CN113094031B - Factor generation method, device, computer equipment and storage medium - Google Patents
Factor generation method, device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN113094031B CN113094031B CN202110281357.1A CN202110281357A CN113094031B CN 113094031 B CN113094031 B CN 113094031B CN 202110281357 A CN202110281357 A CN 202110281357A CN 113094031 B CN113094031 B CN 113094031B
- Authority
- CN
- China
- Prior art keywords
- original
- logic
- factor
- data
- code logic
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 238000004140 cleaning Methods 0.000 claims abstract description 18
- 238000012986 modification Methods 0.000 claims description 46
- 230000004048 modification Effects 0.000 claims description 46
- 238000004590 computer program Methods 0.000 claims description 19
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 230000009471 action Effects 0.000 description 3
- 238000012512 characterization method Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 229910052709 silver Inorganic materials 0.000 description 1
- 239000004332 silver Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/30—Creation or generation of source code
- G06F8/31—Programming languages or programming paradigms
- G06F8/313—Logic programming, e.g. PROLOG programming language
Abstract
The application relates to a factor generation method, a factor generation device, a computer device and a storage medium. The method comprises the following steps: acquiring original data; acquiring factor logic from the original data; analyzing the original data to obtain the structure of the original data; cleaning the original data according to a preset rule; acquiring code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic; acquiring a reference value through the code logic; comparing the reference value with an actual value; and taking the code logic as a factor if the difference between the reference value and the actual value is smaller than a preset threshold value. The method and the device can improve the accuracy of the generated factors.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a factor generating method, apparatus, computer device, and storage medium.
Background
Factors are variables used to characterize information, so as the amount of information increases, factors are increasingly used.
In many fields, when processing data, variables need to be processed, and it is common to implement the variables with codes, i.e. to implement factors with codes. However, in the prior art, when the factor is expressed by a code, the code is repeated more, so that the factor is repeated more. Meanwhile, factor codes and model codes are often interleaved together, making the lookup and repair problems quite complex.
It can be seen that the factor generation method in the prior art is not perfect.
Disclosure of Invention
To solve the above technical problems or at least partially solve the above technical problems, the present application provides a factor generation method, apparatus, computer device, and storage medium.
In a first aspect, the present application provides a factor generating method, wherein the method includes:
acquiring original data;
acquiring factor logic from the original data;
analyzing the original data to obtain the structure of the original data;
cleaning the original data according to a preset rule;
acquiring code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic;
acquiring a reference value through the code logic;
comparing the reference value with an actual value;
and taking the code logic as a factor if the difference between the reference value and the actual value is smaller than a preset threshold value.
In this application implementation, the acquiring the raw data includes:
acquiring original input data and original result data corresponding to the original input data;
the obtaining the factor logic from the original data comprises the following steps:
and logic for obtaining the corresponding original result data from the original input data is obtained as factor logic.
In this application implementation, the preset rule includes:
at least one of a format content rule, a logic rule, a requirement rule, and an association rule;
the cleaning of the original data according to the preset rule includes:
if the cleaned data is the original input data, deleting the original result data corresponding to the original input data, and/or
And if the cleaned data is the original result data, deleting the original input data corresponding to the original result data.
In an implementation of the present application, the code logic includes:
logic implementation, type parameters, attribute parameters, logic between parameters, and associations between parameters;
after the taking the code logic as a factor, the method further comprises:
receiving a modification command to the factor, wherein the modification command comprises a modification target and modification content,
according to the modification target, obtaining a corresponding modification item in the code logic,
modifying the corresponding modification item according to the modification content,
wherein the modification targets include any one or more of logic implementations in code logic, type parameters, attribute parameters, logic between parameters, and associations between parameters.
In this application implementation, the obtaining, by the code logic, the reference value includes:
acquiring an input sample set from the cleaned original input data;
inputting any sample in the input sample set into the code logic to obtain an operation result after the code logic operation;
and taking the operation result as the reference value.
In the implementation of the present application, the comparing the reference value with the actual value includes:
and obtaining original result data corresponding to any sample of the input code logic as the actual numerical value.
In the implementation of the application, the factor further comprises a calling interface, wherein the calling interface is used for receiving a calling command sent by a model system so as to be executed in the model system;
wherein the factor is independent of the model system invoking the factor.
In the implementation of the present application, after taking the code logic as a factor, the method further includes:
assigning a unique identification code to the factor;
the factors are placed in a factor library.
In the implementation of the application, after the factors are placed in the factor library, the method further includes:
receiving a modification command for the factor; and/or
Receiving a call command for the factor; and/or
A query command for the factor is received.
In a second aspect, there is provided a factor generating apparatus, the apparatus comprising:
the original data acquisition unit is used for acquiring original data;
the factor logic acquisition unit is used for acquiring factor logic from the original data;
the analyzing unit is used for analyzing the original data to obtain the structure of the original data;
the cleaning unit is used for cleaning the original data according to a preset rule;
the code logic unit is used for acquiring code logic from the cleaned original data according to the structure of the original data, so that the code logic characterizes the factor logic;
the operation unit is used for acquiring a reference value through the code logic;
a comparison unit for comparing the reference value with an actual value;
and the setting unit is used for taking the code logic as a factor if the difference degree between the reference value and the actual value is smaller than a preset threshold value.
In a third aspect, a computer device is provided comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program implementing the steps of the above method.
In a fourth aspect, a computer readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above method.
In the embodiment of the application, according to the original data, the factor logic is obtained, the original data is cleaned, and according to the structure of the original data, the code logic is obtained, so that the code logic characterizes the factor logic, and the code logic is verified, so that the code logic, namely the factor is determined. In the method, the original data are cleaned, and interference data, error data and the like can be removed, so that the accuracy of the generated factors can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a diagram of an application environment of a factor generation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a factor generation method according to an embodiment of the invention;
FIG. 3 is a flow chart of a factor generation method according to an embodiment of the invention;
FIG. 4 is a block diagram of a factor generating device according to an embodiment of the present invention;
fig. 5 is an internal structural diagram of a computer device in an embodiment of the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
FIG. 1 is a diagram of an application environment for a factor generation method in one embodiment. Referring to fig. 1, the factor generation method is applied to a factor generation system. The factor generation system includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network. The terminal 110 may be a desktop terminal or a mobile terminal, and the mobile terminal may be at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The server 120 may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers.
As shown in fig. 2, in one embodiment, a factor generation method is provided. The present embodiment is mainly exemplified by the application of the method to the terminal 110 or the server 120 in fig. 1. Referring to fig. 2, the factor generation method specifically includes the steps of:
step 210, obtaining original data;
step 220, obtaining factor logic from the original data;
step 230, analyzing the original data to obtain a structure of the original data;
step 240, cleaning the original data according to a preset rule;
step 250, obtaining code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic;
step 260, obtaining a reference value through the code logic;
step 270, comparing the reference value with an actual value;
and step 280, if the difference between the reference value and the actual value is smaller than a preset threshold, taking the code logic as a factor.
In the embodiment of the present application, if the difference between the reference value and the actual value is greater than or equal to the preset threshold, the code logic is modified.
In the embodiment of the application, according to the original data, the factor logic is obtained, the original data is cleaned, and according to the structure of the original data, the code logic is obtained, so that the code logic characterizes the factor logic, and the code logic is verified, so that the code logic, namely the factor is determined. In the method, the original data are cleaned, and interference data, error data and the like can be removed, so that the accuracy of the generated factors can be improved.
In this embodiment, in step 210, the obtaining the raw data includes:
and obtaining original input data and original result data corresponding to the original input data.
In this embodiment, in step 220, the obtaining factor logic from the original data includes:
and logic for obtaining the corresponding original result data from the original input data is obtained as factor logic.
The raw data includes various data, and can be generally divided into raw input data and raw result data. Raw input data may be considered collected data, or raw, unworked data, such as revenue per month, daily output, etc.; the raw result data may be considered as data subjected to a certain process or operation, such as annual income, annual balance, annual income, monthly output, annual output, and the like. The raw input data and the raw result data may also both be acquisition data or measurement data, such as the diameter of a measured circle and the circumference of the circle, the diameter of the circle being the raw input data and the circumference of the circle being the raw result data.
The logic for obtaining the corresponding original result data from the original input data is used as factor logic, and analysis needs to be performed according to the types, kinds, descriptions, data sources, data application scenes and the like of the original input data and the original result data so as to obtain the corresponding original result data.
For example, the original input data is A1, A2, and A3, and the original result data is the sum of A1, A2, and A3, then the logic from the original input data to the original result data is "summation".
In another embodiment of the present application, the raw input data may be revenue for each month and the raw result data may be revenue for the whole year, then the logic from raw input data to raw result data is also "summation".
In another embodiment of the present application, the raw input data may be revenue per month, the raw result data may be annual balance, then annual costs are calculated from raw input data to raw result data, and then the logic between them is "first summing the revenue per month and obtaining annual revenue, and then subtracting the annual balance from the annual revenue".
In this embodiment of the present application, the preset rule includes:
at least one of a format content rule, a logic rule, a requirement rule, and an association rule;
in this embodiment, in step 240, the cleaning the original data according to the preset rule includes:
if the cleaned data is the original input data, deleting the original result data corresponding to the original input data, and/or
And if the cleaned data is the original result data, deleting the original input data corresponding to the original result data.
The original input data and the original result data are corresponding, in the above embodiment, the original input data are A1, A2 and A3, the original result data is the sum a of A1, A2 and A3, and then the logic from the original input data to the original result data is "summation"; similarly, the logic is the same, but the specific data is different, and the original input data is B1, B2 and B3, and the original result data is the sum B of B1, B2 and B3. If A is associated with B1, B2 and B3, incorrect logic is derived. Therefore, in the embodiment of the present application, if the original input data is cleaned, the corresponding original result data is deleted; the original result data is clear, and the corresponding original input data is deleted. Thus, adverse effects of the non-corresponding data on the factor generation can be avoided.
In this embodiment, step 250 obtains code logic from the cleaned original data according to the structure of the original data, so that the code logic characterizes the factor logic, that is, the factor logic is implemented by using code. As in the above embodiment, the original input data is A1, A2 and A3, the original result data is the sum a of A1, A2 and A3, and then the logic from the original input data to the original result data is "summation", and then the code may be: x=x1+x2+x3, where the values of X1, X2, and X3 may be A1, A2, and A3, respectively, and may also be B1, B2, and B3.
In this embodiment of the present application, the code logic may be obtained according to the structure of the original data, which may be implemented by using methods in the prior art, for example, various logic databases, code databases, data models, and so on, which are not described herein.
In an embodiment of the present application, the code logic includes:
logic implementation, type parameters, attribute parameters, logic between parameters, and associations between parameters;
after the taking the code logic as a factor, the method further comprises:
receiving a modification command to the factor, wherein the modification command comprises a modification target and modification content,
according to the modification target, obtaining a corresponding modification item in the code logic,
modifying the corresponding modification item according to the modification content,
wherein the modification targets include any one or more of logic implementations in code logic, type parameters, attribute parameters, logic between parameters, and associations between parameters.
In the embodiment of the application, the factors are implemented by code logic, and the code logic includes logic implementation modes, type parameters, attribute parameters, logic among parameters and relevance among parameters, which can be used as modification targets, and the modification targets can be modified according to requirements. After these modification targets are modified, the code logic is changed, i.e. the code logic is changed as a factor, e.g. the original code logic is a factor M, and after the type parameters in the code logic are modified, the code logic becomes a factor N. For example, where factors based on historical orders of a staged user are originally factors based on historical bills, a new set of factors may be generated by adjusting time variables in parameters in the original code logic.
In the embodiment of the application, the code logic characterization factors can be modified, so that the factors of the code logic characterization can be modified at the same time, namely one factor can be modified into another factor, and the applicability and flexibility of the silver generated by the application are higher.
In this embodiment, in step 260, the obtaining, by the code logic, the reference value includes:
acquiring an input sample set from the cleaned original input data;
inputting any sample in the input sample set into the code logic to obtain an operation result after the code logic operation;
and taking the operation result as the reference value.
In this embodiment, step 270, comparing the reference value with the actual value includes:
and obtaining original result data corresponding to any sample of the input code logic as the actual numerical value.
In the embodiment of the present application, if the difference between the reference value and the actual value is smaller than a preset threshold, taking the code logic as a factor;
and if the difference between the reference value and the actual value is greater than or equal to the preset threshold value, correcting the code logic.
In the embodiment of the application, after the code logic characterizes the factor logic, the code logic needs to be verified. The verification mode is that the root obtains the reference value and compares with the actual value.
A typical factor logic may be the relationship between diameter and circumference length and the code logic used to represent the factor logic may be q=6r, where R represents the diameter and Q represents the circumference length.
In practice, the circumferential rate is an irregular parameter, which can be generally regarded as 3.1415926. The code logic is biased with q=6r, for example, the diameter is 1, the actual value of the circumference is 6.283 … …, but the reference value of the circumference obtained by the code logic is 6.
The above code logic may be used as a factor if the preset threshold is set to 0.3, but may not be used as a factor if the threshold is set to 0.2.
With the calculation of the circumference length being just an example, the code logic may actually be a more complex formula, so setting an appropriate preset threshold may improve the accuracy of the factor.
In this embodiment of the present application, the factor further includes a call interface, where the call interface is configured to receive a call command sent by a model system, so as to be executed in the model system;
wherein the factor is independent of the model system invoking the factor.
In the embodiment of the application, the factor is independent of the model system for calling the factor, and the factor can be called by the model system through a calling interface, so that the factor is executed in the model system. Because the factors are mutually independent with the model system, if the factors have errors, the factors can be directly corrected without modifying the model system; if the model system is in error, the model system can be directly modified without a correction factor. In addition, if a plurality of factors are needed to be used in the model system, or the used factors are replaced by other factors, only the needed factors are required to be called at proper positions, or the called factors are required to be modified into other factors, the model system does not need to be subjected to re-repair construction or modification, the applicability and flexible collar of the model system can be improved, and the construction and modification cost of the model system can be saved.
In an embodiment of the present application, after taking the code logic as a factor, the method further includes:
assigning a unique identification code to the factor;
the factors are placed in a factor library.
In an embodiment of the present application, after the placing the factors in the factor library, the method further includes:
receiving a modification command for the factor; and/or
Receiving a call command for the factor; and/or
A query command for the factor is received.
The unique identification code is allocated to the factors and is put into a factor library, and the factors can be conveniently called by the model system through the unique identification code; the factors can be searched through identification codes, and the like, so that great convenience is provided for users.
Fig. 3 is a schematic flow chart of a factor generation method according to an embodiment of the present application, as shown in fig. 3, where the method includes:
step 310, obtaining original data;
step 320, obtaining factor logic from the original data;
step 330, analyzing the original data to obtain a structure of the original data;
step 340, cleaning the original data according to a preset rule;
step 350, obtaining code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic;
step 360, obtaining a reference value through the code logic;
step 370, comparing the reference value with an actual value;
step 380, determining the relationship between the difference between the reference value and the actual value and the preset threshold, if the difference is smaller than or equal to the preset threshold, going to step 390, if the difference is larger than the preset threshold, going to step 391.
Step 390 takes the code logic as a factor.
Step 391, amending the code logic, goes to step 360.
Fig. 2 and 3 are schematic flow diagrams of the factor generation method in the embodiment. It should be understood that, although the steps in the flowcharts of fig. 2 and 3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2, 3 may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed sequentially, but may be performed alternately or alternately with at least a portion of the other steps or sub-steps of other steps.
Corresponding to the factor generation method, the embodiment of the application also provides a factor generation device, as shown in fig. 4, where the device includes:
an original data acquisition unit 410 for acquiring original data;
a factor logic obtaining unit 420, configured to obtain factor logic from the raw data;
the parsing unit 430 is configured to parse the original data to obtain a structure of the original data;
a cleaning unit 440, configured to clean the raw data according to a preset rule;
a code logic unit 450, configured to obtain code logic from the cleaned original data according to the structure of the original data, so that the code logic characterizes the factor logic;
an operation unit 460, configured to obtain a reference value through the code logic;
a comparing unit 470 for comparing the reference value with an actual value;
and a setting unit 480, configured to take the code logic as a factor if the difference between the reference value and the actual value is smaller than a preset threshold.
The acquiring the original data comprises the following steps:
acquiring original input data and original result data corresponding to the original input data;
the obtaining the factor logic from the original data comprises the following steps:
and logic for obtaining the corresponding original result data from the original input data is obtained as factor logic.
In this embodiment of the present application, the preset rule includes:
at least one of a format content rule, a logic rule, a requirement rule, and an association rule;
the cleaning unit 440 is further configured to:
if the cleaned data is the original input data, deleting the original result data corresponding to the original input data, and/or
And if the cleaned data is the original result data, deleting the original input data corresponding to the original result data.
In an embodiment of the present application, the code logic includes:
logic implementation, type parameters, attribute parameters, logic between parameters, and associations between parameters;
in an implementation of the present application, the apparatus preferably includes a modifying unit configured to:
receiving a modification command to the factor, wherein the modification command comprises a modification target and modification content,
according to the modification target, obtaining a corresponding modification item in the code logic,
modifying the corresponding modification item according to the modification content,
wherein the modification targets include any one or more of logic implementations in code logic, type parameters, attribute parameters, logic between parameters, and associations between parameters.
In the embodiment of the present application, the operation unit 460 is further configured to:
acquiring an input sample set from the cleaned original input data;
inputting any sample in the input sample set into the code logic to obtain an operation result after the code logic operation;
and taking the operation result as the reference value.
The comparing unit 470 is further configured to:
and obtaining original result data corresponding to any sample of the input code logic as the actual numerical value.
In this embodiment of the present application, the apparatus further includes a calling unit:
the method comprises the steps of receiving a call command sent by a model system to be executed in the model system;
wherein the factor is independent of the model system invoking the factor.
In this embodiment of the present application, the apparatus further includes a management unit, configured to:
assigning a unique identification code to the factor;
the factors are placed in a factor library.
In this embodiment of the present application, the management unit is further configured to:
receiving a modification command for the factor; and/or
Receiving a call command for the factor; and/or
A query command for the factor is received.
The factor generation device can improve the reliability and flexibility of the generated factors.
FIG. 5 illustrates an internal block diagram of a computer device in one embodiment. The computer device may be specifically the terminal 110 or the server 120 in fig. 1. As shown in fig. 5, the computer device includes a processor, a memory, a network interface, an input device, and a display screen connected by a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program that, when executed by a processor, causes the processor to implement a factor generation method. The internal memory may also store a computer program which, when executed by the processor, causes the processor to perform the factor generation method
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program: acquiring factor logic from the original data; analyzing the original data to obtain the structure of the original data; cleaning the original data according to a preset rule; acquiring code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic; acquiring a reference value through the code logic; comparing the reference value with an actual value; and taking the code logic as a factor if the difference between the reference value and the actual value is smaller than a preset threshold value.
In one embodiment, the steps of the above method are also implemented when the processor executes the computer program, and are not described herein.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring factor logic from the original data; analyzing the original data to obtain the structure of the original data; cleaning the original data according to a preset rule; acquiring code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic; acquiring a reference value through the code logic; comparing the reference value with an actual value; and taking the code logic as a factor if the difference between the reference value and the actual value is smaller than a preset threshold value.
In one embodiment, the computer program when executed by the processor further implements the steps of the above method, which are not described herein.
In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from a computer-readable storage medium, the processor executing the computer instructions to cause the computer device to perform the steps of: acquiring factor logic from the original data; analyzing the original data to obtain the structure of the original data; cleaning the original data according to a preset rule; acquiring code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic; acquiring a reference value through the code logic; comparing the reference value with an actual value; and taking the code logic as a factor if the difference between the reference value and the actual value is smaller than a preset threshold value.
In an embodiment, the computer program product or the computer program when executed further implements the steps of the above method, which are not described in detail herein.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method of factor generation, the method comprising:
acquiring original data;
acquiring factor logic from the original data;
analyzing the original data to obtain the structure of the original data;
cleaning the original data according to a preset rule;
acquiring code logic from the cleaned original data according to the structure of the original data, and enabling the code logic to represent the factor logic;
acquiring a reference value through the code logic;
comparing the reference value with an actual value;
if the difference between the reference value and the actual value is smaller than a preset threshold value, taking the code logic as a factor;
wherein, the obtaining the original data includes:
acquiring original input data and original result data corresponding to the original input data;
the obtaining the factor logic from the original data comprises the following steps:
logic for obtaining corresponding original result data from the original input data is obtained and is used as factor logic;
the preset rule comprises the following steps:
at least one of a format content rule, a logic rule, a requirement rule, and an association rule;
the cleaning of the original data according to the preset rule includes:
if the cleaned data is the original input data, deleting the original result data corresponding to the original input data, and/or
If the cleaned data is the original result data, deleting the original input data corresponding to the original result data;
the obtaining, by the code logic, the reference value includes:
acquiring an input sample set from the cleaned original input data;
inputting any sample in the input sample set into the code logic to obtain an operation result after the code logic operation;
taking the operation result as the reference value;
the comparing the reference value with the actual value comprises:
and obtaining original result data corresponding to any sample of the input code logic as the actual numerical value.
2. The method of claim 1, wherein the code logic comprises:
logic implementation, type parameters, attribute parameters, logic between parameters, and associations between parameters;
after the taking the code logic as a factor, the method further comprises:
receiving a modification command to the factor, wherein the modification command comprises a modification target and modification content,
according to the modification target, obtaining a corresponding modification item in the code logic,
modifying the corresponding modification item according to the modification content,
wherein the modification targets include any one or more of logic implementations in code logic, type parameters, attribute parameters, logic between parameters, and associations between parameters.
3. The method of claim 1, wherein the factor further comprises a call interface for receiving a call command sent by a model system to be executed in the model system;
wherein the factor is independent of the model system invoking the factor.
4. The method of claim 1, wherein after factoring the code logic, the method further comprises:
assigning a unique identification code to the factor;
the factors are placed in a factor library.
5. The method of claim 4, wherein after the placing the factors into the factor library, the method further comprises:
receiving a modification command for the factor; and/or
Receiving a call command for the factor; and/or
A query command for the factor is received.
6. A factor generation apparatus, the apparatus comprising:
the original data acquisition unit is used for acquiring original data;
the factor logic acquisition unit is used for acquiring factor logic from the original data;
the analyzing unit is used for analyzing the original data to obtain the structure of the original data;
the cleaning unit is used for cleaning the original data according to a preset rule;
the code logic unit is used for acquiring code logic from the cleaned original data according to the structure of the original data, so that the code logic characterizes the factor logic;
the operation unit is used for acquiring a reference value through the code logic;
a comparison unit for comparing the reference value with an actual value;
the setting unit is used for taking the code logic as a factor if the difference degree between the reference value and the actual value is smaller than a preset threshold value;
wherein, the obtaining the original data includes:
acquiring original input data and original result data corresponding to the original input data;
the obtaining the factor logic from the original data comprises the following steps:
logic for obtaining corresponding original result data from the original input data is obtained and is used as factor logic;
the preset rule comprises the following steps:
at least one of a format content rule, a logic rule, a requirement rule, and an association rule;
the cleaning of the original data according to the preset rule includes:
if the cleaned data is the original input data, deleting the original result data corresponding to the original input data, and/or
If the cleaned data is the original result data, deleting the original input data corresponding to the original result data;
the obtaining, by the code logic, the reference value includes:
acquiring an input sample set from the cleaned original input data;
inputting any sample in the input sample set into the code logic to obtain an operation result after the code logic operation;
taking the operation result as the reference value;
the comparing the reference value with the actual value comprises:
and obtaining original result data corresponding to any sample of the input code logic as the actual numerical value.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 5 when the computer program is executed by the processor.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110281357.1A CN113094031B (en) | 2021-03-16 | 2021-03-16 | Factor generation method, device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110281357.1A CN113094031B (en) | 2021-03-16 | 2021-03-16 | Factor generation method, device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113094031A CN113094031A (en) | 2021-07-09 |
| CN113094031B true CN113094031B (en) | 2024-02-20 |
Family
ID=76668272
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110281357.1A Active CN113094031B (en) | 2021-03-16 | 2021-03-16 | Factor generation method, device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113094031B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103631797A (en) * | 2012-08-22 | 2014-03-12 | 阿里巴巴集团控股有限公司 | Operation method and device used for data lists |
| CN108920175A (en) * | 2018-06-26 | 2018-11-30 | 联想(北京)有限公司 | The realization method and system of baseboard management controller BMC code logic |
| CN109784795A (en) * | 2017-11-13 | 2019-05-21 | 阿里巴巴集团控股有限公司 | Inventory status calculation method, device, equipment and medium |
| CN110088754A (en) * | 2016-10-26 | 2019-08-02 | 联邦科学和工业研究组织 | It makes laws to the autocoder of logic |
| CN110096498A (en) * | 2019-03-28 | 2019-08-06 | 阿里巴巴集团控股有限公司 | A kind of data cleaning method and device |
| CN110287180A (en) * | 2019-06-25 | 2019-09-27 | 上海诚数信息科技有限公司 | A kind of air control modeling method based on deep learning |
| CN110647329A (en) * | 2019-08-13 | 2020-01-03 | 平安科技（深圳）有限公司 | Code obfuscation method, apparatus, computer device and storage medium |
| CN111026742A (en) * | 2019-12-05 | 2020-04-17 | 东莞中国科学院云计算产业技术创新与育成中心 | Data quality evaluation method and device, computer equipment and storage medium |
| CN111966356A (en) * | 2020-08-25 | 2020-11-20 | 珠海格力电器股份有限公司 | Program code generation method, program code generation device, storage medium, and electronic device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130144549A1 (en) * | 2011-12-01 | 2013-06-06 | Grigori Temkine | Method for calibrating temperature sensors using reference voltages |
-
2021
- 2021-03-16 CN CN202110281357.1A patent/CN113094031B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103631797A (en) * | 2012-08-22 | 2014-03-12 | 阿里巴巴集团控股有限公司 | Operation method and device used for data lists |
| CN110088754A (en) * | 2016-10-26 | 2019-08-02 | 联邦科学和工业研究组织 | It makes laws to the autocoder of logic |
| CN109784795A (en) * | 2017-11-13 | 2019-05-21 | 阿里巴巴集团控股有限公司 | Inventory status calculation method, device, equipment and medium |
| CN108920175A (en) * | 2018-06-26 | 2018-11-30 | 联想(北京)有限公司 | The realization method and system of baseboard management controller BMC code logic |
| CN110096498A (en) * | 2019-03-28 | 2019-08-06 | 阿里巴巴集团控股有限公司 | A kind of data cleaning method and device |
| CN110287180A (en) * | 2019-06-25 | 2019-09-27 | 上海诚数信息科技有限公司 | A kind of air control modeling method based on deep learning |
| CN110647329A (en) * | 2019-08-13 | 2020-01-03 | 平安科技（深圳）有限公司 | Code obfuscation method, apparatus, computer device and storage medium |
| CN111026742A (en) * | 2019-12-05 | 2020-04-17 | 东莞中国科学院云计算产业技术创新与育成中心 | Data quality evaluation method and device, computer equipment and storage medium |
| CN111966356A (en) * | 2020-08-25 | 2020-11-20 | 珠海格力电器股份有限公司 | Program code generation method, program code generation device, storage medium, and electronic device |
Non-Patent Citations (3)
| Title |
|---|
| Improve DC Motor System using Fuzzy Logic Control by Particle Swarm Optimization in Use Scale Factors;Dr- Noorulden Basil 等;《International Journal of Computer Science and Mobile Computing》;第8卷(第3期);152-160 * |
| 因子分析法在企业绩效评价中的应用——主观评价标准不可由数据逻辑代替;陶黎娟;《财会月刊》(第23期);36-38 * |
| 基于网络爬虫的Web应用程序漏洞检测方法的研究与实现;毛振坡;《中国优秀硕士学位论文全文数据库 信息科技辑》(第3期);I139-76 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113094031A (en) | 2021-07-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108874644B (en) | Data monitoring method and device, computer equipment and storage medium | |
| US20140067461A1 (en) | System and Method for Predicting Customer Attrition Using Dynamic User Interaction Data | |
| CN108920498B (en) | Data query method, device and equipment | |
| CN110069364B (en) | Spare part data error correction method and device, computer equipment and storage medium | |
| CN110750433A (en) | Interface test method and device | |
| CN112328499A (en) | Test data generation method, device, equipment and medium | |
| CN112256318A (en) | Construction method and equipment for dependent product | |
| CN113094031B (en) | Factor generation method, device, computer equipment and storage medium | |
| CN111080352A (en) | Product price determining method and device and server | |
| CN110134898A (en) | Finance product recommended method, device, computer equipment and storage medium | |
| CN114138634B (en) | Test case selection method and device, computer equipment and storage medium | |
| CN113259154B (en) | Method and device for informing middle station data verification, computer equipment and storage medium | |
| CN113064801B (en) | Data point burying method and device, readable storage medium and computer equipment | |
| CN115357598A (en) | Data modification method and device, computer equipment and storage medium | |
| Lokan et al. | Investigating the use of chronological splitting to compare software cross-company and single-company effort predictions | |
| CN111666286B (en) | Method, device, computer equipment and storage medium for detecting database and table | |
| CN114969046A (en) | Hash connection processing method, storage medium and equipment | |
| CN112445829B (en) | Data checking method, device, equipment and storage medium | |
| CN117633316A (en) | Attribution analysis method, attribution analysis device, computer equipment and storage medium | |
| US11068466B2 (en) | Systems and methods for use in data structure validation | |
| CN116775610A (en) | Method, device, equipment and storage medium for checking execution code | |
| CN117454042A (en) | Hotel static information pushing method, system, equipment and storage medium | |
| CN117911170A (en) | New and old insurance system migration configuration verification method and device and computer equipment | |
| CN116798641A (en) | Collation method and apparatus for check items, device and storage medium | |
| CN117632961A (en) | Data query method, device, equipment and medium based on sustainable line segment tree |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |