阅读说明：本技术 一种实体权重评分方法、系统、电子设备及存储介质 (Entity weight scoring method, system, electronic equipment and storage medium ) 是由 张靖南 于 2020-11-23 设计创作，主要内容包括：本发明提出一种实体权重评分方法、系统、电子设备及存储介质,其方法技术方案包括标准库建立步骤,设立一标准库,在所述标准库中设定标准实体属性、标准情感状态、所述标准情感状态的权重值；数据获取步骤,获取电商平台上一商品的原始评价数据；数据预处理步骤,将所述原始评价数据进行冗余信息过滤,并进行分词处理；实体处理步骤,对所述预处理后的原始评价数据进行实体级别处理,并根据所述实体级别处理结果建立一实体情感对应表；评分步骤,根据所述实体情感对应表、所述标准库计算商品的分值。本发明可以解决现有实体权重评分方法对情感状态利用不足、不科学问题。(The invention provides an entity weight scoring method, a system, electronic equipment and a storage medium, wherein the method comprises the following steps of establishing a standard library, setting standard entity attributes, standard emotional states and weight values of the standard emotional states in the standard library; a data acquisition step, namely acquiring original evaluation data of a commodity on an e-commerce platform; a data preprocessing step, namely filtering redundant information of the original evaluation data and performing word segmentation processing; an entity processing step, namely, carrying out entity level processing on the preprocessed original evaluation data, and establishing an entity emotion corresponding table according to the entity level processing result; and a grading step, namely calculating the score of the commodity according to the entity emotion correspondence table and the standard library. The invention can solve the problems of insufficient utilization and unscientific of the existing entity weight scoring method on the emotional state.)

1. An entity weight scoring method, comprising:

establishing a standard library, namely establishing the standard library, and setting standard entity attributes, standard emotional states and weight values of the standard emotional states in the standard library;

a data acquisition step, namely acquiring original evaluation data of a commodity on an e-commerce platform;

a data preprocessing step, namely filtering redundant information of the original evaluation data and performing word segmentation processing;

an entity processing step, namely, carrying out entity level processing on the preprocessed original evaluation data, and establishing an entity emotion corresponding table according to the entity level processing result;

and a grading step, namely calculating the score of the commodity according to the entity emotion correspondence table and the standard library.

2. The entity weight scoring method of claim 1, wherein the entity processing step comprises:

an entity marking step, marking the entity and the entity attribute in the original evaluation data, and acquiring the emotional state of the entity attribute;

an attribute matching step, namely comparing the entity attribute with a standard entity attribute in the standard library, judging that the entity attribute is similar when the similarity reaches a threshold value, and recording the matched standard entity attribute;

an emotion matching step, namely comparing the emotion state of the entity attribute with the standard emotion state in the standard library, judging that the emotion states are similar when the similarity reaches a threshold value, and recording the matched standard emotion state;

and a corresponding table establishing step, namely establishing the entity, the standard entity attribute corresponding to the entity and the standard emotional state as an entity emotion corresponding table.

3. The entity weight scoring method of claim 1 or 2, wherein the entity attribute labeling is implemented by an LSTM-CRF model.

4. The entity weight scoring method according to claim 1 or 2, wherein the similarity matching is achieved by cosine similarity matching.

5. An entity weight scoring system, comprising:

the standard library establishing unit comprises a standard library, wherein standard entity attributes, standard emotional states and weight values of the standard emotional states are set in the standard library;

the data acquisition unit is used for acquiring original evaluation data of a commodity on the E-commerce platform;

the data preprocessing unit is used for filtering redundant information of the original evaluation data and performing word segmentation processing;

the entity processing unit is used for carrying out entity level processing on the preprocessed original evaluation data and establishing an entity emotion corresponding table according to the entity level processing result;

and the scoring unit is used for calculating the score of the commodity according to the entity emotion corresponding table and the standard library.

6. The entity weight scoring system of claim 5, wherein the entity processing unit comprises:

the entity marking module marks the entity and the entity attribute in the original evaluation data and acquires the emotional state of the entity attribute;

the attribute matching module is used for comparing the entity attribute with the standard entity attribute in the standard library, judging that the entity attribute is similar when the similarity reaches a threshold value, and recording the matched standard entity attribute;

the emotion matching module is used for comparing the emotion state of the entity attribute with the standard emotion state in the standard library, judging that the emotion states are similar when the similarity reaches a threshold value, and recording the matched standard emotion state;

and the corresponding table establishing module is used for establishing the entity, the standard entity attribute corresponding to the entity and the standard emotional state into an entity emotion corresponding table.

7. The entity weight scoring system of claim 5 or 6, wherein the entity attribute labeling is implemented by an LSTM-CRF model.

8. The entity weight scoring system of claim 5 or 6, wherein the similarity matching is achieved by cosine similarity matching.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the entity weight scoring method of any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the entity weight scoring method according to any one of claims 1 to 4.

Technical Field

The invention belongs to the field of natural language processing, and particularly relates to an entity weight scoring method, an entity weight scoring system, electronic equipment and a storage medium.

Background

The commodities purchased by people online and offline can be used on the Internet, and the user can have the emotional states of the commodities, and the feedback of the public to the entity can be acquired by capturing the data, and a certain score is given, so that the commodity producer and the advertisement publisher can be directed in the aspects of advertisement delivery and entity performance improvement. However, due to the huge data volume, the data is complicated to arrange simply by manpower, the time consumption is long, a large amount of manpower and financial resources are needed, and meanwhile, the scoring non-objectivity is caused by the influence of artificial subjective emotion in the scoring process.

The evaluation of commodities in the internet is complicated, the evaluation of related entities and entity attributes can be shown in evaluation data, most of the prior art carries out entity recommendation by collecting logs and predicting user-entity analysis and daily behaviors of users, or labels of positive evaluation or negative evaluation are marked for evaluation by analyzing the evaluation logs instead of scoring through weights of the entities and the attributes thereof, so that the aim of optimizing products in a targeted manner is fulfilled. At present, the entity scoring is usually performed through simple manual processing and subjective scoring, and the defects of error, long manual time consumption and the like exist.

Disclosure of Invention

The embodiment of the application provides an entity weight scoring method, an entity weight scoring system, electronic equipment and a storage medium, and aims to at least solve the problems that the existing entity weight scoring method is complicated in data processing process, long in time consumption of manual processing, poor in scoring due to the fact that no standard exists, and poor in scoring and one-sidedness caused by the lack of statistics on evaluation data of entity attributes.

In a first aspect, an embodiment of the present application provides an entity weight scoring method, including: establishing a standard library, namely establishing the standard library, and setting standard entity attributes, standard emotional states and weight values of the standard emotional states in the standard library; a data acquisition step, namely acquiring original evaluation data of a commodity on an e-commerce platform; a data preprocessing step, namely filtering redundant information of the original evaluation data and performing word segmentation processing; an entity processing step, namely, carrying out entity level processing on the preprocessed original evaluation data, and establishing an entity emotion corresponding table according to the entity level processing result; and a grading step, namely calculating the score of the commodity according to the entity emotion correspondence table and the standard library.

Preferably, the entity processing step includes: an entity marking step, marking the entity and the entity attribute in the original evaluation data, and acquiring the emotional state of the entity attribute; an attribute matching step, namely comparing the entity attribute with a standard entity attribute in the standard library, judging that the entity attribute is similar when the similarity reaches a threshold value, and recording the matched standard entity attribute; an emotion matching step, namely comparing the emotion state of the entity attribute with the standard emotion state in the standard library, judging that the emotion states are similar when the similarity reaches a threshold value, and recording the matched standard emotion state; and a corresponding table establishing step, namely establishing the entity, the standard entity attribute corresponding to the entity and the standard emotional state as an entity emotion corresponding table.

Preferably, the entity attribute labeling is implemented by an LSTM-CRF model.

Preferably, the similarity matching is implemented by cosine similarity matching.

In a second aspect, an embodiment of the present application provides an entity weight scoring system, which is suitable for the above entity weight scoring method, and includes: the standard library establishing unit comprises a standard library, wherein standard entity attributes, standard emotional states and weight values of the standard emotional states are set in the standard library; the data acquisition unit is used for acquiring original evaluation data of a commodity on the E-commerce platform; the data preprocessing unit is used for filtering redundant information of the original evaluation data and performing word segmentation processing; the entity processing unit is used for carrying out entity level processing on the preprocessed original evaluation data and establishing an entity emotion corresponding table according to the entity level processing result; and the scoring unit is used for calculating the score of the commodity according to the entity emotion corresponding table and the standard library.

In some of these embodiments, the entity processing unit includes: the entity marking module marks the entity and the entity attribute in the original evaluation data and acquires the emotional state of the entity attribute; the attribute matching module is used for comparing the entity attribute with the standard entity attribute in the standard library, judging that the entity attribute is similar when the similarity reaches a threshold value, and recording the matched standard entity attribute; the emotion matching module is used for comparing the emotion state of the entity attribute with the standard emotion state in the standard library, judging that the emotion states are similar when the similarity reaches a threshold value, and recording the matched standard emotion state; and the corresponding table establishing module is used for establishing the entity, the standard entity attribute corresponding to the entity and the standard emotional state into an entity emotion corresponding table.

In some of these embodiments, the entity attribute labeling is implemented by an LSTM-CRF model.

In some of these embodiments, the similarity matching is implemented by cosine similarity matching.

In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements an entity weight scoring method as described in the first aspect.

In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements an entity weight scoring method as described in the first aspect above.

Compared with the prior art, the entity weight scoring method provided by the embodiment of the application solves the problems of complexity and objectivity of the traditional manual scoring by using the LSTM-CRF model to label the entity for the evaluation data of the commodity, greatly saves labor cost, meanwhile, the model result is matched by using similarity according to the existing standard library, so that the attribute is standardized, and finally, the entity and the final score of the attribute are generated by using a weight scoring mechanism, so that the entity score is used as an index for influencing the score according to the importance of the attribute, the possibility of acquiring commodity comment attitude in a one-sidedness manner is reduced, the scoring result is finally fed back to a brand advertiser of the commodity, and a guidance direction is provided for the brand advertiser in the aspects of entity performance improvement and advertisement investment.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

FIG. 1 is a flow chart of an entity weight scoring method of the present invention;

FIG. 2 is a flowchart illustrating the substeps of step S4 in FIG. 1;

FIG. 3 is a block diagram of an entity weight scoring system of the present invention;

FIG. 4 is a block diagram of an electronic device of the present invention;

in the above figures:

1. a standard library establishing unit; 2. a data acquisition unit; 3. a data preprocessing unit; 4. a physical processing unit; 5. a scoring unit; 41. an entity tagging module; 42. an attribute matching module; 43. an emotion matching module; 60. a bus; 61. a processor; 62. a memory; 63. a communication interface.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.

It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.

Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Natural language processing is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics. Therefore, the research in this field will relate to natural language, i.e. the language that people use everyday, so it is closely related to the research of linguistics, but has important difference. Natural language processing is not a general study of natural language but is directed to the development of computer systems, and particularly software systems therein, that can efficiently implement natural language communications. It is thus part of computer science. Natural Language Processing (NLP) is a field of computer science, artificial intelligence, linguistics that focuses on the interaction between computers and human (natural) language.

A Conditional Random Field (CRF) is proposed by Lafferty et al in 2001, combines the characteristics of a maximum entropy model and a hidden Markov model, is an undirected graph model, and has a good effect in sequence labeling tasks such as word segmentation, part of speech labeling, named entity recognition and the like in recent years. The conditional random field is a typical discriminant model, and the joint probability thereof can be written in the form of multiplication of several potential functions, wherein the most common is the linear chain element random field. If x ═ represents the observed input data sequence, (x1, x2, … xn) and y ═ represents a state sequence, (y1, y2, … yn), the CRF model for the linear chain defines the joint conditional probability of the state sequence given an input sequence as:

p(y|x)＝exp{}(2-14)；

Z(x)＝{}(2-15)；

wherein Z is a probability normalization factor conditioned on the observation sequence x; fj (yi-1, yi, x, i) is an arbitrary characteristic function; is the weight of each feature function.

The Long Short-Term Memory network (LSTM) is a time-cycle neural network, which is specially designed to solve the Long-Term dependence problem of the general RNN (cyclic neural network), and all RNNs have a chain form of repeated neural network modules. In the standard RNN, this repeated structure block has only a very simple structure, e.g. one tanh layer. In recent years, with the rise of artificial intelligence and the rise of the climax, the long-term and short-term memory (LSTM) neural network model overcomes the defect of long-term dependence limitation of a common recurrent neural network model, is widely applied to various tasks of natural language processing and achieves good effects.

The web crawler is a program for automatically extracting web pages, downloads web pages from the world wide web for a search engine, and is an important component of the search engine. The traditional crawler obtains the URL on the initial webpage from the URL of one or a plurality of initial webpages, continuously extracts new URLs from the current webpage and puts the new URLs into a queue in the process of capturing the webpage until certain stop conditions of the system are met. The workflow of the focused crawler is complex, and links irrelevant to the subject need to be filtered according to a certain webpage analysis algorithm, and useful links are reserved and put into a URL queue to be captured. Then, it will select the next web page URL from the queue according to a certain search strategy, and repeat the above process until reaching a certain condition of the system. In addition, all the web pages grabbed by the crawler are stored by the system, certain analysis and filtering are carried out, and indexes are established so as to facilitate later query and retrieval; for focused crawlers, the analysis results obtained by this process may also give feedback and guidance to the subsequent grabbing process.

Cosine similarity measures the similarity between two vectors by measuring their cosine values of their angle. The cosine value of the 0-degree angle is 1, and the cosine value of any other angle is not more than 1; and its minimum value is-1. The cosine of the angle between the two vectors thus determines whether the two vectors point in approximately the same direction. When the two vectors have the same direction, the cosine similarity value is 1; when the included angle of the two vectors is 90 degrees, the value of the cosine similarity is 0; the cosine similarity has a value of-1 when the two vectors point in completely opposite directions. The result is independent of the length of the vector, only the pointing direction of the vector. Cosine similarity is commonly used in the positive space, and therefore gives values between-1 and 1. Note that upper and lower bounds apply to any dimension of vector space, and cosine similarity is most often used in high-dimensional space. For example, in information retrieval, each term is assigned a different dimension, and one dimension is represented by a vector whose values in the respective dimension correspond to the frequency with which the term appears in the document. Cosine similarity may thus give the similarity of two documents in terms of their subject matter. In addition, it is commonly used for file comparison in text mining. Furthermore, in the field of data mining, it is used to measure cohesion inside clusters.

Embodiments of the invention are described in detail below with reference to the accompanying drawings:

fig. 1 is a flowchart of an entity weight scoring method of the present invention, please refer to fig. 1, the entity weight scoring method of the present invention includes the following steps:

s1: a standard library is set in advance, and standard entity attributes, standard emotional states and weight values of the standard emotional states are set in the standard library independently according to actual conditions.

S2: acquiring original evaluation data of a commodity on a commercial platform; optionally, the original evaluation data of the commodity can be automatically acquired through a crawler technology.

S3: the original evaluation data can be filtered according to the redundant information of factors such as time and the like, and the evaluation data is subjected to word segmentation processing for subsequent entity labeling.

S4: carrying out entity level processing on the preprocessed original evaluation data, and establishing an entity emotion corresponding table according to the entity level processing result; optionally, fig. 2 is a flowchart illustrating a sub-step of step S4 in fig. 1, please refer to fig. 2:

s41, marking the entity and the entity attribute in the original evaluation data, and acquiring the emotional state of the entity attribute; optionally, an LSTM-CRF model may be used for automatic entity attribute labeling, that is, the LSTM-CRF model is used for named entity identification of an entity;

s42, comparing the entity attribute with a standard entity attribute in the standard library, judging that the entity attribute is similar when the similarity reaches a threshold value, and recording the matched standard entity attribute; optionally, the matching may be performed by a cosine similarity matching method;

s43, comparing the emotional state of the entity attribute with the standard emotional state in the standard library, judging that the emotional states are similar when the similarity reaches a threshold value, and recording the matched standard emotional state; optionally, the matching may be performed by a cosine similarity matching method;

the cosine similarity matching method has the following formula:

wherein, A is entity attribute, B is standard entity attribute; or, A is the emotional state, B is the standard emotional state;

and S44, establishing the entity, the standard entity attribute corresponding to the entity and the standard emotional state as an entity emotional corresponding table.

S5: calculating the score of the commodity according to the entity emotion corresponding table and the standard library; optionally, the entity attribute evaluation value is first calculated, and the formula is:

V(v₁,v₂,v₃)＝W{w₁,w₂,w₃,…}*E{e_attribute1,e_attribute2,e_attribute3,…}

wherein, W represents entity attribute weight value, E represents the weight value of emotional state in the standard library, each entity attribute is set with different weight values W according to the importance thereof because the importance of each entity attribute in the entity is different, the more important the entity attribute is, the larger the weight value W is, wherein, W represents the weight value of the entity attribute, and the more important the entity attribute is, the more the weight value W is, wherein₁+w₂+…+w_nWhen the emotional state corresponding to the entity attribute does not exist, selecting a preset default emotional state weight constant value;

optionally, the final score is obtained according to an average value of each entity attribute evaluation value of the original evaluation data.

It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.

The embodiment of the application provides an entity weight scoring system, which is suitable for the entity weight scoring method. As used below, the terms "unit," "module," and the like may implement a combination of software and/or hardware of predetermined functions. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware or a combination of software and hardware is also possible and contemplated.

Fig. 3 is a block diagram of an entity weight scoring system according to the present invention, please refer to fig. 3, which includes:

the standard library establishing unit 1 comprises a pre-established standard library, and standard entity attributes, standard emotional states and weighted values of the standard emotional states are set in the standard library according to actual conditions.

The data acquisition unit 2 is used for acquiring original evaluation data of a commodity on the E-commerce platform; optionally, the original evaluation data of the commodity can be automatically acquired through a crawler technology.

And the data preprocessing unit 3 is used for filtering redundant information of the original evaluation data according to factors such as time and the like, and performing word segmentation processing on the evaluation data for subsequent entity labeling.

The entity processing unit 4 is used for carrying out entity level processing on the preprocessed original evaluation data and establishing an entity emotion corresponding table according to the entity level processing result; optionally, the entity processing unit 4 further includes:

an entity labeling module 41, labeling the entity and the entity attribute in the original evaluation data, and acquiring the emotional state of the entity attribute; optionally, an LSTM-CRF model may be used for automatic entity attribute labeling, that is, the LSTM-CRF model is used for named entity identification of an entity;

an attribute matching module 42, which compares the entity attribute with the standard entity attribute in the standard library, determines that the entity attribute is similar when the similarity reaches a threshold, and records the matched standard entity attribute; optionally, the matching may be performed by a cosine similarity matching method;

an emotion matching module 43, which compares the emotion state of the entity attribute with the standard emotion state in the standard library, determines that the emotion states are similar when the similarity reaches a threshold, and records the matched standard emotion state; optionally, the matching may be performed by a cosine similarity matching method;

the cosine similarity matching method has the following formula:

wherein, A is entity attribute, B is standard entity attribute; or, A is the emotional state, B is the standard emotional state;

the correspondence table establishing module 44 establishes the entity, the standard entity attribute corresponding to the entity, and the standard emotional state as an entity emotional correspondence table. A scoring unit 5 for calculating the score of the commodity according to the entity emotion correspondence table and the standard library; optionally, the entity attribute evaluation value is first calculated, and the formula is:

V(v₁,v₂,v₃)＝W{w₁,w₂,w₃,…}*E{e_attribute1,e_attribute2,e_attribute3,…}

wherein, W represents entity attribute weight value, E represents the weight value of emotional state in the standard library, each entity attribute is set with different weight values W according to the importance thereof because the importance of each entity attribute in the entity is different, the more important the entity attribute is, the larger the weight value W is, wherein, W represents the weight value of the entity attribute, and the more important the entity attribute is, the more the weight value W is, wherein₁+w₂+…+w_nWhen the emotional state corresponding to the entity attribute does not exist, selecting a preset default emotional state weight constant value;

optionally, the final score is obtained according to an average value of each entity attribute evaluation value of the original evaluation data.

In addition, an entity weight scoring method described in conjunction with fig. 1 and 2 may be implemented by an electronic device. Fig. 4 is a block diagram of an electronic device of the present invention.

The electronic device may comprise a processor 61 and a memory 62 in which computer program instructions are stored.

Specifically, the processor 61 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 62 may include, among other things, mass storage for data or instructions. By way of example, and not limitation, memory 62 may include a Hard Disk Drive (Hard Disk Drive, abbreviated HDD), a floppy Disk Drive, a Solid State Drive (SSD), flash memory, an optical Disk, a magneto-optical Disk, tape, or a Universal Serial Bus (USB) Drive or a combination of two or more of these. Memory 62 may include removable or non-removable (or fixed) media, where appropriate. The memory 62 may be internal or external to the data processing apparatus, where appropriate. In a particular embodiment, the memory 62 is a Non-Volatile (Non-Volatile) memory. In particular embodiments, Memory 62 includes Read-Only Memory (ROM) and Random Access Memory (RAM). The ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), Electrically rewritable ROM (EAROM), or FLASH Memory (FLASH), or a combination of two or more of these, where appropriate. The RAM may be a Static Random-Access Memory (SRAM) or a Dynamic Random-Access Memory (DRAM), where the DRAM may be a Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), an Extended data output Dynamic Random-Access Memory (EDODRAM), a Synchronous Dynamic Random-Access Memory (SDRAM), and the like.

The memory 62 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions executed by the processor 61.

The processor 61 implements any of the entity weight scoring methods in the above embodiments by reading and executing computer program instructions stored in the memory 62.

In some of these embodiments, the electronic device may also include a communication interface 63 and a bus 60. As shown in fig. 4, the processor 61, the memory 62, and the communication interface 63 are connected via a bus 60 to complete communication therebetween.

The communication port 63 may be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.

The bus 60 includes hardware, software, or both to couple the components of the electronic device to one another. Bus 60 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 60 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Bus (audio Electronics Association), abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 60 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

The electronic device may perform an entity weight scoring method in the embodiments of the present application.

In addition, in combination with the entity weight scoring method in the foregoing embodiments, the embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the entity weight scoring methods in the above embodiments.

And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within 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 invention. 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 patent shall be subject to the appended claims.