CN112580713A - Routing equipment control method and system based on pattern recognition - Google Patents
Routing equipment control method and system based on pattern recognition Download PDFInfo
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Abstract
The invention provides a routing device control method and system based on pattern recognition. The method comprises the steps of collecting user side data of the routing equipment, generating a characteristic vector, classifying user use conditions, constructing a BP neural network classification model and performing model training, generating the user side data of the current routing equipment when the NTP function of the routing equipment is started, inputting the characteristic vector into the trained BP neural network classification model according to the user side data of the current routing equipment to obtain a classification result, and controlling the routing equipment to execute a set function according to the classification result. The invention is based on the pattern recognition theory, judges and classifies the input parameters according to the description of various different characteristics, thereby achieving the effect of making the routing equipment establish different patterns according to different use conditions to execute the setting function, meeting the requirements of different use conditions and simplifying the calculation engineering quantity of the routing equipment.
Description
Technical Field
The present invention relates to the technical field of control of routing devices, and in particular, to a method and a system for controlling a routing device based on pattern recognition.
Background
The routing equipment is used for a long time, a large number of intermediate link caches are accumulated, normal use cannot be influenced by clearing the caches, and the processing speed of the routing equipment can be increased. The factory typically resolves this situation by triggering when the cache reaches a threshold. If the cache does not reach the relevant threshold value and is always close to the threshold line, the manufacturer also has a corresponding solution, such as checking the running time, and triggering is realized when the cache is used for a long time to a certain extent. Or a fixed time, such as performing a cache clean operation every monday in the early morning.
Similar functions (equipment firmware upgrade, security check, etc.) at the same level are also performed by using the same idea. One is threshold, baseline triggered remedial action; the other is a polling mechanism, i.e., the specific time that the device performs a function is fixed. The latter is usually the main case.
A problem arises in that the latter operation does not have a reliable decision mechanism to determine the functional decision of the routing device in general. The needs of most people are met, while the differences of few people are ignored. Taking the example of firmware upgrade of a device, it is feasible to perform the operation late at night, assuming that the user's life is normal work and rest. But the user is an abnormal habit of work and rest, for example, needs to work at night, and the operation is very unsuitable. For the function decision with simple logic and no expansibility, a new decision mechanism needs to be used to meet different use conditions.
Disclosure of Invention
The feasibility that a single and small number of parameters are used as decision conditions in the prior art is not high, and misjudgment is easy to occur, for example, the situation that the use of a user is more than night cannot be judged because the flow of a certain night is too large. To ensure reliable results, analysis needs to be performed with a linear dimensional data volume. In consideration of the difference matching, the dimension of input parameters and the limitation of data samples, the invention provides a routing equipment control method and system based on pattern recognition.
In order to realize the purpose, the invention is realized according to the following technical scheme:
in a first aspect, the present invention provides a method for controlling a routing device based on pattern recognition, including the following steps:
s1, collecting user side data of the routing equipment;
s2, generating a feature vector according to the user side data of the routing equipment collected in the step S1;
s3, classifying the use condition of the user according to the data characteristic value generated in the step S2;
s4, constructing a BP neural network classification model, and performing model training by using the feature vector generated in the step S2 and the expected classification result obtained in the step S3;
s5, checking whether the NTP function of the routing equipment is opened; if yes, collecting user side data of the current routing equipment; otherwise, continuing to check the NTP function state of the routing equipment;
s6, generating a feature vector according to the user side data of the current routing equipment, inputting the feature vector into the BP neural network classification model trained in the step S3, and obtaining a classification result;
and S7, controlling the routing equipment to execute a setting function according to the classification result obtained in the step S5.
Preferably, the step S2 is specifically:
carrying out dichotomy processing on the sum of the collected user side receiving data and the collected sending data of the routing equipment;
judging whether the processing result is larger than a set threshold value or not; if yes, setting the characteristic value of the data to be 1; otherwise, setting the characteristic value of the data to be 0;
and constructing a column matrix by using the data eigenvalue as an eigenvector of the model input.
Preferably, the step S3 is specifically:
setting the working time period and the non-working time period of a user;
judging whether the characteristic values of the user side data of the routing equipment in the working time period are all 0;
if the characteristic values of the user side data of the routing equipment in the working time period are all 0, judging the data as a first classification result;
if the characteristic values of the user side data of the routing equipment in the working time period are all 1, judging the data as a second classification result;
and if the user side data characteristic value of the routing equipment in the working time period comprises 0 and 1, judging the data as a third classification result.
Preferably, the step S5 is specifically: :
s51, checking whether the NTP function of the routing equipment is opened; if yes, collecting user side data of the current routing equipment, and executing step S52; otherwise, continuing to check the NTP function state of the routing equipment;
s52, judging whether the data acquisition of a set period is finished or not; if yes, go to step S6; otherwise, continuing to collect data.
Preferably, the setting function in step S7 specifically includes: firmware upgrade, cache cleaning, reset and restart operations.
Preferably, the step S7 is specifically:
if the classification result obtained in the step S5 is the first classification result, controlling the routing device to execute the setting function during the working period;
if the classification result obtained in the step S5 is the second classification result, controlling the routing device to execute the setting function in the non-working time period;
if the classification result obtained in step S5 is the third classification result, the longest time period when the user-side data characteristic value of the routing device is 0 in all time periods is extracted, and the routing device is controlled to execute the setting function in the longest time period.
In a second aspect, the present invention further provides a routing device control system based on pattern recognition, including:
the data acquisition module is used for acquiring user side data of the routing equipment;
the characteristic vector generating module is used for generating a characteristic vector according to the collected user side data of the routing equipment;
the expected classification module is used for classifying the use condition of the user according to the generated data characteristic value;
the BP neural network classification module is used for constructing a BP neural network classification model and performing model training by using the generated feature vectors and the obtained expected classification result;
the current data acquisition module is used for checking whether the NTP function of the routing equipment is started or not and acquiring user side data of the current routing equipment when the NTP function is started;
the classification module is used for generating a characteristic vector according to the user side data of the current routing equipment and inputting the characteristic vector into the trained BP neural network classification model to obtain a classification result;
and the control module is used for controlling the routing equipment to execute a setting function according to the obtained classification result.
In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for controlling a routing device based on pattern recognition when executing the program.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned pattern recognition-based routing device control method.
According to the method and the system for controlling the routing equipment based on the pattern recognition, provided by the embodiment of the invention, the input parameters are judged and classified based on the pattern recognition theory according to the descriptions of various different characteristics, so that the effect of making the routing equipment establish different patterns according to different use conditions to execute a setting function is achieved, the requirements of different use conditions can be met, and the calculation workload of the routing equipment is simplified.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for controlling a routing device based on pattern recognition according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for controlling a routing device based on pattern recognition according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a routing device control system based on pattern recognition according to an embodiment of the present invention;
fig. 4 is a schematic physical structure diagram according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the embodiment of the present application, the term "and/or" is only one kind of association relationship describing an associated object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" in the embodiments of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, the terms "comprise" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a system, product or apparatus that comprises a list of elements or components is not limited to only those elements or components but may alternatively include other elements or components not expressly listed or inherent to such product or apparatus. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Example 1
Referring to fig. 1 and fig. 2, an embodiment of the present invention provides a method for controlling a routing device based on pattern recognition, including the following steps:
s1, collecting user side data of the routing equipment;
in this embodiment, the present invention constructs the abscissa of 0-1007 by collecting the user-side data of the routing device 6 times per hour from 0 sunday to 24 saturday. Here, the user-side data of the routing device includes LAN and WIFI data.
S2, generating a feature vector according to the user side data of the routing equipment collected in the step S1;
in this embodiment, step S2 specifically includes:
carrying out dichotomy processing on the sum of the collected user side receiving data and the collected sending data of the routing equipment;
judging whether the processing result is greater than a set threshold, wherein the set threshold is defaulted to be one percent of the bandwidth of the routing equipment; if yes, setting the characteristic value of the data to be 1; otherwise, setting the characteristic value of the data to be 0;
the data eigenvalues are mapped to the abscissa of the collected data structure, and a 1008 × 1 column matrix is constructed as the eigenvector of the model input.
S3, classifying the use condition of the user according to the data characteristic value generated in the step S2;
in this embodiment, step S3 specifically includes:
setting the working time period and the non-working time period of a user;
judging whether the characteristic values of the user side data of the routing equipment in the working time period are all 0;
if the characteristic values of the user side data of the routing equipment in the working time period are all 0, judging the data as a first classification result;
if the characteristic values of the user side data of the routing equipment in the working time period are all 1, judging the data as a second classification result;
and if the user side data characteristic value of the routing equipment in the working time period comprises 0 and 1, judging the data as a third classification result.
Specifically, the present invention sets the life of the user's normal work and rest in the desired classification result as a first classification result, the life of the user's abnormal work and rest (day and night reversal) as a second classification result, and the life of the user's irregular work and rest (no regularity) as a third classification result.
The normal work and rest life of the user refers to that the user normally goes to work in a working day and has a rest at night. The corresponding eigenvalue should be 0 during this working period and 1 at night. Otherwise, the opposite situation is abnormal work and rest. For free workers, irregular judgment of the feature vectors can occur, and the situation is different from the former two situations.
S4, constructing a BP neural network classification model, and performing model training by using the feature vector generated in the step S2 and the expected classification result obtained in the step S3;
in this embodiment, the BP neural network classification model is constructed by using a C language architecture, and includes setting the number of hidden layers, training times, and an activation function.
The invention takes the feature vector generated in the step S2 and the expected classification result obtained in the step S3 as training samples, the number of the samples is ensured to be more than 1000, and the number of the samples of different types is close.
The invention trains the BP neural network classification model by using the training sample to obtain the parameters of weight, partial derivative and the like of the hidden layer of the BP neural network, and inputs the parameters into the routing equipment to establish the mathematical model and the classification function.
S5, checking whether the NTP function of the routing equipment is opened; if yes, collecting user side data of the current routing equipment; otherwise, continuing to check the NTP function state of the routing equipment;
in this embodiment, step S5 specifically includes: :
s51, checking whether the NTP function of the routing equipment is started, wherein the NTP function is a protocol of time synchronization and synchronizes time with the server; if yes, collecting user side data of the current routing equipment, and executing step S52; otherwise, continuing to check the NTP function state of the routing equipment;
s52, judging whether the data acquisition of a set period is finished or not; if yes, go to step S6; otherwise, continuing to collect data.
S6, generating a feature vector according to the user side data of the current routing equipment, inputting the feature vector into the BP neural network classification model trained in the step S3, and obtaining a classification result;
and S7, controlling the routing equipment to execute a setting function according to the classification result obtained in the step S5.
In this embodiment, step S7 specifically includes:
if the classification result obtained in the step S5 is the first classification result, controlling the routing device to execute the setting function during the working period; for example, scheduling preset functions to be performed in the evening of monday;
if the classification result obtained in the step S5 is the second classification result, controlling the routing device to execute the setting function in the non-working time period; for example, scheduling preset functions to be performed during monday days;
if the classification result obtained in step S5 is the third classification result, time segment distribution processing is employed to control the routing device to execute the setting function in the longest time segment by extracting the longest time segment when the user-side data characteristic value of the routing device is 0 in all time segments.
Specifically, the setting function of the present invention specifically includes: and upgrading the firmware, clearing the cache, resetting, restarting and the like.
In particular, the present invention needs to perform step S6 again each time a period of data acquisition is completed, so as to ensure adaptive feedback adjustment of user usage.
Example 2
Referring to fig. 3, based on the control method described in embodiment 1, an embodiment of the present invention further provides a routing device control system based on pattern recognition, including:
the data acquisition module is used for acquiring user side data of the routing equipment;
the characteristic vector generating module is used for generating a characteristic vector according to the collected user side data of the routing equipment;
the expected classification module is used for classifying the use condition of the user according to the generated data characteristic value;
the BP neural network classification module is used for constructing a BP neural network classification model and performing model training by using the generated feature vectors and the obtained expected classification result;
the current data acquisition module is used for checking whether the NTP function of the routing equipment is started or not and acquiring user side data of the current routing equipment when the NTP function is started;
the classification module is used for generating a characteristic vector according to the user side data of the current routing equipment and inputting the characteristic vector into the trained BP neural network classification model to obtain a classification result;
and the control module is used for controlling the routing equipment to execute a setting function according to the obtained classification result.
Example 3
Based on the control method described in embodiment 1, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the routing device control method based on pattern recognition described in embodiment 1 when executing the program.
Example 4
Based on the control method described in embodiment 1, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the pattern recognition-based routing device control method as described in embodiment 1.
Based on the same concept, an embodiment of the present invention further provides an entity structure schematic diagram, as shown in fig. 4, the server may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform the steps of the automated batch management CPE method based on the tr069 protocol as described in the embodiments above.
In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Based on the same concept, embodiments of the present invention further provide a non-transitory computer-readable storage medium storing a computer program, where the computer program includes at least one code segment executable by a master device to control the master device to implement the steps of the automated batch management CPE method based on the tr069 protocol according to the foregoing embodiments.
Based on the same technical concept, the embodiment of the present application further provides a computer program, which is used to implement the above method embodiment when the computer program is executed by the main control device.
The program may be stored in whole or in part on a storage medium packaged with the processor, or in part or in whole on a memory not packaged with the processor.
Based on the same technical concept, the embodiment of the present application further provides a processor, and the processor is configured to implement the above method embodiment. The processor may be a chip.
In summary, according to the method for automatically managing the CPEs in batches based on the tr069 protocol provided by the embodiments of the present invention, the scheduled task may be created, the triggered event, the execution range, and the execution time may be set, and the automatic batch management of the CPEs may include automatic upgrade, backup configuration, configuration import, factory reset, and the like, so that a user may conveniently manage the CPEs and the system without manual operation.
The embodiments of the present invention can be arbitrarily combined to achieve different technical effects.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the present application are generated, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid state disk), among others.
One of ordinary skill in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the above method embodiments. And the aforementioned storage medium includes: various media capable of storing program codes, such as ROM or RAM, magnetic or optical disks, etc.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A routing device control method based on pattern recognition is characterized by comprising the following steps:
s1, collecting user side data of the routing equipment;
s2, generating a feature vector according to the user side data of the routing equipment collected in the step S1;
s3, classifying the use condition of the user according to the data characteristic value generated in the step S2;
s4, constructing a BP neural network classification model, and performing model training by using the feature vector generated in the step S2 and the expected classification result obtained in the step S3;
s5, checking whether the NTP function of the routing equipment is opened; if yes, collecting user side data of the current routing equipment; otherwise, continuing to check the NTP function state of the routing equipment;
s6, generating a feature vector according to the user side data of the current routing equipment, inputting the feature vector into the BP neural network classification model trained in the step S3, and obtaining a classification result;
and S7, controlling the routing equipment to execute a setting function according to the classification result obtained in the step S5.
2. The method for controlling routing equipment according to claim 1, wherein the step S2 specifically comprises:
carrying out dichotomy processing on the sum of the collected user side receiving data and the collected sending data of the routing equipment;
judging whether the processing result is larger than a set threshold value or not; if yes, setting the characteristic value of the data to be 1; otherwise, setting the characteristic value of the data to be 0;
and constructing a column matrix by using the data eigenvalue as an eigenvector of the model input.
3. The method for controlling routing equipment according to claim 2, wherein the step S3 specifically comprises:
setting the working time period and the non-working time period of a user;
judging whether the characteristic values of the user side data of the routing equipment in the working time period are all 0;
if the characteristic values of the user side data of the routing equipment in the working time period are all 0, judging the data as a first classification result;
if the characteristic values of the user side data of the routing equipment in the working time period are all 1, judging the data as a second classification result;
and if the user side data characteristic value of the routing equipment in the working time period comprises 0 and 1, judging the data as a third classification result.
4. The method for controlling routing equipment according to claim 3, wherein the step S5 specifically comprises: :
s51, checking whether the NTP function of the routing equipment is opened; if yes, collecting user side data of the current routing equipment, and executing step S52; otherwise, continuing to check the NTP function state of the routing equipment;
s52, judging whether the data acquisition of a set period is finished or not; if yes, go to step S6; otherwise, continuing to collect data.
5. The method for controlling routing equipment according to claim 4, wherein the setting of the function in step S7 specifically includes: firmware upgrade, cache cleaning, reset and restart operations.
6. The method for controlling routing equipment according to claim 5, wherein the step S7 specifically comprises:
if the classification result obtained in the step S5 is the first classification result, controlling the routing device to execute the setting function during the working period;
if the classification result obtained in the step S5 is the second classification result, controlling the routing device to execute the setting function in the non-working time period;
if the classification result obtained in step S5 is the third classification result, the longest time period when the user-side data characteristic value of the routing device is 0 in all time periods is extracted, and the routing device is controlled to execute the setting function in the longest time period.
7. A routing device control system based on pattern recognition, comprising:
the data acquisition module is used for acquiring user side data of the routing equipment;
the characteristic vector generating module is used for generating a characteristic vector according to the collected user side data of the routing equipment;
the expected classification module is used for classifying the use condition of the user according to the generated data characteristic value;
the BP neural network classification module is used for constructing a BP neural network classification model and performing model training by using the generated feature vectors and the obtained expected classification result;
the current data acquisition module is used for checking whether the NTP function of the routing equipment is started or not and acquiring user side data of the current routing equipment when the NTP function is started;
the classification module is used for generating a characteristic vector according to the user side data of the current routing equipment and inputting the characteristic vector into the trained BP neural network classification model to obtain a classification result;
and the control module is used for controlling the routing equipment to execute a setting function according to the obtained classification result.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the pattern recognition based routing device control method according to any of claims 1 to 6 are implemented when the processor executes the program.
9. A non-transitory computer readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the steps of the pattern recognition based routing device control method according to any one of claims 1 to 6.
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