Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that, the user information (including but not limited to the historical evaluation text information, the preset evaluation text, etc.) and the data (including but not limited to the data for analysis, the stored data, the displayed data, etc.) related to the present application are both information and data authorized by the user or fully authorized by each party, and the acquisition, transmission, storage, use and processing of the related data all conform to the related regulations of the national laws and regulations. Content pushed for the user (e.g., service rating text, etc.), the user may reject or may facilitate rejection of the content push, etc. In the embodiments of the present application, some software, components, models, etc. may be mentioned in the industry, and they should be regarded as exemplary only for the purpose of illustrating the feasibility of implementing the technical solution of the present application, but not to mean that the applicant has or must use the solution.
The service evaluation text generation method provided by the embodiment of the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The server 104 acquires historical evaluation text information of a target user and extracts a plurality of first text keywords from the historical evaluation text information, screens an evaluation text set corresponding to each first text keyword from a plurality of preset evaluation texts based on the first text keywords and keyword labels of the plurality of preset evaluation texts, screens target evaluation texts from the evaluation text sets according to the effective evaluation degree labels of each evaluation text in the evaluation text sets aiming at the evaluation text set, and generates service evaluation texts according to the target evaluation texts and pushes the service evaluation texts to the terminal 102. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, projection devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The head-mounted device may be a Virtual Reality (VR) device, an augmented Reality (Augmented Reality, AR) device, smart glasses, or the like. The server 104 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing services.
In an exemplary embodiment, as shown in fig. 2, a service evaluation text generation method is provided, and the method is applied to the server 104 in fig. 1 for illustration, and includes the following steps 202 to 208. Wherein:
Step 202, acquiring historical evaluation text information of a target user, and extracting a plurality of first text keywords from the historical evaluation text information.
The historical evaluation text information in step 202 is evaluation text information of a target user aiming at a preset service, the preset service is a preset service requiring acquisition of service evaluation text, the preset service can be a service provided by a certain service item or a service provided by a certain product, the historical evaluation text information can be evaluation content text information, for example, content text similar to an movie and evaluation, score information and/or option information corresponding to evaluation problem text information, for example, similar to a questionnaire, a problem in the questionnaire is evaluation problem text information, a score corresponding to a problem in the questionnaire is evaluation problem text information, an option corresponding to a problem in the questionnaire is evaluation problem text information, or a combination of the evaluation content text information, the evaluation problem text information and the score information corresponding to the evaluation problem text information, which is not limited herein.
In step 202, the number of target users is single or multiple, and in the case that the number of target users is multiple, the target users may be multiple users belonging to the same user portrait or multiple users belonging to different user portraits, which is not limited herein. The historical evaluation text information of the target user may be the historical evaluation text information under single or multiple evaluations of a plurality of target users, or may be the historical evaluation text information under multiple or single evaluations of a single target user, which is not limited herein.
Illustratively, obtaining historical evaluation text information of the target user includes querying the historical evaluation text information of the target user from a preset evaluation library.
In one embodiment, in response to a service evaluation instruction of a preset service, selecting a target user from users using the preset service, and querying to obtain historical evaluation text information of the target user, specifically, selecting the target user from the users using the preset service includes selecting all users using the preset service as the target user, or selecting users, which belong to continuous users corresponding to the preset service, of all users using the preset service as the target user.
The method for extracting the first text keywords from the historical evaluation text information comprises the steps of obtaining word frequency information of each word in the historical evaluation text information, and selecting the first text keywords from each word in the historical evaluation text information according to the word frequency information of each word in the historical evaluation text information.
The method comprises the steps of obtaining word frequency information of words in historical evaluation text information, determining the word frequency of each word in the historical evaluation text information according to the ratio of the number of times of each word in the historical evaluation text information to the number of all words in the historical evaluation text information, selecting a plurality of first text keywords from each word in the historical evaluation text information according to the word frequency information of each word in the historical evaluation text information, and selecting words, corresponding to each word in the historical evaluation text information, meeting preset word frequency conditions as first text keywords, wherein the preset word frequency conditions can be larger than a preset word frequency threshold value, and the preset word frequency conditions can also be the preset number of words belonging to the front in word frequency sequence, such as frequency top10, top3 and the like.
The word frequency information comprises inverse document frequency and word frequency, word frequency information of each word in the historical evaluation text information is obtained, the word frequency information comprises the logarithm of the ratio between the number of all texts and the number of texts containing the word plus 1 for each word in the historical evaluation text information, the inverse document frequency of the word is determined, a plurality of first text keywords are selected from each word in the historical evaluation text information according to the word frequency information of each word in the historical evaluation text information, the product between the word frequency of each word and the inverse document frequency is obtained for each word in the historical evaluation text information, the product is determined to be a frequency merging value of the word, corresponding to each word in the historical evaluation text information, meeting the preset frequency merging condition is selected to be the first text keywords, wherein the preset frequency merging condition can be larger than a preset frequency merging threshold, the preset frequency merging condition can also be that the words belong to the preset number in the sequence of the frequency merging value, for example, the frequency merging values top10, top3 and the like are selected.
Therefore, the word frequency and the inverse document frequency are used as the basis for selecting the first text keywords, so that the first text keywords can represent most evaluation tendency words of the target user.
As a further embodiment, the word frequency information includes an inverse document frequency and a word frequency, the inverse document frequency of each word in the historical evaluation text information is weighted by a first preset weight to obtain an inverse document weighted frequency, the word frequency of the word is weighted by a second preset weight to obtain a weighted word frequency, the inverse document weighted frequency of the word and the weighted word frequency are aggregated to obtain a frequency combination value of the word, wherein the aggregation mode includes summation or product.
Step 204, screening the evaluation text set corresponding to each first text keyword from the preset evaluation texts based on the first text keyword and the keyword labels of the preset evaluation texts.
