may occur;
a formulating unit 602, configured to establish a migration operation corresponding to each failed node and a target address corresponding to each migration operation, and determine a migration sequence according to failure time and migration required duration;
an executing unit 603, configured to execute the migration operation according to the migration order formulated by the formulating unit 601.
In this embodiment, fig. 7 is a fault tree structure constructed by the present invention; as shown in fig. 7, the lower end sub-service in the fault tree includes:
CPU temperature too high (T): this traffic is typically caused by processor temperature being too high or by other sub-traffic management occurring at two or more lower terminals, processor load being too high and fan speed being too low; as can be seen from fig. 6, the boolean expression of the service T can be written as T ═ E (F ^ G);
insufficient storage space (S): this service may occur when system memory or hard disk is not sufficient; as can be seen from fig. 6, the service S may be represented as S ═ H ═ I;
disk sector failure (D): traffic that occurs when an operation wants to read and write a failed sector of a hard disk or memory. Because, the memory has an automatic repair mechanism. Therefore, the operation of reading and writing the failed sector of the hard disk is a main technical point, and as can be seen from fig. 7, a service D may be represented as D ═ J;
power failure (Y): accidents due to power reduction caused by sudden power failure, excessive load or old power input equipment may occur. Power failures can be discovered by monitoring the voltage values. As can be seen from fig. 6, the service Y may be denoted as Y ═ L. For the entire fault tree, the top traffic can be represented by equation (6):
U＝(E∨(F∧G))∨(H∨I)∨(J)V(L) (6)
the value of the top service U is calculated by combining the cases of 7 leaf nodes (lower terminal services).
When the fault tree is integrated with the multi-step MARA, they can be used to obtain the value of the top traffic U simultaneously at multiple points in time.
FIG. 8 is a time relationship line in online fault prediction, and as shown in FIG. 8, at time t, if U is true, the fault may be in an earlier time period (earlier time period Δ t)lIs predicted. Δ tlCan be calculated by using the number of steps in the multiprocess MARA, Δ tpRepresenting the probability of the system failing during a corresponding basic time period (called the prediction cycle). Detection procedure at size Δ tdObtaining corresponding data in the data window; the shortest alarm time Δ t is also defined in FIG. 7wIf the advance time period is shorter than the warning time, the time for operating the preventive measure or performing the migration operation will be insufficient, and therefore, the warning time Δ twIs set to be equal to the monitoring time Δ tdAre equal.
In the invention, a group of variable data of each device in the force regulation data center can be monitored, after the monitored data is obtained, the future state prediction of the obtained variable data is carried out through the MARA self-adaptive prediction model, and the availability analysis is carried out through the fault tree model, if the prediction result is as follows: if the risk of failure exists at a certain time in the future, it is found that a failure node (equipment) possibly exists in the future, a corresponding failure signal is sent to the management and control unit, and the management and control unit carries out migration operation after marking the failure node; wherein: when a plurality of nodes have the risk of simultaneous failures, the migration sequence is determined according to the failure time (FLT) and the time length required for migration operation migration, and the management and control unit assigns a target address to each migration operation, wherein the nodes with lower failure probability are easier to assign.
Specifically, in the present invention, a set of variable values is obtained, which is used to identify the values of the leaf node states in the fault tree, as shown in table 1.
TABLE 1 variables for identifying values of leaf node states in a fault tree
| Leaf node | Variables of |
| E | Processor temperature (. degree.C.) |
| F | Processor utilization of user Process (%) |
| F | Processor utilization of System Process (%) |
| F | Idle time due to I/O request (%) |
| H | Currently in use/reserved memory space (kB) |
| H | Operating system write reserved storage space (kB) for disk |
| H | Storage space (kB) required by the current workload |
| F，H | Number of switching spaces (kB) |
| H | Exchange memory space of buffer memory (kB) |
| H.I | Storage space (kB) exchanged from disk |
| H，I | Memory space switched to disk (kB) |
| I | Hard disk usage (%) |
| J | Magnetic disk read Rate (kB/s) |
| J | Magnetic disc write rate (kB/s) |
| J | Blocks received from a block device |
| J | Block to be transmitted to block device |
FIG. 8 is an error curve for variable mean prediction using the adaptive MARA model of the present invention; as shown in FIG. 8, the adaptive MARA model was used for variable mean prediction, the accuracy of which was measured using Root Mean Square Error (RMSE), and the results are shown as the percentage of error for each variable calculated from RMSE; the X-axis shows each individual variable in the same order as in fig. 6; the input-output speed of the storage device is significantly dependent on the application.
In the experimental process, a plurality of programs with the characteristic of diversity are operated, the error rates observed in the two variables are less than 1 percent, and the total prediction accuracy is about 9.22 percent. From the results, it can be seen that the RMSE value was improved by 9% by using the adaptive MARA model.
Table 2 illustrates inputting predicted variable values obtained from the MARA model into a fault tree to predict fault conditions of equipment in the power conditioning data center; FIG. 10 is a graph showing the relationship between the sensitivity and specificity of failure prediction in the present invention.
In the experimental process, a confusion matrix is used for evaluating the accuracy of the model for predicting the fault result, and the matrix consists of four parameters: true Positive (TP), False Positive (FP), False Negative (FN) and True Negative (TN). The experimental result shows that the accuracy of the model for predicting the equipment fault result is about 97%.
TABLE 2 confusion matrix for fault trees
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It will be appreciated that the relevant features of the method, apparatus and system described above are referred to one another. In addition, "first", "second", and the like in the above embodiments are for distinguishing the embodiments, and do not represent merits of the embodiments.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the module described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and other divisions may be realized in practice, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The method for locating the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis is characterized by comprising the following steps of: the method comprises the following steps:
establishing a fault tree model;
acquiring a group of variable data corresponding to each device in the power regulation and control data center, and performing maximum and minimum normalization processing on the variable data;
predicting the future state of the acquired variable data through a MARA self-adaptive prediction model, and storing the predicted data into a database;
comparing the threshold value of the predicted data, and outputting the predicted data after binary conversion when the predicted data meets the threshold value;
receiving binary prediction data, and performing availability analysis through a fault tree model;
and receiving fault signals sent by fault nodes which possibly exist in the future, marking, organizing and executing the migration operation.
2. The method for rapidly positioning the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis as claimed in claim 1, wherein: the predicting the future state of the acquired variable data through the MARA adaptive prediction model, and storing the predicted data into a database specifically comprises the following steps:
creating a MARA model;
establishing a multi-step MARA model, and training in a self-adaptive mode to obtain a MARA self-adaptive prediction model based on variable data;
and predicting the future state of the acquired variable data, and storing the predicted data value into a database when the MARA self-adaptive prediction model acquires the predicted data value at the future moment.
3. The method for rapidly positioning the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis as claimed in claim 1, wherein: the establishing of the fault tree model specifically comprises the following steps:
creating a top service for representing a system state, splitting the top service to obtain a lower terminal service, splitting the lower terminal service step by step, and ending the splitting when the maximum value of the interpretable resolution is reached;
connecting the lower sub-service with the top service in a Boolean logic gate index mode, and establishing Boolean expressions which correspond to the sub-services one by one and the Boolean expressions which correspond to the top service;
the receiving binary prediction data and performing availability analysis through a fault tree model specifically include:
the fault tree model decomposes the binary prediction data to simplify the binary prediction data into a Boolean value in the top service;
and analyzing the top service, and when the top service can be carried out, taking the value of the output variable in the fault tree model as 1, or taking the value as 0.
4. The method for rapidly positioning the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis as claimed in claim 1, wherein: the threshold comparison of the prediction data is performed, and when the prediction data meets the threshold, the prediction data is output after binary conversion, specifically including:
setting a plurality of thresholds for indicating the degree of severity;
and comparing the predicted data with a plurality of threshold values, and outputting the predicted data after performing corresponding binary conversion when the predicted data meets a certain threshold value.
5. The method for rapidly positioning the service characteristic fault of the power grid regulation and control data center based on the time sequence and fault tree analysis as claimed in claim 1, wherein: the receiving and marking of fault signals sent by fault nodes which may exist in the future, organizing and executing migration operations specifically include:
receiving fault signals sent by fault nodes which may exist in the future and marking the fault signals, wherein: the fault node comprises: the time of failure that may occur;
establishing a migration operation corresponding to each fault node and a target address corresponding to each migration operation; determining a migration sequence according to the fault time and the time length required by migration;
and executing the migration operation according to the migration sequence.
6. Time series and fault tree analysis-based power grid regulation and control data center service characteristic fault positioning system is characterized in that: the method comprises the following steps:
a building unit (10) for building a fault tree model;
the acquisition unit (20) is used for acquiring a group of variable data corresponding to each equipment in the electric power regulation and control data center and carrying out maximum and minimum normalization processing on the variable data;
the fault prediction unit (30) is used for predicting the future state of the acquired variable data through the MARA adaptive prediction model and storing the predicted data into a database;
a threshold comparison unit (40) for performing threshold comparison on the prediction data, and outputting the prediction data after binary conversion when the prediction data meets the threshold;
an availability analysis unit (50) for receiving the binary prediction data and performing availability analysis by the fault tree model;
and the management and control unit (60) is used for receiving fault signals sent by fault nodes which possibly exist in the future, marking the fault signals, organizing and executing migration operation.
7. The power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis of claim 6, wherein: the failure prediction unit (30) includes:
a creation unit (301) that creates a MARA model;
the training unit (302) is used for creating a multi-step MARA model and training in a self-adaptive mode to obtain a MARA self-adaptive prediction model based on variable data;
and the prediction unit (303) is used for predicting the future state of the acquired variable data, and storing the predicted data value into the database when the MARA adaptive prediction model acquires the predicted data value at the future moment.
8. The power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis of claim 6, wherein: the establishing unit (10) comprises:
a service establishing unit (101) for establishing a top service for representing a system state, splitting the top service to obtain a lower sub-service, splitting the lower sub-service step by step, and ending the splitting when a maximum value of interpretable resolution is reached;
the Boolean expression establishing unit (102) is used for connecting the lower terminal service with the top service in a Boolean logic gate index mode, and establishing Boolean expressions which are in one-to-one correspondence with the sub services and the Boolean expressions which are in correspondence with the top service;
the usability analyzing unit (50) includes:
the decomposition unit (501) is used for decomposing the binary prediction data into a Boolean value in the top service;
and the output unit (502) is used for analyzing the top service, when the top service can be carried out, the value of the variable output in the fault tree model is 1, and otherwise, the value is 0.
9. The power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis of claim 6, wherein: the threshold value comparison unit (40) includes:
a setting unit (401) for setting a plurality of thresholds for indicating severity degrees;
and a comparison unit (402) which compares the prediction data with a plurality of threshold values, and outputs the prediction data after performing corresponding binary conversion when the prediction data meets a certain threshold value.
10. The power grid regulation and control data center service characteristic fault location system based on time series and fault tree analysis of claim 6, wherein: the regulating unit (60) includes:
a receiving unit (601) for receiving and marking a fault signal sent by a fault node which may exist in the future, wherein: the fault node comprises: the time of failure that may occur;
the system comprises a formulation unit (602) for establishing migration operation corresponding to each fault node and a target address corresponding to each migration operation, and determining a migration sequence according to fault time and migration required time;
and the execution unit (603) is used for executing the migration operation according to the migration sequence formulated by the formulation unit (601).
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