CN116307205A - Communication equipment data management system and method based on big data - Google Patents
Communication equipment data management system and method based on big data Download PDFInfo
- Publication number
- CN116307205A CN116307205A CN202310291851.5A CN202310291851A CN116307205A CN 116307205 A CN116307205 A CN 116307205A CN 202310291851 A CN202310291851 A CN 202310291851A CN 116307205 A CN116307205 A CN 116307205A
- Authority
- CN
- China
- Prior art keywords
- cable head
- mounted antenna
- outdoor vehicle
- antenna cable
- plugging
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004891 communication Methods 0.000 title claims abstract description 23
- 238000013523 data management Methods 0.000 title claims abstract description 22
- 238000004458 analytical method Methods 0.000 claims abstract description 78
- 238000007726 management method Methods 0.000 claims abstract description 36
- 238000012360 testing method Methods 0.000 claims description 28
- 238000010276 construction Methods 0.000 claims description 27
- 239000011159 matrix material Substances 0.000 claims description 16
- 238000009825 accumulation Methods 0.000 claims description 8
- 230000009471 action Effects 0.000 claims description 7
- 230000001186 cumulative effect Effects 0.000 claims description 4
- 238000011161 development Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 description 3
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000003313 weakening effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
- G06F17/13—Differential equations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- Algebra (AREA)
- Human Resources & Organizations (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Operations Research (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- General Business, Economics & Management (AREA)
- Computing Systems (AREA)
- Fittings On The Vehicle Exterior For Carrying Loads, And Devices For Holding Or Mounting Articles (AREA)
Abstract
The invention discloses a communication equipment data management system and method based on big data, and relates to the technical field of outdoor vehicle-mounted antenna data management. The system comprises a data acquisition module, a prediction analysis module, a loss analysis module, a fault prediction module and an intelligent early warning management module; the output end of the data acquisition module is connected with the input end of the prediction analysis module; the output end of the prediction analysis module is connected with the input end of the loss analysis module; the output end of the loss analysis module is connected with the input end of the fault prediction module; the output end of the fault prediction module is connected with the input end of the intelligent early warning management module; the invention also provides a data management method of the communication equipment based on big data, which can provide early warning reminding for users by carrying out predictive analysis on the plugging times of the outdoor vehicle-mounted antenna cable head and promote the users to maintain the use of the outdoor vehicle-mounted antenna cable head in time.
Description
Technical Field
The invention relates to the technical field of outdoor vehicle-mounted antenna data management, in particular to a communication equipment data management system and method based on big data.
Background
The outdoor vehicle-mounted antenna generally comprises a cable (feeder), a cable head (a connector between the cable and a radio station), an antenna and an antenna base; firstly, radio frequency signal power output by a radio station is transmitted to an antenna through a feeder line, the antenna radiates out in the form of electromagnetic waves, after the electromagnetic waves reach a receiving place, the electromagnetic waves are received by the antenna and transmitted to the radio station through the feeder line, and an outdoor vehicle-mounted antenna is important communication equipment for transmitting and receiving the electromagnetic waves.
The outdoor vehicle-mounted antenna has multiple functions such as digital broadcasting, mobile television, GPS navigation system and voice equipment, and is used for receiving and enhancing signals, the outdoor vehicle-mounted antenna is commonly used in the automobile industry in China, but the service life of the outdoor vehicle-mounted antenna cable head is limited, on one hand, under the condition that the outdoor vehicle-mounted antenna is not used, a user can pull out and insert the cable head, and frequent plugging and pulling of the outdoor vehicle-mounted antenna cable head can cause certain abrasion of the cable head, and finally faults are generated; on the other hand, under extreme environment, such as frost and snow storm weather, when the outdoor vehicle-mounted antenna is not used by a user, the cable head is pulled out, and in the process, a small amount of water is fed into the cable head due to careless operation, so that the service life of the cable head can be prolonged, and the cable head is failed in advance.
Disclosure of Invention
The invention aims to provide a communication equipment data management system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a method of data management for a communication device based on big data, the method comprising the steps of:
step S1: acquiring historical test data of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head, constructing a gray prediction model, and calculating a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the next service period;
step S2: acquiring historical use data of an outdoor vehicle-mounted antenna cable head, constructing a loss model, and calculating the average value of the plug loss coefficients of the outdoor vehicle-mounted antenna cable head in an extreme environment;
step S3: acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and the plugging times under an extreme environment, constructing a fault prediction model, and calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
step S4: an intelligent management platform is constructed, a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is set, and when the predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is smaller than or equal to the threshold value, early warning and reminding of cable head damage are sent to a user port.
