CN116776901A - Optical fiber distribution frame label management system applied to electric power communication machine room - Google Patents
Optical fiber distribution frame label management system applied to electric power communication machine room Download PDFInfo
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- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B6/00—Light guides; Structural details of arrangements comprising light guides and other optical elements, e.g. couplings
- G02B6/44—Mechanical structures for providing tensile strength and external protection for fibres, e.g. optical transmission cables
- G02B6/4439—Auxiliary devices
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- G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
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Abstract
The application relates to an optical fiber distribution frame label management system for an electric power communication machine room, which comprises an RFID label, a reading unit and an information management unit, wherein the RFID label is used for reading information of the optical fiber distribution frame; the RFID tags are embedded in each row of the optical fiber distribution frame and the two ends of the optical fibers, store the type, the receiving and transmitting end position, the trend information and the system version information of the optical fibers, and further comprise readable tags for realizing smooth transition. The reading unit is used for reading the RFID tag, acquiring optical fiber information and trend information according to the system version information and sending the optical fiber information and trend information to the information management unit; the information management unit comprises a database sub-module and an intelligent analysis sub-module, and is used for storing tag information, predicting the health condition of the optical fiber and optimizing maintenance and management flow; the system effectively integrates management information of the optical fiber distribution frame, and improves the efficiency and accuracy of optical fiber management and maintenance.
Description
Technical Field
The application relates to the field of power communication, in particular to an optical fiber distribution frame label management system applied to a power communication machine room.
Background
Management of fiber optic distribution frames in electrical telecommunication rooms is a complex and important task. With rapid development and widespread use of optical fiber technology, how to efficiently and accurately manage and maintain an optical fiber distribution frame has become more and more critical.
In the prior art, a plurality of optical fibers are arranged on optical transmission equipment of an electric power communication machine room, one end of each optical fiber is connected to the equipment, the other end of each optical fiber is connected to an optical fiber distribution frame, both ends of each optical fiber are provided with readable labels, and the types of the optical fibers and positions of receiving and transmitting ends on the optical fiber distribution frame are marked on the readable labels by characters. Each row on the optical distribution frame is also provided with a readable label, which marks the orientation of each row of optical fibers. The specific orientation of the optical fibers can be obtained by sequentially looking at the readable labels on the optical distribution frame. However, for a long time, the readable label is affected by the environment of the machine room, such as temperature, humidity, etc., and the surface writing of the readable label is blurred, which causes great inconvenience to the management and maintenance of the optical fiber.
Therefore, developing a new optical fiber distribution frame label management system to improve the efficiency and accuracy of optical fiber management and maintenance is a task to be solved.
Disclosure of Invention
The application provides an optical fiber distribution frame label management system of an electric power communication machine room, which is used for improving the efficiency and the accuracy of optical fiber management and maintenance.
The application provides an optical fiber distribution frame label management system, which comprises:
the RFID tags are embedded in each row of the optical fiber distribution frame and two ends of the optical fibers, wherein the type of the optical fibers, the positions of the receiving and transmitting ends on the optical fiber distribution frame and other optical fiber information are stored on the RFID tags embedded in each row of the optical fiber distribution frame, and the trend information of each row of the optical fibers is stored on the RFID tags embedded in the two ends of the optical fibers; the RFID tag also stores system version information, and is used for identifying whether the system is a compatible system which simultaneously exists the RFID tag and the readable tag and is used for realizing smooth transition or a non-compatible system which only exists the RFID tag; in the compatible system, the types of the optical fibers and the positions of the receiving and transmitting ends on the optical fiber distribution frame are marked by characters on the readable labels on the two ends of the optical fibers, and the trend of the optical fibers is marked by characters on the readable labels on each row;
the reading unit is used for reading the RFID tag and acquiring system version information; selecting a corresponding mode according to the system version information to process to obtain the optical fiber information and trend information; the optical fiber information and trend information are sent to an information management unit;
the information management unit comprises a database sub-module and an intelligent analysis sub-module; the database submodule is used for receiving and storing the tag information sent by the reading unit; the intelligent analysis submodule interacts with the database submodule, predicts the health condition of the optical fiber and optimizes maintenance and management flow.
Still further, the optical fiber distribution frame label management system further comprises:
an image capturing unit for capturing an image of the readable labels on each row of the optical distribution frame and on both ends of the optical fibers;
a concealment processing unit that extracts key features of the image captured by the image capturing unit using an encoder of a self-encoder and then converts the key features into concealment data by a decoder of the self-encoder;
and the RFID tag coding unit is used for coding the hidden data converted by the hidden processing unit and storing the coded hidden data in the RFID tag.
Still further, the encoder further includes an input layer, a first convolution layer, a first activation layer, a second convolution layer, a second activation layer, and a full connection layer to extract key features from the captured image; the decoder further includes a first full connection layer, a first deconvolution layer, a second deconvolution layer, and an output layer to convert the extracted key features into hidden data.
Still further, the RFID tag encoding unit includes:
the preprocessing module is used for analyzing the distribution characteristics of a symbol set X formed by the hidden data converted by the hidden processing unit, calculating frequency distribution, counting conditional probability among symbols and constructing a context model;
the self-adaptive Huffman coding module constructs a dynamic Huffman tree based on the context model and the symbol set X to generate the code of the symbol, wherein the Huffman tree is dynamically updated along with the occurrence of a new symbol so as to reflect the new frequency distribution;
and the coding function module is used for realizing a self-defined coding function and representing the optimal path of the hidden data in the form of an adaptive coding number, wherein the optimal path is related to the occurrence frequency of the symbol, so that the hidden data is coded and stored in the RFID tag.
Further, the reading unit further comprises an RFID tag decoding module for reading the system version information on the RFID tag; judging whether the system is a compatible system or not according to the read system version information, if so, converting the hidden data into a first key feature of the image captured by the image capturing unit by applying a decoder in a self-encoder used in the hidden processing unit;
the reading unit further comprises an image recognition module, and the image recognition module is used for obtaining the optical fiber information and the trend information according to the first key characteristics and sending the optical fiber information and the trend information to the information management unit.
Still further, the intelligent analysis sub-module predicts the health of the fiber using the following formula:
Score = w1 A + w2 T + w3(F + C) + w4 (D + S)；
where Score is the health Score of the fiber, a is the fiber usage time, T is the fiber type, F represents the fiber flow, C represents the fiber connection number, D represents the fiber attenuation, S represents the fiber dispersion, w1, w2, w3, and w4 are weights, which can be optimized by historical data and machine learning methods.
Still further, the intelligent analysis sub-module is further configured to perform multi-objective optimization via genetic algorithms, the multi-objective optimization including optimization of objectives such as maintenance cost, maintenance time, and network availability.
Still further, the optimization algorithm execution further includes consideration of the following constraints:
type constraints for ensuring that the assigned maintenance resources match fiber types, considering that different types of fibers require different maintenance tools and skills;
position constraint, considering that the physical position of the optical fiber affects maintenance time and cost;
health condition constraint, determining maintenance frequency and required professional skills according to the health condition of the optical fiber;
historical maintenance record constraints utilize information provided by past maintenance records about failure trends, maintenance personnel efficiencies, and the like to adjust maintenance plans.
Still further, the intelligent analysis sub-module is further configured to perform multi-objective optimization by genetic algorithm, comprising:
creating an initial population;
selecting a parent based on an fitness function, wherein the fitness function is formed by combining an objective function and constraint conditions;
generating offspring by crossing selected parents;
the offspring are mutated with a certain probability.
Further, the database submodule is further used for storing parameters such as fiber use time, fiber type, fiber flow, fiber connection number, fiber attenuation, fiber dispersion and the like.
According to the optical fiber distribution frame label management system provided by the application, the RFID labels are embedded in each row of the optical fiber distribution frame and the two ends of the optical fibers, and the type, position and trend information and system version information of the optical fibers are stored, so that the automatic management of the optical fiber information is realized. This is a significant innovation compared to traditional management methods. In addition, the information management unit is internally provided with a database sub-module and an intelligent analysis sub-module, so that the optical fiber information can be stored, the prediction of the optical fiber health condition can be carried out, and the maintenance and management flow can be optimized. The design breaks through the limitation of the traditional optical fiber management, and realizes higher-level automation and intellectualization.
The technical scheme provided by the application has the following beneficial effects:
(1) Efficiency and accuracy are improved: by automatically reading and processing the optical fiber information in the RFID tag, manual intervention and potential errors are reduced, and management efficiency and accuracy are greatly improved.
(2) Flexibility in fiber management is enhanced: by means of the compatibility of different versions of the system and the readable label, the system and the method can be more easily adapted to different environments and requirements, and the application flexibility of the system and the method is enhanced.
(3) Optimizing and maintaining the flow: the prediction and optimization functions of the intelligent analysis sub-module are beneficial to more accurately identifying the maintenance requirement of the optical fiber, so that maintenance work is purposefully arranged, unnecessary maintenance cost is reduced, and maintenance efficiency is improved.
Drawings
Fig. 1 is a schematic diagram of an optical fiber distribution frame label management system applied to an electric power communication room according to a first embodiment of the present application.
Fig. 2 is a schematic diagram of a self-encoder according to a first embodiment of the present application.
Description of the embodiments
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than those herein described, and those skilled in the art will readily appreciate that the present application may be similarly embodied without departing from the spirit or essential characteristics thereof, and therefore the present application is not limited to the specific embodiments disclosed below.
The first embodiment of the application provides an optical fiber distribution frame label management system applied to an electric power communication machine room. Referring to fig. 1, a schematic diagram of a first embodiment of the present application is shown. A first embodiment of the present application is described in detail below with reference to fig. 1, where the label management system of an optical fiber distribution frame is applied to an electric power communication room.
The system comprises an RFID tag 101, a reading unit 102, an information management unit 103.
The RFID tags 101 are embedded in each row of the optical fiber distribution frame and two ends of the optical fiber, wherein the RFID tags embedded in each row of the optical fiber distribution frame store optical fiber information such as the type of the optical fiber and the position of the transceiver on the optical fiber distribution frame, and the RFID tags embedded in two ends of the optical fiber store trend information of each row of the optical fiber; the RFID tag also stores system version information, and is used for identifying whether the system is a compatible system which simultaneously exists the RFID tag and the readable tag and is used for realizing smooth transition or a non-compatible system which only exists the RFID tag; in the compatible system, the types of the optical fibers and the positions of the receiving and transmitting ends on the optical fiber distribution frame are marked by characters on the readable labels on the two ends of the optical fibers, and the trend of the optical fibers is marked by characters on the readable labels on each row.
In the prior art, a plurality of optical fibers are arranged on optical transmission equipment of an electric power communication machine room, one end of each optical fiber is connected to the equipment, the other end of each optical fiber is connected to an optical fiber distribution frame, both ends of each optical fiber are provided with readable labels, and the types of the optical fibers and positions of receiving and transmitting ends on the optical fiber distribution frame are marked on the readable labels by characters. Each row on the optical distribution frame is also provided with a readable label, which marks the orientation of each row of optical fibers. The specific orientation of the optical fibers can be obtained by sequentially looking at the readable labels on the optical distribution frame. However, for a long time, the readable label is affected by the environment of the machine room, such as temperature, humidity and the like, and the surface writing of the readable label is blurred, which causes great inconvenience to the optical fiber arrangement work.
RFID (radio frequency identification) tags are a wireless communication technology capable of automatically identifying, tracking and managing items associated therewith by radio waves. RFID tags are typically composed of a microelectronic chip and an antenna that can store specific information and communicate with a reader. Aiming at the defects of the readable tag in the prior art, the RFID tag in the embodiment is made of a special material with high temperature resistance and moisture resistance, and can work for a long time in a high-temperature and high-humidity environment of an electric power communication machine room.
The RFID tag also stores system version information for identifying whether the system is a compatible system for achieving smooth transitions in which both the RFID tag and the readable tag are present, or a non-compatible system in which only the RFID tag is present. The optical fiber distribution frame label management system provided by the embodiment can be used for upgrading the existing system. In the existing system, the types of the optical fibers and the positions of the receiving and transmitting ends on the optical fiber distribution frame are marked by characters on the readable labels on the two ends of the optical fibers, and the trend of the optical fibers is marked by the characters on the readable labels on each row. In an upgrade project of an existing system, system version information stored on an RFID tag identifies the system as a compatible system, i.e., the system supports both readable tags and RFID tags. If the system version information read by the reading unit 102 is a compatible system, the system generates an RFID tag through an image capturing unit, a hiding processing unit and an RFID tag encoding unit in the following description; the optical fiber information and trend information are obtained through an RFID tag decoding module and an image recognition module in the following description; and transmitting the optical fiber information and trend information to an information management unit.
In a compatible system, in order to upgrade and reform an existing readable tag, the optical fiber distribution frame tag management system provided in this embodiment may further include an image capturing unit, a hiding processing unit, and an RFID tag encoding unit, where these units are used to generate an RFID tag; these units are described below.
An image capturing unit for capturing images of the readable labels on each row of the optical distribution frame and on both ends of the optical fibers.
The image capturing unit is used to capture images of the readable labels on each row of the optical distribution frame and on both ends of the optical fibers. The image information contains key information about the type, transceiver position, trend and the like of the optical fiber.
The image capturing unit is generally composed of:
(1) A camera head: a camera with sufficient resolution and focusing capability for clearly capturing text or symbols on a readable label.
(2) The light source adjusting module: the light source is adjusted to ensure the definition and the readability of the image, and the problems of light reflection or shadow and the like are prevented.
(3) An image preprocessing module: the captured image is subjected to necessary preprocessing such as denoising, contrast adjustment, saturation adjustment, and the like.
The image capture unit is first positioned to readable labels on each row of the fiber optic distribution frame and on both ends of the optical fibers, and then focused to those labels. Then, an image on the readable label is captured using a camera. Finally, the captured image is subjected to the necessary preprocessing for further analysis and decoding.
The image capturing unit plays a key role in the optical distribution frame tag management system, and acquires key information of the optical fiber by capturing an image of the readable tag.
And a concealment processing unit that extracts key features of the image captured by the image capturing unit using an encoder from an encoder of the encoder and then converts the key features into concealment data by a decoder of the encoder.
The concealment processing unit is intended to extract key features from the image captured by the image capturing unit and convert them into concealment data. This conversion process uses a self-encoder, and this embodiment proposes a self-encoder 200 suitable for use in this embodiment. The following describes the self-encoder 200 in detail with reference to fig. 2. The self-encoder 200 includes an encoder 210 and a decoder 220. The encoder 210 is responsible for extracting key features from the captured image. The decoder 220 is responsible for converting the extracted features into hidden data.
Encoder 210 includes the following:
input layer 211: the captured image is input in a size of 256×256×3.
First convolution layer 212: the data of the input layer is processed through 32 3 x 3 convolution kernels of step 1. The output size was 254×254×32.
First active layer 213: in this layer, the output of the first convolutional layer 212 is processed by a ReLU activation function. The output size is 254×254×32, which is the same as the input.
Second convolution layer 214: this layer processes the output of the first active layer 213. Since 64 convolution kernels of 3×3 with step size 1 are used, the output size is 252×252×64.
Second activation layer 215: interfacing with the previous convolutional layer 214, a ReLU activation function is used. The output size is 252×252×64, which is the same as the input size.
Full connection layer 216: in this layer, first, the output of the second active layer 215 is flattened, becoming a one-dimensional vector. The size was changed from 252×252×64 to 1× (252×252×64). This one-dimensional vector is then processed through a fully connected layer of 128 nodes to produce a feature vector of fixed size.
The fully connected layer 216 is used here to compress information into a more compact representation of features in the high-dimensional feature space extracted from the convolution and activation layers. These successive operations work together to progressively extract the useful information in the image and compress it into a feature vector that can be used for decoding.
Decoder 220 includes the following:
first full connection layer 221: corresponding to the fully connected layer 216 of the encoder 210, there are 128 nodes.
First deconvolution layer 222: 64 3 x 3 convolution kernels are used, with a step size of 1.
The input is 252×252×64, and the output size is: (252-3+1) x (252-3+1) x64=250×250×64.
Second deconvolution layer 223: 32 3 x 3 convolution kernels are used, with a step size of 1.
Output size: (250-3+1) × (250-3+1) ×32=248×248×32.
Output layer 224: the hidden data is output in the form of a 32 x 32 matrix having a specific structure. This layer may be sized from 248 x 32 to 32 x 32 by an additional convolution and pooling layer.
The hiding processing unit obtains hidden data through the processing, so that the security of RFID tag data is improved.
And the RFID tag coding unit is used for coding the hidden data converted by the hidden processing unit and storing the coded hidden data in the RFID tag.
The RFID tag encoding unit is responsible for encoding the hidden data and storing it in the RFID tag. The part can realize the coding of the hidden data through the following modules:
(1) And a pretreatment module: and analyzing the distribution characteristics of the hidden data, and constructing a context model.
First, a frequency distribution is calculated. Statistics is carried out on a symbol set X of the hidden data to obtain symbol frequency distributionFWhereinFiRepresenting symbolsiThe frequency of occurrence in all symbols in set X.
Then, constructA context model ContextModel is built, which may be composed of a conditional probability matrix, in which each elementRepresenting given the previous symbol->In the case of (2), the next symbol isx i Is a probability of (2).
First, a null conditional probability matrix is constructed. Then, the collection is traversedXFor each symbolx i The number of times each other symbol follows is counted and the conditional probability is calculated.
For example: given the symbol a, the number of times it follows a is 1, the number of times it follows b is 0, and the number of times it follows c is 1. Thus, given a, the conditional probabilities of the other symbols are:
P(a∣a) = 1/2；
P(b∣a) = 0/2；
P(c∣a) = 1/2；
this process is repeated and a similar conditional probability can be calculated for each symbol.
The context model describes the association between symbols and can be used as part of adaptive huffman coding in order to code hidden data more efficiently.
(2) An adaptive Huffman coding module:
the module builds a dynamic huffman tree based on the context model and the symbol set X and generates the code.
The symbol code encoding (xi) can be obtained by the following formula:
encode( xi) = BinaryTreeTraversal (xi, HuffmanTree )；
where xi is a specific symbol to be encoded, binaryTreeTraversal is a binary tree traversal function for traversing the Huffman tree to find the path of the symbol xi, starting from the root node, with the left hand branch denoted 0 and the right hand branch denoted 1. HuffmanTree is a huffman tree, which is a binary tree constructed from a symbol frequency distribution for encoding and decoding.
Huffman tree update:
HuffmanTree = update (HuffmanTree,xi , ContextModel)；
update is an update function that incorporates new symbols xi and context model information into the huffman tree, readjusting the tree structure to reflect the new frequency distribution. This process ensures that the huffman tree is dynamically updated as new symbols appear, thereby enabling more efficient encoding.
(3) And a coding function module:
the custom encoding function custom code can be expressed as:
CustomCode( X )=AdaptiveHuffman ( X, ContextModel )；
the adaptive coding function adaptive Huffman can be realized by the following formula:
AdaptiveHuffman ( X, ContextModel )={ optimized_path ( xi )∣xi ∈ X}；
where optimized_path represents finding the optimal path of symbol xi in the huffman tree. The optimality here means that in relation to the coding efficiency, the frequency of occurrence of symbols can be based.
The write operation of the RFID tag may use a standard RFID write protocol, such as ISO 14443, etc., to store the encoded hidden data in a specific memory area of the RFID tag.
After storing the encoded hidden data in a specific storage area of the RFID tag, the embodiment also reads the hidden data in the RFID tag. In particular, the method can be realized by the following modules.
Firstly, the reading unit further comprises an RFID tag decoding module for reading system version information on the RFID tag; judging whether the system is a compatible system according to the read system version information, if so, converting the hidden data into a first key feature of the image captured by the image capturing unit by applying a decoder 220 in a self-encoder 200 used in the hidden processing unit;
the reading unit further comprises an image recognition module which can be realized by using a neural network, obtains the optical fiber information and the trend information according to the first key characteristics, and sends the optical fiber information and the trend information to the information management unit.
The optical fiber distribution frame label management system provided by the embodiment can be also applied to new electric power communication machine room projects. In the new power communication room project, no readable tags are present, only RFID tags are present. The RFID tags are embedded in each row of the optical fiber distribution frame and two ends of the optical fibers, wherein the type of the optical fibers, the positions of the receiving and transmitting ends on the optical fiber distribution frame and other optical fiber information are stored in the RFID tags embedded in each row of the optical fiber distribution frame, and trend information of the optical fibers in each row is stored in the RFID tags embedded in the two ends of the optical fibers. In a new power communication room project, the system version information stored on the RFID tag identifies the system as a non-compatible system, i.e., the system only supports the RFID tag. In a non-compatible system, a reading unit 102 reads the RFID tag to obtain system version information; judging that the system is a non-compatible system according to the system version information, and directly reading the optical fiber information and trend information stored in the RFID tag by the system to obtain the optical fiber information and trend information; and transmitting the optical fiber information and trend information to an information management unit. Here, it should be noted that the content stored in the RFID tag may be encrypted content, and then the reading unit also needs to perform a decryption operation. Since encryption and decryption are common technical means, the embodiments thereof will not be described in detail herein.
The information management unit 103 comprises a database sub-module and an intelligent analysis sub-module; the database submodule is used for receiving and storing the tag information sent by the reading unit; the intelligent analysis submodule interacts with the database submodule, predicts the health condition of the optical fiber, optimizes maintenance and management flow and positions communication fault positions.
The present embodiment provides the following formula to predict the health of the fiber:
Score = w1 A + w2 T + w3(F + C) + w4 (D + S)；
where Score is the health Score of the fiber. A is the fiber usage time, which may be, for example, the current time minus the fiber installation date. T is the fiber type, e.g. a single mode fiber may be set to 1 and a multimode fiber may be set to 2.F represents fiber traffic, e.g., 10Gbps. C represents the number of fiber optic connections, e.g., 100 connections. D represents fiber attenuation, which can be measured by an optical power meter and a light source. The calculation formula is as follows:
；
where Pin is the input power, pout is the output optical power, and L is the fiber length.
S denotes the fiber dispersion, which can be measured by a dispersion tester. For example, the dispersion coefficient may be expressed as 17 ps/(nm km). Where w1, w2, w3 and w4 are weights that can be optimized through historical data and machine learning methods. The fiber use time A, the fiber type T, the fiber flow F, the fiber connection number C, the fiber attenuation D and the fiber dispersion can be obtained from the database sub-module by the intelligent analysis sub-module.
The formula can be used as a prediction index of the health condition of the optical fiber, and possible problems can be found in advance by analyzing the change trend of the data so as to take proper maintenance measures.
The intelligent analysis submodule optimizes maintenance and management flow through the following steps:
s101: the data collection and pretreatment can obtain physical parameters such as attenuation, dispersion and the like and actual use conditions such as the flow, connection number and the like of the optical fiber health status data from a database submodule; fiber optic position data, such as the position of the transceiver end on the fiber optic distribution frame; fiber type data, such as single mode, multimode; historical maintenance records such as previous failures, maintenance personnel, time and cost required, etc.
S102: selection and customization of a multi-objective optimization algorithm:
genetic algorithms (Genetic Algorithm, GA) are chosen as optimization tools, as they can effectively solve the multi-objective optimization problem.
Multiple objective functions are defined, such as minimizing maintenance costs, minimizing maintenance time, maximizing network availability, etc.
The objectives of the optimization provided by the present embodiments generally relate to several key factors, such as maintenance costs, maintenance time, and network availability. The following are detailed steps of how these objective functions are defined:
(1) Minimum maintenance cost:
an objective function may be constructed to minimize maintenance costs, which may consist of:
the labor cost is as follows: this includes wages and extra costs for maintenance personnel. For example, if the engineer's per-hour cost is 50 yuan, then the 4 hour maintenance task would cost 200 yuan.
Tool and equipment costs: calculated according to the required tool and equipment type. For example, the rental fee for a particular test device may be 30 yuan/day.
Travel and traffic costs: calculated from the position of the optical fiber. For example, if maintenance personnel need to travel 10 kilometers, the cost may be 50 yuan.
These costs can be combined into one mathematical formula to represent the total maintenance cost:
C= C labor + C equipment + C travel
wherein C is the total cost, C labor Is the labor cost, C equipment Is the equipment cost, C travel Is the cost of travel.
(2) Minimum optimization time:
reducing maintenance time can improve efficiency and reduce downtime. This objective function may be constructed by:
task time: the time required for each maintenance task.
Arrival time: the time required to reach the site.
Preparation time: the time required to acquire and set the tools and equipment.
These times can be combined into a mathematical formula:
T = T task + T travel + T prep；
wherein T is the total time, T task Is the task time, T travel Is the arrival time, T prep Is the preparation time.
3. Maximizing network availability:
network availability is the degree to which a network can be used at a given time. It can be optimized by reducing the down time and improving the recovery speed. An availability function may be constructed, for example:
；
wherein A is availability, T total Is the total time, T downtime Is the failure time.
With these three objective functions, a multi-objective optimization problem can be created covering key aspects of maintenance cost, time and network availability. The combination of these objective functions can comprehensively analyze and optimize the maintenance and management of the optical fiber network to meet different business requirements and objectives.
The necessary constraints are set according to the type, location and health of the optical fiber.
The multi-objective optimization algorithm needs to take into account some constraints. Depending on the type, location and health of the fiber, the following necessary constraints may be set:
type constraint: different types of optical fibers (e.g., single mode, multimode) may require different maintenance tools and skills. Constraints may ensure that the assigned maintenance resources match the fiber type.
Position constraint: the physical location of the optical fibers may affect maintenance time and costs. For example, a remote location may require more time of arrival and expense.
Health constraints: if the health of the fiber is poor (e.g., high attenuation, dispersion, etc.), more frequent maintenance and more specialized skills may be required.
History maintenance record constraints: past maintenance records may provide information regarding failure propensity, maintenance personnel efficiency, etc., which is analyzed to help adjust maintenance plans.
S103: the optimization algorithm performs:
(1) Initializing:
creating an initial population: for example, an initial population is created that includes 100 maintenance plans. Each maintenance plan consists of a set of maintenance tasks, tool assignments, personnel assignments, etc.
(2) Selection, crossover and mutation:
selection operation: the parent is selected based on the fitness function. The fitness function may be formed by a combination of an objective function and constraints. For example, roulette selection strategies are used.
Crossover operation: the selected parent generates offspring by crossing. For example, single point crossover is used, where the plans of two parents are separated at a random point and the parts are swapped to produce children.
Mutation operation: the offspring are mutated with a certain probability. For example, the time or tool allocation of a maintenance task for a child is randomly altered.
(3) Termination condition:
maximum number of iterations: for example, if 1000 iterations are reached, the algorithm is stopped.
Solution set convergence: for example, if the improvement does not exceed 1% for 50 consecutive iterations, the algorithm is stopped.
S104: result analysis and execution:
(1) Analysis of results:
evaluation of the optimization results:
the optimized solution will be carefully evaluated to ensure that multiple optimization objectives are met. This process may involve the following steps:
(a) Feasibility check of solution: verifying whether the solution meets all constraints, such as budget, time constraints, etc.
(b) Comparison of solutions: different solutions are compared using different metrics, such as cost-effectiveness ratio, response time, etc.
(c) Sensitivity analysis of solutions: the sensitivity of the solution to variations in the various parameters is analyzed to ensure its robustness in different scenarios.
Selecting the best solution:
selecting the best solution may involve the following considerations:
(a) Consistency with business objectives: solutions are selected that most closely agree with the long-term and short-term objectives of the tissue.
(b) Input by stakeholders: feedback from key stakeholders, such as maintenance teams, management layers, and the like, is obtained.
(2) Performing maintenance planning:
performing a maintenance plan requires precisely reconciling multiple elements. The following steps are detailed:
and (3) resource scheduling: and determining the specific requirements of maintenance personnel, tools, equipment and traffic resources, and scheduling.
For example: if the best solution suggests that sunday be serviced, it must be ensured that the required personnel and tools are available on that day.
Notification and communication: ensure that all relevant personnel know the plan and communicate as necessary.
For example: team members, clients, etc. are notified via email or other communication channel.
(3) Updating a database:
(a) Updating a database:
this step involves the accurate entry of new maintenance planning and resource allocation information into the database. This may include:
inserting a new record: the detailed information of the new plan is added to the database.
Updating the existing record: and updating the existing optical fiber information, maintenance records and the like according to the new maintenance plan.
Ensuring data consistency: and verifying the consistency of the new data and the existing data, and ensuring that no conflict or error exists.
(b) Maintaining a database:
in addition, care should be taken in maintaining databases, such as backup, performance monitoring, etc., to ensure reliability and availability of data.
Through the above steps, the result analysis and execution phase ensures an efficient implementation and management of the optimized maintenance plan. From the evaluation and selection of solutions to specific maintenance executions and database updates, each step involves precise coordination and control.
Through the above description, the intelligent analysis submodule automatically adjusts the maintenance plan and the resource allocation according to the health condition, the position, the type and the historical maintenance record of the optical fiber.
While the application has been described in terms of preferred embodiments, it is not intended to be limiting, but rather, it will be apparent to those skilled in the art that various changes and modifications can be made herein without departing from the spirit and scope of the application as defined by the appended claims.
Claims (10)
1. An optical fiber distribution frame label management system applied to an electric power communication machine room, which is characterized by comprising:
the RFID tags are embedded in each row of the optical fiber distribution frame and two ends of the optical fibers, wherein the type information of the optical fibers and the position information of the receiving and transmitting ends on the optical fiber distribution frame are stored on the RFID tags embedded in each row of the optical fiber distribution frame, and the trend information of each row of the optical fibers is stored on the RFID tags embedded in the two ends of the optical fibers; the RFID tag also stores system version information, and is used for identifying whether the system is a compatible system which simultaneously exists the RFID tag and the readable tag and is used for realizing smooth transition or a non-compatible system which only exists the RFID tag; in the compatible system, the types of the optical fibers and the positions of the receiving and transmitting ends on the optical fiber distribution frame are marked by characters on the readable labels on the two ends of the optical fibers, and the trend of the optical fibers is marked by characters on the readable labels on each row;
the reading unit is used for reading the RFID tag and acquiring system version information; selecting a corresponding mode according to the system version information to process to obtain the optical fiber information and trend information; the optical fiber information and trend information are sent to an information management unit;
the information management unit comprises a database sub-module and an intelligent analysis sub-module; the database submodule is used for receiving and storing the tag information sent by the reading unit; the intelligent analysis submodule interacts with the database submodule, predicts the health condition of the optical fiber and optimizes maintenance and management flow.
2. The fiber optic distribution frame label management system of claim 1, further comprising:
an image capturing unit for capturing an image of the readable labels on each row of the optical distribution frame and on both ends of the optical fibers;
a concealment processing unit that extracts key features of the image captured by the image capturing unit using an encoder of a self-encoder and then converts the key features into concealment data by a decoder of the self-encoder;
and the RFID tag coding unit is used for coding the hidden data converted by the hidden processing unit and storing the coded hidden data in the RFID tag.
3. The fiber optic patch panel tag management system of claim 2, wherein the encoder further comprises an input layer, a first convolution layer, a first activation layer, a second convolution layer, a second activation layer, and a full connection layer to extract key features from the captured image; the decoder further includes a first full connection layer, a first deconvolution layer, a second deconvolution layer, and an output layer to convert the extracted key features into hidden data.
4. The fiber optic patch panel tag management system of claim 2, wherein the RFID tag encoding unit comprises:
the preprocessing module is used for analyzing the distribution characteristics of a symbol set X formed by the hidden data converted by the hidden processing unit, calculating frequency distribution, counting conditional probability among symbols and constructing a context model;
the self-adaptive Huffman coding module constructs a dynamic Huffman tree based on the context model and the symbol set X to generate the code of the symbol, wherein the Huffman tree is dynamically updated along with the occurrence of a new symbol so as to reflect the new frequency distribution;
and the coding function module is used for realizing a self-defined coding function and representing the optimal path of the hidden data in the form of an adaptive coding number, wherein the optimal path is related to the occurrence frequency of the symbol, so that the hidden data is coded and stored in the RFID tag.
5. The fiber optic patch panel tag management system of claim 2, wherein the reading unit further comprises an RFID tag decoding module for reading system version information on the RFID tag; judging whether the system is a compatible system or not according to the read system version information, if so, converting the hidden data into a first key feature of the image captured by the image capturing unit by applying a decoder in a self-encoder used in the hidden processing unit;
the reading unit further comprises an image recognition module, and the image recognition module is used for obtaining the optical fiber information and the trend information according to the first key characteristics and sending the optical fiber information and the trend information to the information management unit.
6. The fiber optic distribution frame label management system of claim 1, wherein the intelligent analysis sub-module predicts the health of the optical fiber using the formula:
Score = w1 A + w2 T + w3(F + C) + w4 (D + S)；
where Score is the health Score of the fiber, a is the fiber usage time, T is the fiber type, F represents the fiber flow, C represents the fiber connection number, D represents the fiber attenuation, S represents the fiber dispersion, w1, w2, w3, and w4 are weights, which can be optimized by historical data and machine learning methods.
7. The fiber optic patch panel tag management system of claim 1, wherein the intelligent analysis sub-module is further configured to perform multi-objective optimization via genetic algorithms, the multi-objective optimization including optimization of maintenance costs, maintenance time, and network availability.
8. The fiber optic distribution frame tag management system of claim 7, wherein the multi-objective optimized algorithm execution further comprises consideration of the following constraints:
type constraints for ensuring that the assigned maintenance resources match fiber types, considering that different types of fibers require different maintenance tools and skills;
position constraint, considering that the physical position of the optical fiber affects maintenance time and cost;
health condition constraint, determining maintenance frequency and required professional skills according to the health condition of the optical fiber;
historical maintenance record constraints utilize information provided by past maintenance records regarding failure trends, maintenance personnel efficiencies, and adjust maintenance plans.
9. The fiber optic distribution frame tag management system of claim 7, wherein the intelligent analysis sub-module is further configured to perform multi-objective optimization by genetic algorithm, comprising:
creating an initial population;
selecting a parent based on an fitness function, wherein the fitness function is formed by combining an objective function and constraint conditions;
generating offspring by crossing selected parents;
the offspring are mutated with a certain probability.
10. The fiber optic distribution frame tag management system of claim 1, wherein the database submodule is further configured to store fiber time of use, fiber type, fiber flow, fiber connection count, fiber attenuation, and fiber dispersion parameters.
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