Method and system for carrying out operation and maintenance management on equipment state of 5G communication base station

Abstract

The invention discloses a method and system for operation and maintenance management of the equipment state of a 5G communication base station, and relates to the technical field of data processing, and the method comprises the steps: carrying out the preprocessing of real-time operation state data; constructing an equipment degradation model based on the historical maintenance data to determine a real-time degradation state of each unit in the 5G communication base station equipment; identifying easily-occurring fault points based on historical maintenance data in combination with the real-time operation state data, and identifying potential fault points by using the real-time degradation state of the network based on the tracing reasoning network; state monitoring is carried out on the easily-occurring fault point and the target potential fault point; judging whether equipment maintenance is needed or not based on the state monitoring result, and if yes, making a target maintenance plan based on an operation and maintenance model in combination with equipment load analysis; and carrying out equipment maintenance on the 5G communication base station equipment based on the target maintenance plan, encrypting equipment maintenance information and uploading the equipment maintenance information to the cloud platform. According to the invention, the operation and maintenance management of the 5G communication base station equipment can achieve a more ideal effect.

Description

Technical Field

[0001] The present invention mainly relates to the field of data processing technology, and in particular to a method and system for operation and maintenance management of 5G communication base station equipment status. Background Art

[0002] As a key facility for users to obtain communication services, the safety and reliability of the operation of 5G communication base stations are crucial to the operational stability of the entire communication network. With the rapid development of 5G technology, the number of 5G communication base stations has further increased, which has made the operation and maintenance management of base station equipment status a technical problem that needs to be solved urgently. At present, in the operation and maintenance management of the equipment status of 5G communication base stations, the degradation status of each unit in the 5G communication base station equipment is usually checked by relevant operation and maintenance personnel, but this method is too dependent on the professional quality of the operation and maintenance personnel, and cannot guarantee the accuracy of the inspection of the degradation status of each unit in the communication base station equipment, which may lead to a lack of specific and clear understanding of the status of the communication base station equipment. At the same time, in the current operation and maintenance management of the status of base station equipment, there is a lack of identification of potential fault points, resulting in the inability to timely discover the abnormalities occurring at the potential fault points, affecting the timeliness of the repair of the target units in the communication base station equipment, and having a great impact on the operation and maintenance management of 5G communication base stations. When relevant equipment units in a communication base station need to be repaired, the maintenance plan is usually determined by comparative analysis of operating equipment parameters. However, the reliability of the maintenance plan determined by this method is not high, and resource allocation is not taken into account, which may lead to low quality of equipment maintenance, causing the operation and maintenance management of 5G communication base station equipment to fail to achieve the expected results, affecting the reliability of communication base station operation. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a method and system for operation and maintenance management of the equipment status of a 5G communication base station, which improves the reliability of the operation of the communication base station and enables the operation and maintenance management of the 5G communication base station equipment to achieve a more ideal effect.

[0004] In order to solve the above technical problems, the present invention provides a method for operation and maintenance management of 5G communication base station equipment status, the method comprising:

[0005] Acquire real-time operation status data of each unit in the 5G communication base station equipment, and pre-process the real-time operation status data to obtain pre-processed real-time operation status data;

[0006] Acquire historical maintenance data of each unit, build an equipment degradation model based on the historical maintenance data, and determine the real-time degradation status of each unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operation status data;

[0007] Based on the historical maintenance data combined with the pre-processed real-time operation status data, the fault-prone points are identified, and based on the abductive reasoning network, the real-time degradation status of each unit is used to identify the target potential fault points;

[0008] Performing status monitoring on the prone fault points and target potential fault points to obtain status monitoring results;

[0009] Determine whether equipment maintenance is required based on the condition monitoring results, and if it is determined that equipment maintenance is required, formulate a target maintenance plan based on the operation and maintenance model using the condition monitoring results combined with equipment load analysis;

[0010] Perform equipment maintenance on the 5G communication base station equipment based on the target maintenance plan, encrypt the equipment maintenance information, and upload the encrypted equipment maintenance information to the cloud platform.

[0011] Optionally, preprocessing the real-time operation status data to obtain the preprocessed real-time operation status data includes:

[0012] Performing noise reduction processing on the real-time operation status data to obtain the real-time operation status data after noise reduction processing;

[0013] The real-time operating status data after noise reduction processing is subjected to data transformation processing to obtain the real-time operating status data after preprocessing.

[0014] Optionally, constructing the equipment degradation model based on the historical maintenance data includes:

[0015] Constructing a device operation status parameter sequence and a device health status parameter sequence based on the historical maintenance data, and performing sensitive parameter screening based on the device operation status parameter sequence and the device health status parameter sequence to obtain target sensitive parameters;

[0016] Acquire influencing parameters of the equipment degradation process based on the target sensitive parameters;

[0017] Acquire a number of preset degradation indicators, and establish an edge degradation model of the Wiener process based on the number of preset degradation indicators;

[0018] A device degradation model is constructed based on the influencing parameters and the edge degradation model.

[0019] Optionally, determining the real-time degradation state of each unit in the 5G communication base station device based on the device degradation model using the preprocessed real-time operating state data includes:

[0020] Determining the equipment degradation amount using the preprocessed real-time operating status data based on the equipment degradation model;

[0021] Based on the equipment degradation model, the service life of the target unit in the 5G communication base station equipment is predicted using the preprocessed real-time operating status data, and the real-time degradation status of each unit in the 5G communication base station equipment is determined based on the equipment decay amount and service life.

[0022] Optionally, the identifying the fault-prone points based on the historical maintenance data combined with the pre-processed real-time operation status data includes:

[0023] Calculate the fault parameters of each unit in the 5G communication base station equipment within a preset time period based on the historical maintenance data, and obtain the fault occurrence law based on the fault parameters within the preset time period;

[0024] Based on the fault occurrence law, the fault prone points are identified by using the preset fault classification standard combined with the pre-processed real-time operation status data.

[0025] Optionally, the identifying target potential failure points by using the real-time degradation state of each unit based on the abductive reasoning network includes:

[0026] Construct a three-dimensional digital model of each unit and establish functional association rules between units;

[0027] Based on the three-dimensional digital model and the functional association rules, each unit is simulated to achieve the corresponding extreme working parameters, and based on the corresponding extreme working parameters and normal working parameters, an abductive reasoning rule set is obtained using an abductive reasoning network;

[0028] determining a first potential failure point based on the abductive reasoning rule set;

[0029] Constructing an equipment failure knowledge ontology, extracting knowledge using machine learning based on the historical maintenance data to obtain target knowledge, and constructing a potential failure knowledge base based on the equipment failure knowledge ontology and the target knowledge;

[0030] Determining a second potential fault point based on the potential fault knowledge base combined with the real-time degradation state of each unit;

[0031] A target potential failure point is determined based on the first potential failure point and the second potential failure point.

[0032] Optionally, performing status monitoring on the prone fault point and the target potential fault point to obtain status monitoring results includes:

[0033] The monitoring parameters are adjusted, and status monitoring is performed on the fault-prone points and target potential fault points based on the adjusted monitoring parameters to obtain status monitoring results.

[0034] Optionally, formulating a target maintenance plan based on the operation and maintenance model using the condition monitoring results combined with equipment load analysis includes:

[0035] Acquire operation and maintenance content information, and extract semantic keywords in the operation and maintenance semantic content corresponding to the operation and maintenance content information;

[0036] Performing content-level classification processing on the semantic keywords based on the operation and maintenance semantic content to obtain content-level classification results of the semantic keywords;

[0037] Based on the semantic keywords corresponding to the content level classification results, a tree relationship construction process corresponding to the semantic keywords is performed to obtain a semantic keyword tree structure diagram;

[0038] Indexing the semantic keywords of each node in the semantic keyword tree structure diagram and its corresponding operation and maintenance semantic content to form an operation and maintenance strategy knowledge graph, and constructing an operation and maintenance model based on the operation and maintenance strategy knowledge graph, and determining an initial maintenance plan based on the operation and maintenance model using the condition monitoring results;

[0039] Obtaining the operation load of the operation and maintenance equipment during operation, and adjusting the resource allocation and task scheduling of the operation and maintenance equipment based on the operation load using the status monitoring result combined with the resource scheduling dimension;

[0040] The initial maintenance plan is improved based on the adjusted resource allocation and task scheduling of the operation and maintenance equipment to obtain a target maintenance plan.

[0041] Optionally, performing equipment maintenance on the 5G communication base station equipment based on the target maintenance plan and encrypting equipment maintenance information includes:

[0042] Based on the target maintenance plan, the corresponding operation and maintenance equipment is scheduled to perform equipment maintenance on the 5G communication base station equipment, and the equipment maintenance information is asymmetrically encrypted to obtain the encrypted equipment maintenance information.

[0043] In addition, the present invention also provides a system for operation and maintenance management of 5G communication base station equipment status, the system comprising:

[0044] Data preprocessing module: used to obtain the real-time operation status data of each unit in the 5G communication base station equipment, and preprocess the real-time operation status data to obtain the preprocessed real-time operation status data;

[0045] Real-time degradation state determination module: used to obtain historical maintenance data of each unit, build an equipment degradation model based on the historical maintenance data, and determine the real-time degradation state of each unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operation state data;

[0046] Fault point identification module: used to identify prone fault points based on the historical maintenance data combined with the pre-processed real-time operation status data, and identify target potential fault points based on the abductive reasoning network using the real-time degradation status of each unit;

[0047] Fault point status monitoring module: used to perform status monitoring on the prone fault points and target potential fault points to obtain status monitoring results;

[0048] Maintenance plan formulation module: used to determine whether equipment maintenance is required based on the condition monitoring results. If it is determined that equipment maintenance is required, a target maintenance plan is formulated based on the operation and maintenance model using the condition monitoring results combined with equipment load analysis;

[0049] Equipment maintenance and maintenance information processing module: used to perform equipment maintenance on 5G communication base station equipment based on the target maintenance plan, encrypt the equipment maintenance information, and upload the encrypted equipment maintenance information to the cloud platform.

[0050] In an embodiment of the present invention, an equipment degradation model is constructed based on equipment degradation influencing parameters and edge degradation models generated by historical maintenance data, and the real-time degradation status of each unit in the 5G communication base station equipment is determined according to the equipment degradation model, which can improve the recognition accuracy of the degradation status of each unit in the 5G communication base station equipment, and more clearly understand the specific situation of the communication base station equipment. The target potential fault point is determined by the first potential fault point determined by the abductive reasoning rule set and the second potential fault point determined by the potential fault knowledge base, which can improve the recognition accuracy of the potential fault point, monitor the state of the potential fault point, and timely discover the abnormality of the potential fault point, so as to avoid affecting the timeliness of the repair of the target unit in the communication base station equipment. When it is determined that there are related equipment units in the communication base station that need to be repaired, the target maintenance plan is formulated based on the operation and maintenance model using the state monitoring results combined with the equipment load analysis, so as to improve the reliability of the formulated maintenance plan, improve the equipment maintenance quality, and at the same time realize the improvement of the operation and maintenance management efficiency, effectively solve the problem of low operation and maintenance management efficiency in the prior art, and enable the operation and maintenance management of 5G communication base station equipment to achieve a more ideal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1It is a flowchart of a method for operation and maintenance management of a 5G communication base station device status in a first embodiment of the present invention;

[0053] Figure 2 Schematic diagram of the method for operating and maintaining the 5G communication base station equipment status in the second embodiment of the present invention

[0054] Figure 3 It is a schematic diagram of the structure of a system for operating and maintaining a 5G communication base station device in an embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of the structure of an abductive reasoning network in an embodiment of the present invention;

[0056] Figure 5 It is a schematic diagram of the 5G communication base station network topology in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1

[0059] See also Figure 1 , Figure 1 It is a flow chart of a method for operating and maintaining a 5G communication base station device in a first embodiment of the present invention, and the method includes:

[0060] S11: Acquire real-time operation status data of each unit in the 5G communication base station equipment, and pre-process the real-time operation status data to obtain pre-processed real-time operation status data;

[0061] In the specific implementation process of the present invention, the preprocessing of the real-time operation status data to obtain the preprocessed real-time operation status data includes: performing noise reduction processing on the real-time operation status data to obtain the real-time operation status data after noise reduction processing; performing data transformation processing on the real-time operation status data after noise reduction processing to obtain the preprocessed real-time operation status data.

[0062] Specifically, the real-time operation status data of each unit in the 5G communication base station equipment is obtained through data acquisition equipment. The data acquisition equipment may include image acquisition equipment, pressure sensors, temperature sensors, etc. Each unit in the 5G communication base station equipment may include a baseband processing unit, an active antenna unit, a transmission unit, a power supply and a cooling unit, etc. The real-time operation status data may include the temperature, pressure and load conditions of each unit, such as Figure 5 As shown, the real-time operation status data of each unit in the 5G communication base station equipment is obtained through the perception layer, and the real-time operation status data is input to the platform layer for processing through the relevant data transmission protocol of the access layer, and the real-time operation status data is subjected to noise reduction processing, and the real-time operation status data is input to the noise reduction autoencoder for noise reduction processing to obtain the real-time operation status data after noise reduction processing. Data transformation processing is performed on the real-time operation status data after noise reduction processing, and data format conversion is performed on the real-time operation status data after noise reduction processing to obtain the real-time operation status data after data format conversion. Data transformation and dimensionality reduction are to extract relevant features from the initial features of the data through cluster analysis, reduce the data dimension, and complete the data conversion processing to obtain the pre-processed real-time operation status data.

[0063] S12: Obtain historical maintenance data of each unit, build an equipment degradation model based on the historical maintenance data, and determine the real-time degradation state of each unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operation state data;

[0064] In the specific implementation process of the present invention, the construction of the equipment degradation model based on the historical maintenance data includes: constructing an equipment operation status parameter sequence and an equipment health status parameter sequence based on the historical maintenance data, and screening sensitive parameters based on the equipment operation status parameter sequence and the equipment health status parameter sequence to obtain target sensitive parameters; obtaining influencing parameters of the equipment degradation process based on the target sensitive parameters; obtaining a number of preset degradation indicators, and establishing an edge degradation model of the Wiener process based on the several preset degradation indicators; and constructing the equipment degradation model based on the influencing parameters and the edge degradation model.

[0065] Furthermore, the method of determining the real-time degradation status of each unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operating status data includes: determining the equipment degradation amount based on the equipment degradation model using the preprocessed real-time operating status data; predicting the service life of the target unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operating status data, and determining the real-time degradation status of each unit in the 5G communication base station equipment based on the equipment degradation amount and the service life.

[0066] Specifically, the historical maintenance data of each unit is obtained from the database, and the historical maintenance data includes the fault conditions, number of faults, usage time, time of occurrence of faults, and fault repair information of each unit in the 5G communication base station equipment. Based on the historical maintenance data, a device operation state parameter sequence and a device health state parameter sequence are constructed, and a unit time interval is set. According to the set unit time interval and the historical normal operation data of each unit, the device operation state parameter sequence and the device health state parameter sequence are constructed, and the device operation state parameter sequence includes an abnormal operation state parameter sequence of the device. Based on the device operation state parameter sequence and the device health state parameter sequence, sensitive parameters are screened, and the device operation state parameter sequence is used as a comparison sequence, and the device health state parameter sequence is used as a reference sequence. The gray correlation value between the two is calculated according to the comparison sequence and the reference sequence, and the Pearson correlation coefficient of the comparison sequence and the reference sequence is calculated by the correlation analysis method. The target coefficient is calculated according to the gray correlation value and the Pearson correlation coefficient and its preset weight, and a number of target sensitive parameters are selected from the device operation state parameters according to the target coefficient. The sensitive parameters are used to characterize the equipment degradation data in the equipment operation state monitoring data. Based on the target sensitive parameters, the influencing parameters of the equipment degradation process are obtained, and the external environment influencing parameters are obtained. The external environment influencing parameters include the ambient temperature, humidity and pressure at each time point, etc. The external factor cumulative parameter set of the preset time period is generated according to the external environment influencing parameters, and the Pearson correlation coefficient between the external factor cumulative parameter set and the sensitive parameter set composed of several target sensitive parameters is calculated. According to the Pearson correlation coefficient, the influencing parameters of the equipment degradation process are selected from the external environment influencing parameters and several sensitive parameters. Several preset degradation indicators are obtained. The preset degradation indicators may include the expiration value of the use time of each unit in the communication base station, the number of times the limit load is reached, the coverage interference index and the number of maintenance times, etc. The preset degradation indicators are shown in the following table:

[0067] Preset degradation index classification Indicator name Control surface indicators Reaching the limit load times Duration of use of each unit Business indicators Maintenance times Service delay Average throughput Coverage interference indicators Average bit error rate Channel Utilization

[0068] Based on several preset degradation indicators, a marginal degradation model of the Wiener process is established, a random process model is established for the preset degradation indicators, a state transfer equation and a measurement equation are established according to the random process model, a dependency relationship between several degradation indicators is established according to the state transfer equation and the measurement equation, and a marginal degradation model of the Wiener process is constructed according to the dependency relationship. The marginal degradation model of the Wiener process is used to characterize the degradation in a continuous-time random process. Based on the influencing parameters and the marginal degradation model, a device degradation model is constructed. Based on the influencing parameters and the marginal degradation model, a degradation model with parameter correlation is constructed, which is the device degradation model. Based on the device degradation model, the device degradation amount is determined using the preprocessed real-time operating status data, and the preprocessed real-time operating status data is input into the device degradation model to output the device degradation amount. Based on the equipment degradation model, the service life of the target unit in the 5G communication base station equipment is predicted using the preprocessed real-time operation status data, and the time probability density function of each unit in the base station equipment at each stage is determined according to the equipment degradation model. The total time probability density function is determined according to the time probability density of each stage, and the service life of the target unit in the 5G communication base station equipment is determined according to the total time probability density function combined with the preprocessed real-time operation status data. The service life is the remaining usable life of the target unit, and the real-time degradation state of each unit in the 5G communication base station equipment is determined based on the equipment degradation amount and service life. The real-time degradation state of each unit in the 5G communication base station equipment can be quantified according to the equipment degradation amount and service life combined with the corresponding preset ratio. Thereby improving the accuracy of the description of the degradation process of the base station equipment.

[0069] S13: identifying fault-prone points based on the historical maintenance data combined with the pre-processed real-time operation status data, and identifying target potential fault points based on the abductive reasoning network using the real-time degradation status of each unit;

[0070] In the specific implementation process of the present invention, the identification of fault-prone points based on the historical maintenance data combined with the pre-processed real-time operating status data includes: calculating the fault parameters of each unit in the 5G communication base station equipment within a preset time period based on the historical maintenance data, and obtaining the fault occurrence pattern based on the fault parameters within the preset time period; based on the fault occurrence pattern, using the preset fault classification standard combined with the pre-processed real-time operating status data to identify fault-prone points.

[0071] Furthermore, the method of identifying target potential fault points by using the real-time degradation status of each unit based on the abductive reasoning network includes: constructing a three-dimensional digital model of each unit, and establishing functional association rules between each unit; performing extreme working simulation on each unit based on the three-dimensional digital model and the functional association rules to obtain corresponding extreme working parameters, and obtaining an abductive reasoning rule set based on the corresponding extreme working parameters and normal working parameters by using the abductive reasoning network; determining a first potential fault point based on the abductive reasoning rule set; constructing an equipment fault knowledge ontology, extracting knowledge by using machine learning based on the historical maintenance data to obtain target knowledge, and constructing a potential fault knowledge base based on the equipment fault knowledge ontology and the target knowledge; determining a second potential fault point based on the potential fault knowledge base in combination with the real-time degradation status of each unit; and determining a target potential fault point based on the first potential fault point and the second potential fault point.

[0072] Specifically, the fault parameters of each unit in the 5G communication base station equipment within a preset time period are calculated based on the historical maintenance data. The fault parameters may include fault occurrence time, fault unit and fault type, etc., and the fault occurrence pattern is obtained based on the fault parameters within the preset time period. The number of faults and fault types occurring in each unit within the preset time period are counted according to the fault parameters within the preset time period. A fault frequency distribution is generated according to the number of faults and fault types occurring in each unit within the preset time period. The fault occurrence pattern can be obtained based on the fault frequency distribution. Based on the fault occurrence law, the preset fault classification standard is used in combination with the real-time operating status data after preprocessing to identify fault-prone points. The preset fault classification standard can be a first-level fault, which has little impact on equipment safety and almost no impact on operation; a second-level fault, which has a certain impact on the equipment and a certain impact on operation but is tolerable; a third-level fault, which has a greater impact on equipment safety and a greater impact on operation but can be carried out through backup methods; a fourth-level fault, which has a great impact on equipment safety and a serious impact on operation and needs to be eliminated immediately. The fault types in the fault occurrence frequency are divided according to the preset fault classification standard, and the fault occurrence frequency after classification according to the fault type is combined with the real-time operating status data after preprocessing for comparative analysis to obtain fault-prone points, thereby improving the reliability of identifying fault-prone points. Construct a three-dimensional digital model of each unit, define the key features of each unit in the 5G communication base station equipment, define the corresponding attributes and feature reconstruction methods for each key feature, determine the geometric layer and data layer of the three-dimensional digital model, import the theoretical working principle model of each unit, and construct the corresponding three-dimensional digital model according to the theoretical working principle model of each unit combined with the geometric layer and data layer, and establish functional association rules between each unit, and establish functional association rules between each unit according to the information and physical transmission rules between each unit. Based on the three-dimensional digital model and the functional association rules, each unit is simulated for extreme working. According to the three-dimensional digital model and the functional association rules, each unit is simulated for operation under extreme conditions in the simulation software. The extreme conditions may include high temperature scenes, high load scenes, and high humidity scenes, etc., and its operation data under extreme conditions is obtained, that is, the corresponding extreme working parameters are obtained, and the abductive reasoning rule set is obtained by using the abductive reasoning network based on the corresponding extreme working parameters and normal working parameters. The normal working parameters can be obtained through the parameter database of each unit. The abductive reasoning network can be used to simulate highly nonlinear fault identification problems. The network is a hierarchical network with feedforward function nodes, and multi-layer node functions are composed of simple low-order polynomials. Its 3 or 4-layer polynomial network usually meets the modeling of complex systems. The difference between the corresponding extreme working parameters and the normal working parameters is used as an input variable, and the input variable is input into the abductive reasoning network, and the abductive reasoning rule set is output. The first potential fault point is determined based on the abductive reasoning rule set. The abductive reasoning rule can search for all fault events that have a causal relationship with the fault type, so that the first potential fault point can be determined.The equipment failure knowledge ontology is constructed by using a preset ontology construction tool, and the equipment failure knowledge ontology is constructed from four aspects: class, object attribute, data attribute and instance. Based on the historical maintenance data, machine learning is used to extract knowledge, and the fault prediction model is used to predict the fault event of the historical maintenance data to obtain the fault event prediction result. Event extraction is performed on the fault type in the historical maintenance data, and the fault type with a frequency greater than or equal to the preset occurrence frequency is extracted as a high-frequency event. The high-frequency events are combined, and association rules are generated using an association rule algorithm. Target knowledge is generated according to the association rules, high-frequency events and fault event prediction results, and a potential fault knowledge base is formed based on the equipment failure knowledge ontology and the target knowledge. The constructed potential fault knowledge base can realize the concreteness of fault judgment knowledge. Based on the potential fault knowledge base combined with the real-time degradation state of each unit, the second potential fault point is determined, and the second potential fault point is determined according to the association rules in the potential fault knowledge base combined with the real-time degradation state of each unit. The target potential fault point is determined based on the first potential fault point and the second potential fault point, and the first potential fault point and the second potential fault point are repeatedly eliminated to determine the target potential fault point.

[0073] S14: Performing status monitoring on the prone fault point and the target potential fault point to obtain status monitoring results;

[0074] In the specific implementation process of the present invention, the state monitoring of the fault-prone points and the target potential fault points is performed to obtain the state monitoring results, including: adjusting the monitoring parameters, and performing state monitoring of the fault-prone points and the target potential fault points based on the adjusted monitoring parameters to obtain the state monitoring results.

[0075] Specifically, the monitoring parameters are adjusted, that is, the frequency and range of monitoring of the area of ​​the unit where the fault-prone points and the target potential fault points are located are adjusted. Status detection can be performed through a combination of sensors such as relevant cameras or smart boxes. Status monitoring is performed on the fault-prone points and the target potential fault points based on the adjusted monitoring parameters, that is, data monitoring of the fault-prone points and the target potential fault points is performed according to the adjusted monitoring parameters to obtain status monitoring results.

[0076] S15: judging whether equipment maintenance is required based on the condition monitoring result, and if it is determined that equipment maintenance is required, formulating a target maintenance plan based on the operation and maintenance model by using the condition monitoring result combined with equipment load analysis;

[0077] In the specific implementation process of the present invention, the target maintenance plan is formulated based on the operation and maintenance model using the status monitoring results in combination with equipment load analysis, including: obtaining operation and maintenance content information, extracting semantic keywords in the operation and maintenance semantic content corresponding to the operation and maintenance content information; performing content hierarchy classification processing on the semantic keywords based on the operation and maintenance semantic content to obtain content hierarchy classification results of the semantic keywords; performing tree relationship construction processing corresponding to the semantic keywords based on the semantic keywords corresponding to the content hierarchy classification results to obtain a semantic keyword tree structure diagram; indexing the semantic keywords of each node in the semantic keyword tree structure diagram with its corresponding operation and maintenance semantic content to form an operation and maintenance strategy knowledge graph, and constructing an operation and maintenance model based on the operation and maintenance strategy knowledge graph, and determining an initial maintenance plan based on the operation and maintenance model using the status monitoring results; obtaining the operation load status of the operation and maintenance equipment during operation, and adjusting the resource allocation and task scheduling of the operation and maintenance equipment based on the operation load status using the status monitoring results in combination with the resource scheduling dimension; improving the initial maintenance plan based on the adjusted resource allocation and task scheduling of the operation and maintenance equipment to obtain a target maintenance plan.

[0078] Specifically, the operating data in the status monitoring results of each unit is compared with the preset fault warning threshold. If the preset fault warning threshold is reached, it indicates that the unit needs equipment maintenance. The operation and maintenance content information is obtained. The operation and maintenance content information is the text content of the maintenance strategy of each unit, including the type and quantity of operation and maintenance equipment used. The operation and maintenance content information is input into the semantic recognition model for semantic recognition processing to obtain operation and maintenance semantic content. The semantic recognition model is a convergence model obtained by inputting a sample data set into a deep neural network for training. The semantic keywords in the operation and maintenance semantic content corresponding to the operation and maintenance content information are extracted, and the operation and maintenance semantic content is subjected to keyword extraction processing based on the graph sorting algorithm to obtain semantic keywords. The semantic keywords are subjected to content-level classification processing based on the operation and maintenance semantic content. Content-level classification is performed according to the different operation and maintenance levels of each unit in the communication base station equipment to obtain content-level classification results of the semantic keywords. Based on the semantic keywords corresponding to the content level classification results, a tree relationship construction process corresponding to the semantic keywords is performed, and the node position of each semantic keyword in the tree structure is determined according to the content level classification results, that is, the semantic keywords corresponding to the root node and the remaining nodes in the tree structure are determined, and the semantic keywords corresponding to the content level classification results are filled into the corresponding position centers of the root node and the remaining nodes in the tree structure to obtain a semantic keyword tree structure diagram; the semantic keywords of each node in the semantic keyword tree structure diagram are indexed with the corresponding operation and maintenance semantic content, and the semantic keywords of each node are assigned a corresponding first element, and the operation and maintenance semantic content is assigned a corresponding second element, and an index association is established according to the first element and the second element to form an operation and maintenance strategy knowledge graph, and an operation and maintenance model is constructed based on the operation and maintenance strategy knowledge graph, and the initial maintenance plan is determined based on the operation and maintenance model using the status monitoring results. The operation load of the operation and maintenance equipment during operation is obtained, and the resource allocation and task scheduling of the operation and maintenance equipment are adjusted based on the operation load using the status monitoring results combined with the resource scheduling dimension, and the operation and maintenance equipment with an overloaded operation load is excluded. The initial maintenance plan is improved based on the adjusted resource allocation and task scheduling of the operation and maintenance equipment, and the scheduling of the operation and maintenance equipment in the initial maintenance plan is adjusted to obtain a target maintenance plan.

[0079] S16: Perform equipment maintenance on the 5G communication base station equipment based on the target maintenance plan, encrypt the equipment maintenance information, and upload the encrypted equipment maintenance information to the cloud platform.

[0080] During the specific implementation of the present invention, the 5G communication base station equipment is maintained based on the target maintenance plan, and the equipment maintenance information is encrypted, including: scheduling the corresponding operation and maintenance equipment based on the target maintenance plan to perform equipment maintenance on the 5G communication base station equipment, and asymmetrically encrypting the equipment maintenance information to obtain the encrypted equipment maintenance information.

[0081] Specifically, based on the target maintenance plan, the corresponding operation and maintenance equipment is scheduled to perform equipment maintenance on the 5G communication base station equipment, the equipment maintenance information is asymmetrically encrypted, the equipment maintenance information is encrypted based on a preset public key, the encrypted equipment maintenance information is obtained, and the encrypted equipment maintenance information is uploaded to the cloud platform in the platform layer to prevent the information from being tampered with or maliciously read.

[0082] In an embodiment of the present invention, an equipment degradation model is constructed based on equipment degradation influencing parameters and edge degradation models generated by historical maintenance data, and the real-time degradation status of each unit in the 5G communication base station equipment is determined according to the equipment degradation model, which can improve the recognition accuracy of the degradation status of each unit in the 5G communication base station equipment, and more clearly understand the specific situation of the communication base station equipment. The target potential fault point is determined by the first potential fault point determined by the abductive reasoning rule set and the second potential fault point determined by the potential fault knowledge base, which can improve the recognition accuracy of the potential fault point, monitor the state of the potential fault point, and timely discover the abnormality of the potential fault point, so as to avoid affecting the timeliness of the repair of the target unit in the communication base station equipment. When it is determined that there are related equipment units in the communication base station that need to be repaired, the target maintenance plan is formulated based on the operation and maintenance model using the state monitoring results combined with the equipment load analysis, so as to improve the reliability of the formulated maintenance plan, improve the equipment maintenance quality, and at the same time realize the improvement of the operation and maintenance management efficiency, effectively solve the problem of low operation and maintenance management efficiency in the prior art, and enable the operation and maintenance management of 5G communication base station equipment to achieve a more ideal effect.

[0083] Embodiment 2

[0084] See also Figure 2 , Figure 2 : is a flow chart of a method for operating and maintaining a 5G communication base station device in a second embodiment of the present invention, the method comprising:

[0085] S21: Acquire real-time operation status data of each unit in the 5G communication base station equipment, and pre-process the real-time operation status data to obtain pre-processed real-time operation status data;

[0086] S22: Acquire historical maintenance data of each unit, construct an equipment degradation model based on the historical maintenance data, and determine the real-time degradation state of each unit in the 5G communication base station equipment using the preprocessed real-time operation state data based on the equipment degradation model;

[0087] S23: Identify fault-prone points based on the historical maintenance data combined with the pre-processed real-time operation status data;

[0088] S24: constructing a three-dimensional digital model of each unit and establishing functional association rules between the units;

[0089] In the specific implementation process of the present invention, a three-dimensional digital model of each unit is constructed, the key features of each unit in the 5G communication base station equipment are defined, and the corresponding attributes and feature reconstruction methods are defined for each key feature. The geometric layer and data layer of the three-dimensional digital model are determined, and the theoretical working principle model of each unit is imported. According to the theoretical working principle model of each unit, the corresponding three-dimensional digital model is constructed in combination with the geometric layer and the data layer, and the functional association rules between the units are established. The functional association rules between the units are established according to the information and physical transmission rules between the units.

[0090] S25: performing extreme working simulation on each unit based on the three-dimensional digital model and the functional association rules to obtain corresponding extreme working parameters, and obtaining an abductive reasoning rule set using an abductive reasoning network based on the corresponding extreme working parameters and normal working parameters;

[0091] In the specific implementation process of the present invention, based on the three-dimensional digital model and the functional association rules, each unit is simulated for extreme working conditions. According to the three-dimensional digital model and the functional association rules, each unit is simulated for operation under extreme conditions in the simulation software. The extreme conditions may include high temperature scenes, high load scenes, and high humidity scenes, etc., and its operation data under extreme conditions is obtained, that is, the corresponding extreme working parameters are obtained. Based on the corresponding extreme working parameters and normal working parameters, an abductive reasoning network is used to obtain an abductive reasoning rule set. The normal working parameters can be obtained through the parameter database of each unit. The abductive reasoning network can be used to simulate highly nonlinear fault identification problems, such as Figure 4 As shown, the network is a hierarchical network with feedforward function nodes, which is composed of multi-layer node functions composed of simple low-order polynomials. Its 3-polynomial network usually meets the modeling of complex systems. The first layer is three types of nodes, one is a polynomial input and two is a binomial polynomial input. The second layer includes a type of node, which is a binomial polynomial input. The third layer is a type of node, which is a trinomial polynomial input. Through the processing of multiple types of nodes, the final processing result can be obtained. The difference between the corresponding extreme working parameters and the normal working parameters is used as the input variable, and the input variable is input into the abductive reasoning network, and the abductive reasoning rule set is output.

[0092] S26: Determine a first potential fault point based on the abductive reasoning rule set;

[0093] In a specific implementation of the present invention, the first potential fault point is determined based on the abductive reasoning rule set. The abductive reasoning rule can search for all fault events that have a causal relationship with the fault type, so as to determine the first potential fault point.

[0094] S27: constructing an equipment failure knowledge ontology, extracting knowledge using machine learning based on the historical maintenance data to obtain target knowledge, and constructing a potential failure knowledge base based on the equipment failure knowledge ontology and the target knowledge;

[0095] In the specific implementation process of the present invention, the equipment failure knowledge ontology is constructed by a preset ontology construction tool, and the equipment failure knowledge ontology is constructed from four aspects: class, object attribute, data attribute and instance. Based on the historical maintenance data, machine learning is used to extract knowledge, and the fault prediction model is used to predict fault events in the historical maintenance data to obtain fault event prediction results. Events are extracted from the fault types in the historical maintenance data, and fault types with a frequency greater than or equal to a preset occurrence frequency are extracted as high-frequency events. High-frequency events are combined, and association rules are generated using an association rule algorithm. Target knowledge is generated based on association rules, high-frequency events and fault event prediction results, and a potential fault knowledge base is formed based on the equipment failure knowledge ontology and the target knowledge. The constructed potential fault knowledge base can realize the concretization of fault judgment knowledge.

[0096] S28: determining a second potential fault point based on the potential fault knowledge base and the real-time degradation state of each unit, and determining a target potential fault point based on the first potential fault point and the second potential fault point;

[0097] In the specific implementation process of the present invention, the second potential fault point is determined based on the potential fault knowledge base combined with the real-time degradation state of each unit, and the second potential fault point is determined according to the association rule in the potential fault knowledge base combined with the real-time degradation state of each unit. The target potential fault point is determined based on the first potential fault point and the second potential fault point, and the first potential fault point and the second potential fault point are repeatedly eliminated to determine the target potential fault point.

[0098] S29: Performing status monitoring on the prone fault point and the target potential fault point to obtain status monitoring results;

[0099] S30: Determine whether equipment maintenance is required based on the status monitoring result;

[0100] During the specific implementation of the present invention, data monitoring is performed on the fault-prone points and target potential fault points according to the adjusted monitoring parameters, and whether equipment maintenance is needed is determined based on the obtained monitoring data. If the obtained monitoring data is within the normal range, no maintenance is temporarily required, and step S29 is entered to continue monitoring the fault-prone points and potential fault points. If the obtained monitoring data is abnormal, step S30 is entered to formulate a maintenance plan.

[0101] S31: If it is determined that equipment maintenance is required, a target maintenance plan is formulated based on the operation and maintenance model using the condition monitoring results combined with equipment load analysis;

[0102] S32: Perform equipment maintenance on the 5G communication base station equipment based on the target maintenance plan, encrypt the equipment maintenance information, and upload the encrypted equipment maintenance information to the cloud platform.

[0103] In an embodiment of the present invention, an equipment degradation model is constructed based on equipment degradation influencing parameters and edge degradation models generated by historical maintenance data, and the real-time degradation status of each unit in the 5G communication base station equipment is determined according to the equipment degradation model, which can improve the recognition accuracy of the degradation status of each unit in the 5G communication base station equipment, and more clearly understand the specific situation of the communication base station equipment. The target potential fault point is determined by the first potential fault point determined by the abductive reasoning rule set and the second potential fault point determined by the potential fault knowledge base, which can improve the recognition accuracy of the potential fault point, monitor the state of the potential fault point, and timely discover the abnormality of the potential fault point, so as to avoid affecting the timeliness of the repair of the target unit in the communication base station equipment. When it is determined that there are related equipment units in the communication base station that need to be repaired, the target maintenance plan is formulated based on the operation and maintenance model using the state monitoring results combined with the equipment load analysis, so as to improve the reliability of the formulated maintenance plan, improve the equipment maintenance quality, and at the same time realize the improvement of the operation and maintenance management efficiency, effectively solve the problem of low operation and maintenance management efficiency in the prior art, and enable the operation and maintenance management of 5G communication base station equipment to achieve a more ideal effect.

[0104] Embodiment 3

[0105] See also Figure 3 , Figure 3 : is a schematic diagram of the structure of a system for operating and maintaining a 5G communication base station device in an embodiment of the present invention, the system comprising:

[0106] Data preprocessing module 31: used to obtain real-time operation status data of each unit in the 5G communication base station equipment, and preprocess the real-time operation status data to obtain the preprocessed real-time operation status data;

[0107] A real-time degradation state determination module 32 is used to obtain historical maintenance data of each unit, construct an equipment degradation model based on the historical maintenance data, and determine the real-time degradation state of each unit in the 5G communication base station equipment using the preprocessed real-time operation state data based on the equipment degradation model;

[0108] Fault point identification module 33: used to identify prone fault points based on the historical maintenance data combined with the pre-processed real-time operation status data, and identify target potential fault points based on the abductive reasoning network using the real-time degradation status of each unit;

[0109] Fault point status monitoring module 34: used to perform status monitoring on the prone fault points and target potential fault points to obtain status monitoring results;

[0110] Maintenance plan formulation module 35: used to determine whether equipment maintenance is required based on the condition monitoring results. If it is determined that equipment maintenance is required, a target maintenance plan is formulated based on the operation and maintenance model using the condition monitoring results combined with equipment load analysis;

[0111] Equipment maintenance and maintenance information processing module 36: used to perform equipment maintenance on 5G communication base station equipment based on the target maintenance plan, encrypt the equipment maintenance information, and upload the encrypted equipment maintenance information to the cloud platform.

[0112] In the specific implementation process of the present invention, the specific implementation method of the system item can refer to the implementation method of the above-mentioned method item, which will not be repeated here.

[0113] In an embodiment of the present invention, an equipment degradation model is constructed based on equipment degradation influencing parameters and edge degradation models generated by historical maintenance data, and the real-time degradation status of each unit in the 5G communication base station equipment is determined according to the equipment degradation model, which can improve the recognition accuracy of the degradation status of each unit in the 5G communication base station equipment, and more clearly understand the specific situation of the communication base station equipment. The target potential fault point is determined by the first potential fault point determined by the abductive reasoning rule set and the second potential fault point determined by the potential fault knowledge base, which can improve the recognition accuracy of the potential fault point, monitor the state of the potential fault point, and timely discover the abnormality of the potential fault point, so as to avoid affecting the timeliness of the repair of the target unit in the communication base station equipment. When it is determined that there are related equipment units in the communication base station that need to be repaired, the target maintenance plan is formulated based on the operation and maintenance model using the state monitoring results combined with the equipment load analysis, so as to improve the reliability of the formulated maintenance plan, improve the equipment maintenance quality, and at the same time realize the improvement of the operation and maintenance management efficiency, effectively solve the problem of low operation and maintenance management efficiency in the prior art, and enable the operation and maintenance management of 5G communication base station equipment to achieve a more ideal effect.

[0114] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0115] In addition, the above is a detailed introduction to a method and system for operation and maintenance management of the status of a 5G communication base station equipment provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and scope of application. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for operation and maintenance management of 5G communication base station equipment status, characterized in that: The method comprises: Acquire real-time operation status data of each unit in the 5G communication base station equipment, and pre-process the real-time operation status data to obtain pre-processed real-time operation status data; Acquire historical maintenance data of each unit, build an equipment degradation model based on the historical maintenance data, and determine the real-time degradation status of each unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operation status data; Based on the historical maintenance data combined with the pre-processed real-time operation status data, the fault-prone points are identified, and based on the abductive reasoning network, the real-time degradation status of each unit is used to identify the target potential fault points; Performing status monitoring on the prone fault points and target potential fault points to obtain status monitoring results; Determine whether equipment maintenance is required based on the condition monitoring results, and if it is determined that equipment maintenance is required, formulate a target maintenance plan based on the operation and maintenance model using the condition monitoring results combined with equipment load analysis; Perform equipment maintenance on the 5G communication base station equipment based on the target maintenance plan, encrypt the equipment maintenance information, and upload the encrypted equipment maintenance information to the cloud platform.

2. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: The preprocessing of the real-time operation status data to obtain the preprocessed real-time operation status data includes: Performing noise reduction processing on the real-time operation status data to obtain the real-time operation status data after noise reduction processing; The real-time operating status data after noise reduction processing is subjected to data transformation processing to obtain the real-time operating status data after preprocessing.

3. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: The constructing of the equipment degradation model based on the historical maintenance data comprises: Constructing a device operation status parameter sequence and a device health status parameter sequence based on the historical maintenance data, and performing sensitive parameter screening based on the device operation status parameter sequence and the device health status parameter sequence to obtain target sensitive parameters; Acquire influencing parameters of the equipment degradation process based on the target sensitive parameters; Acquire a number of preset degradation indicators, and establish an edge degradation model of the Wiener process based on the number of preset degradation indicators; A device degradation model is constructed based on the influencing parameters and the edge degradation model.

4. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: Determining the real-time degradation state of each unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operation state data includes: Determining the equipment degradation amount using the preprocessed real-time operating status data based on the equipment degradation model; Based on the equipment degradation model, the service life of the target unit in the 5G communication base station equipment is predicted using the preprocessed real-time operating status data, and the real-time degradation status of each unit in the 5G communication base station equipment is determined based on the equipment decay amount and service life.

5. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: The identifying of the fault-prone points based on the historical maintenance data combined with the pre-processed real-time operation status data includes: Calculate the fault parameters of each unit in the 5G communication base station equipment within a preset time period based on the historical maintenance data, and obtain the fault occurrence law based on the fault parameters within the preset time period; Based on the fault occurrence law, the fault prone points are identified by using the preset fault classification standard combined with the pre-processed real-time operation status data.

6. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: The method of identifying target potential failure points based on the abductive reasoning network using the real-time degradation state of each unit includes: Construct a three-dimensional digital model of each unit and establish functional association rules between units; Based on the three-dimensional digital model and the functional association rules, each unit is simulated to achieve the corresponding extreme working parameters, and based on the corresponding extreme working parameters and normal working parameters, an abductive reasoning rule set is obtained using an abductive reasoning network; determining a first potential failure point based on the abductive reasoning rule set; Constructing an equipment failure knowledge ontology, extracting knowledge using machine learning based on the historical maintenance data to obtain target knowledge, and constructing a potential failure knowledge base based on the equipment failure knowledge ontology and the target knowledge; Determining a second potential fault point based on the potential fault knowledge base combined with the real-time degradation state of each unit; A target potential failure point is determined based on the first potential failure point and the second potential failure point.

7. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: The performing of status monitoring on the prone fault point and the target potential fault point to obtain status monitoring results includes: The monitoring parameters are adjusted, and status monitoring is performed on the fault-prone points and target potential fault points based on the adjusted monitoring parameters to obtain status monitoring results.

8. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: The operation and maintenance model-based method of formulating a target maintenance plan by utilizing the condition monitoring results combined with equipment load analysis includes: Acquire operation and maintenance content information, and extract semantic keywords in the operation and maintenance semantic content corresponding to the operation and maintenance content information; Performing content-level classification processing on the semantic keywords based on the operation and maintenance semantic content to obtain content-level classification results of the semantic keywords; Based on the semantic keywords corresponding to the content level classification results, a tree relationship construction process corresponding to the semantic keywords is performed to obtain a semantic keyword tree structure diagram; Indexing the semantic keywords of each node in the semantic keyword tree structure diagram and its corresponding operation and maintenance semantic content to form an operation and maintenance strategy knowledge graph, and constructing an operation and maintenance model based on the operation and maintenance strategy knowledge graph, and determining an initial maintenance plan based on the operation and maintenance model using the condition monitoring results; Obtaining the operation load of the operation and maintenance equipment during operation, and adjusting the resource allocation and task scheduling of the operation and maintenance equipment based on the operation load using the status monitoring result combined with the resource scheduling dimension; The initial maintenance plan is improved based on the adjusted resource allocation and task scheduling of the operation and maintenance equipment to obtain a target maintenance plan.

9. The method for operation and maintenance management of 5G communication base station equipment status according to claim 1, characterized in that: The performing equipment maintenance on the 5G communication base station equipment based on the target maintenance plan and encrypting the equipment maintenance information includes: Based on the target maintenance plan, the corresponding operation and maintenance equipment is scheduled to perform equipment maintenance on the 5G communication base station equipment, and the equipment maintenance information is asymmetrically encrypted to obtain the encrypted equipment maintenance information.

10. A system for operation and maintenance management of 5G communication base station equipment status, characterized in that: The system comprises: Data preprocessing module: used to obtain the real-time operation status data of each unit in the 5G communication base station equipment, and preprocess the real-time operation status data to obtain the preprocessed real-time operation status data; Real-time degradation state determination module: used to obtain historical maintenance data of each unit, build an equipment degradation model based on the historical maintenance data, and determine the real-time degradation state of each unit in the 5G communication base station equipment based on the equipment degradation model using the preprocessed real-time operation state data; Fault point identification module: used to identify prone fault points based on the historical maintenance data combined with the pre-processed real-time operation status data, and identify target potential fault points based on the abductive reasoning network using the real-time degradation status of each unit; Fault point status monitoring module: used to perform status monitoring on the prone fault points and target potential fault points to obtain status monitoring results; Maintenance plan formulation module: used to determine whether equipment maintenance is required based on the condition monitoring results. If it is determined that equipment maintenance is required, a target maintenance plan is formulated based on the operation and maintenance model using the condition monitoring results combined with equipment load analysis; Equipment maintenance and maintenance information processing module: used to perform equipment maintenance on 5G communication base station equipment based on the target maintenance plan, encrypt the equipment maintenance information, and upload the encrypted equipment maintenance information to the cloud platform.

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