CN117439899B - Communication machine room inspection method and system based on big data - Google Patents
Communication machine room inspection method and system based on big data Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/0677—Localisation of faults
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
Abstract
The invention relates to the field of communication machine room inspection, and discloses a communication machine room inspection method and system based on big data, wherein the method comprises the following steps: collecting the original data of each data source; constructing an asset network according to the data source; in the asset network, generating a patrol task and carrying out patrol of a random path; dividing the data source into areas, calculating the energy of a calculation unit of each area, and rechecking the data source and the energy calculation result of the calculation unit when the energy reaches a threshold value; and after the re-inspection is finished, regenerating an inspection path until the inspection is finished. Through data drive inspection, manual operation is reduced, and inspection speed and frequency are improved. Random path generation based on big data analysis ensures wider coverage and effectively reduces the risks of missed detection and false detection. The intelligent inspection method can more effectively utilize resources, such as energy and manpower, and reduce the overall operation and maintenance cost.
Description
Technical Field
The invention relates to the technical field of communication machine room inspection, in particular to a communication machine room inspection method and system based on big data.
Background
With the rapid development of communication technology, the amount of data handled by a communication room increases dramatically, which results in more complicated data management and analysis. The communication room contains various devices (such as servers, routers and switches) with different performance parameters and maintenance requirements, and data between different devices and systems is difficult to integrate, so that the data utilization rate is low. The traditional inspection method is difficult to accurately identify and locate faults in real time. Traditional inspection processes generally rely on manual operation, are inefficient, and are prone to error.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing inspection method for the communication machine room has the defects of high fault rate, high maintenance cost and how to utilize big data to inspect the communication machine room.
In order to solve the technical problems, the invention provides the following technical scheme: a communication machine room inspection method based on big data comprises the following steps: collecting the original data of each data source; constructing an asset network according to the data source; in the asset network, generating a patrol task and carrying out patrol of a random path; dividing the data source into areas, calculating the energy of a calculation unit of each area, and rechecking the data source and the energy calculation result of the calculation unit when the energy reaches a threshold value; and after the re-inspection is finished, regenerating an inspection path until the inspection is finished.
As a preferable scheme of the big data-based communication machine room inspection method, the invention comprises the following steps: the data source comprises network equipment, a monitoring system and equipment local application; the original data comprise flow data and equipment performance of the network equipment, environment data of the monitoring system and data generated by local application of the equipment.
As a preferable scheme of the big data-based communication machine room inspection method, the invention comprises the following steps: the asset network is built by the following steps: taking each link for data transmission as a virtual asset node, and taking each data source in the link as a basic asset in the virtual asset node; each virtual asset node contains only one link, each data source being capable of being in a plurality of virtual asset nodes; each link is a complete path capable of completing data transmission; taking assets with data transmission among data sources as a class of nodes; taking assets without data transmission among data sources as class II nodes; taking a class-one node and a class-two node virtual space matrix as an asset network; randomly distributing the class II nodes in an M multiplied by N class II virtual space matrix; distributing a class of nodes in an m multiplied by n class of virtual space matrix, forming n columns by taking the space distribution characteristic as the clustering characteristic of the columns, and forming m rows according to the sequence from large flow to small flow by taking the data flow characteristic as the clustering characteristic of the rows; wherein, the empty matrix points in the virtual space matrix are replaced by 0;
wherein M and N represent any two numbers, the sizes of which satisfy the following: m×n is the smallest integer that can accommodate all the two classes of nodes.
