CN117009416A - Parameter maintenance method, device, equipment and medium - Google Patents
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Abstract
The application discloses a parameter maintenance method, device, equipment and medium, and relates to the field of big data or finance. The method comprises the following steps: acquiring an intra-table relationship, an inter-table relationship, a first parameter value and a second parameter value of the parameter; if the first parameter value is the same as the second parameter value, setting the parameter value of the parameter corresponding to the first parameter value and the second parameter value as a fixed value; if the first parameter value is different from the second parameter value, performing a supervised learning algorithm on the intra-table relationship and the inter-table relationship to construct a parameter recommendation model; inputting the target parameters into the parameter recommendation model to obtain target parameter values, wherein the target parameters are parameters which need to be subjected to parameter maintenance. Therefore, when parameter maintenance personnel maintain parameters, constant parameters which do not change along with time are set to be fixed values, target parameter values are automatically given to target parameters with intra-table relations and inter-table relations, so that parameter risk management capability is improved, business management efficiency is improved, and operation risks are reduced.
Description
Technical Field
The present application relates to the field of big data or finance, and in particular, to a method, an apparatus, a device, and a medium for maintaining parameters.
Background
The parameterized design is an important means for the bank system to tamp safe production operation in order to rapidly meet the personalized demands of customers. The aspects of business innovation, business operation, technical management and the like all need to scientifically and reasonably design and centrally and uniformly manage and control parameters in a bank system, and global sharing and multiplexing of the parameters are realized, so that the management efficiency is improved and the operation risk is reduced.
In the related art, when a related technician selects a parameter table from a parameter management module of a banking system for maintenance, on one hand, because parameter values set by certain types of parameters are constant, if the related technician repeatedly maintains the parameters with the constant parameter values, the working efficiency is reduced; on the other hand, the related technicians set special parameter values for certain parameters, and the corresponding relation between the parameters and the special parameter values needs to be recorded, so that the working efficiency is reduced, and meanwhile, the problem of service risk improvement caused by recording errors is easy to occur.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method, an apparatus, a device, and a medium for maintaining parameters, which can improve service management efficiency and reduce operation risk.
The embodiment of the application discloses the following technical scheme:
in a first aspect, the present application provides a method for maintaining parameters, the method comprising:
acquiring an intra-table relation, an inter-table relation, a first parameter value and a second parameter value of a parameter, wherein the first parameter value and the second parameter value are values of the same parameter at different times;
if the first parameter value is the same as the second parameter value, setting the same parameter as a fixed value;
if the first parameter value is different from the second parameter value, performing a supervised learning algorithm on the intra-table relationship and the inter-table relationship to construct a parameter recommendation model;
and inputting the target parameters into the parameter recommendation model to obtain target parameter values, wherein the target parameters are parameters which need to be subjected to parameter maintenance.
Optionally, the performing a supervised learning algorithm on the intra-table relationships and the inter-table relationships to construct a parameter recommendation model includes:
acquiring a training data set, wherein the training data set comprises a first parameter value of training data, a second parameter value of training data, an intra-table relationship of the training data and an inter-table relationship of the training data;
taking the intra-table relation of the training data and the inter-table relation of the training data as inputs, taking the first parameter value of the training data and the second parameter value of the training data as outputs, and training a neural network model according to a supervised learning algorithm to obtain a parameter recommendation model.
Optionally, the method further comprises:
acquiring a test data set, wherein the test data set comprises a first parameter value of test data, a second parameter value of test data, an intra-table relationship of the test data and an inter-table relationship of the test data;
inputting the intra-table relationship of the test data and the inter-table relationship of the test data into the parameter recommendation model to output evaluation maintenance data;
comparing the evaluation maintenance data, the first parameter value of the test data and the second parameter value of the test data to obtain a credibility result;
and optimizing the parameter recommendation model based on the loss function according to the credibility result to obtain an optimized parameter recommendation model.
Optionally, the intra-table relationships include one or more of dependencies, mutual exclusion relationships, and combination relationships.
Optionally, the inter-table relationships include one or more of dependencies, references, alignments, and operational relationships.
In a second aspect, the present application provides a parameter maintenance apparatus, the apparatus comprising: the device comprises an acquisition module, a setting module, a construction module and a maintenance module;
the acquisition module is used for acquiring the intra-table relation, inter-table relation, first parameter values and second parameter values of the parameters, wherein the first parameter values and the second parameter values are values of the same parameter at different times;
the setting module is configured to set the same parameter as a fixed value if the first parameter value is the same as the second parameter value;
the construction module is used for executing a supervised learning algorithm on the table relationships and the inter-table relationships if the first parameter value is different from the second parameter value so as to construct a parameter recommendation model;
the maintenance module is configured to input a target parameter into the parameter recommendation model to obtain a target parameter value, where the target parameter is a parameter that needs to be subjected to parameter maintenance.
Optionally, the intra-table relationships include one or more of dependencies, mutual exclusion relationships, and combination relationships.
Optionally, the inter-table relationships include one or more of dependencies, references, alignments, and operational relationships.
Optionally, the parameters include one or more of parameter table number, parameter table chinese name, parameter table english name, parameter table classification, parameter chinese name, parameter english name, parameter value category, parameter value range, parameter value and parameter table record maintenance time.
In a third aspect, the present application provides a parameter maintenance apparatus comprising: a memory and a processor;
the memory is used for storing programs;
the processor is configured to implement the steps of the parameter maintenance method when executing the computer program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described parameter maintenance method.
Compared with the prior art, the application has the following beneficial effects:
the application discloses a parameter maintenance method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring an intra-table relationship, an inter-table relationship, a first parameter value and a second parameter value of the parameter; if the first parameter value is the same as the second parameter value, setting the parameter value of the parameter corresponding to the first parameter value and the second parameter value as a fixed value; if the first parameter value is different from the second parameter value, performing a supervised learning algorithm on the intra-table relationship and the inter-table relationship to construct a parameter recommendation model; inputting the target parameters into the parameter recommendation model to obtain target parameter values, wherein the target parameters are parameters which need to be subjected to parameter maintenance. Therefore, when parameter maintenance personnel maintain parameters, constant parameters which do not change along with time are set to be fixed values, target parameter values are automatically given to target parameters with intra-table relations and inter-table relations, so that parameter risk management capability is improved, business management efficiency is improved, and operation risks are reduced.
Drawings
In order to more clearly illustrate this embodiment or the technical solutions of the prior art, the drawings that are required for the description of the embodiment or the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for maintaining parameters according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a parameter maintenance apparatus according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a computer readable medium according to an embodiment of the present application;
fig. 4 is a schematic hardware structure of a server according to an embodiment of the present application.
Detailed Description
The parameter maintenance method, the device, the equipment and the medium provided by the application can be used in the big data field or the financial field. The foregoing is merely exemplary, and the application fields of the parameter maintenance method, apparatus, device and medium provided by the present application are not limited.
The parameterized design is an important means for the bank system to tamp safe production operation in order to rapidly meet the personalized demands of customers. The aspects of business innovation, business operation, technical management and the like all need to scientifically and reasonably design and centrally and uniformly manage and control parameters in a bank system, and global sharing and multiplexing of the parameters are realized, so that the management efficiency is improved and the operation risk is reduced.
In the related art, when a related technician selects a parameter table from a parameter management module of a banking system for maintenance, on one hand, because parameter values set by certain types of parameters are constant, if the related technician repeatedly maintains the parameters with the constant parameter values, the working efficiency is reduced; on the other hand, the related technicians set special parameter values for certain parameters, and the corresponding relation between the parameters and the special parameter values needs to be recorded, so that the working efficiency is reduced, and meanwhile, the problem of service risk improvement caused by recording errors is easy to occur.
In view of this, the present application provides a parameter maintenance method, apparatus, device and medium, the method includes: acquiring an intra-table relationship, an inter-table relationship, a first parameter value and a second parameter value of the parameter; if the first parameter value is the same as the second parameter value, setting the parameter value of the parameter corresponding to the first parameter value and the second parameter value as a fixed value; if the first parameter value is different from the second parameter value, performing a supervised learning algorithm on the intra-table relationship and the inter-table relationship to construct a parameter recommendation model; inputting the target parameters into the parameter recommendation model to obtain target parameter values, wherein the target parameters are parameters which need to be subjected to parameter maintenance. Therefore, when parameter maintenance personnel maintain parameters, constant parameters which do not change along with time are set to be fixed values, target parameter values are automatically given to target parameters with intra-table relations and inter-table relations, so that parameter risk management capability is improved, business management efficiency is improved, and operation risks are reduced.
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the flowchart of a parameter maintenance method according to an embodiment of the present application is shown. The method comprises the following steps:
s101: and acquiring the intra-table relation and the inter-table relation of the parameters.
The parameters refer to the data which plays a role in controlling business or technical processing logic in a banking system are designed and developed in advance to meet the requirement of rapid and flexible change, and the formed data can be flexibly configured, maintained and rapidly released to take effect.
When designing the parameter table, the intra-table relationship and the inter-table relationship of the determined parameters are realized according to the service requirements and the technology. In particular, the parameter table refers to a set of one or more parameters formed according to the need of flexible configuration and management, and is a way and means for managing the parameters. The parameter table is a carrier of parameters but not a real table structure in a banking system. One parameter exists in at least one parameter table, and one parameter table manages at least one parameter. The intra-table relationship refers to the association relationship of parameters in one parameter table, and the inter-table relationship refers to the association relationship of parameters among a plurality of parameter tables.
By way of example, the intra-table relationships may include one or more of dependencies, mutual exclusion relationships, and combination relationships.
Specifically, the dependency relationship means that linkage exists among parameters in a parameter table, and the input parameter values influence other parameter values; the mutual exclusion relation refers to that a plurality of parameters in the parameter table are not allowed to exist for value simultaneously; the combination relation refers to that a plurality of parameters in the parameter table form a specific combination to play a role in control together.
In some specific implementations, the dependency relationship may refer to: two parameters are included in a parameter table, parameter 1 is a calendar table applicable to the product, and parameter 2 is a holiday transaction processing limit. When the specific parameter value of parameter 1 is 0, indicating no false calendar, parameter 2 allows selection of the specific parameter value of 0, indicating no limitation. When the specific parameter value of parameter 1 is 1, indicating that there is a false calendar, parameter 2 allows the specific parameter value to select 1, 2, 3, respectively, indicating that financial transactions are not allowed, non-financial transactions are not allowed, and that current financial transactions and non-financial transactions are not allowed, but the parameter value cannot be selected to be 0.
In some specific implementations, the mutually exclusive relationship may refer to: three parameters are included in a parameter table, parameter 1 is the interest rate floating type, parameter 2 is the interest rate floating value, and parameter 3 is the interest rate floating ratio. When the specific parameter value of the parameter 1 is 1 and the sign value is calculated, the parameter 2 allows the input of a numerical value, and the parameter 3 does not allow the input of a numerical value. When the specific parameter value of the parameter 1 is 2, the parameter 3 allows the input of the percentage, and the parameter 2 does not allow the input of the percentage.
For example, the inter-table relationships may include one or more of dependencies, references, alignments, and operational relationships.
Specifically, the dependency relationship means that parameters among a plurality of parameter tables are linked, and the value of a certain parameter table influences the value of other parameter table parameters; the reference relationship refers to that the subsequent parameter table refers to a parameter value maintained by the preceding parameter table, such as a code set or a specific parameter value, and the subsequent parameter table does not allow use of a parameter value not maintained in the preceding parameter table; the comparison relation refers to the correlation of parameter values in a plurality of parameter tables, and the highest value or the lowest value or intersection of the comparison results is achieved, so that a specific rule is realized and the method can be applied to a transaction scene; the operation relation refers to the correlation of the parameter values in the parameter tables, and the values of the parameters are subjected to addition, subtraction, multiplication and division operation to realize a specific rule, so that the operation relation can act on a transaction scene.
S102: first and second parameter values of the parameter are obtained.
And acquiring the first parameter value and the second parameter value, namely acquiring maintenance record data of the parameters. The first parameter value and the second parameter value are values of the same parameter at different times.
It should be noted that the step of acquiring the intra-table relationship and the inter-table relationship of the parameters in S101 may be performed first, and then the step of acquiring the first parameter value and the second parameter value of the parameters in S102 may be performed, or the step of S102 may be performed first, and then the step of S101 may be performed, or the step of S101 and the step of S102 may be performed simultaneously, and the present application is not limited to a specific sequence.
In some specific implementations, the corresponding parameters that obtain the first parameter value and the second parameter value at different times may include one or more of a parameter table number, a parameter table chinese name, a parameter table english name, a parameter table classification, a parameter table hierarchy, a parameter chinese name, a parameter english name, a parameter value category, a parameter value range, a parameter value, a parameter table record maintenance time, a relationship type, a relationship parameter english abbreviation, and a parameter table english abbreviation to which the relationship parameter belongs.
The above-mentioned parameter table classification refers to dividing the parameter table for basic public service into reference data parameter tables according to different service management application level service influence ranges, dividing the parameter table before product on-line into product design parameter tables, dividing the parameter tables for inward and outward service into internal service type and customer service type parameter tables, and dividing the parameter table for risk or trade control into operation control type parameter tables.
The above-mentioned parameter table classification refers to classifying the parameter table into a high risk level parameter table, a medium risk level parameter table or a low risk level parameter table in combination with influencing factors of the parameter table.
The parameter value category refers to a category that can divide parameters into a coding category parameter, a code category parameter, a text category parameter, a numerical value category parameter, an amount category parameter, a proportion category parameter, a mark category parameter, a date category parameter, a time category parameter, a date and time category parameter and the like.
The parameter may have a value range of 0 or 1, where 0 may represent "yes" and 1 may represent "no".
S103: and judging whether the first parameter value and the second parameter value are the same, if so, executing the step S104, and if not, executing the step S105.
S104: the parameter values of the setting parameters are unchanged.
When the first parameter value and the second parameter value are unchanged, the parameter may be defined as a constant fixed value that does not change over time. Therefore, when parameter maintenance personnel maintain parameters, constant parameter setting parameter values which do not change along with time are unchanged, so that parameter risk management capability is improved, service management efficiency is improved, and operation risks are reduced.
It should be noted that, the first parameter value, the second parameter value, and the third parameter value of the parameter may be obtained, and if the first parameter value, the second parameter value, and the third parameter value are all consistent, the parameter value of the parameter is set unchanged. The present application is not limited to a specific number of parameter values.
S105: and executing a supervised learning algorithm on the intra-table relations and inter-table relations of the parameters to train the neural network model and construct a parameter recommendation model.
And (3) combining the intra-table relations and inter-table relations of the parameters acquired in the step (S101), inputting the intra-table relations and inter-table relations into a neural network model, executing a supervised learning algorithm, training through the neural network model algorithm, and mining potential association relations between the intra-table relations and inter-table relations of the parameters, so that a parameter recommendation model can be constructed, and special parameter values for executing parameter maintenance can be output.
In some specific implementations, the step of training the neural network model may be specifically as follows A1 to A5:
a1: a training dataset and an evaluation dataset are obtained.
Firstly, acquiring all parameter table history maintenance data (namely a first parameter value and a second parameter value), and the intra-table relation and the inter-table relation of parameters, so as to construct a training data set and an evaluation data set according to a preset proportion.
Specifically, the training data set and the evaluation data set may be divided according to the parameter table history maintenance data in the training data set, and the table relationships and the inter-table relationships of the parameters accounting for 80% of the parameter table history maintenance data, and the table relationships and the inter-table relationships of the parameters accounting for 20% of the parameter table history maintenance data, and the table relationships and the inter-table relationships of the parameters.
It should be noted that, the present application is not limited to a specific preset ratio.
A2: and taking the intra-table relation and the inter-table relation of the parameters in the training data set as input, taking the history maintenance data of the parameter table as output, and training the neural network model by using a backward propagation neural network algorithm so as to construct a parameter recommendation model.
It should be noted that, other learning algorithms may be selected in addition to the backward propagation neural network algorithm, for example, an intelligent recommendation algorithm based on collaborative filtering, a learning algorithm based on a knowledge-graph feature, and the like. The application is not limited to a specific algorithm.
A3: and inputting the intra-table relationship and the inter-table relationship of the parameters in the evaluation data set into a parameter recommendation model to acquire evaluation maintenance data.
A4: and comparing and evaluating the evaluation maintenance data with the parameter table historical maintenance data in the evaluation data set to obtain a credibility result.
That is, the evaluation maintenance data and the parameter first parameter value and the parameter second parameter value in the evaluation data set are compared, thereby obtaining a reliability result.
A5: and optimizing the parameter recommendation model by using a loss function according to the reliability result to obtain an optimized parameter recommendation model.
S106: and inputting the target parameters into the parameter recommendation model to acquire target parameter values.
The target parameter is a parameter that needs to be maintained. After the parameter recommendation model is built, any number of target parameters can be input into the parameter recommendation model, so that target parameter values are obtained.
Therefore, when parameter maintenance personnel maintain parameters, target parameter values can be automatically given to the parameters with the intra-table relationship and the inter-table relationship, so that the parameter risk management capability is improved, the service management efficiency is further improved, and the operation risk is reduced.
In some specific implementations, after the target parameter value is obtained, the target parameter value may be checked, and if the check is passed, the target parameter value may be used as a parameter maintenance result.
S107: the intra-table relationship, the inter-table relationship, the first parameter value, the second parameter value, and the target parameter value are presented in a banking system.
After the target standard value is obtained, the target parameter value output by the parameter recommendation model, the table association relation among the table and the table of all the parameters, and the maintenance record data (namely the first parameter value and the second parameter value) can be displayed in the bank system so that the user can browse and review.
In summary, the application discloses a parameter maintenance method, which comprises the following steps: acquiring an intra-table relationship, an inter-table relationship, a first parameter value and a second parameter value of the parameter; if the first parameter value is the same as the second parameter value, setting the parameter value of the parameter corresponding to the first parameter value and the second parameter value as a fixed value; if the first parameter value is different from the second parameter value, performing a supervised learning algorithm on the intra-table relationship and the inter-table relationship to construct a parameter recommendation model; inputting the target parameters into the parameter recommendation model to obtain target parameter values, wherein the target parameters are parameters which need to be subjected to parameter maintenance. Therefore, when parameter maintenance personnel maintain parameters, constant parameters which do not change along with time are set to be fixed values, target parameter values are automatically given to target parameters with intra-table relations and inter-table relations, so that parameter risk management capability is improved, business management efficiency is improved, and operation risks are reduced.
Referring to fig. 2, a schematic diagram of a parameter maintenance apparatus according to an embodiment of the present application is shown. The parameter maintenance apparatus 200 includes: an acquisition module 201, a setting module 202, a construction module 203 and a maintenance module 204.
Specifically, the obtaining module 201 is configured to obtain an intra-table relationship, an inter-table relationship, a first parameter value, and a second parameter value of a parameter, where the first parameter value and the second parameter value are values of the same parameter at different times;
the setting module 202 is configured to set the same parameter as a fixed value if the first parameter value is the same as the second parameter value;
the construction module 203 is configured to execute a supervised learning algorithm on the intra-table relationship and the inter-table relationship if the first parameter value is different from the second parameter value, so as to construct a parameter recommendation model;
the maintenance module 204 is configured to input a target parameter into the parameter recommendation model to obtain a target parameter value, where the target parameter is a parameter that needs to be subjected to parameter maintenance.
In some specific implementations, the table-internal relationships include one or more of dependencies, mutual exclusion relationships, and combination relationships.
In some specific implementations, the table relationships include one or more of dependencies, references, alignments, and operational relationships.
In some specific implementations, the building module 203 specifically includes: the device comprises a first building module and a second building module. The first construction module is specifically configured to: acquiring a training data set, wherein the training data set comprises a first parameter value of training data, a second parameter value of training data, an intra-table relationship of the training data and an inter-table relationship of the training data; the second construction module is specifically configured to: and taking the intra-table relationship of the training data and the inter-table relationship of the training data as inputs, taking the first parameter value of the training data and the second parameter value of the training data as outputs, and training the neural network model according to the supervised learning algorithm to obtain the parameter recommendation model.
In some specific implementations, the building module 203 further includes: a third build module, a fourth build module, a fifth build module, and a sixth build module. The third construction module is specifically configured to: acquiring a test data set, wherein the test data set comprises a first parameter value of test data, a second parameter value of test data, an intra-table relationship of the test data and an inter-table relationship of the test data; the fourth building module is specifically configured to: inputting the intra-table relation of the test data and the inter-table relation of the test data into a parameter recommendation model to output evaluation maintenance data; the fifth building module is specifically configured to: comparing the first parameter value of the evaluation maintenance data and the test data with the second parameter value of the test data to obtain a credibility result; the sixth building block is specifically configured to: and optimizing the parameter recommendation model based on the loss function according to the credibility result to obtain an optimized parameter recommendation model.
In summary, the application discloses a parameter maintenance device, which comprises an acquisition module, a setting module, a construction module and a maintenance module, and can set constant parameters which do not change with time as fixed values when parameter maintenance personnel maintain parameters, and automatically assign target parameter values to target parameters with intra-table relations and inter-table relations, so that parameter risk management capability is improved, service management efficiency is improved, and operation risks are reduced.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
Referring to FIG. 3, a schematic diagram of a computer readable medium according to an embodiment of the present application is shown. The computer readable medium 300 has stored thereon a computer program 311, which computer program 311, when executed by a processor, implements the steps of the parameter maintenance method of fig. 1 described above.
It should be noted that in the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable 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. 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.
It should be noted that the machine-readable medium according to the present application may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Referring to fig. 4, which is a schematic diagram of a hardware structure of a server according to an embodiment of the present application, the server 400 may have a relatively large difference due to different configurations or performances, and may include one or more central processing units (central processing units, CPU) 422 (e.g., one or more processors) and a memory 432, and one or more storage media 430 (e.g., one or more mass storage devices) storing application programs 440 or data 444. Wherein memory 432 and storage medium 430 may be transitory or persistent storage. The program stored on the storage medium 430 may include one or more modules (not shown), each of which may include a series of instruction operations on a server. Still further, the central processor 422 may be configured to communicate with the storage medium 430 and execute a series of instruction operations in the storage medium 430 on the server 400.
The server 400 may also include one or more power supplies 426, one or more wired or wireless network interfaces 450, one or more input/output interfaces 458, and/or one or more operating systems 441, such as Windows ServerTM, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.
The steps performed by the parameter maintenance method in the above embodiments may be based on the server structure shown in fig. 4.
It should also be noted that, according to an embodiment of the present application, the process of the parameter maintenance method described in the flowchart of fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow diagram of fig. 1 described above.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
1. A method of maintaining parameters, the method comprising:
acquiring an intra-table relation, an inter-table relation, a first parameter value and a second parameter value of a parameter, wherein the first parameter value and the second parameter value are values of the same parameter at different times;
if the first parameter value is the same as the second parameter value, setting the same parameter as a fixed value;
if the first parameter value is different from the second parameter value, performing a supervised learning algorithm on the intra-table relationship and the inter-table relationship to construct a parameter recommendation model;
and inputting the target parameters into the parameter recommendation model to obtain target parameter values, wherein the target parameters are parameters which need to be subjected to parameter maintenance.
2. The method of claim 1, wherein said performing a supervised learning algorithm on said intra-table relationships and said inter-table relationships to construct a parameter recommendation model comprises:
acquiring a training data set, wherein the training data set comprises a first parameter value of training data, a second parameter value of training data, an intra-table relationship of the training data and an inter-table relationship of the training data;
taking the intra-table relation of the training data and the inter-table relation of the training data as inputs, taking the first parameter value of the training data and the second parameter value of the training data as outputs, and training a neural network model according to a supervised learning algorithm to obtain a parameter recommendation model.
3. The method according to claim 2, wherein the method further comprises:
acquiring a test data set, wherein the test data set comprises a first parameter value of test data, a second parameter value of test data, an intra-table relationship of the test data and an inter-table relationship of the test data;
inputting the intra-table relationship of the test data and the inter-table relationship of the test data into the parameter recommendation model to output evaluation maintenance data;
comparing the evaluation maintenance data, the first parameter value of the test data and the second parameter value of the test data to obtain a credibility result;
and optimizing the parameter recommendation model based on the loss function according to the credibility result to obtain an optimized parameter recommendation model.
4. The method of claim 1, wherein the intra-table relationships comprise one or more of dependencies, mutual exclusion relationships, and combination relationships.
5. The method of claim 1, wherein the inter-table relationships comprise one or more of dependencies, references, alignments, and operational relationships.
6. A parameter maintenance apparatus, the apparatus comprising: the device comprises an acquisition module, a setting module, a construction module and a maintenance module;
the acquisition module is used for acquiring the intra-table relation, inter-table relation, first parameter values and second parameter values of the parameters, wherein the first parameter values and the second parameter values are values of the same parameter at different times;
the setting module is configured to set the same parameter as a fixed value if the first parameter value is the same as the second parameter value;
the construction module is used for executing a supervised learning algorithm on the table relationships and the inter-table relationships if the first parameter value is different from the second parameter value so as to construct a parameter recommendation model;
the maintenance module is configured to input a target parameter into the parameter recommendation model to obtain a target parameter value, where the target parameter is a parameter that needs to be subjected to parameter maintenance.
7. The apparatus of claim 6, wherein the intra-table relationships comprise one or more of dependencies, mutual exclusion relationships, and combination relationships.
8. The apparatus of claim 6, wherein the inter-table relationships comprise one or more of dependencies, references, alignments, and operational relationships.
9. A parameter maintenance apparatus, comprising: a memory and a processor;
the memory is used for storing programs;
the processor being adapted to execute the program to carry out the steps of the method according to any one of claims 1 to 5.
10. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any of claims 1 to 5.
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