The preset evaluation text in step 204 is an evaluation question text and/or an evaluation content text of the preset user using the preset service for the preset service. The evaluation text set is evaluation content of the user using the preset service for the preset service.
Illustratively, step 204 includes obtaining keyword labels of a plurality of preset evaluation texts, and for each first text keyword, screening an evaluation text set corresponding to the first text keyword from the plurality of preset evaluation texts based on a matching degree between the first text keyword and the keyword labels of the plurality of preset evaluation texts.
The method for obtaining the keyword labels of the preset evaluation texts comprises the steps of obtaining problem information corresponding to the preset evaluation texts aiming at each preset evaluation text, extracting keywords from the problem information corresponding to the preset evaluation texts, and obtaining the keyword labels of the preset evaluation texts.
It can be understood that, in the case that the preset evaluation text corresponds to the question information, the preset evaluation text is usually scoring content or option selecting content for the question information, so in this case, the question information corresponding to the preset evaluation text is used as a basis for extracting the keyword, thereby ensuring the accuracy of extracting the keyword label of the preset evaluation text.
The method comprises the steps of obtaining keyword labels of a plurality of preset evaluation texts, wherein the keyword labels comprise extracting keywords from the preset evaluation texts aiming at each preset evaluation text to obtain the keyword labels of the preset evaluation texts.
It can be understood that, in the case that the preset evaluation text does not have corresponding problem information, the preset evaluation text is usually an evaluation feedback text for the preset service, so in this case, the preset evaluation text is used as a basis for extracting keywords, and the accuracy of extracting the keyword labels of the preset evaluation text is ensured.
According to one embodiment, based on the matching degree between the first text keywords and keyword labels of a plurality of preset evaluation texts, screening an evaluation text set corresponding to the first text keywords from the plurality of preset evaluation texts comprises the steps of obtaining the matching degree between the first text keywords and keyword labels of the plurality of preset evaluation texts, and screening an evaluation text set corresponding to the first text keywords, wherein the matching degree of the first text keywords is larger than a preset matching degree threshold value, from the plurality of preset evaluation texts.
Further, obtaining the matching degree between the first text keyword and the keyword labels of the plurality of preset evaluation texts respectively includes: and determining the similarity between the first text keywords and the keyword labels of the plurality of preset evaluation texts respectively, and determining the similarity between the first text keywords and the keyword labels of the plurality of preset evaluation texts respectively as the matching degree between the first text keywords and the keyword labels of the plurality of preset evaluation texts respectively.
Step 206, for each evaluation text set corresponding to the first text keyword, screening the target evaluation text from the evaluation text set according to the effective evaluation degree label of each evaluation text in the evaluation text set.
The validity evaluation level label in step 206 is used to characterize the validity level of the information in the evaluation text for the preset service.
Illustratively, the step 206 includes, for each set of evaluation texts corresponding to the first text keywords, obtaining an effective evaluation degree label of each evaluation text in the set of evaluation texts, and screening target evaluation texts from the set of evaluation texts, wherein the effective evaluation values represented by the corresponding effective evaluation degree labels meet preset evaluation value conditions.
Alternatively, the specific embodiment of obtaining the valid evaluation degree label of each evaluation text in the set of evaluation texts may refer to the specific implementation step of step 304 described below, which is not described herein.
The preset evaluation value condition may be greater than a preset evaluation value threshold, or may be that the effective evaluation values represented by the effective evaluation level label are sorted from large to small, which belongs to a preset number of the front, for example, effective evaluation values top10, top3, and the like are selected.
Therefore, the effective evaluation value of the effective evaluation degree label characterization of the evaluation text corresponding to the selected target evaluation text is ensured to be higher.
And step 208, generating a service evaluation text according to the target evaluation text.
Illustratively, step 208 includes aggregating the target rating text to obtain the service rating text.
In one embodiment, step 208 includes, when the number of target evaluation texts is multiple, respectively arranging and combining each target evaluation text according to the text question type of each target evaluation text to obtain a service evaluation text, wherein specifically, arranging the target evaluation texts with the text question type being the ending feedback type at the rear part position of the service evaluation text, for example, ending the questionnaire, summarizing the feedback, and the like, for the target evaluation texts with the text question type not belonging to the ending feedback type, acquiring the association relationship between the target evaluation texts with the text question type not belonging to the ending feedback type, and sorting the target evaluation texts with the text question type not belonging to the ending feedback type according to the association relationship between the target evaluation texts with the text question type not belonging to the ending feedback type to obtain the service evaluation text.
In this way, the target evaluation texts are reasonably ordered according to the association relation between the text problem types and the target evaluation texts, so that the generated service evaluation texts are logically reasonable in order arrangement, and further the situation that the user evaluation will is reduced under the condition that the generated service evaluation texts are logically disordered in order arrangement is avoided, and therefore the acquisition accuracy of the service evaluation texts is improved.
The service evaluation text generation method comprises the steps of obtaining historical evaluation text information of a target user, extracting a plurality of first text keywords from the historical evaluation text information, screening an evaluation text set corresponding to each first text keyword from the preset evaluation texts based on the first text keywords and keyword labels of the preset evaluation texts, screening target evaluation texts from the evaluation text set corresponding to each first text keyword according to the effective evaluation degree labels of each evaluation text in the evaluation text set, generating service evaluation texts according to the target evaluation texts, and performing a twice screening process on the historical evaluation text information to ensure that the selected target evaluation texts correspond to the historical evaluation text information, wherein more effective evaluation information can be reflected, and further ensure more effective information which can be obtained by the service evaluation texts corresponding to the target evaluation texts, namely, the acquisition accuracy of the service evaluation texts is improved.
It can be appreciated that there may be situations where the generated service valuation text may not be able to obtain valid information, after the service valuation text is generated, the target user and/or other users feedback the generated service valuation text, and this feedback may be used to optimize the service valuation text, so as to ensure that the optimized service valuation text may obtain valid information. Therefore, there is a need for a way to optimize service valuation text.
In one exemplary embodiment, as shown in FIG. 3, after generating the service valuation text from the target valuation text, the manner in which the service valuation text is optimized includes steps 302 through 306. Wherein:
step 302, acquiring an evaluation content text corresponding to the service evaluation text, and extracting a second text keyword of the target evaluation text from the service evaluation text and the evaluation content text.
The evaluation content text is feedback content of the service evaluation text aiming at the preset service by a target user or other users using the preset service.
The method for acquiring the evaluation content text corresponding to the service evaluation text comprises the steps of acquiring the evaluation content text corresponding to the service evaluation text sent by a target user or other user terminals used by users using preset services, or scanning filling content of the service evaluation text by the target user or other users using the preset services to obtain the evaluation content text corresponding to the service evaluation text.
Alternatively, for the specific embodiment of extracting the second text keywords of the target evaluation text from the service evaluation text and the evaluation content text, reference may be made to the specific embodiment of extracting the plurality of first text keyword features from the historical evaluation text information in the above step 202, which is not described herein.
And step 304, based on the evaluation content text, performing effective evaluation degree evaluation on the target evaluation text to obtain an effective evaluation value of the target evaluation text.
As an embodiment, step 304 includes obtaining information complexity of the evaluation content text, obtaining an association relationship between the evaluation content text and a corresponding preset service, and performing effective evaluation degree evaluation on the target evaluation text according to the information complexity and the association relationship to obtain an effective evaluation value of the target evaluation text.
Further, as an embodiment, acquiring the information complexity of the evaluation content text comprises acquiring the field number of the evaluation content text and the field type of the evaluation content text, and generating the information complexity of the evaluation content text according to the field number and the field type of the evaluation content text, wherein the larger the field number of the evaluation content text is, the higher the information complexity of the evaluation content text is, the more the field type of the evaluation content text is, and the higher the information complexity of the evaluation content text is.
Therefore, the number and the types of the fields are used as the evaluation basis of the information complexity, and the information complexity is evaluated from the number and the types of the fields, so that the evaluation accuracy of the information complexity of the evaluation content text is improved.
It will be appreciated that there may be situations where there is a large space in the evaluation content text that is a concatenation word, and then there may be situations where the evaluation of the information complexity of the evaluation content text is inaccurate by using the number of fields and the category dimension described above.
In order to overcome the technical defect that the evaluation of the information complexity of the evaluation content text is inaccurate due to the fact that the large space in the evaluation content text is the connected word, as another embodiment, the method for obtaining the information complexity of the evaluation content text comprises the steps of obtaining the number of words in the evaluation content text and the parts of speech of the words, generating the information complexity of the evaluation content text according to the number of words in the evaluation content text and the parts of speech of the words, wherein the larger the number of the parts of speech of the evaluation content text belongs to the non-connected word, the higher the information complexity of the evaluation content text,
Therefore, the part of speech and the number of words are used as evaluation basis of the information complexity, and the influence of the number of effective words (non-connected words) in the evaluation content text on the information complexity of the evaluation content text is considered, so that the evaluation accuracy of the information complexity of the evaluation content text is improved.
The method comprises the steps of obtaining correlations between words in the evaluation content text and corresponding preset services, and generating the correlations between the evaluation content text and the corresponding preset services according to the correlations between the words in the evaluation content text and the preset services, wherein the higher the ratio of words in the evaluation content text, the higher the correlation between the words in the evaluation content text and the preset services is, and the stronger the correlations represented by the correlations between the evaluation content text and the corresponding preset services are.
According to the information complexity and the association relation, the effective evaluation degree evaluation is carried out on the target evaluation text to obtain an effective evaluation value of the target evaluation text, the method comprises the steps of weighting the information complexity of the evaluation content text through a third preset weight to obtain an information complexity weighting degree, weighting the association relation of the evaluation content text through a fourth preset weight to obtain an association weighting relation, aggregating the association weighting relation and the information complexity weighting degree to obtain aggregate information of the evaluation content text, and carrying out the effective evaluation degree evaluation on the target evaluation text according to the aggregate information of the evaluation content text to obtain the effective evaluation value of the target evaluation text, wherein the larger the aggregate value corresponding to the aggregate information of the evaluation content text is, the larger the effective evaluation value of the target evaluation text is, and the third preset weight and the fourth preset weight can be set by a user according to needs or can be experience values.
Step 306, updating keyword labels of a plurality of preset evaluation texts according to second text keywords of the target evaluation texts, and updating effective evaluation degree labels of the evaluation texts in the evaluation text set according to effective evaluation values of the target evaluation texts.
As one embodiment, updating the keyword labels of the plurality of preset evaluation texts according to the second text keywords of the target evaluation text comprises updating the keyword labels of the plurality of preset evaluation texts according to the difference between the second text keywords of the target evaluation text and the keyword labels of the plurality of preset evaluation texts.
As one embodiment, updating the effective evaluation degree labels of the evaluation texts in the evaluation text set according to the effective evaluation values of the target evaluation texts comprises updating the effective evaluation degree labels of the evaluation texts in the evaluation text set into labels with the corresponding effective evaluation values consistent with the effective evaluation values of the target evaluation texts.
In the embodiment, the second text keywords of the target evaluation text are extracted from the service evaluation text and the evaluation content text by acquiring the evaluation content text corresponding to the service evaluation text, the effective evaluation degree evaluation is carried out on the target evaluation text based on the evaluation content text to obtain the effective evaluation value of the target evaluation text, the keyword labels of a plurality of preset evaluation texts are updated according to the second text keywords of the target evaluation text, the effective evaluation degree labels of the evaluation texts are updated in a concentrated manner according to the effective evaluation value of the target evaluation text, the keyword labels of a plurality of preset evaluation texts are updated based on feedback (the evaluation content text) of a target user and/or other users using preset services to the service evaluation text, and the effective evaluation degree labels of the evaluation texts are updated in a concentrated manner, so that the service evaluation text can be optimized, the evaluation-feedback closed loop logic aiming at the preset service is formed, and the optimization accuracy of the service evaluation text is improved.
It will be appreciated that there may be situations where the feedback of the target user and/or other user to the generated service valuation text does not actually represent the entire community of valuations, and if the service valuation text is optimized as long as there is feedback of the target user and/or other user to the generated service valuation text, there may be situations where the feedback of the target user and/or other user to the generated service valuation text is too extreme, resulting in that the adjusted service valuation text still cannot obtain valid information, and therefore there is a need for an accurate way of optimizing the service valuation text.
In an exemplary embodiment, as shown in fig. 4, the keyword labels of a plurality of preset evaluation texts are updated according to the second text keywords of the target evaluation text, and the valid evaluation degree label of each evaluation text in the set of evaluation texts is updated according to the valid evaluation values of the target evaluation texts, including steps 308 to 310. Wherein:
If the target evaluation text does not meet the update condition, step 308 is executed, and the superimposing step is executed, in which the updated evaluation content text corresponding to the service evaluation text is superimposed with the service evaluation text to obtain a superimposed text, if the updated evaluation content text corresponding to the service evaluation text is detected.
As an embodiment, the update condition may be that the number of evaluation users corresponding to the target evaluation text is greater than a first preset number threshold.
In this way, in the case that the number of evaluation users feeding back the service evaluation text is sufficiently large, that is, in the case that the number of feedback samples can affect the service evaluation text generated by the historical evaluation text information of the target user, the service evaluation text is updated, so that the situation that a single sample makes the update of the service evaluation text actually be a negative effect update of the service evaluation text is avoided, and the optimization accuracy of the service evaluation text is improved.
As another embodiment, the update condition may be that the number of rating users belonging to the same user portrait as the target user corresponding to the target rating text is greater than a second preset number threshold.
Therefore, the optimization basis is further ensured to be the target evaluation text generated by the user belonging to the same user portrait with the target user, the updated service evaluation text is further enabled to be the user portrait attached to the target user, and the optimization accuracy of the service evaluation text is improved.
If the superimposed text does not satisfy the update condition, execution returns to step 308.
If the superimposed text satisfies the update condition, step 310 is executed to update the keyword labels of the plurality of preset evaluation texts according to the second text keywords of the superimposed text, and update the effective evaluation degree labels of each evaluation text in the set of evaluation texts according to the effective evaluation values of the superimposed text.
According to one embodiment, the keyword labels of the preset evaluation texts are updated according to second text keywords of the superimposed text, and the method comprises the steps of selecting a third text keyword and a fourth text keyword from the second text keywords of the superimposed text, wherein the third text keyword is a text keyword which corresponds to a keyword label which does not belong to the preset evaluation texts in the second text keywords of the superimposed text, the fourth text keyword is a text keyword which corresponds to a keyword label which belongs to the preset evaluation texts in the second text keywords of the superimposed text, adding the keyword label corresponding to the third text keyword to the keyword labels of the preset evaluation texts, and deleting the keyword label which corresponds to the fourth text keyword in the keyword labels of the preset evaluation texts.
It can be understood that when the number of superimposed texts is large, the tag addition of the keyword tag of the preset evaluation text is directly performed according to the third text keyword, and the tag deletion of the keyword tag of the preset evaluation text is performed according to the fourth text keyword, which may cause an inaccurate keyword tag of the preset evaluation text obtained by final updating in extreme cases.
In order to overcome the technical defect that the keyword labels of the preset evaluation texts are inaccurate due to the fact that the label adding of the keyword labels of the preset evaluation texts is directly carried out according to the third text keywords and the label deleting of the keyword labels of the preset evaluation texts is carried out according to the fourth text keywords, the keyword labels of the preset evaluation texts obtained through final updating are not accurate in the extreme case, updating the keyword labels of the preset evaluation texts according to the second text keywords of the overlapped texts comprises the steps of obtaining first word frequencies of the third text keywords in the second text keywords of the overlapped texts, obtaining second word frequencies of the fourth text keywords in the second text keywords of the overlapped texts, corresponding to the second word frequencies of the fourth text keywords in the second text keywords of the overlapped texts, and deleting the text keywords corresponding to the second word labels of the preset evaluation texts if the first word frequencies of the third text keywords are larger than the first word frequencies of the first text keywords, and the keyword labels of the fourth text keywords corresponding to the second word labels of the preset evaluation texts are corresponding to the second word labels of the fourth text keywords.
The first preset word frequency threshold value and the second preset word frequency threshold value can be set by a user according to needs, or can be an experience value, and the method is not limited.
Therefore, reasonable addition of the keyword labels of the preset evaluation texts is realized, the keyword labels of the preset evaluation texts obtained by updating can accurately represent the content which can enable the user to provide effective information, and the accuracy of the keyword labels of the preset evaluation texts obtained by updating is improved.
The method comprises the steps of obtaining difference degrees between the effective evaluation value of the superimposed text and the effective evaluation degree label of each evaluation text in the evaluation text set respectively, and updating the effective evaluation degree label of the evaluation text corresponding to the target difference degrees in the evaluation text set according to the effective evaluation value of the superimposed text if the target difference degrees meeting preset difference conditions exist in the difference degrees.
Further, the preset difference condition may be greater than a preset difference degree threshold.
According to an embodiment, the method for updating the effective evaluation degree label of the evaluation text corresponding to the target difference degree in the evaluation text set according to the effective evaluation value of the superimposed text comprises the steps of updating the effective evaluation degree label of the evaluation text corresponding to the target difference degree in the evaluation text set to be a label with the corresponding effective evaluation value consistent with the effective evaluation value of the superimposed text, or updating the effective evaluation degree label of the evaluation text corresponding to the target difference degree in the evaluation text set to be a label with the corresponding effective evaluation value consistent with the average value of the effective evaluation degree labels of the evaluation text corresponding to the target difference degree.
In this way, under the condition that the difference between the overlapped text actually fed back by the target user or the user using the preset service and the effective evaluation degree label of the evaluation text is large, the effective evaluation degree label of the evaluation text is updated, so that the target evaluation text obtained by screening the updated effective evaluation degree label of the evaluation text can be accurately represented by the actually fed back evaluation text capable of acquiring effective information, and the updating accuracy of the service evaluation text is improved.
Optionally, the method further comprises the steps of obtaining a first text quantity of user portraits corresponding to all texts in the superimposed text and the target user belonging to the same user portraits, and obtaining a second text quantity of abnormal texts in the superimposed text, wherein text evaluation information corresponding to the abnormal texts is larger than a preset difference threshold, if the influence degree is larger than the preset degree threshold, the superimposed text is determined to meet an updating condition, if the influence degree is not larger than the preset degree threshold, the superimposed text is determined to not meet the updating condition, specifically, the larger the first text quantity is, the larger the influence degree is, and the larger the second text quantity is, the larger the influence degree is.
The text evaluation information corresponding to the abnormal text comprises a second text keyword and an effective evaluation value corresponding to the abnormal text, and the text evaluation information corresponding to the historical evaluation text information comprises a first text keyword and an effective evaluation degree label corresponding to the historical evaluation text information. The preset difference threshold is a difference threshold preset according to the requirement. The preset degree threshold is an influence degree threshold value preset according to requirements.
Further, the method for obtaining the second text quantity of the abnormal texts in the superimposed text comprises the steps of obtaining first differences between second text keywords of the texts and first text keywords of the historical evaluation text information, obtaining second differences between effective evaluation values of the texts and evaluation values represented by effective evaluation degree labels of the historical evaluation text information, and aggregating the first differences and the second differences to obtain differences between the texts and the historical evaluation text information for each text in the superimposed text.
The method and the device have the advantages that the number of the first texts corresponding to the texts belonging to the same user portrait in the superimposed text is used as the generation basis of the update condition, the basis for optimizing the service evaluation text is guaranteed to be the same user portrait as the target user, the optimization accuracy of the service evaluation text is improved, the number of the second texts of the abnormal texts in the superimposed text is used as the generation basis of the update condition, the fact that large differences, such as aesthetic changes and the like, possibly exist in evaluation along with time change is guaranteed to be captured, and the optimization accuracy of the service evaluation text is improved.
In the embodiment, the overlapping step is executed if the target evaluation text does not meet the updating condition, the updating evaluation content text and the service evaluation text are overlapped under the condition that the updating evaluation content text corresponding to the service evaluation text is detected, the overlapping text is obtained, if the overlapping text does not meet the updating condition, the overlapping step is executed again, if the overlapping text meets the updating condition, the keyword labels of a plurality of preset evaluation texts are updated according to the second text keywords of the overlapping text, the effective evaluation degree label of each evaluation text in the evaluation text set is updated according to the effective evaluation value of the overlapping text, the condition that the keyword labels of the plurality of preset evaluation texts and the effective evaluation degree label of each evaluation text in the evaluation text set are updated is set, the technical defect that the service evaluation text obtained through final updating cannot acquire more effective information due to the calculation condition caused by the fact that the service evaluation text is updated every change is avoided, and therefore reasonable updating of the service evaluation text is guaranteed, and updating accuracy of the service evaluation text is improved.
The method comprises the steps of obtaining historical evaluation text information of a target user, extracting a plurality of first text keywords from the historical evaluation text information, screening an evaluation text set corresponding to each first text keyword from a plurality of preset evaluation texts based on the first text keywords and keyword labels of the preset evaluation texts, screening target evaluation texts from the evaluation text set according to effective evaluation degree labels of each evaluation text in the evaluation text set aiming at the evaluation text set, generating service evaluation texts according to the target evaluation texts, obtaining evaluation content texts corresponding to the service evaluation texts, extracting second text keywords of the target evaluation texts from the service evaluation texts and the evaluation content texts, obtaining information complexity of the evaluation content texts, obtaining association relations between the evaluation content texts and corresponding preset services, and carrying out effective evaluation degree evaluation on the target evaluation texts according to the information complexity and the association relations to obtain effective evaluation values of the target evaluation texts.
Further, if the target evaluation text does not meet the update condition, executing a superposition step, namely superposing the update evaluation content text and the service evaluation text to obtain a superposition text under the condition that the update evaluation content text corresponding to the service evaluation text is detected; obtaining a first text quantity of user portraits corresponding to each text in the superimposed text and a target user belonging to the same user portraits, and obtaining a second text quantity of abnormal texts in the superimposed text, wherein the difference between text evaluation information corresponding to the abnormal texts and text evaluation information corresponding to the historical evaluation text information is larger than a preset difference threshold value, generating the influence degree of the superimposed text on the service evaluation text based on the first text quantity and the second text quantity, determining that the superimposed text meets an updating condition if the influence degree is larger than the preset degree threshold value, determining that the superimposed text does not meet the updating condition if the influence degree is not larger than the preset degree threshold value, returning to execute the superimposed text if the superimposed text does not meet the updating condition, obtaining a first word frequency of a third text keyword in a second text keyword of the superimposed text if the superimposed text meets the updating condition, and obtaining a second word frequency of a fourth text keyword in the second text keyword of the superimposed text, wherein the third text keyword is a keyword of the text keyword corresponding to the text keyword of the superimposed text does not belong to a plurality of preset evaluation keywords, the third text keyword corresponds to a first keyword of the text keyword corresponding to the preset in the text keyword of the text of the superimposed text of the preset to be evaluated text is a plurality of the preset keyword of the text label, the method comprises the steps of adding keyword labels corresponding to a third text keyword to keyword labels of a plurality of preset evaluation texts, deleting the keyword labels corresponding to a fourth text keyword in the keyword labels of the plurality of preset evaluation texts if the second word frequency corresponding to the fourth text keyword is smaller than a second preset word frequency threshold value, obtaining difference degrees between effective evaluation values of the superimposed texts and effective evaluation degree labels of each evaluation text in an evaluation text set respectively, and updating the effective evaluation degree labels of the evaluation texts corresponding to the target difference degrees in the evaluation text set according to the effective evaluation values of the superimposed texts if the target difference degrees meeting preset difference conditions exist in the difference degrees.
The method comprises the steps of obtaining historical evaluation text information of a target user, extracting a plurality of first text keywords from the historical evaluation text information, screening an evaluation text set corresponding to each first text keyword from a plurality of preset evaluation texts based on the first text keywords and keyword labels of the plurality of preset evaluation texts, screening target evaluation texts from the evaluation text set according to the effective evaluation degree labels of each evaluation text in the evaluation text set aiming at the evaluation text set, generating service evaluation texts according to the target evaluation texts, and guaranteeing that the selected target evaluation texts correspond to the historical evaluation text information through a twice screening process, wherein more effective evaluation information can be reflected, and further guaranteeing that more effective information can be obtained by the service evaluation texts corresponding to the target evaluation texts, namely, the obtaining accuracy of the service evaluation texts is improved.
Further, based on feedback (evaluation content text) of a target user and/or other users using preset services to the service evaluation text, keyword labels of a plurality of preset evaluation texts are updated, and effective evaluation degree labels of the evaluation texts are updated in a centralized manner, so that optimization of the service evaluation text can be realized, closed loop logic aiming at evaluation-feedback of the preset services is formed, more effective information can be obtained in the optimized service evaluation text, and conditions for updating the keyword labels of the plurality of preset evaluation texts and the effective evaluation degree labels of each evaluation text in the evaluation text are set, so that the technical defect that the service evaluation text obtained through final updating cannot obtain more effective information due to calculation conditions are avoided, reasonable updating of the service evaluation text is ensured, and updating accuracy of the service evaluation text is improved.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a service evaluation text generation device for realizing the service evaluation text generation method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the device for generating service evaluation text provided below may refer to the limitation of the method for generating service evaluation text hereinabove, and will not be repeated here.
In an exemplary embodiment, as shown in fig. 5, there is provided a service evaluation text generation apparatus 500, including an extraction module 502, a first screening module 504, a second screening module 506, and a generation module 508, wherein:
the extracting module 502 is configured to obtain historical evaluation text information of a target user, and extract a plurality of first text keywords from the historical evaluation text information;
a first screening module 504, configured to screen, based on the first text keywords and keyword labels of a plurality of preset evaluation texts, an evaluation text set corresponding to each first text keyword from the plurality of preset evaluation texts;
A second screening module 506, configured to screen, for each evaluation text set corresponding to the first text keyword, a target evaluation text from the evaluation text set according to the valid evaluation degree label of each evaluation text in the evaluation text set;
and the generating module 508 is used for generating the service evaluation text according to the target evaluation text.
In one embodiment, after generating the service evaluation text according to the target evaluation text, the device further comprises an updating module, which is used for acquiring the evaluation content text corresponding to the service evaluation text, extracting second text keywords of the target evaluation text from the service evaluation text and the evaluation content text, evaluating the effective evaluation degree of the target evaluation text based on the evaluation content text to obtain effective evaluation values of the target evaluation text, updating keyword labels of a plurality of preset evaluation texts according to the second text keywords of the target evaluation text, and updating the effective evaluation degree labels of the evaluation texts in the evaluation text set according to the effective evaluation values of the target evaluation text.
In one embodiment, the updating module is further configured to obtain an information complexity of the evaluation content text, obtain an association relationship between the evaluation content text and a corresponding preset service, and perform an effective evaluation degree evaluation on the target evaluation text according to the information complexity and the association relationship to obtain an effective evaluation value of the target evaluation text.
In one embodiment, the updating module is further configured to perform the stacking step of stacking the updated evaluation content text and the service evaluation text to obtain a stacked text if the updated evaluation content text corresponding to the service evaluation text is detected, return the stacking step if the stacked text does not satisfy the updated condition, update keyword labels of a plurality of preset evaluation texts according to second text keywords of the stacked text if the stacked text satisfies the updated condition, and update valid evaluation degree labels of each evaluation text in the set of evaluation texts according to valid evaluation values of the stacked text.
In one embodiment, the updating module is further configured to obtain a first word frequency of a third text keyword in a second text keyword of the superimposed text, and obtain a second word frequency of a fourth text keyword in the second text keyword of the superimposed text, where the third text keyword is a text keyword corresponding to a keyword tag that does not belong to a plurality of preset evaluation texts in the second text keyword of the superimposed text, the fourth text keyword is a text keyword corresponding to a keyword tag that belongs to a plurality of preset evaluation texts in the second text keyword of the superimposed text, and if the first word frequency corresponding to the third text keyword is greater than a first preset word frequency threshold, the keyword tag corresponding to the third text keyword is added to the keyword tag of the plurality of preset evaluation texts, and if the second word frequency corresponding to the fourth text keyword is less than a second preset word frequency threshold, the keyword tag corresponding to the fourth text keyword tag in the plurality of preset evaluation texts is deleted.
In one embodiment, the updating module is further configured to obtain a difference degree between the effective evaluation value of the superimposed text and the effective evaluation degree label of each evaluation text in the set of evaluation texts, and if a target difference degree satisfying a preset difference condition exists in each difference degree, update the effective evaluation degree label of the evaluation text corresponding to the target difference degree in the set of evaluation texts according to the effective evaluation value of the superimposed text.
In one embodiment, the updating module is further configured to obtain a first number of texts of the user portraits corresponding to each text in the superimposed text and the target user belonging to the same user portraits, and obtain a second number of texts of the abnormal text in the superimposed text, where the text evaluation information corresponding to the abnormal text and the difference between the text evaluation information corresponding to the historical evaluation text information are greater than a preset difference threshold, generate an influence degree of the superimposed text on the service evaluation text based on the first number of texts and the second number of texts, determine that the superimposed text meets the updating condition if the influence degree is greater than the preset degree threshold, and determine that the superimposed text does not meet the updating condition if the influence degree is not greater than the preset degree threshold.
The respective modules in the above-described service evaluation text generation apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one exemplary embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store object information and service information. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a service valuation text generation method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one exemplary embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
Acquiring historical evaluation text information of a target user, and extracting a plurality of first text keywords from the historical evaluation text information;
Screening an evaluation text set corresponding to each first text keyword from a plurality of preset evaluation texts based on the first text keywords and keyword labels of the plurality of preset evaluation texts;
Aiming at the evaluation text set corresponding to each first text keyword, screening target evaluation texts from the evaluation text set according to the effective evaluation degree label of each evaluation text in the evaluation text set;
and generating a service evaluation text according to the target evaluation text.
In one embodiment, the processor further performs the steps of obtaining an evaluation content text corresponding to the service evaluation text, extracting a second text keyword of the target evaluation text from the service evaluation text and the evaluation content text, evaluating the effective evaluation degree of the target evaluation text based on the evaluation content text to obtain an effective evaluation value of the target evaluation text, updating keyword labels of a plurality of preset evaluation texts according to the second text keyword of the target evaluation text, and updating the effective evaluation degree labels of the evaluation texts in the evaluation text set according to the effective evaluation value of the target evaluation text.
In one embodiment, the processor further performs the steps of obtaining the information complexity of the evaluation content text, obtaining the association relation between the evaluation content text and the corresponding preset service, and performing effective evaluation degree evaluation on the target evaluation text according to the information complexity and the association relation to obtain an effective evaluation value of the target evaluation text.
In one embodiment, the processor performs the computer program further comprising the steps of performing the superimposing step of superimposing the updated evaluation content text and the service evaluation text to obtain a superimposed text if the updated evaluation content text corresponding to the service evaluation text is detected, returning to the performing the superimposing step if the superimposed text does not satisfy the updated condition, updating keyword tags of a plurality of preset evaluation texts according to second text keywords of the superimposed text if the superimposed text satisfies the updated condition, and updating the valid evaluation degree tags of each of the evaluation texts in the set of evaluation texts according to the valid evaluation values of the superimposed text.
In one embodiment, the processor further performs the steps of obtaining a first word frequency of a third text keyword in a second text keyword of the superimposed text, and obtaining a second word frequency of a fourth text keyword in the second text keyword of the superimposed text, wherein the third text keyword is a text keyword corresponding to a keyword tag which does not belong to a plurality of preset evaluation texts in the second text keyword of the superimposed text, the fourth text keyword is a text keyword corresponding to a keyword tag which belongs to a plurality of preset evaluation texts in the second text keyword of the superimposed text, adding the keyword tag corresponding to the third text keyword to the keyword tag of the plurality of preset evaluation texts if the first word frequency corresponding to the third text keyword is greater than a first preset word frequency threshold, and deleting the keyword tag corresponding to the fourth text keyword in the keyword tag of the plurality of preset evaluation texts if the second word frequency corresponding to the fourth text keyword is smaller than a second preset word frequency threshold.
In one embodiment, the processor further performs the steps of obtaining a difference degree between the effective evaluation value of the superimposed text and the effective evaluation degree label of each evaluation text in the set of evaluation texts, respectively, and updating the effective evaluation degree label of the evaluation text corresponding to the target difference degree in the set of evaluation texts according to the effective evaluation value of the superimposed text if the target difference degree satisfying the preset difference condition exists in each difference degree.
In one embodiment, the processor further performs the steps of obtaining a first number of texts of the superimposed text, wherein the user portraits corresponding to the texts belong to the same user portraits as the target user, and obtaining a second number of texts of the superimposed text, wherein the text evaluation information corresponding to the abnormal texts, the difference between the text evaluation information corresponding to the historical evaluation text information is larger than a preset difference threshold value, generating the influence degree of the superimposed text on the service evaluation text based on the first number of texts and the second number of texts, determining that the superimposed text meets the update condition if the influence degree is larger than the preset degree threshold value, and determining that the superimposed text does not meet the update condition if the influence degree is not larger than the preset degree threshold value.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
Acquiring historical evaluation text information of a target user, and extracting a plurality of first text keywords from the historical evaluation text information;
Screening an evaluation text set corresponding to each first text keyword from a plurality of preset evaluation texts based on the first text keywords and keyword labels of the plurality of preset evaluation texts;
Aiming at the evaluation text set corresponding to each first text keyword, screening target evaluation texts from the evaluation text set according to the effective evaluation degree label of each evaluation text in the evaluation text set;
and generating a service evaluation text according to the target evaluation text.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining an evaluation content text corresponding to the service evaluation text, extracting a second text keyword of the target evaluation text from the service evaluation text and the evaluation content text, evaluating the effective evaluation degree of the target evaluation text based on the evaluation content text to obtain an effective evaluation value of the target evaluation text, updating keyword labels of a plurality of preset evaluation texts according to the second text keyword of the target evaluation text, and updating the effective evaluation degree labels of the evaluation texts in the evaluation text set according to the effective evaluation value of the target evaluation text.
In one embodiment, the computer program when executed by the processor further comprises the steps of obtaining the information complexity of the evaluation content text, obtaining the association relation between the evaluation content text and the corresponding preset service, and carrying out effective evaluation degree evaluation on the target evaluation text according to the information complexity and the association relation to obtain an effective evaluation value of the target evaluation text.
In one embodiment, the computer program when executed by the processor further implements the steps of executing the superimposing step of superimposing the updated evaluation content text and the service evaluation text to obtain a superimposed text if the updated evaluation content text corresponding to the service evaluation text is detected, returning to execute the superimposing step if the superimposed text does not satisfy the updated condition, updating keyword tags of a plurality of preset evaluation texts according to second text keywords of the superimposed text if the superimposed text satisfies the updated condition, and updating the valid evaluation degree tags of each of the evaluation texts in the set of evaluation texts according to the valid evaluation values of the superimposed text.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a first word frequency of a third text keyword in a second text keyword of the superimposed text, and obtaining a second word frequency of a fourth text keyword in the second text keyword of the superimposed text, wherein the third text keyword is a text keyword corresponding to a keyword tag that does not belong to a plurality of preset evaluation texts in the second text keyword of the superimposed text, the fourth text keyword is a text keyword corresponding to a keyword tag that belongs to a plurality of preset evaluation texts in the second text keyword of the superimposed text, adding the keyword tag corresponding to the third text keyword to the keyword tag of the plurality of preset evaluation texts if the first word frequency corresponding to the third text keyword is greater than a first preset word frequency threshold, and deleting the keyword tag corresponding to the fourth text keyword in the keyword tag of the plurality of preset evaluation texts if the second word frequency corresponding to the fourth text keyword is less than a second preset word frequency threshold.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a difference degree between the effective evaluation value of the superimposed text and the effective evaluation degree label of each evaluation text in the set of evaluation texts, respectively, and updating the effective evaluation degree label of the evaluation text in the set of evaluation texts corresponding to the target difference degree according to the effective evaluation value of the superimposed text if the target difference degree satisfying the preset difference condition exists in each difference degree.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a first number of texts in the superimposed text, where the user portraits corresponding to the texts belong to the same user portraits as the target user, and obtaining a second number of texts in the superimposed text, where the text evaluation information corresponding to the abnormal texts, the difference between the text evaluation information corresponding to the historical evaluation text information is greater than a preset difference threshold, generating an influence degree of the superimposed text on the service evaluation text based on the first number of texts and the second number of texts, determining that the superimposed text satisfies the update condition if the influence degree is greater than the preset degree threshold, and determining that the superimposed text does not satisfy the update condition if the influence degree is not greater than the preset degree threshold.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of:
Acquiring historical evaluation text information of a target user, and extracting a plurality of first text keywords from the historical evaluation text information;
Screening an evaluation text set corresponding to each first text keyword from a plurality of preset evaluation texts based on the first text keywords and keyword labels of the plurality of preset evaluation texts;
Aiming at the evaluation text set corresponding to each first text keyword, screening target evaluation texts from the evaluation text set according to the effective evaluation degree label of each evaluation text in the evaluation text set;
and generating a service evaluation text according to the target evaluation text.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining an evaluation content text corresponding to the service evaluation text, extracting a second text keyword of the target evaluation text from the service evaluation text and the evaluation content text, evaluating the effective evaluation degree of the target evaluation text based on the evaluation content text to obtain an effective evaluation value of the target evaluation text, updating keyword labels of a plurality of preset evaluation texts according to the second text keyword of the target evaluation text, and updating the effective evaluation degree labels of the evaluation texts in the evaluation text set according to the effective evaluation value of the target evaluation text.
In one embodiment, the computer program when executed by the processor further comprises the steps of obtaining the information complexity of the evaluation content text, obtaining the association relation between the evaluation content text and the corresponding preset service, and carrying out effective evaluation degree evaluation on the target evaluation text according to the information complexity and the association relation to obtain an effective evaluation value of the target evaluation text.
In one embodiment, the computer program when executed by the processor further implements the steps of executing the superimposing step of superimposing the updated evaluation content text and the service evaluation text to obtain a superimposed text if the updated evaluation content text corresponding to the service evaluation text is detected, returning to execute the superimposing step if the superimposed text does not satisfy the updated condition, updating keyword tags of a plurality of preset evaluation texts according to second text keywords of the superimposed text if the superimposed text satisfies the updated condition, and updating the valid evaluation degree tags of each of the evaluation texts in the set of evaluation texts according to the valid evaluation values of the superimposed text.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a first word frequency of a third text keyword in a second text keyword of the superimposed text, and obtaining a second word frequency of a fourth text keyword in the second text keyword of the superimposed text, wherein the third text keyword is a text keyword corresponding to a keyword tag that does not belong to a plurality of preset evaluation texts in the second text keyword of the superimposed text, the fourth text keyword is a text keyword corresponding to a keyword tag that belongs to a plurality of preset evaluation texts in the second text keyword of the superimposed text, adding the keyword tag corresponding to the third text keyword to the keyword tag of the plurality of preset evaluation texts if the first word frequency corresponding to the third text keyword is greater than a first preset word frequency threshold, and deleting the keyword tag corresponding to the fourth text keyword in the keyword tag of the plurality of preset evaluation texts if the second word frequency corresponding to the fourth text keyword is less than a second preset word frequency threshold.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a difference degree between the effective evaluation value of the superimposed text and the effective evaluation degree label of each evaluation text in the set of evaluation texts, respectively, and updating the effective evaluation degree label of the evaluation text in the set of evaluation texts corresponding to the target difference degree according to the effective evaluation value of the superimposed text if the target difference degree satisfying the preset difference condition exists in each difference degree.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a first number of texts in the superimposed text, where the user portraits corresponding to the texts belong to the same user portraits as the target user, and obtaining a second number of texts in the superimposed text, where the text evaluation information corresponding to the abnormal texts, the difference between the text evaluation information corresponding to the historical evaluation text information is greater than a preset difference threshold, generating an influence degree of the superimposed text on the service evaluation text based on the first number of texts and the second number of texts, determining that the superimposed text satisfies the update condition if the influence degree is greater than the preset degree threshold, and determining that the superimposed text does not satisfy the update condition if the influence degree is not greater than the preset degree threshold.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile memory and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (RESISTIVE RANDOM ACCESS MEMORY, reRAM), magneto-resistive Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computation, an artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) processor, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the present application.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.