Further, in step S1, the constructing the gray prediction model includes:
acquiring historical test data of the plugging times of the cable head of the outdoor vehicle-mounted antenna, and recording the period from the start of the test to the generation of faults of the outdoor vehicle-mounted antenna as a service period;
taking historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head as an initial use period number sequence, and marking the initial use period number sequence as X (0) ＝{X (0) (1)、X (0) (2)、X (0) (3)、......、X (0) (n) as input to the model;
and performing one-time accumulation generation processing on the initial use period number sequence, namely according to the formula:
generating a cumulative sequence X (1) ＝{X (1) (1)、X (1) (2)、X (1) (3)、......、X (1) (n) }; wherein k=1, 2, 3, &..;
one-time accumulation of the number sequence X (1) The differential equation of (2) is:
wherein a represents a development coefficient; u represents the ash action amount;
record Z (1) For accumulating the sequence X once (1) Generates a series of immediately adjacent means, namely:
Z (1) ＝(Z (1) (2),Z (1) (3),...,Z (1) (n))
wherein Z is (1) (k)＝0.5Z (1) (k)+0.5Z (1) (k-1),k＝2,3,...,n；
The model relation between the initial use period number sequence and the adjacent mean value generation number sequence is constructed as follows:
Y＝mB
wherein Y represents an initial use period number array matrix; m represents a coefficient vector matrix; b represents generating a sequence matrix next to the mean value;
the estimated value of the coefficient vector matrix m obtained by the least square method is as follows:
m＝(a,u) T ＝(B T B) -1 B T Y
the grey prediction model is:
wherein k=1, 2, 3, &..; x is X (0) And (k+1) represents a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the k+1th service cycle.
According to the technical scheme, the gray prediction model can be used for calculating and predicting according to some known historical data, determining the future change trend of the system, performing prediction analysis on the historical test data of the outdoor vehicle-mounted antenna cable head plugging times by using the gray prediction model, weakening the randomness of the original data, and removing information with little meaning, so that the outdoor vehicle-mounted antenna cable head plugging times can be accurately predicted.
Further, in step S2, the acquiring the historical usage data of the outdoor vehicle-mounted antenna cable head includes:
acquiring a historical calibration plugging frequency set of an outdoor vehicle-mounted antenna cable head, and marking the historical calibration plugging frequency set as C= { C 1 、c 2 、c 3 、......、c m -a }; wherein c 1 、c 2 、c 3 、......、c m The 1 st, 2 nd, 3 rd, and the third and fourth historical calibration plug of the outdoor vehicle-mounted antenna cable head are respectively shownThe number of times;
correspondingly, a historical actual plugging frequency set of the outdoor vehicle-mounted antenna cable head is obtained and is recorded as D= { D 1 、d 2 、d 3 、......、d m -a }; wherein d 1 、d 2 、d 3 、......、d m The 1 st, 2 nd, 3 rd, and the third and fourth times of the outdoor vehicle-mounted antenna cable head are respectively represented by the historical actual plugging times of m times;
correspondingly, acquiring a historical plug frequency set of the outdoor vehicle-mounted antenna cable head in an extreme environment, and marking the historical plug frequency set as E= { f 1 、f 2 、f 3 、......、f m -a }; wherein f 1 、f 2 、f 3 、......、f m The 1 st, 2 nd, 3 rd, and third order of the outdoor vehicle-mounted antenna cable head in the extreme environment are respectively represented, and the m times of historical plugging times are represented;
constructing a loss model:
wherein,,
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented; c i Indicating the ith historical calibration plugging times of the outdoor vehicle-mounted antenna cable head; d, d i Representing the i-th historical actual plugging times of the outdoor vehicle-mounted antenna cable head; f represents the i-th historical plugging times of the outdoor vehicle-mounted antenna cable head in an extreme environment.
In the above technical solution, considering that the number of calibration plugging times of the cable head of the outdoor vehicle-mounted antenna is limited, under the condition that such calibration value is reached, the cable head of the outdoor vehicle-mounted antenna will fail, where the number of calibration plugging times is the number of plugging times of the cable head under a non-extreme environment; however, in extreme environments, for example, in extreme weather conditions such as frost, snow, storm and the like, the cable head of the outdoor vehicle smashing antenna is plugged and unplugged, the cable head or the jack can possibly be enabled to enter water under certain conditions, the cable resistance value in the cable head or the jack is increased, the service life of the outdoor vehicle-mounted cable head can be accelerated under certain conditions, and faults occur when the actual plugging frequency of the outdoor vehicle-mounted antenna cable head reaches the calibrated plugging frequency, so that the mean value of plugging loss coefficients under the extreme environments is determined according to historical use data, and the accuracy of the system is improved.
Further, in steps S3-S4, the constructing the fault prediction model includes:
acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head, and marking the current plugging times as c 0 ；
Acquiring the plugging times of an outdoor vehicle-mounted antenna cable head in an extreme environment, and marking the plugging times as f 0 ；
Constructing a fault prediction model:
wherein H represents a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head; x is X (0) (k+1) represents a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the (k+1) th use period;
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented;
setting a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head, and marking the threshold value as H 0 ；
When H is less than or equal to H 0 And when the system sends out the cable head damage early warning prompt to the user port.
According to the technical scheme, the prediction analysis is carried out on the residual plugging times of the outdoor vehicle-mounted antenna cable head based on the current plugging times of the outdoor vehicle-mounted antenna cable head and the plugging times of the outdoor vehicle-mounted antenna cable head in an extreme environment, so that the accuracy of predicting faults of the outdoor vehicle-mounted antenna cable head can be further improved, the residual plugging times of the outdoor vehicle-mounted antenna cable head are updated in real time through the system, the current actual plugging times are monitored to give early warning and reminding to a user, and the user can conveniently and timely check, troubleshoot, replace or maintain the outdoor vehicle-mounted antenna cable head.
A big data based communication device data management system, characterized in that: the system comprises a data acquisition module, a prediction analysis module, a loss analysis module, a fault prediction module and an intelligent early warning management module;
the data acquisition module is used for acquiring historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head, acquiring historical use data of the outdoor vehicle-mounted antenna cable head and acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and the plugging times under an extreme environment; the prediction analysis module is used for constructing a gray prediction model and calculating a predicted value of the plugging times of the outdoor vehicle-mounted antenna cable head in the next use period; the loss analysis module is used for constructing a loss model and calculating the mean value of the plug loss coefficients of the outdoor vehicle-mounted antenna cable head in an extreme environment; the fault prediction module is used for constructing a fault prediction model and calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head; the intelligent early warning management module is used for constructing an intelligent management platform, setting a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head, and sending early warning reminding of cable head damage to a user port when the predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is smaller than or equal to the threshold value;
the output end of the data acquisition module is connected with the input end of the prediction analysis module; the output end of the prediction analysis module is connected with the input end of the loss analysis module; the output end of the loss analysis module is connected with the input end of the fault prediction module; and the output end of the fault prediction module is connected with the input end of the intelligent early warning management module.
Further, the data acquisition module comprises a historical test data acquisition unit, a historical use data acquisition unit and a current use data acquisition unit;
the historical test data acquisition unit is used for acquiring historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head by utilizing big data; the historical use data acquisition unit is used for acquiring historical use data of the outdoor vehicle-mounted antenna cable head by utilizing big data; the current use data acquisition unit is used for acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and plugging times under an extreme environment by using the pressure sensor device.
Further, the prediction analysis module comprises a grey prediction model construction unit and a first analysis unit;
the grey prediction model construction unit is used for constructing a grey prediction model;
the first analysis unit is used for calculating a predicted value of the plugging times of the outdoor vehicle-mounted antenna cable head in the next service period;
the output end of the gray prediction model building unit is connected with the input end of the first analysis unit; the output end of the first analysis unit is connected with the input end of the loss analysis module.
Further, the loss analysis module comprises a loss model construction unit and a second analysis unit;
the loss model construction unit is used for constructing a loss model;
the second analysis unit is used for calculating the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment;
the output end of the loss model building unit is connected with the input end of the second analysis unit; the input end of the second analysis unit is connected with the input end of the fault prediction module.
Further, the fault prediction module comprises a fault prediction model construction unit and a third analysis unit;
the fault prediction model construction unit is used for constructing a fault prediction model;
the third analysis unit is used for calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
the output end of the fault prediction model building unit is connected with the input end of the third analysis unit; and the output end of the third analysis unit is connected with the input end of the intelligent early warning management module.
Further, the intelligent early warning management module comprises an intelligent management platform construction unit, a threshold setting unit and an early warning reminding unit;
the intelligent management platform construction unit is used for constructing an intelligent management platform;
the threshold setting unit is used for setting a threshold of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
the early warning reminding unit is used for sending out early warning reminding of the damage of the cable head to the user port when the predicted value of the residual plugging times of the cable head of the outdoor vehicle-mounted antenna is smaller than or equal to a threshold value;
the output end of the intelligent management platform construction unit is connected with the input end of the threshold setting unit; the output end of the threshold setting unit is connected with the input end of the early warning and reminding unit.
Compared with the prior art, the invention has the following beneficial effects: according to the method, a series of models such as a gray prediction model, a loss model and a fault prediction model are constructed by analyzing historical test data, historical use data, current plug times of the outdoor vehicle-mounted antenna cable head and the plug times under extreme environments, and finally, a predicted value of the residual plug times of the outdoor vehicle-mounted antenna cable head is obtained; and an intelligent management platform is constructed to monitor in real time and send out early warning reminding of cable head damage to the user port. The invention also analyzes the loss influence of plugging the outdoor vehicle-mounted antenna cable head in an extreme environment, further improves the accuracy of system fault prediction, and provides convenience for users to maintain and check the faults of the outdoor vehicle-mounted antenna cable head in time.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a data management system for a big data based communication device according to the present invention;
fig. 2 is a flow chart of a method for managing data of a communication device based on big data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
a method of data management for a communication device based on big data, the method comprising the steps of:
step S1: acquiring historical test data of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head, constructing a gray prediction model, and calculating a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the next service period;
step S2: acquiring historical use data of an outdoor vehicle-mounted antenna cable head, constructing a loss model, and calculating the average value of the plug loss coefficients of the outdoor vehicle-mounted antenna cable head in an extreme environment;
step S3: acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and the plugging times under an extreme environment, constructing a fault prediction model, and calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
step S4: an intelligent management platform is constructed, a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is set, and when the predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is smaller than or equal to the threshold value, early warning and reminding of cable head damage are sent to a user port.
Further, in step S1, the constructing the gray prediction model includes:
acquiring historical test data of the plugging times of the cable head of the outdoor vehicle-mounted antenna, and recording the period from the start of the test to the generation of faults of the outdoor vehicle-mounted antenna as a service period;
plugging and unplugging times of outdoor vehicle-mounted antenna cable headsIs recorded as X as an initial use period series (0) ＝{X (0) (1)、X (0) (2)、X (0) (3)、......、X (0) (n) as input to the model;
and performing one-time accumulation generation processing on the initial use period number sequence, namely according to the formula:
generating a cumulative sequence X (1) ＝{X (1) (1)、X (1) (2)、X (1) (3)、......、X (1) (n) }; wherein k=1, 2, 3, &..;
one-time accumulation of the number sequence X (1) The differential equation of (2) is:
wherein a represents a development coefficient; u represents the ash action amount;
record Z (1) For accumulating the sequence X once (1) Generates a series of immediately adjacent means, namely:
Z (1) ＝(Z (1) (2),Z (1) (3),...,Z (1) (n))
wherein Z is (1) (k)＝0.5Z (1) (k)+0.5Z (1) (k-1),k＝2,3,...,n；
The model relation between the initial use period number sequence and the adjacent mean value generation number sequence is constructed as follows:
Y＝mB
wherein Y represents an initial use period number array matrix; m represents a coefficient vector matrix; b represents generating a sequence matrix next to the mean value;
the estimated value of the coefficient vector matrix m obtained by the least square method is as follows:
m＝(a,u) T ＝(B T B) -1 B T Y
the grey prediction model is:
wherein k=1, 2, 3, &..; x is X (0) And (k+1) represents a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the k+1th service cycle.
Further, in step S2, the acquiring the historical usage data of the outdoor vehicle-mounted antenna cable head includes:
acquiring a historical calibration plugging frequency set of an outdoor vehicle-mounted antenna cable head, and marking the historical calibration plugging frequency set as C= { C 1 、c 2 、c 3 、......、c m -a }; wherein c 1 、c 2 、c 3 、......、c m The 1 st, 2 nd, 3 rd, and the third and fourth times of the outdoor vehicle-mounted antenna cable head are respectively represented, and the m times of historical calibration plugging times are represented;
correspondingly, a historical actual plugging frequency set of the outdoor vehicle-mounted antenna cable head is obtained and is recorded as D= { D 1 、d 2 、d 3 、......、d m -a }; wherein d 1 、d 2 、d 3 、......、d m The 1 st, 2 nd, 3 rd, and the third and fourth times of the outdoor vehicle-mounted antenna cable head are respectively represented by the historical actual plugging times of m times;
correspondingly, acquiring a historical plug frequency set of the outdoor vehicle-mounted antenna cable head in an extreme environment, and marking the historical plug frequency set as E= { f 1 、f 2 、f 3 、......、f m -a }; wherein f 1 、f 2 、f 3 、......、f m The 1 st, 2 nd, 3 rd, and third order of the outdoor vehicle-mounted antenna cable head in the extreme environment are respectively represented, and the m times of historical plugging times are represented;
constructing a loss model:
wherein,,
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented; c i Indicating the ith historical calibration plugging times of the outdoor vehicle-mounted antenna cable head; d, d i Representing the i-th historical actual plugging times of the outdoor vehicle-mounted antenna cable head; f (f) i And (5) representing the i-th historical plugging times of the outdoor vehicle-mounted antenna cable head in an extreme environment.
Further, in steps S3-S4, the constructing the fault prediction model includes:
acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head, and marking the current plugging times as c 0 ；
Acquiring the plugging times of an outdoor vehicle-mounted antenna cable head in an extreme environment, and marking the plugging times as f 0 ；
Constructing a fault prediction model:
wherein H represents a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head; x is X (0) (k+1) represents a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the (k+1) th use period;
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented;
setting a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head, and marking the threshold value as H 0 ；
When H is less than or equal to H 0 And when the system sends out the cable head damage early warning prompt to the user port.
A big data based communication device data management system, characterized in that: the system comprises a data acquisition module, a prediction analysis module, a loss analysis module, a fault prediction module and an intelligent early warning management module;
the data acquisition module is used for acquiring historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head, acquiring historical use data of the outdoor vehicle-mounted antenna cable head and acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and the plugging times under an extreme environment; the prediction analysis module is used for constructing a gray prediction model and calculating a predicted value of the plugging times of the outdoor vehicle-mounted antenna cable head in the next use period; the loss analysis module is used for constructing a loss model and calculating the mean value of the plug loss coefficients of the outdoor vehicle-mounted antenna cable head in an extreme environment; the fault prediction module is used for constructing a fault prediction model and calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head; the intelligent early warning management module is used for constructing an intelligent management platform, setting a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head, and sending early warning reminding of cable head damage to a user port when the predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is smaller than or equal to the threshold value;
the output end of the data acquisition module is connected with the input end of the prediction analysis module; the output end of the prediction analysis module is connected with the input end of the loss analysis module; the output end of the loss analysis module is connected with the input end of the fault prediction module; and the output end of the fault prediction module is connected with the input end of the intelligent early warning management module.
Further, the data acquisition module comprises a historical test data acquisition unit, a historical use data acquisition unit and a current use data acquisition unit;
the historical test data acquisition unit is used for acquiring historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head by utilizing big data; the historical use data acquisition unit is used for acquiring historical use data of the outdoor vehicle-mounted antenna cable head by utilizing big data; the current use data acquisition unit is used for acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and plugging times under an extreme environment by using the pressure sensor device.
Further, the prediction analysis module comprises a grey prediction model construction unit and a first analysis unit;
the grey prediction model construction unit is used for constructing a grey prediction model;
the first analysis unit is used for calculating a predicted value of the plugging times of the outdoor vehicle-mounted antenna cable head in the next service period;
the output end of the gray prediction model building unit is connected with the input end of the first analysis unit; the output end of the first analysis unit is connected with the input end of the loss analysis module.
Further, the loss analysis module comprises a loss model construction unit and a second analysis unit;
the loss model construction unit is used for constructing a loss model;
the second analysis unit is used for calculating the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment;
the output end of the loss model building unit is connected with the input end of the second analysis unit; the input end of the second analysis unit is connected with the input end of the fault prediction module.
Further, the fault prediction module comprises a fault prediction model construction unit and a third analysis unit;
the fault prediction model construction unit is used for constructing a fault prediction model;
the third analysis unit is used for calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
the output end of the fault prediction model building unit is connected with the input end of the third analysis unit; and the output end of the third analysis unit is connected with the input end of the intelligent early warning management module.
Further, the intelligent early warning management module comprises an intelligent management platform construction unit, a threshold setting unit and an early warning reminding unit;
the intelligent management platform construction unit is used for constructing an intelligent management platform;
the threshold setting unit is used for setting a threshold of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
the early warning reminding unit is used for sending out early warning reminding of the damage of the cable head to the user port when the predicted value of the residual plugging times of the cable head of the outdoor vehicle-mounted antenna is smaller than or equal to a threshold value;
the output end of the intelligent management platform construction unit is connected with the input end of the threshold setting unit; the output end of the threshold setting unit is connected with the input end of the early warning and reminding unit.
In this embodiment:
acquiring historical test data of the plugging times of the cable head of the outdoor vehicle-mounted antenna, and recording the period from the start of the test to the generation of faults of the outdoor vehicle-mounted antenna as a service period;
taking historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head as an initial use period number sequence, and marking the initial use period number sequence as X (0) ＝{X (0) (1)、X (0) (2)、X (0) (3)、......、X (0) (n) as input to the model;
and performing one-time accumulation generation processing on the initial use period number sequence, namely according to the formula:
generating a cumulative sequence X (1) ＝{X (1) (1)、X (1) (2)、X (1) (3)、......、X (1) (n) }; wherein k=1, 2, 3, &..;
one-time accumulation of the number sequence X (1) The differential equation of (2) is:
wherein a represents a development coefficient; u represents the ash action amount;
record Z (1) For accumulating the sequence X once (1) Generates a series of immediately adjacent means, namely:
Z (1) ＝(Z (1) (2),Z (1) (3),...,Z (1) (n))
wherein Z is (1) (k)＝0.5Z (1) (k)+0.5Z (1) (k-1),k＝2,3,...,n；
The model relation between the initial use period number sequence and the adjacent mean value generation number sequence is constructed as follows:
Y＝mB
wherein Y represents an initial use period number array matrix; m represents a coefficient vector matrix; b represents generating a sequence matrix next to the mean value;
the estimated value of the coefficient vector matrix m obtained by the least square method is as follows:
m＝(a,u) T ＝(B T B) -1 B T Y
the grey prediction model is:
wherein k=1, 2, 3, &..; x is X (0) And (k+1) represents a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the k+1th service cycle.
X can be obtained from the above gray prediction model (0) (k+1)＝1000；
Acquiring a historical calibration plugging frequency set of an outdoor vehicle-mounted antenna cable head, and marking the historical calibration plugging frequency set as C= { C 1 、c 2 、c 3 、......、c m -a }; wherein c 1 、c 2 、c 3 、......、c m The 1 st, 2 nd, 3 rd, and the third and fourth times of the outdoor vehicle-mounted antenna cable head are respectively represented, and the m times of historical calibration plugging times are represented;
correspondingly, a historical actual plugging frequency set of the outdoor vehicle-mounted antenna cable head is obtained and is recorded as D= { D 1 、d 2 、d 3 、......、d m -a }; wherein d 1 、d 2 、d 3 、......、d m The 1 st, 2 nd, 3 rd, and the third and fourth times of the outdoor vehicle-mounted antenna cable head are respectively represented by the historical actual plugging times of m times;
corresponding toThe historical plug frequency set of the outdoor vehicle-mounted antenna cable head in the extreme environment is obtained and is marked as E= { f 1 、f 2 、f 3 、......、f m -a }; wherein f 1 、f 2 、f 3 、......、f m The 1 st, 2 nd, 3 rd, and third order of the outdoor vehicle-mounted antenna cable head in the extreme environment are respectively represented, and the m times of historical plugging times are represented;
constructing a loss model:
wherein,,
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented; c i Indicating the ith historical calibration plugging times of the outdoor vehicle-mounted antenna cable head; d, d i Representing the i-th historical actual plugging times of the outdoor vehicle-mounted antenna cable head; f (f) i And (5) representing the i-th historical plugging times of the outdoor vehicle-mounted antenna cable head in an extreme environment.
From the loss model, it is possible to obtain
Acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head, and marking the current plugging times as c 0 ＝400；
Acquiring the plugging times of an outdoor vehicle-mounted antenna cable head in an extreme environment, and marking the plugging times as f 0 ＝80；
Constructing a fault prediction model:
wherein H represents a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head; x is X (0) (k+1) is a number of times of plugging/unplugging the cable head of the outdoor vehicle-mounted antenna in the (k+1) th service cycleMeasuring a value;
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented;
setting a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head, and marking the threshold value as H 0 ＝100；
Because of H>H 0 Therefore, the system does not send out early warning reminding of the damage of the cable head to the user port.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for managing data of a communication device based on big data, the method comprising the steps of:
step S1: acquiring historical test data of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head, constructing a gray prediction model, and calculating a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the next service period;
step S2: acquiring historical use data of an outdoor vehicle-mounted antenna cable head, constructing a loss model, and calculating the average value of the plug loss coefficients of the outdoor vehicle-mounted antenna cable head in an extreme environment;
step S3: acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and the plugging times under an extreme environment, constructing a fault prediction model, and calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
step S4: an intelligent management platform is constructed, a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is set, and when the predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is smaller than or equal to the threshold value, early warning and reminding of cable head damage are sent to a user port.
2. The big data based communication device data management method according to claim 1, wherein: in step S1, the constructing a gray prediction model includes:
acquiring historical test data of the plugging times of the cable head of the outdoor vehicle-mounted antenna, and recording the period from the start of the test to the generation of faults of the outdoor vehicle-mounted antenna as a service period;
taking historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head as an initial use period number sequence, and marking the initial use period number sequence as X (0) ＝{X (0) (1)、X (0) (2)、X (0) (3)、......、X (0) (n) as input to the model;
and performing one-time accumulation generation processing on the initial use period number sequence, namely according to the formula:
one-time accumulation of the number sequence X (1) The differential equation of (2) is:
wherein a represents a development coefficient; u represents the ash action amount;
record Z (1) For accumulating the sequence X once (1) Generates a series of immediately adjacent means, namely:
Z (1) ＝(Z (1) (2),Z (1) (3),...,Z (1) (n))
wherein Z is (1) (k)＝0.5Z (1) (k)+0.5Z (1) (k-1),k＝2,3,...,n；
The model relation between the initial use period number sequence and the adjacent mean value generation number sequence is constructed as follows:
Y＝mB
wherein Y represents an initial use period number array matrix; m represents a coefficient vector matrix; b represents generating a sequence matrix next to the mean value;
the estimated value of the coefficient vector matrix m obtained by the least square method is as follows:
m＝(a,u) T ＝(B T B) -1 B T Y
the grey prediction model is:
wherein k=1, 2, 3, &..; x is X (0) And (k+1) represents a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the k+1th service cycle.
3. The big data based communication device data management method according to claim 2, wherein: in step S2, the acquiring historical usage data of the outdoor vehicle-mounted antenna cable head includes:
acquiring a historical calibration plugging frequency set of an outdoor vehicle-mounted antenna cable head, and marking the historical calibration plugging frequency set as C= { C 1 、c 2 、c 3 、......、c m -a }; wherein c 1 、c 2 、c 3 、......、c m The 1 st, 2 nd, 3 rd, and the third and fourth times of the outdoor vehicle-mounted antenna cable head are respectively represented, and the m times of historical calibration plugging times are represented;
correspondingly, a historical actual plugging frequency set of the outdoor vehicle-mounted antenna cable head is obtained and is recorded as D= { D 1 、d 2 、d 3 、......、d m -a }; wherein d 1 、d 2 、d 3 、......、d m The 1 st, 2 nd, 3 rd, and the third and fourth times of the outdoor vehicle-mounted antenna cable head are respectively represented by the historical actual plugging times of m times;
correspondingly, acquiring a historical plug frequency set of the outdoor vehicle-mounted antenna cable head in an extreme environment, and marking the historical plug frequency set as E= { f 1 、f 2 、f 3 、......、f m -a }; wherein f 1 、f 2 、f 3 、......、f m The 1 st, 2 nd, 3 rd, and third order of the outdoor vehicle-mounted antenna cable head in the extreme environment are respectively represented, and the m times of historical plugging times are represented;
constructing a loss model:
wherein,,
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented; c i Indicating the ith historical calibration plugging times of the outdoor vehicle-mounted antenna cable head; d, d i Representing the i-th historical actual plugging times of the outdoor vehicle-mounted antenna cable head; f (f) i Indicating the ith historical plug of the outdoor vehicle-mounted antenna cable head in extreme environmentTimes.
4. A method of data management for a big data based communication device according to claim 3, wherein: in steps S3-S4, the constructing a fault prediction model includes:
acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head, and marking the current plugging times as c 0 ；
Acquiring the plugging times of an outdoor vehicle-mounted antenna cable head in an extreme environment, and marking the plugging times as f 0 ；
Constructing a fault prediction model:
wherein H represents a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head; x is X (0) (k+1) represents a predicted value of the number of plugging and unplugging of the outdoor vehicle-mounted antenna cable head in the (k+1) th use period;
the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment is represented;
setting a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head, and marking the threshold value as H 0 ；
When H is less than or equal to H 0 And when the system sends out the cable head damage early warning prompt to the user port.
5. A big data based communication device data management system, characterized in that: the system comprises a data acquisition module, a prediction analysis module, a loss analysis module, a fault prediction module and an intelligent early warning management module;
the data acquisition module is used for acquiring historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head, acquiring historical use data of the outdoor vehicle-mounted antenna cable head and acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and the plugging times under an extreme environment; the prediction analysis module is used for constructing a gray prediction model and calculating a predicted value of the plugging times of the outdoor vehicle-mounted antenna cable head in the next use period; the loss analysis module is used for constructing a loss model and calculating the mean value of the plug loss coefficients of the outdoor vehicle-mounted antenna cable head in an extreme environment; the fault prediction module is used for constructing a fault prediction model and calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head; the intelligent early warning management module is used for constructing an intelligent management platform, setting a threshold value of the residual plugging times of the outdoor vehicle-mounted antenna cable head, and sending early warning reminding of cable head damage to a user port when the predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head is smaller than or equal to the threshold value;
the output end of the data acquisition module is connected with the input end of the prediction analysis module; the output end of the prediction analysis module is connected with the input end of the loss analysis module; the output end of the loss analysis module is connected with the input end of the fault prediction module; and the output end of the fault prediction module is connected with the input end of the intelligent early warning management module.
6. The big data based communication device data management system of claim 5, wherein: the data acquisition module comprises a historical test data acquisition unit, a historical use data acquisition unit and a current use data acquisition unit;
the historical test data acquisition unit is used for acquiring historical test data of the plugging times of the outdoor vehicle-mounted antenna cable head by utilizing big data; the historical use data acquisition unit is used for acquiring historical use data of the outdoor vehicle-mounted antenna cable head by utilizing big data; the current use data acquisition unit is used for acquiring the current plugging times of the outdoor vehicle-mounted antenna cable head and plugging times under an extreme environment by using the pressure sensor device.
7. The big data based communication device data management system of claim 5, wherein: the prediction analysis module comprises a gray prediction model construction unit and a first analysis unit;
the grey prediction model construction unit is used for constructing a grey prediction model;
the first analysis unit is used for calculating a predicted value of the plugging times of the outdoor vehicle-mounted antenna cable head in the next service period;
the output end of the gray prediction model building unit is connected with the input end of the first analysis unit; the output end of the first analysis unit is connected with the input end of the loss analysis module.
8. The big data based communication device data management system of claim 5, wherein: the loss analysis module comprises a loss model construction unit and a second analysis unit;
the loss model construction unit is used for constructing a loss model;
the second analysis unit is used for calculating the average value of the plug loss coefficient of the outdoor vehicle-mounted antenna cable head in an extreme environment;
the output end of the loss model building unit is connected with the input end of the second analysis unit; the input end of the second analysis unit is connected with the input end of the fault prediction module.
9. The big data based communication device data management system of claim 5, wherein: the fault prediction module comprises a fault prediction model construction unit and a third analysis unit;
the fault prediction model construction unit is used for constructing a fault prediction model;
the third analysis unit is used for calculating a predicted value of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
the output end of the fault prediction model building unit is connected with the input end of the third analysis unit; and the output end of the third analysis unit is connected with the input end of the intelligent early warning management module.
10. The big data based communication device data management system of claim 5, wherein: the intelligent early warning management module comprises an intelligent management platform construction unit, a threshold setting unit and an early warning reminding unit;
the intelligent management platform construction unit is used for constructing an intelligent management platform;
the threshold setting unit is used for setting a threshold of the residual plugging times of the outdoor vehicle-mounted antenna cable head;
the early warning reminding unit is used for sending out early warning reminding of the damage of the cable head to the user port when the predicted value of the residual plugging times of the cable head of the outdoor vehicle-mounted antenna is smaller than or equal to a threshold value;
the output end of the intelligent management platform construction unit is connected with the input end of the threshold setting unit; the output end of the threshold setting unit is connected with the input end of the early warning and reminding unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310291851.5A CN116307205A (en) | 2023-03-23 | 2023-03-23 | Communication equipment data management system and method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310291851.5A CN116307205A (en) | 2023-03-23 | 2023-03-23 | Communication equipment data management system and method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116307205A true CN116307205A (en) | 2023-06-23 |
Family
ID=86781391
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310291851.5A Pending CN116307205A (en) | 2023-03-23 | 2023-03-23 | Communication equipment data management system and method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116307205A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116846074A (en) * | 2023-07-04 | 2023-10-03 | 深圳市利业机电设备有限公司 | Intelligent electric energy supervision method and system based on big data |
-
2023
- 2023-03-23 CN CN202310291851.5A patent/CN116307205A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116846074A (en) * | 2023-07-04 | 2023-10-03 | 深圳市利业机电设备有限公司 | Intelligent electric energy supervision method and system based on big data |
| CN116846074B (en) * | 2023-07-04 | 2024-03-19 | 深圳市利业机电设备有限公司 | Intelligent electric energy supervision method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110658807A (en) | Vehicle fault diagnosis method, device and system | |
| CN102759684B (en) | For detecting and position the apparatus and method of the defect in underground cables | |
| CN116307205A (en) | Communication equipment data management system and method based on big data | |
| US20130343445A1 (en) | Noise detection method, noise detection apparatus, simulation method, simulation apparatus, and communication system | |
| CN115170000B (en) | Remote monitoring method and system based on electric energy meter communication module | |
| CN113258995A (en) | Fault prediction method, device and computer readable storage medium | |
| CN110221979A (en) | Performance test methods, device, equipment and the storage medium of application program | |
| US20070207739A1 (en) | Testing systems and methods for aircraft radios | |
| KR102515853B1 (en) | Abnormality diagnosis device, system and method based on battery cell unit | |
| CN113839712B (en) | Optical module-based signal transmission monitoring method, equipment, storage medium and device | |
| CN117556359A (en) | Power distribution equipment health state evaluation system and method | |
| CN113660009B (en) | Testing system and testing method for power distribution and utilization communication | |
| CN117169639B (en) | Product detection method and system for power adapter production | |
| CN117424658B (en) | Phased array antenna fault detection system | |
| CN115766297B (en) | Information data safety protection method based on Internet of things | |
| CN111695735A (en) | Railway bow net real-time early warning method, system and device based on flow calculation | |
| CN116756505A (en) | Photovoltaic equipment intelligent management system and method based on big data | |
| WO2023045365A1 (en) | Video quality evaluation method and apparatus, electronic device, and storage medium | |
| CN113098640B (en) | Frequency spectrum anomaly detection method based on channel occupancy prediction | |
| CN115187880A (en) | Communication optical cable defect detection method and system based on image recognition and storage medium | |
| CN111245531B (en) | Method for adaptive parameter adjustment by analyzing device module power | |
| CN110703012B (en) | Distributed fault diagnosis method for power transmission line | |
| CN101640571B (en) | Multipath E1 error code tester and multipath E1 error code testing method | |
| CN115913356A (en) | Intelligent substation optical power testing and comparing method and device comprising switch and storage medium | |
| CN118278924B (en) | Inspection system and method based on digital twinning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |