阅读说明：本技术 一种股票资讯信息推送方法、设备及存储介质 (Stock information pushing method, equipment and storage medium ) 是由 黎原 于 2021-08-26 设计创作，主要内容包括：本发明公开了一种股票资讯信息推送方法、设备及存储介质,该方法包括以下步骤：获取至少一条资讯信息；对所述至少一条资讯信息中每条资讯信息进行语义分析,得到至少一条资讯信息的关键实体；分别计算所述至少一条资讯信息中每条资讯信息的关键实体与偏好表中偏好项的匹配度,筛选出与偏好表中偏好项的匹配度超过一匹配阈值的关键实体；推送与偏好表中偏好项的匹配度超过一匹配阈值的关键实体对应的资讯信息。本方案通过对资讯信心进行语义分析得到关键实体,通过关键实体与偏好表中偏好项的匹配度筛选用户关心且对用户投资有用的资讯,提高资讯信息对用户投资的可参考性。其推送用户关心的资讯,提高用户投资的可参考性。(The invention discloses a stock information pushing method, equipment and a storage medium, wherein the method comprises the following steps: acquiring at least one piece of information; performing semantic analysis on each piece of information in the at least one piece of information to obtain a key entity of the at least one piece of information; respectively calculating the matching degree of the key entity of each piece of information in the at least one piece of information and the preference item in the preference table, and screening out the key entities of which the matching degree with the preference item in the preference table exceeds a matching threshold; pushing information corresponding to the key entities with the matching degree of the preference items in the preference table exceeding a matching threshold. According to the scheme, the key entities are obtained by performing semantic analysis on the information confidence, the information which is concerned by the user and is useful for the user investment is screened according to the matching degree of the key entities and the preference items in the preference table, and the referential of the information to the user investment is improved. It pushes the information concerned by the user and improves the referential of the investment of the user.)

1. A stock information pushing method is characterized by comprising the following steps:

acquiring at least one piece of information;

performing semantic analysis on each piece of information in the at least one piece of information to obtain a key entity of the at least one piece of information;

respectively calculating the matching degree of the key entity of each piece of information in the at least one piece of information and the preference item in the preference table, and screening out the key entities of which the matching degree with the preference item in the preference table exceeds a matching threshold;

pushing information corresponding to the key entities with the matching degree of the preference items in the preference table exceeding a matching threshold.

2. The method as claimed in claim 1, wherein the performing semantic analysis on each piece of information in the at least one piece of information comprises:

segmenting the at least one piece of information to obtain at least one section;

performing word segmentation processing on each paragraph in the at least one paragraph to obtain a word segmentation set of each paragraph, wherein the word segmentation set comprises at least one word segmentation;

respectively carrying out named entity recognition of entity classes on the participles in the participle set to obtain a named entity of each participle set;

classifying the named entities to obtain at least one category, wherein the category comprises a first category and a second category, the first category comprises organization names, organization codes, person names and security plate names, and the second category comprises named entities except the first category;

determining a keyword set according to named entities in the first category, the keyword set comprising at least one keyword;

respectively calculating the matching degrees of all named entities in the second category and all keywords in the keyword set;

and screening the named entities in the second category according to the matching degree, and forming the screened named entities in the second category and the named entities in the first category together into key entities corresponding to the information.

3. The method as claimed in claim 2, wherein the step of calculating the matching degree between the key entities of each piece of information in the at least one piece of information and the preference items in the preference table and selecting the key entities with the matching degree exceeding a matching threshold with the preference items in the preference table comprises:

respectively calculating the matching degree between all named entities of the first category in each information key entity and the preference item in the preference table,

if at least one named entity in the first category is completely matched with the preference item in the preference table, calculating the matching degree of all the named entities in the second category and the preference item in the preference table;

screening out named entities which are completely matched with the preference items in the preference table in the second category to obtain a named entity set to be confirmed, wherein the named entity set to be confirmed comprises at least one named entity;

and calculating the times of all the named entities in the named entities to be confirmed in the information, and taking the named entities with the times exceeding a matching threshold value in the second category as key entities.

4. The method of claim 1, further comprising:

and updating the preference items in the preference table.

5. The method as claimed in claim 4, wherein the updating of the preferences in the preference table comprises:

acquiring a self-selection stock item of a user, wherein the self-selection stock item comprises a stock name, a stock code and plate information;

constructing a preference table according to the self-selection stock item;

acquiring user marked content, wherein the marked content comprises marked strands and at least one piece of marked content corresponding to the marked strands;

respectively carrying out entity-class named entity identification on each piece of marked content in the at least one piece of marked content corresponding to the marked strand to obtain a named entity of the marked content;

and constructing a corresponding relation between the named entity of the marked content and the marked stock, and updating the preference item according to the corresponding relation.

6. A stock information pushing device is characterized by comprising an information acquisition unit, a key entity acquisition unit, a matching unit and a pushing unit which are sequentially in signal connection;

the information acquisition unit is used for acquiring at least one piece of information;

the key entity obtaining unit is used for performing semantic analysis on each piece of information in the at least one piece of information to obtain a key entity of the at least one piece of information;

the matching unit is used for respectively calculating the matching degree of the key entity of each piece of information in the at least one piece of information and the preference item in the preference table, and screening out the key entities of which the matching degree with the preference item in the preference table exceeds a matching threshold;

the pushing unit is used for pushing information corresponding to the key entities with the matching degree of the preference items in the preference table exceeding a matching threshold.

7. The apparatus of claim 6, wherein the key entity acquiring unit comprises a segmentation unit, a named entity recognition unit, a classification unit, a keyword acquiring unit, a matching degree calculating unit and a confirmation unit, which are connected in sequence;

the segmentation unit is used for segmenting the at least one piece of information to obtain at least one paragraph;

the word segmentation unit is used for performing word segmentation processing on each paragraph in the at least one paragraph to obtain a word segmentation set of each paragraph, and the word segmentation set comprises at least one word segmentation;

the named entity recognition unit is used for respectively carrying out named entity recognition of entity classes on the participles in the participle set to obtain a named entity of each participle set;

the classification unit is used for classifying the named entities to obtain at least one class, wherein the class comprises a first class and a second class, the first class comprises organization names, organization codes, person names and security plate names, and the second class comprises the named entities except the first class;

the keyword acquisition unit is used for determining a keyword set according to the named entities in the first category, wherein the keyword set comprises at least one keyword;

the matching degree calculation unit is used for calculating the matching degrees of all named entities in the second category and all keywords in the keyword set respectively;

the confirmation unit is used for screening the named entities in the second category according to the matching degree, and the screened named entities in the second category and the named entities in the first category jointly form key entities corresponding to the information.

8. The utility model provides a stock information pushing equipment which characterized in that, includes memory and the controller of communication connection in proper order, the computer program is stored to the memory, its characterized in that: the processor is used for reading the computer program and executing a stock information pushing method according to any one of claims 1-5.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: when the instructions are executed on a computer, a method for pushing stock information according to any one of claims 1 to 5 is performed.

Technical Field

The invention belongs to the technical field of information push, and particularly relates to a stock information push method, equipment and a storage medium.

Background

In platforms such as securities APP or websites and the like, information is recommended to users for the users to refer to, and the users can obtain the information from the information so as to better manage the investment of the users. However, in the existing platforms such as securities APP or websites, the information provided by the securities vertical industry is too flat and has a single dimension, that is, consistent content is pushed in a uniform window, the content seen by all users is the same, and the user association degree is low. The user can obtain the requirement of the associated information from the mass information, the time consumption and the accuracy of the user can be greatly reduced, and the effect of providing accurate information reference for the user is not strong.

Disclosure of Invention

In order to solve the problem that the relevance between the push information of the existing stock class platform and a user is low, the invention provides a method, equipment and a storage medium for pushing stock information, which are used for pushing information concerned by the user and improving the referential property of investment of the user.

The invention is realized by the following technical scheme:

the first aspect of the invention provides a stock information pushing method, which comprises the following steps:

acquiring at least one piece of information;

performing semantic analysis on each piece of information in the at least one piece of information to obtain a key entity of the at least one piece of information;

respectively calculating the matching degree of the key entity of each piece of information in the at least one piece of information and the preference item in the preference table, and screening out the key entities of which the matching degree with the preference item in the preference table exceeds a matching threshold;

pushing information corresponding to the key entities with the matching degree of the preference items in the preference table exceeding a matching threshold.

According to the scheme, the key entities are obtained by performing semantic analysis on the information confidence, the information which is concerned by the user and is useful for the user investment is screened according to the matching degree of the key entities and the preference items in the preference table, and the referential of the information to the user investment is improved.

In one possible design, the semantically analyzing each of the at least one information message includes:

segmenting the at least one piece of information to obtain at least one section;

performing word segmentation processing on each paragraph in the at least one paragraph to obtain a word segmentation set of each paragraph, wherein the word segmentation set comprises at least one word segmentation;

respectively carrying out named entity recognition of entity classes on the participles in the participle set to obtain a named entity of each participle set;

classifying the named entities to obtain at least one category, wherein the category comprises a first category and a second category, the first category comprises organization names, organization codes, person names and security plate names, and the second category comprises named entities except the first category;

determining a keyword set according to named entities in the first category, the keyword set comprising at least one keyword;

respectively calculating the matching degrees of all named entities in the second category and all keywords in the keyword set;

and screening the named entities in the second category according to the matching degree, and forming the screened named entities in the second category and the named entities in the first category together into key entities corresponding to the information.

The scheme carries out named entity recognition of entity class by each participle after information is subjected to segmentation word segmentation processing, identifies each entity in the information, classifies each named entity, and can screen information which is not related to securities investment in information contents related to organization names, organization codes and names by carrying out proportion calculation on the named entities in the second category, so that the accuracy of key entity recognition is improved, and the accuracy of information recommendation is improved.

In a possible design, the respectively calculating the matching degree between the key entity of each piece of information in the at least one piece of information and the preference item in the preference table, and screening out the key entities whose matching degree with the preference item in the preference table exceeds a matching threshold, includes:

respectively calculating the matching degree between all named entities of the first category in each information key entity and the preference item in the preference table,

if at least one named entity in the first category is completely matched with the preference item in the preference table, calculating the matching degree of all the named entities in the second category and the preference item in the preference table;

screening out named entities which are completely matched with the preference items in the preference table in the second category to obtain a named entity set to be confirmed, wherein the named entity set to be confirmed comprises at least one named entity;

and calculating the times of all the named entities in the named entities to be confirmed in the information, and taking the named entities with the times exceeding a matching threshold value in the second category as key entities.

In the security investment, a user cares about the investment target interested by the user, the named entity in the first category is matched with the preference item in the preference table to realize the identification of the investment target, and after the investment target interested by the user is determined, the content interested by the user is screened through the matching of the named entity in the second category, so that the accuracy of information recommendation is improved.

In one possible design, the method further includes:

and updating the preference items in the preference table.

According to the scheme, the matching degree of the information content is improved and the accuracy of information recommendation is improved by updating the preference items in the preference table.

In one possible design, updating the preference table includes:

acquiring a self-selection stock item of a user, wherein the self-selection stock item comprises a stock name, a stock code and plate information;

constructing a preference table according to the self-selection stock item;

acquiring user marked content, wherein the marked content comprises marked strands and at least one piece of marked content corresponding to the marked strands;

respectively carrying out entity type named entity recognition on each piece of marked content in the at least one piece of marked content corresponding to the marked strand to obtain a named entity of the marked content;

and constructing a corresponding relation between the named entity of the marked content and the marked stock, and updating the preference item according to the corresponding relation.

The second aspect of the invention provides a stock information pushing device, which comprises an information acquisition unit, a key entity acquisition unit, a matching unit and a pushing unit which are sequentially in signal connection;

the information acquisition unit is used for acquiring at least one piece of information;

the key entity obtaining unit is used for performing semantic analysis on each piece of information in the at least one piece of information to obtain a key entity of the at least one piece of information;

the matching unit is used for respectively calculating the matching degree of the key entity of each piece of information in the at least one piece of information and the preference item in the preference table, and screening out the key entities of which the matching degree with the preference item in the preference table exceeds a matching threshold;

the pushing unit is used for pushing information corresponding to the key entities with the matching degree of the preference items in the preference table exceeding a matching threshold.

In one possible design, the key entity obtaining unit comprises a segmentation unit, a word segmentation unit, a named entity identification unit, a classification unit, a keyword obtaining unit, a matching degree calculation unit and a confirmation unit which are connected in sequence through signals;

the segmentation unit is used for segmenting the at least one piece of information to obtain at least one paragraph;

the word segmentation unit is used for performing word segmentation processing on each paragraph in the at least one paragraph to obtain a word segmentation set of each paragraph, and the word segmentation set comprises at least one word segmentation;

the named entity recognition unit is used for respectively carrying out entity type named entity recognition on the participles in the participle set to obtain a named entity of each participle set;

the classification unit is used for classifying the named entities to obtain at least one class, wherein the class comprises a first class and a second class, the first class comprises organization names, organization codes, person names and security plate names, and the second class comprises the named entities except the first class;

the keyword acquisition unit is used for determining a keyword set according to the named entities in the first category, wherein the keyword set comprises at least one keyword;

the matching degree calculation unit is used for calculating the matching degrees of all named entities in the second category and related keys in the key word set respectively;

the confirmation unit is used for screening the named entities in the second category according to the matching degree, and the screened named entities in the second category and the named entities in the first category jointly form key entities corresponding to the information.

A third aspect of the present invention provides a stock information pushing apparatus, including a memory and a controller, which are sequentially connected in a communication manner, where the memory stores a computer program thereon, and the processor is configured to read the computer program and execute the stock information pushing method according to the first aspect and any one of the possibilities thereof.

A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon instructions for executing the stock information pushing method according to the first aspect and any one of the possibilities when the instructions are run on a computer.

Compared with the prior art, the invention at least has the following advantages and beneficial effects:

the invention obtains the key entity by carrying out semantic analysis on the information confidence, screens the information which is concerned by the user and is useful for the user investment by the matching degree of the key entity and the preference items in the preference table, and improves the referential property of the information to the user investment.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.

FIG. 1 is a flow chart of the present invention.

Detailed Description

The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments of the present invention.

It should be understood that, for the term "and/or" as may appear herein, it is merely an associative relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, B exists alone, and A and B exist at the same time.

It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be understood that specific details are provided in the following description to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may not be shown in unnecessary detail in order to avoid obscuring example embodiments.

As shown in fig. 1, the present embodiment discloses a method for pushing stock information, which may be applied to an electronic device, such as a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an Augmented Reality (AR)/Virtual Reality (VR) device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a Personal Digital Assistant (PDA), and other electronic devices, or may also be an independent application program

Step S101, at least one piece of information is obtained. In this step, the information may be information uploaded by the relevant terminal, information stored by the cloud server, or information stored locally. The information can be obtained in a wired or wireless manner, preferably in a periodic manner, so as to reduce the frequency of content push, where the periodicity may be once a day, once a week, or once every certain time.

Step S102, performing semantic analysis on each piece of information in the at least one piece of information to obtain a key entity of the at least one piece of information. Specifically, the step includes step S1021 to step S1027.

Step S1021, segmenting the at least one information message to obtain at least one segment. Each information message may have multiple segments, and the information message is processed in segments to reduce the workload of the subsequent processing. The segments may be divided according to the number of preset text words, or may be divided according to actual paragraphs, which is not limited herein.

Step S1022, performing word segmentation processing on each paragraph in the at least one paragraph to obtain a word segmentation set of each paragraph, where the word segmentation set includes at least one word segmentation.

In the step, the paragraph is segmented, and can be realized by adopting a neural network model finished by pre-training, and the selection of the neural network model can be a LeNet network, an AlexNet network, a VGGNet network, a NiN network, a GooLeNet network, a ResNet network or a DenseNet network. Specifically and preferably, the YOLO model is used. When the YOLO model is used for word segmentation processing, the method has the advantages of high speed, low error rate and high universality.

And S1023, respectively carrying out named entity recognition of entity classes on the participles in the participle sets to obtain named entities of each participle set.

The named entities comprise a plurality of categories, such as entity categories, time categories, number categories and the like, and the scheme only needs to identify the named entities of the entity categories for the word segmentation, so that the identification amount is reduced, and the calculation amount of a subsequent method is increased. The named entity recognition can be realized in various ways, for example, the named entity recognition can be realized by adopting a bidirectional pre-training language model Bert, the bidirectional pre-training language model Bert needs to be trained by utilizing a training set, and preferably, the training set is related corpus data of the securities industry.

Step S1024, classifying the named entities to obtain at least one category, wherein the category comprises a first category and a second category, the first category comprises organization names, organization codes, person names and security plate names, and the second category comprises named entities except the first category. Here, the legal names and the important leaders are named as the names of the related organizations. The organization name, the organization code, the person name and the security board name are related to the object secret mark in the security trade, and the information matching degree is improved by classifying the person name and the security board name into one class.

Taking a certain segment of information as an example, the contents are: the company plans to cooperate with a certain stock to establish a joint venture company at the Jinmen, the joint venture company concentrates on manufacturing a lithium ion battery isolation film and a coating film, the annual capacity is 16 hundred million square meters wet-process basal film and the coating film completely matched with the annual capacity, and preferentially supplies the company and the subsidiary companies, and the project plan investment sum is 52 billion yuan. The registered capital of the joint venture company is 16 hundred million yuan, wherein, a certain share designates the payers to pay 8.8 hundred million yuan and holds 55 percent of the share rights of the joint venture company; the company accepts 7.2 million yuan and has 45% of the share right of the joint venture company. A certain share of lithium battery diaphragm tap as one of four main raw materials of lithium battery has increased by more than 140% since 2 months ago this year. The latest stock price is 252 yuan per stock, and the market value is up to 2247 million yuan.

The named entities in the first category obtained through the processing in the steps S1022 to S1024 include lithium batteries, some lithium energy, 3000 × and some share, and the named entities in the second category include joint materials, isolation films, coating films, wet-process base films, spreading, share prices and the like.

Step S1025, determining a keyword set according to the named entities in the first category, wherein the keyword set comprises at least one keyword. The keywords here are similar to tags, i.e., information about industries, products, etc. that the named entity in the first category may be involved in. Taking a certain lithium energy (3000 x) as an example, the information of travel information of the company organization can be prevented from being pushed to the user.

The keywords and the named entities in the first category have corresponding relations, preferably, the keywords and the named entities in the first category can be stored locally, and when all the named entities in the first category are confirmed, the keywords can be directly called according to the corresponding relations. The corresponding relation between the keywords and the named entities in the first category can be updated in real time, so that the accuracy of information matching is improved.

And step S1026, respectively calculating the matching degrees of all the named entities in the second category and all the keywords in the keyword set. Taking the above information as an example, if a keyword set according to a certain lithium energy (3000), includes a separation film, a coating film, a wet process base film, NPL, etc., the separation film, the coating film, and the wet process base film can be completely matched with the keyword, and the joint ventures, the spread, the stock price, etc. cannot be matched.

Step S1027, the named entities in the second category are screened according to the matching degree, and the screened named entities in the second category and the named entities in the first category jointly form the key entities corresponding to the information. Specifically, named entities that can be completely matched with the keywords are taken as key entities. In this case, the obtained key entities include lithium batteries, certain lithium energy, 3000 ×, certain share, separator, coating film, wet process base film.

Step S103, respectively calculating the matching degree of the key entities of each piece of information in the at least one piece of information and the preference items in the preference table, and screening out the key entities of which the matching degree with the preference items in the preference table exceeds a matching threshold.

Specifically, the step first calculates the matching degree between all named entities of the first category in each information key entity and the preference item in the preference table. In this step, the preference items in the preference table are generated according to the user behavior, and the preference items in the preference table need to be updated, and the updating can be performed at any time.

The updating method of the preference items in the preference table comprises the following steps: acquiring a self-selection stock item of a user, wherein the self-selection stock item comprises a stock name, a stock code and plate information; the plate information here includes, but is not limited to, products, raw materials, etc. of a stock name corresponding company. Constructing a preference table according to the self-selection stock item; acquiring user marked content, wherein the marked content comprises marked strands and at least one piece of marked content corresponding to the marked strands; respectively carrying out entity-class named entity identification on each piece of marked content in the at least one piece of marked content corresponding to the marked strand to obtain a named entity of the marked content; and constructing a corresponding relation between the named entity of the marked content and the marked stock, and updating the preference item according to the corresponding relation.

If the user does not mark the content at this time, the preference item in the preference table generated by the user comprises the stock name, the stock code and the plate information.

And if at least one named entity in the first category is completely matched with the preference item in the preference table, calculating the matching degree of all the named entities in the second category and the preference item in the preference table.

And screening out the named entities which are completely matched with the preference items in the preference table in the second category to obtain a named entity set to be confirmed, wherein the named entity set to be confirmed comprises at least one named entity. At this time, the named entity set to be confirmed includes a barrier film, a coating film, a wet-process base film.

And finally, calculating the times of all named entities in the named entities to be confirmed in the information, and taking the named entities with the times exceeding a matching threshold value in the second category as key entities. If the times of occurrence of the isolating film, the coating film and the wet-process base film in the information are 3, 1 and 1 respectively, and the matching threshold value is 3, the information is confirmed as the information to be pushed.

Step S104, pushing information corresponding to the key entities with the matching degree of the preference items in the preference table exceeding a matching threshold.

The second aspect of the invention provides a stock information pushing device, which comprises an information acquisition unit, a key entity acquisition unit, a matching unit and a pushing unit which are sequentially in signal connection;

the information acquisition unit is used for acquiring at least one piece of information;

the key entity obtaining unit is used for performing semantic analysis on each piece of information in the at least one piece of information to obtain a key entity of the at least one piece of information;

the matching unit is used for respectively calculating the matching degree of the key entity of each piece of information in the at least one piece of information and the preference item in the preference table, and screening out the key entities of which the matching degree with the preference item in the preference table exceeds a matching threshold;

the pushing unit is used for pushing information corresponding to the key entities with the matching degree of the preference items in the preference table exceeding a matching threshold.

In one possible design, the key entity obtaining unit comprises a segmentation unit, a word segmentation unit, a named entity identification unit, a classification unit, a keyword obtaining unit, a matching degree calculation unit and a confirmation unit which are connected in sequence through signals;

the segmentation unit is used for segmenting the at least one piece of information to obtain at least one paragraph;

the word segmentation unit is used for performing word segmentation processing on each paragraph in the at least one paragraph to obtain a word segmentation set of each paragraph, and the word segmentation set comprises at least one word segmentation;

the named entity recognition unit is used for respectively carrying out entity type named entity recognition on the participles in the participle set to obtain a named entity of each participle set;

the classification unit is used for classifying the named entities to obtain at least one class, wherein the class comprises a first class and a second class, the first class comprises organization names, organization codes, person names and security plate names, and the second class comprises the named entities except the first class;

the keyword acquisition unit is used for determining a keyword set according to the named entities in the first category, wherein the keyword set comprises at least one keyword;

the matching degree calculation unit is used for calculating the matching degrees of all named entities in the second category and related keys in the key word set respectively;

the confirmation unit is used for screening the named entities in the second category according to the matching degree, and the screened named entities in the second category and the named entities in the first category jointly form key entities corresponding to the information.

A third aspect of the present invention provides a stock information pushing apparatus, including a memory and a controller, which are sequentially connected in a communication manner, where the memory stores a computer program thereon, and the processor is configured to read the computer program and execute the stock information pushing method according to the first aspect and any one of the possibilities thereof. For example, the Memory may include, but is not limited to, a Random-Access Memory (RAM), a Read-Only Memory (ROM), a Flash Memory (Flash Memory), a First-in First-out (FIFO), a First-in Last-out (FILO), and/or a First-in Last-out (FILO); the processor may not be limited to the use of a microprocessor of the model number STM32F105 family. Furthermore, the computer device may also include, but is not limited to, a power supply unit, a display screen, and other necessary components.

The operation principle of the apparatuses disclosed in the second and third aspects of the present invention is the same as that of the first aspect, and therefore, the descriptions thereof are omitted.

A fourth aspect of the present invention provides a computer-readable storage medium, which stores instructions that, when executed on a computer, perform a stock information pushing method according to the first aspect or any one of the possibilities. The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, floppy disks, optical disks, hard disks, flash memories, flash disks and/or Memory sticks (Memory sticks), etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: modifications may be made to the embodiments described above, or equivalents may be substituted for some of the features described. And such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.