CN118400314A - Informationized machine room monitoring and management system - Google Patents
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Abstract
The invention relates to the technical field of network configuration management, in particular to an informationized machine room monitoring and management system which comprises a network state monitoring module, a topology dynamic updating module, a device health detection module, a link quality analysis module, a path optimization strategy module, a bandwidth dynamic management module and a network adjustment implementation module. In the invention, through dynamic topology updating, real-time accurate reflection of the network structure becomes possible, and powerful support is provided for fault prediction and equipment maintenance. The link quality analysis and path optimization strategy not only improves the data transmission efficiency, but also realizes the maximization of resource utilization, and effectively reduces network delay and data loss. The bandwidth dynamic management and network adjustment implementation further improves the network adaptability and service quality, and ensures the stable operation of key applications and services.
Description
Technical Field
The invention relates to the technical field of network configuration management, in particular to an informationized machine room monitoring and management system.
Background
The technical field of network configuration management is a field that focuses on the efficient configuration, monitoring, and management of networks and their resources. The network configuration management not only comprises the physical and logical configuration of the equipment, but also covers the key aspects of monitoring the network performance, fault detection and recovery, safety management and the like. Through the fine management of network resources, the field aims to ensure the high efficiency and stability of data communication and ensure the reliability and the safety of network services. This is critical to support various applications in modern information technology environments, particularly in data centers, cloud computing environments, and large enterprise networks.
The informationized machine room monitoring and managing system is an integrated solution, and aims to optimize and ensure the running states of IT resources such as networks, servers, storage devices and the like in the machine room. The method mainly aims to ensure high efficiency, high availability and high safety of machine room operation through real-time monitoring, data analysis and automatic management means. The system can help an operation and maintenance team to discover and solve problems in time, and improves the stability and response speed of an information system, so that the core business operation of enterprises or organizations is supported, the system downtime is reduced, and the user satisfaction is improved.
The traditional system has a plurality of defects in real-time performance, dynamic adaptability and intelligent management. The assessment of the health condition of the equipment and the link quality is not accurate enough, so that the faults are difficult to effectively predict, and the complexity and the cost of network maintenance are increased. The fixed bandwidth allocation strategy can not adapt to the dynamic change of the network demand, especially in the peak period of network traffic, so that the key service performance is damaged, and the enterprise operation and user experience are affected. These limitations highlight the deficiencies of conventional systems in dealing with modern network environment challenges, ensuring network performance and quality of service.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an informationized machine room monitoring and management system.
In order to achieve the above purpose, the present invention adopts the following technical scheme: the informationized machine room monitoring and managing system comprises:
The network state monitoring module collects real-time information based on the real-time flow of the network equipment and the connection data, acquires running state information comprising interface rate and bandwidth utilization rate by inquiring a network equipment database, and marks a time stamp to generate a network state snapshot;
The topology dynamic updating module updates the network topology structure in real time based on the network state snapshot, adjusts element values in the adjacent matrix in real time by updating the connection state information among the network devices, and recalculates connectivity and path cost of the graph to generate a network topology graph;
the equipment health detection module evaluates the performance and failure rate of the equipment on the network topological graph, and identifies potential problem equipment by checking the state parameters of each equipment and comparing the state parameters with preset rules to generate equipment health state indexes;
The link quality analysis module is used for measuring delay and packet loss rate of multiple links in a network based on the equipment health status index, performing performance evaluation on each link by analyzing the measurement result, and generating link quality analysis data based on the performance identification link;
The path optimization strategy module uses the link quality analysis data to conduct transmission path optimization planning of the data streams, and generates a path optimization scheme by calculating the optimal transmission path of each data stream and referring to the link quality data as weight;
The bandwidth dynamic management module performs bandwidth allocation adjustment of multiple links in a network according to the path optimization scheme, and reserves critical bandwidth for critical applications and services by setting the maximum transmission rate of the data stream so as to generate a bandwidth management strategy;
the network adjustment implementation module monitors network traffic based on the bandwidth management strategy, establishes network connection, obtains the current bandwidth use condition, adjusts network configuration according to the bandwidth upper limit, verifies the validity of an adjustment scheme by using a test environment, and generates a network adjustment operation result.
As a further scheme of the invention, the network state snapshot comprises a device identifier, an interface rate and a bandwidth utilization rate, the network topology map specifically comprises a device connection relation, a link state and network connectivity, the device health state index comprises a CPU utilization rate, a memory utilization rate and a device restarting frequency, the link quality analysis data comprises a link delay, a packet loss rate and a link rating, the path optimization scheme comprises a data flow optimal path, an expected transmission efficiency and a path connectivity, the bandwidth management strategy comprises a key service bandwidth reservation, a non-key traffic bandwidth limitation and a dynamic bandwidth allocation, and the network adjustment operation specifically comprises a routing rule update, a bandwidth limitation adjustment and a test verification result.
As a further scheme of the invention, the network state monitoring module comprises an interface monitoring sub-module, a bandwidth analysis sub-module and a snapshot integration sub-module;
The interface monitoring sub-module establishes data monitoring service based on real-time flow of network equipment and connection data, captures network data packets in real time, analyzes data packet head information, extracts interface rate information and interface information, and generates interface rate information;
The bandwidth analysis submodule performs time sequence analysis based on the interface rate information, sets a threshold value of bandwidth utilization rate, marks data points exceeding the threshold value, calculates overall bandwidth utilization rate and obtains a bandwidth utilization analysis result;
The snapshot integration sub-module formats the time stamp based on the bandwidth utilization analysis result, resamples the time sequence, fills the missing value in the forward direction, time stamps the missing value by using the current time stamp, integrates the time sequence data and the time stamp data by using the data integration method, and generates a complete network state result to obtain the network state snapshot.
As a further scheme of the invention, the topology dynamic updating module comprises a network state monitoring sub-module, an adjacent matrix updating sub-module and a connectivity and path cost calculating sub-module;
the network state monitoring submodule monitors the connection state change of network equipment in real time by adopting a real-time network monitoring technology based on the network state snapshot, records the changed equipment and port states thereof and generates a network state change record;
the adjacent matrix updating submodule updates the adjacent matrix based on the network state change record to reflect the current connection state among the devices, adjusts the element value of the corresponding matrix and generates an updated adjacent matrix;
the connectivity and path cost calculation submodule calculates the shortest path and cost from the initial node to the target node by creating a graph object and adding edges and weights according to the adjacency matrix based on the updated adjacency matrix, and displays the calculation result in a graph mode to generate a network topology graph;
The network state monitoring technology adopts a formula
Wherein P' (k=k) is a probability of observing K times of state change in a given time period after considering a network complexity factor, e is a base number of natural logarithms, λ is a number of network state change events observed averagely in a unit time, t is an observation time length, K is an observed event number, C is a network connection number, S is a network service type number, a is a network activity intensity index, B is a network load balance index, and w1, w2, w3, w4 are weight coefficients.
As a further scheme of the invention, the equipment health detection module comprises a performance evaluation sub-module, a fault rate analysis sub-module, a problem equipment identification sub-module and a health state index generation sub-module;
the performance evaluation submodule analyzes historical data of each equipment state parameter based on a network topological graph, predicts future performance trend, compares the future performance trend with a preset performance threshold, identifies equipment with performance lower than expected, and generates a performance analysis result;
The fault rate analysis sub-module carries out fault prediction model training according to the historical fault record and the performance parameters of the equipment based on the performance analysis result, calculates the fault probability of each equipment, carries out risk equipment identification, and generates a fault probability evaluation result;
The problem equipment identification submodule classifies the problem equipment according to the fault probability and the performance analysis result of the equipment and the associated input characteristics based on the fault probability evaluation result, classifies the risk grades of the equipment, marks the risk grades and generates a risk grade distribution table;
and the health state index generation submodule carries out weighted calculation on the performance analysis result, the fault probability evaluation and the risk level of each device based on the risk level distribution table to obtain the health state score of each device, and carries out sequencing to generate the health state index of the device.
As a further scheme of the invention, the link quality analysis module comprises a delay packet loss measurement sub-module, a link performance analysis sub-module and a quality grading and grading sub-module;
The delay packet loss measurement submodule is used for carrying out delay and packet loss rate measurement on a plurality of links in a network by using Ping and Traceroute commands based on equipment health status indexes, and obtaining link delay packet loss data by sending an ICMP echo request and determining data packet path record delay;
The link performance analysis submodule performs performance analysis on the data by using Z-score standardization processing based on the link delay packet loss data, compares delay and packet loss conditions of differential links, evaluates the performance of each link and generates a link performance analysis result;
the quality grading archiving submodule performs quality grading on the link based on the link performance analysis result by constructing a decision tree model, takes the link performance data as input, sets the quality grade of the link according to the performance evaluation result, and archives the link according to the quality grade to generate link quality analysis data.
As a further scheme of the invention, the path optimization strategy module comprises a link quality analysis sub-module, a path planning sub-module and an optimization scheme generation sub-module;
The link quality analysis submodule calculates a multi-link quality score based on link quality analysis data in a network, comprehensively evaluates a plurality of indexes including delay, bandwidth and packet loss rate, synthesizes the plurality of indexes through a weighted average method, and generates a link quality scoring table;
The path planning submodule carries out shortest path search based on a link quality scoring table, uses Dijkstra algorithm as path cost according to the link quality scoring, captures an optimal transmission path for each data stream based on cost, and obtains an optimal path list;
The optimization scheme generation submodule performs optimization planning on the data flow transmission path based on the optimal path list, performs path distribution and adjustment according to the optimal path by analyzing the current network state and the data flow requirement, and obtains a path optimization scheme.
As a further scheme of the invention, the bandwidth dynamic management module comprises a path analysis sub-module, a bandwidth adjustment sub-module and a strategy application sub-module;
The path analysis submodule builds a network graph model based on a path optimization scheme, analyzes a network topological structure, traverses each path, selects the shortest path between two points by circularly traversing node combinations, acquires the performance index of a link on the path, evaluates the link performance of each path, selects an optimal performance path based on a total delay minimum principle, and generates an optimized path analysis result;
the bandwidth adjustment submodule performs network flow analysis based on the optimized path analysis result, adjusts bandwidth allocation according to current network flow and application priority, matches the requirements of key applications and services, and acquires a bandwidth allocation adjustment parameter table;
The strategy application submodule monitors network conditions based on the bandwidth allocation adjustment parameter table, dynamically adjusts the bandwidth allocation strategy according to the monitoring result and the bandwidth requirement of the application service, and generates a bandwidth management strategy.
As a further scheme of the invention, the network adjustment implementation module comprises a bandwidth monitoring sub-module, a connection establishment sub-module and an adjustment scheme testing sub-module;
The bandwidth monitoring sub-module captures and analyzes network flow data based on a bandwidth management strategy, records network bandwidth use conditions including time stamps, source and target IP, and data packet sizes, and generates bandwidth use detail data;
The connection establishment submodule analyzes network load conditions based on bandwidth use detail data, establishes optimized network connection, records a graph model of the connection and a shortest path selection algorithm, and generates network connection optimization detail conditions;
the adjustment scheme testing submodule sends ICMP data packets based on the network connection optimization detail condition, configures network equipment, sets routing table items, hop limit and timeout time, monitors network delay and packet loss rate by using a Ping tool, adjusts weight parameters and generates a network adjustment operation result;
The shortest path selection algorithm adopts a formula
D(v)＝min{D(v),D(u)+C(u,v)+α·L(u,v)+β·T(u)+γ·S(v)}
Wherein D (v) is the shortest distance from the source node to the node v, D (u) is the shortest distance from the source node to the node u, C (u, v) is the path cost from the node u to the node v, L (u, v) is the load balancing coefficient between the node u and the node v, T (u) is the flow processing capacity of the node u, S (v) is the security level of the node v, and alpha, beta and gamma are the weight coefficients.
Compared with the prior art, the invention has the advantages and positive effects that:
in the invention, through dynamic topology updating, real-time accurate reflection of the network structure becomes possible, and powerful support is provided for fault prediction and equipment maintenance. The link quality analysis and path optimization strategy not only improves the data transmission efficiency, but also realizes the maximization of resource utilization, and effectively reduces network delay and data loss. The bandwidth dynamic management and network adjustment implementation further improves the network adaptability and service quality, and ensures the stable operation of key applications and services.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system framework of the present invention;
FIG. 3 is a flow chart of a network status monitoring module according to the present invention;
FIG. 4 is a flow chart of a topology dynamic update module of the present invention;
FIG. 5 is a flow chart of the device health detection module of the present invention;
FIG. 6 is a flow chart of a link quality analysis module of the present invention;
FIG. 7 is a flow chart of a path optimization strategy module of the present invention;
fig. 8 is a flow chart of a bandwidth dynamic management module according to the present invention;
fig. 9 is a flow chart of a network adjustment implementation module according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1 to 2, the information machine room monitoring and management system includes:
The network state monitoring module collects real-time information based on the real-time flow of the network equipment and the connection data, acquires running state information comprising interface rate and bandwidth utilization rate by inquiring a network equipment database, and marks a time stamp to generate a network state snapshot;
The topology dynamic updating module updates the network topology structure in real time based on the network state snapshot, adjusts element values in the adjacent matrix in real time by updating the connection state information among the network devices, and recalculates connectivity and path cost of the graph to generate a network topology graph;
The equipment health detection module evaluates the performance and failure rate of the equipment on the network topological graph, and identifies potential problem equipment by checking the state parameters of each equipment and comparing the state parameters with preset rules to generate equipment health state indexes;
The link quality analysis module is used for measuring delay and packet loss rate of multiple links in a network based on equipment health status indexes, performing performance evaluation on each link by analyzing measurement results, and generating link quality analysis data based on performance identification links;
The path optimization strategy module uses the link quality analysis data to carry out the optimization planning of the transmission path of the data stream, and generates a path optimization scheme by calculating the optimal transmission path of each data stream and referring to the link quality data as weight;
The bandwidth dynamic management module performs bandwidth allocation adjustment of multiple links in a network according to a path optimization scheme, and reserves key bandwidth for key applications and services by setting the maximum transmission rate of a data stream so as to generate a bandwidth management strategy;
The network adjustment implementation module monitors network traffic based on the bandwidth management strategy, establishes network connection, obtains the current bandwidth use condition, adjusts network configuration according to the bandwidth upper limit, verifies the validity of an adjustment scheme by using the test environment, and generates a network adjustment operation result.
The network state snapshot comprises a device identifier, an interface rate and a bandwidth utilization rate, the network topology is specifically a device connection relation, a link state and network connectivity, the device health state index comprises a CPU utilization rate, a memory utilization rate and a device restarting frequency, the link quality analysis data comprises a link delay, a packet loss rate and a link rating, the path optimization scheme comprises a data flow optimal path, an expected transmission efficiency and a path connectivity, the bandwidth management strategy comprises key service bandwidth reservation, non-key traffic bandwidth limitation and dynamic bandwidth allocation, and the network adjustment operation is specifically a routing rule update, bandwidth limitation adjustment and test verification result.
Referring to fig. 3, the network state monitoring module includes an interface monitoring sub-module, a bandwidth analysis sub-module, and a snapshot integration sub-module;
the interface monitoring sub-module establishes data monitoring service based on the network equipment and the real-time flow of the connection data, captures the network data packet in real time, analyzes the packet head information, extracts interface rate information and interface information, and generates interface rate information;
The interface monitoring sub-module is based on real-time flow of network equipment and connection data, a network data capturing tool is adopted, a Wireshark is utilized to establish a data monitoring service, a network data packet is captured in real time through a capturing and filtering grammar tcp port 80or udp port 443, data packet head information is analyzed through-n parameters of the tcpdump tool, data packets of all interfaces are captured through-i any parameters, interface rate information and interface information are extracted through-v parameters, and interface rate information is generated.
The bandwidth analysis submodule performs time sequence analysis based on the interface rate information, sets a threshold value of bandwidth utilization rate, marks data points exceeding the threshold value, calculates overall bandwidth utilization rate, and obtains a bandwidth utilization analysis result;
the bandwidth analysis submodule adopts a time sequence analysis tool based on interface rate information, utilizes Pandas library to perform time sequence analysis, resamples interface rate data at 5-minute intervals by using a dataframe (' 5T ') and mean () method, calculates an average value, sets the threshold value of the bandwidth utilization ratio to be 75%, marks data points exceeding the threshold value by using dataframe ' query (' utilization >75 '), and calculates the overall bandwidth utilization ratio by using DATAFRAME [ ' utilization ' ]. Mean (), so as to obtain a bandwidth utilization analysis result.
The snapshot integration sub-module formats the time stamp based on the bandwidth utilization analysis result, resamples the time sequence, simultaneously fills the missing value in the forward direction, time stamps the missing value by using the current time stamp, integrates the time sequence data with the time stamp data by using the data integration method, and generates a complete network state result to obtain a network state snapshot;
The snapshot integrating sub-module adopts a data integrating method based on a bandwidth utilization analysis result, utilizes a datetime module of Python to format a time stamp, performs time stamp formatting through datatime.strftime ('%Y-%m-%d%H%M%S'), performs time sequence resampling, simultaneously performs forward filling on missing values, uses a dataframe.filena (method= 'ffill') of Pandas to operate, performs time marking by utilizing a current time stamp, integrates time sequence data and time stamp data through a dataframe.merge () method, and generates a complete network state result to obtain a network state snapshot.
Referring to fig. 4, the topology dynamic update module includes a network status monitor sub-module, an adjacency matrix update sub-module, and a connectivity and path cost calculation sub-module;
The network state monitoring submodule monitors the connection state change of network equipment in real time by adopting a real-time network monitoring technology based on the network state snapshot, records the changed equipment and port states thereof and generates a network state change record;
The network state monitoring sub-module adopts a real-time network monitoring technology based on the network state snapshot, utilizes Nagios to monitor the connection state change of the network equipment in real time, and defines a service check command check_tcp-! -p 80 and check_udp-! -p 443 records the status of the changed device and its ports, automatically records the status change using Nagios event handler event_handler script, generating a network status change record.
The network state monitoring technology adopts the formula
Wherein P' (k=k) is a probability of observing K times of state change in a given time period after considering a network complexity factor, e is a base number of natural logarithms, λ is a number of network state change events observed averagely in a unit time, t is an observation time length, K is an observed event number, C is a network connection number, S is a network service type number, a is a network activity intensity index, B is a network load balance index, and w1, w2, w3, w4 are weight coefficients.
Four new parameters of a network connection number C, a network service type number S, a network activity intensity index A and a network load balance index B are introduced, so that the probability of network state change is reflected more accurately, the complexity of the network and the influence of the network on state change monitoring are considered, wherein C is the total number of all active connections in the network, S is the total number of service types provided on the network, A is the comprehensive intensity index of network activities (such as data transmission quantity, request frequency and the like) in a given time, B is the balance degree of network loads and is used for representing the uniformity of network load distribution, and w1, w2, w3 and w4 are weight coefficients and are used for adjusting the influence of all factors on final probability calculation.
Based on the network state change record, the adjacent matrix updating submodule updates the adjacent matrix to reflect the current connection state between the devices, adjusts the corresponding matrix element values and generates an updated adjacent matrix;
Based on the network state change record, the adjacent matrix updating sub-module adopts a matrix operation method, updates the adjacent matrix by using NumPy library to reflect the current connection state between the devices, adjusts the corresponding matrix element value by using a numpy.where (condition, x, y) method, sets the corresponding value of the connected device as 1, sets the corresponding value of the unconnected device as 0, and generates the updated adjacent matrix.
The connectivity and path cost calculation submodule calculates the shortest path and cost from the initial node to the target node by creating a graph object and adding edges and weights according to the adjacency matrix based on the updated adjacency matrix, and displays the calculation result in a graph mode to generate a network topological graph;
The connectivity and path cost calculation submodule adopts a graph theory analysis method to create a graph object by utilizing NetworkX based on the updated adjacency matrix, adds edges and weights according to the adjacency matrix through network x, from_ numpy _matrix, calculates the shortest path and cost from a starting node to a target node by adopting Dijkstra algorithm network x, dijkstra_path (G, source, target), and displays the calculation result in a graph mode by utilizing Matplotlib library matplotlib.
Referring to fig. 5, the device health detection module includes a performance evaluation sub-module, a failure rate analysis sub-module, a problem device identification sub-module, and a health status index generation sub-module;
The performance evaluation sub-module analyzes historical data of each equipment state parameter based on the network topological graph, predicts future performance trend, compares the future performance trend with a preset performance threshold, identifies equipment with performance lower than expected, and generates a performance analysis result;
The performance evaluation submodule is based on a network topological graph, adopts a time sequence analysis method, utilizes an ARIMA model to analyze historical data of state parameters of each device, predicts future performance trend by determining parameters p=2, d=1 and q=2 of the ARIMA model to fit the time sequence data, compares the future performance trend with a preset performance threshold, and uses conditions to judge if performance < threshold to identify devices with performance lower than expected, so as to generate a performance analysis result.
The fault rate analysis sub-module carries out fault prediction model training according to the historical fault record and the performance parameters of the equipment based on the performance analysis result, calculates the fault probability of each equipment, carries out risk equipment identification, and generates a fault probability assessment result;
The fault rate analysis submodule adopts a machine learning training method based on a performance analysis result, performs the history fault record of the equipment and the fault prediction model training of the performance parameters by utilizing a random forest algorithm, constructs a model by setting parameters n_ estimators =100 and max_depth=10, calculates the fault probability of each equipment, performs risk equipment identification, and performs High risk equipment marking by using dataframe [ DATAFRAME [ 'fault_probability' ] >0.5 and 'risk' ] = 'High', so as to generate a fault probability evaluation result.
The problem equipment identification submodule classifies the problem equipment according to the fault probability and the performance analysis result of the equipment and the associated input characteristics based on the fault probability evaluation result, classifies the risk grades of the equipment, marks the risk grades and generates a risk grade distribution table;
Based on the fault probability evaluation result, the problem equipment identification submodule adopts a classification algorithm, performs problem equipment classification according to the fault probability and the performance analysis result of the equipment and the associated input characteristics by using a K-nearest neighbor (KNN) algorithm, classifies the risk grades of the equipment by setting n_neighbors=5 and a distance weight weights = 'distance', and marks the risk grades to generate a risk grade distribution table.
The health state index generation submodule carries out weighted calculation on the performance analysis result, the fault probability evaluation and the risk level of each device based on the risk level distribution table to obtain the health state score of each device, and carries out sequencing to generate the health state index of the device;
the health state index generation submodule adopts a weighted calculation method based on a risk level distribution table, calculates the performance analysis result, the fault probability evaluation and the score of the risk level of each device by using weighted average, and sets weights
Performance_weight=0.5, fault_probability_weight=0.3, and risk_level_weight=0.2, and a health state score of each device is obtained and sequenced to generate a device health state index.
Referring to fig. 6, the link quality analysis module includes a delay packet loss measurement sub-module, a link performance analysis sub-module, and a quality grading and grading sub-module;
The delay packet loss measurement submodule is used for carrying out delay and packet loss rate measurement on a plurality of links in a network by using Ping and Traceroute commands based on the equipment health status index, and obtaining link delay packet loss data by sending an ICMP echo request and determining the data packet path record delay;
The delay packet loss measurement submodule adopts a network diagnosis command to carry out delay measurement based on equipment health status indexes, sends 4 ICMP echo requests through a set parameter-c 4, determines a data packet path through a Traceroute command, sets a maximum TTL value to 30 through a parameter-m 30, records delay, and analyzes link delay and packet loss rate through the output of the Ping and Traceroute commands to obtain link delay packet loss data.
The link performance analysis submodule performs performance analysis on the data by using Z-score standardization processing based on the link delay packet loss data, compares the delay and packet loss conditions of the differential links, evaluates the performance of each link and generates a link performance analysis result;
the link performance analysis submodule carries out performance analysis on the data by utilizing Z score standardization processing based on the link delay packet loss data by adopting a statistical analysis method, and generates a link performance analysis result by calculating a formula Z= (X-mu)/sigma, wherein X is original data, mu is an average value, sigma is a standard deviation, comparing delay and packet loss conditions of differential links and evaluating the performance of each link.
The quality grading archiving submodule performs quality grading on the link by constructing a decision tree model based on the link performance analysis result, takes the link performance data as input, sets the quality grade of the link according to the performance evaluation result, and archives the link according to the quality grade to generate link quality analysis data;
The quality classification archiving submodule adopts a decision tree model based on a link performance analysis result, builds the decision tree model by utilizing scikit-learn library to carry out link quality classification, sets the dividing quality standard of the decision tree as a base coefficient and the maximum depth as 3 by parameter criterion= 'gini', max_depth=3, takes the link performance data as input, sets the quality class of the link according to the performance evaluation result, and archives the link according to the quality class to generate link quality analysis data.
Referring to fig. 7, the path optimization policy module includes a link quality analysis sub-module, a path planning sub-module, and an optimization scheme generation sub-module;
The link quality analysis submodule calculates a multi-link quality score based on link quality analysis data in a network, comprehensively evaluates a plurality of indexes including delay, bandwidth and packet loss rate, synthesizes the plurality of indexes through a weighted average method, and generates a link quality scoring table;
The link quality analysis submodule calculates a multi-link quality score based on link quality analysis data in a network, comprehensively evaluates a plurality of indexes including delay, bandwidth and packet loss rate, adopts a weighted average method, utilizes the weight of delay as 0.4, the weight of bandwidth as 0.3 and the weight of packet loss rate as 0.3, synthesizes the plurality of indexes through the formula score = delay 0.4+ bandwidth 0.3+ packet loss rate 0.3, and generates a link quality scoring table.
The path planning submodule carries out shortest path search based on a link quality scoring table, uses Dijkstra algorithm as path cost according to the link quality score, captures an optimal transmission path for each data stream based on cost, and obtains an optimal path list;
The path planning submodule performs shortest path search based on a link quality scoring table, adopts Dijkstra algorithm, captures an optimal transmission path for each data stream based on cost according to a link quality score as path cost, and executes Dijkstra_path (graph, source, target, weight= 'weight') by setting a starting point and an ending point and defining path cost through the link quality score to obtain an optimal path list.
The optimization scheme generation submodule performs optimization planning on a data flow transmission path based on the optimal path list, performs path allocation and adjustment according to the optimal path by analyzing the current network state and the data flow requirement, and acquires a path optimization scheme;
The optimization scheme generation submodule performs optimization planning on a data flow transmission path based on an optimal path list, performs path distribution and adjustment according to the optimal path by analyzing the current network state and the data flow requirement and adopting a path adjustment algorithm, considers the load condition of each node in the network, sets the adjustment parameters to be 75% including the node load threshold, searches for an alternative path when the node load exceeds the threshold, and performs dynamic adjustment on the path based on the alternative path to obtain a path optimization scheme.
Referring to fig. 8, the bandwidth dynamic management module includes a path analysis sub-module, a bandwidth adjustment sub-module, and a policy application sub-module;
The path analysis submodule builds a network graph model based on a path optimization scheme, analyzes a network topological structure, traverses each path, selects the shortest path between two points by circularly traversing node combinations, acquires the performance index of a link on the path, evaluates the link performance of each path, selects an optimal performance path based on a total delay minimum principle, and generates an optimized path analysis result;
The path analysis submodule builds a network graph model based on a path optimization scheme, analyzes a network topological structure, traverses each path by using NetworkX libraries by adopting a graph theory analysis method, and traverses node combinations by circulation
Network x.all_ pairs _ shortest _path (G), selecting the shortest path between two points, acquiring the performance index of the link on the path, calculating the total delay of the path by adopting a custom function, evaluating the link performance of each path, selecting the optimal performance path based on the minimum principle of total delay min (total_delay_ paths), and generating an optimized path analysis result.
The bandwidth adjustment submodule analyzes the network flow based on the analysis result of the optimized path, adjusts bandwidth allocation according to the current network flow and the application priority, matches the requirements of key applications and services, and acquires a bandwidth allocation adjustment parameter table;
The bandwidth adjustment submodule analyzes network flow based on an optimized path analysis result, adopts a flow monitoring technology, utilizes an SNMP protocol to collect network flow data, adjusts bandwidth allocation according to current network flow and application priority through a self-defined algorithm, adopts if application_priority= 'high' logic to match the requirements of key applications and services, acquires a bandwidth allocation adjustment parameter table, and generates the bandwidth allocation adjustment parameter table.
The strategy application sub-module monitors network conditions based on the bandwidth allocation adjustment parameter table, dynamically adjusts the bandwidth allocation strategy according to the monitoring result and the bandwidth requirement of the application service, and generates a bandwidth management strategy;
the policy application submodule monitors network conditions based on the bandwidth allocation adjustment parameter table, adopts a dynamic bandwidth management policy, utilizes a Python script to combine with an SNMP protocol to monitor network conditions in real time, dynamically adjusts the bandwidth allocation policy through an adjust_bandwidth () function according to a monitoring result and the bandwidth requirement of application service, and generates the bandwidth management policy.
Referring to fig. 9, the network adjustment implementation module includes a bandwidth monitoring sub-module, a connection establishment sub-module, and an adjustment scheme testing sub-module;
The bandwidth monitoring sub-module captures and analyzes network flow data based on a bandwidth management strategy, records network bandwidth use conditions including time stamps, source and target IP (Internet protocol) and data packet sizes, and generates bandwidth use detail data;
the bandwidth monitoring submodule captures and analyzes network traffic data based on a bandwidth management strategy, a traffic monitoring tool is adopted, network traffic is captured by utilizing a Wireshark, and the network traffic is filtered through a filter ip.src= { source_ip } and ip.dst=
{ Destination_ip } records network bandwidth usage, including timestamp frame.time, source and destination IPip.src, ip.dst, packet size frame.len, and generates bandwidth usage details data.
The connection establishment submodule analyzes the network load condition based on the bandwidth use detail data, establishes optimized network connection, records a graph model of the connection and a shortest path selection algorithm, and generates network connection optimization detail condition;
The connection establishment submodule analyzes network load conditions based on bandwidth use detail data, establishes optimized network connection by using a Python script and NetworkX library by adopting a network analysis method, records a graph model and shortest path selection of the connection by using a network x.short_path (G, weight= 'delay'), and generates network connection optimization detail conditions based on network traffic and a shortest path algorithm.
The shortest path selection algorithm adopts a formula
D(v)＝min{D(v),D(u)+C(u,v)+α·L(u,v)+β·T(u)+γ·S(v)}
Wherein D (v) is the shortest distance from the source node to the node v, D (u) is the shortest distance from the source node to the node u, C (u, v) is the path cost from the node u to the node v, L (u, v) is the load balancing coefficient between the node u and the node v, T (u) is the flow processing capacity of the node u, S (v) is the security level of the node v, and alpha, beta and gamma are the weight coefficients.
Firstly, original path cost C (u, v) is calculated to reflect the load or delay of direct connection between two nodes, then L (u, v) is introduced to consider the load balance of a network, so that the path selection is not only based on the shortest distance, but also considers the overall load condition of the network, T (u) represents the processing capacity of the nodes, the selected path is ensured to effectively process the passing flow, S (v) ensures that the path selection considers the network safety, the nodes with lower safety level are prevented from being passed through, alpha, beta and gamma are used as weight coefficients, the adjustment is carried out according to the actual requirement of the network, the values of the coefficients are determined according to the historical load data and the safety requirement of the network, and the influence of the path cost, the load balance, the processing capacity and the safety is balanced.
The adjustment scheme testing submodule sends ICMP data packets based on the network connection optimization detail condition, configures network equipment, sets routing table items, hop limit and overtime time, monitors network delay and packet loss rate by using a Ping tool, adjusts weight parameters and generates a network adjustment operation result;
The adjustment scheme test sub-module sends ICMP data packets based on the network connection optimization detail condition, configures network equipment, adopts a network configuration command, and utilizes ping-c { count } -W { timeout } { destination_ip } and traceroute } { to optimize the detail condition of the network connection
{ Destination_ip } monitors network delay and packet loss rate, dynamically adjusts route entry route add, hop limit-m { max_ttl }, timeout time-W { timeout }, adjusts weight parameters to optimize network performance, and generates network adjustment operation results.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (9)
1. Informationized computer lab control and management system, its characterized in that: the system comprises:
The network state monitoring module collects real-time information based on the real-time flow of the network equipment and the connection data, acquires running state information comprising interface rate and bandwidth utilization rate by inquiring a network equipment database, and marks a time stamp to generate a network state snapshot;
The topology dynamic updating module updates the network topology structure in real time based on the network state snapshot, adjusts element values in the adjacent matrix in real time by updating the connection state information among the network devices, and recalculates connectivity and path cost of the graph to generate a network topology graph;
the equipment health detection module evaluates the performance and failure rate of the equipment on the network topological graph, and identifies potential problem equipment by checking the state parameters of each equipment and comparing the state parameters with preset rules to generate equipment health state indexes;
The link quality analysis module is used for measuring delay and packet loss rate of multiple links in a network based on the equipment health status index, performing performance evaluation on each link by analyzing the measurement result, and generating link quality analysis data based on the performance identification link;
The path optimization strategy module uses the link quality analysis data to conduct transmission path optimization planning of the data streams, and generates a path optimization scheme by calculating the optimal transmission path of each data stream and referring to the link quality data as weight;
The bandwidth dynamic management module performs bandwidth allocation adjustment of multiple links in a network according to the path optimization scheme, and reserves critical bandwidth for critical applications and services by setting the maximum transmission rate of the data stream so as to generate a bandwidth management strategy;
the network adjustment implementation module monitors network traffic based on the bandwidth management strategy, establishes network connection, obtains the current bandwidth use condition, adjusts network configuration according to the bandwidth upper limit, verifies the validity of an adjustment scheme by using a test environment, and generates a network adjustment operation result.
2. The informationized room monitoring and management system of claim 1, wherein: the network state snapshot comprises a device identifier, an interface rate and a bandwidth utilization rate, the network topology is specifically a device connection relation, a link state and network connectivity, the device health state index comprises a CPU utilization rate, a memory utilization rate and a device restarting frequency, the link quality analysis data comprises a link delay, a packet loss rate and a link rating, the path optimization scheme comprises a data flow optimal path, an expected transmission efficiency and a path connectivity, the bandwidth management strategy comprises a key service bandwidth reservation, a non-key traffic bandwidth limitation and a dynamic bandwidth allocation, and the network adjustment operation is specifically a routing rule update, a bandwidth limitation adjustment and a test verification result.
3. The informationized room monitoring and management system of claim 1, wherein: the network state monitoring module comprises an interface monitoring sub-module, a bandwidth analysis sub-module and a snapshot integration sub-module;
The interface monitoring sub-module establishes data monitoring service based on real-time flow of network equipment and connection data, captures network data packets in real time, analyzes data packet head information, extracts interface rate information and interface information, and generates interface rate information;
The bandwidth analysis submodule performs time sequence analysis based on the interface rate information, sets a threshold value of bandwidth utilization rate, marks data points exceeding the threshold value, calculates overall bandwidth utilization rate and obtains a bandwidth utilization analysis result;
The snapshot integration sub-module formats the time stamp based on the bandwidth utilization analysis result, resamples the time sequence, fills the missing value in the forward direction, time stamps the missing value by using the current time stamp, integrates the time sequence data and the time stamp data by using the data integration method, and generates a complete network state result to obtain the network state snapshot.
4. The informationized room monitoring and management system of claim 1, wherein: the topology dynamic updating module comprises a network state monitoring sub-module, an adjacent matrix updating sub-module and a connectivity and path cost calculating sub-module;
the network state monitoring submodule monitors the connection state change of network equipment in real time by adopting a real-time network monitoring technology based on the network state snapshot, records the changed equipment and port states thereof and generates a network state change record;
the adjacent matrix updating submodule updates the adjacent matrix based on the network state change record to reflect the current connection state among the devices, adjusts the element value of the corresponding matrix and generates an updated adjacent matrix;
the connectivity and path cost calculation submodule calculates the shortest path and cost from the initial node to the target node by creating a graph object and adding edges and weights according to the adjacency matrix based on the updated adjacency matrix, and displays the calculation result in a graph mode to generate a network topology graph;
The network state monitoring technology adopts a formula
Wherein P' (k=k) is a probability of observing K times of state change in a given time period after considering a network complexity factor, e is a base number of natural logarithms, λ is a number of network state change events observed averagely in a unit time, t is an observation time length, K is an observed event number, C is a network connection number, S is a network service type number, a is a network activity intensity index, B is a network load balance index, and w1, w2, w3, w4 are weight coefficients.
5. The informationized room monitoring and management system of claim 1, wherein: the equipment health detection module comprises a performance evaluation sub-module, a fault rate analysis sub-module, a problem equipment identification sub-module and a health state index generation sub-module;
the performance evaluation submodule analyzes historical data of each equipment state parameter based on a network topological graph, predicts future performance trend, compares the future performance trend with a preset performance threshold, identifies equipment with performance lower than expected, and generates a performance analysis result;
The fault rate analysis sub-module carries out fault prediction model training according to the historical fault record and the performance parameters of the equipment based on the performance analysis result, calculates the fault probability of each equipment, carries out risk equipment identification, and generates a fault probability evaluation result;
The problem equipment identification submodule classifies the problem equipment according to the fault probability and the performance analysis result of the equipment and the associated input characteristics based on the fault probability evaluation result, classifies the risk grades of the equipment, marks the risk grades and generates a risk grade distribution table;
and the health state index generation submodule carries out weighted calculation on the performance analysis result, the fault probability evaluation and the risk level of each device based on the risk level distribution table to obtain the health state score of each device, and carries out sequencing to generate the health state index of the device.
6. The informationized room monitoring and management system of claim 1, wherein: the link quality analysis module comprises a delay packet loss measurement sub-module, a link performance analysis sub-module and a quality grading and grading sub-module;
The delay packet loss measurement submodule is used for carrying out delay and packet loss rate measurement on a plurality of links in a network by using Ping and Traceroute commands based on equipment health status indexes, and obtaining link delay packet loss data by sending an ICMP echo request and determining data packet path record delay;
The link performance analysis submodule performs performance analysis on the data by using Z-score standardization processing based on the link delay packet loss data, compares delay and packet loss conditions of differential links, evaluates the performance of each link and generates a link performance analysis result;
the quality grading archiving submodule performs quality grading on the link based on the link performance analysis result by constructing a decision tree model, takes the link performance data as input, sets the quality grade of the link according to the performance evaluation result, and archives the link according to the quality grade to generate link quality analysis data.
7. The informationized room monitoring and management system of claim 1, wherein: the path optimization strategy module comprises a link quality analysis sub-module, a path planning sub-module and an optimization scheme generation sub-module;
The link quality analysis submodule calculates a multi-link quality score based on link quality analysis data in a network, comprehensively evaluates a plurality of indexes including delay, bandwidth and packet loss rate, synthesizes the plurality of indexes through a weighted average method, and generates a link quality scoring table;
The path planning submodule carries out shortest path search based on a link quality scoring table, uses Dijkstra algorithm as path cost according to the link quality scoring, captures an optimal transmission path for each data stream based on cost, and obtains an optimal path list;
The optimization scheme generation submodule performs optimization planning on the data flow transmission path based on the optimal path list, performs path distribution and adjustment according to the optimal path by analyzing the current network state and the data flow requirement, and obtains a path optimization scheme.
8. The informationized room monitoring and management system of claim 1, wherein: the bandwidth dynamic management module comprises a path analysis sub-module, a bandwidth adjustment sub-module and a strategy application sub-module;
The path analysis submodule builds a network graph model based on a path optimization scheme, analyzes a network topological structure, traverses each path, selects the shortest path between two points by circularly traversing node combinations, acquires the performance index of a link on the path, evaluates the link performance of each path, selects an optimal performance path based on a total delay minimum principle, and generates an optimized path analysis result;
the bandwidth adjustment submodule performs network flow analysis based on the optimized path analysis result, adjusts bandwidth allocation according to current network flow and application priority, matches the requirements of key applications and services, and acquires a bandwidth allocation adjustment parameter table;
The strategy application submodule monitors network conditions based on the bandwidth allocation adjustment parameter table, dynamically adjusts the bandwidth allocation strategy according to the monitoring result and the bandwidth requirement of the application service, and generates a bandwidth management strategy.
9. The informationized room monitoring and management system of claim 1, wherein: the network adjustment implementation module comprises a bandwidth monitoring sub-module, a connection establishment sub-module and an adjustment scheme testing sub-module;
The bandwidth monitoring sub-module captures and analyzes network flow data based on a bandwidth management strategy, records network bandwidth use conditions including time stamps, source and target IP, and data packet sizes, and generates bandwidth use detail data;
The connection establishment submodule analyzes network load conditions based on bandwidth use detail data, establishes optimized network connection, records a graph model of the connection and a shortest path selection algorithm, and generates network connection optimization detail conditions;
the adjustment scheme testing submodule sends ICMP data packets based on the network connection optimization detail condition, configures network equipment, sets routing table items, hop limit and timeout time, monitors network delay and packet loss rate by using a Ping tool, adjusts weight parameters and generates a network adjustment operation result;
The shortest path selection algorithm adopts a formula
D(v)＝min{D(v),D(u)+C(u,v)+α·L(u,v)+β·T(u)+γ·S(v)}
Wherein D (v) is the shortest distance from the source node to the node v, D (u) is the shortest distance from the source node to the node u, C (u, v) is the path cost from the node u to the node v, L (u, v) is the load balancing coefficient between the node u and the node v, T (u) is the flow processing capacity of the node u, S (v) is the security level of the node v, and alpha, beta and gamma are the weight coefficients.
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