CN116931843B - User online management system based on large language model - Google Patents
User online management system based on large language model Download PDFInfo
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Abstract
The application discloses a user online management system based on a large language model, which comprises a first data acquisition module, a second data acquisition module, a central processing unit, a comparison module and a prompt module; the first data acquisition module acquires data management information of a user on-line management system based on a large language model during operation, processes the acquired data management information and transmits the processed data management information to the central processing unit. According to the application, through monitoring the process of storing the user online management system data based on the large language model, when the abnormal hidden danger of data storage possibly exists in the process of storing the data by the system, the user is prompted in time, so that the user information, interaction history and other key data are effectively prevented from being lost or damaged due to the abnormality of the data stored by the system, and meanwhile, the stored data are effectively prevented from becoming unavailable or unresolvable, and the user experience and the business process are ensured.
Description
Technical Field
The application relates to the technical field of user online management, in particular to a user online management system based on a large language model.
Background
The user on-line management system based on the large language model is a software system utilizing the large pre-training language model, and is used for carrying out natural language interaction with a user, responding to inquiry, providing information, solving problems and the like. The goal of such a system is to provide a highly personalized and intelligent user experience through natural language processing techniques. The system integrates a large pre-trained language model with powerful language understanding and generation capabilities that can understand user inputs and generate natural language responses.
The data storage of the system plays a key role and has important influence on the functions, user experience and subsequent analysis and improvement of the system.
The prior art has the following defects: when the system generates abnormal hidden trouble of data storage in the process of data storage, the system cannot intelligently sense, and as the system continues to operate, the abnormal data stored by the system can be possibly caused, and when the abnormal data storage of the system occurs, the user information, interaction history and other key data can be possibly lost or damaged, so that the data becomes unavailable or unresolvable, and finally the user experience and the business flow are seriously influenced.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The application aims to provide a large language model-based user online management system, which monitors the process of storing data of a large language model-based user online management system, and prompts a user in time when abnormal hidden danger of data storage possibly exists in the process of storing the data by the system, so that user information, interaction history and other key data are effectively prevented from being lost or damaged due to the abnormal data stored by the system, and meanwhile, the stored data are effectively prevented from becoming unavailable or unresolved, and user experience and business process are guaranteed, so that the problems in the background technology are solved.
In order to achieve the above object, the present application provides the following technical solutions: the user online management system based on the large language model comprises a first data acquisition module, a second data acquisition module, a central processing unit, a comparison module and a prompt module;
the first data acquisition module acquires data management information of a user on-line management system based on a large language model during operation, processes the acquired data management information and transmits the processed data management information to the central processor;
the second data acquisition module acquires system performance information of a user on-line management system based on a large language model when the user on-line management system runs, and transmits the system performance information to the central processing unit after processing the system performance information;
the central processing unit is used for comprehensively analyzing the processed data management information and system performance information when the user online management system based on the large language model runs, generating an abnormality index and transmitting the abnormality index to the comparison module;
the comparison module is used for comparing and analyzing the abnormality index generated during the operation of the user online management system based on the large language model with a preset abnormality index reference threshold value to generate an abnormality signal, transmitting the signal to the prompt module and sending out a warning through the prompt module.
Preferably, the data management information of the user on-line management system based on the large language model comprises a data backup frequency floating coefficient and a data storage space abnormal change coefficient when the user on-line management system operates, and after the data backup frequency floating coefficient and the data storage space abnormal change coefficient are acquired, the first data acquisition module respectively calibrates the data backup frequency floating coefficient and the data storage space abnormal change coefficient asAnd->The system performance information of the user on-line management system based on the large language model comprises a system response time abnormal stability coefficient, and after acquisition, the second data acquisition module marks the system response time abnormal stability coefficient as +.>。
Preferably, the logic for obtaining the floating coefficient of the data backup frequency is as follows:
a101, acquiring an optimal data backup frequency range in unit time when a user online management system based on a large language model performs data backup, and calibrating the optimal data backup frequency range as；
A102, acquiring actual data backup frequency of unit time in different time periods within T time in the running process of the system, and calibrating the actual data backup frequency of unit time as，xRepresenting actual unit time data backup frequency of different time periods in T time in system operation processThe number of the rate at which the data is to be transmitted,x=1、2、3、4、……、m，mis a positive integer;
a103, acquiring the system in the process of running in the T time to be smaller thanIs recalibrated to +.>，kRepresenting less than->A number of actual data backup frequencies per unit time,k=1、2、3、4、……、N，Nis a positive integer;
a104, the optimal data backup frequency range in unit time when data backup is carried out through the systemAnd actual data backup frequency per unit time of different time periods in T time in system operation processCalculating the floating coefficient of the data backup frequency, wherein the calculated expression is as follows:in which, in the process,mand the total data backup frequency of the actual unit time acquired in the T time in the running process of the system is represented.
Preferably, the logic for obtaining the abnormal change coefficient of the data storage space is as follows:
b101, obtaining the maximum limit capacity value of the user online management system based on the large language model for data storage, and calibrating the maximum limit capacity value as；
B102, acquiring actual data storage capacity values of different moments in T time in the running process of the user online management system based on the large language model, and comparingThe actual data storage capacity value is calibrated as，yA number representing the actual data storage capacity value at different times during system operation during time T,y=1、2、3、4、……、n，nis a positive integer;
b103 maximum limit capacity value when data storage is performed by the systemAnd the actual data storage capacity values at different moments in time T during the operation of the system +.>Calculating abnormal change coefficients of the data storage space, wherein the calculated expression is as follows: />。
Preferably, the logic for acquiring the abnormal stability coefficient of the system response time length is as follows:
c101, acquiring a plurality of actual response time durations generated in T time in the running process of a user online management system based on a large language model, and calibrating the actual response time durations as，vA number representing a response time period generated during the system operation in T time,v=1、2、3、4、……、M，Mis a positive integer;
c102, calculating an actual response time length standard deviation generated in the T time in the running process of the system, and calibrating the actual response time length standard deviation asQStandard deviation ofQThe calculation formula of (2) is as follows:wherein->For the average value of the actual response time length generated when the system runs in the T time during the running process,the obtained expression is:；
and C103, outputting an abnormal stability coefficient of the system response time through an actual response time standard deviation, an actual response time average value and a preset actual response time standard deviation reference threshold value and an actual response time average value reference threshold value, which are generated in the T time in the system operation process, wherein the specific output process is as follows:
if the average value of the actual response time length is larger than or equal to the reference threshold value of the average value of the actual response time length and the standard deviation of the actual response time length is smaller than the reference threshold value of the standard deviation of the actual response time length, outputting an abnormal stability coefficient of the response time length of the system,；
if the average value of the actual response time length is larger than or equal to the reference threshold value of the average value of the actual response time length and the standard deviation of the actual response time length is equal to the reference threshold value of the standard deviation of the actual response time length, obtaining the abnormal stability coefficient of the response time length of the system,；
if the average value of the actual response time is smaller than the reference threshold value of the average value of the actual response time and the standard deviation of the actual response time is larger than or equal to the reference threshold value of the standard deviation of the actual response time, obtaining the abnormal stability coefficient of the response time of the system,；
if the average value of the actual response time is smaller than the reference threshold value of the average value of the actual response time and the standard deviation of the actual response time is smaller than the reference threshold value of the standard deviation of the actual response time, obtaining the abnormal stability coefficient of the response time of the system,。
preferably, the CPU obtains the floating coefficient of the data backup frequencyAbnormal change coefficient of data storage space>Abnormal stability coefficient of system response time length +.>Then, a data analysis model is built to generate an abnormality index +.>The formula according to is: />Wherein->、/>、/>Frequency floating coefficient for data backup respectively>Abnormal change coefficient of data storage space>Abnormal stability coefficient of system response time length +.>Is a preset proportionality coefficient of>、/>、/>Are all greater than 0.
Preferably, the comparison module compares and analyzes the abnormality index generated during the operation of the user online management system based on the large language model with a preset abnormality index reference threshold, if the abnormality index is greater than or equal to the abnormality index reference threshold, a high abnormality signal is generated through the comparison module and transmitted to the prompt module, and the prompt module gives out a warning, if the abnormality index is less than the abnormality index reference threshold, a low abnormality signal is generated through the comparison module and transmitted to the prompt module, and the prompt module does not give out a warning.