As a preferable scheme of the big data-based communication machine room inspection method, the invention comprises the following steps: the routing inspection of the random paths comprises the steps of setting the number of steps of routing inspection paths in a virtual space matrix as t virtual asset nodes, and routing inspection is performed by a virtual robot with routing inspection computing capability; wherein t is greater than n; when the inspection is carried out in a virtual space matrix, randomly extracting a non-0 matrix point from each column in the virtual space matrix, calculating the flow occurrence probability of each virtual asset node, and selecting the rest t-n virtual asset nodes by taking the calculation result of the probability as a basis; when the rest virtual asset nodes are selected, randomly selecting the routing inspection nodes in unselected nodes by taking the traffic occurrence probability as the probability basis of the selected nodes; the virtual robot sequentially performs inspection in all the selected virtual asset nodes according to the sequence from the big flow occurrence probability to the small flow occurrence probability; the calculating the traffic occurrence probability of each virtual asset node is expressed as:
；
wherein,the flow size of the node with coordinates (i, j) from the last inspection to the current inspection; />Representing the sum of the flow of all nodes in the matrix from the last inspection to the current inspection; k represents a row; l represents a column; if the flow path of the traffic of the virtual asset node is not a complete link but is only an intermediate link of the complete link, A kl The value is recorded as 0; setting the length of a routing inspection path in the second-class virtual space matrix as T virtual asset nodes, and selecting a virtualWhen the asset nodes are selected, randomly selecting the selected nodes according to the probability basis of using the frequency as the selected node, and sequentially carrying out patrol on the selected nodes according to the sequence from high to low of the frequency by using the virtual robot with the patrol computing capability; the frequency of use is expressed as:
；
wherein,the working time of the second class node from the last inspection to the current inspection is represented;the sum of the working time lengths of the class II nodes from the last inspection to the current inspection is represented, u represents the number of the class II nodes, h represents the node serial numbers of the class II nodes, and +.>And the working time of the h node from the last inspection to the second class node in the period from the last inspection is shown.
As a preferable scheme of the big data-based communication machine room inspection method, the invention comprises the following steps: the regional division comprises taking a computing unit with computing capability as a regional center if the computing unit with computing capability exists; dividing a data source used by a computing unit serving as a region center in energy computing into regions serving as region contents, and recording the divided regions as regionsWhere V denotes a region sequence number, each data source can be divided into a plurality of regions.
As a preferable scheme of the big data-based communication machine room inspection method, the invention comprises the following steps: the energy calculation includes, for each region, an evaluation of an energy threshold:
；
according to the data flow calculated by each calculation unit, energy calculation is performed:
；
wherein n represents a set threshold coefficient, and n is more than 2;representing the average value of each data flow calculation; e (E) max A threshold value representing the energy of the region; e (E) V Representing the cumulative amount of energy; f (.+ -.) represents a cumulative function for accumulating the sum of the energy of each data flow, clearing 0 after finishing the patrol of the area; f represents the data traffic size of a single calculation; z represents an expansion coefficient, which is equal to the addition of 1 to the anomaly rate of the single-time calculated data during inspection; j represents the load factor of the computing unit at each computation;
；
wherein j represents a single calculated amount; y represents the optimal calculation amount of the calculation unit;
if E V ≥E max The virtual robot breaks away from the patrol task after finishing the patrol of the current node, and the virtual robot is used for the areaRechecking the data source and the calculation result of the calculation unit; the rechecking is to check the calculation result of the data source in the area and the calculation unit in the area; the method comprises the steps of inspecting data flow and checking calculation accuracy of a calculation unit, and specifically comprises the following steps: setting the routing inspection path length as D virtual asset nodes for one type of nodes in the area, and taking the number of nodes with flow data as the routing inspection path length if the number of nodes with flow data is smaller than D from the last routing inspection to the current routing inspection; if from aboveThe method comprises the steps that after the secondary inspection is finished until the inspection is started, the number of nodes with flow data is not smaller than D, random selection is carried out on the inspected nodes by taking the flow occurrence probability as a probability basis for selecting the nodes, and the selected nodes are inspected sequentially from high to low according to the flow occurrence probability through a virtual robot with inspection computing capability; wherein D represents a path length preset by a technician when the inspection of one type of node is executed;
in the areaThe probability basis of the inspection is expressed as:
；
wherein,probability basis representing the q-th data, +.>The q-th node indicates the flow from the last inspection to the current inspection; />Representing the sum of the flow of all nodes in the area from the last inspection to the current inspection; r represents the number of all nodes in the region; r represents the sequence number of the node in the region; if the flow path of the traffic of the virtual asset node is not a complete link but is only an intermediate link of the complete link, A r The value is recorded as 0;
the second class nodes in the area are inspected according to the inspection mode of the second class virtual space matrix by taking the inspection path length as O; wherein, O represents the path length preset by a technician when the inspection of the class II nodes is executed; after the inspection in the area is finished, an inspection result is obtained, the inspection result is compared with the calculation result of the calculation unit corresponding to the inspection content, if the inspection result is inconsistent, an error alarm is sent out to the calculation unit, and meanwhile, the threshold coefficient n is reduced by one unit step.
As a preferable scheme of the big data-based communication machine room inspection method, the invention comprises the following steps: the regenerating of the inspection path comprises the step of continuing to execute inspection on the original inspection process of the first-class virtual space matrix and the second-class virtual space matrix after the re-inspection of the area is completed; when the inspection is executed, if the data is abnormal, immediately giving out early warning and feeding back the abnormal condition of the data to a management system of a data source; when the inspection task is completed, automatically switching to the next inspection task; when the L-wheel inspection task is completed, performing comprehensive inspection of the communication machine room; the patrol object comprises all data from the last patrol of each asset node to the current.
On the other hand, the invention provides a system adopting the communication machine room inspection method based on big data, which comprises the following steps: the data acquisition module acquires the original data of each data source; the inspection module builds an asset network according to the data source; in the asset network, generating a patrol task and carrying out patrol of a random path; the rechecking module is used for dividing the data source into areas, calculating the energy of the calculation unit of each area, and rechecking the data source and the energy calculation result of the calculation unit when the energy reaches a threshold value; and after the re-inspection is finished, regenerating an inspection path until the inspection is finished.
A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: and the processor realizes the steps of the communication machine room inspection method based on big data when executing the computer program.
A computer-readable storage medium having stored thereon a computer program, characterized by: and the computer program realizes the steps of the communication machine room inspection method based on big data when being executed by a processor.
The invention has the beneficial effects that: through data drive inspection, manual operation is reduced, and inspection speed and frequency are improved. Random path generation based on big data analysis ensures wider coverage and effectively reduces the risks of missed detection and false detection. The intelligent inspection method can more effectively utilize resources, such as energy and manpower, and reduce the overall operation and maintenance cost.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a communication room inspection method based on big data according to a first embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, a method for inspecting a communication room based on big data is provided, including:
s1: raw data for each data source is collected.
Further, the data source comprises network equipment, a monitoring system and equipment local application; the original data comprise flow data and equipment performance of the network equipment, environment data of the monitoring system and data generated by local application of the equipment, wherein the flow data and the equipment performance of the network equipment comprise data of all servers and storage equipment in a machine room, such as system logs, operation records, performance indexes and the like, and relate to all network related equipment, such as flow data, state logs and configuration information of routers, switches and firewalls; the environmental data of the monitoring system comprise environmental data such as a temperature and humidity sensor, a power supply monitoring system, a UPS (uninterrupted power supply) state and the like; the data generated by the local application of the device refers to data generated by various application programs running on the server, including transaction records, user activity logs and the like.
Through effective inspection and analysis of the original data, efficient management of a communication machine room can be realized, potential problems can be found in advance, and stable and safe operation of the machine room is ensured. The big data based method can provide deeper insight and optimize operation and maintenance strategies.
S2: constructing an asset network according to the data source; and in the asset network, generating a patrol task, and carrying out patrol of a random path on the original data.
Further, building the asset network includes the steps of:
taking each link for data transmission as a virtual asset node, and taking each data source in the link as a basic asset in the virtual asset node; each virtual asset node contains only one link, each data source being capable of being in a plurality of virtual asset nodes; and each link is a complete path capable of completing data transmission. Taking assets with data transmission among data sources as a class of nodes; taking assets without data transmission among data sources as class II nodes; taking a class-one node and a class-two node virtual space matrix as an asset network; randomly distributing the class II nodes in a class II virtual space matrix; distributing a class of nodes in an m multiplied by n class of virtual space matrix, forming n columns by taking the space distribution characteristic as the clustering characteristic of the columns, and forming m rows according to the sequence from large flow to small flow by taking the data flow characteristic as the clustering characteristic of the rows; wherein, the empty matrix points in the virtual space matrix are replaced by 0, wherein M and N represent any two numbers, and the sizes of the M and N satisfy the following conditions: m×n is the smallest integer that can accommodate all the two classes of nodes.
It should be noted that, the second class node is generally an asset including no data transmission between data sources, for example, a monitoring system and a device local application may not have a data transmission relationship with other devices, but also needs a data source for inspection. When such nodes are inspected, they are typically not data-streamed because they are separate accounting devices, and the length of time they are in use becomes a feature of their use. While a class of nodes are typically network devices that can perform data transfer, there must be data flows between the underlying assets of such nodes, and they may have significant space and traffic characteristics. By classifying the two types of data sources into two network models, the two networks can be separately inspected.
It is known that the path of data transmission is used as a virtual asset node, and when the node is patrolled, the inspection can be ensured that the inspected content is a complete data stream. For example, the data is transmitted from the device a to the device B and then transmitted to the device C to the device D, and the ABCD is a complete data stream, so that when the accuracy of the data is checked, the problem of which link occurs can be accurately obtained, and thus a maintenance instruction is sent. For example, the data from a to B has no problem, but the data from B to C has a problem, which inevitably leads to the output data of D being abnormal, and by inspection of such a virtual asset node, the occurrence position of the data abnormality can be accurately obtained. The traditional method only carries out inspection on the data terminal, and can carry out tracing after the problem is found, but the tracing result at the moment is possibly poor, and the equipment position where the problem occurs can be locked only through multiple fishing tests.
Meanwhile, the data volume on each link can be clear through the asset network, so that the use state and the use frequency of each device can be evaluated by technicians. In this way, not only can the tracing of data be completed at the fastest speed, but also the improvement situation based on the asset network can be rapidly analyzed.
It is also known that a telecommunications closet, also commonly referred to as a data center or server room, is a space dedicated to the storage of telecommunications equipment and associated support equipment. The n columns are formed with the spatial distribution features as the cluster features of the columns. Typically there will be one computational unit per column of each spatial distribution. This also constitutes a spatial feature in the asset network, which is a clustering feature for each column based on this spatial distribution feature. This ensures that the data information in each computing unit can be walked around. In the inspection, the calculation accuracy of the calculation unit can be verified as long as the nodes in the column are inspected. In addition, since there are many nodes in a column and few nodes, there is a case where "empty nodes" exist, and the calculation is not affected by marking these empty matrix points as 0.
The routing inspection of the random paths comprises the steps of setting the number of steps of routing inspection paths in a virtual space matrix as t virtual asset nodes, and routing inspection is performed by a virtual robot with routing inspection computing capability; wherein t > n.
It is to be noted that the "virtual robot having inspection calculation capability" referred to herein is virtual in order to simulate the inspection process. According to the algorithm requirement of the inspection, the technician embeds the algorithm used when the inspection is performed in the computing module, and then when the inspection is performed on a certain node, the computing module starts the algorithm to inspect the data of the node, and at the moment, the virtual robot with the inspection computing capability performs the inspection task on the node. By introducing the concept of 'virtual robot with patrol computing capability', the current patrol position can be clearly found out on the visualized system interface. The "virtual robot with patrol calculation capability" itself is a calculation process executed by an algorithm embedded in a calculation module, and the visualization of the patrol position is realized by virtually existence of such a "robot".
When the inspection is carried out in a virtual space matrix, randomly extracting a non-0 matrix point from each column in the virtual space matrix, calculating the flow occurrence probability of each virtual asset node, and selecting the rest t-n virtual asset nodes based on the calculation result of the probability; when the rest virtual asset nodes are selected, randomly selecting the routing inspection nodes in unselected nodes by taking the traffic occurrence probability as the probability basis of the selected nodes; the virtual robot sequentially performs inspection in all the selected virtual asset nodes according to the sequence from the big flow occurrence probability to the small flow occurrence probability; the calculating the traffic occurrence probability of each virtual asset node is expressed as:
；
wherein,the flow size of the node with coordinates (i, j) from the last inspection to the current inspection; />Representing the sum of the flow of all nodes in the matrix from the last inspection to the current inspection; k represents a row; l represents a column; if the flow path of the traffic of the virtual asset node is not a complete link but is only an intermediate link of the complete link, A kl And is noted as 0.
It will be appreciated that there may be a "inclusive" relationship for some links, for example, long links Z-X-C-V-B-N-M may be used as complete links for data transmission. At the same time, a short link B-N-M exists, and complete data traffic can be completed. At this time, the information in the short link is also collected by the long link. In order to avoid repeated collection of data traffic, the traffic of the long link is recorded as 0. Similarly, data in a long link is also erased by recording it as 0 in order to avoid repeated metering in a short link.
It is to be noted that the above-mentioned "randomly selecting with the probability of occurrence of the traffic as the probability basis for selecting a node" means that each node is selected by using a probability distribution as the basis for selecting and using "randomly" as the core and "probability" as the means. Therefore, the selected data has integral characteristics, and is not selected by a single highest value, and certain randomness and accuracy can be ensured. In addition, each space is randomly selected with one node, so that the spatial characteristics of each space can be guaranteed to be considered, and meanwhile, the nodes with low probability can be inspected with high probability.
Setting the routing inspection path length in the second-class virtual space matrix as T virtual asset nodes, randomly selecting routing inspection nodes according to the probability basis of using the frequency as the selection node in unselected nodes when the virtual asset nodes are selected, and sequentially inspecting the selected nodes according to the order of using the frequency from high to low through the virtual robot with routing inspection computing capability; the frequency of use is expressed as:
；
wherein,the working time of the second class node from the last inspection to the current inspection is represented;the sum of the working time lengths of the class II nodes from the last inspection to the current inspection is represented, u represents the number of the class II nodes, h represents the node serial numbers of the class II nodes, and +.>And the working time of the h node from the last inspection to the second class node in the period from the last inspection is shown.
S3: dividing the data source into areas, calculating the energy of a calculation unit of each area, and rechecking the data source and the energy calculation result of the calculation unit when the energy reaches a threshold value; and after the re-inspection is finished, regenerating an inspection path until the inspection is finished.
The regional division comprises taking a computing unit with computing capability as a regional center if the computing unit with computing capability exists; dividing a data source used by a computing unit serving as a region center in energy computing into regions serving as region contents, and recording the divided regions as regionsWhere V represents a region sequence number, each data source can be divided among a plurality ofIn the region. Because the communication machine room serves as a data center, data from various parts are gathered, and most of each part is provided with a computing unit for edge computing, the computing unit can complete computing of equipment information of a certain area. By inspecting the certain area, the computing unit can check whether the recognition is accurate or not when the recognition abnormality occurs, and if the recognition is inaccurate, the bug is proved to exist and needs to be corrected. When the energy of each area reaches the threshold value, the calculated amount of data reaches a certain amount from the last inspection to the present. At this time, it is necessary to inspect each region. The erroneous calculation amount of the calculation unit is prevented from being accumulated. And nodes without calculation units are indicated as possibly simple devices and do not need calculation; or directly connected with the communication room, and is calculated by the communication room. The node without the calculation unit does not need accumulated energy and can directly carry out normal inspection.
The energy calculation includes, for each region, an evaluation of an energy threshold:
；
according to the data flow calculated by each calculation unit, energy calculation is performed:
；
wherein n represents a set threshold coefficient, and n is more than 2;representing the average value of each data flow calculation; e (E) max A threshold value representing the energy of the region; e (E) V Representing the cumulative amount of energy; f (.+ -.) represents a cumulative function for accumulating the sum of the energy of each data flow, and after finishing the inspection of the area, the inspection of the area and the inspection of the normal area cannot be compared because the computing power of the computing unit cannot be compared in the ordinary and conventional inspection (the inspection of the first-class virtual space matrix and the second-class virtual space matrix)The regular inspection is parallel, and the conventional inspection cannot affect the energy storage, so that the energy metering is performed by taking the area as the metering basis; f represents the data traffic size of a single calculation; z represents an expansion coefficient, which is equal to the anomaly rate of single-time calculated data during inspection plus 1, for example, 10 anomalies are found during inspection, the number of inspection is 100, the anomaly rate is 10%, and the higher the value of Z=110% is, the more interesting the higher the value of Z=110% is, and the higher the energy of Z is; j represents the load factor of the calculation unit at each calculation.
；
Wherein j represents a single calculated amount; y represents the optimal calculation amount of the calculation unit.
