Data integration management method based on machine room integration

Abstract

The invention discloses a data integration management method based on machine room integration. The management method comprises the steps of obtaining a plurality of machine room abnormal events in a first preset time window; obtaining a plurality of non-independent event groups based on a plurality of machine room abnormal events in a first preset time window; based on a plurality of machine room abnormal events of the same non-independent event group, obtaining associated equipment of the non-independent event group and associated feature data corresponding to each associated equipment; based on the associated devices of the non-independent event group and the associated feature data corresponding to each associated device, obtaining a plurality of exception causes and exception elimination operation corresponding to each exception cause; and obtaining a solution of the non-independent event group based on the plurality of abnormal reasons of the non-independent event group and the abnormal elimination operation corresponding to each abnormal reason. The technical problem of low management efficiency in the data integration management process in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, in particular to a data integration management method based on computer room integration. Background Art

[0002] Computer room integration refers to the architecture and integration of multiple physical infrastructure devices within a computer room into a unified management system. Integrated computer rooms gather a vast amount of complex information, with data structures drawn from multiple sources. Existing technologies use data integration to consolidate heterogeneous data from these multiple sources onto a single platform for management.

[0003] Currently, data integration involves converting data in different formats into a unified format and then aggregating them on a data integration platform. Data management still relies on manual labor to obtain and evaluate relevant data on the platform, resulting in reduced data management efficiency.

[0004] From this we can see that low management efficiency in the data integration management process is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of the present invention is to provide a data integration management method based on computer room integration, which solves the technical problem of low management efficiency in the data integration management process in the prior art.

[0006] The present invention provides a data integration management method based on computer room integration, the management method comprising:

[0007] Obtaining several abnormal events in the computer room within a first preset time window;

[0008] Based on a number of abnormal events in the computer room within a first preset time window, a plurality of non-independent event groups are obtained;

[0009] Based on multiple abnormal events in the same non-independent event group, obtain the associated devices of the non-independent event group and the associated feature data corresponding to each associated device;

[0010] Based on the associated devices of the non-independent event group and the associated feature data corresponding to each associated device, a number of abnormal causes and the abnormality elimination operation corresponding to each abnormal cause are obtained;

[0011] Based on multiple abnormal causes of the non-independent event group and the abnormality elimination operation corresponding to each abnormal cause, a solution for the non-independent event group is obtained.

[0012] Furthermore, based on a number of abnormal events in the computer room within the first preset time window, a plurality of non-independent event groups are obtained, including:

[0013] Based on a number of abnormal events in the computer room within the first preset time window, a plurality of candidate arrangement schemes are obtained in a permutation and combination manner; each candidate arrangement scheme includes a plurality of candidate event groups and the number of abnormal events in each candidate event group;

[0014] Based on several abnormal events of the computer rooms in the event group to be selected, obtaining the event group correlation degree of the event group to be selected;

[0015] Screening the candidate arrangement schemes according to the first preset screening condition to obtain a plurality of check arrangement schemes;

[0016] A plurality of check-selected arrangement schemes are screened according to a second preset screening condition to obtain a final selection arrangement scheme; the final selection arrangement scheme includes a plurality of non-independent event groups.

[0017] Furthermore, based on several abnormal events in the computer room in the event group to be selected, the event group correlation of the event group to be selected is obtained, including:

[0018] Arrange several abnormal events of computer rooms in the event group to be selected in chronological order, and obtain the event arrangement order and the time interval between abnormal events of adjacent computer rooms;

[0019] Based on the order of events and the time intervals between abnormal events in adjacent computer rooms, the event confidence level corresponding to each abnormal event in the computer room is obtained;

[0020] Based on the event confidence levels corresponding to multiple abnormal events in the computer rooms, the event group correlation is obtained.

[0021] Furthermore, based on the order of events and the time intervals between abnormal events in adjacent computer rooms, the event confidence level corresponding to each abnormal event in the computer room is obtained, including:

[0022] Based on the order of events, the standard confidence between the current computer room abnormal event and each computer room abnormal event after the current computer room abnormal event is obtained one by one;

[0023] Based on the time intervals between abnormal events in adjacent computer rooms, the corresponding weight parameters between the abnormal event in the current computer room and each abnormal event in the subsequent computer room are obtained one by one;

[0024] Based on the standard confidence and weight parameters corresponding to the current computer room abnormal event and each computer room abnormal event after the current computer room abnormal event, the event confidence corresponding to the current computer room abnormal event is obtained.

[0025] Furthermore, the first preset screening condition includes:

[0026] The event confidence level corresponding to the first abnormal event in the computer room in each event group to be selected in the selection arrangement scheme is greater than the first preset event confidence threshold;

[0027] The standard confidence between the last abnormal event in each of the candidate event groups in the candidate arrangement scheme and at least one abnormal event in the previous abnormal events in the plurality of abnormal events in the computer rooms is greater than a first standard confidence threshold;

[0028] The event confidence level corresponding to each abnormal event in each event group to be selected in the selection arrangement scheme is greater than the second preset event confidence threshold.

[0029] Furthermore, the second preset screening condition includes:

[0030] The number of different events between any two check event groups in the check permutation scheme is greater than the preset number of different events;

[0031] The first computer room abnormal event and the last computer room abnormal event between any two check event groups in the check arrangement scheme are different.

[0032] Furthermore, based on the associated devices of the non-independent event group and the associated feature data corresponding to each associated device, several abnormal causes and abnormality elimination operations corresponding to each abnormal cause are obtained, including:

[0033] Obtaining a non-independent anomaly analysis model;

[0034] Input the associated feature data corresponding to each associated device in the non-independent event group into the non-independent anomaly analysis model to obtain several anomaly causes;

[0035] Based on the cause of the exception, the corresponding exception troubleshooting operations are obtained through the preset troubleshooting database.

[0036] Furthermore, based on multiple abnormal causes of the dependent event group and the abnormality elimination operation corresponding to each abnormal cause, a solution to the dependent event group is obtained, including:

[0037] Get the operation object corresponding to each exception elimination operation;

[0038] Based on the operation objects corresponding to the multiple exception elimination operations, obtain the operation constraints between the multiple operation objects;

[0039] Based on the operation constraints between multiple operation objects, multiple exception elimination operations are constrained to obtain solutions; the constraint operations include filtering and sorting.

[0040] Furthermore, the management method further includes:

[0041] Based on a plurality of computer room abnormal events and a plurality of dependent event groups within a first preset time window, obtaining a plurality of independent abnormal events within the current first preset time window;

[0042] Based on the exception type and processing period of the independent abnormal event, confirm whether it can be eliminated within the current first preset time window;

[0043] If so, obtain the exception elimination operation corresponding to the independent exception event.

[0044] Furthermore, a number of abnormal events in the computer room within the first preset time window are obtained, including:

[0045] Acquire computer room monitoring data within a first preset time window;

[0046] Based on the computer room monitoring data, obtain abnormal events in the computer room.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] In the present invention, a non-independent event group is obtained by analyzing multiple computer room abnormal events within a first preset time window, so that multiple computer room abnormal events in the same non-independent event group can be comprehensively analyzed, making the analyzed abnormal causes more accurate. Solutions are obtained based on the multiple abnormal causes in the same non-independent event group to handle multiple computer room abnormal events, thereby improving the efficiency of handling computer room abnormal events. This solves the technical problem of low management efficiency in the data integration management process in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a method step diagram of the data integration management method for the computer room of the present invention. DETAILED DESCRIPTION

[0050] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] like Figure 1 As shown, the present invention provides a data integration management method based on computer room integration, the management method comprising:

[0052] S1: Acquire several abnormal events of the computer room within a first preset time window;

[0053] In this embodiment, the step of obtaining a number of abnormal events in the computer room includes obtaining computer room monitoring data within a first preset time window; obtaining abnormal events in the computer room based on the computer room monitoring data.

[0054] In this embodiment, several items of computer room monitoring data are pre-set based on the data integration situation in the computer room, such as the temperature data of some important equipment. Each item of computer room monitoring data is assigned a corresponding normal operating range. When a piece of computer room monitoring data exceeds the corresponding normal operating range, this is treated as a computer room abnormality event. Setting up computer room monitoring data can reduce the workload of managing large amounts of multi-source heterogeneous data during the integrated computer room data integration process.

[0055] In this embodiment, the length of the first preset time window includes 10 minutes, 15 minutes, and 20 minutes.

[0056] S2: Based on a number of abnormal events in the computer room within the first preset time window, obtain a plurality of non-independent event groups;

[0057] In this embodiment, the plurality of abnormal events in the computer room within the first preset time window are divided into a plurality of non-independent event groups, which can improve the effect of data integration management, avoid isolated processing of abnormal events in the computer room, and improve the management effect of abnormal events in the computer room.

[0058] S3: Based on multiple abnormal events in the same non-independent event group, obtain the associated devices of the non-independent event group and the associated feature data corresponding to each associated device;

[0059] In this embodiment, each abnormal event in the computer room in the non-independent event group is pre-set with a corresponding associated device and associated characteristic data corresponding to each associated device, so as to more accurately obtain the cause of the abnormality.

[0060] In this embodiment, the associated devices corresponding to each abnormal event in the computer room and the associated feature data corresponding to each associated device are obtained through expert experience or big data.

[0061] S4: Based on the associated devices of the non-independent event group and the associated feature data corresponding to each associated device, obtain several abnormal causes and the abnormality elimination operation corresponding to each abnormal cause;

[0062] S5: Based on multiple abnormal causes of the non-independent event group and the abnormality elimination operation corresponding to each abnormal cause, a solution to the non-independent event group is obtained.

[0063] The specific implementation process of this embodiment includes:

[0064] In this embodiment, by analyzing several abnormal events in a computer room within a first preset time window to obtain a dependent event group, a comprehensive analysis of multiple abnormal events in the same dependent event group can be performed, making the analyzed abnormal causes more accurate. Solutions are obtained based on the multiple abnormal causes in the same dependent event group to handle multiple abnormal events in the computer room, thereby improving the efficiency of handling abnormal events in the computer room. This solves the technical problem of low management efficiency in the data integration management process in the prior art.

[0065] In this embodiment, based on a number of abnormal events in the computer room within the first preset time window, multiple non-independent event groups are obtained, including:

[0066] S21: Based on a number of abnormal events in the computer room within the first preset time window, a plurality of candidate arrangement schemes are obtained in a permutation and combination manner; each candidate arrangement scheme includes a plurality of candidate event groups and the number of abnormal events in each candidate event group;

[0067] In this embodiment, a plurality of abnormal events in the computer room are randomly selected multiple times within the first preset time window in a permutation and combination manner to obtain a plurality of event groups to be selected; and the plurality of event groups to be selected constitute a permutation scheme to be selected.

[0068] S22: Based on a number of abnormal events in the computer room in the event group to be selected, obtain the event group correlation of the event group to be selected;

[0069] In this embodiment, the correlation between several abnormal events in the computer rooms in the candidate event group is evaluated by the event group correlation degree. The higher the event group correlation degree is, the stronger the correlation between the abnormal events in the computer rooms is.

[0070] S23: Screening the candidate arrangement schemes according to the first preset screening condition to obtain several selected arrangement schemes;

[0071] S24: screening a plurality of selected arrangement schemes according to a second preset screening condition to obtain a final selection arrangement scheme; the final selection arrangement scheme includes a plurality of non-independent event groups.

[0072] The specific implementation process of this embodiment includes:

[0073] In this embodiment, based on a number of abnormal events in the computer room in the event group to be selected, the event group correlation of the event group to be selected is obtained, including:

[0074] S221: Arrange several abnormal events of computer rooms in the event group to be selected in chronological order, and obtain the event arrangement order and the time interval between abnormal events of adjacent computer rooms;

[0075] In this embodiment, the abnormal events are arranged in the order of the start time of the abnormal events in the computer rooms. The time interval between the abnormal events in adjacent computer rooms includes the interval length between the start times of the abnormal events in adjacent computer rooms.

[0076] S222: Obtaining the event confidence corresponding to each abnormal event in each computer room based on the event arrangement sequence and the time interval between abnormal events in adjacent computer rooms;

[0077] S223: Obtaining an event group correlation degree based on event confidences corresponding to multiple abnormal events in the computer rooms.

[0078] In this embodiment, step S222 obtains the event confidence corresponding to each abnormal event in the computer room based on the order of the events and the time interval between abnormal events in adjacent computer rooms, including:

[0079] Based on the order of events, the standard confidence between the current computer room abnormal event and each computer room abnormal event after the current computer room abnormal event is obtained one by one;

[0080] In this embodiment, the standard confidence between the current computer room abnormal event and each subsequent computer room abnormal event is obtained one by one through expert experience and big data. The range of the standard confidence in this embodiment includes [0, 1]. The higher the standard confidence, the stronger the correlation between the current computer room abnormal event and the abnormal event in another computer room. If the standard confidence between the current computer room abnormal event and the abnormal event in another computer room is 0, it means that there is no necessary connection between the current computer room abnormal event and the abnormal event in another computer room. If the standard confidence between the current computer room abnormal event and the abnormal event in another computer room is 1, it means that there is a necessary connection between the current computer room abnormal event and the abnormal event in another computer room.

[0081] Based on the time intervals between abnormal events in adjacent computer rooms, the corresponding weight parameters between the abnormal event in the current computer room and each abnormal event in the subsequent computer room are obtained one by one;

[0082] In this embodiment, the time interval between the abnormal events in each adjacent computer room is used to obtain the time interval between the current computer room abnormal event and each subsequent abnormal event in the computer room. In this embodiment, the standard confidence between the abnormal events in the two computer rooms is set with a corresponding standard time interval range. When the actual time interval between the abnormal events in the two computer rooms is not within the standard time interval range, the actual confidence between the abnormal events in the two computer rooms is affected by the actual time interval. In this embodiment, the calculation formula of the weight parameter includes:

[0083]

[0084] Among them, Q is the weight parameter, T S is the actual time interval between abnormal events in the two computer rooms, t minis the lower limit of the standard time interval between abnormal events in the two computer rooms, t max is the upper limit of the standard event interval between abnormal events in the two computer rooms, and a is a fixed value.

[0085] It should be noted that, in this embodiment, when the actual event interval is within the corresponding standard event interval range, the weight parameter is 1.

[0086] Based on the standard confidence and weight parameters corresponding to the current computer room abnormal event and each computer room abnormal event after the current computer room abnormal event, the event confidence corresponding to the current computer room abnormal event is obtained.

[0087] In this embodiment, the calculation formula for the event confidence corresponding to the current abnormal event in the computer room includes:

[0088] QS=∑Q i *X i ;

[0089] Among them, QS is the event confidence corresponding to the current computer room abnormal event, Qi is the weight parameter corresponding to the current computer room abnormal event and the subsequent abnormal events of the i-th computer room; Xi is the standard confidence corresponding to the current computer room abnormal event and the subsequent abnormal events of the i-th computer room.

[0090] In this embodiment, the first preset screening condition includes:

[0091] The event confidence level corresponding to the first abnormal event in the computer room in each event group to be selected in the selection arrangement scheme is greater than the first preset event confidence threshold;

[0092] The standard confidence between the last abnormal event in each of the candidate event groups in the candidate arrangement scheme and at least one abnormal event in the previous abnormal events in the plurality of abnormal events in the computer rooms is greater than a first standard confidence threshold;

[0093] The event confidence level corresponding to each abnormal event in each event group to be selected in the selection arrangement scheme is greater than the second preset event confidence threshold.

[0094] In this embodiment, the second preset screening condition includes:

[0095] The number of different events between any two check event groups in the check permutation scheme is greater than the preset number of different events;

[0096] In this embodiment, the abnormal events between two check event groups include all abnormal events in one check event group except the abnormal events in the same computer room as in the other check event group.

[0097] The first computer room abnormal event and the last computer room abnormal event between any two check event groups in the check arrangement scheme are different.

[0098] In this embodiment, based on the associated devices of the non-independent event group and the associated feature data corresponding to each associated device, several abnormal causes and the abnormality elimination operations corresponding to each abnormal cause are obtained, including:

[0099] Obtaining a non-independent anomaly analysis model;

[0100] In this embodiment, the non-independent anomaly analysis model includes a neural network model. The non-independent anomaly analysis model is obtained by constructing a mapping relationship between associated feature data and anomaly causes corresponding to a non-independent event group and then training the neural network model.

[0101] Input the associated feature data corresponding to each associated device in the non-independent event group into the non-independent anomaly analysis model to obtain several anomaly causes;

[0102] Based on the cause of the exception, the corresponding exception troubleshooting operations are obtained through the preset troubleshooting database.

[0103] In this embodiment, the abnormality elimination operation corresponding to each abnormality cause is obtained through expert experience and big data.

[0104] In this embodiment, based on multiple abnormal causes of the dependent event group and the abnormality elimination operation corresponding to each abnormal cause, a solution to the dependent event group is obtained, including:

[0105] Get the operation object corresponding to each exception elimination operation;

[0106] Based on the operation objects corresponding to the multiple exception elimination operations, obtain the operation constraints between the multiple operation objects;

[0107] Based on the operation constraints between multiple operation objects, multiple exception elimination operations are constrained to obtain solutions; the constraint operations include filtering and sorting.

[0108] In this embodiment, corresponding operation constraints are pre-set between the operation objects corresponding to the multiple exception elimination operations. The operation constraints include sequence constraints and parameter constraints.

[0109] In this embodiment, the management method further includes:

[0110] Based on a plurality of computer room abnormal events and a plurality of dependent event groups within a first preset time window, obtaining a plurality of independent abnormal events within the current first preset time window;

[0111] Based on the exception type and processing period of the independent abnormal event, confirm whether it can be eliminated within the current first preset time window;

[0112] In this embodiment, exception types include independent handling and continuous observation. When the exception type is independent handling, or the exception type is continuous observation but the processing deadline expires at the beginning of the next first preset time window, the independent exception event must be eliminated within the current first preset time window. When the exception type is continuous observation and the processing deadline has not expired at the beginning of the next first preset time window, the independent exception event is determined not to be eliminated within the current first preset time window.

[0113] If so, obtain the exception elimination operation corresponding to the independent exception event.

[0114] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0115] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A data integration management method based on computer room integration, characterized by: The management method includes: Obtaining several abnormal events in the computer room within a first preset time window; Based on a number of abnormal events in the computer room within a first preset time window, a plurality of non-independent event groups are obtained; Based on multiple abnormal events in the same non-independent event group, obtain the associated devices of the non-independent event group and the associated feature data corresponding to each associated device; Based on the associated devices of the non-independent event group and the associated feature data corresponding to each associated device, a number of abnormal causes and the abnormality elimination operation corresponding to each abnormal cause are obtained; Based on multiple abnormal causes of the non-independent event group and the abnormality elimination operation corresponding to each abnormal cause, a solution for the non-independent event group is obtained.

2. The data integration management method based on computer room integration according to claim 1, characterized in that: Based on a number of abnormal events in the computer room within the first preset time window, a plurality of non-independent event groups are obtained, including: Based on a number of abnormal events in the computer room within the first preset time window, a plurality of candidate arrangement schemes are obtained in a permutation and combination manner; each candidate arrangement scheme includes a plurality of candidate event groups and the number of abnormal events in each candidate event group; Based on several abnormal events of the computer rooms in the event group to be selected, obtaining the event group correlation degree of the event group to be selected; Screening the candidate arrangement schemes according to the first preset screening condition to obtain a plurality of check arrangement schemes; A plurality of check-selected arrangement schemes are screened according to a second preset screening condition to obtain a final selection arrangement scheme; the final selection arrangement scheme includes a plurality of non-independent event groups.

3. The data integration management method based on computer room integration according to claim 2, characterized in that: Based on several abnormal events in the computer room in the event group to be selected, the event group correlation of the event group to be selected is obtained, including: Arrange several abnormal events of computer rooms in the event group to be selected in chronological order, and obtain the event arrangement order and the time interval between abnormal events of adjacent computer rooms; Based on the order of events and the time intervals between abnormal events in adjacent computer rooms, the event confidence level corresponding to each abnormal event in the computer room is obtained; Based on the event confidence levels corresponding to multiple abnormal events in the computer rooms, the event group correlation is obtained.

4. The data integration management method based on computer room integration according to claim 3 is characterized in that: Based on the order of events and the time intervals between abnormal events in adjacent computer rooms, the event confidence level corresponding to each abnormal event in the computer room is obtained, including: Based on the order of events, the standard confidence between the current computer room abnormal event and each computer room abnormal event after the current computer room abnormal event is obtained one by one; Based on the time intervals between abnormal events in adjacent computer rooms, the corresponding weight parameters between the abnormal event in the current computer room and each abnormal event in the subsequent computer room are obtained one by one; Based on the standard confidence and weight parameters corresponding to the current computer room abnormal event and each computer room abnormal event after the current computer room abnormal event, the event confidence corresponding to the current computer room abnormal event is obtained.

5. The data integration management method based on computer room integration according to claim 4 is characterized in that: The first preset screening condition includes: The event confidence level corresponding to the first abnormal event in the computer room in each event group to be selected in the selection arrangement scheme is greater than the first preset event confidence threshold; The standard confidence between the last abnormal event in each of the candidate event groups in the candidate arrangement scheme and at least one abnormal event in the previous abnormal events in the plurality of abnormal events in the computer rooms is greater than a first standard confidence threshold; The event confidence level corresponding to each abnormal event in each event group to be selected in the selection arrangement scheme is greater than the second preset event confidence threshold.

6. The data integration management method based on computer room integration according to claim 5 is characterized in that: The second preset screening condition includes: The number of different events between any two check event groups in the check permutation scheme is greater than the preset number of different events; The first computer room abnormal event and the last computer room abnormal event between any two check event groups in the check arrangement scheme are different.

7. The data integration management method based on computer room integration according to claim 1 is characterized in that: Based on the associated devices of the non-independent event group and the associated feature data corresponding to each associated device, several abnormal causes and the abnormality elimination operations corresponding to each abnormal cause are obtained, including: Obtaining a non-independent anomaly analysis model; Input the associated feature data corresponding to each associated device in the non-independent event group into the non-independent anomaly analysis model to obtain several anomaly causes; Based on the cause of the exception, the corresponding exception troubleshooting operations are obtained through the preset troubleshooting database.

8. The data integration management method based on computer room integration according to claim 7 is characterized in that: Based on the multiple abnormal causes of the dependent event group and the abnormality elimination operations corresponding to each abnormal cause, obtain the solution of the dependent event group, including: Get the operation object corresponding to each exception elimination operation; Based on the operation objects corresponding to the multiple exception elimination operations, obtain the operation constraints between the multiple operation objects; Based on the operation constraints between multiple operation objects, multiple exception elimination operations are constrained to obtain solutions; the constraint operations include filtering and sorting.

9. The data integration management method based on computer room integration according to claim 6, characterized in that: Management methods also include: Based on a plurality of computer room abnormal events and a plurality of dependent event groups within a first preset time window, obtaining a plurality of independent abnormal events within the current first preset time window; Based on the exception type and processing period of the independent abnormal event, confirm whether it can be eliminated within the current first preset time window; If so, obtain the exception elimination operation corresponding to the independent exception event.

10. The data integration management method based on computer room integration according to claim 1, characterized in that: Obtain several abnormal events in the computer room within the first preset time window, including: Acquire computer room monitoring data within a first preset time window; Based on the computer room monitoring data, obtain abnormal events in the computer room.

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