Preferably, the system further comprises an operation and maintenance management evaluation module;
the operation and maintenance management evaluation module is used for comprehensively analyzing a plurality of abnormal index establishment analysis sets output by the central processing unit when the user online management system based on the large language model is in operation and maintenance management, and judging the operation and maintenance management condition of the system;
the operation and maintenance management evaluation module establishes an analysis set for a plurality of abnormal indexes output by a central processing unit during operation and maintenance management of a user online management system based on a large language model, and marks the analysis set asZThen，jA number representing the abnormality index within the analysis set,j=1、2、3、4、……、p，pis a positive integer;
calculating standard deviation and average value of a plurality of abnormality indexes in the analysis set, and respectively calibrating the standard deviation and average value of the abnormality indexes asAnd->Standard deviation->The calculation formula of (2) is as follows: />Wherein->For analyzing the average value of a plurality of abnormal indexes in the collection, the obtained expression is: />；
Comparing the standard deviation of the abnormality index and the average value of the abnormality index with a preset standard deviation reference threshold value of the abnormality index and a preset average value reference threshold value of the abnormality index respectively, and calibrating the standard deviation reference threshold value of the abnormality index and the average value reference threshold value of the abnormality index respectively asAnd->The following cases are generated:
if it isGenerating a signal of failure of system operation and maintenance management through the operation and maintenance management evaluation module, transmitting the signal to a mobile terminal, and prompting an operation and maintenance manager to continue maintenance through the mobile terminal;
if it isGenerating a signal with poor system operation and maintenance management stability through an operation and maintenance management evaluation module, transmitting the signal to a mobile terminal, and prompting an operation and maintenance manager to continue maintenance through the mobile terminal;
if it isAnd generating a signal of successful system operation and maintenance management through the operation and maintenance management evaluation module, transmitting the signal to the mobile terminal, and prompting an operation and maintenance manager that the system operation and maintenance management is successful through the mobile terminal.
In the technical scheme, the application has the technical effects and advantages that:
according to the application, through monitoring the process of storing the user online management system data based on the large language model, when the abnormal hidden danger of data storage possibly exists in the process of storing the data by the system, the user is prompted in time, so that the user information, interaction history and other key data are effectively prevented from being lost or damaged due to the abnormality of the data stored by the system, and meanwhile, the stored data are effectively prevented from becoming unavailable or unresolvable, and the user experience and the business process are ensured;
according to the application, the operation and maintenance management evaluation module is additionally arranged on the system, and the operation and maintenance management evaluation module is used for comprehensively analyzing a plurality of abnormal indexes output by the central processing unit during operation and maintenance management of the system, so that the operation and maintenance management condition of the system is judged, and the condition that the operation and maintenance management of the system fails or is unstable after the operation and maintenance management of the system is effectively prevented.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a block diagram of a large language model based user on-line management system of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The application provides a user online management system based on a large language model as shown in fig. 1, which comprises a first data acquisition module, a second data acquisition module, a central processing unit, a comparison module and a prompt module;
the first data acquisition module acquires data management information of a user on-line management system based on a large language model during operation, processes the acquired data management information and transmits the processed data management information to the central processor;
the data management information of the user on-line management system based on the large language model during operation comprises a data backup frequency floating coefficient and a data storage space abnormal change coefficient, and after acquisition, the first data acquisition module respectively calibrates the data backup frequency floating coefficient and the data storage space abnormal change coefficient intoAnd->；
The low frequency of data backup may cause errors in the data storage of the large language model-based user online management system, especially in the event of data loss, data corruption or system failure, although not necessarily immediately, the risk of data loss may increase, thereby negatively affecting the data storage and integrity of the system, for possible reasons:
risk of data loss: the lower backup frequency can result in older versions of the backed-up data, and if data is lost or corrupted between backups, the restored data can be older, resulting in newer data being lost;
data corruption problem: if data corruption occurs between backups, but the backup frequency is low, the system may not be able to recover to a normal, undamaged state in time;
recovery difficulties increase: if the backup frequency is low, the system may not be able to provide a timely backup version for data recovery, which may make it more difficult to recover the data after a failure occurs;
data inconsistency: data changes between backups may cause inconsistencies between the backup data and the actual data, thereby affecting the data integrity of the system;
therefore, the problem that the potential abnormality occurs in data storage due to the fact that the system data backup frequency becomes low can be found in time by monitoring the data backup frequency when the user online management system based on the large language model runs;
the logic for obtaining the data backup frequency floating coefficient is as follows:
a101, acquiring an optimal data backup frequency range in unit time when a user online management system based on a large language model performs data backup, and calibrating the optimal data backup frequency range as；
It should be noted that, firstly, knowing the service requirement of the system, different services may have different requirements on the recovery time of data and the tolerance of data loss, for example, the financial field may need more frequent backup, while some other fields may be more flexible, analyze the change speed of data in the system, secondly, if the data changes frequently, more frequent backup may need to be needed to capture the latest change, if the data changes slowly, the backup frequency may be relatively low, so the optimal data backup frequency range when the system performs data backup is not limited specifically, and may be adjusted according to the actual situation and requirement;
a102, acquiring actual data backup frequency of unit time in different time periods (the time in the time period can be equal or unequal in value or in a crossed form of the two in the running process of the system, and not specifically limited herein), and calibrating the actual data backup frequency of unit time as，xA number representing the actual data backup frequency per unit time of different time periods within the time T during the system operation,x=1、2、3、4、……、m，mis a positive integer;
a103, acquiring the system in the process of running in the T time to be smaller thanIs recalibrated to +.>，kRepresenting less than->A number of actual data backup frequencies per unit time,k=1、2、3、4、……、N，Nis a positive integer;
a104, the optimal data backup frequency range in unit time when data backup is carried out through the systemAnd actual data backup frequency per unit time of different time periods in T time in system operation processCalculating the floating coefficient of the data backup frequency, wherein the calculated expression is as follows:in which, in the process,mrepresenting the total amount of data backup frequency of the actual unit time acquired in the T time in the running process of the system;
the calculation formula of the data backup frequency floating coefficient can show that the larger the representation value of the data backup frequency floating coefficient generated in the T time in the running process of the user on-line management system based on the large language model is, the larger the hidden danger of the data storage abnormality is shown when the system stores the data, otherwise, the smaller the hidden danger of the data storage abnormality is shown when the system stores the data;
when the data storage space of the user online management system based on the large language model is insufficient, which means that the capacity for storing data in the system is close to or reaches the maximum limit capacity, more data cannot be stored continuously (it should be noted that when the capacity for storing data in the system exceeds the maximum limit capacity, the system usually gives a prompt, and therefore, this situation is not specifically considered here), when the data storage space of the system is insufficient, abnormal data storage can occur, because the insufficient storage space can cause the system to fail to perform data writing, reading and management normally, so that the normal operation of the system is affected, and the following possible abnormal situations are:
data write failure: when the storage space is insufficient, the system may not be able to write new data to the storage device, resulting in a write operation failure, which may prevent new data entered by the user from being stored correctly;
data loss: insufficient storage space may cause existing data to be overwritten or deleted, resulting in loss of stored data;
data corruption: insufficient storage space may cause data to be written or truncated incorrectly, thereby damaging the data;
incomplete data: insufficient storage space may result in only a portion of the data being stored such that the stored data becomes incomplete;
system errors: insufficient storage space may cause performance degradation or even breakdown of the system, and if the system cannot allocate enough storage space when processing data, memory errors or other anomalies may be caused, and finally, error in system data storage is caused;
limited functionality: if the storage space is insufficient, the system may not be able to provide certain functions or services because the required data cannot be stored;
therefore, the problem that the abnormal hidden trouble occurs in the data storage due to insufficient data storage space of the system can be timely found out by monitoring the data storage space of the user on-line management system based on a large language model;
the logic for acquiring the abnormal change coefficient of the data storage space is as follows:
b101, obtaining the maximum limit capacity value of the user online management system based on the large language model for data storage, and calibrating the maximum limit capacity value as；
It should be noted that, the design documents, technical specifications and user manuals of the system will generally provide information about the limitation of the storage capacity of the system, and these documents contain detailed information about the data storage architecture, database configuration and storage devices, so that the maximum limitation capacity value when the system performs data storage can be obtained in the above manner;
b102, acquiring actual data storage capacity values of different moments in T time in the running process of the user online management system based on the large language model, and calibrating the actual data storage capacity values as，yA number representing the actual data storage capacity value at different times during system operation during time T,y=1、2、3、4、……、n，nis a positive integer;
it should be noted that, the usage monitoring tools can track the resource usage of the system in real time, including storage capacity, and these tools can provide real-time data and trend analysis about the usage of storage space, for example, prometaus is an open-source monitoring and alarm tool, can collect and store index data of various systems and applications, including storage capacity, prometaus provides powerful query language and dashboard, and is used for real-time monitoring of the usage of storage, and for example, grafana and Prometaus are integrated with other data sources, and is used for creating a real-time monitoring dashboard, and can display indexes such as storage capacity in the form of a graph;
b103 maximum limit capacity value when data storage is performed by the systemAnd the actual data storage capacity values at different moments in time T during the operation of the system +.>Calculating abnormal change coefficients of the data storage space, wherein the calculated expression is as follows: />；
The calculation formula of the abnormal change coefficient of the data storage space can show that the smaller the representation value of the abnormal change coefficient of the data storage space generated in the time T in the running process of the user on-line management system based on the large language model is, the greater the hidden danger of abnormal data storage occurs when the system stores data, otherwise, the smaller the hidden danger of abnormal data storage occurs when the system stores data;
the second data acquisition module acquires system performance information of a user on-line management system based on a large language model when the user on-line management system runs, and transmits the system performance information to the central processing unit after processing the system performance information;
the system performance information of the user on-line management system based on the large language model comprises a system response time abnormal stability coefficient, and after acquisition, the second data acquisition module marks the system response time abnormal stability coefficient as；
Poor stability of response time of a user online management system based on a large language model may cause errors in data storage, and especially in some specific cases, unstable response time may cause problems in data storage and processing, including the following cases:
concurrency conflict: if the response time of the system is unstable, data conflict can be caused under concurrent operation, and when a plurality of users operate simultaneously, data reading and writing can be performed under the condition that the response is slow, so that data inconsistency or errors can be caused;
write failure: unstable response time may cause data writing failure, and if delay or timeout occurs in writing data, writing operation may be caused to fail;
data loss: an unstable response time may cause some write operations to be unable to complete successfully, resulting in data loss or partial data writing, affecting data storage accuracy and integrity;
incomplete data: if the response time is unstable, the read operation may be interrupted or failed in some cases, so that incomplete data is returned, thereby affecting the integrity of data storage;
therefore, the response time length of the user on-line management system based on the large language model is monitored, and the problem that the stability of the response time length of the system number is poor, so that abnormal hidden danger occurs in data storage can be timely found;
the logic for acquiring the abnormal stability coefficient of the system response time length is as follows:
c101, acquiring a plurality of actual response time durations generated in T time in the running process of a user online management system based on a large language model, and calibrating the actual response time durations as，vA number representing a response time period generated during the system operation in T time,v=1、2、3、4、……、M，Mis a positive integer;
it should be noted that, the system may record the response start time and the response end time when processing the request, then record these information into the log file, and calculate the response duration of each request by analyzing the log file, so as to obtain the actual response duration information in the running process of the system;
c102, calculating an actual response time length standard deviation generated in the T time in the running process of the system, and calibrating the actual response time length standard deviation asQStandard deviation ofQThe calculation formula of (2) is as follows:wherein->For the average value of the actual response time length generated when the system runs in the T time in the running process, the obtained expression is as follows:；
and C103, outputting an abnormal stability coefficient of the system response time through an actual response time standard deviation, an actual response time average value and a preset actual response time standard deviation reference threshold value and an actual response time average value reference threshold value, which are generated in the T time in the system operation process, wherein the specific output process is as follows:
if the average value of the actual response time length is larger than or equal to the reference threshold value of the average value of the actual response time length and the standard deviation of the actual response time length is smaller than the reference threshold value of the standard deviation of the actual response time length, outputting an abnormal stability coefficient of the response time length of the system,；
if the average value of the actual response time length is larger than or equal to the reference threshold value of the average value of the actual response time length and the standard deviation of the actual response time length is equal to the reference threshold value of the standard deviation of the actual response time length, obtaining the abnormal stability coefficient of the response time length of the system,；
if the average value of the actual response time is smaller than the reference threshold value of the average value of the actual response time and the standard deviation of the actual response time is larger than or equal to the reference threshold value of the standard deviation of the actual response time, obtaining the abnormal stability coefficient of the response time of the system,；
if the average value of the actual response time is smaller than the reference threshold value of the average value of the actual response time and the standard deviation of the actual response time is smaller than the reference threshold value of the standard deviation of the actual response time, obtaining the abnormal stability coefficient of the response time of the system,；
the obtaining process of the abnormal change coefficient of the data storage space can know that the larger the representation value of the abnormal change coefficient of the data storage space generated in the T time in the running process of the user on-line management system based on the large language model is, the larger the hidden danger of the abnormal data storage is indicated to be generated when the system stores the data, otherwise, the hidden danger of the abnormal data storage is indicated to be generated when the system stores the data;
the central processing unit is used for comprehensively analyzing the processed data management information and system performance information when the user online management system based on the large language model runs, generating an abnormality index and transmitting the abnormality index to the comparison module;
the central processing unit obtains the floating coefficient of the data backup frequencyAbnormal change coefficient of data storage space>Abnormal stability coefficient of system response time length +.>Then, a data analysis model is built to generate an abnormality index +.>The formula according to is: />Wherein->、/>、/>Frequency floating coefficient for data backup respectively>Abnormal change coefficient of data storage space>Abnormal stability coefficient of system response time length +.>Is a preset proportionality coefficient of>、/>、/>Are all greater than 0;
as can be seen from the calculation formula, the larger the data backup frequency floating coefficient generated in the T time during the running process of the user online management system based on the large language model, the smaller the abnormal change coefficient of the data storage space, the larger the abnormal stability coefficient of the system response time length, namely the abnormal index generated in the T time during the running process of the systemThe larger the representation value of (2) is, the larger the hidden danger of data storage abnormality of the system is shown when the data is stored, the smaller the data backup frequency floating coefficient generated in the T time is generated in the running process of the user online management system based on a large language model, the larger the data storage space abnormality variation coefficient is, the smaller the system response time abnormality stability coefficient is, namely the abnormality index generated in the T time is the greater the abnormality index generated in the running process of the system is>The smaller the representation value of the data storage system is, the smaller the hidden danger of abnormal data storage occurs when the system stores the data;
the comparison module is used for comparing and analyzing an abnormality index generated when the user online management system based on the large language model runs with a preset abnormality index reference threshold value to generate an abnormality signal, transmitting the signal to the prompt module and sending a warning through the prompt module;
the comparison module is used for comparing and analyzing an abnormality index generated when a user online management system based on a large language model runs with a preset abnormality index reference threshold, generating a high abnormality signal through the comparison module if the abnormality index is larger than or equal to the abnormality index reference threshold, transmitting the signal to the prompt module, sending out a warning through the prompt module, prompting that an operation and maintenance manager is required to carry out operation and maintenance management on the system when the user online management system possibly has abnormal hidden dangers of data storage in the process of storing the data, generating a low abnormality signal through the comparison module if the abnormality index is smaller than the abnormality index reference threshold, transmitting the signal to the prompt module, and sending out a warning through the prompt module;
it should be noted that, the selection of the above-mentioned T time is a time period with a relatively short time, the time in the time period is not limited in detail herein, and can be set according to practical situations, so as to monitor the situation of the system in different time periods (T time) when the user online management system based on the large language model stores data, thereby monitoring the data storage situation of the system in the T time period;
the system also comprises an operation and maintenance management evaluation module;
the operation and maintenance management evaluation module is used for comprehensively analyzing a plurality of abnormal index establishment analysis sets output by the central processing unit when the user online management system based on the large language model is in operation and maintenance management, and judging the operation and maintenance management condition of the system;
the operation and maintenance management evaluation module establishes an analysis set for a plurality of abnormal indexes output by a central processing unit during operation and maintenance management of a user online management system based on a large language model, and marks the analysis set asZThen，jA number representing the abnormality index within the analysis set,j=1、2、3、4、……、p，pis a positive integer;
calculating standard deviation and average value of a plurality of abnormality indexes in the analysis set, and respectively calibrating the standard deviation and average value of the abnormality indexes asAnd->Standard deviation->The calculation formula of (2) is as follows: />Wherein->For analyzing the average value of a plurality of abnormal indexes in the collection, the obtained expression is: />；
Comparing the standard deviation of the abnormality index and the average value of the abnormality index with a preset standard deviation reference threshold value of the abnormality index and a preset average value reference threshold value of the abnormality index respectively, and calibrating the standard deviation reference threshold value of the abnormality index and the average value reference threshold value of the abnormality index respectively asAnd->The following cases are generated:
if it isGenerating a signal of failure of system operation and maintenance management through the operation and maintenance management evaluation module, transmitting the signal to a mobile terminal, and prompting an operation and maintenance manager to continue maintenance through the mobile terminal;
if it isGenerating a signal with poor system operation and maintenance management stability through the operation and maintenance management evaluation module, sending the signal to the mobile terminal, prompting operation and maintenance management personnel to continue maintenance through the mobile terminal, and indicating that the system operation stability after the operation and maintenance management is poor by the signal with poor system operation and maintenance management is easy to occur in the operation process;
if it isGenerating a signal that the system operation and maintenance management is successful through the operation and maintenance management evaluation module, sending the signal to the mobile terminal, and prompting an operation and maintenance manager that the system operation and maintenance management is successful through the mobile terminal;
according to the application, through monitoring the process of storing the user online management system data based on the large language model, when the abnormal hidden danger of data storage possibly exists in the process of storing the data by the system, the user is prompted in time, so that the user information, interaction history and other key data are effectively prevented from being lost or damaged due to the abnormality of the data stored by the system, and meanwhile, the stored data are effectively prevented from becoming unavailable or unresolvable, and the user experience and the business process are ensured;
according to the application, the operation and maintenance management evaluation module is additionally arranged on the system, and the operation and maintenance management evaluation module is used for comprehensively analyzing a plurality of abnormal indexes output by the central processing unit during operation and maintenance management of the system, so that the operation and maintenance management condition of the system is judged, and the condition that the operation and maintenance management of the system fails or is unstable after the operation and maintenance management of the system is effectively prevented.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
While certain exemplary embodiments of the present application have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the application. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the application, which is defined by the appended claims.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The user online management system based on the large language model is characterized by comprising a first data acquisition module, a second data acquisition module, a central processing unit, a comparison module and a prompt module;
the first data acquisition module acquires data management information of a user on-line management system based on a large language model during operation, processes the acquired data management information and transmits the processed data management information to the central processor;
the data management information of the user on-line management system based on the large language model during operation comprises a data backup frequency floating coefficient and a data storage space abnormal change coefficient, and after acquisition, the first data acquisition module respectively calibrates the data backup frequency floating coefficient and the data storage space abnormal change coefficient intoAnd->；
The logic for obtaining the data backup frequency floating coefficient is as follows:
a101, acquiring an optimal data backup frequency range in unit time when a user online management system based on a large language model performs data backup, and calibrating the optimal data backup frequency range as；
A102, acquiring actual data backup frequency of unit time in different time periods within T time in the running process of the system, and calibrating the actual data backup frequency of unit time as，xA number representing the actual data backup frequency per unit time of different time periods within the time T during the system operation,x=1、2、3、4、……、m，mis a positive integer;
a103, acquiring the system in the process of running in the T time to be smaller thanIs recalibrated to +.>，kRepresenting less than->A number of actual data backup frequencies per unit time,k=1、2、3、4、……、N，Nis a positive integer;
a104, the optimal data backup frequency range in unit time when data backup is carried out through the systemAnd actual data backup frequency per unit time of different time periods in T time in system operation processCalculating the frequency floating coefficient of data backupThe expression is:in which, in the process,mrepresenting the total amount of data backup frequency of the actual unit time acquired in the T time in the running process of the system;
the logic for acquiring the abnormal change coefficient of the data storage space is as follows:
b101, obtaining the maximum limit capacity value of the user online management system based on the large language model for data storage, and calibrating the maximum limit capacity value as；
B102, acquiring actual data storage capacity values of different moments in T time in the running process of the user online management system based on the large language model, and calibrating the actual data storage capacity values as，yA number representing the actual data storage capacity value at different times during system operation during time T,y=1、2、3、4、……、n，nis a positive integer;
b103 maximum limit capacity value when data storage is performed by the systemAnd the actual data storage capacity values at different moments in time T during the operation of the system +.>Calculating abnormal change coefficients of the data storage space, wherein the calculated expression is as follows: />；
The second data acquisition module acquires system performance information of a user on-line management system based on a large language model when the user on-line management system runs, and transmits the system performance information to the central processing unit after processing the system performance information;
the system performance information of the user on-line management system based on the large language model comprises a system response time abnormal stability coefficient, and after acquisition, the second data acquisition module marks the system response time abnormal stability coefficient as；
The logic for acquiring the abnormal stability coefficient of the system response time length is as follows:
c101, acquiring a plurality of actual response time durations generated in T time in the running process of a user online management system based on a large language model, and calibrating the actual response time durations as，vA number representing a response time period generated during the system operation in T time,v=1、2、3、4、……、M，Mis a positive integer;
c102, calculating an actual response time length standard deviation generated in the T time in the running process of the system, and calibrating the actual response time length standard deviation asQStandard deviation ofQThe calculation formula of (2) is as follows:wherein->For the average value of the actual response time length generated when the system runs in the T time in the running process, the obtained expression is as follows:；
and C103, outputting an abnormal stability coefficient of the system response time through an actual response time standard deviation, an actual response time average value and a preset actual response time standard deviation reference threshold value and an actual response time average value reference threshold value, which are generated in the T time in the system operation process, wherein the specific output process is as follows:
if actually make a soundThe response time length average value is larger than or equal to the actual response time length average value reference threshold value, the actual response time length standard deviation is smaller than the actual response time length standard deviation reference threshold value, the abnormal stability coefficient of the response time length of the output system,；
if the average value of the actual response time length is larger than or equal to the reference threshold value of the average value of the actual response time length and the standard deviation of the actual response time length is equal to the reference threshold value of the standard deviation of the actual response time length, obtaining the abnormal stability coefficient of the response time length of the system,；
if the average value of the actual response time is smaller than the reference threshold value of the average value of the actual response time and the standard deviation of the actual response time is larger than or equal to the reference threshold value of the standard deviation of the actual response time, obtaining the abnormal stability coefficient of the response time of the system,；
if the average value of the actual response time is smaller than the reference threshold value of the average value of the actual response time and the standard deviation of the actual response time is smaller than the reference threshold value of the standard deviation of the actual response time, obtaining the abnormal stability coefficient of the response time of the system,；
the central processing unit is used for comprehensively analyzing the processed data management information and system performance information when the user online management system based on the large language model runs, generating an abnormality index and transmitting the abnormality index to the comparison module;
the comparison module is used for comparing and analyzing the abnormality index generated during the operation of the user online management system based on the large language model with a preset abnormality index reference threshold value to generate an abnormality signal, transmitting the signal to the prompt module and sending out a warning through the prompt module.
2. The large language model based user on-line management system of claim 1, wherein the central processing unit obtains the data backup frequency floating coefficientAbnormal change coefficient of data storage space>Abnormal stability coefficient of system response time length +.>Then, a data analysis model is built to generate an abnormality index +.>The formula according to is:wherein->、/>、/>Frequency floating coefficient for data backup respectively>Abnormal change coefficient of data storage space>Abnormal stability coefficient of system response time length +.>Is a preset proportionality coefficient of>、/>、/>Are all greater than 0.
3. The large language model-based user online management system according to claim 2, wherein the comparison module compares an abnormality index generated when the large language model-based user online management system is operated with a preset abnormality index reference threshold value, generates a high abnormality signal through the comparison module if the abnormality index is greater than or equal to the abnormality index reference threshold value, transmits the signal to the prompt module, sends a warning through the prompt module, generates a low abnormality signal through the comparison module if the abnormality index is less than the abnormality index reference threshold value, and transmits the signal to the prompt module without sending the warning through the prompt module.
4. The large language model based user online management system of claim 3, further comprising an operation and maintenance management assessment module;
the operation and maintenance management evaluation module is used for comprehensively analyzing a plurality of abnormal index establishment analysis sets output by the central processing unit when the user online management system based on the large language model is in operation and maintenance management, and judging the operation and maintenance management condition of the system;
the operation and maintenance management evaluation module establishes an analysis set for a plurality of abnormal indexes output by a central processing unit during operation and maintenance management of a user online management system based on a large language model, and marks the analysis set asZThen，jA number representing the abnormality index within the analysis set,j=1、2、3、4、……、p，pis a positive integer;
calculating and analyzing a plurality of different types in the collectionStandard deviation and average value of constant indexes, and respectively calibrating the standard deviation and average value of abnormality indexes asAnd->Standard deviation->The calculation formula of (2) is as follows: />Wherein, the method comprises the steps of, wherein,for analyzing the average value of a plurality of abnormal indexes in the collection, the obtained expression is: />；
Comparing the standard deviation of the abnormality index and the average value of the abnormality index with a preset standard deviation reference threshold value of the abnormality index and a preset average value reference threshold value of the abnormality index respectively, and calibrating the standard deviation reference threshold value of the abnormality index and the average value reference threshold value of the abnormality index respectively asAnd->The following cases are generated:
if it isGenerating a signal of failure of system operation and maintenance management through the operation and maintenance management evaluation module, transmitting the signal to a mobile terminal, and prompting an operation and maintenance manager to continue maintenance through the mobile terminal;
if it isGenerating a signal with poor system operation and maintenance management stability through an operation and maintenance management evaluation module, transmitting the signal to a mobile terminal, and prompting an operation and maintenance manager to continue maintenance through the mobile terminal;
if it isAnd generating a signal of successful system operation and maintenance management through the operation and maintenance management evaluation module, transmitting the signal to the mobile terminal, and prompting an operation and maintenance manager that the system operation and maintenance management is successful through the mobile terminal.
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