CN115618288A - Rank determination method, apparatus, device, storage medium, and program product - Google Patents
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Abstract
The present application relates to a rank determination method, apparatus, device, storage medium, and program product. The method comprises the following steps: the method comprises the steps of obtaining power data of a target object in a power system and resource data of a resource system, carrying out feature extraction on the power data to obtain first target feature data, carrying out feature extraction on the resource data to obtain second target feature data, and determining a grade evaluation result of the target object according to the first target feature data and the second target feature data. The accuracy of the obtained grade of the object is improved.
Description
Technical Field
The present application relates to the field of power technologies, and in particular, to a rank determination method, apparatus, device, storage medium, and program product.
Background
Analyzing the grade of the object according to the classification result of the object, and avoiding resource loss according to the grade of the object has become a conventional choice for various industries.
The grade of the object to be graded is determined mainly according to the resource data of the object, and the problem that the accuracy of the grade of the obtained object is poor exists.
Disclosure of Invention
In view of the above, it is desirable to provide a rank determination method, apparatus, device, storage medium, and program product capable of improving accuracy of a obtained rank of an object in view of the above technical problem.
In a first aspect, the present application provides a rank determination method. The method comprises the following steps:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
In one embodiment, the performing feature extraction on the power data to obtain first target feature data includes:
extracting first characteristic data corresponding to a first resource transfer model, second characteristic data corresponding to a second resource transfer model and third characteristic data corresponding to a resource loss model from the electric power data;
wherein the first target feature data includes the first feature data, the second feature data, and the third feature data.
In one embodiment, the performing feature extraction on the resource data to obtain second target feature data includes:
extracting fourth characteristic data corresponding to the first resource transfer model, fifth characteristic data corresponding to the second resource transfer model and sixth characteristic data corresponding to the resource loss model from the resource data;
wherein the second target feature data includes the fourth feature data, the fifth feature data, and the sixth feature data.
In one embodiment, the determining the rating of the target object according to the first target feature data and the second target feature data includes:
inputting the first characteristic data and the fourth characteristic data into the first resource transfer model to obtain a first resource transfer probability;
inputting the second characteristic data and the fifth characteristic data into the second resource transfer model to obtain a second resource transfer probability;
inputting the third characteristic data and the sixth characteristic data into the resource loss model to obtain a resource loss probability;
and determining a grade evaluation result of the target object according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
In one embodiment, the determining the grade evaluation result of the target object according to the first resource transition probability, the second resource transition probability and the resource loss probability includes:
and determining the grade evaluation result of the target object according to the target quantization value of the target object and the corresponding relation between different quantization values and different grade evaluation results.
In one embodiment, the weighted voting method is a Bagging ensemble learning method.
In a second aspect, the present application further provides a rank determination apparatus. The device comprises:
the acquisition module is used for acquiring the power data of the target object in the power system and the resource data in the resource system;
the first extraction module is used for extracting the characteristics of the electric power data to obtain first target characteristic data; the second extraction module is used for extracting the characteristics of the resource data to obtain second target characteristic data;
and the determining module is used for determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
In a third aspect, the application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
According to the grade determining method, the device, the equipment, the storage medium and the program product, the power data of the target object in the power system and the resource data of the resource system are obtained, the power data are subjected to feature extraction to obtain first target feature data, the resource data are subjected to feature extraction to obtain second target feature data, and the grade evaluation result of the target object is determined according to the first target feature data and the second target feature data. In the traditional technology, the grade of an object to be graded is determined mainly according to the resource data of the object, and the problem that the accuracy of the grade of the obtained object is poor exists. The power data are introduced, and the power data and the resource data are jointly used as the basis, so that the accuracy of the grade of the obtained object is improved.
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Fig. 1 is an internal structural diagram of a computer device provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of a rank determination method according to an embodiment of the present application;
fig. 3 is one of the flow diagrams of a method for determining a rating evaluation result of a target object according to an embodiment of the present application;
fig. 4 is a second flowchart of a method for determining a grade evaluation result of a target object according to an embodiment of the present application;
fig. 5 is a second flowchart of a grade determining method according to an embodiment of the present application;
fig. 6 is a block diagram of a structure of a rank determination apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
The embodiment provided in the present application may be applied to a computer device as shown in fig. 1, and referring to fig. 1, fig. 1 is an internal structural diagram of the computer device provided in the embodiment of the present application. The computer device may be a terminal. The computer device comprises a processor, a memory, a communication interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for communicating with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a resource scaling method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, as shown in fig. 2, fig. 2 is a schematic flowchart of a level determination method provided in an embodiment of the present application, and is described by taking the example that the method is applied to the computer device in fig. 1, including the following steps:
s201, acquiring power data of a target object in a power system and resource data of a resource system.
The target object represents an object in which the power system and the resource system both store corresponding data. For example, when data of the object a, the object B, and the object C are stored in the power system, and data of the object a, the object B, and the object D are stored in the resource system, the object a and the object B are determined as target objects.
The target object may be determined by aligning objects stored in the power system with objects stored in the resource system using a federal learning approach. The federal learning method can comprise a hash algorithm and an asymmetric encryption algorithm. The use of the federal learning method does not result in the leakage of data for other objects, such as object C and object D described above.
S202, performing feature extraction on the electric power data to obtain first target feature data.
Specifically, for example, taking enterprise a as a target object, the power data of enterprise a may be subjected to feature extraction according to the power consumption behavior features and the power data integrity features of the enterprise. First target characteristic data of the enterprise A is obtained.
And S203, performing feature extraction on the resource data to obtain second target feature data.
Specifically, for example, taking enterprise a as a target object, feature extraction may be performed on the resource data of enterprise a according to enterprise data integrity, enterprise historical level evaluation results, enterprise resource quantity features, and enterprise resource loss features of enterprise a, so as to obtain second target feature data of enterprise a.
And S204, determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
Specifically, for example, the first feature data and the second feature data of the enterprise a are brought into the relevant probability calculation model to obtain the relevant probability of the enterprise a, the accumulated sum of the relevant probabilities may be used as the target quantization value of the enterprise a, and the grade evaluation result of the enterprise a is determined according to the target quantization value of the enterprise a.
In the grade determining method, the power data of the target object in the power system and the resource data of the resource system are obtained, the power data are subjected to feature extraction to obtain first target feature data, the resource data are subjected to feature extraction to obtain second target feature data, and the grade evaluation result of the target object is determined according to the first target feature data and the second target feature data. By introducing the power data, the power data and the resource data are jointly used as evaluation basis, and the accuracy of the grade of the obtained object is improved.
In one embodiment, in step S202, the feature extraction is performed on the power data to obtain first target feature data, and the method may be implemented as follows:
and extracting first characteristic data corresponding to the first resource transfer model, second characteristic data corresponding to the second resource transfer model and third characteristic data corresponding to the resource loss model from the power data.
Wherein the first target feature data includes first feature data, second feature data, and third feature data.
For example, with enterprise a as the target object, the first target feature data of enterprise a includes: annual power consumption, annual power consumption same-ratio growth rate, monthly power consumption and monthly power consumption same-ratio growth rate of the enterprise A corresponding to the first resource transfer model are extracted from the power data and used as first characteristic data. And extracting annual power consumption, annual power consumption same-ratio growth rate, monthly power consumption same-ratio growth rate of the enterprise A corresponding to the second resource transfer model as second characteristic data. And extracting annual power consumption, annual power consumption same-ratio growth rate, monthly power consumption and monthly power consumption same-ratio growth rate of the enterprise A corresponding to the resource loss model as third characteristic data.
In the embodiment of the application, first characteristic data corresponding to a first resource transfer model, second characteristic data corresponding to a second resource transfer model and third characteristic data corresponding to a resource loss model are extracted from power data. The electric power data is used as the basis of the grade evaluation result, and the timeliness of the grade evaluation result is improved.
In one embodiment, in step S203, performing feature extraction on the resource data to obtain second target feature data, includes:
and extracting fourth characteristic data corresponding to the first resource transfer model, fifth characteristic data corresponding to the second resource transfer model and sixth characteristic data corresponding to the resource loss model from the resource data.
Wherein the second target feature data includes fourth feature data, fifth feature data, and sixth feature data.
For example, with enterprise a as the target object, the second target feature data of enterprise a includes: social resources, historical grade evaluation results, asset liability ratio, interest support multiple, total asset profitability, business profit rate, total asset turnover rate, snap-action rate and enterprise resource value. Social resources, historical grade evaluation results and enterprise resource values of the enterprise A corresponding to the first resource transfer model are extracted from the resource data and serve as fourth feature data. And extracting social resources, historical grade evaluation results, asset liability ratio, interest guarantee multiples, total asset profitability, business profit rate, total asset turnover rate and quick action rate of the enterprise A corresponding to the second resource transfer model as fifth characteristic data, and extracting social resources, historical grade evaluation results and enterprise resource values of the enterprise A corresponding to the resource loss model as sixth characteristic data.
In the embodiment of the application, the fourth characteristic data corresponding to the first resource transfer model, the fifth characteristic data corresponding to the second resource transfer model, and the sixth characteristic data corresponding to the resource loss model are extracted from the resource data. The resource data is used as the basis of the grade evaluation result, so that the accuracy of the grade evaluation result is improved.
Fig. 3 is a schematic flowchart of a method for determining a rating result of a target object according to an embodiment of the present application, where this embodiment relates to a possible implementation manner of how to determine a rating result of a target object according to first target feature data and second target feature data, and on the basis of the foregoing embodiment, as shown in fig. 3, the foregoing S204 includes:
s301, inputting the first characteristic data and the fourth characteristic data into the first resource transfer model to obtain a first resource transfer probability.
Specifically, taking enterprise a as an example, annual power consumption geometric growth rate, monthly power consumption, monthly power geometric growth rate, social resources, historical grade evaluation results, and enterprise resource value of enterprise a are input into the first resource transfer model, so as to obtain a first resource transfer probability of enterprise a.
S302, inputting the second characteristic data and the fifth characteristic data into a second resource transfer model to obtain a second resource transfer probability.
Specifically, taking enterprise a as an example for explanation, annual power consumption same-ratio growth rate, monthly power consumption same-ratio growth rate, social resources, historical grade evaluation results, asset liability rate, interest guarantee multiple, total asset profitability, business profit rate, total asset turnover rate, and snap-action rate of enterprise a are input into the second resource transfer model, so as to obtain a second resource transfer probability of enterprise a.
And S303, inputting the third characteristic data and the sixth characteristic data into a resource loss model to obtain the resource loss probability.
Specifically, taking enterprise a as an example, annual power consumption geometric growth rate, monthly power consumption, monthly power geometric growth rate, social resources, historical grade evaluation results, and enterprise resource value of enterprise a are input into the resource loss model, so as to obtain the resource loss probability of enterprise a.
S304, determining a grade evaluation result of the target object according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
Specifically, taking enterprise a as an example, according to a first resource transition probability of enterprise a, a second resource transition probability of enterprise a, and a resource loss probability, a product of the cumulative sum of the probabilities and a first preset coefficient may be used as a target quantization value of enterprise a, and a level evaluation result of enterprise a is determined according to the target quantization value of enterprise a.
In the embodiment of the application, the first characteristic data and the fourth characteristic data are input into the first resource transfer model to obtain the first resource transfer probability, the second characteristic data and the fifth characteristic data are input into the second resource transfer model to obtain the second resource transfer probability, the third characteristic data and the sixth characteristic data are input into the resource loss model to obtain the resource loss probability, the grade evaluation result of the target object is determined according to the first resource transfer probability, the second resource transfer probability and the resource loss probability, and the accuracy of the grade evaluation result of the target object is improved.
Fig. 4 is a second flowchart of a method for determining a rating result of a target object according to an embodiment of the present application, where this embodiment relates to a possible implementation manner of how to determine a rating result of a target object according to a first resource transition probability, a second resource transition probability, and a resource loss probability, and on the basis of the foregoing embodiment, as shown in fig. 4, the foregoing S304 includes:
s401, determining a target quantization value of the target object by using a weighted voting method according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
The weighted voting method is a voting method in which weights are added.
Specifically, taking enterprise a as an example, the target quantization value of enterprise a is determined by a weighted voting method according to the first resource transition probability, the second resource transition probability, and the resource loss probability of enterprise a.
S402, determining the grade evaluation result of the target object according to the target quantization value of the target object and the corresponding relation between the different quantization values and the different grade evaluation results.
Specifically, the different quantization values correspond to different rating evaluation results, taking enterprise a as an example for explanation, assuming that the quantization value is higher than 80 points, the corresponding rating evaluation result is a, the quantization value is higher than 60 points, the corresponding rating evaluation result is B, the rating evaluation results corresponding to the remaining quantization values are all C, and if the target quantization value of enterprise a obtained through the above steps is 90 points, the rating evaluation result corresponding to the target quantization value of enterprise a is a.
In the embodiment of the application, a target quantization value of a target object is determined by using a weighted voting method according to the first resource transfer probability, the second resource transfer probability and the resource loss probability, and a grade evaluation result of the target object is determined according to the target quantization value of the target object and the corresponding relation between different quantization values and different grade evaluation results. The conversion from the related resource probability to the grade evaluation result is realized.
In one embodiment, the weighted voting method is a Bagging ensemble learning method.
Specifically, taking enterprise a as an example, the first resource transition probability, the second resource transition probability, and the resource loss probability of enterprise a may be used as input variables, and the target quantization value corresponding to enterprise a may be determined by a Bagging ensemble learning method.
In the embodiment of the application, the first resource transfer probability, the second resource transfer probability and the resource loss probability of the target object are processed through a Bagging ensemble learning method, the target quantization value of the target object is determined, and the conversion from the related resource probability to the target quantization value is realized.
Fig. 5 is a second schematic flowchart of a rank determination method according to an embodiment of the present application, and as shown in fig. 5, the method includes:
acquiring power data of a target object in a power system and resource data of a resource system; extracting first characteristic data corresponding to the first resource transfer model, second characteristic data corresponding to the second resource transfer model and third characteristic data corresponding to the resource loss model from the electric power data; inputting the first characteristic data and the fourth characteristic data into a first resource transfer model to obtain a first resource transfer probability; inputting the second characteristic data and the fifth characteristic data into a second resource transfer model to obtain a second resource transfer probability; inputting the third characteristic data and the sixth characteristic data into a resource loss model to obtain a resource loss probability; determining a target quantization value of the target object by using a weighted voting method according to the first resource transfer probability, the second resource transfer probability and the resource loss probability; and determining the grade evaluation result of the target object according to the target quantization value of the target object and the corresponding relation between the different quantization values and the different grade evaluation results.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the present application further provides a grade determining apparatus for implementing the above-mentioned grade determining method. The implementation scheme for solving the problem provided by the apparatus is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the level determining apparatus provided below may refer to the limitations in the above level determining method, and details thereof are not described herein again.
In one embodiment, as shown in fig. 5, there is provided a rank determination apparatus 500 comprising: an obtaining module 601, a first extracting module 602, a second extracting module 603 and a determining module 604, wherein:
the obtaining module 601 is configured to obtain power data of a target object in a power system and resource data of a resource system.
The first extraction module 602 is configured to perform feature extraction on the power data to obtain first target feature data.
The second extraction module 603 performs feature extraction on the resource data to obtain second target feature data.
The determining module 604 is configured to determine a grade evaluation result of the target object according to the first target feature data and the second target feature data.
In one embodiment, the first extracting module 602 is specifically configured to extract, from the power data, first feature data corresponding to a first resource transfer model, second feature data corresponding to a second resource transfer model, and third feature data corresponding to a resource loss model; the first target characteristic data comprises first characteristic data, second characteristic data and third characteristic data.
In one embodiment, the second extracting module 603 is specifically configured to extract, from the resource data, fourth feature data corresponding to the first resource transfer model, fifth feature data corresponding to the second resource transfer model, and sixth feature data corresponding to the resource loss model; wherein the second target feature data includes fourth feature data, fifth feature data, and sixth feature data.
In one embodiment, the determining module 604 comprises:
the first determining unit is used for inputting the first characteristic data and the fourth characteristic data into the first resource transfer model to obtain a first resource transfer probability;
the second determining unit is used for inputting the second characteristic data and the fifth characteristic data into a second resource transfer model to obtain a second resource transfer probability;
a third determining unit, configured to input the third feature data and the sixth feature data into the resource loss model to obtain a resource loss probability;
and the fourth determining unit is used for determining the grade evaluation result of the target object according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
In one embodiment, the fourth determining unit is specifically configured to determine a target quantization value of the target object by using a weighted voting method according to the first resource transition probability, the second resource transition probability and the resource loss probability, and determine a level evaluation result of the target object according to the target quantization value of the target object and a corresponding relationship between different quantization values and different level evaluation results.
In one embodiment, the fourth determining unit is specifically configured to determine that the weighted voting method is a Bagging ensemble learning method.
The respective modules in the above-described level determination means may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
extracting first characteristic data corresponding to a first resource transfer model, second characteristic data corresponding to a second resource transfer model and third characteristic data corresponding to a resource loss model from the power data;
wherein the first target feature data includes first feature data, second feature data, and third feature data.
In one embodiment, the processor when executing the computer program further performs the steps of:
extracting fourth characteristic data corresponding to the first resource transfer model, fifth characteristic data corresponding to the second resource transfer model and sixth characteristic data corresponding to the resource loss model from the resource data;
wherein the second target feature data includes fourth feature data, fifth feature data, and sixth feature data.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
inputting the first characteristic data and the fourth characteristic data into a first resource transfer model to obtain a first resource transfer probability;
inputting the second characteristic data and the fifth characteristic data into a second resource transfer model to obtain a second resource transfer probability;
inputting the third characteristic data and the sixth characteristic data into a resource loss model to obtain a resource loss probability;
and determining a grade evaluation result of the target object according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining a target quantization value of the target object by using a weighted voting method according to the first resource transfer probability, the second resource transfer probability and the resource loss probability;
and determining the grade evaluation result of the target object according to the target quantization value of the target object and the corresponding relation between the different quantization values and the different grade evaluation results.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
the weighted voting method is a Bagging ensemble learning method.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting first characteristic data corresponding to the first resource transfer model, second characteristic data corresponding to the second resource transfer model and third characteristic data corresponding to the resource loss model from the electric power data;
wherein the first target feature data includes first feature data, second feature data, and third feature data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting fourth characteristic data corresponding to the first resource transfer model, fifth characteristic data corresponding to the second resource transfer model and sixth characteristic data corresponding to the resource loss model from the resource data;
wherein the second target feature data includes fourth feature data, fifth feature data, and sixth feature data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the first characteristic data and the fourth characteristic data into a first resource transfer model to obtain a first resource transfer probability;
inputting the second characteristic data and the fifth characteristic data into a second resource transfer model to obtain a second resource transfer probability;
inputting the third characteristic data and the sixth characteristic data into a resource loss model to obtain a resource loss probability;
and determining the grade evaluation result of the target object according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a target quantization value of the target object by using a weighted voting method according to the first resource transfer probability, the second resource transfer probability and the resource loss probability;
and determining the grade evaluation result of the target object according to the target quantization value of the target object and the corresponding relation between the different quantization values and the different grade evaluation results.
In one embodiment, the computer program when executed by the processor further performs the steps of:
the weighted voting method is a Bagging ensemble learning method.
In one embodiment, a computer program product is provided, comprising a computer program which when executed by a processor performs the steps of:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting first characteristic data corresponding to the first resource transfer model, second characteristic data corresponding to the second resource transfer model and third characteristic data corresponding to the resource loss model from the electric power data;
wherein the first target feature data includes first feature data, second feature data, and third feature data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting fourth characteristic data corresponding to the first resource transfer model, fifth characteristic data corresponding to the second resource transfer model and sixth characteristic data corresponding to the resource loss model from the resource data;
wherein the second target feature data includes fourth feature data, fifth feature data, and sixth feature data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the first characteristic data and the fourth characteristic data into a first resource transfer model to obtain a first resource transfer probability;
inputting the second characteristic data and the fifth characteristic data into a second resource transfer model to obtain a second resource transfer probability;
inputting the third characteristic data and the sixth characteristic data into a resource loss model to obtain a resource loss probability;
and determining the grade evaluation result of the target object according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a target quantization value of the target object by using a weighted voting method according to the first resource transfer probability, the second resource transfer probability and the resource loss probability;
and determining the grade evaluation result of the target object according to the target quantization value of the target object and the corresponding relation between the different quantization values and the different grade evaluation results.
In one embodiment, the computer program when executed by the processor further performs the steps of:
the weighted voting method is a Bagging ensemble learning method.
It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware that is instructed by a computer program, and the computer program may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method for rank determination, the method comprising:
acquiring power data of a target object in a power system and resource data of a resource system;
performing feature extraction on the electric power data to obtain first target feature data;
performing feature extraction on the resource data to obtain second target feature data;
and determining a grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
2. The method of claim 1, wherein the performing feature extraction on the power data to obtain first target feature data comprises:
extracting first characteristic data corresponding to a first resource transfer model, second characteristic data corresponding to a second resource transfer model and third characteristic data corresponding to a resource loss model from the electric power data;
wherein the first target feature data comprises the first feature data, the second feature data, and the third feature data.
3. The method of claim 1, wherein the performing feature extraction on the resource data to obtain second target feature data comprises:
extracting fourth characteristic data corresponding to the first resource transfer model, fifth characteristic data corresponding to the second resource transfer model and sixth characteristic data corresponding to the resource loss model from the resource data;
wherein the second target feature data comprises the fourth feature data, the fifth feature data, and the sixth feature data.
4. The method of claim 1, wherein determining a rating assessment result for the target object based on the first target feature data and the second target feature data comprises:
inputting the first characteristic data and the fourth characteristic data into the first resource transfer model to obtain a first resource transfer probability;
inputting the second characteristic data and the fifth characteristic data into the second resource transfer model to obtain a second resource transfer probability;
inputting the third characteristic data and the sixth characteristic data into the resource loss model to obtain a resource loss probability;
and determining the grade evaluation result of the target object according to the first resource transfer probability, the second resource transfer probability and the resource loss probability.
5. The method of claim 4, wherein determining the target object's rank assessment result according to the first resource transition probability, the second resource transition probability, and the resource loss probability comprises:
determining a target quantization value of the target object by using a weighted voting method according to the first resource transfer probability, the second resource transfer probability and the resource loss probability;
and determining the grade evaluation result of the target object according to the target quantization value of the target object and the corresponding relation between different quantization values and different grade evaluation results.
6. The method of claim 5, wherein the weighted voting method is a Bagging ensemble learning method.
7. A rank determination apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring the power data of the target object in the power system and the resource data in the resource system;
the first extraction module is used for extracting the characteristics of the electric power data to obtain first target characteristic data;
the second extraction module is used for extracting the features of the resource data to obtain second target feature data;
and the determining module is used for determining the grade evaluation result of the target object according to the first target characteristic data and the second target characteristic data.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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