CN112364900A - Equipment alarm management method, device, client and medium for intelligent building - Google Patents
Equipment alarm management method, device, client and medium for intelligent building Download PDFInfo
- Publication number
- CN112364900A CN112364900A CN202011174753.6A CN202011174753A CN112364900A CN 112364900 A CN112364900 A CN 112364900A CN 202011174753 A CN202011174753 A CN 202011174753A CN 112364900 A CN112364900 A CN 112364900A
- Authority
- CN
- China
- Prior art keywords
- alarm
- equipment
- value
- target
- clustering
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 46
- 238000012544 monitoring process Methods 0.000 claims description 39
- 230000002159 abnormal effect Effects 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 18
- 238000004422 calculation algorithm Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 5
- 238000000034 method Methods 0.000 abstract description 23
- 230000008569 process Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000012806 monitoring device Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23211—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with adaptive number of clusters
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Alarm Systems (AREA)
Abstract
The application is applicable to the technical field of Internet of things, and particularly relates to an equipment alarm management method, device, client and medium for an intelligent building. The method includes clustering equipment fault probabilities corresponding to target attributes of target equipment, determining equipment reported values corresponding to K central points, comparing the K equipment reported values with equipment alarm values input by a user to determine an alarm tolerance range, and giving an alarm according to the condition that the monitored values of the target attributes fall within the alarm tolerance range.
Description
Technical Field
The application belongs to the technical field of Internet of things, and particularly relates to an equipment alarm management method, device, client and medium for an intelligent building.
Background
At present, when a monitoring value of a certain attribute of equipment during operation exceeds a certain threshold value, the equipment operation fails, and even the equipment fails seriously or goes down. Therefore, in order to avoid the situations of equipment failure and the like, corresponding alarm values are set manually for the attributes to carry out alarm management, and when the attribute values reported by the equipment exceed alarm threshold values, an alarm is output to prompt corresponding staff. However, the manually set alarm value is inaccurate, and when the alarm value is a range value, the manual operation cannot give an accurate range, which often causes false alarm and false negative alarm of the equipment alarm, and does not consider the change of the equipment state along with time. Therefore, the accuracy of alarm management is low, and the service life of the equipment is influenced.
Disclosure of Invention
The embodiment of the application provides an equipment alarm management method, device, client and medium for an intelligent building, and can solve the problem of low accuracy of the existing alarm management.
In a first aspect, an embodiment of the present application provides an equipment alarm management method for an intelligent building, where the equipment alarm management method includes:
acquiring equipment fault probability corresponding to each equipment reported value in N equipment reported values aiming at target attributes of target equipment to obtain N equipment fault probabilities, wherein N is an integer larger than zero;
clustering the N equipment fault probabilities to obtain K clustering center points, wherein K is an integer which is greater than zero and less than or equal to N;
acquiring an equipment alarm value input by a user, and calculating the maximum value of the difference value between the equipment alarm value corresponding to the K clustering central points and the equipment alarm value;
and determining the alarm tolerance range of the target attribute according to the equipment alarm value and the maximum value.
In a second aspect, an embodiment of the present application provides an equipment alarm management device for an intelligent building, where the equipment alarm management device includes:
the probability acquisition module is used for acquiring the equipment fault probability corresponding to each equipment reported value in N equipment reported values aiming at the target attribute of the target equipment to obtain N equipment fault probabilities, wherein N is an integer larger than zero;
the clustering processing module is used for clustering the N equipment fault probabilities to obtain K clustering central points, wherein K is an integer which is greater than zero and less than or equal to N;
the first alarm value acquisition module is used for acquiring an equipment alarm value input by a user and calculating the maximum value of the difference value between the equipment alarm value and the equipment alarm value corresponding to the K clustering central points;
and the range determining module is used for determining the alarm tolerance range of the target attribute according to the equipment alarm value and the maximum value.
In a third aspect, an embodiment of the present application provides a client, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the device alarm management method according to the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where a computer program is stored, and when executed by a processor, the computer program implements the device alarm management method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a client, causes the client to execute the device alarm management method according to the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that: this application is through clustering the equipment fault probability that the target attribute corresponds to the target device, confirm the equipment reported value that K central point corresponds, it reports the tolerance scope to report an emergency and ask for help or increased vigilance to carry out the comparison with the equipment alarm value of user input again to combine K equipment reported value to confirm, this application can combine the equipment reported value of equipment reported value and the equipment alarm value of user input to confirm and report an emergency and ask for help or increased vigilance the tolerance scope, the wrong report that the manual work brought according to subjective experience setting alarm value and the problem of failing to report an emergency and ask for help or increased vigilance the accuracy of reporting an emergency and asking for help the management of reporting an emergency and asking for help.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating an alarm management method for a device according to an embodiment of the present application;
fig. 2 is a schematic flowchart of an alarm management method for a device according to a second embodiment of the present application;
fig. 3 is a schematic structural diagram of an alarm management apparatus of a device according to a third embodiment of the present application;
fig. 4 is a schematic structural diagram of a client according to a fourth embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The alarm management method of the device provided by the embodiment of the application can be applied to clients such as a palm computer, a desktop computer, a notebook computer, a super-mobile personal computer (UMPC), a netbook, a cloud server, a Personal Digital Assistant (PDA) and the like, wherein a database is arranged in the client, and the database can be a memory database when an event accesses the database, and the embodiment of the application does not limit the specific type of the client.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
In order to explain the technical means of the present application, the following description will be given by way of specific examples.
Referring to fig. 1, which is a schematic flowchart of an alarm management method for a device according to an embodiment of the present application, where the alarm management method is applicable to a client, as shown in fig. 1, the alarm management method may include the following steps:
step S101, obtaining the equipment fault probability corresponding to each equipment reported value in the N equipment reported values aiming at the target attribute of the target equipment, and obtaining N equipment fault probabilities.
N is an integer greater than zero, the target device may be a device connected to the client, and the range indicated by the target device is wide, for example, common air conditioning devices such as a heater and an air conditioner, and industrial devices such as a processing center; the target attribute may refer to some attributes for determining whether an operation of the target device is abnormal, for example, an outdoor unit temperature attribute of an air conditioner; the device report value may refer to a preset value of a target attribute for the target device, where the preset value is used to indicate a failure of the target device, but the target device may not fail when the preset value occurs, so that the target device has a certain failure probability when the target attribute is the preset value, that is, one device report value of the target attribute corresponds to one device failure probability.
The device failure probability can be stored in the corresponding storage device, the client sends a probability obtaining instruction of the device failure probability for the N device reported values of the target attribute of the target device to the storage device, the storage device sends the device failure probability to the client after receiving the probability obtaining instruction, and the client can also obtain the device failure probability in other manners.
Optionally, the obtaining of the device failure probability corresponding to each device reported value in N device reported values of the target attribute for the target device includes:
acquiring the occurrence frequency of each device reported value in N device reported values aiming at the target attribute of the target device and the corresponding device failure frequency;
and dividing the equipment failure times of the reported value of each equipment by the occurrence times to obtain the equipment failure probability of the reported value of each equipment.
The number of occurrences of the device reporting value may refer to the number of times that the value of the target attribute is the device reporting value, the corresponding device failure number may refer to the number of times that the target device fails when the value of the target attribute is the device reporting value, the device failure probability corresponding to the device reporting value may be obtained by dividing the device failure number by the number of occurrences, and the N device failure probabilities may be obtained by traversing the N device reporting values.
Probability of equipment failure PiThe calculation formula of (a) is as follows:
where i represents the ith device reported value, riThe number of equipment failures corresponding to the reported value of the ith equipment, niThe number of occurrences of the reported value for the ith device.
And S102, clustering the N equipment fault probabilities to obtain K cluster center points.
Wherein K is an integer greater than zero and less than or equal to N, clustering is an unsupervised learning method, and for a given set of data points, each data point can be divided into a specific set using a clustering algorithm, and theoretically, the data points in the same set should have similar attributes and/or characteristics, while the data points in different sets should have highly different attributes and/or characteristics; in the application, one equipment fault probability is one data point, and at least one cluster center point can be obtained by clustering N equipment fault probabilities, wherein the cluster center point is one equipment fault probability in the N equipment fault probabilities.
The clustering method may adopt a K-means clustering algorithm, a K-center clustering algorithm, a K nearest neighbor algorithm, etc., and the present application is not limited herein.
Optionally, clustering the N device failure probabilities includes:
and clustering the N equipment fault probabilities by using a K nearest neighbor algorithm.
The K nearest neighbor algorithm is an online technology, new equipment failure probability can be directly added into a data set of the equipment failure probability without retraining, the algorithm is simple in theory, easy to implement, high in accuracy and high in tolerance to abnormal values and noise. The K nearest neighbor algorithm can cluster the N equipment fault probabilities by adopting distance measurement, can take Euclidean distance as a distance measuring standard when adopting the distance measurement, and can also cluster by adopting similarity, so that the K nearest neighbor algorithm is not limited in the application.
Step S103, acquiring the equipment alarm value input by the user, and calculating the maximum value of the difference value between the equipment alarm value and the equipment alarm value corresponding to the K cluster central points.
The device alarm value is a value input by a user, namely the user sets an alarm value according to the target attribute of the target device according to the self requirement when using the client so as to achieve the purpose of alarming.
In addition, K cluster center points are obtained in step S102, and since the device reporting value corresponds to the device failure probability one to one, and the cluster center point is one device failure probability of the N device failure probabilities, each cluster center point corresponds to a device reporting value.
And subtracting the equipment reported values corresponding to the K clustering central points from the equipment alarm values to obtain K difference values, and solving the maximum value of the K difference values, such as a formula: x ═ max | ξj-ξ0Wherein j is more than or equal to 1 and less than or equal to K, X is the maximum value, xijReporting value xi for the device corresponding to the jth cluster center point0Is a device alarm value.
Optionally, before acquiring the device alarm value input by the user, the method further includes:
acquiring an alarm value aiming at the target attribute of other equipment input by a user, wherein the other equipment is at least one equipment of the same product as the target equipment;
clustering alarm values input by a user and aiming at target attributes of other equipment, and determining the alarm value corresponding to a clustering central point as a target alarm value;
and displaying a target alarm value, wherein the target alarm value is used for prompting a user to input a device alarm value according to the target alarm value.
The other devices do not include the target device and are devices of the same product as the target device, the other devices include at least one device, and the alarm value input by the user for the target attribute of the other devices can be used for prompting the device alarm value input by the user for the target device, so that reference is provided for the user, and the use by the user is facilitated.
And clustering the alarm values input by the user aiming at the target attributes of other equipment, determining clustering central points, wherein the number of the clustering central points is 1, displaying the alarm values corresponding to the clustering central points, and prompting the user to input the equipment alarm values according to the alarm values, wherein the clustering method in the step S102 can be adopted, and the K value in the clustering method is 1.
And step S104, determining the alarm tolerance range of the target attribute according to the equipment alarm value and the maximum value.
The maximum value represents the maximum deviation between the K cluster center points and the device alarm value, and when the monitored value of the target attribute falls within the maximum deviation of the device alarm value, a target device fault may be caused, so that the maximum deviation of the device alarm value is the alarm tolerance range of the target attribute.
According to the reported value of the equipment and the fault probability of the equipment, the preset alarm value input by the user is adjusted to be within the alarm tolerance range, so that the alarm rule operated in the client is more reasonable.
Optionally, determining the alarm tolerance range of the target attribute according to the device alarm value and the maximum value includes:
taking the value obtained by subtracting the maximum value from the equipment alarm value as the lowest value of the alarm tolerance range of the target attribute;
and taking the value obtained by adding the maximum value to the device alarm value as the highest value of the alarm tolerance range of the target attribute.
Wherein, the lowest value of the alarm tolerance range is the value obtained by subtracting the maximum value from the device alarm value (namely, the lower limit), and the highest value of the alarm tolerance range is the value obtained by adding the maximum value to the device alarm value (namely, the upper limit). When the value of the target attribute of the monitored target device falls between the upper limit and the lower limit, outputting an alarm to prompt a user to check the target device.
This application is through clustering the equipment fault probability that the target attribute corresponds to the target device, confirm the equipment reported value that K central point corresponds, it reports an emergency and asks for help or increased vigilance tolerance scope to combine K equipment reported value and the equipment alarm value of user input to carry out the comparison and confirm to report an emergency and ask for help or increased vigilance tolerance scope to the condition that the monitoring value of this target attribute falls into to report an emergency and ask for help or increased vigilance tolerance scope, this application can combine equipment reported value and the equipment alarm value of user input to report an emergency and ask for help or increased vigilance tolerance scope, manual setting has been avoided, the accuracy of reporting an emergency and asking for help or increased vigilance the management to equipment.
Referring to fig. 2, which is a schematic flow chart of an alarm management method for a device according to a second embodiment of the present application, where the alarm management method is applicable to a client, as shown in fig. 2, the alarm management method may include the following steps:
step S201, obtaining an equipment failure probability corresponding to each equipment reported value in N equipment reported values of the target attribute of the target equipment, to obtain N equipment failure probabilities.
And S202, clustering the N equipment fault probabilities to obtain K cluster central points.
Step S203, acquiring the equipment alarm value input by the user, and calculating the maximum value of the difference value between the equipment alarm value and the equipment alarm value corresponding to the K cluster central points.
And step S204, determining the alarm tolerance range of the target attribute according to the equipment alarm value and the maximum value.
The contents of steps S201 to S204 are the same as those of steps S101 to S104, and the descriptions of steps S101 to S104 can be referred to, and are not repeated herein.
Step S205, acquiring the monitoring value of the target attribute.
The monitoring device is used for detecting the value of the target attribute of the target equipment to obtain a monitoring value, the client is connected with the monitoring device, the client sends a monitoring value acquisition instruction to the monitoring device, the monitoring device acquires the value of the target attribute after receiving the monitoring value acquisition instruction, and the acquired result is sent to the client.
And step S206, if the monitoring values are within the alarm tolerance range, continuously acquiring M monitoring values aiming at the target attribute.
The method can give an alarm when the monitoring value falls into the alarm tolerance range, and can continuously monitor the target attribute to obtain M monitoring values when the monitoring value falls into the alarm tolerance range in order to reduce the occurrence of false alarm condition, wherein M is an integer greater than zero.
Step S207, judging whether the abnormal rate of the M monitoring values is larger than the target abnormal rate.
The abnormal rate of the M monitoring values is the ratio of the number of the monitoring values of the M monitoring values in the alarm tolerance range to the M, if the M is 10, the number of the monitoring values of the 10 monitoring values in the alarm tolerance range is 8, and the abnormal rate of the 10 monitoring values is 0.8; the target abnormality rate is a value set in advance according to the demand, for example, 0.6.
And step S208, if the abnormal rate of the M monitoring values is greater than the target abnormal rate, outputting alarm information.
The abnormal rate of the M monitoring values is greater than the target abnormal rate, which can be used to indicate that the value of the target attribute is in an abnormal state, and the target equipment is likely to be in a fault, so that an alarm is output when the abnormal rate of the M monitoring values is greater than the target abnormal rate.
Optionally, after the alarm information is output, the method further includes:
acquiring equipment information of target equipment;
acquiring information of alarm linkage equipment according to the equipment information and the alarm information, wherein the alarm linkage equipment is equipment used for starting when the target attribute alarms;
and triggering the alarm linkage equipment to start according to the information of the alarm linkage equipment.
The device information of the target device may include information such as a device name and a model, the alarm information of the target device may include information such as an alarm code and an alarm position, a preset linkage relation table may be searched according to the device information and the alarm information of the target device in the application, that is, if an alarm linkage device corresponding to the device information and the alarm information of the target device exists in the preset linkage relation table, the information of the alarm linkage device is obtained, and if an alarm linkage device corresponding to the device information and the alarm information of the target device does not exist in the preset linkage relation table, alarm linkage cannot be performed.
The alarm linkage device is a device that is started when the target attribute of the target device alarms, for example, the alarm linkage device is a standby device of the target device, and starting the standby device can avoid the target device from malfunctioning, and for example, the alarm linkage device is a device that interferes with the target attribute of the target device, and starting the device can interfere with the target attribute, so as to avoid the value abnormality of the target attribute.
The client is connected with the alarm linkage equipment, the client sends a starting instruction to the alarm linkage equipment, and the alarm linkage equipment is started in response to the starting instruction.
According to the method and the device, the target attribute is monitored, whether the alarm is given or not is judged according to the abnormal rate of the M monitoring values of the target attribute, the possibility of false alarm is reduced, the alarm accuracy is improved, and in addition, the alarm can be effectively processed by being linked with the alarm linkage equipment.
Corresponding to the alarm management method of the device in the foregoing embodiment, fig. 3 shows a structural block diagram of an alarm management apparatus of the device provided in the second embodiment of the present application, and for convenience of description, only the relevant parts of the embodiment of the present application are shown.
Referring to fig. 3, the alarm management apparatus includes:
a probability obtaining module 31, configured to obtain an equipment failure probability corresponding to each of N equipment reported values of a target attribute of a target device, to obtain N equipment failure probabilities, where N is an integer greater than zero;
the clustering processing module 32 is configured to cluster the N device failure probabilities to obtain K clustering center points, where K is an integer greater than zero and less than or equal to N;
the first alarm value acquisition module 33 is configured to acquire an equipment alarm value input by a user, and calculate a maximum value of a difference between an equipment alarm value and an equipment alarm value corresponding to the K cluster center points;
and the range determining module 34 is configured to determine an alarm tolerance range of the target attribute according to the device alarm value and the maximum value.
Optionally, the probability obtaining module 31 includes:
the device comprises a frequency acquisition unit, a frequency acquisition unit and a frequency generation unit, wherein the frequency acquisition unit is used for acquiring the occurrence frequency of each device reported value in N device reported values aiming at the target attribute of target devices and the corresponding device failure frequency;
and the probability determining unit is used for dividing the equipment failure times of each equipment reported value by the occurrence times to obtain the equipment failure probability of each equipment reported value.
Optionally, the range determining module 34 includes:
a minimum value determining unit, configured to use a value obtained by subtracting the maximum value from the device alarm value as a minimum value of the alarm tolerance range of the target attribute;
and the maximum value determining unit is used for taking the value obtained by adding the maximum value to the equipment alarm value as the maximum value of the alarm tolerance range of the target attribute.
Optionally, the alarm management apparatus further includes:
the second alarm value acquisition module is used for acquiring an alarm value aiming at the target attribute of other equipment, which is input by a user, wherein the other equipment is at least one equipment of the same product as the target equipment;
the target determination module is used for clustering the alarm values input by the user and aiming at the target attributes of other equipment and determining the alarm value corresponding to the clustering center point as a target alarm value;
and the display module is used for displaying the target alarm value, and the target alarm value is used for prompting a user to input the equipment alarm value according to the target alarm value.
Optionally, the clustering module 32 is specifically configured to:
and clustering the N equipment fault probabilities by using a K nearest neighbor algorithm.
Optionally, the alarm management apparatus further includes:
the monitoring value acquisition module is used for acquiring a monitoring value of the target attribute;
the continuous acquisition module is used for continuously acquiring M monitoring values aiming at the target attribute if the monitoring values are within the alarm tolerance range;
the judging module is used for judging whether the abnormal rate of the M monitoring values is greater than the target abnormal rate or not, wherein the abnormal rate of the M monitoring values is the ratio of the number of the monitoring values of the M monitoring values in the alarm tolerance range to the M;
and the alarm module is used for outputting an alarm if the abnormal rate of the M monitoring values is greater than the target abnormal rate.
Optionally, the alarm management apparatus further includes:
the information acquisition module is used for acquiring equipment information and alarm information of the target equipment;
the linkage acquisition module is used for acquiring information of alarm linkage equipment according to the equipment information and the alarm information, wherein the alarm linkage equipment is equipment which is started when the target attribute alarms;
and the triggering module is used for triggering the alarm linkage equipment to start according to the information of the alarm linkage equipment.
It should be noted that, because the contents of information interaction, execution process, and the like between the modules are based on the same concept as that of the embodiment of the method of the present application, specific functions and technical effects thereof may be specifically referred to a part of the embodiment of the method, and details are not described here.
Fig. 4 is a schematic structural diagram of a client according to a third embodiment of the present application. As shown in fig. 4, the client 4 of this embodiment includes: at least one processor 40 (only one shown in fig. 4), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, the steps in any of the various apparatus alarm management method embodiments described above being implemented when the computer program 42 is executed by the processor 40.
The client may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of a client 4, and does not constitute a limitation on the client 4, and may include more or fewer components than those shown, or some components in combination, or different components, such as input output devices, network access devices, etc.; as another example, the client entity such as RGB camera, robot arm, etc. needs to have components.
The Processor 40 may be a Central Processing Unit (CPU), and the Processor 40 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 41 may in some embodiments be an internal storage unit of the client 4, such as a hard disk or a memory of the client 4. The memory 41 may also be an external storage device of the client 4 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the client 4. Further, the memory 41 may also include both an internal storage unit of the client 4 and an external storage device. The memory 41 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of a computer program. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method of the embodiments described above can be implemented by a computer program, which can be stored in a computer readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying the computer program code, recording medium, computer Memory, Read-Only Memory (ROM), Random-Access Memory (RAM), electrical carrier wave signals, telecommunications signals, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
When the computer program product runs on a client, the steps in the method embodiments can be implemented when the client executes the computer program product.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/client and method may be implemented in other ways. For example, the above-described apparatus/client embodiments are merely illustrative, and for example, a division of modules or units is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An equipment alarm management method for an intelligent building, the equipment alarm management method comprising:
acquiring equipment fault probability corresponding to each equipment reported value in N equipment reported values aiming at target attributes of target equipment to obtain N equipment fault probabilities, wherein N is an integer larger than zero;
clustering the N equipment fault probabilities to obtain K clustering center points, wherein K is an integer which is greater than zero and less than or equal to N;
acquiring an equipment alarm value input by a user, and calculating the maximum value of the difference value between the equipment alarm value corresponding to the K clustering central points and the equipment alarm value;
and determining the alarm tolerance range of the target attribute according to the equipment alarm value and the maximum value.
2. The device alarm management method according to claim 1, wherein the obtaining of the device failure probability corresponding to each device reported value of N device reported values of the target attribute for the target device comprises:
acquiring the occurrence frequency of each device reported value in N device reported values aiming at the target attribute of the target device and the corresponding device failure frequency;
and dividing the equipment failure times of the reported value of each equipment by the occurrence times to obtain the equipment failure probability of the reported value of each equipment.
3. The device alarm management method of claim 1 wherein said determining an alarm tolerance range for said target attribute based on said device alarm value and said maximum value comprises:
taking the value obtained by subtracting the maximum value from the equipment alarm value as the lowest value of the alarm tolerance range of the target attribute;
and adding the maximum value to the equipment alarm value to obtain a value which is taken as the highest value of the alarm tolerance range of the target attribute.
4. The device alarm management method of claim 1 wherein prior to said obtaining a device alarm value entered by a user, further comprising:
acquiring an alarm value of the target attribute, which is input by a user and aims at other equipment, wherein the other equipment is at least one equipment of the same product as the target equipment;
clustering the alarm values input by the user and aiming at the target attributes of other equipment, and determining the alarm value corresponding to the clustering center point as a target alarm value;
and displaying the target alarm value, wherein the target alarm value is used for prompting a user to input a device alarm value according to the target alarm value.
5. The device alarm management method of claim 1 wherein said clustering of N device failure probabilities comprises:
and clustering the N equipment fault probabilities by using a K nearest neighbor algorithm.
6. The device alarm management method of any of claims 1 to 5, further comprising, after determining the alarm tolerance range for the target attribute:
acquiring a monitoring value of the target attribute;
if the monitoring value is within the alarm tolerance range, continuously acquiring M monitoring values aiming at the target attribute, wherein M is an integer larger than zero;
judging whether the abnormal rate of the M monitoring values is greater than a target abnormal rate or not, wherein the abnormal rate of the M monitoring values is the ratio of the number of the monitoring values of the M monitoring values within the alarm tolerance range to M;
and if the abnormal rate of the M monitoring values is greater than the target abnormal rate, outputting alarm information.
7. The device alarm management method of claim 6, further comprising, after said outputting alarm information:
acquiring equipment information of the target equipment;
acquiring information of alarm linkage equipment according to the equipment information and the alarm information, wherein the alarm linkage equipment is equipment for starting when the target attribute alarms;
and triggering the alarm linkage equipment to start according to the information of the alarm linkage equipment.
8. An equipment alarm management device for smart buildings, the equipment alarm management device comprising:
the probability acquisition module is used for acquiring the equipment fault probability corresponding to each equipment reported value in N equipment reported values aiming at the target attribute of the target equipment to obtain N equipment fault probabilities, wherein N is an integer larger than zero;
the clustering processing module is used for clustering the N equipment fault probabilities to obtain K clustering central points, wherein K is an integer which is greater than zero and less than or equal to N;
the first alarm value acquisition module is used for acquiring an equipment alarm value input by a user and calculating the maximum value of the difference value between the equipment alarm value and the equipment alarm value corresponding to the K clustering central points;
and the range determining module is used for determining the alarm tolerance range of the target attribute according to the equipment alarm value and the maximum value.
9. A client comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the device alarm management method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a device alarm management method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011174753.6A CN112364900A (en) | 2020-10-28 | 2020-10-28 | Equipment alarm management method, device, client and medium for intelligent building |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011174753.6A CN112364900A (en) | 2020-10-28 | 2020-10-28 | Equipment alarm management method, device, client and medium for intelligent building |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112364900A true CN112364900A (en) | 2021-02-12 |
Family
ID=74511257
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011174753.6A Pending CN112364900A (en) | 2020-10-28 | 2020-10-28 | Equipment alarm management method, device, client and medium for intelligent building |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112364900A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113923103A (en) * | 2021-12-06 | 2022-01-11 | 深圳市城市交通规划设计研究中心股份有限公司 | Alarm method, alarm device, cloud service platform and readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017117964A1 (en) * | 2016-01-05 | 2017-07-13 | 中兴通讯股份有限公司 | Alarm monitoring method and apparatus |
| CN109446017A (en) * | 2018-09-03 | 2019-03-08 | 平安科技（深圳）有限公司 | A kind of alarm algorithm generation method, monitoring system and terminal device |
| CN110704283A (en) * | 2019-09-05 | 2020-01-17 | 北京浪潮数据技术有限公司 | Method, device and medium for uniformly generating alarm information |
| CN111752811A (en) * | 2020-06-29 | 2020-10-09 | 平安普惠企业管理有限公司 | Abnormal alarm information processing method, electronic device and storage medium |
-
2020
- 2020-10-28 CN CN202011174753.6A patent/CN112364900A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017117964A1 (en) * | 2016-01-05 | 2017-07-13 | 中兴通讯股份有限公司 | Alarm monitoring method and apparatus |
| CN109446017A (en) * | 2018-09-03 | 2019-03-08 | 平安科技（深圳）有限公司 | A kind of alarm algorithm generation method, monitoring system and terminal device |
| CN110704283A (en) * | 2019-09-05 | 2020-01-17 | 北京浪潮数据技术有限公司 | Method, device and medium for uniformly generating alarm information |
| CN111752811A (en) * | 2020-06-29 | 2020-10-09 | 平安普惠企业管理有限公司 | Abnormal alarm information processing method, electronic device and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113923103A (en) * | 2021-12-06 | 2022-01-11 | 深圳市城市交通规划设计研究中心股份有限公司 | Alarm method, alarm device, cloud service platform and readable storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022068645A1 (en) | Database fault discovery method, apparatus, electronic device, and storage medium | |
| WO2022078186A1 (en) | Data real-time monitoring method and apparatus based on machine learning | |
| US11650908B1 (en) | Processing data streams received from instrumented software in real time using incremental-decremental implementation of the KPSS stationarity statistic | |
| CN112148768A (en) | Index time series abnormity detection method, system and storage medium | |
| CN110532119B (en) | Method for detecting abnormal running point of power system | |
| WO2020079860A1 (en) | Equipment failure diagnosis support system and equipment failure diagnosis support method | |
| US20180234746A1 (en) | Maintenance device, presentation system, and program | |
| CN110570544A (en) | method, device, equipment and storage medium for identifying faults of aircraft fuel system | |
| CN116502166B (en) | Method, device, equipment and medium for predicting faults of target equipment | |
| CN116010220A (en) | Alarm diagnosis method, device, equipment and storage medium | |
| CN115081969A (en) | Abnormal data determination method and related device | |
| CN112364900A (en) | Equipment alarm management method, device, client and medium for intelligent building | |
| CN113487086B (en) | Method, device, computer equipment and medium for predicting residual service life of equipment | |
| CN114637649A (en) | Alarm root cause analysis method and device based on OLTP database system | |
| CN112380073B (en) | Fault position detection method and device and readable storage medium | |
| CN116702006A (en) | Abnormality determination method, abnormality determination device, computer device, and storage medium | |
| CN116414608A (en) | Abnormality detection method, abnormality detection device, abnormality detection apparatus, and storage medium | |
| CN115796582A (en) | Operation risk assessment method and device based on multidimensional data | |
| EP3042258A2 (en) | Apparatus and method for model adaptation | |
| CN114297034A (en) | Cloud platform monitoring method and cloud platform | |
| CN113946493A (en) | Monitoring threshold determination and monitoring alarm method, device, equipment and medium | |
| CN111176931A (en) | Operation monitoring method, operation monitoring device, server and storage medium | |
| CN116112339B (en) | Root cause alarm positioning method, device, equipment and medium | |
| CN116127149B (en) | Quantification method and system for health degree of graph database cluster | |
| CN117170914A (en) | Fault locating method, device, computer system and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |