Intelligent data analysis device and method for integrated wiring management system

Abstract

The present invention includes an intelligent data analysis device of a comprehensive wiring management system, characterized by generating a data table for predicting a failure or a state and a life of a network device and a connected cable of at least one of a comprehensive wiring frame or an intermediate wiring frame, the data table is grouped into one data set classified according to an algorithm and a set condition for operating a set condition of each diagnosis item of an object device, classified analysis is performed on raw data collected by an information collection unit according to the set condition, an input value calculated based on the corresponding data set is input to a conditional sentence in which a failure or a life prediction condition is set for each diagnosis item, and a failure diagnosis and a life prediction value are calculated by comparing a calculated result value with the set condition.

Description

Technical Field

[0001] This invention relates to an intelligent data analysis device and method for an integrated wiring management system. Background Technology

[0002] The integrated wiring management system includes an integrated patch panel and at least one network device among the following: a patch panel, a fiber optic splitter (fiber optic patch panel), and an L2 switch or an L3 switch. It manages and comprehensively monitors the wiring connected to the outlets in the home.

[0003] To cope with frequent maintenance, equipment changes, expansion construction, and fault handling, this integrated wiring management system needs to accurately identify the wire connection status of each device. When a communication failure occurs in a specific device or user, it needs a function to immediately identify the communication network path causing the failure and take measures to reduce downtime and cost losses.

[0004] However, in most field settings, managing circuit connection status via handwritten notes or spreadsheets (Excel) becomes difficult due to frequent maintenance and repair work causing circuit changes, making accurate wire number management challenging. Consequently, when a fault occurs, the communication path cannot be accurately identified, and identifying and repairing the circuit consumes significant time and involves unnecessary workflows.

[0005] Furthermore, existing integrated wiring management systems receive various signals from network devices installed on integrated wiring racks to detect faults and anomalies, and have the function of organizing the received data and outputting it as visualized data.

[0006] To address this, existing integrated wiring management systems construct databases to store and analyze the received data, and are equipped with various software programs for anomaly detection and statistics.

[0007] However, it is predicted that smart buildings will become more common in the future. Correspondingly, integrated wiring management systems will also need high-performance management systems that can process large amounts of data quickly and in real time. Therefore, when building such systems, not only are high-performance data processing devices required, but also, as the amount of computing power increases, the power consumption will also increase, resulting in increased setup and manufacturing costs.

[0008] Existing technical documents

[0009] Patent documents

[0010] Patent document 0001: KR 10-2665920 B1 (May 9, 2024) Summary of the Invention

[0011] This invention is proposed to solve existing problems. The purpose of this invention is to provide an intelligent data analysis device and method for an integrated wiring management system that can analyze large amounts of data with relatively little computing power and predict and diagnose faults or malfunctions in the integrated wiring system through data analysis.

[0012] To achieve the objectives described above, the present invention includes the following embodiments.

[0013] The intelligent data analysis device of the integrated wiring management system of this invention includes a management terminal for monitoring the cable number management and faults of at least one of a comprehensive distribution frame (MDF) and an intermediate distribution frame (IDF) equipped with multiple network devices. The management terminal includes: a cable number management unit for constructing a network topology connecting the MDF, IDF, outlets, and user terminals; an information collection unit for collecting raw data from network devices; a data analysis unit for generating a data table to predict the faults, status, and lifespan of network devices and connected cables installed in either the MDF or IDF, wherein the data table is grouped into a dataset classified according to an algorithm and set conditions for calculating the status of the target device for each set diagnostic item; the raw data collected by the information collection unit is classified and analyzed according to the set conditions; the input value calculated based on the corresponding dataset is input into a conditional statement that sets fault or lifespan prediction conditions for each diagnostic item; the calculated result value is compared with the set conditions to calculate the fault diagnosis and lifespan prediction value; and a warning unit for warning of faults and remaining lifespan based on the diagnostic results of the data analysis unit.

[0014] In the embodiment, the data table includes: an operation condition clause, which sets the calculation algorithm for the input value input to the condition clause; and a condition value, which inputs the original data corresponding to the set condition and inputs it into the operation condition clause.

[0015] Furthermore, the data analysis department includes: a raw database for storing data collected by the information collection department; a data classification module for classifying and inputting the condition values ​​of the data tables by analyzing the raw data in the raw database based on the set conditions of each data table; a classification database for storing the data tables of various diagnostic items input by the data classification module; and a decision module for substituting the condition values ​​of the data tables stored in the classification database into the calculation algorithm set in the calculation condition statement to calculate the input value of the condition statement, and calculating the fault diagnosis and life prediction values ​​through its result value.

[0016] In addition, the data analysis department also includes a decision tool database, which stores the calculation algorithms set in the data tables.

[0017] The junction box may be equipped with at least one of a wiring sensor and a detection device. The wiring sensor is used to detect whether the cable fastened to the port is secure, and the detection device is used to detect the internal resistance of the cable, the reflection frequency of the cable, the resistance of the port, and the abnormal power supply of the port.

[0018] Among them, the wiring sensor may include a reflective optical sensor.

[0019] Furthermore, the management terminal sends information to the building control system, which controls at least one of the following: power system, parking management system, gas system, fire protection system, access control system, refrigeration and heating system, video system, and lighting system.

[0020] According to another embodiment of the present invention, an intelligent data analysis method for an integrated wiring management system includes: step a) collecting raw data from network devices installed in at least one integrated patch panel and intermediate patch panel; step b) classifying and inputting the raw data that meets the set conditions of a data table for predicting the faults or conditions and lifespan of the network devices and connected cables, wherein the data table is grouped into a dataset classified according to an algorithm and set conditions for calculating the status of the object devices for each set diagnostic item; step c) inputting the condition values ​​input to the corresponding dataset into a calculation condition statement to calculate the input value of the condition statement; step d) inputting the input values ​​into a condition statement that sets fault or lifespan prediction conditions for each diagnostic item and comparing them with the set conditions to calculate the fault diagnosis and lifespan prediction results; and step e) selectively and automatically executing a plan based on the fault diagnosis and lifespan prediction results to output a warning including the fault or lifespan prediction results.

[0021] According to the embodiment, in step e), a warning is sent by the building control system, which controls at least one of the following: power system, parking management system, gas system, fire protection system, access control system, refrigeration and heating system, imaging system, and lighting system.

[0022] Therefore, this invention is based on grouping the collected data into conditional statements by predefining the dataset of the original data through classification analysis and correlation analysis. As a result, large amounts of data can be analyzed with less computing power, thus reducing manufacturing costs. Moreover, the analysis can be performed with less computing power, thus saving energy. Attached Figure Description

[0023] Figure 1 This is a block diagram illustrating the intelligent data analysis device of the integrated wiring management system of the present invention.

[0024] Figure 2 This is a structural diagram of the management terminal.

[0025] Figure 3 A block diagram showing the data analysis department.

[0026] Figure 4 This is a diagram illustrating an example of the data table being categorized and analyzed.

[0027] Figure 5 A diagram illustrating an embodiment of the correlation analysis.

[0028] Figure 6 This is a flowchart illustrating the intelligent data analysis method of the integrated wiring management system.

[0029] Explanation of reference numerals in the attached figures

[0030] 10: Management Terminal; 20: Integrated Distribution Frame

[0031] 30: Intermediate patch panel 40: Port

[0032] 110: Line Number Management Department; 120: Information Collection Department

[0033] 130: Data Analysis Department; 131: Raw Database

[0034] 132: Data Classification Module 133: Classification Database

[0035] 134: Decision Module 135: Decision Tool Database

[0036] 140: Warning Department; 150: Visual Information Provision Department

[0037] 210: Connector board; 220: Fiber optic connector board

[0038] 230: Sensor Detailed Implementation

[0039] This invention can be modified in many ways and has many embodiments; however, it is only used to illustrate specific embodiments in conjunction with the accompanying drawings. This does not mean that the invention is limited to a specific implementation, but can be understood as corresponding to one of the modifications, equivalents, and alternatives within the inventive concept and technical scope for connecting and / or fixing structures extending in different directions.

[0040] In this specification, the terminology used is for illustrative purposes only and is not intended to limit the invention. Unless the context clearly indicates otherwise, singular expressions include plural expressions.

[0041] In this specification, terms such as “comprising” or “having” are used to specify the presence of features, figures, steps, operations, structural elements, components or combinations thereof described in the specification, and should be understood as not precluding the presence or additional possibility of one or more other features, figures, steps, operations, structural elements, components or combinations thereof.

[0042] The following describes in detail, with reference to the accompanying drawings, the intelligent data analysis device and method of the integrated wiring management system of the present invention according to preferred embodiments.

[0043] Figure 1 To illustrate the block diagram of the intelligent data analysis device of the integrated wiring management system of the present invention, Figure 2 This is a structural diagram of the management terminal 10.

[0044] Reference Figure 1 and Figure 2 The integrated wiring management system of the present invention includes: an integrated patch panel 20 for constructing an integrated patch panel; multiple intermediate patch panels 30 for constructing intermediate patch panels; a socket 40 for connecting to indoor equipment; and a management terminal 10 for monitoring whether there are obstacles or faults in network equipment and lines through the integrated patch panel 20.

[0045] The integrated patch panel 20 and the intermediate patch panel 30 have a frame structure equipped with at least one of a junction box 210, a fiber optic distribution box, an L2 switch, or an L3 switch. That is, the intermediate patch panel 30 serves as the integrated patch panel 20, such as an MDF. Hereinafter, the integrated patch panel and the intermediate patch panel will be collectively referred to as integrated patch panels 20 and 30 for explanation.

[0046] Furthermore, the equipment and wiring installed in such integrated patch panels 20 and 30 are generally known and their descriptions will be omitted.

[0047] The management terminal 10 detects the output of wire number management and connection information of the integrated patch panel 20 and whether events occur, and sends them to a remote server or setting terminal.

[0048] As an example, the management terminal 10 performs network monitoring by calculating multiple performance indicators that can measure network performance, such as hourly traffic, transmission speed, and inter-node latency, of the network connected to the user terminals connected to the integrated patch panels 20 and 30 via the socket 40.

[0049] Furthermore, the management terminal 10 is equipped with an artificial intelligence model based on big data algorithm pre-learning, which calculates structural elements (e.g., nodes and user terminals) with a high probability of failure by analyzing the measured data of performance indicators accumulated during the set period.

[0050] Furthermore, the management terminal 10 focuses on monitoring the nodes of user terminals for which faults have been predicted. If a fault occurs, it outputs a warning message to the user terminal and / or the registered terminals. In this case, the management terminal 10 can transmit a voice message as a warning message to the user terminal via an IP-PBX (not shown).

[0051] Furthermore, the management terminal 10 predicts and calculates the faults, component replacement periods, and lifespan of network devices such as the patch panel 210 and L2 switch installed in the integrated patch panel 20 due to contact defects and deterioration.

[0052] Furthermore, in order to predict the faults or failures and lifespan of network equipment and network lines within the integrated patch panel 20, the management terminal 10 can perform data processing and analysis by collecting various types of data and converting them into a set format.

[0053] Typically, smart buildings can collect several times more data than existing buildings, thus requiring high-performance processors for rapid computation.

[0054] However, the present invention is characterized in that the management terminal 10 performs classification analysis on the collected data, and performs correlation analysis through classification analysis of the data, so as to further and quickly process large-capacity data.

[0055] Therefore, the management terminal 10 includes a line number management unit 110, an information collection unit 120, a data analysis unit 130, an alarm unit 140, and a visual information provision unit 150.

[0056] The wire number management unit 110 constructs the network topology. The network topology and the wire number device are connected together to the visual information providing unit 150, which outputs visual information (e.g., a two-dimensional or three-dimensional virtual model) to a display (not shown).

[0057] The wire number management unit 110 assigns numbers to the wires connected to the wiring sensors through the terminal block 210, and outputs whether the corresponding wire is connected or disconnected.

[0058] The wiring sensor is installed in the connector of the wiring board 210. When the wiring connector is connected, it outputs a detection signal of connection, contact defect, or no connection. The wiring board 210 is equipped with a light-emitting unit that emits light in different colors to visually confirm the connection status of the wiring.

[0059] The visual information providing unit 150 can visualize and output the wire number information and network topology of the lines connecting the junction box 210 to the intermediate patch panel and socket 40 as a three-dimensional electronic diagram. In this case, the visual information providing unit 150 sends it to a management server outside its own display or a building control system (not shown) inside the building.

[0060] The information collection unit 120 collects data in real time from at least one of the network devices, including patch panels 210, L2 switches, L3 switches, and fiber optic patch panels, which construct and support networks that connect user terminals via integrated patch panels 20, 30, and jacks 40.

[0061] For example, the information collection unit 120 can measure in real time multiple performance indicators that can determine network performance (e.g., traffic at a set time, transmission speed, throughput that can be transmitted within a set time, delay measured by the movement time of data packets, jitter that measures whether delay occurs between data packets, and packet loss that measures the number of data packets lost during transmission).

[0062] The performance metrics of each node (e.g., a router or switching hub connected to port 40) can be received via RS485 communication between the node's spare port and the spare port of the junction box 210, and can then be directly connected to the management terminal 10 via the spare port of the junction box 210.

[0063] Furthermore, the information collection unit 120 collects port activation and fault data from the junction box 210, L3 switch, L2 switch, node (or user terminal connected to the socket 40 or device including router or switching hub). In this case, for example, the information collection unit 120 may be connected to sensors (wiring sensors, environmental sensors), ports, power distribution units (PDUs), dry contact terminals, and trunk terminals to collect information.

[0064] The sensor includes at least one of a temperature sensor, a humidity sensor, a smoke sensor, and a vibration sensor, and collects environmental data such as temperature, humidity, smoke, and vibration, and outputs it to the information collection unit 120.

[0065] Furthermore, for example, the information collection unit 120 receives at least one of the following from the junction box 210 and the fiber optic junction box 220: cable short circuit information, micro-resistance value, cable internal resistance, and cable reflection frequency value.

[0066] For example, the wiring sensor of the junction box 210 sends cable short-circuit detection information, including whether the cable connector is short-circuited at the port of the junction box 210, to the information collection unit 120. Furthermore, the junction box 210 may include a resistance measuring device or resistance sensor for measuring the micro-resistance of the port, and may include a cable measuring device for measuring frequency reflections within the cable and the internal resistance of the cable.

[0067] That is, the information collection unit 120 collects data in real time from the device that determines whether the junction box 210 and the network equipment installed in other integrated patch panels 20 are in an abnormal state.

[0068] The data analysis unit 130 sequentially performs classification and correlation analysis on the data collected from the information collection unit 120 to predict the lifespan of the equipment, such as whether there are any malfunctions or when to replace parts and the equipment. (See below for reference.) Figures 3 to 5 Explanation of Data Analysis Department 130.

[0069] Figure 3 A block diagram of the data analysis department is shown.

[0070] Reference Figure 3 The data analysis department 130 includes a raw database 131, a data classification module 132, a decision-making module 134, a classification database 133, and a decision-making tool database 135.

[0071] The raw database 131 stores the raw data collected by the information collection unit 120. The raw data is data collected in real time by the information collection unit 120 from network devices and measuring devices within the network.

[0072] The data classification module 132 converts the collected data into data in a set format. In the converted data, data tables can be generated according to the diagnostic items. Based on the set conditions, the classified data is grouped into a dataset according to the device status, port faults, contact signals, power signals, information flow obstacles, earthquakes, fires, tilt, etc. of the target equipment or diagnostic items (e.g., cable faults, L2 switches, L3 switches, fiber optic junction boxes 220 (fiber optic splitters) and junction boxes 210).

[0073] To this end, the data classification module 132 includes a deep learning-based classification analysis artificial intelligence model that uses existing data related to network devices and the presence of network obstacles as existing learning data for classification.

[0074] The classification analysis artificial intelligence model of the data classification module 132 is equipped with a classification analysis algorithm. It classifies the required data according to the input values ​​of the operation condition sentences required for predicting the operation life and the condition values ​​used to calculate the input values.

[0075] The classification database 133 stores data tables classified and analyzed by the data classification module 132. That is, the classification database 133 stores data tables that are required to predict failures or lifespans according to the inputs of each device.

[0076] The decision module 134 inserts the data input to the data table stored in the classification database 133 into the conditional clauses required for predicting failure or lifespan to calculate whether a failure exists, predict lifespan, and calculate the results. The decision module 134 is equipped with a deep learning-based correlation analysis artificial intelligence model.

[0077] The correlation analysis artificial intelligence model is based on the classification analysis artificial intelligence model. The raw data is classified and analyzed, and the input value is set. The input value of the conditional statement is calculated through the data table. The calculated input value is substituted into the conditional statement, and the result is calculated based on the result to perform fault diagnosis and life prediction.

[0078] The conditional statement, used for correlation analysis to set the conditions required for fault diagnosis and lifespan prediction, is defined as a statement that includes conditions that can predict faults and lifespan based on the input values. Alternatively, the conditional statement is defined as a selective combination of at least one of a mathematical expression that substitutes the input values, a comparison condition, and an algorithm that calculates the result by substituting the input values ​​and conditions.

[0079] Therefore, the decision module 134 calculates the input value based on the condition values ​​of the original data input into the classified data table according to the grouping of each diagnostic item, based on the classification analysis results, and substitutes the input value into the condition clause to calculate the result value. Furthermore, the decision module 134 calculates the fault diagnosis and / or life prediction results based on the result value.

[0080] The following is for reference Figure 4 and Figure 5 The data table classified and analyzed by the data classification module 132 and the conditional statements used by the decision module 134 to calculate fault, obstacle and / or life prediction values ​​through the data table are further described in detail.

[0081] Figure 4 To illustrate the example of the data table being categorized and analyzed, Figure 5 A diagram illustrating an embodiment of the correlation analysis.

[0082] Reference Figure 4 and Figure 5 The data table sets the input values ​​for the conditional statements used to set fault diagnosis items and the conditional values ​​(raw data) used to calculate those input values.

[0083] The condition values ​​(A1, B1, C1...) are equivalent to the raw data corresponding to the set conditions (e.g., time, below the set value, or above the set value).

[0084] The conditional statement sets conditions, mathematical expressions, or computational algorithms to calculate the input values ​​(A, B, C) that are input into the conditional statement by substituting the original data.

[0085] That is, the operation condition statement is a mathematical expression or algorithm used to calculate the input value of the condition statement, and the input is a condition value calculated according to the set conditions. For example, the operation condition statement is a mathematical expression or operation algorithm used to calculate the average flow rate or the total number of cable short circuits or the short circuit duration, the average vibration intensity of each set time period, the number of smoke detections and / or the duration, and the number of odor durations and / or detections.

[0086] Furthermore, the input values ​​(A, B, C) are input into the set conditional statements in order to calculate fault diagnosis items (e.g., cable disconnection, cable damage, low speed, short circuit, etc.).

[0087] The data classification model performs classification analysis on the raw data stored in the original database 131 through the loaded data classification artificial intelligence model. It searches for condition values ​​and classifies them based on the set conditions set by each data table according to the input values ​​of the condition sentences used to calculate in order to predict failure and lifespan, and inputs them into the data tables respectively.

[0088] The data tables are set according to the input values ​​of the set condition sentences in order to predict fault diagnosis items or lifespan. The same raw data can be input into multiple data tables.

[0089] Furthermore, the conditional statement applies mathematical formulas or algorithms calculated for fault diagnosis and lifespan prediction. If an input value is input, the result value is calculated based on the mathematical formula, algorithm, or condition contained in the corresponding statement. Moreover, the decision module 134 calculates the existence of faults and / or lifespan predictions based on this result value.

[0090] For example, to detect whether there is a port fault on the junction box 210, the condition statement can consist of at least one condition statement. For example, the condition statement is set as follows: if the value of A is less than a', then it is 1 (condition statement 1-1); if the value of B is greater than a'', then it is 0; otherwise, it is 1 (condition statement 1-2); if the sum of condition statement 1-1 and condition statement 1-2 is 0, then it is set to drive the preset (condition statement 1-3).

[0091] In this context, A, B, and C are the values ​​from the original data that satisfy the conditions, which are then substituted into the calculation of the conditional statement. a' and a'' are the input condition values. The sum of the results of conditional statements 1-1 and 1-2 corresponds to the pre-plan driving condition. That is, conditional statement 1-3 corresponds to the result statement.

[0092] The result clause defines the next process based on the result value of the condition clause, which is set to activate the warning department's contingency plan function.

[0093] As described above, condition sentences 1-1 to 1-3 are one of the multiple embodiments. The condition sentences are set separately according to the fault diagnosis project or life prediction object, and the input values ​​substituted into the corresponding condition sentences and the calculation algorithm or setting conditions for calculating the input values ​​are changed.

[0094] Furthermore, the playbook automatically sends warnings based on the fault diagnosis and lifespan prediction results. That is, the decision module 134 automatically drives the remote server or warning unit 140 based on the result value of the conditional statement.

[0095] Furthermore, calculation algorithms, including mathematical formulas or rules set in the conditional statements and operational conditional statements, are stored in the decision tool database 135. Therefore, if condition values ​​and input values ​​are input to the operational conditional statements and conditional statements, the decision module 134 searches for and executes the calculation algorithms set in the corresponding data tables (e.g., cable reflection frequency calculation algorithm, port internal resistance calculation algorithm, cable resistance calculation algorithm, information flow calculation algorithm, etc.) from the decision tool database 135 to calculate the result value.

[0096] That is, the decision module 134 calculates the input value of the conditional statement based on the data table stored in the classification database 133, and drives the calculation algorithm of the operation conditional statement set in the data table according to the conditional statement set for fault diagnosis and life prediction. The calculated input value is then input into the conditional statement to calculate the result. Moreover, the decision module 134 automatically executes the plan based on the calculation result to automatically issue warnings and send the results of fault detection and life prediction.

[0097] For example, in the data table, the operation condition sentence includes an algorithm for calculating the average value of the performance index values ​​of the node and the user terminal according to the set time. The condition sentence is classified into upward adjustment, maintenance, and downward adjustment levels based on the change of the average value calculated over time. In the corresponding level, the content set for the key monitoring object is set according to the change.

[0098] Furthermore, the conditional statement includes a baseline value to determine whether an obstacle has been predicted, based on measurement data from nodes continuously classified as downgraded within a specified period or from user terminals (the amount of change in the average value calculated over the entire period).

[0099] Furthermore, the conditional statement sets the conditions for the amount of change. If at least one of the following conditions is met, the obstacle can be predicted: for example, the change in the downward value is greater than the average of the previous change, the measurement is the same as the previous continuous downward change, the downward change gradually increases, the change in the final measured average value is greater than the average of the initial measured average value, and the final measured value corresponds to the set reference value.

[0100] Furthermore, the data table sets the raw data corresponding to the set conditions of the cable reflection frequency as the condition value, sets the calculation algorithm for calculating the average value of the cable reflection frequency over time based on the condition value, and sets the calculation algorithm in the operation condition sentence to calculate the input value of the condition sentence used for cable fault or life prediction.

[0101] In this case, the conditional clause used for cable fault or life prediction is set to calculate the life prediction value based on whether the average value of the cable reflection frequency calculated over time is greater than or less than a set value, or it can be combined with a conditional clause that includes changes in the internal resistance value of the power supply to calculate the cable life prediction result.

[0102] Alternatively, the port fault of the terminal block 210 or the replacement time of each port component of the terminal block 210 can be calculated by the change in the port grounding resistance value of the terminal block 210. For example, the data table can use the raw data of measuring the resistance value of each port grounding terminal above the set value as the condition value input of the data table, and the operation condition sentence sets the average value calculation algorithm for calculating the average resistance value.

[0103] Furthermore, if the input value calculated based on the conditional statement is greater than the set conditions a' and a'', it is calculated as 1; otherwise, it is calculated as 0, and the pre-plan is automatically executed based on the result.

[0104] Furthermore, conditional statements can be set to determine environmental factors such as fires or earthquakes. For example, a data table can set smoke detection data, vibration detection data, temperature detection data, and odor detection data as conditional values, and set computational conditional statements to define the algorithms required to calculate average temperature, the deviation between average temperature and recent temperature, odor duration, vibration frequency per hour, and average or maximum temperature. Moreover, the conditional statements are generated as sentences that include the conditions and input values ​​(vibration frequency, difference between average temperature and recent temperature, presence of odor and smoke, duration) for determining earthquakes or fires.

[0105] Furthermore, in this invention, the conditional statement can detect whether an earthquake has occurred using a reflective wiring sensor.

[0106] Typically, wired sensors include transmissive and reflective types. In this case, depending on how the light output from the output terminal of the transmissive type is input to the opposite light-receiving device, and with the cable securely fastened to the port of the wiring board 210, even if vibration occurs, the impact on the detection signal is negligible.

[0107] If a reflective wiring sensor is used, the light from the output device is reflected by a mirror and then used by the light-receiving device to detect whether the cable port is secure. In this case, the output device and the light-receiving device are located on the same surface. Therefore, the light output by the output device is reflected by the reflective surface opposite and then incident on the light-receiving device adjacent to the output device.

[0108] In the event of vibration, even if the light output by the output device is incident on the receiving device, this type of reflective wiring sensor may generate a large level of noise, or cause a significant reduction in the level of the output signal.

[0109] Therefore, the present invention detects attitude disturbances of the integrated patch panel or junction box 210 using a reflective port signal sensor. For example, the data table sets the output value of the input wiring sensor to be raw data above the average value, and the operation condition statement sets at least one of an operation algorithm and a tilt calculation algorithm to calculate the change in the output value of the wiring sensor, such as the deviation between the maximum and minimum values ​​or the deviation between the current average value and the maximum or average value of the most recently set time period.

[0110] The conditional statement is generated as at least one conditional statement that uses the output value change and tilt of the vibration-based wiring sensor as input values, and can be set to automatically drive the program by the result (or the sum of the results).

[0111] Therefore, the present invention can monitor whether the orientation of the junction box 210 or the integrated patch panel 20 changes due to earthquakes, internal vibrations or impacts.

[0112] Furthermore, as another embodiment, the management terminal 10 can send information to a building control system that controls at least one of the following: power system, parking management system, gas system, fire protection system, access control system, refrigeration and heating system, video system, and lighting system.

[0113] That is, the integrated wiring management system of the present invention is installed in intelligent buildings and connected to the building control system for integrated control of the systems within the building to predict whether network devices will malfunction or predict their lifespan.

[0114] The present invention includes the structure described above. The following describes the intelligent data analysis method of the integrated wiring management system of the present invention.

[0115] Figure 6 This is a flowchart illustrating the intelligent data analysis method of the integrated wiring management system.

[0116] Reference Figure 6The present invention includes: step S100, collecting raw data; step S200, performing classification analysis on the raw data based on set conditions; step S300, calculating the input value of the operation condition sentence using a data table; step S400, substituting the input value into the condition sentence; and step S500, calculating the result value and outputting the diagnostic result.

[0117] Step S100 is the step in which the information collection unit 120 collects data from network devices. Specifically, the management terminal 10 collects data from nodes (e.g., user terminals, routers, and / or hubs) of the junction box 210, fiber optic distribution board, and connection adapters. In this case, network devices such as the junction box 210 include devices or sensing devices for collecting inherent status information of each component. For example, the junction box 210 may include a connection sensor capable of detecting whether a cable is securely fastened to a port, a port micro-resistance measuring device, a resistance measuring device for the connecting cable, a cable internal reflection frequency measuring device, an information flow detection device, etc. These measuring devices are existing known technologies, therefore, detailed descriptions will be omitted.

[0118] The data analysis unit 130 stores the data (raw data) collected by the information collection unit 120 in the raw database 131.

[0119] Step S200 involves the data analysis unit classifying the raw data stored in the raw database 131 according to the various items set for fault diagnosis items and life prediction target components or devices. The data analysis unit 130 performs classification analysis to input condition values ​​from the raw data that meet set conditions as condition values ​​for the data tables. These condition values ​​can be input into other data tables simultaneously, depending on the items in each data table.

[0120] Step S300 is the step in which the data analysis unit 130 performs correlation analysis on the classified and analyzed data table to calculate the input values ​​of each conditional statement set for fault diagnosis and life prediction. The decision module 134 of the data analysis unit 130 runs the correlation analysis artificial intelligence model and inputs each data input to the data table into the set operation conditional statements to calculate the input values ​​of the conditional statements.

[0121] Step S400 involves substituting the input values ​​calculated by the data analysis unit 130 into the conditional statement. The decision module 134 inputs the conditional values ​​for each data table into the calculation algorithm set in the defined calculation conditional statement to calculate the input values.

[0122] Step S500 is the step in which the data analysis unit 130 substitutes input values ​​into the conditional statement and calculates the fault and life prediction results based on the calculated result values. The data analysis unit 130 inserts input values ​​into the conditional statement and automatically executes a pre-plan to output warnings to a remote server or display based on its result values.

[0123] That is, the present invention performs the process of analyzing data and calculating result values ​​through artificial intelligence models step by step through classification analysis and correlation analysis. The input values ​​set for correlation analysis can be calculated by modeling the grouped datasets and substituting them into conditional statements. Thus, the data analysis process can be further shortened and faster computation can be achieved.

[0124] While the above description illustrates one embodiment of the present invention, those skilled in the art can make various modifications and alterations to the present invention by adding, changing, deleting, or supplementing structural elements without departing from the scope of the invention as described in the claims, and these modifications and alterations also fall within the scope of the invention claims.

Claims

1. An intelligent data analysis device for an integrated wiring management system, characterized in that, Includes a management terminal for monitoring cable number management and faults in at least one of a comprehensive patch panel and an intermediate patch panel containing multiple network devices. The management terminal includes: The cable management department constructs a network topology that connects the integrated patch panel to intermediate patch panels, sockets, and user terminals. The information collection department collects raw data from network devices; The data analysis department generates a data table to predict the faults, status, and lifespan of network devices and connected cables located in any of the integrated patch panels or intermediate patch panels. This data table is grouped into a dataset categorized according to the algorithm and setting conditions used to calculate the status of the target equipment for each set diagnostic item. The department also classifies and analyzes the raw data collected by the information collection department according to the setting conditions, inputs the calculated input values ​​based on the corresponding datasets into conditional statements that set fault or lifespan prediction conditions for each diagnostic item, and compares the calculated results with the setting conditions to calculate fault diagnosis and lifespan prediction values. The warning department alerts to faults and remaining lifespan based on the diagnostic results from the data analysis department.

2. The intelligent data analysis device for the integrated wiring management system according to claim 1, characterized in that, The data table includes: The operation condition clause sets the calculation algorithm for the input value input to the condition clause; as well as Condition value: Input the original data corresponding to the set conditions and input it into the operation condition statement.

3. The intelligent data analysis device for the integrated wiring management system according to claim 2, characterized in that, The data analytics department includes: The raw database is used to store data collected by the information gathering department; The data classification module analyzes the raw data in the original database based on the set conditions of each data table to classify and input the condition values ​​of the data tables; A classification database is used to store data tables for each diagnostic item input by the data classification module; and The decision module substitutes the condition values ​​from the data tables stored in the classification database into the calculation algorithm set in the operation condition statement to calculate the input value of the condition statement, and calculates the fault diagnosis and life prediction values ​​based on the result value.

4. The intelligent data analysis device for the integrated wiring management system according to claim 3, characterized in that, The data analysis department also includes a decision tool database, which stores the calculation algorithms set in the data tables.

5. The intelligent data analysis device for the integrated wiring management system according to claim 3, characterized in that, The terminal block is equipped with at least one of a wiring sensor and a detection device. The wiring sensor is used to detect whether the cable fastened to the port is secure, and the detection device is used to detect the internal resistance of the cable, the reflection frequency of the cable, the resistance of the port, and the abnormal power supply of the port.

6. The intelligent data analysis device for the integrated wiring management system according to claim 5, characterized in that, Wiring sensors include reflective optical sensors.

7. The intelligent data analysis device for the integrated wiring management system according to claim 1, characterized in that, The management terminal sends information to the building control system, which controls at least one of the following systems: power system, parking management system, gas system, fire protection system, access control system, refrigeration and heating system, video system, and lighting system.

8. An intelligent data analysis method for an integrated wiring management system, characterized in that, include: Step a) Collect raw data from at least one network device located in the integrated patch panel and intermediate patch panel; Step b) To predict the faults or conditions and lifespan of network devices and connected cables, raw data that meets the set conditions of a data table is classified and input, the data table being a dataset classified according to the algorithm and set conditions used to calculate the state of the object device for each set diagnostic item. Step c) Input the condition values ​​from the corresponding dataset into the conditional statement to calculate the input value of the conditional statement; Step d) involves inputting the input values ​​into a conditional statement that sets fault or life prediction conditions for each diagnostic item, and comparing them with the set conditions to calculate the fault diagnosis and life prediction results; and Step e) Based on the fault diagnosis and life prediction results, the plan is selectively and automatically executed to output warnings including fault or life prediction results.

9. The intelligent data analysis method for the integrated wiring management system according to claim 8, characterized in that, In step e), a warning is sent by the building control system, which controls at least one of the following systems: power system, parking management system, gas system, fire protection system, access control system, refrigeration and heating system, video system, and lighting system.

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