CN116781529A - Network planning system and method based on big data analysis - Google Patents
Network planning system and method based on big data analysis Download PDFInfo
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Abstract
The invention relates to the technical field of network planning, which is used for solving the problem that the existing network planning mode cannot accurately judge the network service demand state, so that accurate guidance cannot be provided for the network construction; the system and the method for planning the network based on big data analysis comprise a data acquisition module, a network service demand analysis module, a network topology analysis module, a network transmission performance analysis module, a network security state analysis module, a cloud storage library and a display terminal. According to the invention, through a data analysis mode, the comprehensive auxiliary judgment of the network planning state is carried out on the service demand level, the topology structure construction level, the transmission performance and the safety performance level, and a powerful data support is provided for constructing a reasonable network environment.
Description
Technical Field
The invention relates to the technical field of network planning, in particular to a network planning system and method based on big data analysis.
Background
Network planning refers to a process of designing and deploying a network system according to network topology, hardware devices, software applications, security protection, and other factors in order to meet specific service requirements and targets.
With the rapid advance of network technology, network planning has become an indispensable step in network engineering construction, and whether the network is available is determined by the quality of network planning. It is therefore important to define the state of the network planning.
However, in the existing network planning mode, accurate judgment on the network service demand state cannot be achieved, so that accurate guidance cannot be provided for the network construction;
and the accurate analysis of the transmission performance and the security level of the network planning is difficult to realize, so that the security and the stability of the later network cannot be ensured.
Disclosure of Invention
The invention aims to solve the problem that the existing network planning method cannot accurately judge the network service demand state, so that accurate guidance cannot be provided for the network construction; the method is characterized in that the network planning system comprises a network planning system, a network planning system and a network planning method, wherein the network planning system comprises a network planning system, a network planning system and a network planning system, wherein the network planning system comprises a network planning system, a topology structure construction layer, a network planning system and a network planning system.
The aim of the invention can be achieved by the following technical scheme: the network planning system based on big data analysis comprises a data acquisition module, a network service demand analysis module, a network topology analysis module, a network transmission performance analysis module, a network security state analysis module, a cloud storage library and a display terminal;
the data acquisition module is used for acquiring service demand information, topological structure information, transmission performance information and safety state information of a target network in a network planning process and respectively transmitting the service demand information, the topological structure information, the transmission performance information and the safety state information to the network service demand analysis module, the network topological structure analysis module, the network transmission performance analysis module and the network safety state analysis module;
the network service demand analysis module is used for monitoring service demand information of the target network, so as to analyze the demand state of the target network;
the network topology analysis module is used for monitoring network topology information of the target network, so that the topology state of the target network is analyzed;
the network transmission performance analysis module is used for monitoring the transmission performance information of the target network, so as to analyze the transmission performance state of the target network;
the network security state analysis module is used for monitoring security state information of the target network, so that the security state of the target network is analyzed;
the cloud storage is used for storing a topological structure type table of the target network, storing a transmission performance grade grading table of the target network and storing a security performance grade grading table of the target network.
Preferably, the monitoring and analyzing the service requirement information of the target network specifically includes the following steps:
acquiring bandwidth requirement value, delay requirement value and service data volume in service requirement information of target network, respectively calibrating the bandwidth requirement value, delay requirement value and service data volume as dk, sy and s l, performing formulated analysis, and according to a set formulaObtaining a business demand coefficient bdc of a target network, wherein lambda 1, lambda 2 and lambda 3 are respectively weight factor coefficients of a bandwidth demand value, a time delay demand value and a business data volume, and lambda 1, lambda 2 and lambda 3 are natural numbers larger than 0;
the bandwidth requirement value refers to a data value of a bandwidth size required by a network service when the network service is running, and the delay requirement value is used for representing a data value of a maximum delay limit allowed in the operation of a target network;
setting a first demand comparison interval, a second demand comparison interval and a third demand comparison interval of service demand coefficients of a target network, substituting the service demand coefficients of the target network into the preset first demand comparison interval, second demand comparison interval and third demand comparison interval, and comparing and analyzing;
when the business demand coefficient of the target network is in a preset first demand comparison interval, a low-demand business signal is generated, when the business demand coefficient of the target network is in a preset second demand comparison interval, a medium-demand business signal is generated, and when the business demand coefficient of the target network is in a preset third demand comparison interval, a high-demand business signal is generated.
Preferably, the monitoring of the topology information of the target network includes the following specific monitoring process:
obtaining the number of devices and the total number of cables to be connected with a target network, calibrating the number of devices and the total number of cables as i and d l respectively, carrying out normalization analysis on the number of devices and the total number of cables, and obtaining the structural coefficient td of the target network according to a set formula td=mu 1*i +mu 2 d l, wherein mu 1 and mu 2 are normalization factor coefficients of the number of devices and the total number of cables respectively, and mu 1 and mu 2 are natural numbers larger than 0;
based on the number of devices to be connected by the target network, obtaining the bandwidth magnitude, delay magnitude, throughput and encryption level in the performance parameters of each target device, and calibrating the bandwidth magnitude, delay magnitude, throughput and encryption level as bv respectively i 、dv i 、tc i And sg (g) i And carrying out formulated analysis on the sample according to a set formulaThereby obtaining the performance feedback coefficient fkx of each target device i Where i=1, 2,3 … … n, and i represents the number of target devices, γ1, γ2, γ3, and γ4 are correction factor coefficients of bandwidth magnitude, delay magnitude, throughput, and encryption level, respectively, and γ1, γ2, γ3, and γ4 are natural numbers greater than 0;
average analysis is carried out on the performance feedback coefficients of all target devices, and the formula is used for the average analysisObtaining an average of all devices corresponding to the target network topologyCoefficient of performance feedback fkx * 。
Preferably, the analyzing the network topology status of the target network includes the following specific steps:
obtaining a structural coefficient, an average performance feedback coefficient, the number of nodes and a construction cost value in topology structure information of a target network, carrying out formulated analysis on the structural coefficient, the average performance feedback coefficient, the number of nodes and the construction cost value, and carrying out formulated analysis according to a set formula tpx=ρ1×td+ρ2× fkx * Obtaining a topological structure discrimination coefficient tpx of the target network by +ρ3×ps+ρ4× cb, wherein ps is expressed as the node number, cb is expressed as a construction cost value, ρ1, ρ2, ρ3 and ρ4 are respectively a structure coefficient, an average performance feedback coefficient, the node number and an error factor coefficient of the construction cost value, and ρ1, ρ2, ρ3 and ρ4 are natural numbers larger than 0;
and comparing and matching the topology structure discrimination coefficients of the target networks with a topology structure type table stored in a database to obtain corresponding network topology structures, wherein each topology structure discrimination coefficient of the target network corresponds to one network topology structure.
Preferably, the monitoring of the transmission performance information of the target network specifically includes the following steps:
equally dividing a period of time into j time points, and j=1, 2,3 … … m;
capturing the transmission rate of the target network for a period of time, and calculating the standard deviation of the transmission rate according to the formulaObtaining a transmission rate fluctuation value sigma of the target network, wherein csl j Represents the jth time data point, csl * Is the average of all data, i.e. the average of the transmission rate over a period of time, m is the size of all data sets;
respectively recording the data packet quantity sent by the target network and the received data packet quantity, respectively recording the data packet quantity as fmb and smb, performing formula analysis on the two data, and according to a set formulaAnd obtaining a data transmission packet loss rate ut of the target network, wherein t represents the transmission duration.
Preferably, the analyzing the transmission performance state of the target network specifically includes the following steps:
acquiring a transmission rate fluctuation value, a data transmission packet loss rate and a data transmission rate in transmission performance information of a target network, carrying out formulation analysis on three data, and carrying out formulation analysis according to a set formulaObtaining a transmission performance index Nc of a target network, wherein l v represents the data transmission rate of the target network, and delta 1, delta 2 and delta 3 are respectively a transmission rate fluctuation value, a data transmission packet loss rate and a data transmission rate proportion factor coefficient;
and comparing and matching the transmission performance indexes of the target networks with a transmission performance grade grading table stored in a database, thereby obtaining corresponding transmission performance grades of the target networks, wherein each transmission performance index of the target networks corresponds to one transmission performance grade.
Preferably, the monitoring and the analyzing of the security status information of the target network specifically include the following steps:
acquiring a user identity authentication mode value, an emergency response alternative plan value and an encryption key length value in security state information of a target network, calibrating the user identity authentication mode value, the emergency response alternative plan value and the encryption key length value as s1, s2 and s3 respectively, comprehensively analyzing the user identity authentication mode value, the emergency response alternative plan value and the encryption key length value, and obtaining a security performance coefficient sty of the target network according to a set formula sty=s1+s2+ zhy ×s3, wherein zhy is a conversion factor coefficient of the encryption key length value, and zhy is a natural number larger than 0;
and comparing and matching the security performance coefficient of the target network with a security performance level grading table stored in a database, thereby obtaining the corresponding security performance level of the target network, wherein the security performance coefficient of each target network corresponds to one security performance level.
The network planning method based on big data analysis comprises the following steps:
step one: acquiring service demand information, topology structure information, transmission performance information and safety state information of a target network in a network planning process;
step two: monitoring service demand information of a target network, analyzing the demand state of the target network, and sending the obtained service demand state judgment result to a display terminal for feedback description;
step three: monitoring the topology structure information of the target network, analyzing the topology structure state of the target network, and sending the obtained network topology structure judgment result to a display terminal for feedback description;
step four: monitoring transmission performance information of a target network, analyzing the transmission performance state of the target network, and sending an obtained transmission performance grade judging result to a display terminal for feedback description;
step five: and monitoring the safety state information of the target network, analyzing the safety state of the target network, and sending the obtained safety performance grade judging result to a display terminal for feedback description.
The invention has the beneficial effects that:
the business demand level of the target network is defined by means of data calibration, formula calculation and substitution analysis of the gradient interval, and a foundation is laid for realizing reasonable network planning;
the network topology information of the target network is determined by means of normalization analysis and mean analysis, the topology state of the target network is analyzed, and the planning state of the target network is judged and analyzed from the topology layer by means of formula analysis and database comparison and matching analysis;
the transmission performance information of the target network is defined by adopting the modes of time equivalent setting, standard deviation calculation and data duty ratio analysis, and based on the transmission performance information, the transmission performance state of the target network is clearly judged and analyzed by adopting the modes of comprehensive analysis and data substitution comparison analysis;
auxiliary analysis is performed on the planning mode of the target network from the network security performance level through the data physical quantity conversion and data superposition modes, and a foundation is laid for realizing reasonable planning and construction of the network;
the comprehensive auxiliary judgment of the network planning state is carried out on the service demand level, the topology structure construction level, the transmission performance and the security performance level, and a powerful data support is provided for constructing a reasonable network environment.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a system block diagram of the present invention;
fig. 2 is a system flow diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1, the present invention is a network planning system based on big data analysis, comprising: the system comprises a data acquisition module, a network service demand analysis module, a network topology analysis module, a network transmission performance analysis module, a network security state analysis module, a cloud storage library and a display terminal.
The data acquisition module, the network service demand analysis module, the network topology analysis module, the network transmission performance analysis module and the network security state analysis module are respectively connected with the cloud storage library, and the network service demand analysis module, the network topology analysis module, the network transmission performance analysis module and the network security state analysis module are respectively connected with the display terminal.
The data acquisition module is used for acquiring service demand information, topological structure information, transmission performance information and security state information of a target network in the network planning process and respectively transmitting the service demand information, the topological structure information, the transmission performance information and the security state information to the network service demand analysis module, the network topological structure analysis module, the network transmission performance analysis module and the network security state analysis module.
The cloud storage is used for storing the topology structure type table of the target network, storing the transmission performance level grading table of the target network and storing the security performance level grading table of the target network.
The network service demand analysis module is used for monitoring service demand information of the target network and analyzing the demand state of the target network, and the specific process is as follows:
acquiring bandwidth requirement value, delay requirement value and service data volume in service requirement information of target network, respectively calibrating the bandwidth requirement value, delay requirement value and service data volume as dk, sy and s l, performing formulated analysis, and according to a set formulaObtaining a business demand coefficient bdc of a target network, wherein lambda 1, lambda 2 and lambda 3 are respectively weight factor coefficients of a bandwidth demand value, a time delay demand value and a business data volume, lambda 1, lambda 2 and lambda 3 are natural numbers larger than 0, and the weight factor coefficients are used for balancing the duty ratio weights of various data in formula calculation, so that the accuracy of a calculation result is promoted;
the bandwidth requirement value refers to a data value of a bandwidth size required by a network service when the network service is running, and the delay requirement value is used for representing a data value of a maximum delay limit allowed in the operation of a target network;
setting a first demand comparison interval, a second demand comparison interval and a third demand comparison interval of service demand coefficients of a target network, substituting the service demand coefficients of the target network into the preset first demand comparison interval, the second demand comparison interval and the third demand comparison interval for comparison analysis, wherein the values of the first demand comparison interval, the second demand comparison interval and the third demand comparison interval are increased in a gradient manner;
when the business demand coefficient of the target network is in a preset first demand comparison interval, a low-demand business signal is generated, when the business demand coefficient of the target network is in a preset second demand comparison interval, a medium-demand business signal is generated, when the business demand coefficient of the target network is in a preset third demand comparison interval, a high-demand business signal is generated, and an obtained business demand state judging result is sent to a display terminal for feedback explanation.
The network topology analysis module is used for monitoring network topology information of the target network, and the specific monitoring process is as follows:
obtaining the number of devices and the total number of cables to be connected with a target network, calibrating the number of devices and the total number of cables as i and d l respectively, carrying out normalization analysis on the number of devices and the total number of cables, and obtaining a structural coefficient td of the target network according to a set formula td=mu 1*i +mu 2 x d l, wherein mu 1 and mu 2 are normalization factor coefficients of the number of devices and the total number of cables respectively, mu 1 and mu 2 are natural numbers larger than 0, and the normalization factor coefficients are used for representing the conversion of various data of the number of devices and the total number of cables into a non-dimensional form;
based on the number of devices to be connected by the target network, obtaining the bandwidth magnitude, delay magnitude, throughput and encryption level in the performance parameters of each target device, and calibrating the bandwidth magnitude, delay magnitude, throughput and encryption level as bv respectively i 、dv i 、tc i And sg (g) i And carrying out formulated analysis on the sample according to a set formulaThereby obtaining the performance feedback coefficient fkx of each target device i Wherein i=1, 2,3 … … n, and i represents the number of target devices, γ1, γ2, γ3 and γ4 are correction factor coefficients of bandwidth magnitude, delay magnitude, throughput and encryption level, respectively, and γ1, γ2, γ3 and γ4 are natural numbers greater than 0, the correction factor coefficients are used for correcting deviations of various parameters occurring in the formula calculation process, so that the calculation is more accurateA count data;
it should be noted that, the bandwidth magnitude refers to a data transmission rate supported by the target device, the throughput refers to an effective data flow that can be processed and transmitted by the target device, and the encryption level refers to an intensity level of an encryption algorithm and a protocol supported by the target device;
average analysis is carried out on the performance feedback coefficients of all target devices, and the formula is used for the average analysisObtaining average performance feedback coefficient fkx of all devices corresponding to target network topology * ；
The topological structure state of the target network is analyzed, and the specific analysis steps are as follows:
obtaining a structural coefficient, an average performance feedback coefficient, the number of nodes and a construction cost value in topology structure information of a target network, carrying out formulated analysis on the structural coefficient, the average performance feedback coefficient, the number of nodes and the construction cost value, and carrying out formulated analysis according to a set formula tpx=ρ1×td+ρ2× fkx * Obtaining a topological structure discrimination coefficient tpx of a target network by +ρ3×ps+ρ4× cb, wherein ps is expressed as the node number, cb is expressed as a construction cost value, ρ1, ρ2, ρ3 and ρ4 are error factor coefficients of the structure coefficient, the average performance feedback coefficient, the node number and the construction cost value respectively, and ρ1, ρ2, ρ3 and ρ4 are natural numbers larger than 0, and the error factor coefficients are used for adjusting errors of the structure coefficient, the average performance feedback coefficient, the node number and the construction cost value in the calculation process so as to reduce calculation result errors;
and comparing and matching the topology structure discrimination coefficients of the target networks with a topology structure type table stored in a database, thereby obtaining corresponding network topology structures, wherein each topology structure discrimination coefficient of the target network corresponds to one network topology structure, and sending the obtained network topology structure discrimination results to a display terminal for feedback description.
The network transmission performance analysis module is used for monitoring the transmission performance information of the target network, and the specific monitoring process is as follows:
equally dividing a period of time into j time points, and j=1, 2,3 … … m;
capturing the transmission rate of the target network for a period of time, and calculating the standard deviation of the transmission rate according to the formulaObtaining a transmission rate fluctuation value sigma of a target network, wherein cs l j Represents the jth time data point, cl * Is the average of all data, i.e. the average of the transmission rate over a period of time, m is the size of all data sets;
respectively recording the data packet quantity sent by the target network and the received data packet quantity, respectively recording the data packet quantity as fmb and smb, performing formula analysis on the two data, and according to a set formulaObtaining a data transmission packet loss rate ut of a target network, wherein t represents transmission time;
the transmission performance state of the target network is analyzed, and the specific analysis steps are as follows:
acquiring a transmission rate fluctuation value, a data transmission packet loss rate and a data transmission rate in transmission performance information of a target network, carrying out formulation analysis on three data, and carrying out formulation analysis according to a set formulaObtaining a transmission performance index Nc of the target network, wherein l v represents the data transmission rate of the target network, delta 1, delta 2 and delta 3 are respectively a transmission rate fluctuation value, a data transmission packet loss rate and a data transmission rate specific gravity factor coefficient, and the specific gravity factor coefficient is used for reducing the error of a calculation result;
and comparing and matching the transmission performance indexes of the target networks with a transmission performance grade grading table stored in a database, thereby obtaining corresponding transmission performance grades of the target networks, wherein each transmission performance index of the target networks corresponds to one transmission performance grade, and sending the obtained transmission performance grade judging result to a display terminal for feedback description.
The network security state analysis module is used for monitoring security state information of the target network and analyzing the security state of the target network, and the specific process is as follows:
acquiring a user identity authentication mode value, an emergency response alternative plan value and an encryption key length value in security state information of a target network, calibrating the user identity authentication mode value, the emergency response alternative plan value and the encryption key length value as s1, s2 and s3 respectively, comprehensively analyzing the user identity authentication mode value, the emergency response alternative plan value and the encryption key length value, and obtaining a security performance coefficient sty of the target network according to a set formula sty=s1+s2+ zhy ×s3, wherein zhy is a conversion factor coefficient of the encryption key length value, zhy is a natural number larger than 0, and the conversion factor coefficient is used for converting physical quantity of the encryption key length value into a data coefficient of the same physical quantity as the user identity authentication mode value and the emergency response alternative plan value;
it should be noted that, the user identity authentication mode value is used to represent the type value of the target network capable of performing identity authentication, where the identity authentication modes include password authentication, short message authentication code authentication, fingerprint authentication, etc., and if the target network can perform two identity authentication modes of password authentication and short message authentication code authentication at the same time, the user identity authentication mode value is 2; the emergency response alternative plan value is used for representing the alternative plan number of the emergency response plans formulated by the target network during planning, the emergency response plans are response plans used for coping with various network security events, and the emergency response alternative plan value is 3 on the premise that 3 emergency response plans are formulated for the target network; the encryption key length value is used for measuring the security level of the target network planning, and when the key length is longer, the security of the target network planning is higher;
and comparing and matching the security performance coefficient of the target network with a security performance level grading table stored in a database, thereby obtaining the corresponding security performance level of the target network, wherein the security performance coefficient of each target network corresponds to one security performance level, and sending the obtained security performance level judgment result to a display terminal for feedback description.
Embodiment two:
referring to fig. 2, the present invention is a network planning method based on big data analysis, comprising the following steps:
step one: acquiring service demand information, topology structure information, transmission performance information and safety state information of a target network in a network planning process;
step two: monitoring service demand information of a target network, analyzing the demand state of the target network, and sending the obtained service demand state judgment result to a display terminal for feedback description;
step three: monitoring the topology structure information of the target network, analyzing the topology structure state of the target network, and sending the obtained network topology structure judgment result to a display terminal for feedback description;
step four: monitoring transmission performance information of a target network, analyzing the transmission performance state of the target network, and sending an obtained transmission performance grade judging result to a display terminal for feedback description;
step five: and monitoring the safety state information of the target network, analyzing the safety state of the target network, and sending the obtained safety performance grade judging result to a display terminal for feedback description.
When the method is used, the service demand level of the target network is defined by monitoring the service demand information of the target network and analyzing the demand state and adopting the modes of data calibration, formula calculation and substitution analysis of the gradient interval, and a foundation is laid for realizing reasonable network planning;
the network topology information of the target network is determined by monitoring the network topology information of the target network in a normalization analysis and average analysis mode, the topology state of the target network is analyzed, and the planning state of the target network is judged and analyzed from the topology layer by a formula analysis and database comparison matching analysis mode;
the transmission performance information of the target network is determined by monitoring the transmission performance information of the target network in a mode of time equivalent setting, standard deviation calculation and data duty ratio analysis, and based on the transmission performance information, the transmission performance state of the target network is determined and analyzed definitely by utilizing a mode of comprehensive analysis and data substitution comparison analysis;
the safety state information of the target network is monitored and analyzed, the data physical quantity conversion and data superposition modes are adopted, the auxiliary analysis is performed on the planning mode of the target network from the network safety performance level, and a foundation is laid for realizing reasonable planning and construction of the network;
the comprehensive auxiliary judgment of the network planning state is carried out on the service demand level, the topology structure construction level, the transmission performance and the security performance level, and a powerful data support is provided for constructing a reasonable network environment.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (8)
1. A network planning system based on big data analysis, comprising:
the data acquisition module is used for acquiring service demand information, topological structure information, transmission performance information and safety state information of a target network in a network planning process;
the network service demand analysis module is used for monitoring service demand information of the target network, so as to analyze the demand state of the target network;
the network topology analysis module is used for monitoring network topology information of the target network, so that the topology state of the target network is analyzed;
the network transmission performance analysis module is used for monitoring the transmission performance information of the target network, so as to analyze the transmission performance state of the target network;
the network security state analysis module is used for monitoring security state information of the target network, so that the security state of the target network is analyzed;
the cloud storage is used for storing the topology structure type table of the target network, storing the transmission performance level grading table of the target network and storing the security performance level grading table of the target network.
2. The network planning system based on big data analysis of claim 1, wherein the monitoring and analyzing the service requirement information of the target network comprises the following specific procedures:
acquiring a bandwidth demand value, a time delay demand value and a business data volume in business demand information of a target network, and carrying out formulated analysis on the bandwidth demand value, the time delay demand value and the business data volume to obtain a business demand coefficient of the target network;
setting a first demand comparison interval, a second demand comparison interval and a third demand comparison interval of service demand coefficients of a target network, substituting the service demand coefficients of the target network into the preset first demand comparison interval, second demand comparison interval and third demand comparison interval, and comparing and analyzing;
when the business demand coefficient of the target network is in a preset first demand comparison interval, a low-demand business signal is generated, when the business demand coefficient of the target network is in a preset second demand comparison interval, a medium-demand business signal is generated, and when the business demand coefficient of the target network is in a preset third demand comparison interval, a high-demand business signal is generated.
3. The network planning system based on big data analysis of claim 1, wherein the monitoring of the topology information of the target network is performed as follows:
obtaining the number of devices and the total number of cables to be connected of a target network, calibrating the number of devices and the total number of cables as i and dl respectively, carrying out normalization analysis on the number of devices and the total number of cables, and obtaining the structural coefficient td of the target network according to a set formula td=μ 1*i +μ2, wherein μ1 and μ2 are normalization factor coefficients of the number of devices and the total number of cables respectively, and μ1 and μ2 are natural numbers larger than 0;
based on the number of devices to be connected by the target network, obtaining bandwidth magnitude, delay magnitude, throughput and encryption level in the performance parameters of each target device, and carrying out formulated analysis on the bandwidth magnitude, the delay magnitude, the throughput and the encryption level, thereby obtaining performance feedback coefficients of each target device;
and carrying out average analysis on the performance feedback coefficients of all the target devices to obtain average performance feedback coefficients of all the devices corresponding to the target network topology structure.
4. The network planning system based on big data analysis of claim 1, wherein the analyzing the network topology status of the target network comprises the following steps:
obtaining a structural coefficient, an average performance feedback coefficient, a node number and a construction cost value in topology structure information of a target network, and carrying out formulated analysis on the structural coefficient, the average performance feedback coefficient, the node number and the construction cost value to obtain a topology structure discrimination coefficient of the target network;
and comparing and matching the topology structure discrimination coefficients of the target networks with a topology structure type table stored in a database to obtain corresponding network topology structures, wherein each topology structure discrimination coefficient of the target network corresponds to one network topology structure.
5. The network planning system based on big data analysis of claim 1, wherein the monitoring of the transmission performance information of the target network is performed by the following specific monitoring process:
equally dividing a period of time into j time points, and j=1, 2,3 … … m;
capturing the transmission rate of the target network for a period of time, and calculating the standard deviation of the transmission rate according to the formulaObtaining a transmission rate fluctuation value sigma of the target network, wherein csl j Represents the jth time data point, csl * Is the average of all data, i.e. the average of the transmission rate over a period of time, m is the size of all data sets;
respectively recording the data packet quantity sent by the target network and the received data packet quantity, respectively recording the data packet quantity as fmb and smb, performing formula analysis on the two data, and according to a set formulaAnd obtaining a data transmission packet loss rate ut of the target network, wherein t represents the transmission duration.
6. The network planning system based on big data analysis of claim 1, wherein the analyzing the transmission performance status of the target network comprises the following specific steps:
acquiring a transmission rate fluctuation value, a data transmission packet loss rate and a data transmission rate in transmission performance information of a target network, carrying out formulation analysis on three data, and carrying out formulation analysis according to a set formulaObtaining a transmission performance index Nc of a target network, wherein lv represents the data transmission rate of the target network, and δ1, δ2 and δ3 are respectively a transmission rate fluctuation value, a data transmission packet loss rate and a data transmission rate proportion factor coefficient;
and comparing and matching the transmission performance indexes of the target networks with a transmission performance grade grading table stored in a database, thereby obtaining corresponding transmission performance grades of the target networks, wherein each transmission performance index of the target networks corresponds to one transmission performance grade.
7. The network planning system based on big data analysis of claim 1, wherein the monitoring and the analysis of the security status information of the target network are as follows:
acquiring a user identity authentication mode value, an emergency response alternative plan value and an encryption key length value in security state information of a target network, calibrating the user identity authentication mode value, the emergency response alternative plan value and the encryption key length value as s1, s2 and s3 respectively, comprehensively analyzing the user identity authentication mode value, the emergency response alternative plan value and the encryption key length value, and obtaining a security performance coefficient sty of the target network according to a set formula sty=s1+s2+ zhy ×s3, wherein zhy is a conversion factor coefficient of the encryption key length value, and zhy is a natural number larger than 0;
and comparing and matching the security performance coefficient of the target network with a security performance level grading table stored in a database, thereby obtaining the corresponding security performance level of the target network, wherein the security performance coefficient of each target network corresponds to one security performance level.
8. The network planning method based on big data analysis is characterized by comprising the following steps:
step one: acquiring service demand information, topology structure information, transmission performance information and safety state information of a target network in a network planning process;
step two: monitoring service demand information of a target network, analyzing the demand state of the target network, and sending the obtained service demand state judgment result to a display terminal for feedback description;
step three: monitoring the topology structure information of the target network, analyzing the topology structure state of the target network, and sending the obtained network topology structure judgment result to a display terminal for feedback description;
step four: monitoring transmission performance information of a target network, analyzing the transmission performance state of the target network, and sending an obtained transmission performance grade judging result to a display terminal for feedback description;
step five: and monitoring the safety state information of the target network, analyzing the safety state of the target network, and sending the obtained safety performance grade judging result to a display terminal for feedback description.
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