CN117097677A - Flow management distribution system and analysis method based on big data - Google Patents
Flow management distribution system and analysis method based on big data Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
- H04L47/2425—Traffic characterised by specific attributes, e.g. priority or QoS for supporting services specification, e.g. SLA
- H04L47/2433—Allocation of priorities to traffic types
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/50—Queue scheduling
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract
The invention belongs to the technical field of flow distribution management, and particularly relates to a flow management distribution system and an analysis method based on big data. According to the invention, when the required flow of the flow demand target exceeds the rated allocation amount, the flow demand target is gradually optimized, and partial flow execution targets are stopped to continuously acquire the flow if necessary, so that the response speed of the flow demand target is ensured, the flow allocation amount of the flow demand target stopping flow allocation is improved after the response is completed, the experience of a user is ensured not to be influenced, meanwhile, when the allowance exists in the flow allocation process, corresponding allowable allocation queues can be generated, different allowable allocation queues correspond to different combined flow demand targets, and then when the corresponding allowable allocation queues respond, balance flow can be rapidly determined according to the queue matching result, and accordingly, the response speed of the flow demand target acquired flow can be improved.
Description
Technical Field
The invention belongs to the technical field of flow distribution management, and particularly relates to a flow management distribution system and an analysis method based on big data.
Background
In recent years, big data technology is widely applied in various fields, valuable information and rules can be mined through collection, storage, processing and analysis of mass data, support is provided for decision making, a big data-based flow management distribution system can utilize a data analysis algorithm to predict problems in aspects of user behavior mode, network bandwidth utilization rate optimization, user experience quality improvement and the like, and a traditional network flow management method is often used for carrying out flow distribution based on manual experience and fixed rules, cannot adapt to dynamic requirements of different users, different applications and different scenes, and is used for optimizing network bandwidth utilization rate and improving user experience quality.
In the prior art, the flow management allocation mode is mainly allocation according to the need, the response speed is generally taken as the allocation basis, and the allocation flow of part of flow demand targets is excessive in the allocation process, which can definitely lead to the reduction of the flow allocated by the newly-added flow demand targets or other flow demand targets, and meanwhile, when the demand flow of the flow demand targets is excessive, other flow demand targets can still normally respond, but the response speed and the response time are excessively prolonged, which obviously leads to the reduction of the experience of users.
Disclosure of Invention
The invention aims to provide a flow management distribution system and an analysis method based on big data, which can optimize flow demand targets step by step and realize reasonable distribution of demand flows of all the flow demand targets.
The technical scheme adopted by the invention is as follows:
an analysis method for traffic management allocation based on big data, comprising:
acquiring a management area and flow demand targets in the management area, wherein the flow demand targets are provided with a plurality of flow demand targets;
the demand flow of each flow demand target is obtained in real time, calibrated to be distributed parameters, and the distributed parameters under the current node are summarized to be parameters to be evaluated;
obtaining rated allocation quantity and comparing the rated allocation quantity with the parameter to be evaluated;
if the parameter to be evaluated is smaller than the rated allocation amount, indicating that each flow demand target can acquire the demand flow, and inputting the parameter to be evaluated and the rated allocation amount into an evaluation model together to obtain an allowable allocation queue;
if the parameter to be evaluated is greater than or equal to the rated allocation amount, inputting the parameter to be evaluated and the rated allocation amount into a verification model together to obtain an execution deviation amount, arranging the flow demand targets according to the flow demand amount from high to low, and synchronously generating a list to be optimized;
inputting the flow demand targets in the list to be optimized into a hierarchical model together to obtain the optimization grade of each flow demand target;
inputting the corresponding flow demand into an optimization model one by one according to the optimization level of each flow demand target and the sequence from high to low to obtain an optimization execution amount;
and comparing the optimized execution amount with the execution deviation amount, sending an alarm signal when the optimized execution amount is smaller than the execution deviation amount, and stopping flow distribution one by one according to the optimization grade of each flow demand target until the optimized execution amount is larger than or equal to the execution deviation amount.
In a preferred embodiment, the step when the flow demand target obtains the flow includes:
acquiring the flow demand of the flow demand target, and calibrating the flow demand as a parameter to be distributed;
acquiring a response interval and a flow distribution function;
and inputting the parameters to be distributed and the lower limit value of the response interval into a distribution function, and calibrating the output result as the distributed parameters.
In a preferred embodiment, the step of inputting the parameter to be evaluated and the rated allocation into an evaluation model to obtain an allowable allocation queue includes:
acquiring the parameter to be evaluated and the rated allocation amount;
invoking an evaluation function from the evaluation model;
inputting the parameter to be evaluated and the rated allocation into an evaluation function together to obtain an allowable allocation;
and inputting the allowable allocation amount into a screening model to obtain a plurality of executable allocation parameters, randomly combining the executable allocation parameters, and calibrating the combination result as an allowable allocation queue.
In a preferred embodiment, the step of inputting the allowable allocation amount into a screening model to obtain a plurality of executable allocation parameters includes:
acquiring an allowable allocation amount;
screening a function from the screening model, inputting the flow demand of each flow demand target and the upper limit value of a response interval into the screening function together, and calibrating the output result as a parameter to be screened;
comparing the parameters to be screened with allowable distribution amounts;
if the allowable allocation amount is greater than or equal to the parameter to be screened, calibrating the allowable allocation amount as an unexecutable allocation parameter, and synchronously screening the unexecutable allocation parameter;
and if the allowable allocation amount is smaller than the parameter to be screened, calibrating the allowable allocation amount as an executable allocation parameter.
In a preferred embodiment, the step of randomly combining the executable allocation parameters and calibrating the combination result to be an allowable allocation queue includes:
acquiring the executable allocation parameters, and carrying out random combination to obtain a plurality of groups of queues to be evaluated;
summing the executable allocation parameters in each group of the queues to be evaluated to obtain parameters to be verified;
comparing the parameter to be verified with an allowable dispensing amount;
if the parameter to be verified is larger than the allowable allocation amount, judging the corresponding queue to be evaluated as an invalid queue, and synchronously screening out the queue to be evaluated;
and if the parameter to be verified is smaller than or equal to the allowable allocation amount, marking the corresponding queue to be evaluated as the allowable allocation queue.
In a preferred embodiment, the step of inputting the parameter to be evaluated and the rated allocation amount into a verification model to obtain the execution deviation amount includes:
acquiring the parameter to be evaluated and rated allocation quantity;
obtaining a check function from the check model;
and inputting the parameter to be evaluated and the rated allocation quantity into a check function, and calibrating an output result of the parameter to be evaluated as an execution deviation quantity.
In a preferred embodiment, the step of inputting the flow demand targets in the to-be-optimized list into a hierarchical model together to obtain an optimization level of each flow demand target includes:
acquiring a flow demand target in the list to be optimized, and calibrating the flow demand target as a target to be classified;
acquiring the waiting response time length and unassigned flow of the waiting grading target;
calling a classification function from the classification model, inputting the duration to be responded and the unassigned flow into the classification function, and calibrating an output result of the duration to be responded and the unassigned flow into parameters to be classified;
and arranging all the parameters to be classified according to the sequence from large to small, and calibrating the arrangement order as the optimization grade of the flow demand target.
In a preferred solution, the step of inputting the corresponding flow demand into the optimization model one by one according to the optimization level of each flow demand target from high to low to obtain the optimization execution amount includes:
obtaining the flow demand of each flow demand target and the allocated flow according to the optimization grade, and performing difference processing on the flow demand and the allocated flow to obtain parameters to be optimized;
acquiring the allowable allocation duration of each flow demand target;
calling an optimization function from the optimization model;
and inputting the parameters to be optimized and the allowable allocation time length into an optimization function, and calibrating the output result as an optimization execution amount.
The invention also provides a flow management distribution system based on big data, which is applied to the analysis method based on the flow management distribution of the big data, and comprises the following steps:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a management area and flow demand targets in the management area, and a plurality of flow demand targets are arranged;
the second acquisition module is used for acquiring the required flow of each flow demand target in real time, calibrating the required flow as an allocated parameter, and summarizing the allocated parameter under the current node as a parameter to be evaluated;
the evaluation module is used for acquiring rated allocation quantity and comparing the rated allocation quantity with the parameter to be evaluated;
if the parameter to be evaluated is smaller than the rated allocation amount, indicating that each flow demand target can acquire the demand flow, and inputting the parameter to be evaluated and the rated allocation amount into an evaluation model together to obtain an allowable allocation queue;
if the parameter to be evaluated is greater than or equal to the rated allocation amount, inputting the parameter to be evaluated and the rated allocation amount into a verification model together to obtain an execution deviation amount, arranging the flow demand targets according to the flow demand amount from high to low, and synchronously generating a list to be optimized;
the grading module is used for inputting the flow demand targets in the list to be optimized into the grading model together to obtain the optimization grade of each flow demand target;
the optimization module is used for inputting the corresponding flow demand into an optimization model one by one according to the optimization grade of each flow demand target and the sequence from high to low to obtain an optimization execution amount;
and the execution module is used for comparing the optimized execution quantity with the execution deviation quantity, sending out an alarm signal when the optimized execution quantity is smaller than the execution deviation quantity, and stopping flow distribution one by one according to the optimization grade of each flow demand target until the optimized execution quantity is larger than or equal to the execution deviation quantity.
And, a traffic management distribution terminal based on big data, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the above-described analysis method of big data based traffic management allocation.
The invention has the technical effects that:
according to the invention, when the required flow of the flow demand target exceeds the rated allocation amount, the flow demand target is gradually optimized, and partial flow execution targets are stopped to continuously acquire the flow if necessary, so that the response speed of the flow demand target is ensured, the flow allocation amount of the flow demand target stopping flow allocation is improved after the response is completed, the experience of a user is ensured not to be influenced, meanwhile, when the allowance exists in the flow allocation process, corresponding allowable allocation queues can be generated, different allowable allocation queues correspond to different combined flow demand targets, and then when the corresponding allowable allocation queues respond, balance flow can be rapidly determined according to the queue matching result, and accordingly, the response speed of the flow demand target acquired flow can be improved.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
fig. 2 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1 and 2, the present invention provides an analysis method for traffic management allocation based on big data, which includes:
s1, acquiring a management area and flow demand targets in the management area, wherein the flow demand targets are provided with a plurality of flow demand targets;
s2, acquiring the demand flow of each flow demand target in real time, calibrating the demand flow as an allocated parameter, and summarizing the allocated parameter under the current node as a parameter to be evaluated;
s3, obtaining rated allocation amount and comparing the rated allocation amount with parameters to be evaluated;
if the parameter to be evaluated is smaller than the rated allocation amount, indicating that each flow demand target can acquire the demand flow, and inputting the parameter to be evaluated and the rated allocation amount into an evaluation model to obtain an allowable allocation queue;
if the parameter to be evaluated is greater than or equal to the rated allocation amount, inputting the parameter to be evaluated and the rated allocation amount into a verification model together to obtain an execution deviation amount, arranging each flow demand target according to the flow demand amount from high to low, and synchronously generating a list to be optimized;
s4, inputting the flow demand targets in the list to be optimized into the grading model together to obtain the optimization grade of each flow demand target;
s5, according to the optimization level of each flow demand target, inputting the corresponding flow demand into an optimization model one by one according to the sequence from high to low to obtain an optimization execution amount;
s6, comparing the optimized execution amount with the execution deviation amount, sending an alarm signal when the optimized execution amount is smaller than the execution deviation amount, and stopping flow distribution one by one according to the optimization level of each flow demand target until the optimized execution amount is larger than or equal to the execution deviation amount.
As described in the above steps S1-S6, with the continuous development and popularization of the internet technology, network traffic management becomes more and more important, the conventional network traffic management method often performs traffic distribution based on manual experience and fixed rules, and cannot adapt to dynamic demands under different users, different applications and different scenarios, so as to optimize network bandwidth utilization and improve user experience quality, how to implement an intelligent, efficient and expandable traffic management distribution system to one of the problems to be solved currently, in this embodiment, firstly, a management area is determined, and traffic demand targets in the management area, and there are a plurality of traffic demand targets, then traffic demand amounts of the traffic demand targets are calibrated as distributed parameters, and the distributed parameters under the current node are summarized as parameters to be evaluated, then the parameters to be evaluated are compared with preset rated distribution amounts, and when the parameters to be evaluated are smaller than the rated distribution amounts, an allowable distribution queue is output, when the parameters to be evaluated are greater than or equal to the rated distribution amounts, it is indicated that the total amount of traffic demand of the current traffic demand targets cannot be met, through checking the execution of a model can be output, and the amount of demand of the optimization model can be optimized, and the optimal demand amount can be calculated by comparing the amount with the execution order to obtain the optimal demand amount in the optimization demand amount by the optimization execution order, and the optimization demand amount in the optimization order of the optimization class is calculated by comparing the execution order to obtain the optimal execution order, and the optimal execution order is better to obtain the optimal execution order, and the optimal execution order to be better, and judging whether the optimization result can finish reasonable allocation of the flow, and when the optimization execution amount is smaller than the execution deviation amount, indicating that the optimization is invalid, and stopping the flow allocation one by one according to the optimization level of each flow demand target until the optimization execution amount is larger than or equal to the execution deviation amount so as to ensure reasonable allocation of the network flow.
In a preferred embodiment, the step of obtaining the flow from the flow demand target includes:
step 1, acquiring the flow demand of a flow demand target, and calibrating the flow demand as a parameter to be distributed;
step 2, obtaining a response interval and a flow distribution function;
and step 3, inputting the parameters to be distributed and the lower limit value of the response interval into the distribution function, and calibrating the output result as the distributed parameters.
As described in the above steps Stp1-Stp3, when the flow demand target obtains the flow, the new flow demand corresponding to the new flow demand is required to be obtained, and in this embodiment, the new flow demand is calibrated to be a parameter to be allocated, then the lower limit value is obtained from the response interval, and then the remaining parameters to be allocated are input into the flow allocation function together, so as to measure and calculate the allocated parameters, where the expression of the flow allocation function is:wherein->Representing the assigned parameters->Indicating flow demand, +.>And representing the lower limit value of the response interval, and obtaining the allocated parameters of each flow demand target based on the formula, wherein the allocated parameters are smaller than or equal to the flow demand of the flow demand target.
In a preferred embodiment, the step of inputting the parameter to be evaluated and the rated allocation into the evaluation model to obtain the allowable allocation queue includes:
s301, acquiring parameters to be evaluated and rated allocation quantity;
s302, calling an evaluation function from the evaluation model;
s303, inputting parameters to be evaluated and rated allocation into an evaluation function together to obtain allowable allocation;
s304, the allowable allocation amount is input into a screening model to obtain a plurality of executable allocation parameters, the executable allocation parameters are randomly combined, and the combination result is calibrated to be an allowable allocation queue.
As described in the above steps S301 to S304, when the flow is allocated to each flow demand target, because the flow that can be allocated in unit time has an upper limit due to factors such as the network speed and the equipment response delay, the present embodiment determines the flow demand target as a rated allocation amount, and when the parameter to be evaluated is smaller than the rated allocation amount, it indicates that each flow demand target can respond according to the shortest response time, then an evaluation function is called from an evaluation model, and then the parameter to be evaluated and the rated allocation amount are input into the evaluation function together, where the expression of the evaluation function is:wherein->Indicating allowable dispensing amount, ++>Indicating (I)>Representing the parameters to be evaluated, directly inputting the parameters into a screening model after the allowable allocation amount is determined, obtaining executable allocation parameters, and randomly combining the executable allocation parameters so as to determine an allowable allocation queue.
In a preferred embodiment, the step of inputting the allowable allocation into the screening model to obtain a plurality of executable allocation parameters comprises:
stp1, obtaining allowable allocation amount;
stp2, screening a function from a screening model, inputting the flow demand of each flow demand target and the upper limit value of a response interval into the screening function together, and calibrating the output result as a parameter to be screened;
stp3, comparing the parameters to be screened with the allowable distribution quantity;
if the allowable allocation amount is greater than or equal to the parameter to be screened, calibrating the allowable allocation amount as an inexecutable allocation parameter, and synchronously screening the inexecutable allocation parameter;
if the allowable allocation is less than the parameter to be screened, it is calibrated as an executable allocation parameter.
As described in the above steps Stp1-Stp3, when the screening model is executed, firstly, a screening function is called from the screening model to determine parameters to be screened, wherein the expression of the screening function is:wherein->Representing parameters to be screened, < > for>The upper limit value of the response interval is represented, based on which the minimum required flow rate of each flow rate requirement target in unit time can be obtained, and the embodiment marks the minimum required flow rate as the parameter to be screened and then compares the minimum required flow rate with the allowable fractionAnd comparing the metering, and when the parameter to be screened is smaller than or equal to the allowable metering, calibrating the parameter to be screened as an inexecutable dispensing parameter, otherwise, directly calibrating the parameter to be the executable dispensing parameter, and providing corresponding data support for the construction of the subsequent allowable dispensing queue.
In a preferred embodiment, the step of randomly combining the executable allocation parameters and calibrating the combination result as an allowable allocation queue comprises the steps of:
stp4, acquiring executable allocation parameters, and carrying out random combination to obtain a plurality of groups of queues to be evaluated;
stp5, summing the executable allocation parameters in each group of queues to be evaluated to obtain parameters to be verified;
stp6, comparing the parameter to be verified with the allowable distribution quantity;
if the parameter to be verified is larger than the allowable allocation amount, judging the corresponding queue to be evaluated as an invalid queue, and synchronously screening out the queue to be evaluated;
and if the parameter to be verified is smaller than or equal to the allowable allocation amount, marking the corresponding queue to be evaluated as the allowable allocation queue.
As described in the above steps Stp4-Stp6, after the executable allocation parameters are determined, the executable allocation parameters are randomly combined to obtain a plurality of groups of queues to be evaluated, wherein the number of the executable allocation parameters in the queues to be evaluated is 1-n, the value of n is a positive integer, the executable allocation parameters in each group of queues to be evaluated are summed to obtain a parameter to be verified, and the parameter to be verified is compared with the allowable allocation amount to determine whether the corresponding queues to be evaluated are valid, and based on the parameter to be verified, the allowable allocation queues under the allowable allocation amount can be determined one by one.
In a preferred embodiment, the step of inputting the parameter to be evaluated and the rated allocation into the verification model to obtain the execution deviation comprises the following steps:
s305, acquiring parameters to be evaluated and rated allocation amount;
s306, acquiring a check function from the check model;
s307, the parameter to be evaluated and the rated allocation quantity are input into the check function together, and the output result is calibrated as the execution deviation quantity.
As described in the above steps S305-S307, when the parameter to be evaluated exceeds the rated allocation amount, it indicates that the required flow of each flow requirement target cannot be satisfied under the current node, and the execution deviation amount can be calculated by inputting the parameter to be evaluated and the rated allocation amount into the check function, where the expression of the check function is:wherein->Indicating the execution deviation amount, and performing optimization operation according to the optimization level of the flow demand target after the execution deviation amount is determined, so as to ensure that each flow demand target under the rated allocation amount can obtain reasonable flow allocation.
In a preferred embodiment, the step of inputting the flow demand targets in the list to be optimized into the hierarchical model together to obtain the optimization level of each flow demand target includes:
s401, acquiring a flow demand target in a list to be optimized, and calibrating the flow demand target as a target to be classified;
s402, acquiring a waiting response time length of a target to be classified and unallocated flow;
s403, calling a grading function from the grading model, inputting the duration to be responded and the unassigned flow into the grading function, and calibrating the output result as parameters to be graded;
s404, arranging all parameters to be classified according to the sequence from large to small, and calibrating the arrangement order as the optimization grade of the flow demand target.
After the generation of the list to be optimized, the flow demand targets are first calibrated as targets to be classified, and then the time length to be responded and the unassigned flow of the targets to be classified are obtained in real time, and are synchronously input into a value classification function, wherein the expression of the classification function is as follows:wherein->Representing parameters to be ranked->Weight coefficient representing duration to be responded to, +.>Indicating the duration of waiting for response->Weight coefficient representing unassigned traffic, +.>And representing unassigned flow, arranging the parameters to be classified according to the order from large to small based on the values of the parameters to be classified, and determining the optimization level of the flow demand target according to the arrangement level, wherein the higher the arrangement level is, the higher the priority of executing optimization is.
In a preferred embodiment, according to the optimization level of each flow demand target, the steps of inputting the corresponding flow demand into the optimization model one by one according to the order from high to low to obtain the optimized execution amount include:
s501, obtaining the flow demand of each flow demand target and the allocated flow according to the optimization grade, and performing difference processing on the flow demand and the allocated flow to obtain parameters to be optimized;
s502, acquiring allowable allocation time length of each flow demand target;
s503, calling an optimization function from the optimization model;
s504, inputting parameters to be optimized and allowable allocation time length into an optimization function, and calibrating an output result thereof as an optimization execution amount.
After the optimization level of the flow demand targets is determined, the flow demand and the allocated flow of each flow demand target are obtained according to the optimization level as described in the above steps S501 to S504The unallocated flow of the flow demand target can be obtained by differentiating the flow demand of the flow demand target and the allocated flow, and the method is calibrated to be a parameter to be optimized, and then the parameter and the allowable allocation duration of the flow demand target are input into an optimization function, wherein the expression of the optimization function is as follows:wherein->Representing the amount of execution optimized, ++>Current allocated flow representing flow demand target, +.>Representing parameters to be optimized->And indicating the allowable allocation duration, based on the allowable allocation duration, determining the optimal execution quantity of each flow demand target one by one, finally summarizing the optimal execution quantity and comparing the optimal execution quantity with the execution deviation quantity, sending out an alarm signal when the optimal execution quantity is smaller than the execution deviation quantity, and stopping flow allocation one by one according to the optimization grade of each flow demand target until the optimal execution quantity is larger than or equal to the execution deviation quantity, thereby realizing reasonable allocation of the demand flow of each flow demand target.
The invention also provides a flow management distribution system based on big data, which is applied to the analysis method based on the flow management distribution of the big data, and comprises the following steps:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a management area and flow demand targets in the management area, and a plurality of flow demand targets are arranged;
the second acquisition module is used for acquiring the demand flow of each flow demand target in real time, calibrating the demand flow as an allocated parameter, and summarizing the allocated parameter under the current node as a parameter to be evaluated;
the evaluation module is used for acquiring rated allocation quantity and comparing the rated allocation quantity with parameters to be evaluated;
if the parameter to be evaluated is smaller than the rated allocation amount, indicating that each flow demand target can acquire the demand flow, and inputting the parameter to be evaluated and the rated allocation amount into an evaluation model to obtain an allowable allocation queue;
if the parameter to be evaluated is greater than or equal to the rated allocation amount, inputting the parameter to be evaluated and the rated allocation amount into a verification model together to obtain an execution deviation amount, arranging each flow demand target according to the flow demand amount from high to low, and synchronously generating a list to be optimized;
the grading module is used for inputting the flow demand targets in the list to be optimized into the grading model together to obtain the optimization grade of each flow demand target;
the optimization module is used for inputting the corresponding flow demand into the optimization model one by one according to the optimization level of each flow demand target and the sequence from high to low to obtain the optimization execution amount;
and the execution module is used for comparing the optimized execution amount with the execution deviation amount, sending out an alarm signal when the optimized execution amount is smaller than the execution deviation amount, and stopping flow distribution one by one according to the optimization level of each flow demand target until the optimized execution amount is larger than or equal to the execution deviation amount.
When the distribution system is executed, the management area is acquired through the first acquisition module, the flow demand target in the management area is acquired through the second acquisition module, the flow demand target is calibrated into the distributed parameter and summarized into the parameter to be evaluated, the parameter to be evaluated is compared with the rated distribution amount through the evaluation module, the parameter to be evaluated and the rated distribution amount are input into the evaluation model together when the parameter to be evaluated is smaller than the rated distribution amount, the allowable distribution queue is obtained, the parameter to be evaluated and the rated distribution amount are input into the verification model together when the parameter to be evaluated is larger than or equal to the rated distribution amount, the execution deviation amount is obtained, the flow demand target is classified through the classification module, the optimization level of each flow demand target is determined, the optimization module is combined to perform optimization processing according to the optimization level of each flow demand target, and accordingly the optimal execution amount of the flow demand target can be determined, the execution deviation amount and the optimal execution amount are compared through the execution module, and the executable performance can be determined.
And, a traffic management distribution terminal based on big data, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the analysis method of big data based traffic management allocation described above.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (10)
1. An analysis method for flow management distribution based on big data is characterized in that: comprising the following steps:
acquiring a management area and flow demand targets in the management area, wherein the flow demand targets are provided with a plurality of flow demand targets;
the demand flow of each flow demand target is obtained in real time, calibrated to be distributed parameters, and the distributed parameters under the current node are summarized to be parameters to be evaluated;
obtaining rated allocation quantity and comparing the rated allocation quantity with the parameter to be evaluated;
if the parameter to be evaluated is smaller than the rated allocation amount, indicating that each flow demand target can acquire the demand flow, and inputting the parameter to be evaluated and the rated allocation amount into an evaluation model together to obtain an allowable allocation queue;
if the parameter to be evaluated is greater than or equal to the rated allocation amount, inputting the parameter to be evaluated and the rated allocation amount into a verification model together to obtain an execution deviation amount, arranging the flow demand targets according to the flow demand amount from high to low, and synchronously generating a list to be optimized;
inputting the flow demand targets in the list to be optimized into a hierarchical model together to obtain the optimization grade of each flow demand target;
inputting the corresponding flow demand into an optimization model one by one according to the optimization level of each flow demand target and the sequence from high to low to obtain an optimization execution amount;
and comparing the optimized execution amount with the execution deviation amount, sending an alarm signal when the optimized execution amount is smaller than the execution deviation amount, and stopping flow distribution one by one according to the optimization grade of each flow demand target until the optimized execution amount is larger than or equal to the execution deviation amount.
2. The method for analyzing traffic management allocation based on big data according to claim 1, wherein: the step when the flow demand target obtains the flow comprises the following steps:
acquiring the flow demand of the flow demand target, and calibrating the flow demand as a parameter to be distributed;
acquiring a response interval and a flow distribution function;
and inputting the parameters to be distributed and the lower limit value of the response interval into a distribution function, and calibrating the output result as the distributed parameters.
3. The method for analyzing traffic management allocation based on big data according to claim 1, wherein: the step of inputting the parameter to be evaluated and the rated allocation into an evaluation model to obtain an allowable allocation queue comprises the following steps:
acquiring the parameter to be evaluated and the rated allocation amount;
invoking an evaluation function from the evaluation model;
inputting the parameter to be evaluated and the rated allocation into an evaluation function together to obtain an allowable allocation;
and inputting the allowable allocation amount into a screening model to obtain a plurality of executable allocation parameters, randomly combining the executable allocation parameters, and calibrating the combination result as an allowable allocation queue.
4. A method of analyzing traffic management allocation based on big data according to claim 3, wherein: the step of inputting the allowable allocation amount into a screening model to obtain a plurality of executable allocation parameters includes:
acquiring an allowable allocation amount;
screening a function from the screening model, inputting the flow demand of each flow demand target and the upper limit value of a response interval into the screening function together, and calibrating the output result as a parameter to be screened;
comparing the parameters to be screened with allowable distribution amounts;
if the allowable allocation amount is greater than or equal to the parameter to be screened, calibrating the allowable allocation amount as an unexecutable allocation parameter, and synchronously screening the unexecutable allocation parameter;
and if the allowable allocation amount is smaller than the parameter to be screened, calibrating the allowable allocation amount as an executable allocation parameter.
5. A method of analyzing traffic management allocation based on big data according to claim 3, wherein: the step of randomly combining the executable allocation parameters and calibrating the combination result as an allowable allocation queue comprises the following steps:
acquiring the executable allocation parameters, and carrying out random combination to obtain a plurality of groups of queues to be evaluated;
summing the executable allocation parameters in each group of the queues to be evaluated to obtain parameters to be verified;
comparing the parameter to be verified with an allowable dispensing amount;
if the parameter to be verified is larger than the allowable allocation amount, judging the corresponding queue to be evaluated as an invalid queue, and synchronously screening out the queue to be evaluated;
and if the parameter to be verified is smaller than or equal to the allowable allocation amount, marking the corresponding queue to be evaluated as the allowable allocation queue.
6. The method for analyzing traffic management allocation based on big data according to claim 1, wherein: the step of inputting the parameter to be evaluated and the rated allocation amount into a verification model to obtain the execution deviation amount comprises the following steps:
acquiring the parameter to be evaluated and rated allocation quantity;
obtaining a check function from the check model;
and inputting the parameter to be evaluated and the rated allocation quantity into a check function, and calibrating an output result of the parameter to be evaluated as an execution deviation quantity.
7. The method for analyzing traffic management allocation based on big data according to claim 1, wherein: the step of inputting the flow demand targets in the to-be-optimized list into a hierarchical model together to obtain the optimization level of each flow demand target comprises the following steps:
acquiring a flow demand target in the list to be optimized, and calibrating the flow demand target as a target to be classified;
acquiring the waiting response time length and unassigned flow of the waiting grading target;
calling a classification function from the classification model, inputting the duration to be responded and the unassigned flow into the classification function, and calibrating an output result of the duration to be responded and the unassigned flow into parameters to be classified;
and arranging all the parameters to be classified according to the sequence from large to small, and calibrating the arrangement order as the optimization grade of the flow demand target.
8. The method for analyzing traffic management allocation based on big data according to claim 1, wherein: the step of inputting the corresponding flow demand into an optimization model one by one according to the optimization level of each flow demand target and the order from high to low to obtain the optimization execution amount comprises the following steps:
obtaining the flow demand of each flow demand target and the allocated flow according to the optimization grade, and performing difference processing on the flow demand and the allocated flow to obtain parameters to be optimized;
acquiring the allowable allocation duration of each flow demand target;
calling an optimization function from the optimization model;
and inputting the parameters to be optimized and the allowable allocation time length into an optimization function, and calibrating the output result as an optimization execution amount.
9. A big data based flow management distribution system, applied to the analysis method of big data based flow management distribution according to any one of claims 1 to 8, characterized in that: comprising the following steps:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring a management area and flow demand targets in the management area, and a plurality of flow demand targets are arranged;
the second acquisition module is used for acquiring the required flow of each flow demand target in real time, calibrating the required flow as an allocated parameter, and summarizing the allocated parameter under the current node as a parameter to be evaluated;
the evaluation module is used for acquiring rated allocation quantity and comparing the rated allocation quantity with the parameter to be evaluated;
if the parameter to be evaluated is smaller than the rated allocation amount, indicating that each flow demand target can acquire the demand flow, and inputting the parameter to be evaluated and the rated allocation amount into an evaluation model together to obtain an allowable allocation queue;
if the parameter to be evaluated is greater than or equal to the rated allocation amount, inputting the parameter to be evaluated and the rated allocation amount into a verification model together to obtain an execution deviation amount, arranging the flow demand targets according to the flow demand amount from high to low, and synchronously generating a list to be optimized;
the grading module is used for inputting the flow demand targets in the list to be optimized into the grading model together to obtain the optimization grade of each flow demand target;
the optimization module is used for inputting the corresponding flow demand into an optimization model one by one according to the optimization grade of each flow demand target and the sequence from high to low to obtain an optimization execution amount;
and the execution module is used for comparing the optimized execution quantity with the execution deviation quantity, sending out an alarm signal when the optimized execution quantity is smaller than the execution deviation quantity, and stopping flow distribution one by one according to the optimization grade of each flow demand target until the optimized execution quantity is larger than or equal to the execution deviation quantity.
10. The utility model provides a flow management distribution terminal based on big data which characterized in that: comprising the following steps:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the analysis method of big data based traffic management allocation of any of claims 1 to 8.
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