CN114662337A - Data evaluation system, device and storage medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a data evaluation system, a data evaluation device and a storage medium. The data evaluation system includes: a processor configured to perform the steps of: acquiring first type data and second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence coefficient of the first type data is attenuated along with time; respectively determining a first level weight of the first type of data and a first level weight of the second type of data; determining a second hierarchical weight for the first type of data based on the non-updated time for the first type of data, and determining a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data; determining a data evaluation result of the object based on the first and second hierarchical weights of the first type of data and the first and second hierarchical weights of the second type of data. By the technical scheme, the reliability of the data evaluation result is improved.
Description
Technical Field
The embodiment of the disclosure relates to the field of data processing, in particular to a data evaluation system, a data evaluation device and a storage medium.
Background
At present, some psychological or physical attention software is available on the market, but the weights of all physical and psychological factors are difficult to assign due to complex and various data.
In the prior art, a fixed weight is often adopted to score data authorized by a user, so that the reliability of an evaluation result is poor.
Disclosure of Invention
The embodiment of the disclosure provides a data evaluation system, a data evaluation device and a storage medium, so as to improve the reliability of evaluation results.
In a first aspect, an embodiment of the present disclosure provides a data evaluation system, including:
a processor configured to perform the steps of:
acquiring first type data and second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence coefficient of the first type data is attenuated along with time;
determining a first hierarchical weight of the first type of data and a first hierarchical weight of the second type of data, respectively;
determining a second hierarchical weight for the first type of data based on the non-update time for the first type of data, and determining a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data;
determining a data evaluation result of the object based on the first hierarchical weight and the second hierarchical weight of the first type of data and the first hierarchical weight and the second hierarchical weight of the second type of data.
In a second aspect, an embodiment of the present disclosure further provides a data evaluation apparatus, where the apparatus is configured in a processor, and includes:
the data acquisition module is used for acquiring first type data and second type data, wherein the first type data and the second type data are related data of the same object, and the confidence coefficient of the first type data is attenuated along with time;
a first-level weight determining module, configured to determine a first-level weight of the first type of data and a first-level weight of the second type of data respectively;
a second level weight determination module to determine a second level weight for the first type of data based on the non-update time for the first type of data and to determine a second level weight for the second type of data based on the second level weight for the first type of data;
and the evaluation result determining module is used for determining the data evaluation result of the object based on the first level weight and the second level weight of the first type data and the first level weight and the second level weight of the second type data.
In a third aspect, embodiments of the present disclosure also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a data evaluation method, the method comprising:
acquiring first type data and second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence coefficient of the first type data is attenuated along with time;
determining a first level weight of the first type of data and a first level weight of the second type of data, respectively;
determining a second hierarchical weight for the first type of data based on the non-update time for the first type of data, and determining a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data;
determining a data evaluation result of the object based on the first and second hierarchical weights of the first type of data and the first and second hierarchical weights of the second type of data.
According to the technical scheme of the embodiment of the disclosure, a first class weight of the first type data and a first class weight of a second type data are respectively determined by acquiring the first type data and the second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence coefficient of the first type data decays along with time; determining a second level weight of the first type data based on the non-updated time of the first type data, so that the second level weight can be adjusted along with the change of the non-updated time, and the self-adaptive adjustment of the weight is realized; furthermore, the data evaluation result of the object is determined according to the adjusted first level weight and second level weight of the first type data and the adjusted first level weight and second level weight of the second type data, so that the reliability of the data evaluation result is improved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1 is a schematic flowchart illustrating a method executed by a processor in a data evaluation system according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating a method executed by a processor in a data evaluation system according to a second embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a data evaluation apparatus provided in a third embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
Example one
Fig. 1 is a schematic flowchart of a method executed by a processor in a data evaluation system according to a first embodiment of the present disclosure, where the first embodiment of the present disclosure is adapted to a situation where weights of different types of data are adaptively adjusted, the data evaluation system may be implemented in a form of software and/or hardware, and specifically, the data evaluation system includes the processor, and the processor is configured and executes the following steps:
s110, obtaining first type data and second type data, wherein the first type data and the second type data are related data of the same object, and the confidence degree of the first type data is attenuated along with time.
And S120, respectively determining the first level weight of the first type of data and the first level weight of the second type of data.
S130, determining a second level weight of the first type data based on the non-updating time of the first type data, and determining a second level weight of the second type data based on the second level weight of the first type data.
S140, determining a data evaluation result of the object based on the first level weight and the second level weight of the first type data and the first level weight and the second level weight of the second type data.
In the embodiment of the present disclosure, the first type data and the second type data are associated data of the same object, wherein the object may include a person, an animal, and the like. The confidence of the first type of data decays over time, i.e., the reliability of the first type of data decreases over time, indicating that the first type of data is time-sensitive. The first type data and the second type data can be data set to be public or authorized by a user, and the processor can call or receive the first type data and the second type data sent by other equipment from a preset storage position.
It should be noted that the first type data and the second type data may be incomplete data, that is, the first type data and the second type data currently include partial index data due to collection loss, incomplete data, or data validity. When the first type data and the second type data are incomplete, the evaluation result of the data is calculated by using the original fixed weight, which results in poor reliability of the evaluation result of the data.
Furthermore, in order to solve the problem of poor reliability of the evaluation result, the method and the device realize the readjustment of the weight by respectively determining the first level weight and the second level weight of each type of data, determine the data evaluation result according to the adjusted weight, and improve the reliability of the data evaluation result.
Specifically, the processor determines a first hierarchical weight for the first type of data and a first hierarchical weight for the second type of data, respectively. It will be appreciated that the first hierarchical weights for the first type of data are calculated separately from the first hierarchical weights for the second type of data to enable differentiation between the first and second types of data.
On the basis of the foregoing embodiments, the determining the first level weight of the first type data and the first level weight of the second type data respectively includes: determining the weight corresponding to each index data in the first type of data, and determining the first level weight corresponding to the first type of data based on the weight corresponding to each index data in the first type of data; determining the weight corresponding to each index data in the second type of data, and determining the first level weight corresponding to the second type of data based on the weight corresponding to each index data in the second type of data.
The first type of data may include a plurality of index data, and different index data may be in a table form, a text form, or a graph form, which is not limited herein.
Exemplarily, the weight corresponding to each index data in the first type data can be determined through an analytic hierarchy process, and the weights corresponding to each index data in the first type data are summarized to obtain a first hierarchical weight corresponding to the first type data; similarly, the weights corresponding to the index data in the second type of data can be determined through an analytic hierarchy process, and the weights corresponding to the index data in the second type of data are summarized to obtain the first hierarchical weight corresponding to the second type of data.
It should be noted that when each index data in the first type data changes, the weight corresponding to each index data in the first type data also changes, for example, if a new index data is added to the first type data, the first level weight corresponding to the first type data is determined again according to the added new index data and the previous index data. Similarly, when each index data in the second type data changes, the first level weight corresponding to the second type data also changes.
Further, a second hierarchical weight of the first type of data is determined based on the non-update time of the first type of data, and a second hierarchical weight of the second type of data is determined based on the second hierarchical weight of the first type of data.
Wherein the non-updated time may be understood as a time difference between the current time stamp and the historical update time stamp of the first type of data. It can be understood that the confidence of the first type data is attenuated with time, and the second level weight of the first type data is adjusted according to the non-updated time, so that the second level weight can be adjusted with the increase of the non-updated time, and the self-adaptive adjustment of the weight is realized.
It can be understood that the second hierarchical weight is composed of the second hierarchical weight of the first type of data and the second hierarchical weight of the second type of data, and on the basis of obtaining the second hierarchical weight of the first type of data, the second hierarchical weight of the second type of data can be quickly determined according to the second hierarchical weight of the first type of data, so that the second hierarchical weight determination speed is improved.
Further, a data evaluation result of the object is determined based on the first hierarchical weight and the second hierarchical weight of the first type of data and the first hierarchical weight and the second hierarchical weight of the second type of data.
The data evaluation result may be understood as an evaluation result of the object-related data, and may be in a score form or an analysis result form, which is not limited herein.
For example, after the first level weight and the second level weight are obtained, a mathematical operation may be performed according to the scores of the types of data, and the first level weight and the second level weight to obtain the data evaluation score of the current object. The first type data may include an evaluation score of the a index data and an evaluation score of the B index data, weights of the a index data and the B index data may be 0.4 and 0.6, respectively, the second type data may include an evaluation score of the C index data and an evaluation score of the D index data, weights of the C index data and the D index data may be 0.3 and 0.7, respectively; the second hierarchical weight of the first type of data and the second hierarchical weight of the second type of data may be 0.6 and 0.4, respectively; the calculation formula is as follows:
data evaluation result ═ a × 0.4+ b × 0.6+ (c × 0.3+ d × 0.7) × 0.4
Wherein a represents the evaluation score of the a index data, B represents the evaluation score of the B index data, C represents the evaluation score of the C index data, and D represents the evaluation score of the D index data.
According to the technical scheme of the embodiment of the disclosure, a processor respectively determines a first level weight of first type data and a first level weight of second type data by acquiring the first type data and the second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence degree of the first type data is attenuated along with time; determining a second level weight of the first type data based on the non-updated time of the first type data, so that the second level weight can be adjusted along with the change of the non-updated time, and the self-adaptive adjustment of the weight is realized; furthermore, the data evaluation result of the object is determined according to the adjusted first level weight and second level weight of the first type data and the adjusted first level weight and second level weight of the second type data, so that the reliability of the data evaluation result is improved.
Example two
Fig. 2 is a schematic flowchart of a method executed by a processor in a data evaluation system according to an embodiment of the present disclosure, and the system according to this embodiment and various alternatives in the data evaluation system according to the foregoing embodiments may be combined. The data evaluation system provided by the embodiment is further optimized. Optionally, the determining a second hierarchical weight of the first type of data based on the non-update time of the first type of data includes: and inputting the non-updated time of the first type of data into a weight attenuation model to obtain a second level weight of the first type of data. The method is executed by the following steps:
s210, obtaining first type data and second type data, wherein the first type data and the second type data are related data of the same object, and the confidence degree of the first type data is attenuated along with time.
S220, respectively determining the first level weight of the first type data and the first level weight of the second type data.
S230, inputting the non-updated time of the first type of data to a weight attenuation model to obtain a second level weight of the first type of data, and determining the second level weight of the second type of data based on the second level weight of the first type of data.
S240, determining a data evaluation result of the object based on the first level weight and the second level weight of the first type data and the first level weight and the second level weight of the second type data.
The weight attenuation model may be a preset function model. Specifically, the processor 110 inputs the non-updated time of the first type data as input data to the weight attenuation model for data calculation to obtain the second level weight of the first type data, so that the second level weight can be adjusted along with the change of the non-updated time, thereby realizing the self-adaptive adjustment of the weight.
On the basis of the foregoing embodiments, the inputting the non-updated time of the first type of data into a weight attenuation model to obtain a second hierarchical weight of the first type of data includes:
w1＝A-0.1αeday/30
wherein, w1A second level weight representing a first type of data, a representing a second level initial weight of the first type of data, a representing a decay prevention factor, which may be set empirically, and day representing an un-updated time, wherein the un-updated time is a time difference between a current timestamp and a historical update timestamp of the first type of data.
Wherein, the first type data history update timestamp may be a timestamp of a last time the first type data changed.
It should be noted that, in some embodiments, in the case that the second-level weight of the first type data decays to a preset threshold, the decay of the second-level weight of the first type data is stopped, so as to ensure the reasonableness of calculating the weight.
On the basis of the foregoing embodiments, the determining the second hierarchical weight of the second type of data based on the second hierarchical weight of the first type of data includes:
w2＝1-w1；
accordingly, the second hierarchical weight comprises:
W＝[w1,w2]
wherein W represents a second level weight, W2A second hierarchical weight representing a second type of data.
It can be understood that the second hierarchical weight of the first type data and the second hierarchical weight of the second type data together form a second hierarchical weight, and the second hierarchical weight of the second type data can be obtained through simple mathematical operation on the basis of obtaining the second hierarchical weight of the first type data, so that the weight determination efficiency is improved.
On the basis of the foregoing embodiments, the steps executed and configured by the processor further include:
updating the first type data, and updating the first hierarchical weight based on the updated first type data;
setting the second-level weight as an initial weight.
Illustratively, when the processor receives new first-type data, the new first-type data and the historical first-type data are stored together to update the first-type data, and further, the updated first-type data is re-evaluated through expert evaluation or an analytic hierarchy process to obtain current first-level weights, and the historical first-level weights are replaced by the current first-level weights to complete updating of the first-level weights. In some embodiments, the second-level weight may also be set as the initial weight, i.e., the attenuated weight is restored to the initial weight, so that the second-level weight is more reasonable.
On the basis of the above embodiments, the first type data packet block includes psychological index data authorized by the user, and the second type data packet block includes physiological index data authorized by the user.
The physiological index data refers to data related to the body of the object to be evaluated, and can be real-time data, namely, the data is updated every moment, so that the validity of the data is ensured. Physiological indicator data may include, but is not limited to, systolic blood pressure data, diastolic blood pressure data, step count, sitting time, weight excursion data, sleep time, sleep quality, smoking data, and the like. The physiological index data can be acquired by data acquisition equipment, and the acquisition equipment comprises but is not limited to a mobile phone, a smart watch and the like. Psychological index data refers to psychologically related data of a subject to be evaluated and may include, but is not limited to, an exen personality questionnaire, a concise mood state scale, a symptom self-rating scale, a cornell CMI health questionnaire, a close relationship experience scale, a parenthood diagnostic test data, a human relationship trust scale, a psychological stress scale, and a depressive factor, etc. The psychological index data can be acquired continuously within a preset time, that is, the acquisition is not completed at one time.
Illustratively, the subject to be evaluated completes the filling of the data of the index a and the data of the index B in the physiological index data 32 days ago, the processor may receive the data of the index a and the data of the index B, completes the filling of the data of the index C in the physiological index data 3 days ago, and sends the data of the index C to the processor. Further, the C index data may be defined as an event, and the first hierarchical weight of the psychological index data before the event is Amind ═ 0.202,0.798, which may be calculated by combining expert scoring and analytic hierarchy process; the overall weight of physiology and psychology (i.e. the second level weight) is Amind _ body ═ 0.3601,0.6399], since there is no new psychometric data update 29 days before the event, the second level weight of psychometric data is attenuated from the second level initial weight 0.623 to 0.3601 according to the weight attenuation model, the attenuation formula in the weight attenuation model is as follows:
weightmind＝0.623-0.1αeday/30
in some embodiments, the first and second tier weights are normalized where they are not unique.
According to the technical scheme of the embodiment of the disclosure, the non-update time of the first type data is used as input data and is input to the weight attenuation model for data calculation to obtain the second level weight of the first type data, so that the second level weight can be adjusted along with the change of the non-update time, and the self-adaptive adjustment of the weight is realized. Furthermore, the data evaluation result of the object is determined according to the adjusted first level weight and second level weight of the first type data and the adjusted first level weight and second level weight of the second type data, so that the reliability of the data evaluation result is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a data evaluation apparatus provided in an embodiment of the present disclosure. As shown in fig. 3, the apparatus includes:
a data obtaining module 310, configured to obtain first type data and second type data, where the first type data and the second type data are associated data of the same object, and a confidence of the first type data decreases with time;
a first-level weight determining module 320, configured to determine a first-level weight of the first type of data and a first-level weight of the second type of data, respectively;
a secondary weight determination module 330 for determining a second hierarchical weight for the first type of data based on the non-update time for the first type of data and for determining a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data;
an evaluation result determining module 340, configured to determine a data evaluation result of the object based on the first hierarchical weight and the second hierarchical weight of the first type data and the first hierarchical weight and the second hierarchical weight of the second type data.
Optionally, the primary weight determining module 320 is further configured to:
determining the weight corresponding to each index data in the first type of data, and determining the first level weight corresponding to the first type of data based on the weight corresponding to each index data in the first type of data;
determining the weight corresponding to each index data in the second type of data, and determining the first level weight corresponding to the second type of data based on the weight corresponding to each index data in the second type of data.
Optionally, the secondary weight determining module 330 includes:
and the weight attenuation unit is used for inputting the non-updated time of the first type of data into a weight attenuation model to obtain a second level weight of the first type of data.
Optionally, the weight attenuation unit is specifically configured to:
w1＝A-0.1αeday/30
wherein w1And B, representing a second level weight of the first type data, A representing a second level initial weight of the first type data, alpha representing a decay prevention factor, and day representing the non-updated time, wherein the non-updated time is the time difference between the current timestamp and the historical update timestamp of the first type data.
Optionally, the secondary weight determining module 330 is further configured to:
w2＝1-w1；
accordingly, the second hierarchical weight comprises:
W＝[w1,w2]
wherein W represents a second hierarchical weight, W2A second hierarchical weight representing a second type of data.
Optionally, the apparatus further comprises:
the first hierarchical weight updating module is used for updating the first type of data and updating the first hierarchical weight based on the updated first type of data;
a second level weight resetting module for resetting the second level weight to an initial weight.
Optionally, the first hierarchical weight updating module is further configured to:
carrying out weight evaluation on the updated first type data through an analytic hierarchy process to obtain the current first-level weight;
and replacing the current first-level weight with the historical first-level weight to finish the updating of the first-level weight.
Optionally, the first type of data packet block includes physiological index data authorized by a user, and the second type of data includes psychological index data authorized by the user.
The device provided by the embodiment of the disclosure can execute the steps provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of the steps.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the embodiments of the present disclosure.
Example four
The disclosed embodiments provide a computer storage medium on which a computer program is stored, the program, when executed by a processor, implementing the data evaluation method provided by the above embodiments, the method including:
acquiring first type data and second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence coefficient of the first type data is attenuated along with time;
determining a first level weight of the first type of data and a first level weight of the second type of data, respectively;
determining a second hierarchical weight for the first type of data based on the non-update time for the first type of data, and determining a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data;
determining a data evaluation result of the object based on the first and second hierarchical weights of the first type of data and the first and second hierarchical weights of the second type of data.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the system described above; or may exist separately and not be assembled into the system.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, including conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit/module does not in some cases constitute a limitation of the unit itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A data evaluation system, comprising:
a processor configured to perform the steps of:
acquiring first type data and second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence coefficient of the first type data is attenuated along with time;
determining a first level weight of the first type of data and a first level weight of the second type of data, respectively;
determining a second hierarchical weight for the first type of data based on the non-update time for the first type of data, and determining a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data;
determining a data evaluation result of the object based on the first and second hierarchical weights of the first type of data and the first and second hierarchical weights of the second type of data.
2. The system of claim 1, wherein the determining the first hierarchical weight for the first type of data and the first hierarchical weight for the second type of data separately comprises:
determining the weight corresponding to each index data in the first type of data, and determining the first level weight corresponding to the first type of data based on the weight corresponding to each index data in the first type of data;
determining the weight corresponding to each index data in the second type of data, and determining the first level weight corresponding to the second type of data based on the weight corresponding to each index data in the second type of data.
3. The system of claim 1, wherein the determining a second hierarchical weight for the first type of data based on the time of non-update of the first type of data comprises:
and inputting the non-updated time of the first type of data into a weight attenuation model to obtain a second level weight of the first type of data.
4. The system of claim 3, wherein inputting the non-updated time of the first type of data to a weight decay model, resulting in a second level of weight of the first type of data, comprises:
w1＝A-0.1αeday/30
wherein w1And B, representing a second level weight of the first type data, A representing a second level initial weight of the first type data, alpha representing a decay prevention factor, and day representing the non-updated time, wherein the non-updated time is the time difference between the current timestamp and the historical update timestamp of the first type data.
5. The system of claim 4, wherein the determining the second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data comprises:
w2＝1-w1；
accordingly, the second hierarchical weight comprises:
W＝[w1,w2]
wherein W represents a second level weight, W2A second hierarchical weight representing a second type of data.
6. The system of claim 1, wherein the processor is configured to perform the steps further comprising:
updating the first type data, and updating the first hierarchical weight based on the updated first type data;
setting the second-level weight as an initial weight.
7. The system of claim 6, wherein updating the first hierarchical weight based on the updated first type of data comprises:
carrying out weight evaluation on the updated first type data through an analytic hierarchy process to obtain the current first-level weight;
and replacing the current first-level weight with the historical first-level weight to finish the updating of the first-level weight.
8. The system of claim 1, wherein the first type of data packet block is user-authorized physiological metric data and the second type of data packet block comprises user-authorized psychological metric data.
9. A data evaluation device, the device being configured in a processor, comprising:
the data acquisition module is used for acquiring first type data and second type data, wherein the first type data and the second type data are related data of the same object, and the confidence coefficient of the first type data is attenuated along with time;
a first-level weight determining module, configured to determine a first-level weight of the first type of data and a first-level weight of the second type of data respectively;
a secondary weight determination module to determine a second hierarchical weight for the first type of data based on the non-updated time for the first type of data and to determine a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data;
and the evaluation result determining module is used for determining the data evaluation result of the object based on the first level weight and the second level weight of the first type data and the first level weight and the second level weight of the second type data.
10. A storage medium containing computer-executable instructions, which when executed by a computer processor, perform a method for data evaluation, the method comprising:
acquiring first type data and second type data, wherein the first type data and the second type data are associated data of the same object, and the confidence coefficient of the first type data is attenuated along with time;
determining a first level weight of the first type of data and a first level weight of the second type of data, respectively;
determining a second hierarchical weight for the first type of data based on the non-update time for the first type of data, and determining a second hierarchical weight for the second type of data based on the second hierarchical weight for the first type of data;
determining a data evaluation result of the object based on the first and second hierarchical weights of the first type of data and the first and second hierarchical weights of the second type of data.
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| CN202210418527.0A CN114662337A (en) | 2022-04-20 | 2022-04-20 | Data evaluation system, device and storage medium |
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