CN117214571A - RRU/AAU health state evaluation method, device and medium - Google Patents
RRU/AAU health state evaluation method, device and medium Download PDFInfo
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Abstract
The invention provides a method, a device and a medium for evaluating RRU/AAU health status, wherein the method comprises the following steps: collecting temperature data of a historical RRU/AAU; clustering the acquired temperature data to obtain the average baseline temperature of RRU/AAU under the same judgment scene; and comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain the temperature situation category of the RRU/AAU to be evaluated. The method, the device and the medium can solve the problems that the existing RRU/AAU can be used for station-surfing correction after the occurrence of the faults, the faults are generated at the moment and can only be passively maintained afterwards, and the faults can not be processed in time easily.
Description
Technical Field
The present invention relates to the field of network technologies, and in particular, to a method, an apparatus, and a medium for evaluating RRU/AAU health status.
Background
The electrolytic capacitor is the most sensitive device with service life, and RRU (radio remote unit, remote radio unit)/AAU (active antenna unit, active antenna processing unit) equipment works at abnormal ultra-high temperature for a long time, and can have adverse effect on service life of devices such as the electrolytic capacitor in the equipment, so that RRU performance is affected.
However, the traditional means can be used for station-surfing correction after the occurrence of the faults, and the faults are generated and belong to post passive maintenance, so that the faults cannot be timely processed in remote positions or concurrent faults due to limited input of resources such as personnel.
Disclosure of Invention
The invention aims to solve the technical problems of the prior art, and provides a method, a device and a medium for evaluating the health state of RRU/AAU, which are used for solving the problems that the prior RRU/AAU can be on site for rectification after faults occur, the faults are generated at the moment, only can be passively maintained after the faults occur, and the faults are easy to cause incapability of timely treatment.
In a first aspect, the present invention provides a method for evaluating the health status of an RRU/AAU, the method comprising:
collecting temperature data of a historical RRU/AAU;
clustering the acquired temperature data to obtain the average baseline temperature of RRU/AAU under the same judgment scene;
and comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain the temperature situation category of the RRU/AAU to be evaluated.
Further, the same decision scenario comprises at least one of: the same area, the same terrain, the same station type, the same model, the same power consumption, the same time and the same weather.
Further, the clustering of the collected temperature data to obtain an average baseline temperature of the RRU/AAU in the same judgment scene specifically includes:
and clustering the acquired temperature data based on a flexible multidimensional clustering algorithm to obtain the average baseline temperature of the RRU/AAU under the same judgment scene.
Further, the temperature situation categories comprise situation easy over-temperature, normal temperature situation and situation temperature data missing, wherein the situation easy over-temperature is classified into three grades of high risk, serious and general.
Further, comparing the average baseline temperature of the RRU/AAU under the same judgment scene with the temperature value of the RRU/AAU to be evaluated to obtain the temperature situation category of the RRU/AAU to be evaluated, which specifically comprises the following steps:
comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain a deviation value;
and comparing the deviation value with a preset easy-to-overheat deviation threshold, and obtaining the temperature situation category of the RRU/AAU to be evaluated according to a comparison result.
Further, after comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU in the same decision scene to obtain the temperature situation category of the RRU/AAU to be evaluated, the method further includes:
and defining the processing time limit, the closed-loop period and the processing priority of the temperature situation category, so that related personnel process the RRU/AAU to be evaluated according to the processing priority within the processing time limit and the closed-loop period.
Further, defining the processing time limit and the closed loop period of the temperature situation category, which specifically comprises the following steps:
defining a processing time limit and a closed-loop period of the temperature situation category according to the key performance index KPI corresponding to the RRU/AAU to be evaluated;
the KPI comprises a Radio Resource Control (RRC) access success rate, a handover success rate, a disconnection rate and traffic volume.
In a second aspect, the present invention provides an apparatus for evaluating health status of an RRU/AAU, including:
the temperature data acquisition module is used for acquiring temperature data of the historical RRU/AAU;
the average baseline temperature acquisition module is connected with the temperature data acquisition module and used for clustering the acquired temperature data to obtain the average baseline temperature of the RRU/AAU under the same judgment scene;
the temperature situation type acquisition module is connected with the average baseline temperature acquisition module and is used for comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU in the same judgment scene to obtain the temperature situation type of the RRU/AAU to be evaluated.
In a third aspect, the present invention provides an apparatus for estimating the health status of an RRU/active antenna processing unit AAU, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to implement the method for estimating the health status of an RRU/AAU according to the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the RRU/AAU health status assessment method of the first aspect described above.
The RRU/AAU health state evaluation method, device and medium provided by the invention firstly collect temperature data of historical RRU/AAU; then clustering the acquired temperature data to obtain the average baseline temperature of the RRU/AAU under the same judgment scene; and comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain the temperature situation category of the RRU/AAU to be evaluated. The invention enables related personnel to find hidden trouble in advance through the temperature situation category, promotes the related personnel to actively get on the station before the fault occurs, belongs to the prior active maintenance, and solves the problems that the prior RRU/AAU can be adjusted after the fault occurs, the fault occurs at the moment, only can be passively maintained after the fault occurs, and the problem that the prior RRU/AAU cannot be processed in time is easy to cause.
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Fig. 1 is a flowchart of an RRU/AAU health status evaluation method according to embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of an RRU/AAU health status assessment apparatus according to embodiment 2 of the present invention;
fig. 3 is a schematic structural diagram of an RRU/AAU health status assessment apparatus according to embodiment 3 of the present invention.
Detailed Description
In order to make the technical scheme of the present invention better understood by those skilled in the art, the following detailed description of the embodiments of the present invention will be given with reference to the accompanying drawings.
It is to be understood that the specific embodiments and figures described herein are merely illustrative of the invention, and are not limiting of the invention.
It is to be understood that the various embodiments of the invention and the features of the embodiments may be combined with each other without conflict.
It is to be understood that only the portions relevant to the present invention are shown in the drawings for convenience of description, and the portions irrelevant to the present invention are not shown in the drawings.
It should be understood that each unit and module in the embodiments of the present invention may correspond to only one physical structure, may be formed by a plurality of physical structures, or may be integrated into one physical structure.
It will be appreciated that the terms "first," "second," and the like in embodiments of the present invention are used to distinguish between different objects or to distinguish between different processes on the same object, and are not used to describe a particular order of objects.
It will be appreciated that, without conflict, the functions and steps noted in the flowcharts and block diagrams of the present invention may occur out of the order noted in the figures.
It is to be understood that the flowcharts and block diagrams of the present invention illustrate the architecture, functionality, and operation of possible implementations of systems, apparatuses, devices, methods according to various embodiments of the present invention. Where each block in the flowchart or block diagrams may represent a unit, module, segment, code, or the like, which comprises executable instructions for implementing the specified functions. Moreover, each block or combination of blocks in the block diagrams and flowchart illustrations can be implemented by hardware-based systems that perform the specified functions, or by combinations of hardware and computer instructions.
It should be understood that the units and modules related in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, for example, the units and modules may be located in a processor.
Example 1:
the embodiment provides a method for evaluating the health status of RRU/AAU, as shown in fig. 1, the method comprises the following steps:
step S101: and collecting temperature data of the historical RRU/AAU.
In this embodiment, the temperature data of the historical RRU/AAU may be obtained by collecting temperature sensor data or optical module data. Specifically, each RRU/AAU may be labeled first, and the label information may include: information such as time, weather, area, city, scene, topography, station type, RRU/AAU model, power consumption and the like, and then corresponding temperature data are acquired. For example, the tag information of the RRU/AAU may be shown in table 1, and it should be noted that the tag information may be specifically set according to the actual situation, which is not limited in the present invention.
Table 1: tag information of RRU/AAU
Step S102: and clustering the acquired temperature data to obtain the average baseline temperature of the RRU/AAU under the same judgment scene.
Optionally, the same decision scenario comprises at least one of: the same area, the same terrain, the same station type, the same model, the same power consumption, the same time and the same weather.
In this embodiment, a clustering algorithm may be used to cluster the collected temperature data to obtain an average baseline temperature of the RRU/AAU in the same decision scene, where the same decision scene is preferably the same area, the same station, the same model, the same time (such as the same month), and the same weather.
Algorithm input: historical RRU/AAU temperature data, such as: temperature sensor data collected at 12 pm a day history
Algorithm output: clustering according to the conditions of the same month, the same weather, the same area, the same station type and the same modelCalculating the temperature data of the same category according to the data acquisition value in the unit period to obtain the average baseline temperature L a 。
Wherein L is 1 、L 2 To L n The temperature data currently collected, and the overtemperature situation coefficients of the time grid where the temperature data are located are shown in table 2, for example, the overtemperature situation coefficient is shown in table 2, the overtemperature situation coefficient is shown as A7 when the data collection of L1 is a sunny day in 7 months, and the overtemperature situation coefficient is shown as A5 when the data collection of L2 is shown as A5 month in 5 months (at the moment, the overtemperature situation coefficient A5 is smaller than A7).
Table 2: overtemperature situation coefficient
Optionally, the clustering is performed on the collected temperature data to obtain an average baseline temperature of the RRU/AAU in the same judgment scene, which specifically includes:
and clustering the acquired temperature data based on a flexible multidimensional clustering algorithm to obtain the average baseline temperature of the RRU/AAU under the same judgment scene.
In this embodiment, the clustering of the collected temperature data based on the flexible multidimensional clustering algorithm includes the following steps:
(1) Collecting data and constructing a multidimensional data tensor according to the data;
for example, for a current region, a three-dimensional data tensor is constructed from the data characteristics, the first dimension is a station type, the second dimension is a model, and the third dimension is time.
(2) Decomposing the multidimensional data tensor to obtain Zhang Liangkuai structure and multi-membership clustering information;
(3) Tensor filling is carried out according to the Zhang Liangkuai structure and the multi-membership clustering information, and convergence is judged;
(4) The previous steps are circulated until the post-filling tensor reaches the convergence threshold value, and the post-filling tensor is output;
(5) And combining the output post-filling tensor with a recommendation target to generate information recommendation.
Step S103: and comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain the temperature situation category of the RRU/AAU to be evaluated.
In this embodiment, the temperature situation categories include a situation that is prone to overtemperature, a temperature situation that is normal, and a situation that is prone to temperature data loss, where the situation is prone to overtemperature and is classified into three levels of high risk, severe, and general.
Optionally, comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU in the same decision scene to obtain the temperature situation category of the RRU/AAU to be evaluated, which specifically includes:
comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain a deviation value;
and comparing the deviation value with a preset easy-to-overheat deviation threshold, and obtaining the temperature situation category of the RRU/AAU to be evaluated according to a comparison result.
In this embodiment, the preset easy-over-temperature deviation threshold includes L i1 、L i2 、L i3 Three easy-over-temperature deviation thresholds can be initially defined for three grades which are easy to over-temperature in a situation, and then the easy-over-temperature deviation thresholds are calculated by pushing the alarm for 7 days forward and backward according to the temperature data of the historical RRU/AAU, so that the easy-over-temperature deviation thresholds are further close to the threshold value with faults. For example, 2023, 8 months, sunny days, outdoor temperature 31 degrees, where L a For average baseline temperature of the same-station type RRU/AAU in the area, an easy over-temperature deviation threshold L is set i1 >L i2 >L i3 Wherein L is i1 、L i2 、L i3 The initial temperature is 15 degrees, 10 degrees and 5 degrees, and then is iteratively updated in unit time according to a large number of data neural networks, for example, the L is calculated according to the first 7 days of data of the fault RRU/AAU with high-risk overtemperature alarm in 9 months and 24 hours i1 16 degrees.
In this embodiment, RRU/AAU temperature situation definitions may be as shown in table 3, where table 3 shows RRU/AAU temperature situation definitions for a month B weather, where a certain period supports 15 minutes, half an hour, 1 hour, 7 days, month level, etc.
Table 3: RRU/AAU temperature situation definition for A month B weather
It should be noted that, since weather has a certain influence on the temperature of RRU/AAU at different times, the invention performs over-temperature situation analysis based on time (mainly month) and weather.
Optionally, after comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperatures of the RRU/AAU in the same decision scene to obtain the temperature situation category of the RRU/AAU to be evaluated, the method further includes:
and defining the processing time limit, the closed-loop period and the processing priority of the temperature situation category, so that related personnel process the RRU/AAU to be evaluated according to the processing priority within the processing time limit and the closed-loop period.
In this embodiment, in order to facilitate the improvement of the working efficiency during the practical application, the processing time limit, the closed-loop period and the processing priority corresponding to the temperature situation types of different risks may be perfected. Specifically, different processing time limits and closed loop periods of temperature situation types are defined, and the city or county is defined as a unit according to the inspection fund. The processing priority comprises four stages, wherein the first stage is the situation easy to overheat (high risk), the second stage is the situation easy to overheat (serious), the third stage is the situation easy to overheat (general), the fourth stage is the situation temperature data missing, the processing priority of the first stage is the highest, the second stage is the second stage, and so on.
Optionally, defining a processing time limit and a closed loop period of the temperature situation category, which specifically include:
defining the processing time limit and the closed loop period of the temperature situation category according to KPI (Key Performance Indication, key performance indicator) indexes corresponding to the RRU/AAU to be evaluated;
the KPI indexes comprise RRC (Radio Resource Control ) access success rate, switching success rate, disconnection rate and traffic volume.
In this embodiment, a KPI threshold may be first determined according to an average value of KPI indexes of the same type and same time in the same area, then a KPI index corresponding to the RRU/AAU to be evaluated is compared with the KPI threshold, if the KPI index is lower than the KPI threshold, the KPI is abnormal, otherwise, the KPI is normal, and then a processing time limit and a closed loop period of the temperature situation category are defined according to whether the KPI index is abnormal or normal. It should be noted that, the determination of different KPI averages may be performed according to the market, and then a threshold is formulated, if there are more abnormal RRUs/AAUs than the average, the threshold is further adjusted until an abnormal KPI value of 10% of RRUs/AAUs is set, and note that 10% of the KPI values may be adjusted.
For example, taking city 1 as an example, the three-dimensional inspection period is 1 time/month, and the high-level early warning is preferentially processed by combining budget and customizing RRU/AAU over-temperature risk processing and closed loop period as shown in the following table 4.
Table 4: RRU/AAU over-temperature risk treatment and closed loop period customization
For example, the actual prediction and processing of the over-temperature alarm is shown in Table 5
Table 5: practical prediction and processing examples of over-temperature alarms
The invention can find hidden trouble in advance, is convenient for actively standing up and processing before the fault occurs, belongs to the field of active maintenance in advance, and is convenient for improving the working efficiency in practical application by perfecting the priority of different risk priority processing.
In a specific embodiment, the RRU/AAU health status evaluation method is applied to an OMC (network management platform), where the OMC implements network management and energy-saving management functions, and includes 3 unit modules, where each module functions as follows:
(1) The acquisition unit: and information acquisition of the state, the area information, the temperature threshold value and the like of the base station is realized.
(2) And a processing unit: and training an AI unit (training a neural network) according to the historical RRU/AAU temperature data, and then processing to perform threshold judgment and prediction.
(3) And a storage unit: information such as base station status, area information, temperature threshold, etc. is stored.
The RRU/AAU health state evaluation method is based on the following principle: the RRU/AAU is composed of a power amplifier or the like, which is an integrated circuit composed of components, and in order to prevent the components inside the RRU/AAU from overheating or even being burned out, a plurality of temperature sensors are usually placed in or near the main heating components and temperature monitoring is performed all the time. When the temperature read by the temperature sensor reaches a set threshold value, if the temperature still continues to rise, the RRU/AAU can perform the operations of closing a channel, closing a power amplifier, powering down and the like until the temperature drops below the threshold value and then gradually returns to normal output power. This mechanism is called over temperature handling, and once the components are damaged or destroyed, the above procedure will perform an error, affecting RRU/AAU performance, which provides a theoretical basis for temperature-based fault prediction.
The RRU/AAU health state evaluation method can comprise the following steps:
(1) Data mining
And classifying and gradually associating the collected temperature sensor data and mining.
Specifically, the hierarchical deep drilling can be performed based on big data, for example, the temperature sensor data is obtained by associating step by step according to areas, then according to station type, model type and the like. Each RRU/AAU has corresponding tag information in advance, and each RRU/AAU carries out different attribution according to the tags.
(2) Temperature situational awareness
Algorithm input: temperature sensor data collected at 12 pm every day (it should be noted that 12 pm can be adjusted, as long as the time reference is the same)
Algorithm output: clustering according to the same month, the same weather, the same area, the same station type and the same model, calculating the temperature data of the same category according to the data acquisition value in the unit period, and calculating to obtain the average baseline temperature L a 。
Wherein, the clustering adopts flexible multidimensional clustering.
(3) Risk handling priority
And defining different processing time limits and closed loop periods of temperature situation types, and customizing the units of the city or county according to the inspection funds. Taking city 1 as an example, the three-dimensional inspection period is 1 time/month, and the high-level early warning is preferentially processed by combining budget and carrying out RRU/AAU over-temperature risk processing and closed loop period self-definition as shown in a table 4.
The invention divides all samples under the decision scenes of model, film area, time (month/week etc.), weather and the like based on a large amount of temperature sensor data collected by RRU/AAU, calculates the average baseline temperature L of RRU/AAU under the same decision scene a The temperature value of each RRU/AAU is equal to the average baseline temperature L a Compared with the RRU/AAU with the temperature higher than the average baseline temperature, the easy over-temperature judgment of the AAU environment is carried out according to the comparison of the deviation value and the easy over-temperature deviation threshold, so that the hidden trouble of the fault can be found in advance.
For example, according to the RRU temperature situation, the RRU of a certain area in a certain city is predicted in 8 months 2022, among 314 RRUs, the serious situation is easy to overheat, and the general situation is easy to overheat, and is normal, and is required to further check the reasons, and the result is shown in table 6:
table 6: prediction result example of RRU
For example, the outdoor temperature is 37 ℃ and the outdoor temperature is clear on day 8 in 2022, the temperature detection is carried out on each station at the same time, a certain type 5G AAU of a certain equipment manufacturer is arranged on a fully-closed square column, the temperature is 79.6 ℃, a certain type 5G AAU of a certain equipment manufacturer is arranged on a shutter square column, the temperature is 57.4 ℃, and the equipment manufacturer needs to be subjected to on-site treatment for 1 week and rectification within 24 hours before the fault occurs.
For example, the 2022 month 2 weather is clear, the outdoor temperature is 5 degrees, the temperature detection is carried out on each station at the same time, the same station type is a fully-closed beautifying cover, the AAU working environment is not ventilated and poor in heat dissipation, the highest temperature of the AAU back radiating fin is 64 degrees, and the field treatment is carried out within 1 week after 24 hours before the fault occurs.
The RRU/AAU health state evaluation method provided by the embodiment of the invention comprises the steps of firstly, collecting temperature data of historical RRU/AAU; then clustering the acquired temperature data to obtain the average baseline temperature of the RRU/AAU under the same judgment scene; and comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain the temperature situation category of the RRU/AAU to be evaluated. The invention enables related personnel to find hidden trouble in advance through the temperature situation category, promotes the related personnel to actively get on the station before the fault occurs, belongs to the prior active maintenance, and solves the problems that the prior RRU/AAU can be adjusted after the fault occurs, the fault occurs at the moment, only can be passively maintained after the fault occurs, and the problem that the prior RRU/AAU cannot be processed in time is easy to cause.
Example 2:
as shown in fig. 2, this embodiment provides an apparatus for evaluating the health status of RRU/AAU, which is configured to execute the method for evaluating the health status of RRU/AAU, including:
the temperature data acquisition module 11 is used for acquiring temperature data of the historical RRU/AAU;
the average baseline temperature acquisition module 12 is connected with the temperature data acquisition module 11 and is used for clustering the acquired temperature data to obtain the average baseline temperature of the RRU/AAU under the same judgment scene;
the temperature situation type obtaining module 13 is connected with the average baseline temperature obtaining module 12, and is configured to compare the average baseline temperature of the RRU/AAU under the same decision scene with the temperature value of the RRU/AAU to be evaluated, so as to obtain the temperature situation type of the RRU/AAU to be evaluated.
Optionally, the same decision scenario comprises at least one of: the same area, the same terrain, the same station type, the same model, the same power consumption, the same time and the same weather.
Optionally, the average baseline temperature acquisition module 12 is specifically configured to:
and clustering the acquired temperature data based on a flexible multidimensional clustering algorithm to obtain the average baseline temperature of the RRU/AAU under the same judgment scene.
Optionally, the temperature situation categories include situation easy over-temperature, normal temperature situation and situation temperature data missing, wherein the situation easy over-temperature is classified into three grades of high risk, serious and general.
Optionally, the temperature situation category obtaining module 13 specifically includes:
the comparison unit is used for comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain a deviation value;
and the comparison unit is used for comparing the deviation value with a preset easy-over-temperature deviation threshold and obtaining the temperature situation category of the RRU/AAU to be evaluated according to a comparison result.
Optionally, the apparatus further comprises:
the definition module is used for defining the processing time limit, the closed-loop period and the processing priority of the temperature situation category so that related personnel can process the RRU/AAU to be evaluated according to the processing priority in the processing time limit and the closed-loop period.
Optionally, the definition module includes:
the first defining unit is used for defining the processing time limit and the closed loop period of the temperature situation category according to the key performance index KPI corresponding to the RRU/AAU to be evaluated;
the KPI comprises a Radio Resource Control (RRC) access success rate, a handover success rate, a disconnection rate and traffic volume.
Example 3:
referring to fig. 3, the present embodiment provides an apparatus for estimating the health status of RRU/AAU, which includes a memory 21 and a processor 22, wherein the memory 21 stores a computer program, and the processor 22 is configured to run the computer program to execute the method for estimating the health status of RRU/AAU in embodiment 1.
The memory 21 is connected to the processor 22, the memory 21 may be a flash memory, a read-only memory, or other memories, and the processor 22 may be a central processing unit or a single chip microcomputer.
Example 4:
the present embodiment provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the RRU/AAU health status assessment method in embodiment 1 described above.
Computer-readable storage media include volatile or nonvolatile, removable or non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, computer program modules or other data. Computer-readable storage media includes, but is not limited to, RAM (Random Access Memory ), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory, charged erasable programmable Read-Only Memory), flash Memory or other Memory technology, CD-ROM (Compact Disc Read-Only Memory), digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.
In summary, the method, the device and the medium for evaluating the RRU/AAU health status provided by the embodiment of the invention collect the temperature data of the historical RRU/AAU; then clustering the acquired temperature data to obtain the average baseline temperature of the RRU/AAU under the same judgment scene; and comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain the temperature situation category of the RRU/AAU to be evaluated. The invention enables related personnel to find hidden trouble in advance through the temperature situation category, promotes the related personnel to actively get on the station before the fault occurs, belongs to the prior active maintenance, and solves the problems that the prior RRU/AAU can be adjusted after the fault occurs, the fault occurs at the moment, only can be passively maintained after the fault occurs, and the problem that the prior RRU/AAU cannot be processed in time is easy to cause.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.
Claims (10)
1. The method for evaluating the health state of the RRU/AAU is characterized by comprising the following steps:
collecting temperature data of a historical RRU/AAU;
clustering the acquired temperature data to obtain the average baseline temperature of RRU/AAU under the same judgment scene;
and comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain the temperature situation category of the RRU/AAU to be evaluated.
2. The method of claim 1, wherein the same decision scenario comprises at least one of: the same area, the same terrain, the same station type, the same model, the same power consumption, the same time and the same weather.
3. The method of claim 2, wherein the clustering the collected temperature data to obtain an average baseline temperature of RRU/AAU in the same decision scene specifically includes:
and clustering the acquired temperature data based on a flexible multidimensional clustering algorithm to obtain the average baseline temperature of the RRU/AAU under the same judgment scene.
4. The method of claim 1, wherein the temperature profile categories include profile prone over temperature, temperature profile normal, and profile temperature data missing, wherein the profile prone over temperature is classified into three classes of high risk, severe, and general.
5. The method of claim 4, wherein comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU in the same decision scene to obtain the temperature situation category of the RRU/AAU to be evaluated specifically comprises:
comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU under the same judgment scene to obtain a deviation value;
and comparing the deviation value with a preset easy-to-overheat deviation threshold, and obtaining the temperature situation category of the RRU/AAU to be evaluated according to a comparison result.
6. The method of claim 5, wherein the comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU in the same decision scenario, after obtaining the temperature situation category of the RRU/AAU to be evaluated, further comprises:
and defining the processing time limit, the closed-loop period and the processing priority of the temperature situation category, so that related personnel process the RRU/AAU to be evaluated according to the processing priority within the processing time limit and the closed-loop period.
7. The method according to claim 6, characterized in that defining a processing time limit, a closed loop period of the temperature profile class, in particular comprises:
defining a processing time limit and a closed-loop period of the temperature situation category according to the key performance index KPI corresponding to the RRU/AAU to be evaluated;
the KPI comprises a Radio Resource Control (RRC) access success rate, a handover success rate, a disconnection rate and traffic volume.
8. An evaluation device for health status of an RRU/AAU, comprising:
the temperature data acquisition module is used for acquiring temperature data of the historical RRU/AAU;
the average baseline temperature acquisition module is connected with the temperature data acquisition module and used for clustering the acquired temperature data to obtain the average baseline temperature of the RRU/AAU under the same judgment scene;
the temperature situation type acquisition module is connected with the average baseline temperature acquisition module and is used for comparing the temperature values of the RRU/AAU to be evaluated with the average baseline temperature of the RRU/AAU in the same judgment scene to obtain the temperature situation type of the RRU/AAU to be evaluated.
9. A device for assessing the health of an RRU/active antenna processing unit AAU, comprising a memory and a processor, said memory having stored therein a computer program, said processor being arranged to run said computer program to implement a method for assessing the health of an RRU/AAU as claimed in any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the RRU/AAU health status assessment method according to any one of claims 1-7.
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