JP2009015450A - Device and method for predicting number of lightning accident failures, program and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve the efficiency of a maintenance operation by predicting the occurrence of a lightning failure in an arbitrary period, and securing repair components or maintenance personnel according to the scale of the failure in the period. <P>SOLUTION: The device includes: a failure number prediction part 2; a storage part 3; a prediction data output part 4; a lightning data input part 5; a service area information input part 6; lightning database 7; an equipment database 8; a building information input part 100; and a building information database 101. Information relating to lightning is acquired from the lightning database 7. Information relating to equipment is acquired from the equipment database 8. A failure number prediction part 1 assigns the information to a prediction formula: Nd2=B×k2×(Nt2×Ld2)<SP>x</SP>to calculate the predicted failure number. In the formula, Nd2 is lightning damage failure density; Nt2 is storage density; Ld2 is lightning density; k2 is regression coefficient; x is regression multiplier; and B is building coefficient. The value of x is set so as to be 0.5, 1 or any value which is larger than 0.3 and is smaller than 1 according to the equipment circumstances of a service area in which failure number prediction is carried out. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a lightning damage failure prediction apparatus and method, a program, and a recording medium for predicting the occurrence of a lightning damage failure.

  Conventionally, along with the progress of semiconductor technology and telecommunications technology for electrical equipment, the functions of electrical equipment and communication equipment used in the field have been advanced, but the improvement of lightning damage resistance is still in the process. .

In addition, when a device malfunctions due to lightning damage that has occurred, it is particularly required to repair the device in a short time when the device is in use. However, since lightning damage itself does not occur regularly throughout the year, we always have personnel, repair parts, and transportation means to carry out repairs immediately when a lightning strike occurs. Furthermore, various preparations are taken into consideration in order to carry out the worker who actually performs the repair work efficiently and without overloading the worker so as to meet the customer's request (see Patent Document 1).
JP 2004-62521 A

  However, in the conventional technology, although communication equipment and electrical equipment are made resistant to lightning surges assumed in the installation location (general home environment or office environment), this lightning surge is a natural phenomenon. Therefore, it was impossible to specify the maximum surge.

  In addition, the areas where lightning damage is likely to occur are ascertained from past observations and statistical data, and it is possible to limit the areas where lightning damage occurs from past data, but from the viewpoint of providing products at low cost. In this case, it is actually difficult to take countermeasures against surges as products for specific areas that are prone to lightning damage, considering the case where the owner of the product frequently moves.

  The present invention has been made in view of the above-mentioned problems, and its purpose is to predict the occurrence of lightning damage in an arbitrary period and to secure repair parts and maintenance personnel according to the scale of failure in that period. And to improve the efficiency of maintenance operations.

In order to solve the problem, the present invention according to claim 1 is a lightning strike for inputting lightning strike data relating to a lightning strike in a lightning damage failure number prediction device for predicting the number of equipment failures in a service area caused by a lightning strike. Based on the data input unit, the service area information input unit for inputting facility data related to the equipment arranged in the service area, the lightning strike data and the facility data, and within the service area within a predetermined period The relationship between the generated lightning strike density (Ld2), the accommodation density of the equipment in the service area (Nt2), the total number of buildings in the service area and the number of buildings over 6 stories is 1- (6 stories) Building coefficient (B), regression coefficient (k2), regression multiplier (x), lightning damage density (Nd2) The relationship, Nd2 = B × k2 × ( Nt2 × Ld2) and number of faults prediction unit for processing as ^x, storage for being connected to said failure count prediction unit stores the results and registered number of the processing And a prediction data output unit for outputting a result of the arithmetic processing in the failure number prediction unit.

Further, the present invention described in claim 2 is a lightning damage data input unit for inputting lightning strike data related to lightning strikes in a lightning damage failure number prediction device for predicting the number of equipment failures in a service area due to lightning strikes, Based on the lightning strike data and the equipment data, a lightning density (lightning density generated in the service area within a predetermined period) based on the lightning strike data and the equipment data. Ld2), the density of the device in the service area (Nt2), the total number of buildings existing in the service area, and the number of buildings of 6 stories or more: 1− (number of buildings of 6 stories or more / The building coefficient (B) calculated as the total number of buildings), the total length of all communication cables existing in the service area, and the underground communication cable installed underground The communication cable coefficient (T), the regression coefficient (k2), the regression multiplier (x), and the lightning damage density (Nd2) obtained as 1− (underground communication cable length / total communication cable length) , Nd2 = B × T × k2 × (Nt2 × Ld2) ^x is calculated as a failure number predicting unit, and connected to the failure number predicting unit to store the result of the calculation process and the number of entries And a prediction data output unit for outputting the result of the arithmetic processing in the failure number prediction unit.

  According to a third aspect of the present invention, in the first or second aspect, the regression multiplier (x) is any of 0.5, 1, and more than 0.3 and less than 1.

  Moreover, as for this invention of Claim 4, in any one of Claims 1-3, the said regression coefficient (k2) shows the tolerance with respect to the said lightning strike of the said apparatus.

Further, the present invention described in claim 5 is a lightning damage failure number prediction method for predicting the number of equipment failures in a service area caused by a lightning strike, the step of inputting lightning strike data relating to a lightning strike in a lightning strike data input unit, In the service area information input unit, input the facility data related to the equipment arranged in the service area, and in the service area information input unit within the service area within a predetermined period based on the lightning strike data and the facility data in the failure number prediction unit Relationship between generated lightning strike density (Ld2), accommodation density (Nt2) of the equipment in the service area, regression coefficient (k2), total number of buildings existing in the service area, and the number of buildings of 6 stories or more 1− (the number of buildings over 6 stories / the total number of buildings), the building coefficient (B), the regression multiplier (x), Detrimental failure density (Nd2), a relationship, Nd2 = B × k2 × ( Nt2 × Ld2) a step of processing the ^x, connected to said failure count prediction unit in the storage unit of the arithmetic processing result and the numeric And a step of outputting the result of the arithmetic processing in the failure number prediction unit in the prediction data output unit.

According to a sixth aspect of the present invention, in the lightning damage failure number prediction method for predicting the number of device failures in a service area due to a lightning strike, the step of inputting lightning strike data related to lightning strike in a lightning strike data input unit; In the service area information input unit, input the facility data related to the equipment arranged in the service area, and in the service area information input unit within the service area within a predetermined period based on the lightning strike data and the facility data in the failure number prediction unit Relationship between generated lightning strike density (Ld2), accommodation density (Nt2) of the equipment in the service area, regression coefficient (k2), total number of buildings existing in the service area, and the number of buildings of 6 stories or more 1− (number of buildings of 6 stories or more / total number of buildings) and the service area Communication cable coefficient (T) obtained from 1- (underground communication cable length / total communication cable length) and the regression multiplier (x) And the lightning damage failure density (Nd2), the step of calculating as Nd2 = B × T × k2 × (Nt2 × Ld2) ^x , and the calculation processing connected to the failure number prediction unit in the storage unit And a step of storing the result of the arithmetic processing in the failure number prediction unit in the prediction data output unit.

  In addition, according to a seventh aspect of the present invention, in the fifth or sixth aspect, the regression multiplier (x) is any of 0.5, 1, and more than 0.3 and less than 1.

Further, the present invention according to claim 8 is a computer-readable lightning damage failure number prediction program for predicting the number of device failures in a service area caused by a lightning strike. A step of inputting, a step of inputting facility data relating to the device arranged in the service area in the service area information input unit, and a predetermined number of times based on the lightning strike data and the facility data in the failure number prediction unit The relationship between the density of lightning strikes (Ld2) generated in the service area, the accommodation density (Nt2) of the equipment in the service area, the total number of buildings existing in the service area, and the number of buildings of 6 stories or more is 1 -Building coefficient (B) calculated as (number of buildings over 6 stories / total number of buildings) and regression Number and (k2), and regression multiplier (x), the steps of processing a lightning fault density (Nd2), a relationship, as Nd2 = B × k2 × (Nt2 × Ld2) x, the failure in the storage unit The computer is caused to function as a step connected to a number predicting unit and storing a result of the arithmetic processing and a numerical value and a step of outputting a result of the arithmetic processing in the failure number predicting unit in a prediction data output unit.

According to a ninth aspect of the present invention, there is provided a computer-readable lightning damage failure number prediction program for predicting the number of device failures in a service area caused by a lightning strike. A step of inputting, a step of inputting facility data relating to the device arranged in the service area in the service area information input unit, and a predetermined number of times based on the lightning strike data and the facility data in the failure number prediction unit Lightning density (Ld2) generated in the service area, accommodation density (Nt2) of the equipment in the service area, regression coefficient (k2), total number of buildings existing in the service area, and 6 stories or more The building was calculated as 1- (number of buildings over 6 stories / total number of buildings). Communication cable coefficient obtained by calculating 1− (underground communication cable length / total communication cable length) between the coefficient (B) and the total length of all communication cables existing in the service area and the underground communication cable length of the underground laying (T), a regression multiplier (x), and a lightning damage fault density (Nd2) are calculated as Nd2 = B × T × k2 × (Nt2 × Ld2) ^x , The computer is caused to function as a step connected to a failure number predicting unit and storing the result of the arithmetic processing and a numerical value and a step of outputting a result of the arithmetic processing in the failure number predicting unit in a prediction data output unit.

  According to a tenth aspect of the present invention, the lightning damage failure number prediction program according to the eighth or ninth aspect is recorded in a computer-readable manner.

  According to the present invention, it is possible to predict the occurrence of lightning damage in an arbitrary period, and to secure repair parts and maintenance personnel according to the scale of failure in that period, thereby improving the efficiency of maintenance operation.

<First Embodiment>
FIG. 1 shows a configuration diagram relating to a lightning damage failure number predicting apparatus 1 of the first embodiment.

  A lightning damage failure number prediction device 1 shown in FIG. 1 includes a failure number prediction unit 2, a storage unit 3, a prediction data output unit (output unit) 4, a lightning strike data input unit 5, and a service area information input unit 6 A lightning strike database 7, an equipment database 8, a building information input unit 100, and a building information database 101.

  FIG. 2 is an explanatory diagram for explaining a method for predicting the number of failures of equipment in the service area by the lightning damage failure number prediction device 1 shown in FIG.

  In FIG. 2, the lightning strike data input unit 5 is linked to the lightning strike database 7 and acquires information on the lightning strike stored in the lightning strike database 7 as necessary. The information that the lightning strike data input unit 5 acquires from the lightning strike database 7 includes the number of lightning strikes, the occurrence time of lightning strikes, position information, the lightning strike scale, and other information.

  Moreover, the service area information input part 6 is linked to the equipment database 8, and acquires the information regarding the equipment memorize | stored in this equipment database 8 as needed. Information acquired by the service area information input unit 6 from the equipment database 8 includes the area of the service area, position information, the number of devices to be serviced, the name of the exchange center, and the like. The service area refers to the area that is subject to the failure of equipment installed due to lightning damage, and telephones and other devices installed (accommodated) in this area are telephone lines. It is connected to an exchange center that relays communications in that area. The service area area is an area calculated with a distance twice as long as the shortest distance between branches (distance to the nearest exchange center among adjacent exchange centers) as one side.

  Lightning strikes indicate all lightning data (including location information) observed by the lightning observation system. Lightning is difficult to estimate position information (latitude and longitude) because many lines diverge and reach various points. The position accuracy of lightning observed by the lightning observation system is generally set to 500 m, but it is presumed that more observation errors actually occur.

Moreover, the building information input unit 100 is linked to the building information database 101, and acquires information on the building stored in the building information database 101 as necessary. The information acquired by the building information input unit 100 from the building information database 101 includes the total number of buildings existing in the service area, the number of buildings having a height of 6 floors (6F) or higher, and the number of these buildings. The building-rise ratio of 6 stories or more, which is the high-rise ratio obtained from the above, and the density of buildings in the service area. Note that the congestion rate indicating the building density around the replacement center in the service area is acquired as the number of buildings / km ² .

  Note that lightning rods are required to be installed in buildings of 6 stories or more (buildings with a height above 20m), and lightning damage is less than buildings without lightning rods. That is, if there are many buildings of 6 stories or more in one service area, the number of lightning rods increases and the probability of equipment damage due to lightning damage is reduced. In order to handle the presence or absence of such lightning rods as a parameter, the building information rate and the building density of 6 stories or more are collected and accumulated in the building information database 101 and used.

  In the failure number prediction unit 2, the information obtained from the lightning strike data input unit 5, the service area information input unit 6 and the building information input unit 100 is substituted into a preset prediction formula, and the predicted number of failures is calculated. Ask. This calculation is based on the product of the lightning density (Ld2) generated in the service area and the accommodation density (Nt2) of the devices accommodated in the service area, the total number of buildings existing in the service area, and 6 Deriving the relationship between the building coefficient (B) calculated as 1- (number of buildings over 6 stories / total number of buildings) and the lightning damage density (Nd2) by regression analysis, Predict the number of lightning damages in the service area from the lightning density and the housing density.

  The prediction formula for determining the number of lightning damages is derived from information on communication equipment failures and lightning strikes over the past several years and information on the number of units accommodated.

Prediction formula: Nd2 = B × k2 × (Nt2 × Ld2) ^X
Here, Nd2: lightning damage failure density, Nt2: accommodation density, Ld2: lightning strike density, k2: regression coefficient, x: regression multiplier, B: building coefficient. In addition, the value of the prediction formula x is applied by selecting any one of the values larger than 0.5, 1, 0.3 and smaller than 1 depending on the equipment condition of the service area where the number of failures is predicted. The

  Note that the storage unit 3 shown in FIG. 1 is linked to the failure number prediction unit 2 and stores data used for calculation by the failure number prediction unit 2 and calculation results. These stored contents are read from and written to the failure prediction unit 2 as necessary.

  By calculating the prediction formula, a failure prediction in a specific period and a specific area is calculated, and the number of failures per line is also predicted. Then, in order to repair the failure within a specific period, the securing of personnel, prediction of the time when the personnel are required, and calculation of the location where the failure occurs as the dispatch destination of the personnel are performed.

  Further, since the predicted failure number obtained in this way is calculated for each service area, it is possible to create a lightning damage failure occurrence risk list for each service area and to rank the service areas. In addition, maintenance personnel in a service area with a low risk can be assigned to a service area with a high risk.

  According to the embodiment described above, for each service area, the past number of lightning strikes, the number of devices, service area area information and building information are substituted into the prediction formula, and the number of failures expected to occur in each service area is calculated. It can be calculated by processing. The information on the number of failures calculated by the number-of-failures prediction unit can be used to specify the time when maintenance personnel are required in each service area, to set the number of personnel involved in failure repair, the dispatch destination of maintenance personnel, and the like.

<Second Embodiment>
FIG. 3 is an explanatory diagram for explaining a failure number prediction method by the lightning strike failure number prediction apparatus 10 according to the second embodiment.

  3 is different from the already described first embodiment in that a failure data input unit 9 and a communication cable information input unit 102 are provided. The failure data input unit 9 acquires information such as the device tolerance against lightning strikes, the number of devices, and the device name in the service area that is the target of the failure number prediction. In addition, the building information input unit 100 acquires information such as a building ratio of 6 stories or more and a building density.

  Further, the communication cable information input unit 102 acquires the undergroundization rate by the communication cable embedded in the underground from the communication cables such as the network cable laid in the target service area. In general, communication cables are roughly classified into two types: metal cables and optical cables. An optical cable is not a conductor and therefore has a low risk of lightning strikes. Therefore, the possibility of lightning damage is low when it is laid on the ground or underground. Compared to this, the metal cable is made of a conductor and is easily affected by lightning damage caused by lightning. Especially in the case of metal cables laid on the ground, damage due to lightning damage is significant. Therefore, the undergrounding rate of the communication cable is obtained by comparing the ratio of the metal cable laid on the ground that is easily damaged by lightning and the metal cable that is buried underground that is not easily damaged by lightning. The type of communication cable is not limited to a metal cable, and an optical cable may be included. Although the lightning damage occurrence rate of the optical cable is very low and can be ignored, the guide wire of the optical cable may use a conductive metal wire or the like, and its slight influence may be taken into consideration.

  Then, the failure prediction unit 2 substitutes the proof strength of the device as a regression coefficient k2 into the prediction formula, and calculates the estimated number of failures of the specific device due to lightning in the service area. Since k2 (regression coefficient) of the prediction formula indicates the resistance against lightning damage that is different for each device, the k2 of each device is input in advance, and the specific device is assigned by substituting the accommodation number of the specific device into the prediction formula. Calculate the number of failures.

  As a prediction formula, the product of the lightning density (Ld2) generated in the service area and the accommodation density (Nt2) of the devices accommodated in the service area and the buildings existing in the service area within a specific period The building coefficient (B) obtained by calculating the relationship between the total number and the number of buildings of 6 stories or more as 1- (number of buildings of 6 stories or more / total number of buildings) and the total communication cable length existing in the service area The relationship between the communication cable coefficient (T) calculated as 1- (underground communication cable length / total communication cable length) and the lightning damage density (Nd2) is derived by regression analysis. The number of lightning damages in the service area is predicted from the lightning density and the housing density.

  The prediction formula for determining the number of lightning damages is derived from information on communication equipment failures and lightning strikes over the past several years and information on the number of units accommodated.

Prediction formula: Nd2 = B × T × k2 × (Nt2 × Ld2) ^X
Here, Nd2: lightning damage failure density, Nt2: accommodation density, Ld2: lightning strike density, k2: regression coefficient, x: regression multiplier, B: building coefficient, T: communication cable coefficient. In addition, the value of the prediction formula x is applied by selecting any one of the values larger than 0.5, 1, 0.3 and smaller than 1 depending on the equipment condition of the service area where the number of failures is predicted. The

  According to the embodiment described above, each failure number predicting unit calculates each information from the information such as the number of lightning strikes and the number of specific devices in each service area, the area information of each service area, and the building information and communication cable information. The predicted number of failures related to the specific device in the service area can be calculated by arithmetic processing. The prediction information calculated by the failure number prediction unit is failure prediction information related to a specific device within a specific period. From the predicted failure information, the number of personnel involved in failure repair, the arrangement of necessary repair parts, and maintenance personnel It is possible to set the required time, the dispatch destination of maintenance personnel, etc. by specifying the location of the failure.

<Third Embodiment>
FIG. 4 shows a configuration diagram according to the failure number prediction apparatus 20 of the third embodiment.

  The lightning damage failure number prediction device 20 shown in FIG. 4 includes a failure number prediction unit 2, a storage unit 3, a prediction data output unit 4, a predicted lightning strike data input unit 10, and a service area information input unit 6. A database 11, a weather database 12, a lightning strike database 13, an equipment database 8, a building information input unit 100, a building information database 101, a communication cable information input unit 102, and a communication cable information database 103; Yes.

  The number-of-failures prediction method by the lightning damage failure number prediction device 20 shown in FIG. 4 is based on, for example, the correlation between the weather information such as the temperature and the number of lightning strikes two to three months ago, and the weather information and lightning information for the past several years. The number of lightning strikes after 1 to 3 months is predicted, and the number of failures occurring in a specific period of the year (for example, summer) is calculated by the failure number prediction unit.

  Specifically, meteorological data (temperature, number of lightning strikes, etc.) for a specific area is acquired from the meteorological database 12 and the lightning strike database 13, and the correlation between the past meteorological data acquired in the correlation database 11 and the past lightning strike data is observed. This correlation is input to the predicted lightning strike data input unit 10 to predict the occurrence of a lightning strike and send the result to the failure number prediction unit 2.

  On the other hand, information regarding a specific area (service area) based on the facility database 8 is provided from the service area information input unit 6 to the failure number prediction unit 2.

  Moreover, the building information input unit 100 is linked to the building information database 101, and acquires information on the building stored in the building information database 101 as necessary. The information acquired by the building information input unit 100 from the building information database 101 includes the total number of buildings existing in the service area, the number of buildings having a height of 6 floors (6F) or higher, and the number of these buildings. The building ratio of 6 floors or more calculated from the above, the density of buildings in the service area, and the like.

  Further, the communication cable information input unit 102 is linked to the communication cable information database 103, and acquires information on the communication cable stored in the communication cable information database 103 as necessary. The information acquired from the communication cable information database 103 by the communication cable information input unit 102 includes the total number (length) of communication cables existing in the service area and the total number of communication cables buried underground in the total number of communication cables. Is a communication cable coefficient (T) based on

  The failure number prediction unit 2 calculates the number of failures that are expected to occur in a specific region in the near future from the predicted lightning strike information, the number of devices in the specific region, and the area of the specific region, by calculation processing using a prediction formula, Output from the predicted data output unit 4.

  As a prediction formula, the product of the lightning density (Ld2) generated in the service area and the accommodation density (Nt2) of the devices accommodated in the service area and the buildings existing in the service area within a specific period The building coefficient (B) obtained by calculating the relationship between the total number and the number of buildings of 6 stories or more as 1- (number of buildings of 6 stories or more / total number of buildings) and the total communication cable length existing in the service area The relationship between the communication cable coefficient (T) calculated as 1- (underground communication cable length / total communication cable length) and the lightning damage density (Nd2) is derived by regression analysis. The number of lightning damages in the service area is predicted from the lightning density and the housing density.

  The prediction formula for determining the number of lightning damages is derived from information on communication equipment failures and lightning strikes over the past several years and information on the number of units accommodated.

Prediction formula: Nd2 = B × T × k2 × (Nt2 × Ld2) ^X
Here, Nd2: lightning damage failure density, Nt2: accommodation density, Ld2: lightning strike density, k2: regression coefficient, x: regression multiplier, B: building coefficient, T: communication cable coefficient. In addition, the value of the prediction formula x is applied by selecting any one of the values larger than 0.5, 1, 0.3 and smaller than 1 depending on the equipment condition of the service area where the number of failures is predicted. The

  According to the embodiment described above, the prediction information calculated by the failure number prediction unit 2 includes failure information related to a specific device within a specific period, and therefore, for a failure expected to occur in the near future. In addition, it is possible to set up personnel necessary for failure repair, arrangement of necessary repair parts, time when maintenance personnel are needed, dispatch destination of maintenance personnel by specifying the location of failure.

<Fourth embodiment>
In FIG. 5, the block diagram concerning the lightning damage failure number prediction apparatus 30 of 4th Embodiment is shown.

  The lightning damage failure number prediction device 30 shown in FIG. 5 includes a failure number prediction unit 2, a storage unit 3, a prediction data output unit 4, a transmission unit 15, an exchange center 16, and a service area information input unit 6. , Equipment database 8, building information input unit 100, building information database 101, communication cable information input unit 102, and communication cable information database 103. Further, the information input to the failure number predicting unit 2 is selected from two types, and the information from the lightning strike data input unit 5 linked to the lightning strike database 7 in (1) in the figure and the weather in (2) in the figure. This is information from the predicted lightning strike data input unit 10 linked to the correlation database 11 that correlates the database 12 and the lightning strike database 13.

  FIG. 6 is an explanatory diagram for explaining a failure number prediction method by the lightning damage failure number prediction device 30 shown in FIG.

  In FIG. 6, the failure number prediction result calculated by the configuration of the first to third embodiments is stored in the storage unit 3. The storage unit 3 associates the calculated date and time information and information used for the calculation with respect to each failure number prediction result stored.

  The failure number prediction information calculated by the failure number prediction unit 2 and the past failure number prediction information stored in the storage unit 3 are distributed to a network such as the Internet (not shown) via the prediction data output unit 4 and the transmission unit 15. Is done. The distributed failure number prediction information is distributed to the exchange center 16 in the nearby service area, and the failure prediction information can be shared in the distribution destination service area. Therefore, it is possible to easily predict when and how many maintenance workers are required in each service area.

<Fifth embodiment>
FIG. 7 and FIG. 8 show configuration diagrams related to the lightning damage failure number prediction apparatus 40 of the fifth embodiment.

  In FIG. 7, as the configuration of the lightning damage failure number prediction device 40, the failure number prediction unit 2, the storage unit 3, the prediction data output unit 4, the lightning strike data input unit 5, and the service area information input unit 6 The lightning strike database 7, the facility database 8, the building information input unit 100, the building information database 101, the communication cable information input unit 102, the communication cable information database 103, the personnel placement adjustment unit 17, and the transmission unit 15 It is shown. The transmission unit 15 is connected to the exchange center 16 so as to be communicable.

  Furthermore, as shown in FIG. 12, the configuration of the staff assignment adjustment unit 17 includes a maintenance staff registration unit 21, a predicted maintenance staff number storage unit 22, an excess / deficiency staff number calculation unit 23, a staff placement list creation unit 24, And a staffing list output unit 25. The staffing list output unit 25 is connected to the transmission unit 15, and the transmission unit 15 can transmit arbitrary information to the exchange center 16 by mail or the like via a network such as the Internet (not shown).

  8 shows almost the same configuration as that of FIG. 7 except that the lightning strike data input unit 5 and the lightning strike database 7 shown in FIG. A correlation database 11, a weather database 12 linked to the correlation database 11, and a lightning strike database 13 are shown.

  Next, FIG. 9 shows an explanatory diagram for explaining a failure number prediction method by the lightning damage failure number prediction device 40 shown in FIG. 7.

  First, in the configuration of the fourth embodiment shown in FIG. 6, the failure prediction information is transmitted from the transmission unit 15 to the neighboring service area, and the failure prediction information can be shared between the service areas. Therefore, in the configuration shown in FIG. 9, the personnel arrangement adjusting unit 17 is further installed so that the personnel arrangement of maintenance personnel can be adjusted between service areas.

  Each service area transmits information such as the number of maintenance personnel in the service area and the number of personnel that can be dispatched to the staff placement adjustment unit 17 via the network. The staff assignment adjustment unit 17 creates a staff assignment list of maintenance personnel necessary in each service area using information received from the nearby service area and the failure prediction information extracted from the storage unit 3. The created personnel assignment list is transmitted to each service area, and information is shared between the service areas. The information created by the staff placement adjustment unit 17 is stored in the storage unit 3 and is shared among the service areas.

  Next, FIG. 10 shows an explanatory diagram for explaining a modification of the lightning damage failure number prediction method shown in FIG.

  In FIG. 9, the personnel allocation adjustment unit 17 can transmit the personnel allocation list after the personnel allocation adjustment to each service area. Therefore, in the configuration shown in FIG. 10, final adjustment work is performed between each service area and the staff placement adjustment unit 17 for information such as a staff placement list transmitted from the staff placement adjustment unit 17. Then, by managing the result by the “operation status management / storage unit 18 of failure response personnel in the service area” linked to the personnel allocation adjustment unit 17, efficient personnel allocation or failure in each service area can be achieved. It is possible to build a system that can respond quickly and to procure necessary parts smoothly.

  Next, FIG. 11 shows an explanatory diagram for explaining another modification of the lightning damage failure number prediction method shown in FIG.

  In FIG. 10, for each service area, personnel allocation related to failure handling can be efficiently performed by adjusting personnel between service areas. Therefore, in the configuration shown in FIG. 11, in order to be able to manage and search information related to failure repair such as the progress status and repair status of the failure work, the person in charge of the failure work will be notified of the “service area etc. The operation status management / storage unit 18 "of the failure handling personnel is accessed via the network so that the failure status can be registered. The “operation area management / storage unit 18 for failure response personnel in the service area” is provided with an operation state grasping means (not shown). For example, a person in charge of breakdown work (personnel) can access the “service area etc. The operation status management / storage unit 18 "of the failure handling personnel is accessed, and the operation status can be input to the operation status input screen (operation status grasping means). By this work, it is possible to manage the progress of failure repair.

  According to the first to fifth embodiments described above, the number of failures in the near future in an area is predicted using past lightning strike data, service area information, failure information, weather data, building information, and communication cable information. It is possible to deal with failures efficiently and at a minimum cost, and to provide high-quality services.

  In addition, it is possible to estimate lightning damage failures of communication devices and electric devices that occur in the field.

  It also features thorough processing of lightning damage information, lightning strike information, and weather information on communication equipment and electrical equipment to estimate the location where lightning damage occurs and the scale of lightning damage. From the information, it is possible to realize an estimation method and an estimation device for estimating a near future lightning damage failure.

  Estimated near-future prediction information is notified between neighboring switching centers via the network, and information sharing between switching centers allows personnel adjustment and management of faulty operation status between switching centers.・ It is possible to understand the status of repairs.

In addition, by predicting the number of lightning damages occurring in a specific area during a specific period from the number of lightning strikes and the number of lines of equipment accommodated in the service area, arrangement of repair parts (type and number of necessary parts), each It becomes possible to grasp in advance the number of maintenance personnel required in the service area.

  In addition, if we can arrange repair parts and secure the necessary maintenance personnel in advance, we will provide high-quality services by reducing the parts cost necessary for repair and prompt failure response for failure operation concentrated in a specific area for a specific period. Can do.

  In addition, by sharing failure prediction information between adjacent service areas, it is possible to adjust personnel involved in failure operation, and it is possible to reduce personnel costs and the like by efficient personnel assignment.

  In addition, using the lightning damage failure data, past lightning strike data, and past meteorological data, the correlation between the failure caused by lightning and its scale is obtained using a regression equation. By taking into account current weather data, it is possible to predict failures in the near future.

  In addition, it is easy to secure maintenance personnel by sharing the forecast information obtained by the above method via the network between service areas and adjusting the personnel between service areas, etc. It can respond quickly.

  Then, it is possible to predict the occurrence of lightning damage in an arbitrary period, and to secure repair parts and maintenance personnel according to the failure scale in that period, thereby improving the efficiency of maintenance operation.

The block diagram concerning the lightning damage failure number prediction apparatus of 1st Embodiment is shown. The explanatory view for explaining the prediction method of the number of failures by the lightning damage failure number prediction device shown in FIG. 1 is shown. Explanatory drawing for demonstrating the prediction method of the failure number by the lightning strike failure number prediction apparatus of 2nd Embodiment is shown. The block diagram which concerns on the failure number prediction apparatus of 3rd Embodiment is shown. The block diagram which concerns on the lightning damage failure number prediction apparatus of 4th Embodiment is shown. FIG. 6 is an explanatory diagram for explaining a method for predicting the number of failures by the lightning damage failure number prediction apparatus shown in FIG. 5. The block diagram which concerns on the lightning damage failure number prediction apparatus of 5th Embodiment is shown. The block diagram which concerns on the lightning damage failure number prediction apparatus of 5th Embodiment is shown. An explanatory view for explaining a failure number prediction method by the lightning damage failure number prediction apparatus shown in FIG. 7 is shown. An explanatory view for explaining a modification of the lightning damage failure number prediction method shown in FIG. 9 is shown. Explanatory drawing for demonstrating another modification of the lightning damage failure number prediction method shown in FIG. The structure of the staff arrangement | positioning adjustment part which concerns on embodiment is shown.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1, 20, 30, 40 ... Lightning damage failure number prediction apparatus 2 ... Failure prediction part 3 ... Memory | storage part 4 ... Prediction data output part 5 ... Lightning strike data input part 6 ... Service area information input part 7 ... Lightning strike database 8 ... Equipment database DESCRIPTION OF SYMBOLS 100 ... Building information input part 101 ... Building information database 102 ... Communication cable information input part 103 ... Communication cable information database

Claims (10)

In the lightning damage failure number prediction device to predict the number of device failures in the service area due to lightning strikes,
Lightning data input part for inputting lightning data related to lightning,
A service area information input unit for inputting facility data related to the device arranged in the service area;
Based on the lightning strike data and the equipment data, the density of lightning strikes (Ld2) generated in the service area within a predetermined period, the accommodation density (Nt2) in the service area of the device, and the presence in the service area The building coefficient (B), the regression coefficient (k2), and the regression multiplier (x), which are calculated as 1- (number of buildings 6 floors or more / total number of buildings) And a lightning damage failure density (Nd2), a failure number prediction unit for calculating the relationship as Nd2 = B × k2 × (Nt2 × Ld2) ^x ,
A storage unit connected to the failure number predicting unit for storing the result of the arithmetic processing and the stored number;
A prediction data output unit for outputting the result of the arithmetic processing in the failure number prediction unit;
An apparatus for predicting the number of lightning damages. In the lightning damage failure number prediction device to predict the number of device failures in the service area due to lightning strikes,
Lightning data input part for inputting lightning data related to lightning,
A service area information input unit for inputting facility data related to the device arranged in the service area;
Based on the lightning strike data and the equipment data, the density of lightning strikes (Ld2) generated in the service area within a predetermined period, the accommodation density (Nt2) in the service area of the device, and the presence in the service area The building coefficient (B) obtained as 1− (the number of buildings over 6 stories / the total number of buildings) and the total communication existing in the service area The communication cable coefficient (T), the regression coefficient (k2), and the regression multiplier (x) calculated as 1- (underground communication cable length / total communication cable length). A failure number prediction unit for calculating the relationship between the lightning damage failure density (Nd2) and Nd2 = B × T × k2 × (Nt2 × Ld2) ^x ,
A storage unit connected to the failure number predicting unit for storing the result of the arithmetic processing and the stored number;
A prediction data output unit for outputting the result of the arithmetic processing in the failure number prediction unit;
An apparatus for predicting the number of lightning damages. The regression multiplier (x) is
The lightning damage failure number predicting apparatus according to claim 1 or 2, wherein the number of lightning damage failures is one of 0.5, 1 and more than 0.3 and less than 1. The regression coefficient (k2) is
The lightning damage failure number prediction apparatus according to any one of claims 1 to 3, wherein the device has a lightning strike resistance. In the lightning damage failure number prediction method for predicting the number of device failures in the service area due to lightning strikes,
A step of inputting lightning strike data regarding lightning strikes in the lightning strike data input section;
A step of inputting facility data relating to the device arranged in the service area in a service area information input unit;
Based on the lightning strike data and the equipment data in the failure number prediction unit, the lightning strike density (Ld2) generated in the service area within a predetermined period, the accommodation density (Nt2) in the service area of the device, and the regression A coefficient (k2), a building coefficient (B) obtained by calculating a relationship between the total number of buildings existing in the service area and the number of buildings of 6 stories or more as 1- (number of buildings of 6 stories or more / total number of buildings); Calculating the relationship between the regression multiplier (x) and the lightning damage density (Nd2) as Nd2 = B × k2 × (Nt2 × Ld2) ^x ;
A step of storing a result of the arithmetic processing and a number connected to the failure number prediction unit in a storage unit;
Outputting a result of the arithmetic processing in the failure number prediction unit in the prediction data output unit;
A method for predicting the number of lightning damage failures. In the lightning damage failure number prediction method for predicting the number of device failures in the service area due to lightning strikes,
A step of inputting lightning strike data regarding lightning strikes in the lightning strike data input section;
A step of inputting facility data relating to the device arranged in the service area in a service area information input unit;
Based on the lightning strike data and the equipment data in the failure number prediction unit, the lightning strike density (Ld2) generated in the service area within a predetermined period, the accommodation density (Nt2) in the service area of the device, and the regression A coefficient (k2), a building coefficient (B) obtained by calculating a relationship between the total number of buildings existing in the service area and the number of buildings of 6 stories or more as 1- (number of buildings of 6 stories or more / total number of buildings); Communication cable coefficient (T) obtained by calculating the relationship between the total communication cable length existing in the service area and the underground communication cable length of the underground lay as 1- (underground communication cable length / total communication cable length), and regression Calculating the relationship between the multiplier (x) and the lightning damage density (Nd2) as Nd2 = B × T × k2 × (Nt2 × Ld2) ^x ;
A step of storing a result of the arithmetic processing and a number connected to the failure number prediction unit in a storage unit;
Outputting a result of the arithmetic processing in the failure number prediction unit in the prediction data output unit;
A method for predicting the number of lightning damage failures. The regression multiplier (x) is
7. The lightning damage failure number prediction method according to claim 5, wherein the method is any one of 0.5, 1 and more than 0.3 and less than 1. 7. In a computer-readable lightning damage failure number prediction program for predicting the number of device failures in a service area due to lightning strikes,
A step of inputting lightning strike data regarding lightning strikes in the lightning strike data input section;
A step of inputting facility data relating to the device arranged in the service area in a service area information input unit;
Based on the lightning strike data and the equipment data in the failure number prediction unit, the lightning strike density (Ld2) generated in the service area within a predetermined period, the accommodation density (Nt2) in the service area of the device, and the regression A coefficient (k2), a building coefficient (B) obtained by calculating a relationship between the total number of buildings existing in the service area and the number of buildings of 6 stories or more as 1- (number of buildings of 6 stories or more / total number of buildings); Calculating the relationship between the regression multiplier (x) and the lightning damage density (Nd2) as Nd2 = B × k2 × (Nt2 × Ld2) ^x ;
A step of storing a result of the arithmetic processing and a number connected to the failure number prediction unit in a storage unit;
Outputting a result of the arithmetic processing in the failure number prediction unit in the prediction data output unit;
And causing the computer to function. In a computer-readable lightning damage failure number prediction program for predicting the number of device failures in a service area due to lightning strikes,
A step of inputting lightning strike data regarding lightning strikes in the lightning strike data input section;
A step of inputting facility data relating to the device arranged in the service area in a service area information input unit;
Based on the lightning strike data and the equipment data in the failure number prediction unit, the lightning strike density (Ld2) generated in the service area within a predetermined period, the accommodation density (Nt2) in the service area of the device, and the regression A coefficient (k2), a building coefficient (B) obtained by calculating a relationship between the total number of buildings existing in the service area and the number of buildings of 6 stories or more as 1- (number of buildings of 6 stories or more / total number of buildings); Communication cable coefficient (T) obtained by calculating the relationship between the total communication cable length existing in the service area and the underground communication cable length of the underground lay as 1- (underground communication cable length / total communication cable length), and regression Calculating the relationship between the multiplier (x) and the lightning damage density (Nd2) as Nd2 = B × T × k2 × (Nt2 × Ld2) ^x ;
A step of storing a result of the arithmetic processing and a number connected to the failure number prediction unit in a storage unit;
Outputting a result of the arithmetic processing in the failure number prediction unit in the prediction data output unit;
And causing the computer to function.   A recording medium on which the lightning damage failure number prediction program according to claim 8 or 9 is recorded so as to be readable by a computer.

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