If E V ≥E max The virtual robot breaks away from the patrol task after finishing the patrol of the current node, and the virtual robot is used for the areaRechecking the data source and the calculation result of the calculation unit; the rechecking is to check the calculation result of the data source in the area and the calculation unit in the area; the method comprises the steps of inspecting data flow and checking calculation accuracy of a calculation unit, and specifically comprises the following steps: setting the routing inspection path length as D virtual asset nodes for one type of nodes in the area, and taking the number of nodes with flow data as the routing inspection path length if the number of nodes with flow data is smaller than D from the last routing inspection to the current routing inspection; if the number of nodes with flow data is not less than D from the last inspection to the current inspection, randomly selecting the inspected nodes according to the probability of the flow occurrence probability serving as a probability basis for selecting the nodes, and sequentially inspecting the selected nodes according to the sequence of the flow occurrence probability from high to low by using a virtual robot with inspection computing capability, wherein D represents the path length preset by a technician when the inspection of one type of nodes is executed; in area->The probability basis of the inspection is expressed as:
；
wherein,probability basis representing the q-th data, +.>The q-th node indicates the flow from the last inspection to the current inspection; />Representing the sum of the flow of all nodes in the area from the last inspection to the current inspection; r represents the number of all nodes in the region; r represents the sequence number of the node in the region; if the flow path of the traffic of the virtual asset node is not a complete link but is only an intermediate link of the complete link, A r And is noted as 0.
The second class nodes in the area are inspected according to the inspection mode of the second class virtual space matrix by taking the inspection path length as O; wherein, O represents the path length preset by a technician when the inspection of the class II nodes is executed; after the inspection in the area is finished, an inspection result is obtained, the inspection result is compared with the calculation result of a calculation unit corresponding to the inspection content, if the inspection result is inconsistent, an error alarm is sent out to the calculation unit, and meanwhile, the threshold coefficient n is reduced by one unit of step length, wherein the step length is default to 1 which is adjusted manually.
The regenerating of the inspection path comprises the step of continuing to execute inspection on the original inspection process of the first-class virtual space matrix and the second-class virtual space matrix after the re-inspection of the area is completed; when the inspection is executed, if the data is abnormal, immediately giving out early warning and feeding back the abnormal condition of the data to a management system of a data source; when the inspection task is completed, automatically switching to the next inspection task; when the L-wheel inspection task is completed, performing comprehensive inspection of the communication machine room; the patrol object comprises all data from the last patrol of each asset node to the current.
And after the re-inspection of the area is finished, the inspection task is continuously executed, so that the continuity and the integrity of the inspection process are ensured, and the omission of a key inspection point due to interruption or restarting is avoided. Meanwhile, the routing inspection path is not specified again, so that the complexity of calculation can be reduced. When the inspection is performed, abnormal data is monitored in real time, and early warning is immediately sent out when the abnormality is detected, so that quick response is allowed to prevent the problem from expanding. The abnormal conditions are fed back to the management system of the data source, important information is provided for the operation and maintenance team, and the operation and maintenance team is helped to locate the problem and take measures more quickly. After the inspection task is finished, the next round of inspection is automatically carried out, so that manual intervention is reduced, and efficiency is improved. Provision is made to perform a complete inspection of the communication room after the inspection of a specific number of rounds (e.g., L rounds) is completed, ensuring that each asset node is thoroughly inspected over a long period of time, including all data from the end of the last inspection to the present.
On the other hand, this embodiment also provides a communication computer lab inspection system based on big data, and it includes:
and the data acquisition module acquires the original data of each data source.
The inspection module builds an asset network according to the data source; and in the asset network, generating a patrol task and carrying out patrol of the random path.
The rechecking module is used for dividing the data source into areas, calculating the energy of the calculation unit of each area, and rechecking the data source and the energy calculation result of the calculation unit when the energy reaches a threshold value; and after the re-inspection is finished, regenerating an inspection path until the inspection is finished.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 2
In the following, for one embodiment of the invention, a communication machine room inspection method based on big data is provided, and in order to verify the beneficial effects of the invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
Table 1 is obtained by using a simulation test performed under the same environment by the conventional method and the present invention to compare the performance of the conventional communication room inspection method and the present invention method.
Table 1 data comparison table
As can be seen from table 1, the method of the present invention has significant advantages over conventional inspection methods in terms of improved efficiency, reduced cost, and improved quality. By using the invention, the communication machine room can improve the utilization rate of resources in the long-term use process, and can provide technical support for inspection for scale expansion.
The following are test results of the method of the present invention under different test environments, which are data integration of different scales (sequentially increasing the scale by 1 time from environment a to environment E), as shown in table 2.
TABLE 2 test sheets for different environments for the method of the present invention
It can be seen that as the amount of data increases, the error rate gradually increases, but remains low overall. This shows that the present invention can effectively control the error rate despite the increase in the data amount. The increase in the data volume results in a relatively flat maintenance cost. This reflects that the present invention can still remain highly cost effective in processing large-scale data. As the amount of data increases, the operating efficiency of the system is slightly reduced. The invention acquires the path nodes through probability distribution during the inspection, which means that the smaller the data is, the larger the probability of inspecting the same data source data twice is, and the smaller the data quantity of the inspection is, so the average running time is short. However, the invention can still maintain certain stability when facing a large amount of data, and the more the data amount is, the more stable the single running time is.
In conclusion, the invention can be analyzed in tests to obtain that the invention has certain superiority and stability in performance.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (5)
1. The communication machine room inspection method based on big data is characterized by comprising the following steps of:
collecting the original data of each data source;
constructing an asset network according to the data source;
in the asset network, generating a patrol task and carrying out patrol of a random path;
dividing the data source into areas, calculating the energy of a calculation unit of each area, and rechecking the data source and the energy calculation result of the calculation unit when the energy reaches a threshold value;
after the re-inspection is finished, regenerating an inspection path until the inspection is finished;
the asset network is built by the following steps:
taking each link for data transmission as a virtual asset node, and taking each data source in the link as a basic asset in the virtual asset node;
each virtual asset node contains only one link, and each data source can exist in a plurality of virtual asset nodes; each link is a complete path capable of completing data transmission;
taking assets with data transmission among data sources as a class of nodes; taking assets without data transmission among data sources as class II nodes;
taking a class-one node and a class-two node virtual space matrix as an asset network; randomly distributing the class II nodes in an M multiplied by N class II virtual space matrix; distributing a class of nodes in an m multiplied by n class of virtual space matrix, forming n columns by taking the space distribution characteristic as the clustering characteristic of the columns, and forming m rows according to the sequence from large flow to small flow by taking the data flow characteristic as the clustering characteristic of the rows; wherein, the empty matrix points in the virtual space matrix are replaced by 0;
wherein M and N represent any two numbers, the sizes of which satisfy the following: m×n is the smallest integer that can accommodate all class two nodes;
the data source comprises network equipment, a monitoring system and equipment local application;
the original data comprise flow data of the network equipment, equipment performance, environment data of the monitoring system and data generated by local application of the equipment;
the routing inspection of the random paths comprises the steps of setting the number of routing inspection path steps in a virtual space matrix as t nodes, and routing inspection is performed by a virtual robot with routing inspection computing capability; wherein t is greater than n;
when the inspection is carried out in a virtual space matrix, randomly extracting a non-0 matrix point from each column in the virtual space matrix, calculating the flow occurrence probability of each virtual asset node, and selecting the rest t-n virtual asset nodes based on the calculation result of the probability; when the rest virtual asset nodes are selected, randomly selecting the routing inspection nodes in unselected nodes by taking the traffic occurrence probability as the probability basis of the selected nodes; the virtual robot sequentially performs inspection in all the selected virtual asset nodes according to the sequence from the big flow occurrence probability to the small flow occurrence probability;
the calculating the traffic occurrence probability of each virtual asset node is expressed as:
；
wherein,the flow size of the node with coordinates (i, j) from the last inspection to the current inspection;representing the sum of the flow of all nodes in the matrix from the last inspection to the current inspection; k represents a row; l represents a column; if the flow path of the traffic of the virtual asset node is not a complete link but is only an intermediate link of the complete link, A kl The value is recorded as 0;
setting the routing inspection path length in the second-class virtual space matrix as T virtual asset nodes, randomly selecting routing inspection nodes according to the probability basis of using the frequency as the selection node in unselected nodes when the virtual asset nodes are selected, and sequentially inspecting the selected nodes according to the order of using the frequency from high to low through the virtual robot with routing inspection computing capability;
the frequency of use is expressed as:
；
wherein,the working time of the second class node from the last inspection to the current inspection is represented;the sum of the working time lengths of the class II nodes from the last inspection to the current inspection is represented, u represents the number of the class II nodes, h represents the node serial numbers of the class II nodes, and +.>And the working time of the h node from the last inspection to the second class node in the period from the last inspection is shown.
2. The big data based communication room inspection method as claimed in claim 1, wherein: the regional division comprises taking a computing unit with computing capability as a regional center if the computing unit with computing capability exists; dividing a data source used by a computing unit serving as a region center in energy computing into regions serving as region contents, and recording the divided regions as regionsWhere V denotes a region sequence number, each data source can be divided into a plurality of regions.
3. The big data based communication room inspection method as claimed in claim 2, wherein: the energy calculation includes, for each region, an evaluation of an energy threshold:
；
according to the data flow calculated by each calculation unit, energy calculation is performed:
；
wherein n represents a set threshold coefficient, and n is more than 2;representing the average value of each data flow calculation; e (E) max A threshold value representing the energy of the region; e (E) V Representing the cumulative amount of energy; f (.+ -.) represents a cumulative function for accumulating the sum of the energy of each data flow, clearing 0 after finishing the patrol of the area; f represents the data traffic size of a single calculation; z represents an expansion coefficient, which is equal to the addition of 1 to the anomaly rate of the single-time calculated data during inspection; j represents the load factor of the computing unit at each computation;
；
wherein j represents a single calculated amount; y represents the optimal calculation amount of the calculation unit;
if E V ≥E max The virtual robot breaks away from the patrol task after finishing the patrol of the current node, and the virtual robot is used for the areaRechecking the data source and the calculation result of the calculation unit;
the rechecking is to check the calculation result of the data source in the area and the calculation unit in the area; the method comprises the steps of inspecting data flow and checking calculation accuracy of a calculation unit, and specifically comprises the following steps:
setting the routing inspection path length as D virtual asset nodes for one type of nodes in the area, and taking the number of nodes with flow data as the routing inspection path length if the number of nodes with flow data is smaller than D from the last routing inspection to the current routing inspection; if the number of the nodes with flow data is not less than D from the last inspection to the current inspection, randomly selecting the inspected nodes according to the probability of the flow occurrence probability as a probability basis for selecting the nodes, and sequentially inspecting the selected nodes according to the sequence of the flow occurrence probability from high to low by using a virtual robot with inspection computing capability; wherein D represents a path length preset by a technician when the inspection of one type of node is executed;
in the areaThe probability basis of the inspection is expressed as:
；
wherein,probability basis representing the q-th data, +.>The q-th node indicates the flow from the last inspection to the current inspection; />Representing the sum of the flow of all nodes in the area from the last inspection to the current inspection; r represents the number of all nodes in the region; r represents the sequence number of the node in the region; if the flow path of the traffic of the virtual asset node is not a complete link but is only an intermediate link of the complete link, A r The value is recorded as 0;
the second class nodes in the area are inspected according to the inspection mode of the second class virtual space matrix by taking the inspection path length as O; wherein, O represents the path length preset by a technician when the inspection of the class II nodes is executed;
after the inspection in the area is finished, an inspection result is obtained, the inspection result is compared with the calculation result of the calculation unit corresponding to the inspection content, if the inspection result is inconsistent, an error alarm is sent out to the calculation unit, and meanwhile, the threshold coefficient n is reduced by one unit step.
4. The method for inspecting a communication machine room based on big data as claimed in claim 3, wherein: the regenerating of the inspection path comprises the step of continuing to execute inspection on the original inspection process of the first-class virtual space matrix and the second-class virtual space matrix after the re-inspection of the area is completed;
when the inspection is executed, if the data is abnormal, immediately giving out early warning and feeding back the abnormal condition of the data to a management system of a data source;
when the inspection task is completed, automatically switching to the next inspection task; when the L-wheel inspection task is completed, performing comprehensive inspection of the communication machine room; the patrol object comprises all data from the last patrol of each asset node to the current.
5. A big data based communication room inspection system employing the method of any of claims 1-4, characterized by:
the data acquisition module acquires the original data of each data source;
the inspection module builds an asset network according to the data source; in the asset network, generating a patrol task and carrying out patrol of a random path;
the rechecking module is used for dividing the data source into areas, calculating the energy of the calculation unit of each area, and rechecking the data source and the energy calculation result of the calculation unit when the energy reaches a threshold value; and after the re-inspection is finished, regenerating an inspection path until the inspection is finished.
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