JPH06177968A - Communication network abnormality impact assessment device - Google Patents

Abstract

(57) [Summary] [Purpose] To facilitate quick and appropriate countermeasures by maintenance personnel when an abnormality occurs in the communication network, and improve the reliability and safety of the communication network. [Structure] In order to select an appropriate measure for a communication network abnormality, in an abnormality impact degree evaluation device that predicts the impact degree of damage to customers, the abnormality occurrence location information notified by the monitoring device and the accommodation of customers / services A first impact degree prediction unit that estimates the degree of impact on each customer and service by using the normal use status of the resources allocated to the customer and service affected by the abnormality based on the information; First, select customers and services whose impacts exceed a predetermined value as measures, and select appropriate measures for each measure.
And a measure determination section of.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technique for evaluating the degree of influence of an abnormality on a customer or service suffering damage, which is necessary for automatic selection of measures to be taken when an abnormality occurs in a communication network. The present invention relates to a communication network abnormality influence degree evaluation device suitable for more accurate selection of measures.

[0002]

2. Description of the Related Art Conventionally, when an abnormality occurs in a communication network, in order to select an appropriate measure to cope with the abnormality, for example, as disclosed in Japanese Patent Application No. 4-44503 by the proposers of the present invention. A technique is used to evaluate the degree of influence that represents the degree of trouble for each customer and the degree of trouble for each service due to this abnormality. In this prior art, when selecting a measure corresponding to an alarm from each device in the communication network, a failure occurrence point is specified, and the degree of influence at the time of the failure occurrence is automatically evaluated for each specified failure point. To do.

In this technique, the degree of influence is evaluated when a failure occurs, and an appropriate measure is selected based on the evaluation result. However, the degree of influence changes with the passage of time, and therefore, when the action is actually taken, the selected action may be different from the appropriate one.
In addition, consideration is given to the evaluation of the impact of each service and each customer, the prediction of the effect of implementing the selected measures, and the recording of the history of the predicted measures for each damaged service and customer. Not yet, it was not enough to let the maintainers choose the appropriate measures.

[0004]

The problems to be solved are that the prior art does not evaluate the degree of influence at the time of actually taking measures against a failure, and the degree of influence for each service and each customer. It does not take into consideration the evaluation of the above, the prediction of the effect of implementing the selected measures, and the recording of the history of the predicted measures for each damaged customer. The object of the present invention is to solve these problems of the prior art, to predict the time transition of the degree of influence for each service and each customer, to determine a more accurate measure, and to implement the selected measure. In the case of a communication network, it is possible to predict the effects of such cases and to promptly respond to failure reports by accumulating and saving the degree of influence derived at the time of an abnormality, thus improving the reliability and safety of the communication network. The present invention is to provide a device for evaluating anomalous influence.

[0005]

In order to achieve the above object, the abnormality influence evaluation apparatus for a communication network according to the present invention (1) selects an appropriate measure against this abnormality when an abnormality occurs in the communication network. In addition, in a communication network abnormality impact degree evaluation device that predicts the degree of impact of damage to customers suffering from this abnormality, when an abnormality occurs in the communication network, an abnormality notified from the monitoring device that monitors the communication network Based on the information of the location where the error occurred and the accommodation information of the customer and service, the customers and services that are affected by the anomaly are extracted, and the normal use status of the resources allocated to each of the extracted customers and services The first impact degree prediction unit that estimates the impact degree of the damage caused by the abnormality on the extracted customer and service by using this usage state, and the impact degree estimated by the first impact degree prediction unit But A first measure determination unit that selects a customer and a service that exceed a set threshold value as a measure target and selects a measure that avoids the influence of abnormality on the customer and the service is provided for each of the selected measure targets. It is characterized by (2) In the communication network abnormality impact degree evaluation device described in (1) above, the measure selected by the first measure determination unit and the usage status of the resources allocated to the customer and the service at the time of the abnormality occurrence. Based on the above, a first measure post-implementation impact degree prediction unit for estimating the improvement situation of the impact degree when the measure selected by the first measure determination unit is implemented is provided. Further, (3) when an abnormality occurs in the communication network, in order to select an appropriate measure for this abnormality, the abnormality influence evaluation device of the communication network for predicting the influence degree of the damage to the customer suffering from this abnormality At the time of occurrence of an abnormality in the communication network, the customer and service affected by the abnormality are identified on the basis of the information of the location where the abnormality is notified from the monitoring device that monitors the communication network and the accommodation information of the customer and service. Extract and grasp the time transition of the normal use status of the resources allocated to each extracted customer and service, and predict the resource use status after the occurrence of an abnormality based on this time transition of the use status Then, based on the predicted resource usage status, a second impact degree prediction unit that predicts a time transition of the impact degree at and after the abnormality occurrence, and a second impact degree predictor at a specified time. Select the customers and services whose impact degree predicted by the department exceeds a preset threshold as the target of measures, and select the measures to avoid the influence of abnormalities on the customers and services for each of the selected targets. And a second measure deciding unit for carrying out.
Further, (4) in the communication network abnormality impact degree evaluation device described in (3) above, the measure selected by the second measure determination unit,
Estimate the time transition of the improvement situation of the impact degree when the measure selected by the second measure determination unit is implemented based on the time transition of the usage condition of the resource allocated to the customer and the service at the time of the abnormality occurrence. The feature is that a post impact measure impact prediction unit is provided. Also, (5) above (1)
In the communication network abnormality impact degree evaluation device according to any one of (1) to (4), a data management unit that records the impact degree of damage to a customer who is predicted to be damaged when an abnormality occurs in the communication network, and the customer With respect to the failure report of (1), an impact degree search unit is provided for searching the impact degree of this customer from the record of the impact degree accumulated in the data management unit.

[0006]

In the present invention, when the abnormality occurrence in the communication network is notified, the first influence degree predicting unit grasps the service affected by the abnormality and the usage status of the resources allocated to the customer. Then, based on the usage status, the degree of influence at the time of occurrence of an abnormality is estimated for each extracted customer or service. Then, the first measure determination unit selects a measure target for each customer or service based on the influence degree estimated by the first influence degree prediction unit, and determines an appropriate measure for each selected measure subject. To do.
The determined measure and the influence degree estimated by the first influence degree prediction unit are displayed to the maintenance person on the display unit of the terminal or the like. In this way, the maintenance person can know the degree of influence at the time of occurrence of an abnormality and appropriate measures for each customer or service suffered by the abnormality.

When the maintenance staff selects an arbitrary measure from the displayed contents, the first post-implementation impact degree predicting unit determines the selected measure and each service grasped by the first impact degree predicting unit. Based on the usage status of the resource allocated to the customer and the customer, the usage status of the resource when the selected measure is implemented is estimated, and based on the changed usage status, the first impact degree prediction unit Similar to the above, the degree of impact after the implementation of measures is estimated. The degree of influence is notified to the terminal unit,
Displayed to maintenance personnel. This allows the maintenance person to simulate the effect of the action prior to the implementation of the action.

Further, in the configuration provided with the second impact degree predicting unit, the second measure determining unit, and the second measure post-implementing influence degree predicting unit, the time transition of the resource usage condition, and Each processing is performed based on the time transition of the degree of influence. That is, the second impact degree predicting unit grasps the time transition of the normal use status of the resource affected by the affected service and the customer, and based on the time transition of the use status, after the occurrence of the abnormality. The usage status of the resource is predicted, and based on the prediction result, the time transition of the time of the abnormality occurrence and the influence degree after the abnormality occurrence is estimated. Using this estimated time transition, the second measure determination unit selects the measure target for each service or customer and determines the measure for each selected measure target. Then, the second post-implementation impact degree prediction unit estimates the time transition of the impact degree when the determined measure is implemented. The time transition of the impact degree estimated in this way, the time transition of the impact degree estimated by the second impact degree prediction unit, and the measures determined by the second action determination unit are Displayed to maintenance personnel.

Further, the influence degree at the time of occurrence of the abnormality estimated by the first or second influence degree predicting section is recorded by the data managing section. Then, in response to the customer's failure report, the maintenance person instructs the terminal to search the degree of influence and the search condition, and the influence degree search unit causes the data management unit to determine the degree of influence related to the customer who has reported the failure. According to the search conditions,
The search result is notified to the terminal unit. Then, the terminal unit displays the search result notified from the influence degree search unit to the maintenance person. As a result, the maintenance person can quickly obtain the past impact degree related to the customer.

[0010]

Embodiments of the present invention will now be described in detail with reference to the drawings. FIG. 1 is a block diagram showing a first embodiment of a configuration relating to the present invention of an abnormality influence degree evaluation device for a communication network of the present invention. In the figure, 1 is an abnormality influence degree evaluation device according to the present invention, 2 is a monitoring device for monitoring the normality of each station in the communication network, and 7 is a communication network. The communication network 7 includes a network service management station 3 that manages contract information and definition information regarding customers and services, a plurality of network service control stations 4a and 4b that perform call processing regarding customers and services, and a plurality of call connections. And exchanges 5a to 5c. The network service management station 3 transfers contract information and definition information relating to managed customers and services to the network service control stations 4a and 4b, and collects and accumulates operation information from the network service control stations 4a and 4b. . The exchanges 5a to 5c, the network service control stations 4a and 4b, the network service control unit 4a,
4b and network service management unit 3, network service management unit 3, anomaly impact degree evaluation device 1, monitoring device 2 and network service control unit 4
a, 4b, and the monitoring device 2 and the abnormality impact degree evaluation device 1
Are connected to each other via the information transfer network 6.

When an abnormality occurs in the communication network 7, the abnormality impact degree evaluation apparatus 1 determines the influence of the abnormality based on the information about the location of the abnormality notified from the monitoring apparatus 2 and the accommodation information of the customer and the service. Extract the customers and services to be incurred, grasp the normal usage status of the resources allocated to each of the extracted customers and services, and use this usage status to determine the damage caused by anomalies to the extracted customers and services. First impact degree predicting unit 12 according to the present invention for estimating the impact degree of
And the impact degree estimated by the first impact degree predicting unit 12 is
A first measure determination according to the present invention, in which a customer and a service exceeding a preset threshold value are selected as measures, and a measure for avoiding the influence of abnormality on the customer and the service is selected for each selected measure. Based on the unit 13, the measure selected by the first measure determination unit 13, and the use status of the resources allocated to the customer and the service at the time of the occurrence of the abnormality, the measure selected by the first measure determination unit 13 is determined. The first post-implementation impact degree predicting unit 14 and the first impact degree predicting unit 1 for estimating the improvement situation of the impact degree when implemented
The data management unit 16 according to the present invention for recording the degree of damage to the customer who is predicted to be damaged when an abnormality occurs in the communication network according to 2, and the data management unit 16 for the failure report from the customer. The influence degree search unit 15 according to the present invention, which retrieves the influence degree related to this customer from the record of the influence degree accumulated in the above, the display of the processing result of the abnormality influence degree evaluation apparatus 1, and the maintenance to the abnormality influence degree evaluation apparatus 1. It is composed of a terminal unit 17 for input from a person, a communication unit 11 for controlling communication with the monitoring device 2 and the network service management station 3 of the communication network 7.

The operation according to the present invention of the abnormality influence degree evaluation apparatus 1 having such a configuration will be described below. When an abnormality occurs in the communication network 7 and the monitoring apparatus 2 notifies the abnormality occurrence, the first impact degree prediction unit 12 extracts the service and the customer affected by the abnormality, and the influences of these are extracted. Understand the usage status of services that have been incurred and resources allocated to customers. Then, the first impact degree prediction unit 12 estimates the impact degree at the time of occurrence of an abnormality for each extracted customer or service based on the usage status. First measure determination unit 13
Uses the degree of influence estimated by the first degree-of-impact estimation unit 12 to select an action target for each customer or service and determine an appropriate action for each selected action target. The determined measure and the influence degree estimated by the first influence degree prediction unit 12 are displayed to the maintenance person on the display unit of the terminal unit 17 or the like. In this way, the maintenance person can know the degree of influence at the time of occurrence of an abnormality and appropriate measures for each customer or service suffered by the abnormality.

Further, for example, when the maintenance person selects an arbitrary measure from the displayed contents, the first post-implementation influence degree prediction unit 14 causes the selected measure and the first influence degree prediction unit 12 to be selected. Based on the usage status of each service and the customer's allocated resource that was affected by the above, the usage status of the resource when this selected measure was implemented is estimated, and Then, similarly to the first impact degree prediction unit 12, the impact degree after the implementation of the measure is estimated. The degree of influence is notified to the terminal unit 17, and the maintenance person can simulate the effect of the measure before performing the measure.

Further, the data management unit 16 records the degree of influence at the time of occurrence of the abnormality estimated by the first influence degree predicting unit 12. When the maintenance person receives the failure report from the customer via the terminal unit 17, when the maintenance person gives an instruction for the influence degree search and the search condition via the terminal unit 17, the influence degree search unit 15
Searches for the degree of influence related to the customer who has reported the failure from the data management unit 16 according to the search condition, and sends it to the terminal unit 17.
Notify search results. Then, the terminal unit 17 displays the search result notified from the influence degree search unit 15 to the maintenance person. By this, the maintenance person can obtain the past influence degree related to the customer who has reported the failure.
The data management unit 16 is composed of various tables, as shown in FIG. 2 below, and the abnormality influence degree evaluation apparatus 1 uses these tables to perform the processing operation according to the present invention. Hereinafter, the processing operation according to the present invention of the abnormality influence degree evaluation device 1 will be described in more detail with reference to FIG.

FIG. 2 is a block diagram showing an embodiment of the structure relating to the present invention of the abnormality influence degree evaluation apparatus in FIG. The present embodiment particularly shows a detailed example of the table in the data management unit 16 of the abnormality influence degree evaluation device 1 in FIG. 1, and the data management unit 16 includes the following tables. The abnormality occurrence local table 21 is used to grasp the station in which the abnormality has occurred, and has elements of the abnormality occurrence time, its local number, and the content of the abnormality. The disability service / customer table 22 is used for temporarily storing a list of services and customers that have been affected by an abnormality, and has elements of the service / customer identification number and the effect type indicating the type of the effect. Here, the type of influence includes call disabled, call disabled, sometimes call disabled, sometimes call disabled, call disconnect. The resource usage status table 23 is used for temporarily storing the measured value of the resource usage status, and has the service / customer identification number, the measurement period, and the number of outgoing calls as elements. The predicted resource usage status table 24 is used for temporarily storing the predicted value of the resource usage status, and has the same elements as the above-described resource usage status table 23. The influence table 25 is
Used to temporarily store the impact degree derived based on the measured traffic and the predicted traffic, and includes the service / customer identification number, the time, the service at that time, and the impact degree of the customer. And The impact recording table 26 is used to record and accumulate the impact derived based on the measured traffic for a long period of time, and has the same elements as the impact table 25 described above. The important customer table 27 is used to store a list of important customers, and has the customer identification number and the importance of the customer as elements. The measure target table 28 is used to temporarily store a list of services and customers to be preferentially taken, and has elements of service / customer identification numbers, priority order of measures, and measures.

Hereinafter, an abnormality occurs in the network service control station 4a in the communication network 7 in FIG.
a and the network service management station 3, and the communication between the network service control station 4a and the exchanges 5a to 5c is interrupted, the customers and services accommodated in the network service control station 4a are assumed. Will be stored in the network service control station 4b and recovered. In this example, due to the capability of the network service control station 4b, all the customers and services accommodated in the network service control station 4a are handled by the network service control station 4b.
It is assumed that it is impossible to transfer to b.

When the abnormality assumed above occurs,
In FIG. 1, the monitoring device 2 that monitors an abnormality in the communication network detects that an abnormality has occurred in the network service control station 4a, and detects the location of the abnormality and the status of the abnormality via the information transfer network 6. The communication unit 11 in the abnormality impact degree evaluation device 1 is notified.
The communication unit 11 notifies the data management unit 16 of the abnormal place and the abnormal situation notified from the monitoring device 2 of FIG.
Further, the first impact degree prediction unit 12 is notified of the occurrence of an abnormality. The data management unit 16 registers the abnormality occurrence location and the abnormality situation notified from the communication unit 11 in the abnormality occurrence local table, and in response to the inquiry about the abnormality occurrence location from the first impact degree prediction unit 12, The abnormality occurrence local table 21 is searched and the location of the abnormality occurrence is answered.

When the first impact degree predicting unit 12 is notified of the occurrence of an abnormality by the communication unit 11, the first influence degree predicting unit 12 inquires of the data management unit 16 about the location of the abnormality, and retrieves the location of the abnormality answered by the data management unit 16. As a condition, the list of services and customers accommodated in the abnormal place was inquired to the network service management station 3 via the communication unit 11, and the service was accommodated in the abnormal place answered by the network service management station 3. The data management unit 16 is notified of the list of services and customers. The data management unit 16 registers the list of services and customers notified by the first impact degree prediction unit 12 in the disordered service / customer table 22. Further, the first impact degree prediction unit 12 uses the communication service unit 3 to determine, via the communication unit 11, the service status returned from the network service management station 3 and the usage status of the resource allocated to the customer before the abnormality occurs. 3, and notifies the data management unit 16 of the usage status of the resource, which is returned from the network service management station 3 before the abnormality occurs. The data management unit 16 registers, in the resource usage status table 23, the usage status of the resource that was notified by the first impact degree prediction unit 12 before the abnormality occurred.

Further, the first impact degree predicting unit 12 determines the influence of each service and customer accommodated at the location where the abnormality occurs based on the usage status of the resource before the abnormality occurrence, which is returned from the network service management station 3. Predicting the degree of influence, notifying the data management unit 16 of the result of predicting the degree of influence, notifying the terminal unit 17 of the degree of influence of the service and the customer having a significant influence from the result of the degree of influence prediction, and taking the first measure. The determination unit 13 is notified of the end of the impact degree prediction. The data management unit 16 registers the influence degree prediction result notified from the first influence degree prediction unit 12 in the influence degree recording table 26. The terminal unit 17 displays, on the terminal screen, the influence degree of the service and the customer whose noticeable influence is notified from the first influence degree predicting unit 12.

When the first measure determining unit 13 is notified by the first predicting unit 12 of the end of the predicting the influence, the data managing unit 16 receives the influence of the abnormality and registers the important information. Inquires about the list of customers and notifies the data management unit 16 of the inquiry result. The data management unit 16 is the first
In response to an inquiry from the measure determining unit 13 of No. 1, the customers included in both the disability service / customer table 22 and the important customer table 27 are extracted, and the first measure determining unit 1
3 is answered, and the inquiry result notified from the first measure determination unit 13 is registered in the measure target table 28. In addition, the first measure determination unit 13 includes the data management unit 16
To a list of influence degrees, and extracts services and customers whose influence degree exceeds a preset threshold value, and notifies the data management unit 16 of the extraction result. The data management unit 16 responds to the inquiry from the first measure determination unit 13 with the contents of the impact degree table 25,
The extraction result notified from the measure determination unit 13 is additionally registered in the measure target table 28.

The first measure determination unit 13 is the data management unit 1
Inquire about the measures to be registered in step 6 first, give priority numbers to the important customers in descending order of their impact, and then, in the order of greatest impact among non-important customers, A number subsequent to the priority number assigned by the important customer is assigned, and the data management unit 16 is notified of the priority number assigned in this way. The data management unit 16 registers the priority number notified from the first measure determination unit 13 in the measure target table 28 for each measure target. Also, the first
The measure determination unit 13 inquires from the data management unit 16 about the measure target, and in this embodiment, for each measure target answered from the data management unit 16, the network service control station 4 in FIG.
From a, the network service control station 4b selects a measure for accommodation change, notifies the data management unit 16 of the measure for each measure target, and notifies the terminal unit 17 of the measure target and the measure.

When the maintainer instructs the terminal unit 17 to predict the improvement status of the degree of influence when the measure is taken, together with the target measure, the terminal unit 17 causes the terminal unit 17 to measure the influence level after the measure is taken. The improvement status prediction request is notified to the post-first measures post-execution impact degree prediction unit 14 together with the designated measure target. After the first measure implementation, the impact degree prediction unit 14
When the request for prediction of the improvement situation of the impact degree after the implementation of the measures is notified, the data management unit 16 is inquired about the list of services and customers accommodated in the location where the abnormality occurred, and the terminal unit is notified from the inquiry result. Except for the specified measures to be taken, the data management unit 16 is inquired about the removed services and the resource usage status of the customer, and the inquiry result is notified to the data management unit 16. The data management unit 16 responds to the inquiry of the list of services and customers accommodated in the abnormal place from the post-first-influence-degree impact prediction unit 14 with the contents of the disability service / customer table 22. Further, in response to the inquiry about the resource usage status regarding the service and the customer, the content of the resource usage status table 23 is answered, and the notified inquiry result is registered in the predicted resource usage status table 24.

In addition, the post-implementation impact degree prediction unit 14
Asks the data management unit 16 about the resource usage status regarding services and customers, and based on the usage status,
The data management unit 1 predicts the improvement status of the impact degree for each service and customer when the measure is taken for the measure object notified from the terminal unit, and predicts the impact degree when the measure is taken.
6 of the results of the impact of the measures taken, the impact of services and customers with significant impact,
Notify the terminal unit 17. The data management unit 16 registers, in the impact degree table 25, the impact degree when the measure notified from the post-implementation impact degree prediction unit 14 is taken. In addition, the terminal unit 17 includes the post-implementation impact degree prediction unit 14
The service and the impact degree of the customer noticed by the noticer are displayed on the screen.

Next, the processing operation according to the present invention of the first influence degree predicting unit 12, the first measure determining unit 13, and the first measure post-execution influence degree predicting unit 14 will be described with reference to FIGS. Will be described respectively. FIG. 3 is a flow chart showing an embodiment of the processing operation according to the present invention of the first influence degree prediction section in FIG. When the first impact degree predicting unit 12 in FIG. 2 is notified of the occurrence of an abnormality by the communication unit 11 of FIG. 2, first, the data managing unit 16 of FIG. 2 is queried for the location of the abnormality, and the location of the abnormality is grasped. (Step 30
1). Then, using the location of the abnormality returned from the data management unit 16 of FIG. 2 as a search condition, the network service management station 3 is inquired of the list of services and customers accommodated in the location of the abnormality via the communication unit ( Step 30
2). Further, the data management unit 16 is notified of the list of services and customers accommodated in the abnormality occurrence location, which is returned from the network service management station 3 of FIG. 2, and is registered in the disordered service / customer table 22 of FIG. (Step 30
3).

Next, the service replied from the network service management station 3 of FIG. 2 and the usage status of the resource allocated to the customer before the abnormality occurs are communicated via the communication unit 11 of FIG. The service management station 3 is inquired (step 304). The usage status before the abnormality occurrence of the resource, which is replied from the network service management station 3 of FIG. 2, is shown in FIG.
The data management unit 16 is notified and registered in the resource usage status table 23 of FIG. 2 (step 305).

Further, based on the usage status of the resource before the abnormality occurrence, which is returned from the network service management station 3, the influence degree of each service and customer accommodated in the abnormality occurrence place is predicted and the influence degree is derived. Perform (step 306). The prediction result is notified to the data management unit 16 of FIG.
Is registered in the impact record table 26 (step 30).
7). From the prediction result of the influence degree, the noticeable degree of influence of the service and the customer is notified to the terminal unit 17 of FIG.
It is displayed on the screen (step 308). Then, the measure method determination unit 13 of FIG. 2 is notified of the end of the impact degree prediction (step 309).

FIG. 4 is a flow chart showing an embodiment of the processing operation of the first measure determination unit in FIG. 2 according to the present invention. The first measure determination unit 13 in FIG.
When notified of the end of the influence degree prediction from the influence degree prediction unit 12, the processing contents described below are sequentially executed. First, the data management unit 16 in FIG. 2 is inquired about a list of important registered customers who have been affected by an abnormality, and is included in both the disability service / customer table 22 and the important customer table 27 in FIG. Existing customers are extracted, and important customers with disabilities are extracted (step 401). Then, the result (distressed important customer) is notified to the data management unit 16 in FIG. 2 and registered in the action target table 28 in FIG. 2 (step 40).
2).

Further, the data management unit 16 in FIG. 2 is inquired about the list of the degree of influence, and from the contents of the degree of influence table 25 in FIG. 2, the service and the customer whose influence degree exceeds a preset threshold value. Is extracted, and the recommended action target is extracted (step 403). Then, the data management unit 16 of FIG. 2 is notified of the extraction result (target of recommended measures),
It is additionally registered in the measure target table 28 of FIG. 2 (step 404).

Next, the data management unit 16 of FIG. 2 is inquired about the previously registered measures, and first, among the important customers,
Priority numbers are assigned in descending order of impact, and then numbers other than important customers are assigned in descending order of impact, following the priority numbers assigned by important customers. 2 is notified to the data management unit 16 of FIG. 2, and the priority number is registered in the measure object table 28 of FIG. 2 for each measure object (step 40).
5). Further, the data management unit 16 in FIG. 2 inquires about the action target, and for example, for each responded action target, the network service control station 4a in FIG. 1 to the network service control station 4 in FIG.
The measure for accommodation relocation to b is selected, the measure for each measure is notified to the data management unit 16 in FIG. 2, and the recommended measure is registered (step 406). Then, the measure target and measure are notified to the terminal unit 17 and displayed (step 407).

Here, when the maintenance person instructs the terminal unit 17 of FIG. 2 to predict the improvement status of the degree of influence when the measures are taken together with the target action target, the terminal unit 17 of FIG. 2 notifies the first post-implementation impact degree prediction unit 14 of FIG. 2 together with the designated action target of the request for predicting the improvement status of the impact degree after the implementation of the measures. When the first post-implementation impact degree prediction unit 14 in FIG. 2 is notified of a request for prediction of the improvement status of the impact degree after the implementation of such measures from the terminal unit 17 in FIG. 2, the following FIG. The processing contents shown are sequentially executed.

FIG. 5 is a flow chart showing an embodiment of the processing operation of the first post-implementation influence degree predicting section in FIG. 2 according to the present invention. When the first post-implementation impact degree prediction unit 14 in FIG. 2 is notified by the terminal unit 17 of FIG. 2 of a request for prediction of the post-measurement impact improvement situation, first, the data management unit of FIG. 16 is inquired about the list of services and customers accommodated in the abnormal place, and the inquiry result, that is, the injured service / customer table in FIG. 2 for the inquiry of the list of services and customers accommodated in the abnormal place 22 from the contents of FIG.
The disability service / customer list correction processing is performed except for the designated measure object notified from the terminal section 17 (step 501).

Then, the data management unit 16 of FIG. 2 is inquired about the resource usage status regarding the excluded service and the customer, that is, the content of the resource usage status table 23 of FIG. 2 is notified from the terminal unit 17 of FIG. The object excluding the specified designated action target is taken out and the resource usage status is corrected (step 502). The inquiry result is notified to the data management unit 16 of FIG.
The data management unit 16 of the above registers the notified inquiry result in the predicted resource usage status table 24 of FIG.

Further, in the case where the data management unit 16 of FIG. 2 is inquired about the resource usage status regarding the service and the customer, and based on the usage status, a measure is taken for the measure object notified from the terminal unit 17 of FIG. The improvement status of the influence degree for each service and each customer is predicted, and the influence degree is derived (step 503). Then, the prediction result of the influence degree when the measure is taken is notified to the data management unit 16 of FIG. 2, and the influence degree is registered in the influence degree table 25 (step 504). Further, from the prediction result of the influence degree when the measures are taken, the influence degree of the service and the customer having a remarkable influence is notified to the terminal unit 17 of FIG. 2 and displayed on the screen (step 505).

As a result, the maintenance person can simulate the improvement situation of the influence degree for each service and each customer when the measure is taken. Next, the influence degree search unit 15 according to the present invention in FIG. 1 will be described with reference to FIGS. 6 and 7.

FIG. 6 is a block diagram showing an embodiment of a configuration relating to the operation of the present invention of the influence degree search unit in FIG. In the figure, when the influence degree search unit 15 is notified of the influence degree search from the terminal unit 17 together with the search condition, the influence degree search unit 15 notifies the data management unit 16 of the search condition notified from the terminal unit 17, and The search result replied from the data management unit 16 is notified to the terminal unit 17. The data management unit 16
Based on the search condition notified from the influence degree search unit 15, FIG.
The influence record table 26 is searched, and the search result is returned to the influence search unit 15. The terminal unit 17 displays the search result notified from the influence degree search unit 15 on the screen. Thus, when the maintenance person receives the failure report from the customer, the maintenance person can search for the service regarding the customer and the past influence degree of the customer.

FIG. 7 is a flow chart showing an embodiment of the processing operation relating to the operation of the present invention of the influence degree searching section in FIG. When the influence degree search unit 15 of FIG. 6 is notified of the influence degree search together with the search condition from the terminal unit 17 of FIG. 6, first, the influence degree search unit 15 notifies the data management unit 16 of FIG. 6 of the notified search condition. Then, the influence record table 26 of FIG. 2 is searched, and the influence is searched (step 701). Then, the search result replied from the data management unit 16 of FIG. 6 is notified to the terminal unit 17 of FIG. 6, and the search result (degree of influence) is displayed on the screen (step 702).

By doing so, the maintainer can easily obtain the past degree of influence on the customer.
As described above, in the first embodiment described with reference to FIGS. 1 to 7,
The degree of influence is predicted when an abnormality occurs in the communication network, and the measure selected by this prediction is not always optimal at the time of actual implementation. In order to solve such a problem, the second embodiment shown below is effective.

FIG. 8 is a block diagram showing a second embodiment of the configuration relating to the present invention of the abnormality influence degree evaluation apparatus for a communication network of the present invention. The abnormality influence degree evaluation apparatus of the second embodiment is
Similar to the anomaly impact degree evaluation apparatus of the first embodiment shown in FIG. 2, the communication unit 11 that controls communication with the communication network 7 and the monitoring apparatus 2 and the monitoring apparatus 2 when an abnormality occurs in the communication network 7 Based on the information on the location of the abnormality notified from the customer and the accommodation information of the customer and service, the customers and services affected by the abnormality are extracted, and the resources allocated to each extracted customer and service are extracted. Grasping the time transition of the usage status under normal conditions, predicting the resource usage status after the occurrence of an abnormality based on this time transition of the usage status, and based on the predicted resource usage status The second aspect of the present invention for predicting the time transition of the degree of influence after occurrence
Influence degree prediction unit 12a and the second at the designated time
The impact degree predicted by the impact degree predicting unit 12a selects a customer and a service that exceed a preset threshold value as an action target, and determines the influence of the abnormality on the customer and the service for each of the selected action targets. A second measure determination unit 13a and a second measure determination unit 13a according to the present invention for selecting measures to avoid.
Based on the measure selected in step 1 and the time transition of the usage status of the resources allocated to the customer and the service at the time of the occurrence of the abnormality, the improvement status of the degree of impact when the measure selected by the second measure determination unit 13a is implemented. A second post-implementation impact degree prediction unit 14a for estimating the time transition of
It is composed of a terminal unit 17 for displaying a processing result in each unit and an input from a maintenance person, and a data management unit 16 including each table (abnormality occurrence local table 21 to action target table 28) described in FIG. ing. The anomaly influence degree evaluation apparatus of the second embodiment having such a configuration is connected to the communication network 7 having the configuration shown in FIG.

The operation of the second embodiment will be described below assuming an abnormal state similar to that of the first embodiment described with reference to FIG. That is, in FIG. 1, an abnormality occurs in the network service control station 4a in the communication network 7, and between the network service control station 4a and the network service management station 3 and between the network service control station 4a and the switching center 5a. Assuming that communication with 5c is interrupted, a procedure for replacing the customer and service accommodated in the network service control station 4a with the network service control station 4b and recovering the service will be described. Here, because of the capability of the network service control station 4b, all the customers and services accommodated in the network service control station 4a are handled by the network service control station 4b.
It is assumed that it is impossible to transfer to b.

When the abnormality assumed above occurs,
The monitoring device 2 detects that an abnormality has occurred in the network service control station 4a in FIG. 1 in the communication network 7 and notifies the communication unit 11 of it. The communication unit 11 notifies the data management unit 16 of the location of the abnormality notified by the monitoring device 2 and the status of the abnormality, and further notifies the second impact degree prediction unit 12a of the occurrence of the abnormality. The data management unit 16 registers the abnormality occurrence location and the abnormality situation notified from the communication unit 11 in the abnormality occurrence local table 21 and responds to the inquiry about the abnormality occurrence location from the second impact degree prediction unit 12a. , The abnormality occurrence local table 21 is searched, and the location of the abnormality occurrence is answered.

The second influence degree predicting unit 12a includes a communication unit 11
When notified of the occurrence of an abnormality, the processing contents shown in FIG. 9 are sequentially executed. FIG. 9 is a flow chart showing an embodiment of the processing operation according to the present invention of the second influence degree prediction section in FIG. The second influence degree prediction unit 12a in FIG.
When notified of the occurrence of an abnormality from the communication unit 11 of FIG. 8, first, the data management unit 16 of FIG. 8 is queried for the location of the abnormality, and the location of the abnormality is identified (step 90).
1). Then, the list of services and customers accommodated in the abnormal place is returned to the communication unit 1 in FIG. 8 using the abnormal place returned from the data management unit 16 in FIG. 8 as a search condition.
1, the network service management station 3 in FIG. 1 is inquired to make an inquiry about the disorder service / customer list (step 902). Further, the data management unit 16 is notified of the list of services and customers accommodated in the abnormal place, which is replied from the network service management station 3 of FIG. 1, and is registered in the disability service / customer table 22 of FIG. , Disability service / customer registration (step 903).

Next, the network service management station 3 in FIG.
Inquiries about the service and the usage status before the abnormality occurrence of the resources allocated to the customer from the network service management station 3 in FIG. 1 via the communication unit 11 in FIG. Is performed (step 904). Then, the time transition of the usage status before the abnormality occurrence of the resource, which is replied from the network service management station 3 in FIG. 1, is notified to the data management unit 16 in FIG. 8 and registered in the resource usage status table 23 in FIG. The resource usage status is registered (step 905).

Next, based on the time transition of the usage status before the abnormality occurrence of the resource, which is replied from the network service management station 3 of FIG. 1, the predicted resource usage status of the normal situation after the abnormality occurrence is shown. The time transition is predicted and the resource usage status is predicted (step 906). Then, the prediction result of the temporal change of the resource usage status is notified to the data management unit 16 of FIG. 8, and the predicted resource usage status table 2 of FIG.
4 and the resource usage status prediction result is registered (step 907).

Subsequently, based on the prediction result of the time transition of the resource usage status, the time transition of the influence degree of each service and customer accommodated in the abnormal place is predicted and the influence degree is derived (step 908). ). Then, the prediction result of the time transition of the influence degree is notified to the data management unit 16 in FIG.
It is registered in the influence degree table 25 of FIG. 8 and the influence degree prediction result is registered (step 909). Further, from the prediction result of the time transition of the influence degree, the influence degree of the service and the customer whose influence is remarkable at the designated time is notified to the terminal unit 17 of FIG. 8 and displayed on the screen, and the influence degree prediction result is displayed. Display is performed (step 910). Then, the second measure determination unit 13a of FIG. 8 is notified of the end of the impact degree prediction (step 91).
1). In this way, it is possible to predict the degree of influence in consideration of the time when the measures are actually taken. Further, when notified of the end of the impact degree prediction from the second impact degree prediction unit 12a in FIG. 8, the second measure determination unit 13a in FIG.
The processing contents shown in are sequentially executed.

FIG. 10 is a flow chart showing an embodiment of the processing operation of the second measure determination unit in FIG. 8 according to the present invention. When the second measure determination unit 13a in FIG. 8 is notified of the end of the impact prediction from the second impact prediction unit 12a in FIG. 8, first, the data management unit 16 in FIG. Inquiring about the list of the important customers who have been registered in advance, and extracting the customers included in both the disability service / customer table 22 and the important customer table 27 in FIG.
An important customer for disability is extracted (step 101). Then, the inquiry result is notified to the data management unit 16 of FIG. 8 to be registered in the measure target table 28 of FIG. 8 and the disability important customer is registered (step 102).

Next, the data management unit 16 of FIG. 8 is queried for a list of the time transition of the influence degree, the contents of the influence degree table 25 of FIG. 8 are obtained as an answer, and the influence degree is previously set at the designated time. Services and customers exceeding the set threshold value are extracted, and recommended action targets are extracted (step 103). Then, the extraction result is notified to the data management unit 16 of FIG. 8 to be additionally registered in the measure target table 28, and the recommended measure target is registered (step 104).
In addition, the data management unit 16 in FIG. 8 is inquired about the previously registered measures, firstly, the priority numbers are given to the important customers in descending order of the degree of their influence, and then the non-important customers are selected. Then, in order of decreasing impact, the numbers following the priority numbers assigned by the important customers are assigned and prioritized, and the priority numbers thus assigned are notified to the data management unit 16 in FIG. , Is registered in the measure target table 28 of FIG. 8 for each measure target (step 105).

Subsequently, the data management unit 16 in FIG. 8 inquires about the action target, and in this embodiment, the network service control station 4a in FIG. 1 sends the network for each action target answered from the data management unit 16 in FIG. The measure for relocation is selected to the service control station 4b, the measure for each measure is notified to the data management unit 16 and the terminal unit 17 in FIG. 8, and the recommended measure is registered (step 106) and displayed (step 107). ).

In this way, the priority measure target can be extracted in consideration of the time when the measure is actually taken.
Here, the maintenance person predicts the improvement status of the time transition of the influence degree when the measure is implemented on the terminal unit 17 of FIG.
When the instruction is given together with the target measures, the terminal unit 1 in FIG.
7 notifies the post-measurement effect degree prediction unit 14a of FIG. 8 of the request for prediction of the improvement status of the time transition of the effect degree after the measure is carried out, together with the designated measure object. When the post-measurement impact degree prediction unit 14a of FIG. 8 is notified of the request for prediction of the improvement state of the post-measurement impact degree from the terminal unit 17 of FIG. 8, the following FIG.
The processing contents shown in 1 are sequentially executed.

FIG. 11 is a flow chart showing an embodiment of the processing operation according to the present invention of the second post-implementation influence degree prediction section in FIG. When the second measure post-implementation impact degree prediction unit 14a of FIG. 8 is notified from the terminal unit 17 of FIG. 8 of the request for prediction of the improvement status of the post-measurement impact degree, first,
The data management unit 16 in FIG. 8 is inquired about the list of services and customers accommodated in the abnormal place, and the result of the inquiry, that is, the contents of the disability service / customer table 22 in FIG. Except for the designated measure object notified from 17, the disability service / customer list is corrected (step 111). Then, the data management unit 16 of FIG. 8 is inquired about the time transition of the resource usage status regarding the removed service and the customer, and the inquiry result, that is, the content of the resource usage status table 23 of FIG. The unit 16 is notified and registered in the predicted resource usage status table 24 of FIG. 8 to correct the resource usage status (step 112).

Next, the data management unit 16 of FIG. 8 is inquired about the time transition of the resource usage status related to the service and the customer, and based on the usage status, the measures to be taken are taken from the terminal unit 17 of FIG. Predict the improvement situation of the time transition of the impact degree for each service and customer when performing,
The degree of influence is derived (step 113). Then, the prediction result of the time transition of the degree of influence when the measures are taken is shown in FIG.
8 to the data management unit 16 of FIG.
5, and the impact degree prediction result is registered (step 114). Further, from the prediction results of the time transition of the impact degree when the measures are taken, the impact degree of the service and the customer having a significant impact is notified to the terminal unit 17 of FIG. Is displayed (step 11)
5). As a result, the maintenance person can estimate the time transition of the degree of influence when the measure is taken for the measure target.

As described above with reference to FIGS. 1 to 11, according to the anomaly influence degree evaluation apparatus for a communication network of this embodiment,
When an anomaly occurs in the communication network, the impact of the anomaly on the service and the customer affected by the anomaly is predicted, and priority measures are taken into consideration of the important service, the customer, the magnitude of the impact, and the time when the action is actually taken. Extracting the target and predicting the degree of impact when measures are taken against the target,
Upon receipt of a failure report from a customer, it becomes possible to search for the service regarding this customer and the past influence degree of the customer.

The present invention is not limited to the embodiment described with reference to FIGS. 1 to 11, but various modifications can be made without departing from the scope of the invention. For example, in FIG.
It is also possible to perform the search operation similar to that of the first embodiment by providing the abnormality influence degree evaluation device of the second embodiment shown in FIG. 6 with the influence degree search unit 15 described with reference to FIG. is there.

[0053]

According to the present invention, when an abnormality occurs in the communication network, it is possible to predict the time transition of the influence degree for each service and customer affected by the abnormality, and to determine the influence degree at the time when the measures are taken. The priority measure can be selected based on the above, and when responding to a failure report, the influencer related to the filer can be searched according to the content of the report, and the maintainer can promptly and appropriately respond to an abnormality in the communication network. It becomes possible to improve the reliability and safety of the communication network.

[Brief description of drawings]

FIG. 1 is a block diagram showing a first embodiment of a configuration relating to the present invention of an abnormality influence degree evaluation device for a communication network of the present invention.

FIG. 2 is a block diagram showing an embodiment of a configuration according to the present invention of the abnormality influence degree evaluation device in FIG.

FIG. 3 is a flowchart showing an embodiment of a processing operation according to the present invention of the first influence degree prediction section in FIG.

FIG. 4 is a flowchart showing an embodiment of the processing operation of the first measure determination unit in FIG. 2 according to the present invention.

5 is a flowchart showing an example of a processing operation according to the present invention of the post-implementation impact degree prediction unit in FIG.

6 is a block diagram showing an embodiment of a configuration relating to the operation of the present invention of the influence degree search unit in FIG.

FIG. 7 is a flowchart showing an example of a processing operation relating to the operation of the present invention by the influence degree search unit in FIG.

FIG. 8 is a block diagram showing a second embodiment of the configuration relating to the present invention of the abnormality influence degree evaluation device for a communication network of the present invention.

9 is a flowchart showing an example of a processing operation according to the present invention of a second influence degree prediction section in FIG.

10 is a flowchart showing an embodiment of the processing operation of the second measure determination unit in FIG. 8 according to the present invention.

FIG. 11 is a flow chart showing an example of the processing operation according to the present invention of the post-implementation impact degree prediction unit in FIG.

[Explanation of symbols]

 1 Abnormality Impact Evaluation Device 2 Monitoring Device 3 Network Service Management Station 4a, 4b Network Service Control Station 5a-5c Switching Station 6 Information Transfer Network 7 Communication Network 11 Communication Section 12 First Impact Prediction Section 12a Second Impact Level Prediction unit 13 First measure determination unit 13a Second measure determination unit 14 First measure post-implementation impact degree prediction unit 14a Second measure post-implementation impact degree prediction unit 15 Impact degree search unit 16 Data management unit 17 Terminal unit 21 Abnormal Occurrence Local Table 22 Disability Service / Customer Table 23 Resource Usage Status Table 24 Predicted Resource Usage Status Table 25 Impact Level Table 26 Impact Level Recording Table 27 Important Customer Table 28 Action Target Table

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Claims (5)

[Claims] 1. When an abnormality occurs in a communication network, in order to select an appropriate measure for the abnormality, a communication network abnormality impact degree evaluation device that predicts the impact degree of damage to a customer suffering from the abnormality. At the time of occurrence of an abnormality in the communication network,
Based on the information on the location of the abnormality notified from the monitoring device for monitoring the inside of the communication network and the accommodation information of the customer and the service, the customer and the service affected by the abnormality are extracted, and each extracted customer And a normal use state of the resources allocated to the service, and using the use state, the impact degree of the damage caused by the abnormality to the extracted customer and service is estimated. Means and a customer and a service whose influence degree estimated by the first influence degree predicting means exceeds a preset threshold value are selected as action targets, and the customer and the service are selected for each selected action subject. A first measure determining means for selecting a measure for avoiding the influence of an abnormality, and an abnormality influence degree evaluation device for a communication network. 2. The abnormality impact degree evaluation device for a communication network according to claim 1, wherein the measures selected by the first measure determination means and the resources allocated to the customer and the service at the time of the abnormality occur. The communication network is characterized in that a post-implementation impact degree predicting means for estimating the improvement situation of the impact degree when the measure selected by the first measure determining means is implemented based on the usage situation is provided. Anomalous impact assessment system 3. When an abnormality occurs in the communication network, in order to select an appropriate measure for the abnormality, the abnormality influence evaluation device of the communication network predicts the influence degree of the damage to the customer suffering from the abnormality. At the time of occurrence of an abnormality in the communication network,
Based on the information on the location of the abnormality notified from the monitoring device for monitoring the inside of the communication network and the accommodation information of the customer and the service, the customer and the service affected by the abnormality are extracted, and each extracted customer And grasp the time transition of the normal usage status of the resources assigned to the service,
Based on the time transition of the usage status, the usage status of the resource after the occurrence of the abnormality is predicted, and based on the predicted usage status of the resource, the time transition of the impact point at the time of the failure occurrence and the impact degree after the failure occurrence is predicted. And a second impact degree predicting unit that performs the impact degree predicted by the second impact degree predicting unit at a designated time exceeds a preset threshold value, and selects a customer and a service as action targets, An abnormality impact degree evaluation device for a communication network, comprising: a second action determination means for selecting an action for avoiding the influence of an abnormality on a customer and a service for each of the selected action targets. 4. The abnormality impact assessment device for a communication network according to claim 3, wherein the measures selected by the second measure determination means and resources allocated to the customer and the service at the time of the occurrence of the abnormality are included. The second post-implementation impact degree prediction means for estimating the time transition of the improvement status of the impact degree when the measure selected by the second measure determination means is implemented based on the time transition of the usage situation is provided. Characteristic communication network abnormality impact degree evaluation device. 5. The abnormality impact degree evaluation device for a communication network according to claim 1, wherein the impact degree of damage to a customer suffering the damage predicted when an abnormality occurs in the communication network. And a degree-of-effect retrieving means for retrieving the degree of influence of the customer from a record of the degree of influence accumulated in the data-control means with respect to a failure report from the customer. Anomaly impact degree evaluation device for communication networks.