CN116342099A - Operation and maintenance work assisting method, device, equipment, medium and program product - Google Patents
Operation and maintenance work assisting method, device, equipment, medium and program product Download PDFInfo
- Publication number
- CN116342099A CN116342099A CN202310271301.7A CN202310271301A CN116342099A CN 116342099 A CN116342099 A CN 116342099A CN 202310271301 A CN202310271301 A CN 202310271301A CN 116342099 A CN116342099 A CN 116342099A
- Authority
- CN
- China
- Prior art keywords
- target
- transaction
- transaction amount
- vector
- predicted
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 55
- 239000013598 vector Substances 0.000 claims abstract description 171
- 238000005070 sampling Methods 0.000 claims description 35
- 239000011159 matrix material Substances 0.000 claims description 33
- 238000004590 computer program Methods 0.000 claims description 25
- 238000012986 modification Methods 0.000 claims description 14
- 230000004048 modification Effects 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 13
- 238000005259 measurement Methods 0.000 claims description 5
- 238000011524 similarity measure Methods 0.000 claims 1
- 238000013473 artificial intelligence Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000003993 interaction Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004883 computer application Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/70—Software maintenance or management
- G06F8/71—Version control; Configuration management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Game Theory and Decision Science (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Technology Law (AREA)
- Educational Administration (AREA)
- Computer Security & Cryptography (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an operation and maintenance work assisting method, an operation and maintenance work assisting device, operation and maintenance work assisting equipment, an operation and maintenance work assisting medium and a program product. The invention relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a target transaction code sent by a client, and acquiring first vectors of n time points according to version time of the target transaction code, wherein the target transaction code is a transaction code which is changed by iteration of a system version, and the transaction code is used for identifying a system component and component calling information, and determining transaction amount through the transaction code; traversing the historical transaction amount data according to the first vector to obtain transaction amount similarity; and determining predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, and sending the predicted transaction amount to the client, wherein the client determines a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount, so that the problem that the current time point at which risks possibly occur after the iteration of the version is judged to be online based on human experience is solved.
Description
Technical Field
The embodiment of the invention relates to the technical field of artificial intelligence, in particular to an operation and maintenance work assisting method, an operation and maintenance work assisting device, operation and maintenance work assisting equipment, an operation and maintenance work assisting medium and a program product.
Background
The version of the financial computer application system needs to be changed repeatedly frequently, and some emergency events can be caused after the new version is online. In order to deal with emergency events in time, operation and maintenance personnel need to go to a production machine room to perform system inspection and emergency preparation in advance. The time point of entering the machine room in advance to deal with the emergency event can only be judged through human experience at present. This may ignore some risk points.
Disclosure of Invention
The embodiment of the invention provides an operation and maintenance work assisting method, device, equipment, medium and program product, which are used for solving the problem that the time point at which risk possibly occurs after the iteration of a manually determined version is online is incomplete.
In a first aspect, an embodiment of the present invention provides an operation and maintenance work assisting method, including:
acquiring a target transaction code sent by a client, and acquiring first vectors of n time points according to version time of the target transaction code, wherein the target transaction code is a transaction code which is changed by iteration of a system version, and the transaction code is used for identifying a system component and component calling information, and determining transaction amount through the transaction code;
Traversing the historical transaction amount data according to the first vector to obtain transaction amount similarity;
and determining predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, sending the predicted transaction amount to the client, and determining a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount by the client.
In a second aspect, an embodiment of the present invention provides an operation and maintenance work assisting method, including:
transmitting a target transaction code to a server, determining a predicted transaction amount corresponding to the target transaction code through the server, wherein the target transaction code is a transaction code which is iteratively caused to be changed by a system version, and the transaction code is used for identifying a system component and component calling information, and determining the transaction amount through the transaction code;
acquiring predicted transaction amount sent by the server, and determining a target time point corresponding to a target transaction amount in the predicted transaction amount;
modifying an initial attention degree vector according to the target time point to obtain a target attention degree vector, wherein the initial attention degree vector comprises attention levels of components corresponding to target transaction codes at future set time points;
And determining a attention degree schedule associated with operation and maintenance work according to the target attention degree vector, and displaying the attention degree schedule.
In a third aspect, an embodiment of the present invention further provides an operation and maintenance work assisting device, including:
the system comprises a vector acquisition module, a transaction code generation module and a transaction code generation module, wherein the vector acquisition module is used for acquiring a target transaction code sent by a client, and acquiring first vectors of n time points according to the version time of the target transaction code, wherein the target transaction code is a transaction code which is changed due to system version iteration, and the transaction code is used for identifying a system component and component call information and determining transaction quantity through the transaction code;
the similarity acquisition module is used for traversing the historical transaction amount data according to the first vector to acquire transaction amount similarity;
and the transaction amount determining module is used for determining a predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, sending the predicted transaction amount to the client, and determining a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount through the client.
In a fourth aspect, an embodiment of the present invention further provides an operation and maintenance work auxiliary device, where the device includes:
the transaction code sending module is used for sending a target transaction code to the server, determining a predicted transaction amount corresponding to the target transaction code through the server, wherein the target transaction code is a transaction code which is iteratively caused to be changed by a system version, and the transaction code is used for identifying a system component and component calling information, and determining the transaction amount through the transaction code;
The time point determining module is used for obtaining the predicted transaction amount sent by the server and determining a target time point corresponding to the target transaction amount in the predicted transaction amount;
the vector modification module is used for modifying the initial attention degree vector according to the target time point to obtain a target attention degree vector, wherein the initial attention degree vector comprises attention levels of components corresponding to the target transaction code at the future set time point;
and the schedule determining module is used for determining a concerned degree schedule associated with operation and maintenance work according to the target concerned degree vector and displaying the concerned degree schedule.
In a fifth aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of being executed by the processor, where the processor executes the computer program to implement the operation and maintenance work support method according to any one of the embodiments of the present invention.
In a sixth aspect, an embodiment of the present invention further provides a computer readable storage medium, where a computer program is stored, where the program when executed by a processor implements the operation and maintenance work support method according to any one of the embodiments of the present invention.
In a seventh aspect, embodiments of the present invention further provide a computer program product comprising a computer program which, when executed by a processor, implements an operation and maintenance work assisting method according to any of the embodiments of the present invention.
In the embodiment of the invention, a target transaction code sent by a client is obtained, and first vectors of n time points are obtained according to the version time of the target transaction code, wherein the target transaction code is a transaction code which is changed due to iteration of a system version, and the transaction code is used for identifying a system component and component call information, and the transaction amount is determined through the transaction code; traversing the historical transaction amount data according to the first vector to obtain transaction amount similarity; and determining predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, and sending the predicted transaction amount to the client, wherein the client determines a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount, so that the problem that the current time point at which risks possibly occur after the iteration of the version is judged to be online based on human experience is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an operation and maintenance work assisting method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of three-party interaction according to an embodiment of the present invention;
FIG. 3 is a flow chart of a determination of predicted transaction amount according to an embodiment of the present invention;
FIG. 4 is a flowchart of another operation and maintenance work assisting method according to an embodiment of the present invention;
FIG. 5 is a flow chart illustrating a determination of a attention schedule according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an operation and maintenance auxiliary device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of another operation and maintenance auxiliary device according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance. The technical scheme of the invention obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.
Fig. 1 is a flowchart of an operation and maintenance work assisting method according to an embodiment of the present invention, where the method may be performed by an operation and maintenance work assisting device, and the operation and maintenance work assisting device may be implemented in hardware and/or software, and the operation and maintenance work assisting device may be configured in an electronic device.
As shown in fig. 1, the method includes:
The transaction code is a functional identifier and has a mapping relation with the system component. The system component comprises a method, a class and the like, wherein the transaction code can be used as a key, and the method and the class which are included by the system component can be used as a value. For example, the function of a component, which components are invoked by or which other components may be determined by the transaction code, which may be inherited. The version time is the system version update time. The time point may be a sampling time of the transaction amount, the time point is a time point which is uniformly distributed, and a unit time length is between two adjacent time points.
Fig. 2 is a schematic diagram of three-party interaction according to an embodiment of the present invention. As shown in fig. 2, the user may send the changed target transaction code to the server by inputting the transaction code at the client according to the system version iteration update, system function change, and the like.
The obtaining the first vectors of the n time points according to the version time of the target transaction code includes: acquiring a first transaction amount matrix of n sampling times before the version time of the target transaction code, wherein the transaction amount matrix comprises a transaction code, sampling time and transaction amount; and determining the first vector according to the historical transaction amount in the first transaction code matrix.
Wherein, there is a correspondence between the transaction code, the sampling time and the transaction amount. For example, the transaction amount of each transaction code is periodically collected according to the sampling time to obtain a historical transaction amount matrix.
FIG. 3 is a flow chart of a determination of predicted transaction amount according to an embodiment of the present invention. As shown in fig. 3, n sampling times and corresponding transaction amounts may be obtained from a historical transaction amount matrix based on a version time of a certain transaction code, for example, n sampling times and transaction amounts before the version time of the target transaction amount c are selected in the historical transaction amount matrix, and the first transaction amount matrix is determined. For example, if the current version time is m, n=100, the first transaction amount matrix may be expressed as follows:
Where c represents a transaction code, t represents a sampling time, and y represents a transaction amount.
Determining the first vector based on the historical transaction amount in the first transaction code matrix, the first vector may be:
Traversing the historical transaction amount data according to the first vector to obtain transaction amount similarity, wherein the traversing comprises the following steps: acquiring a second transaction amount matrix of the target transaction code before the version time; under the condition that the number of sampling time which is not traversed in the second transaction amount matrix is larger than n, acquiring historical transaction amounts of n sampling times in the second transaction amount matrix according to the sampling time sequence to obtain a second vector; and for each second vector, carrying out similarity measurement on the first vector and the second vector to obtain transaction amount similarity.
The second transaction amount matrix is a matrix formed by sampling time and transaction amount corresponding to the target transaction code in the historical transaction amount matrix, and the sampling time contained in the second transaction amount matrix can be a time point before the version time of the target transaction code. For example, assuming that the current version time of the target transaction code is 1000 time points before the current version time, the time points in the first transaction amount matrix are 100 time points before the current version time, and the sequence numbers 901 to 1000, the sequence numbers of the sampling times in the second transaction amount matrix may be 1 to 1000.
As shown in fig. 3, the second transaction amount matrix may be expressed as:
wherein c represents a transaction code, t represents sampling time, x represents transaction amount, and N > N.
Taking the sampling time i in a certain second transaction amount matrix as a starting time point, and selecting the transaction amounts of n time points as second vectors, wherein the transaction amount in the second vectors is the same as the transaction amount in the first vectors. Assuming n=100, the second vector can be expressed in the form:
and obtaining a plurality of second vectors based on the sampling time sequence of the second transaction amount matrix, and carrying out similarity measurement on the second vectors according to the first vector for each second vector to obtain transaction amount similarity.
For each second vector, performing similarity measurement on the first vector and the second vector to obtain transaction amount similarity, including: calculating Euclidean distance for each second vector, and taking the Euclidean distance as an alternative transaction amount similarity for the first vector and the second vector; and comparing the alternative transaction amount similarity to obtain the transaction amount similarity.
As shown in fig. 3, based on the above examples of the first vector and the second vector, the euclidean distance can be calculated by the following formula:
The smaller the euclidean distance, the higher the similarity of the two vectors. The Euclidean distance is used as the alternative transaction amount similarity. Obtaining a plurality of candidate transaction amount similarity according to the Euclidean distance between the second vectors and the first vectors, and obtaining the minimum candidate transaction amount similarity D (j) As transaction amount similarity.
And 130, determining a predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, sending the predicted transaction amount to the client, and determining a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount by the client.
The determining a predicted transaction amount according to the transaction amount similarity and the historical transaction amount data comprises: determining target sampling time according to the transaction amount similarity; and acquiring n target historical transaction amounts from the historical transaction amount data according to the target sampling time, and taking the target historical transaction amounts as predicted transaction amounts.
Wherein the target sampling time is a starting time point of the second vector most similar to the first vector.
Specifically, if the transaction amount similarity is D (j) And if the target sampling time is j, the corresponding second vector is composed of n transaction amounts taking j as the starting time, and n historical transaction amounts taking j+n as the starting time are taken as predicted transaction amounts. As shown in FIG. 3, when n is 100, the vector of the predicted transaction amount is
As shown in fig. 2, the server returns the predicted transaction amount to the client after obtaining the predicted transaction amount, and the client determines the attention degree schedule associated with the operation and maintenance work according to the predicted transaction amount and displays the attention degree schedule to the user. The method for determining the attention level schedule is described in the following examples, and is not described in detail herein.
According to the embodiment of the invention, the target transaction code sent by the client is obtained, and the first vectors of n time points are obtained according to the version time of the target transaction code, wherein the target transaction code is a transaction code which is changed due to iteration of a system version, and the transaction code is used for identifying a system component and component call information, and the transaction quantity is determined through the transaction code; traversing the historical transaction amount data according to the first vector to obtain transaction amount similarity; and determining predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, sending the predicted transaction amount to the client, and determining a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount by the client. According to the technical scheme, the transaction amount can be automatically predicted based on the transaction code, and the attention degree schedule related to operation and maintenance work is determined based on the predicted transaction amount, so that the problem that the time point at which risks possibly occur after the iteration of the version is judged on line based on human experience is solved.
In a specific embodiment, fig. 4 is a flowchart of another operation and maintenance work assisting method according to an embodiment of the present invention. The present embodiment refines the step of determining the attention degree schedule associated with the operation and maintenance work based on the predicted transaction amount on the basis of the above embodiment. As shown in fig. 4, the operation and maintenance work assisting method includes:
FIG. 5 is a flow chart of a determination of a attention level schedule according to an embodiment of the present invention. As shown in fig. 5, the predicted transaction amount corresponding to the target transaction code is obtained on the basis of the above-described embodiment.
The client obtains the target transaction code which is input by the user and is changed by updating the system version, and sends the target transaction code to the server. The method for determining the predicted transaction amount based on the target transaction code by the server is described in the above embodiment, and will not be described here.
The target transaction amount may be a transaction amount of which a value in the predicted transaction amount satisfies a set condition, including a target non-zero transaction amount and a second predicted transaction amount. The target time point may be a time point corresponding to the target transaction amount, including a first target time point and a second target time point.
The determining the target time point corresponding to the target transaction amount in the predicted transaction amounts comprises the following steps: acquiring a first target time point corresponding to a target non-zero transaction amount in the predicted transaction amount; the method comprises the steps of obtaining the median of non-zero transaction amounts in the predicted transaction amounts, obtaining a first predicted transaction amount in the predicted transaction amounts according to the median, determining a second predicted transaction amount in the predicted transaction amounts according to the average value of the first predicted transaction amounts, and obtaining a second target time point corresponding to the second predicted transaction amount.
Wherein the first predicted transaction amount is a median transaction amount having a transaction amount greater than the non-zero transaction amount. The second predicted transaction amount is a transaction amount having a transaction amount greater than a mean of the first predicted transaction amounts.
The obtaining a first predicted transaction amount of the predicted transaction amounts according to the median, and determining a second predicted transaction amount of the predicted transaction amounts according to a mean value of the first predicted transaction amounts, includes: acquiring a first predicted transaction amount greater than the median in the predicted transaction amounts, and determining a mean value of the first predicted transaction amount; and acquiring a second predicted transaction amount greater than the average value in the predicted transaction amounts.
After a second target time point corresponding to the second predicted transaction amount is obtained, determining a target time point corresponding to the target transaction amount according to the first target time point and the second target time point.
Specifically, the first target time point and the second target time point are determined as target time points.
And step 230, modifying the initial attention degree vector according to the target time point to obtain a target attention degree vector, wherein the initial attention degree vector comprises attention levels of components corresponding to the target transaction code at future set time points.
The step of modifying the initial attention degree vector according to the target time point to obtain a target attention degree vector comprises the following steps: and under the condition that the number of the target transaction codes is equal to 1, modifying an initial attention level corresponding to the target time point in an initial attention level vector to obtain a target attention level vector, wherein the attention level of each future time point in the initial attention level vector is an initial attention level, and the initial attention level is a low attention level.
Specifically, when the transaction code input by the user is one, the initial attention level of each future time point may be set to a low attention level, for example, a value of 1. And then modifying the initial attention level of the first target time point and the second target time point, and taking the initial attention level vector after the modification of the initial attention level as a target attention level vector. As shown in fig. 5, the target attention vector may be
The step of modifying the initial attention degree vector according to the target time point to obtain a target attention degree vector comprises the following steps: when the number of the target transaction codes is greater than 1, for each target transaction code, modifying an initial attention level corresponding to the target time point in an initial attention level vector to obtain an alternative attention level vector, wherein the attention level of each future time point in the initial attention level vector is an initial attention level, and the initial attention level is a low attention level; and comparing the attention levels of all time points in the alternative attention degree vectors, and determining a target attention degree vector according to the comparison result.
Specifically, when the transaction code input by the user is plural, the initial attention level of each future point in time may be set to a low attention level, for example, a value of 1. And then modifying the initial attention level of the first target time point and the second target time point, and taking the initial attention level vector after the modification of the initial attention level as an alternative attention level vector.
The modifying the initial attention level corresponding to the target time point in the initial attention level vector comprises the following steps: for the initial attention degree vector, modifying the initial attention level corresponding to the first target time point to be a high attention level, modifying the initial attention level corresponding to the minimum second target time point to be a high attention level, and modifying the initial attention level corresponding to the rest second target time points to be a medium attention level.
Specifically, as shown in fig. 5, the initial attention level at the first target time point is modified to a high attention level, for example, assigned 3. The initial focus level at the smallest of the second target time points is modified to a high focus level, e.g., assigned 3, and the initial focus levels at the remaining time points in the second target time points are modified to medium focus levels, e.g., assigned 2.
The comparing the attention level of each time point in the alternative attention level vector, and determining the target attention level vector according to the comparison result comprises the following steps: comparing the attention levels of the same time points in the alternative attention degree vectors; and determining a target attention degree vector according to the maximum attention level of each time point.
Specifically, since the transaction code input by the user is plural, there may be cases where there is more than one attention level at the same time point. If there are two or more different attention levels at the same time point, the highest attention level is set as the attention level at that time point. As shown in FIG. 5, the target attention vector may be expressed as
And 240, determining a attention degree schedule associated with operation and maintenance work according to the target attention degree vector, and displaying the attention degree schedule.
The attention degree schedule comprises attention degrees corresponding to time points in the target attention degree vector.
Specifically, the attention degree of each time point is displayed to the user according to the attention grade assignment of the time and the time point in the target attention degree vector.
Transmitting a target transaction code to a server, determining a predicted transaction amount corresponding to the target transaction code through the server, wherein the target transaction code is a transaction code which is iteratively caused to be changed by a system version, and the transaction code is used for identifying a system component and component calling information, and determining the transaction amount through the transaction code;
according to the embodiment of the invention, the target time point corresponding to the target transaction amount in the predicted transaction amount is determined by acquiring the predicted transaction amount sent by the server; modifying an initial attention degree vector according to the target time point to obtain a target attention degree vector, wherein the initial attention degree vector comprises attention levels of components corresponding to target transaction codes at future set time points; and determining a attention degree schedule associated with operation and maintenance work according to the target attention degree vector, and displaying the attention degree schedule. According to the method and the device, the attention degree schedule related to operation and maintenance work is automatically determined based on the predicted transaction amount determined by the server, and the problem that the time point at which risks possibly occur after the iteration of the version is judged to be online based on human experience is solved.
Fig. 6 is a schematic structural diagram of an operation and maintenance auxiliary device according to an embodiment of the present invention. As shown in fig. 6, the apparatus includes:
the vector obtaining module 310 is configured to obtain a target transaction code sent by a client, and obtain first vectors of n time points according to version time of the target transaction code, where the target transaction code is a transaction code that is changed by iteration of a system version, and the transaction code is used to identify a system component and component call information, and determine a transaction amount according to the transaction code;
a similarity obtaining module 320, configured to traverse the historical transaction amount data according to the first vector, to obtain a transaction amount similarity;
the transaction amount determining module 330 is configured to determine a predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, send the predicted transaction amount to the client, and determine, by the client, a degree of attention schedule associated with the operation and maintenance work according to the predicted transaction amount.
According to the embodiment of the invention, the target transaction code sent by the client is obtained, and the first vectors of n time points are obtained according to the version time of the target transaction code, wherein the target transaction code is a transaction code which is changed due to iteration of a system version, and the transaction code is used for identifying a system component and component call information, and the transaction quantity is determined through the transaction code; traversing the historical transaction amount data according to the first vector to obtain transaction amount similarity; and determining predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, sending the predicted transaction amount to the client, and determining a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount by the client. According to the technical scheme, the transaction amount can be automatically predicted based on the transaction code, and the attention degree schedule related to operation and maintenance work is determined based on the predicted transaction amount, so that the problem that the time point at which risks possibly occur after the iteration of the version is judged on line based on human experience is solved.
Optionally, the vector obtaining module 310 includes:
the first acquisition sub-module is used for acquiring a first transaction amount matrix of N sampling times before the version time of the target transaction code, wherein the transaction amount matrix comprises a transaction code, sampling time and transaction amount;
and the determining submodule is used for determining the first vector according to the historical transaction amount in the first transaction code matrix.
Optionally, the similarity obtaining module 320 includes:
a second obtaining sub-module, configured to obtain a second transaction amount matrix of the target transaction code before the version time;
a third obtaining sub-module, configured to obtain, according to a sampling time sequence, historical transaction amounts of N sampling times in the second transaction amount matrix, to obtain a second vector, where the number of sampling times that have not been traversed in the second transaction amount matrix is greater than N;
and the transaction amount similarity determination submodule is used for carrying out similarity measurement on the first vector and the second vector for each second vector to obtain transaction amount similarity.
Optionally, the transaction amount similarity determining submodule is specifically configured to calculate, for each second vector, a euclidean distance between the first vector and the second vector, and take the euclidean distance as an alternative transaction amount similarity; and comparing the alternative transaction amount similarity to obtain the transaction amount similarity.
Optionally, the transaction amount determining module 330 is specifically configured to determine a target sampling time according to the transaction amount similarity; and acquiring N target historical transaction amounts from the historical transaction amount data according to the target sampling time, and taking the target historical transaction amounts as predicted transaction amounts.
Fig. 7 is a schematic structural diagram of another operation and maintenance auxiliary device according to an embodiment of the present invention. As shown in fig. 7, the apparatus includes:
the transaction code sending module 410 is configured to send a target transaction code to a server, and determine a predicted transaction amount corresponding to the target transaction code through the server, where the target transaction code is a transaction code that is iteratively caused to change by a system version, and the transaction code is used to identify a system component and component call information, and determine the transaction amount through the transaction code;
a time point determining module 420, configured to obtain a predicted transaction amount sent by the server, and determine a target time point corresponding to a target transaction amount in the predicted transaction amount;
the vector modification module 430 is configured to modify an initial attention degree vector according to the target time point to obtain a target attention degree vector, where the initial attention degree vector includes attention levels of components corresponding to the target transaction code at a set time point in the future;
The schedule determining module 440 is configured to determine a schedule of attention associated with the operation and maintenance according to the target attention vector, and display the schedule of attention.
According to the embodiment of the invention, the target time point corresponding to the target transaction amount in the predicted transaction amount is determined by acquiring the predicted transaction amount sent by the server; modifying an initial attention degree vector according to the target time point to obtain a target attention degree vector, wherein the initial attention degree vector comprises attention levels of components corresponding to target transaction codes at future set time points; and determining a attention degree schedule associated with operation and maintenance work according to the target attention degree vector, and displaying the attention degree schedule. According to the method and the device, the attention degree schedule related to operation and maintenance work is automatically determined based on the predicted transaction amount determined by the server, and the problem that the time point at which risks possibly occur after the iteration of the version is judged to be online based on human experience is solved.
Optionally, the time point determining module 420 includes:
the first target time point acquisition sub-module is used for acquiring a first target time point corresponding to a target non-zero transaction amount in the predicted transaction amount;
A second target time point obtaining sub-module, configured to obtain a median of non-zero transaction amounts in the predicted transaction amounts, obtain a first predicted transaction amount in the predicted transaction amounts according to the median, determine a second predicted transaction amount in the predicted transaction amounts according to a mean value of the first predicted transaction amounts, and obtain a second target time point corresponding to the second predicted transaction amount;
and the target time point determining submodule is used for determining a target time point corresponding to the target transaction amount according to the first target time point and the second target time point.
Optionally, the second target time point obtaining sub-module is specifically configured to:
acquiring a first predicted transaction amount greater than the median in the predicted transaction amounts, and determining a mean value of the first predicted transaction amount;
and acquiring a second predicted transaction amount greater than the average value in the predicted transaction amounts.
Optionally, the vector modification module includes:
and the first modification submodule is used for modifying the initial attention level corresponding to the target time point in the initial attention level vector to obtain the target attention level vector under the condition that the number of the target transaction codes is equal to 1, wherein the attention level of each future time point in the initial attention level vector is an initial attention level, and the initial attention level is a low attention level.
Optionally, the vector modification module 430 includes:
the second modification submodule is used for modifying the initial attention level corresponding to the target time point in the initial attention level vector for each target transaction code under the condition that the number of the target transaction codes is larger than 1 to obtain an alternative attention level vector, wherein the attention level of each future time point in the initial attention level vector is an initial attention level, and the initial attention level is a low attention level; and comparing the attention levels of all time points in the alternative attention degree vectors, and determining a target attention degree vector according to the comparison result.
Optionally, the first modification sub-module and the second modification sub-module include:
the modifying unit is used for modifying the initial attention level corresponding to the first target time point into a high attention level, modifying the initial attention level corresponding to the smallest second target time point into a high attention level and modifying the initial attention level corresponding to the rest second target time points into a medium attention level for the initial attention level vector.
Optionally, the second modification sub-module includes:
a comparison unit for comparing attention levels of the same time points in the alternative attention degree vectors;
And the determining unit is used for determining the target attention degree vector according to the maximum attention level of each time point.
The operation and maintenance work auxiliary device provided by the embodiment of the invention can execute the steps executed by the operation and maintenance work auxiliary method provided by the embodiment of the method of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 8, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the operation and maintenance work assistance method.
In some embodiments, the operation and maintenance work support method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above-described operation and maintenance work assistance method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the operation and maintenance work assistance method in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (17)
1. An operation and maintenance work assisting method, comprising:
acquiring a target transaction code sent by a client, and acquiring first vectors of n time points according to version time of the target transaction code, wherein the target transaction code is a transaction code which is changed by iteration of a system version, and the transaction code is used for identifying a system component and component calling information, and determining transaction amount through the transaction code;
traversing the historical transaction amount data according to the first vector to obtain transaction amount similarity;
And determining predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, sending the predicted transaction amount to the client, and determining a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount by the client.
2. The method of claim 1, wherein the obtaining the first vectors for the n time points according to the version time of the target transaction code comprises:
acquiring a first transaction amount matrix of n sampling times before the version time of the target transaction code, wherein the transaction amount matrix comprises a transaction code, sampling time and transaction amount;
and determining the first vector according to the historical transaction amount in the first transaction code matrix.
3. The method of claim 1, wherein traversing historical transaction amount data according to the first vector to obtain transaction amount similarity comprises:
acquiring a second transaction amount matrix of the target transaction code before the version time;
under the condition that the number of sampling time which is not traversed in the first transaction amount matrix is larger than n, acquiring historical transaction amounts of n sampling times in the second transaction amount matrix according to the sampling time sequence to obtain a second vector;
And for each second vector, carrying out similarity measurement on the first vector and the second vector to obtain transaction amount similarity.
4. A method according to claim 3, wherein for each second vector, the similarity measure is performed on the first vector and the second vector to obtain a transaction amount similarity, including:
calculating Euclidean distance for each second vector, and taking the Euclidean distance as an alternative transaction amount similarity for the first vector and the second vector;
and comparing the alternative transaction amount similarity to obtain the transaction amount similarity.
5. The method of claim 1, wherein said determining a predicted transaction amount based on said transaction amount similarity and historical transaction amount data comprises:
determining target sampling time according to the transaction amount similarity;
and acquiring n target historical transaction amounts from the historical transaction amount data according to the target sampling time, and taking the target historical transaction amounts as predicted transaction amounts.
6. An operation and maintenance work assisting method, comprising:
transmitting a target transaction code to a server, determining a predicted transaction amount corresponding to the target transaction code through the server, wherein the target transaction code is a transaction code which is iteratively caused to be changed by a system version, and the transaction code is used for identifying a system component and component calling information, and determining the transaction amount through the transaction code;
Acquiring predicted transaction amount sent by the server, and determining a target time point corresponding to a target transaction amount in the predicted transaction amount;
modifying an initial attention degree vector according to the target time point to obtain a target attention degree vector, wherein the initial attention degree vector comprises attention levels of components corresponding to target transaction codes at future set time points;
and determining a attention degree schedule associated with operation and maintenance work according to the target attention degree vector, and displaying the attention degree schedule.
7. The method of claim 6, wherein the determining a target point in time for a target transaction amount of the predicted transaction amounts comprises:
acquiring a first target time point corresponding to a target non-zero transaction amount in the predicted transaction amount;
acquiring the median of non-zero transaction amounts in the predicted transaction amounts, acquiring a first predicted transaction amount in the predicted transaction amounts according to the median, determining a second predicted transaction amount in the predicted transaction amounts according to the average value of the first predicted transaction amounts, and acquiring a second target time point corresponding to the second predicted transaction amount;
and determining a target time point corresponding to the target transaction amount according to the first target time point and the second target time point.
8. The method of claim 7, wherein the obtaining a first one of the predicted transaction amounts from the median, determining a second one of the predicted transaction amounts from a mean of the first predicted transaction amounts, comprises:
acquiring a first predicted transaction amount greater than the median in the predicted transaction amounts, and determining a mean value of the first predicted transaction amount;
and acquiring a second predicted transaction amount greater than the average value in the predicted transaction amounts.
9. The method of claim 7, wherein modifying the initial attention vector based on the target point in time results in a target attention vector, comprising:
and under the condition that the number of the target transaction codes is equal to 1, modifying an initial attention level corresponding to the target time point in an initial attention level vector to obtain a target attention level vector, wherein the attention level of each future time point in the initial attention level vector is an initial attention level, and the initial attention level is a low attention level.
10. The method of claim 7, wherein modifying the initial attention vector based on the target point in time results in a target attention vector, comprising:
When the number of the target transaction codes is greater than 1, for each target transaction code, modifying an initial attention level corresponding to the target time point in an initial attention level vector to obtain an alternative attention level vector, wherein the attention level of each future time point in the initial attention level vector is an initial attention level, and the initial attention level is a low attention level;
and comparing the attention levels of all time points in the alternative attention degree vectors, and determining a target attention degree vector according to the comparison result.
11. The method according to claim 9 or 10, wherein modifying the initial attention level in the initial attention level vector corresponding to the target point in time comprises:
for the initial attention degree vector, modifying the initial attention level corresponding to the first target time point to be a high attention level, modifying the initial attention level corresponding to the minimum second target time point to be a high attention level, and modifying the initial attention level corresponding to the rest second target time points to be a medium attention level.
12. The method of claim 10, wherein comparing the attention levels of each time point in the candidate attention vector, and determining the target attention vector according to the comparison result, comprises:
Comparing the attention levels of the same time points in the alternative attention degree vectors;
and determining a target attention degree vector according to the maximum attention level of each time point.
13. An operation and maintenance work auxiliary device, characterized by comprising:
the system comprises a vector acquisition module, a transaction code generation module and a transaction code generation module, wherein the vector acquisition module is used for acquiring a target transaction code sent by a client, and acquiring first vectors of n time points according to the version time of the target transaction code, wherein the target transaction code is a transaction code which is changed due to system version iteration, and the transaction code is used for identifying a system component and component call information and determining transaction quantity through the transaction code;
the similarity acquisition module is used for traversing the historical transaction amount data according to the first vector to acquire transaction amount similarity;
and the transaction amount determining module is used for determining a predicted transaction amount according to the transaction amount similarity and the historical transaction amount data, sending the predicted transaction amount to the client, and determining a concerned degree schedule associated with operation and maintenance work according to the predicted transaction amount through the client.
14. An operation and maintenance work auxiliary device, characterized by comprising:
the transaction code sending module is used for sending a target transaction code to the server, determining a predicted transaction amount corresponding to the target transaction code through the server, wherein the target transaction code is a transaction code which is iteratively caused to be changed by a system version, and the transaction code is used for identifying a system component and component calling information, and determining the transaction amount through the transaction code;
The time point determining module is used for obtaining the predicted transaction amount sent by the server and determining a target time point corresponding to the target transaction amount in the predicted transaction amount;
the vector modification module is used for modifying the initial attention degree vector according to the target time point to obtain a target attention degree vector, wherein the initial attention degree vector comprises attention levels of components corresponding to the target transaction code at the future set time point;
and the schedule determining module is used for determining a concerned degree schedule associated with operation and maintenance work according to the target concerned degree vector and displaying the concerned degree schedule.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, wherein the processor implements the operation and maintenance work support method according to any one of claims 1-12 when executing the computer program.
16. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the operation and maintenance work support method according to any of claims 1-12.
17. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the operation and maintenance work support method according to any of claims 1-12.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310271301.7A CN116342099A (en) | 2023-03-17 | 2023-03-17 | Operation and maintenance work assisting method, device, equipment, medium and program product |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310271301.7A CN116342099A (en) | 2023-03-17 | 2023-03-17 | Operation and maintenance work assisting method, device, equipment, medium and program product |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116342099A true CN116342099A (en) | 2023-06-27 |
Family
ID=86888946
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310271301.7A Pending CN116342099A (en) | 2023-03-17 | 2023-03-17 | Operation and maintenance work assisting method, device, equipment, medium and program product |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116342099A (en) |
-
2023
- 2023-03-17 CN CN202310271301.7A patent/CN116342099A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113095336B (en) | Method for training key point detection model and method for detecting key points of target object | |
| CN113205037B (en) | Event detection method, event detection device, electronic equipment and readable storage medium | |
| CN113657289B (en) | Training method and device of threshold estimation model and electronic equipment | |
| CN114218931A (en) | Information extraction method and device, electronic equipment and readable storage medium | |
| CN115691206B (en) | Parking stall recommendation method, device, equipment and storage medium | |
| CN116342099A (en) | Operation and maintenance work assisting method, device, equipment, medium and program product | |
| CN113761379B (en) | Commodity recommendation method and device, electronic equipment and medium | |
| CN113313049A (en) | Method, device, equipment, storage medium and computer program product for determining hyper-parameters | |
| CN113713378B (en) | Content generation method and device | |
| CN115168727B (en) | User habit mining method and device and electronic equipment | |
| CN113360258B (en) | Data processing method, device, electronic equipment and storage medium | |
| CN114648672A (en) | Method and device for constructing sample image set, electronic equipment and readable storage medium | |
| CN115131453A (en) | Color filling model training method, color filling device, and electronic equipment | |
| CN115934987A (en) | Sample text generation method and device and electronic equipment | |
| CN117539602A (en) | Method and device for task speculation behind, electronic equipment and storage medium | |
| CN116597209A (en) | Image classification method, device, equipment and storage medium | |
| CN117668113A (en) | Data synchronization method, device, equipment and medium | |
| CN115437955A (en) | Project testing method, device, equipment and storage medium | |
| CN117668192A (en) | Data processing method, device, equipment and storage medium | |
| CN113713378A (en) | Content generation method and device | |
| CN116502841A (en) | Event processing method and device, electronic equipment and medium | |
| CN114898060A (en) | Method, apparatus, device, medium and product for processing data | |
| CN115563182A (en) | Data processing method, device, electronic equipment and medium | |
| CN117041182A (en) | Main server node selection method, device, equipment and medium of distributed component | |
| CN117495285A (en) | Approval method, device, electronic equipment and storage medium for applying for event |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |