CN112309586A - Method and system for improving adaptation degree of pushing information of medical robot and user diseases - Google Patents
Method and system for improving adaptation degree of pushing information of medical robot and user diseases Download PDFInfo
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Abstract
The invention discloses a method and a system for improving the adaptation degree of pushing information and user diseases of a medical robot, which relate to the technical field of robots and comprise the following steps: acquiring disease information about diseases on a network, and adding disease labels to the disease information respectively; retrieving the occurred user history behavior of the user using the medical robot through history voice description; scoring the user historical behavior according to the integrity of the historical voice description, wherein the integrity of the historical voice description is in direct proportion to the score of the user historical behavior; matching the scores of the historical behaviors of the user with the disease labels corresponding to the historical behaviors of the user to obtain a matching result; when the current voice description of the user is collected, matching keywords in the current voice description with a disease label with the highest historical behavior score in a matching result, and pushing disease information represented by the disease label with the highest historical behavior score to the user; thereby improving the accuracy of the illness results pushed for the user.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a method and a system for improving the adaptation degree of pushing information and user diseases of a medical robot.
Background
A Robot (Robot) is a machine device that automatically performs work. It can accept human command, run the program programmed in advance, and also can operate according to the principle outline action made by artificial intelligence technology. Its task is to assist or replace the work of human work.
And along with the development of technique, the robot that has appeared using in various fields, wherein just including domestic doctor type robot, user's disease can be differentiateed according to the user data who gathers to domestic doctor type robot for the user can monitor the health status of oneself at home constantly.
The existing household doctor-type robot captures a disease label in the voice description of a user by acquiring the voice description of the user through voice, and pushes a disease result matched with the disease label to the user after matching the disease label with a corresponding disease; however, when the household doctor-type robot collects the voice description of the user, it is likely that the wrong disease label cannot be grasped or grasped due to external causes, thereby reducing the accuracy of the disease result pushed to the user.
Disclosure of Invention
The invention mainly aims to provide a method and a system for improving the adaptation degree of pushing information of a medical robot and a disease state of a user, and aims to solve the technical problem that in the prior art, due to external reasons, a wrong disease state label cannot be grabbed or grabbed, so that the accuracy of a disease state result pushed for the user is reduced.
In order to achieve the above object, a first aspect of the present invention provides a method for improving the adaptation degree of push information of a medical robot to a user condition, including: acquiring disease information about diseases on a network, and adding disease labels to the disease information respectively; retrieving the occurred user history behavior of the user using the medical robot through history voice description; scoring the user historical behavior according to the integrity of the historical speech description, wherein the integrity of the historical speech description is proportional to the score of the user historical behavior; matching the scores of the user historical behaviors with disease labels corresponding to the user historical behaviors to obtain matching results; when the current voice description of the user is collected, matching the keywords in the current voice description with the disease label with the highest user historical behavior score in the matching result, and pushing the disease information represented by the disease label with the highest user historical behavior score to the user.
Further, the method further comprises: marking the scored historical user behaviors; and when the user historical behaviors are scored, scoring the unmarked user historical behaviors.
Further, the collecting disease information about diseases on the network and adding disease labels to the disease information respectively comprises: collecting disease information about a disease on a network; classifying the disease information to obtain a class label; extracting disease condition keywords in the disease condition information to obtain keyword labels; and adding the category label and the keyword label to corresponding disease information to generate a disease label of the disease information.
Further, the method further comprises: detecting the number of disease labels with the highest user historical behavior score in the matching result matched with the keywords in the current voice description; if the number of the disease label with the highest user historical behavior score is at least two, extracting a category label and a keyword label contained in each disease label respectively to obtain an extraction result of each disease label; calculating the proportion value of the keywords in the current voice description in each extraction result; and pushing the disease information represented by the disease label corresponding to the maximum proportion value to a user.
Further, the scoring the user historical behavior according to the completeness of the historical speech description comprises: detecting a voice decibel value of the user voice in the historical voice description; deleting the part of the historical voice description, of which the voice decibel value is smaller than a preset decibel value, to obtain a processed voice description; calculating a ratio of the processed speech description to the historical speech description; when the ratio is greater than a preset first threshold and less than or equal to 1, marking a high score for the historical behavior mark of the user corresponding to the historical voice description, when the ratio is greater than a preset second threshold and less than or equal to the first threshold, marking a medium score for the historical behavior mark of the user corresponding to the historical voice description, and when the ratio is less than the second threshold, marking a low score for the historical behavior mark of the user corresponding to the historical voice description.
The second aspect of the present invention provides a system for improving the adaptation degree of the pushing information of a medical robot and the disease symptoms of a user, comprising: the acquisition adding module is used for acquiring disease information about diseases on a network and respectively adding disease labels to the disease information; the user historical behavior retrieval module is used for retrieving the occurred user historical behaviors of the user using the medical robot through the historical voice description; the scoring module is used for scoring the historical behaviors of the user according to the integrality of the historical voice description retrieved by the historical behavior retrieval module of the user, and the integrality of the historical voice description is in direct proportion to the scores of the historical behaviors of the user; the matching module is used for matching the scores of the user historical behaviors obtained by the scoring module with the disease labels corresponding to the user historical behaviors to obtain matching results; and the pushing module is used for matching the keywords in the current voice description with the disease label with the highest user historical behavior score in the matching result obtained by the matching module when the current voice description of the user is collected, and pushing the disease information represented by the disease label with the highest user historical behavior score to the user.
Further, the system further comprises: and the marking module is used for marking the user historical behaviors searched by the user historical behavior searching module and scored by the scoring module so as to score the user historical behaviors which are not marked by the marking module when the user historical behaviors searched by the user historical behavior searching module are scored by the scoring module.
Further, the acquisition adding module comprises: the disease information acquisition unit is used for acquiring disease information about diseases on a network; the classification unit is used for classifying the disease information acquired by the disease information acquisition unit to obtain a class label; the keyword extraction unit is used for extracting disease keywords in the disease information acquired by the disease information acquisition unit to obtain keyword labels; and the adding unit is used for adding the category label obtained by the classifying unit and the keyword label obtained by the keyword extracting unit to corresponding disease information to generate a disease label of the disease information.
Further, the system further comprises: the disease label detection module is used for detecting the number of disease labels with the highest user historical behavior score in the matching result obtained by the matching module matched with the keywords in the current voice description; the disease label extraction module is used for respectively extracting the category label and the keyword label contained in each disease label to obtain the extraction result of each disease label under the condition that the disease label detection module detects that the number of the disease labels with the highest user historical behavior score is at least two; the proportion value calculation module is used for calculating the proportion value of the key words in the current voice description in each extraction result obtained by the disease label extraction module; and the selection pushing unit is used for selecting the disease label generated by the addition acquisition module corresponding to the maximum ratio calculated by the proportional value calculation module.
Further, the scoring module includes: a decibel value detection unit, configured to detect a decibel value of the voice of the user in the historical voice description retrieved by the user historical behavior retrieval module; the voice deleting unit is used for deleting the part of the historical voice description detected by the decibel value detecting unit, of which the voice decibel value is smaller than the preset decibel value, so as to obtain the processed voice description; a ratio calculation unit for calculating the ratio of the processed voice description obtained by the voice deletion unit to the historical voice description detected by the decibel value detection unit; the score marking unit is used for marking a high score for the historical behavior of the user corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is greater than a preset first threshold and less than or equal to 1, marking a medium score for the historical behavior of the user corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is greater than a preset second threshold and less than or equal to the first threshold, and marking a low score for the historical behavior of the user corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is less than the second threshold.
The invention provides a method and a system for improving the adaptation degree of pushing information of a medical robot and user diseases, which have the advantages that: the historical behaviors of the user using the medical robot are retrieved and scored through historical voice description, the score of the historical behaviors of the user with high integrity is higher, the integrity of the historical behaviors of the user is high, namely, the more clear and complete keywords can be obtained from the historical voice description, so that the disease information close to or the same as the user can be more accurately obtained during searching, and the historical voice description with high integrity and the captured disease label can play a role in correcting the disease label captured by the current voice description under the condition that the current voice cannot capture or capture the wrong disease label due to external factors by using the scoring method, so that the accuracy of the disease result pushed for the user is improved.
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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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic block diagram of a flow of a method for improving the adaptation degree of push information of a medical robot to a user disease state according to an embodiment of the present application;
fig. 2 is a block diagram schematically illustrating the structure of a system for improving the adaptation degree of the pushing information of the medical robot to the user symptoms.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for improving the adaptation degree of the push information of a medical robot to the user's disease, including: s1, collecting disease information about diseases on the network, and adding disease labels to the disease information respectively; s2, retrieving the historical behaviors of the user using the medical robot described by the historical voice; s3, scoring the historical behaviors of the user according to the integrality of the historical voice description, wherein the integrality of the historical voice description is in direct proportion to the fraction of the historical behaviors of the user; s4, matching the scores of the user historical behaviors with the disease labels corresponding to the user historical behaviors to obtain matching results; and S5, when the current voice description of the user is collected, matching the keywords in the current voice description with the disease label with the highest historical behavior score in the matching result, and pushing the disease information represented by the disease label with the highest historical behavior score to the user.
The method for improving the adaptation degree of the pushing information of the medical robot and the disease symptoms of the user further comprises the following steps: marking the marked historical behaviors of the user; when scoring the user historical behaviors, scoring the unmarked user historical behaviors.
By marking the marked user historical behaviors, the marked user historical behaviors are not required to be marked when the user historical behaviors are marked, so that the marking time is reduced, and the speed of pushing disease information for the user is increased.
Collecting disease information about diseases on a network, and adding disease labels to the disease information respectively comprises the following steps: collecting disease information about a disease on a network; classifying the disease information to obtain a class label; extracting disease key words in the disease information to obtain key word labels; and adding the category label and the keyword label to corresponding disease information to generate a disease label of the disease information.
By using the category labels and the keyword labels to generate the disease labels, one disease label can contain a plurality of keyword labels or disease labels, so that when the keyword of the user is matched, the disease information corresponding to the disease label can be more accurately attached to the description of the user, and the accuracy of the disease result pushed for the user is improved.
The method for improving the adaptation degree of the pushing information of the medical robot and the disease symptoms of the user further comprises the following steps: detecting the number of disease labels with the highest user historical behavior scores in matching results matched with the keywords in the current voice description; if the number of the disease label with the highest historical behavior score of the user is at least two, extracting a category label and a keyword label contained in each disease label respectively to obtain an extraction result of each disease label; calculating the proportion value of the keywords in the current voice description in each extraction result; and pushing the disease information represented by the disease label corresponding to the maximum proportion value to the user.
By calculating the proportion value, under the condition that the number of disease labels with the highest user historical behavior scores in the matching result matched with the keywords in the current voice description is multiple, the disease label most fit with the keywords in the previous voice description is extracted from the multiple disease labels, so that the disease label closer to the current voice description of the user is found, and the accuracy of the disease result pushed for the user is improved.
Scoring the user's historical behavior according to the completeness of the historical speech description comprises: detecting a voice decibel value of user voice in historical voice description; deleting the part of the historical voice description, of which the voice decibel value is smaller than the preset decibel value, to obtain a processed voice description; calculating the ratio of the processed voice description to the historical voice description; when the ratio is greater than a preset first threshold and less than or equal to 1, marking a high score for the historical behavior mark of the user corresponding to the historical voice description, when the ratio is greater than a preset second threshold and less than or equal to the first threshold, marking a medium score for the historical behavior mark of the user corresponding to the historical voice description, and when the ratio is less than the second threshold, marking a low score for the historical behavior mark of the user corresponding to the historical voice description.
By marking the high score, the medium score and the low score and limiting the ranges of the high score, the medium score and the low score by using the first threshold and the second threshold, the score described by the historical voice can be more definite, so that the historical behaviors of the user with the high score can be more accurately screened, and the disease information represented by the disease label corresponding to the historical behaviors of the user with the high score can be more accurately pushed to the user.
The application provides a method for improving the adaptation degree of pushing information and user diseases of a medical robot, which has the following principle: the historical behaviors of the user using the medical robot are retrieved and scored through historical voice description, the score of the historical behaviors of the user with high integrity is higher, the integrity of the historical behaviors of the user is high, namely, the more clear and complete keywords can be obtained from the historical voice description, so that the disease information close to or the same as the user can be more accurately obtained during searching, and the historical voice description with high integrity and the captured disease label can play a role in correcting the disease label captured by the current voice description under the condition that the current voice cannot capture or capture the wrong disease label due to external factors by using the scoring method, so that the accuracy of the disease result pushed for the user is improved.
Referring to fig. 2, an embodiment of the present invention further provides a system for improving the adaptation degree of the push information of the medical robot to the user's disease, including: the system comprises an acquisition adding module 1, a user historical behavior retrieval module 2, a scoring module 3, a matching module 4 and a pushing module 5; the acquisition adding module is used for acquiring disease information about diseases on the network and respectively adding disease labels to the disease information; the user historical behavior retrieval module is used for retrieving the occurred user historical behaviors of the user using the medical robot through the historical voice description; the scoring module is used for scoring the historical behaviors of the user according to the integrality of the historical voice description retrieved by the historical behavior retrieval module of the user, and the integrality of the historical voice description is in direct proportion to the scores of the historical behaviors of the user; the matching module is used for matching the scores of the user historical behaviors obtained by the scoring module with the disease labels corresponding to the user historical behaviors to obtain matching results; the pushing module is used for matching the keywords in the current voice description with the disease label with the highest user historical behavior score in the matching result obtained by the matching module when the current voice description of the user is collected, and pushing the disease information represented by the disease label with the highest user historical behavior score to the user.
The system for improving the adaptation degree of the pushing information of the medical robot and the disease symptoms of the user further comprises: and the marking module is used for marking the user historical behaviors searched by the user historical behavior searching module which are marked by the marking module so as to mark the user historical behaviors which are not marked by the marking module when the user historical behaviors searched by the user historical behavior searching module are marked by the marking module.
The acquisition adding module comprises: the system comprises a disease information acquisition unit, a classification unit, a keyword extraction unit and an addition unit; the disease information acquisition unit is used for acquiring disease information about diseases on a network; the classification unit is used for classifying the disease information acquired by the disease information acquisition unit to obtain a class label; the keyword extraction unit is used for extracting disease keywords in the disease information acquired by the disease information acquisition unit to obtain a keyword label; the adding unit is used for adding the category label obtained by the classifying unit and the keyword label obtained by the keyword extracting unit to the corresponding disease information to generate a disease label of the disease information.
The system for improving the adaptation degree of the pushing information of the medical robot and the disease symptoms of the user further comprises: the system comprises a disease label detection module, a disease label extraction module, a proportional value calculation module and a selection pushing unit; the disease label detection module is used for detecting the number of disease labels with the highest user historical behavior score in the matching result obtained by the matching module matched with the keywords in the current voice description; the disease label extraction module is used for respectively extracting the category label and the keyword label contained in each disease label to obtain the extraction result of each disease label under the condition that the disease label detection module detects that the number of the disease labels with the highest user historical behavior score is at least two; the proportion value calculation module is used for calculating the proportion value of the keywords in the current voice description in each extraction result obtained by the disease label extraction module; the selection pushing unit is used for selecting the disease label generated by the adding acquisition module corresponding to the maximum ratio calculated by the proportional value calculation module.
The scoring module includes: the system comprises a decibel value detection unit, a voice deleting unit, a ratio calculating unit and a score marking unit; the decibel value detection unit is used for detecting the voice decibel value of the user voice in the historical voice description retrieved by the user historical behavior retrieval module; the voice deleting unit is used for deleting the part of the historical voice description detected by the decibel value detecting unit, of which the voice decibel value is smaller than the preset decibel value, so as to obtain a processed voice description; the ratio calculation unit is used for calculating the ratio of the processed voice description obtained by the voice deletion unit to the historical voice description detected by the decibel value detection unit; the score marking unit is used for marking a high score for the historical behavior mark corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is greater than a preset first threshold and less than or equal to 1, marking a medium score for the historical behavior mark corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is greater than a preset second threshold and less than or equal to the first threshold, and marking a low score for the historical behavior mark corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is less than the second threshold.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the above description, for those skilled in the art, there are variations on the specific implementation and application scope according to the ideas of the embodiments of the present invention, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for improving the adaptation degree of pushing information of a medical robot and user diseases is characterized by comprising the following steps:
acquiring disease information about diseases on a network, and adding disease labels to the disease information respectively;
retrieving the occurred user history behavior of the user using the medical robot through history voice description;
scoring the user historical behavior according to the integrity of the historical speech description, wherein the integrity of the historical speech description is proportional to the score of the user historical behavior;
matching the scores of the user historical behaviors with disease labels corresponding to the user historical behaviors to obtain matching results;
when the current voice description of the user is collected, matching the keywords in the current voice description with the disease label with the highest user historical behavior score in the matching result, and pushing the disease information represented by the disease label with the highest user historical behavior score to the user.
2. The method for improving the adaptation degree of the push information of the medical robot to the user symptoms according to claim 1, further comprising:
marking the scored historical user behaviors;
and when the user historical behaviors are scored, scoring the unmarked user historical behaviors.
3. The method for improving the adaptation degree of the pushing information of the medical robot to the disease of the user according to claim 1, wherein the collecting the disease information about the disease on the network and adding the disease labels to the disease information respectively comprises:
collecting disease information about a disease on a network;
classifying the disease information to obtain a class label;
extracting disease condition keywords in the disease condition information to obtain keyword labels;
and adding the category label and the keyword label to corresponding disease information to generate a disease label of the disease information.
4. The method for improving the adaptation degree of the push information of the medical robot to the user symptoms according to claim 3, further comprising:
detecting the number of disease labels with the highest user historical behavior score in the matching result matched with the keywords in the current voice description;
if the number of the disease label with the highest user historical behavior score is at least two, extracting a category label and a keyword label contained in each disease label respectively to obtain an extraction result of each disease label;
calculating the proportion value of the keywords in the current voice description in each extraction result;
and pushing the disease information represented by the disease label corresponding to the maximum proportion value to a user.
5. The method for improving the adaptation degree of the pushing information of the medical robot to the user symptoms according to claim 1, wherein the scoring the historical behaviors of the user according to the completeness of the historical voice description comprises the following steps:
detecting a voice decibel value of the user voice in the historical voice description;
deleting the part of the historical voice description, of which the voice decibel value is smaller than a preset decibel value, to obtain a processed voice description;
calculating a ratio of the processed speech description to the historical speech description;
when the ratio is greater than a preset first threshold and less than or equal to 1, marking a high score for the historical behavior mark of the user corresponding to the historical voice description, when the ratio is greater than a preset second threshold and less than or equal to the first threshold, marking a medium score for the historical behavior mark of the user corresponding to the historical voice description, and when the ratio is less than the second threshold, marking a low score for the historical behavior mark of the user corresponding to the historical voice description.
6. A system for improving the adaptation degree of pushing information of a medical robot and user diseases is characterized by comprising:
the acquisition adding module is used for acquiring disease information about diseases on a network and respectively adding disease labels to the disease information;
the user historical behavior retrieval module is used for retrieving the occurred user historical behaviors of the user using the medical robot through the historical voice description;
the scoring module is used for scoring the historical behaviors of the user according to the integrality of the historical voice description retrieved by the historical behavior retrieval module of the user, and the integrality of the historical voice description is in direct proportion to the scores of the historical behaviors of the user;
the matching module is used for matching the scores of the user historical behaviors obtained by the scoring module with the disease labels corresponding to the user historical behaviors to obtain matching results;
and the pushing module is used for matching the keywords in the current voice description with the disease label with the highest user historical behavior score in the matching result obtained by the matching module when the current voice description of the user is collected, and pushing the disease information represented by the disease label with the highest user historical behavior score to the user.
7. The system for improving the adaptation degree of the pushing information of the medical robot to the user's disease according to claim 6, further comprising:
and the marking module is used for marking the user historical behaviors searched by the user historical behavior searching module and scored by the scoring module so as to score the user historical behaviors which are not marked by the marking module when the user historical behaviors searched by the user historical behavior searching module are scored by the scoring module.
8. The system for improving the adaptation degree of the pushing information of the medical robot to the symptoms of the user according to claim 6, wherein the collecting and adding module comprises:
the disease information acquisition unit is used for acquiring disease information about diseases on a network;
the classification unit is used for classifying the disease information acquired by the disease information acquisition unit to obtain a class label;
the keyword extraction unit is used for extracting disease keywords in the disease information acquired by the disease information acquisition unit to obtain keyword labels;
and the adding unit is used for adding the category label obtained by the classifying unit and the keyword label obtained by the keyword extracting unit to corresponding disease information to generate a disease label of the disease information.
9. The system for improving the adaptation of the pushing information of the medical robot to the user's disease according to claim 8, further comprising:
the disease label detection module is used for detecting the number of disease labels with the highest user historical behavior score in the matching result obtained by the matching module matched with the keywords in the current voice description;
the disease label extraction module is used for respectively extracting the category label and the keyword label contained in each disease label to obtain the extraction result of each disease label under the condition that the disease label detection module detects that the number of the disease labels with the highest user historical behavior score is at least two;
the proportion value calculation module is used for calculating the proportion value of the key words in the current voice description in each extraction result obtained by the disease label extraction module;
and the selection pushing unit is used for selecting the disease label generated by the addition acquisition module corresponding to the maximum ratio calculated by the proportional value calculation module.
10. The system for improving the adaptation degree of the pushing information of the medical robot to the user symptoms according to claim 6, wherein the scoring module comprises:
a decibel value detection unit, configured to detect a decibel value of the voice of the user in the historical voice description retrieved by the user historical behavior retrieval module;
the voice deleting unit is used for deleting the part of the historical voice description detected by the decibel value detecting unit, of which the voice decibel value is smaller than the preset decibel value, so as to obtain the processed voice description;
a ratio calculation unit for calculating the ratio of the processed voice description obtained by the voice deletion unit to the historical voice description detected by the decibel value detection unit;
the score marking unit is used for marking a high score for the historical behavior of the user corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is greater than a preset first threshold and less than or equal to 1, marking a medium score for the historical behavior of the user corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is greater than a preset second threshold and less than or equal to the first threshold, and marking a low score for the historical behavior of the user corresponding to the historical voice description when the ratio calculated by the ratio calculating unit is less than the second threshold.
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