CN115150295A - Method and device for detecting working condition data - Google Patents
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Abstract
The application discloses a method and a device for detecting working condition data, which can be applied to the field of Internet of things or the field of big data. The embodiment of the application provides a method and a device for detecting working condition data, which are used for acquiring the working condition data, acquiring clustering parameters corresponding to the working condition data, clustering the working condition data according to the clustering parameters, and determining the data state type in the working condition data according to a clustering result. According to the method, the abnormal data of the working conditions of the enterprise can be identified more timely and more truly through screening and judging the data states corresponding to the working condition data, and the enterprise or a third-party detection mechanism is reminded on the basis of the judgment result.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a device for detecting working condition data.
Background
With the wide application of the internet of things and intelligent equipment, more and more enterprises or third-party detection mechanisms introduce an environment monitoring system in order to realize real-time monitoring of working condition data. In the process of applying these common environmental monitoring systems, current working condition data of an enterprise are usually collected through terminal equipment, the collected data are integrated and uploaded, and an alarm rule for data abnormality needs to be manually set, for example, an alarm is given when the smoke concentration in the environment reaches 15%.
That is to say, in the current data monitoring method, each working condition type and the working condition data corresponding to each working condition type need to be marked manually, but because the working condition data are numerous and numerous, the process of marking each working condition data is time-consuming and labor-consuming, and when insufficient data setting experience is lacked, the accuracy of the data cannot be ensured. The data detection monitoring method has poor accuracy and low efficiency.
Disclosure of Invention
In view of this, the embodiment of the present application provides a method and an apparatus for detecting working condition data, and aims to implement real-time monitoring on the working condition data.
In a first aspect, an embodiment of the present application provides a method and an apparatus for detecting operating condition data, where the method includes:
acquiring working condition data;
acquiring a clustering parameter corresponding to the working condition data, wherein the clustering parameter is a clustering division basis in a clustering algorithm;
and clustering the working condition data according to the clustering parameters, and determining the data state type in the working condition data according to the clustering result, wherein the data state type comprises normal or abnormal.
Optionally, the obtaining of the clustering parameter corresponding to the operating condition data includes:
the clustering parameters comprise a first parameter and a second parameter, the first parameter is a difference value between normal working condition data and abnormal working condition data, and the second parameter is a difference value between the same state type working condition data.
Optionally, the method further includes:
acquiring a first alarm threshold corresponding to the clustering parameter, wherein the first alarm data threshold is a limit value of data abnormal alarm;
judging whether the first parameter is matched with the first alarm data threshold value;
generating alert information in response to the first parameter being greater than or equal to the first alert data threshold.
Optionally, after determining abnormal data in the working condition data according to the clustering result, the method further includes:
acquiring a data acquisition scene corresponding to the abnormal data, wherein the data acquisition scene comprises acquisition equipment and an access mode;
acquiring an alarm rule corresponding to the data acquisition scene, wherein the alarm rule comprises a second alarm data threshold value, and the second alarm data threshold value is a limit value of a data abnormal alarm corresponding to the acquisition scene;
judging whether the abnormal data is matched with the second alarm data threshold value or not;
and generating alarm information in response to the abnormal data being greater than or equal to the second alarm data threshold.
Optionally, after determining abnormal data in the operating condition data according to the clustering result, the method further includes:
acquiring a first request, wherein the first request is an operation request for the abnormal data or the normal data;
acquiring a first user corresponding to the first request and a user permission rule corresponding to the first user, wherein the user permission rule is an operation restriction rule of the user on data;
and performing corresponding operation on the abnormal data or the normal data according to the first request and the user authority rule.
In a second aspect, an embodiment of the present application provides an apparatus for detecting operating condition data, where the apparatus includes:
the working condition data acquisition module is used for acquiring working condition data;
a clustering parameter obtaining module for obtaining a clustering parameter corresponding to the working condition data, wherein the clustering parameter is a clustering division basis in a clustering algorithm;
the clustering module is used for clustering the working condition data according to the clustering parameters;
and the state type determining module is used for determining the data state type in the working condition data according to the clustering result, wherein the data state type comprises normal or abnormal.
Optionally, the clustering parameters include a first parameter and a second parameter, the first parameter is a difference between normal operating condition data and abnormal operating condition data, and the second parameter is a difference between operating condition data of the same state type.
Optionally, the apparatus further comprises:
a first alarm threshold obtaining module, configured to obtain a first alarm threshold corresponding to the clustering parameter, where the first alarm data threshold is a limit value of a data abnormal alarm;
the first alarm threshold matching module is used for judging whether the first parameter is matched with the first alarm data threshold;
and the first alarm information generation module is used for responding to the first parameter which is larger than or equal to the first alarm data threshold value and generating alarm information.
Optionally, the apparatus further comprises:
the acquisition scene acquisition module is used for acquiring a data acquisition scene corresponding to the abnormal data, and the data acquisition scene comprises acquisition equipment and an access mode;
the alarm rule obtaining module is used for obtaining an alarm rule corresponding to the data acquisition scene, wherein the alarm rule comprises a second alarm data threshold value, and the second alarm data threshold value is a limit value of a data abnormal alarm corresponding to the acquisition scene;
the second alarm threshold matching module is used for judging whether the abnormal data is matched with the second alarm data threshold;
and the second alarm information generation module is used for responding to the abnormal data which is greater than or equal to the second alarm data threshold value and generating alarm information.
Optionally, the apparatus further comprises:
a first request obtaining module, configured to obtain a first request, where the first request is an operation request for the abnormal data or the normal data;
a user rule obtaining module, configured to obtain a first user corresponding to the first request and a user permission rule corresponding to the first user, where the user permission rule is an operation restriction rule of a user on data; and performing corresponding operation on the abnormal data or the normal data according to the first request and the user authority rule.
The embodiment of the application provides a method and a device for detecting working condition data. Acquiring working condition data when the method is executed; acquiring a clustering parameter corresponding to the working condition data, wherein the clustering parameter is a clustering division basis in a clustering algorithm; and clustering the working condition data according to the clustering parameters, and determining the data state type in the working condition data according to the clustering result. Therefore, abnormal data in the working condition data can be rapidly identified according to the clustering algorithm, and an alarm rule of the abnormal data can be set on the basis. Therefore, the effect of automatically, timely and efficiently identifying abnormal data is achieved. Therefore, the efficiency and timeliness of data state identification can be improved.
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To illustrate the technical solutions in the present embodiment or the prior art more clearly, the drawings needed to be used in the description of the embodiment or the prior art will be briefly introduced 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 creative efforts.
FIG. 1 is a flow chart of a method for detecting condition data according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a method for detecting condition data according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of a method for detecting condition data according to an embodiment of the present disclosure;
FIG. 4 is a flow chart of a method for detecting condition data according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of detection of operating condition data according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As described above, when the state type of the condition data is identified, it is generally necessary to consider that an early warning rule for data abnormality is set, and the forming of the algorithm model requires a large amount of training data and a large amount of priori knowledge, which is usually accompanied by the human input of manual labeling.
In order to solve the problem, the embodiment of the application provides a method and a device for detecting working condition data, and the working condition data are obtained; acquiring a clustering parameter corresponding to the working condition data, wherein the clustering parameter is a clustering division basis in a clustering algorithm; and clustering the working condition data according to the clustering parameters, and determining the data state type in the working condition data according to the clustering result. Therefore, abnormal data in the working condition data can be rapidly identified and screened according to the clustering algorithm, and the alarm rule of the abnormal data can be set on the basis. Therefore, according to the detection method of the working condition data, manual marking of each type of working condition data in the prior art is not needed, and therefore the high efficiency and timeliness of data state identification are improved.
The method provided by the embodiment of the application is executed by the detection equipment, the background server, the management system and the computing system, for example, the corresponding detection equipment can be selected according to the requirements of a detection party on various working condition data, and the target object behaviors and the corresponding working condition data are obtained through the equipment. And transmitting the collected data to a computing system by the gateway equipment mobile phone for the collected original working condition data. And analyzing and clustering the working condition data by using a clustering algorithm so as to obtain processed data. And uploading the data to a management system for storage, transmission and backup, wherein the management system can realize functions of data storage call, message notification, rule generation and the like. The background server may be one server device, or a server cluster composed of a plurality of servers.
The following describes a method for detecting operating condition data provided by the present application, by using an embodiment. Referring to fig. 1, fig. 1 is a flowchart of a method for detecting operating condition data according to an embodiment of the present disclosure, including:
s101: and acquiring working condition data.
And acquiring working condition data, wherein the working condition data can comprise carbon emission, sewage discharge, flow of people and vehicles, water and electricity consumption and other data.
In a specific application scenario, data acquisition equipment can be selected according to the requirements of enterprises or third-party monitoring mechanisms on various working condition data. In some embodiments, the data acquisition device comprises a camera, a thermometer, a hygrometer, a range finder, an infrared sensor, a location tag, a radio frequency identification instrument, a water meter, an electricity meter, and the like. After various devices are installed and deployed to corresponding sites of enterprises, the devices are accessed through gateways and various protocols, for example: and the MQTT, the MQTT-SN, the CoAP, the LwM2M and the like converge the acquired original data to prepare for the subsequent calculation process.
The MQTT protocol is a message queue transmission protocol, a subscription and release mechanism is adopted, a subscriber only receives data subscribed by the subscriber, and non-subscribed data are not received, so that necessary data exchange is guaranteed, and storage and processing caused by invalid data are avoided. Therefore, the method is widely applied to the industrial Internet of things. CoAP is a computer protocol, is applied to the Internet of things and is based on the REST architecture.
MQTT-SN (Sensor Networks) is a Sensor version of the MQTT protocol, the earliest used in zigBee wireless Networks, mainly facing devices with limited processor and memory capabilities powered by batteries. MQTT based TCP is also too heavily loaded for some sensors, which may have only tens of bytes of memory and cannot run TCP.
CoAP is a generic term for a restricted Application Protocol (Constrained Application Protocol). But implementing the TCP and HTTP protocols is obviously an undue requirement for small devices. In order to allow small devices to access the internet, the CoAP protocol was designed. CoAP is an application layer protocol that runs on top of UDP rather than TCP as HTTP does. The CoAP protocol is very compact, with the smallest packet being only 4 bytes. The CoAp is a complete binary application layer protocol, the design of the HTTP protocol is used for reference, the format of a protocol packet is simplified, and the learning cost of a developer is reduced.
LwM2M (Lightweight M2M ), proposed by the development mobile alliance (OMA), is a Lightweight, standard and general internet of things device management protocol, and can be used for rapidly deploying internet of things services in a client/server mode. LwM2M establishes a set of standards for management and application of Internet of things equipment, and provides a portable and small safe communication interface and an efficient data model to realize management and service support of M2M equipment.
S102: and acquiring the clustering parameters corresponding to the working condition data.
The clustering parameters are clustering division bases in a clustering algorithm. The clustering parameters comprise a first parameter and a second parameter, the first parameter is a difference value between normal working condition data and abnormal working condition data, and the second parameter is a difference value between the same state type working condition data.
In a specific application scenario, the parameter may be set by the detection party according to a personal detection requirement, or may be automatically configured by the system according to a conventional situation.
S103: and clustering the working condition data according to the clustering parameters.
And clustering the acquired original working condition data according to the clustering parameters acquired in the steps.
The data processing algorithm of the local unsupervised learning in the method comprises but is not limited to: k-means, mean shift clustering, density-based clustering, and gaussian mixture clustering, etc. Clustering algorithms are density-based, distance-based, hierarchy-based, and the like. The clustering algorithm may divide the data points into different clusters of classes according to different attributes of the data points, for example: in the distance-based clustering algorithm, the distance between abnormal data points (second parameter) is similar, the distance between normal data points (second parameter) is also similar, and the distance between the abnormal data points and the normal data points (first parameter) is larger, so that the clustering algorithm can identify different data types, and therefore, the abnormal data points and the normal data points can be distinguished.
S104: and determining the data state type in the working condition data according to the clustering result.
According to the clustering process, the data state types are determined to include normal or abnormal.
Anomalous data includes, but is not limited to: environmental anomaly data, such as temperature and humidity anomalies; artificially introduced abnormal data, such as artificial interference of equipment for pollution discharge data counterfeiting and the like; dirty data, or noise data that needs to be filtered, such as image ghosting caused by a failure in a certain area of the camera, white noise data, etc.
The following describes in detail a method for detecting operating condition data provided by an embodiment of the present application. Referring to fig. 2, fig. 2 is another schematic flow chart of a method for detecting passing condition data according to an embodiment of the present disclosure. The specific process is as follows:
s201: and acquiring working condition data.
And acquiring required working condition data.
S202: and acquiring the clustering parameters corresponding to the working condition data.
And acquiring clustering parameters corresponding to the current original working condition data, wherein the clustering parameters comprise a first parameter and a second parameter.
S203: and clustering the working condition data according to the clustering parameters.
And acquiring a related clustering processing algorithm according to the current detection requirement to realize clustering processing of the current working condition data.
S204: and determining the data state type in the working condition data according to the clustering result.
And determining the state information of the working condition data according to the data obtained after the original working condition data are processed, wherein the state information corresponds to normal working condition data or abnormal working condition data.
S205: and acquiring a first alarm threshold corresponding to the clustering parameter.
The first alarm data threshold is a limit value of data abnormal alarm.
For the clustering parameter obtained in S202, a first alarm threshold corresponding to the parameter is obtained, where the first alarm threshold includes a distance difference (first parameter) between normal data and abnormal data or densities of the two types of data, and the alarm threshold is set according to a requirement.
In a specific application scenario, subsequent judgment can be performed according to any one of the first alarm threshold values, and the two threshold values can also be combined and applied to subsequent judgment matching.
S206: judging whether the first parameter is matched with the first alarm data threshold value; generating alert information in response to the first parameter being greater than or equal to the first alert data threshold.
When the difference value of the distances between the working condition data and the normal data is larger than or equal to the threshold value, the current data is in an abnormal state and an alarm condition for triggering the abnormal data is achieved.
And generating alarm information according to the current working condition data and the related information, and in an actual application scene, integrating the working condition data and the related information into report information and sending the report information to a detection party terminal.
The following describes in detail a method for detecting operating condition data provided by an embodiment of the present application. Referring to fig. 3, this figure is a schematic flow chart of a method for detecting operating condition data according to an embodiment of the present application, including:
s301: and acquiring working condition data.
And acquiring working condition data required to be detected.
S302: and acquiring the clustering parameters corresponding to the working condition data.
And acquiring clustering parameters corresponding to the current original working condition data, wherein the clustering parameters comprise a first parameter and a second parameter.
S303: and clustering the working condition data according to the clustering parameters.
And acquiring a related clustering processing algorithm according to the current detection requirement to realize clustering processing of the current working condition data.
S304: and determining the data state type in the working condition data according to the clustering result.
And determining the state information of the working condition data according to the data obtained after the original working condition data is processed, wherein the state information corresponds to normal working condition data or abnormal working condition data.
S305: acquiring a data acquisition scene corresponding to the abnormal data, wherein the data acquisition scene comprises acquisition equipment and an access mode;
the data acquisition scene comprises information of acquisition equipment and information of an access mode.
In some possible implementation manners, a user can add different access devices and select access manners of the devices for different projects and scenes in the management system, complete a data chain from the device acquisition to the data detection judgment type to the transmission stored in the management system, and set the scene needing warning during monitoring of the enterprise working conditions in advance through the design of the rule chain.
For example, in a specific application scenario, when data of the same type is acquired, there may be a deviation in the sensitivity and accuracy of different devices, and after various devices are installed and deployed to corresponding sites of an enterprise, access is performed through a gateway and various protocols, for example: MQTT, MQTT-SN, coAP, lwM2M, etc. Therefore, different types of equipment and different types of access modes have different degrees of influence on the acquisition and judgment of the working condition data.
S306: and acquiring an alarm rule corresponding to the data acquisition scene.
The alarm rule comprises a second alarm data threshold value, and the second alarm data threshold value is a limit value of the data abnormal alarm corresponding to the acquisition scene.
In some possible implementation manners, the threshold may be determined according to any one of the acquisition devices or access manners in the acquisition scene, or the threshold corresponding to the current scene may be determined according to a manner of combining the two types of information.
S307: judging whether the abnormal data is matched with the second alarm data threshold value or not; and generating alarm information in response to the abnormal data being greater than or equal to the second alarm data threshold.
When the operating condition data subjected to clustering processing is larger than or equal to the threshold value, the current data is in an abnormal state and an alarm condition for triggering abnormal data is achieved.
And generating alarm information according to the current working condition data and the related information, and in an actual application scene, integrating the working condition data and the related information into report information and sending the report information to a detection party terminal.
The following describes in detail the process of the method for detecting the operating condition data provided by the embodiment of the present application. Referring to fig. 4, this figure is a schematic flow chart of a method for detecting operating condition data according to an embodiment of the present application, including:
s401: and acquiring working condition data.
And acquiring working condition data required by the current inspection scene.
S402: and acquiring the clustering parameters corresponding to the working condition data.
And acquiring clustering parameters corresponding to the current original working condition data, wherein the clustering parameters comprise a first parameter and a second parameter.
S403: and clustering the working condition data according to the clustering parameters.
And acquiring a related clustering processing algorithm according to the current detection requirement to realize clustering processing of the current working condition data.
S404: and determining the data state type in the working condition data according to the clustering result.
And determining the state information of the working condition data according to the data obtained after the original working condition data are processed, wherein the state information corresponds to normal working condition data or abnormal working condition data.
S405: a first request is obtained.
The first request is an operation request for the abnormal data or the normal data, and the operation request comprises accessing, calling and sending different types of data in the working condition data.
S406: and acquiring a first user corresponding to the first request and a user authority rule corresponding to the first user.
The user authority rules are operation limit rules of the user on the data.
The first user refers to various users, the various users include but are not limited to roles of enterprise responsible persons, third-party detection mechanisms, government workers, developers and the like, in a specific application scene, different characters correspond to different system interaction interfaces, and control and viewing permissions of different projects, scenes and equipment are possessed.
The operation restriction rule can be set by a data provider or adjusted by a management system worker according to actual requirements.
S407: and performing corresponding operation on the abnormal data or the normal data according to the first request and the user authority rule.
According to the limiting rule corresponding to the user obtained in the step, the system limits the subsequent data operation of the user, and in a specific application scene, the system interactive interface can be switched and adjusted according to the user information. When the restriction rule requires that the client can not access the error data, the system automatically terminates the current action process when the client initiates an access action, and closes the operation port, and a message distribution function module in the system can distribute a message prompt for terminating the operation to the user.
The foregoing provides some specific implementation manners of the method for monitoring aquaculture risks, and based on this, the present application also provides a corresponding apparatus. The device provided by the embodiment of the present application will be described in terms of functional modularity.
Please refer to fig. 5, fig. 5 is a schematic structural diagram of an apparatus for monitoring aquaculture risk according to an embodiment of the present disclosure.
In this embodiment, the apparatus may include:
a working condition data acquisition module 500 for acquiring working condition data;
a clustering parameter obtaining module 501, configured to obtain a clustering parameter corresponding to the operating condition data, where the clustering parameter is a clustering partition basis in a clustering algorithm;
a clustering module 502, configured to cluster the operating condition data according to the clustering parameters;
and a state type determining module 503, configured to determine a data state type in the working condition data according to the clustering result, where the data state type includes normal or abnormal.
Optionally, the clustering parameters include a first parameter and a second parameter, the first parameter is a difference between normal operating condition data and abnormal operating condition data, and the second parameter is a difference between operating condition data of the same state type.
Optionally, the apparatus further comprises:
a first alarm threshold obtaining module 504, configured to obtain a first alarm threshold corresponding to the clustering parameter, where the first alarm data threshold is a limit value of a data abnormal alarm;
a first alarm threshold matching module 505, configured to determine whether the first parameter matches the first alarm data threshold;
a first alarm information generating module 506, configured to generate alarm information in response to the first parameter being greater than or equal to the first alarm data threshold.
Optionally, the apparatus further comprises:
a collection scene obtaining module 507, configured to obtain a data collection scene corresponding to the abnormal data, where the data collection scene includes collection equipment and an access mode;
an alarm rule obtaining module 508, configured to obtain an alarm rule corresponding to the data acquisition scenario, where the alarm rule includes a second alarm data threshold, and the second alarm data threshold is a limit value of a data abnormal alarm corresponding to the data acquisition scenario;
a second alarm threshold matching module 509, configured to determine whether the abnormal data matches the second alarm data threshold;
and a second alarm information generating module 510, configured to generate alarm information in response to that the abnormal data is greater than or equal to the second alarm data threshold.
Optionally, the apparatus further comprises:
a first request obtaining module 511, configured to obtain a first request, where the first request is an operation request for the abnormal data or the normal data;
a user rule obtaining module 512, configured to obtain a first user corresponding to the first request and a user permission rule corresponding to the first user, where the user permission rule is an operation restriction rule of a user on data; and performing corresponding operation on the abnormal data or the normal data according to the first request and the user authority rule.
The method and the device for detecting the working condition data provided by the application are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It should be noted that the method and the device for detecting the working condition data provided by the invention can be applied to the field of internet of things or the field of big data. The above is only an example, and does not limit the application field of the method for detecting the working condition data provided by the present invention.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for detecting operating condition data, the method comprising:
acquiring working condition data;
acquiring a clustering parameter corresponding to the working condition data, wherein the clustering parameter is a clustering division basis in a clustering algorithm;
and clustering the working condition data according to the clustering parameters, and determining the data state types in the working condition data according to clustering results, wherein the data state types comprise normal or abnormal.
2. The method according to claim 1, wherein the obtaining of the clustering parameter corresponding to the operating condition data includes:
the clustering parameters comprise a first parameter and a second parameter, the first parameter is a difference value between normal working condition data and abnormal working condition data, and the second parameter is a difference value between the same state type working condition data.
3. The method of claim 2, further comprising:
acquiring a first alarm threshold corresponding to the clustering parameter, wherein the first alarm data threshold is a limit value of data abnormal alarm;
judging whether the first parameter is matched with the first alarm data threshold value;
generating alert information in response to the first parameter being greater than or equal to the first alert data threshold.
4. The method of claim 1, wherein after determining the data state type in the condition data according to the clustering result, the method further comprises:
acquiring a data acquisition scene corresponding to the abnormal data, wherein the data acquisition scene comprises acquisition equipment and an access mode;
acquiring an alarm rule corresponding to the data acquisition scene, wherein the alarm rule comprises a second alarm data threshold value, and the second alarm data threshold value is a limit value of a data abnormal alarm corresponding to the acquisition scene;
judging whether the abnormal data is matched with the second alarm data threshold value;
and generating alarm information in response to the abnormal data being greater than or equal to the second alarm data threshold.
5. The method according to claim 2, wherein after determining the abnormal data in the operating condition data according to the clustering result, the method further comprises:
acquiring a first request, wherein the first request is an operation request for the abnormal data or the normal data;
acquiring a first user corresponding to the first request and a user permission rule corresponding to the first user, wherein the user permission rule is an operation restriction rule of the user on data;
and performing corresponding operation on the abnormal data or the normal data according to the first request and the user authority rule.
6. An apparatus for detecting operating condition data, the apparatus comprising:
the working condition data acquisition module is used for acquiring working condition data;
the clustering parameter acquisition module is used for acquiring clustering parameters corresponding to the working condition data, and the clustering parameters are clustering division bases in a clustering algorithm;
the clustering module is used for clustering the working condition data according to the clustering parameters;
and the state type determining module is used for determining the data state type in the working condition data according to the clustering result, wherein the data state type comprises normal or abnormal.
7. The apparatus of claim 6, wherein the clustering parameters comprise a first parameter and a second parameter, the first parameter is a difference between normal operating condition data and abnormal operating condition data, and the second parameter is a difference between same state type operating condition data.
8. The method of claim 7, wherein the apparatus further comprises:
a first alarm threshold obtaining module, configured to obtain a first alarm threshold corresponding to the clustering parameter, where the first alarm data threshold is a limit value of a data abnormal alarm;
the first alarm threshold matching module is used for judging whether the first parameter is matched with the first alarm data threshold;
and the first alarm information generation module is used for responding to the first parameter which is larger than or equal to the first alarm data threshold value and generating alarm information.
9. The method of claim 6, wherein the apparatus further comprises:
the acquisition scene acquisition module is used for acquiring a data acquisition scene corresponding to the abnormal data, and the data acquisition scene comprises acquisition equipment and an access mode;
the alarm rule obtaining module is used for obtaining an alarm rule corresponding to the data acquisition scene, wherein the alarm rule comprises a second alarm data threshold value, and the second alarm data threshold value is a limit value of a data abnormal alarm corresponding to the acquisition scene;
the second alarm threshold matching module is used for judging whether the abnormal data is matched with the second alarm data threshold;
and the second alarm information generation module is used for responding to the abnormal data which is greater than or equal to the second alarm data threshold value and generating alarm information.
10. The method of claim 7, wherein the apparatus further comprises:
a first request obtaining module, configured to obtain a first request, where the first request is an operation request for the abnormal data or the normal data;
a user rule obtaining module, configured to obtain a first user corresponding to the first request and a user permission rule corresponding to the first user, where the user permission rule is an operation restriction rule of a user on data; and performing corresponding operation on the abnormal data or the normal data according to the first request and the user authority rule.
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