CN116779087B - Automatic data management system and method based on AI engine - Google Patents
Automatic data management system and method based on AI engine Download PDFInfo
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- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Abstract
The invention relates to the technical field of data automation management, in particular to an automatic data management system and method of a basic AI engine, comprising the steps of monitoring the use operation record of a user using each intelligent diagnosis guiding device arranged at each area position in a medical institution; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target using operation sequence, calculating the characteristic value of each characteristic operation sequence, and screening each characteristic operation sequence based on the characteristic value; calculating user portrait characteristic values for users corresponding to the characteristic operation records; locking the characteristic user; marking the related guiding signboards arranged in the medical institution; setting period duration, accumulating the marking times of each guiding signpost in each period duration, and feeding back the guiding signpost with accumulated times larger than a time threshold value to a manager.
Description
Technical Field
The invention relates to the technical field of data automation management, in particular to an automatic data management system and method based on an AI engine.
Background
The AI service engine is mainly used for providing diversified scene services, so that the equipment is more intelligent, the user experience of a user is enhanced, AI refers to artificial intelligence, and the AI service engine is a new technical science, and is used for researching and developing theories, methods, technologies and application systems for simulating, expanding and expanding human intelligence. Artificial intelligence is a branch of computer science. It attempts to understand the nature of intelligence and to produce a new intelligent machine that can respond in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image recognition, natural language processing, and expert systems.
The basic environment and the hospital guiding identification system of most modern hospitals are under the current situation of continuous optimized development, and aim to fully develop the care concept of the hospitals based on people and establish the overall image of the hospitals. The complete guiding system can help the doctor who enters the hospital for the first time to quickly know the overall layout of the hospital, quickly find the corresponding treatment position and receive the diagnosis and treatment of the doctor; when the identification system is distributed, the situation of hospital layout and different consultants needs to be fully considered, the method is studied carefully, the method is perfected and modified continuously, and finally the scheme is determined.
Disclosure of Invention
The invention aims to provide an automatic data management system and method based on an AI engine, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an automatic data management method based on an AI engine, the method comprising:
step S100: whenever the duration of continuous use of any user on any intelligent diagnosis guiding device is longer than a duration threshold, triggering any intelligent diagnosis guiding device to start an AI service engine for any user, and acquiring a target diagnosis guiding service item of any user; performing operation record monitoring on users who use the intelligent diagnosis guiding devices arranged at the positions of all areas in the medical institution; setting a target diagnosis guiding service item as a user use operation record for acquiring relevant diagnosis guiding navigation and is set as a characteristic operation record;
step S200: extracting a target use operation sequence for characteristic analysis of user expansion from each characteristic operation record; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target using operation sequence, calculating the characteristic value of each characteristic operation sequence, and screening each characteristic operation sequence based on the characteristic value;
step S300: calculating user portrait characteristic values for users corresponding to each characteristic operation record based on the characteristic operation sequence distribution condition in each characteristic operation record and the use operation rule presented by the users; setting a characteristic threshold value, and locking a characteristic user larger than the characteristic threshold value;
step S400: acquiring an optimal travel route recommended by an AI service engine for each characteristic user, and finishing marking the related guide signboards arranged in the medical institution according to the deviation condition of the actual travel route of the corresponding characteristic user and the optimal travel route;
step S500: setting period duration, accumulating the marking times of each guiding signpost in each period duration, and feeding back the guiding signpost with accumulated times larger than a time threshold value to a manager.
Further, in each characteristic operation record, all the using operations generated in time sequence from the beginning of the user typing in the first using operation to wake up the intelligent diagnosis guiding device until the intelligent diagnosis guiding device starts the AI service engine are formed, and a target using operation sequence of characteristic analysis is developed for each user generating each characteristic operation record.
Further, step S200 includes:
step S201: respectively acquiring an operation interface to which each use operation belongs in a target use operation sequence corresponding to each characteristic operation record; capturing the same using operation of the corresponding operation interface in each target using operation sequence in sequence; in each target use operation sequence, sequentially taking two use operations with the same operation interface and the closest sequence as endpoints of a section of characteristic operation sequence, and intercepting to obtain a plurality of characteristic operation sequences; acquiring a using operation set contained in each characteristic operation sequence; the characteristic operation sequences with the same operation interface corresponding to the endpoints belong to one type of characteristic operation sequences;
the intercepting mode of the characteristic operation sequence is used for capturing the trial-and-error behavior of a user when searching for a target service item because the user is unfamiliar with the operation and the use of the intelligent diagnosis guiding equipment by using the operation sequence for each target; the two using operations with the same operation interface and the closest sequence are used as the end points of a section of characteristic operation sequence, because from the arrangement of the using operations, the user repeatedly returns to the same operation interface, and because of unfamiliarity, the probability of random trial and error is larger;
step S202: if the j-th type characteristic operation sequence with the operation interface X corresponding to the end point exists, the i-th segment characteristic operation sequence, wherein the end point with the earlier sequence of the i-th segment characteristic operation sequence is a using operation a, and the end point with the later sequence is a using operation b; extracting a using operation set contained in the ith characteristic operation sequence, acquiring a using operation total number N corresponding to the using operation set, and collecting operation interface information corresponding to each using operation in the using operation set to obtain an interface information set S1;
step S203: when the j-th characteristic operation sequence with the operation interface X corresponding to the end point is not provided with the i+1th characteristic operation sequence, extracting all the using operations after the using operation b in the corresponding target using operation sequence, recording the total number of the using operations after the using operation b as M, if M < N, collecting operation interface information corresponding to the M using operations after the using operation b, obtaining an interface information set S2, and if M is more than or equal to N, collecting operation interface information of the N using operations which sequentially appear after the using operation b, and obtaining an interface information set S2; when the ith+1th section of characteristic operation sequence exists in the jth class of characteristic operation sequence with the operation interface X corresponding to the endpoint, extracting a using operation set contained in the ith+1th section of characteristic operation sequence, and collecting operation interface information corresponding to each using operation in the using operation set to obtain an interface information set S2;
step S204: calculating a characteristic value U=N (S1N S2)/(S1U S2) corresponding to the i-th characteristic operation sequence, and reserving the i-th characteristic operation sequence as a characteristic operation sequence corresponding to the user when trial-and-error behavior occurs in a corresponding characteristic operation record when U is smaller than a threshold value; when U is larger than a threshold value, eliminating the ith section of characteristic operation sequence;
the calculation of the characteristic values is performed on all the intercepted characteristic operation sequences, and the final screening is completed based on the characteristic values, because from the aspect of arrangement of using operation, a user repeatedly returns to the same operation interface, besides the reason that random trial and error occurs because of unfamiliarity, the reason that the requirement of objective operation or other operation rule influence is also caused, if N is smaller, the follow-up operation generated by the user after entering the operation interface corresponding to the endpoint operation is also smaller, from the aspect of operation, the possibility that the user returns to the initial interface is larger because the follow-up operation interface is inconsistent with the operation interface with a certain function sought by the target; if [ (S1U S2)/(S1U S2) ] is smaller, the probability of selecting different operation functions in the same operation interface is larger, and the probability of user trial and error is also larger.
Further, step S300 includes:
step S301: acquiring an average speed V of a user operation, a total number R of characteristic operation sequences and a minimum use operation item K required from an operation page before an AI service engine is started to an operation page for acquiring relevant diagnosis navigation guidance in each characteristic operation record in corresponding intelligent diagnosis guiding equipment; extracting all historical visit records with the time interval within a threshold range from the current time for a user generating each characteristic operation record, wherein the total number of the historical visit records is L; acquiring an average visit interval time Tr presented between all of the historical visit records;
step S302: the user portrait characteristic value p=v×r×k+tr×l is calculated for the user who generates each characteristic operation record.
Further, step S400 includes:
step S401: acquiring an optimal travel route recommended by an AI service engine for each characteristic user, setting a travel time threshold based on the optimal travel route, when a monitoring camera away from the end point of the optimal travel route leaves the intelligent diagnosis guiding equipment from the characteristic user to start the full time threshold, still capturing the corresponding characteristic user, retrieving video monitoring information of a medical institution, and extracting the actual travel route of the characteristic user;
step S402: and comparing the actual travel path with the optimal travel path, locking all position information of the travel path deviation of the characteristic user, and marking all guide signboards which can assist in guiding the user to the optimal travel path within the range of each position information threshold value.
The system comprises a characteristic operation record extraction management module, a characteristic operation sequence screening management module, a characteristic user capturing management module, a guiding signpost mark processing module and an early warning feedback management module;
the characteristic operation record extraction management module is used for monitoring the operation record of the user using each intelligent diagnosis guiding device arranged at each area position in the medical institution; setting a target diagnosis guiding service item as a user use operation record for acquiring relevant diagnosis guiding navigation and is set as a characteristic operation record;
the characteristic operation sequence screening management module is used for extracting target use operation sequences for characteristic analysis of user development from each characteristic operation record respectively; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target using operation sequence, calculating the characteristic value of each characteristic operation sequence, and screening each characteristic operation sequence based on the characteristic value;
the characteristic user capturing management module is used for calculating user portrait characteristic values for the users corresponding to the characteristic operation records according to the characteristic operation sequence distribution condition in the characteristic operation records and the use operation rules presented by the users; setting a characteristic threshold value, and locking a characteristic user larger than the characteristic threshold value;
the guiding signpost marking processing module is used for acquiring the optimal travel route recommended by the AI service engine to each characteristic user and finishing marking processing on the related guiding signpost arranged in the medical institution according to the deviation condition of the actual travel route of the corresponding characteristic user and the optimal travel route;
the early warning feedback management module is used for setting period duration, accumulating the marking times of each guiding signpost in each period duration, and feeding back the guiding signpost with the accumulated times being greater than the time threshold value to management staff.
Further, the characteristic operation sequence screening management module comprises a characteristic operation sequence interception management unit and a characteristic value calculation management unit;
the characteristic operation sequence intercepting and managing unit is used for extracting a target use operation sequence for characteristic analysis of user development from each characteristic operation record; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target use operation sequence;
the characteristic value calculation management unit is used for calculating characteristic values of the characteristic operation sequences and screening the characteristic operation sequences for the characteristic operation records based on the characteristic values;
further, the characteristic user capturing management module comprises a user portrait characteristic value calculation unit and a characteristic user capturing unit;
the user portrait characteristic value calculation unit is used for calculating a user portrait characteristic value corresponding to each characteristic operation record according to the characteristic operation sequence distribution condition in each characteristic operation record and the use operation rule presented by the user;
and the characteristic user capturing unit is used for setting a characteristic threshold value and locking the characteristic user larger than the characteristic threshold value.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, through collecting the use operation generated by the user at the intelligent diagnosis guiding equipment end, based on the use operation rule of the user and the distribution condition of the history diagnosis records, the user is calculated to obtain the familiarity degree of the user to the medical institution, the actual diagnosis condition of the user meeting the feature image is analyzed, the leading mark needing diagnosis guiding optimization in the medical institution is marked according to the actual diagnosis condition of the user, and the manager port is fed back, so that the continuous perfection of the guiding system in the medical institution is realized, the whole layout of the hospital is quickly known by the doctor entering the hospital for the first time or after a longer period of time, the corresponding diagnosis position is quickly found, the diagnosis and treatment of the doctor are accepted, and the diagnosis efficiency is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of an automated data management method based on an AI engine of the present invention;
fig. 2 is a schematic diagram of an automated data manager system based on an AI engine according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: an automatic data management method based on an AI engine, the method comprising:
step S100: whenever the duration of continuous use of any user on any intelligent diagnosis guiding device is longer than a duration threshold, triggering any intelligent diagnosis guiding device to start an AI service engine for any user, and acquiring a target diagnosis guiding service item of any user; performing operation record monitoring on users who use the intelligent diagnosis guiding devices arranged at the positions of all areas in the medical institution; setting a target diagnosis guiding service item as a user use operation record for acquiring relevant diagnosis guiding navigation and is set as a characteristic operation record;
all the using operations generated in the time sequence from the beginning of the first item of using operation input by the user to wake up the intelligent diagnosis guiding device to the beginning of the AI service engine starting by the intelligent diagnosis guiding device in each characteristic operation record form a target using operation sequence for developing characteristic analysis for each user generating each characteristic operation record;
step S200: extracting a target use operation sequence for characteristic analysis of user expansion from each characteristic operation record; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target using operation sequence, calculating the characteristic value of each characteristic operation sequence, and screening each characteristic operation sequence based on the characteristic value;
wherein, step S200 includes:
step S201: respectively acquiring an operation interface to which each use operation belongs in a target use operation sequence corresponding to each characteristic operation record; capturing the same using operation of the corresponding operation interface in each target using operation sequence in sequence; in each target use operation sequence, sequentially taking two use operations with the same operation interface and the closest sequence as endpoints of a section of characteristic operation sequence, and intercepting to obtain a plurality of characteristic operation sequences; acquiring a using operation set contained in each characteristic operation sequence; the characteristic operation sequences with the same operation interface corresponding to the endpoints belong to one type of characteristic operation sequences;
step S202: if the j-th type characteristic operation sequence with the operation interface X corresponding to the end point exists, the i-th segment characteristic operation sequence, wherein the end point with the earlier sequence of the i-th segment characteristic operation sequence is a using operation a, and the end point with the later sequence is a using operation b; extracting a using operation set contained in the ith characteristic operation sequence, acquiring a using operation total number N corresponding to the using operation set, and collecting operation interface information corresponding to each using operation in the using operation set to obtain an interface information set S1;
step S203: when the j-th characteristic operation sequence with the operation interface X corresponding to the end point is not provided with the i+1th characteristic operation sequence, extracting all the using operations after the using operation b in the corresponding target using operation sequence, recording the total number of the using operations after the using operation b as M, if M < N, collecting operation interface information corresponding to the M using operations after the using operation b, obtaining an interface information set S2, and if M is more than or equal to N, collecting operation interface information of the N using operations which sequentially appear after the using operation b, and obtaining an interface information set S2; when the ith+1th section of characteristic operation sequence exists in the jth class of characteristic operation sequence with the operation interface X corresponding to the endpoint, extracting a using operation set contained in the ith+1th section of characteristic operation sequence, and collecting operation interface information corresponding to each using operation in the using operation set to obtain an interface information set S2;
step S204: calculating a characteristic value U=N (S1N S2)/(S1U S2) corresponding to the i-th characteristic operation sequence, and reserving the i-th characteristic operation sequence as a characteristic operation sequence corresponding to the user when trial-and-error behavior occurs in a corresponding characteristic operation record when U is smaller than a threshold value; when U is larger than a threshold value, eliminating the ith section of characteristic operation sequence
Step S300: calculating user portrait characteristic values for users corresponding to each characteristic operation record based on the characteristic operation sequence distribution condition in each characteristic operation record and the use operation rule presented by the users; setting a characteristic threshold value, and locking a characteristic user larger than the characteristic threshold value;
wherein, step S300 includes:
step S301: acquiring an average speed V of a user operation, a total number R of characteristic operation sequences and a minimum use operation item K required from an operation page before an AI service engine is started to an operation page for acquiring relevant diagnosis navigation guidance in each characteristic operation record in corresponding intelligent diagnosis guiding equipment; extracting all historical visit records with the time interval within a threshold range from the current time for a user generating each characteristic operation record, wherein the total number of the historical visit records is L; acquiring an average visit interval time Tr presented between all of the historical visit records;
step S302: calculating a user portrait characteristic value P=V×R×K+Tr×L for a user generating each characteristic operation record;
step S400: acquiring an optimal travel route recommended by an AI service engine for each characteristic user, and finishing marking the related guide signboards arranged in the medical institution according to the deviation condition of the actual travel route of the corresponding characteristic user and the optimal travel route;
wherein, step S400 includes:
step S401: acquiring an optimal travel route recommended by an AI service engine for each characteristic user, setting a travel time threshold based on the optimal travel route, when a monitoring camera away from the end point of the optimal travel route leaves the intelligent diagnosis guiding equipment from the characteristic user to start the full time threshold, still capturing the corresponding characteristic user, retrieving video monitoring information of a medical institution, and extracting the actual travel route of the characteristic user;
step S402: comparing the actual travel path with the optimal travel path, locking all position information of the travel path deviation of the characteristic user, and marking all guide signboards which can assist in guiding the user to the optimal travel path within the range of each position information threshold;
for example, the actual travel path is compared with the optimal travel path, and all the position information of the travel path deviation of the characteristic user is obtained after locking: position 1, position 2, position 3;
in the position 1, if the user needs to walk into the optimal travel route and needs to go straight at the current position, all the guide signboards which can assist in guiding the user to go straight are marked within the threshold range of the position 1;
in the position 2, if the user needs to walk into the optimal travel route and needs to turn right at the current position, all the guiding signboards which can assist in guiding the user to turn right are marked within the threshold range of the position 2;
in the position 3, if the user needs to walk into the optimal travel route and needs to turn left at the current position, all the guiding signboards which can assist in guiding the user to turn left are marked within the threshold range of the position 3;
step S500: setting period duration, accumulating the marking times of each guiding signpost in each period duration, and feeding back the guiding signpost with accumulated times larger than a time threshold value to a manager.
The system comprises a characteristic operation record extraction management module, a characteristic operation sequence screening management module, a characteristic user capturing management module, a guiding signpost mark processing module and an early warning feedback management module;
the characteristic operation record extraction management module is used for monitoring the operation record of the user using each intelligent diagnosis guiding device arranged at each area position in the medical institution; setting a target diagnosis guiding service item as a user use operation record for acquiring relevant diagnosis guiding navigation and is set as a characteristic operation record;
the characteristic operation sequence screening management module is used for extracting target use operation sequences for characteristic analysis of user development from each characteristic operation record respectively; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target using operation sequence, calculating the characteristic value of each characteristic operation sequence, and screening each characteristic operation sequence based on the characteristic value;
the characteristic operation sequence screening management module comprises a characteristic operation sequence interception management unit and a characteristic value calculation management unit;
the characteristic operation sequence intercepting and managing unit is used for extracting a target use operation sequence for characteristic analysis of user development from each characteristic operation record; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target use operation sequence;
the characteristic value calculation management unit is used for calculating characteristic values of the characteristic operation sequences and screening the characteristic operation sequences for the characteristic operation records based on the characteristic values;
the characteristic user capturing management module is used for calculating user portrait characteristic values for the users corresponding to the characteristic operation records according to the characteristic operation sequence distribution condition in the characteristic operation records and the use operation rules presented by the users; setting a characteristic threshold value, and locking a characteristic user larger than the characteristic threshold value;
the characteristic user capturing management module comprises a user portrait characteristic value calculation unit and a characteristic user capturing unit;
the user portrait characteristic value calculation unit is used for calculating a user portrait characteristic value corresponding to each characteristic operation record according to the characteristic operation sequence distribution condition in each characteristic operation record and the use operation rule presented by the user;
the characteristic user capturing unit is used for setting a characteristic threshold value and locking a characteristic user larger than the characteristic threshold value;
the guiding signpost marking processing module is used for acquiring the optimal travel route recommended by the AI service engine to each characteristic user and finishing marking processing on the related guiding signpost arranged in the medical institution according to the deviation condition of the actual travel route of the corresponding characteristic user and the optimal travel route;
the early warning feedback management module is used for setting period duration, accumulating the marking times of each guiding signpost in each period duration, and feeding back the guiding signpost with the accumulated times being greater than the time threshold value to management staff.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An automated data management method based on an AI engine, the method comprising:
step S100: whenever the continuous use time of any user on any intelligent diagnosis guiding equipment is longer than a time threshold, triggering the any intelligent diagnosis guiding equipment to start an AI service engine for the any user, and acquiring a target diagnosis guiding service item of the any user; performing operation record monitoring on users who use the intelligent diagnosis guiding devices arranged at the positions of all areas in the medical institution; setting a target diagnosis guiding service item as a user use operation record for acquiring relevant diagnosis guiding navigation and is set as a characteristic operation record;
step S200: extracting a target use operation sequence for characteristic analysis of user expansion from each characteristic operation record; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target using operation sequence, calculating the characteristic value of each characteristic operation sequence, and screening the characteristic operation sequence for each characteristic operation record based on the characteristic value;
step S300: calculating user portrait characteristic values for users corresponding to each characteristic operation record based on the characteristic operation sequence distribution condition in each characteristic operation record and the use operation rule presented by the users; setting a characteristic threshold value, and locking a characteristic user larger than the characteristic threshold value;
step S400: acquiring an optimal travel route recommended by an AI service engine for each characteristic user, and finishing marking the related guide signboards arranged in the medical institution according to the deviation condition of the actual travel route of the corresponding characteristic user and the optimal travel route;
step S500: setting period duration, accumulating the marking times of each guiding signpost in each period duration, and feeding back the guiding signpost with accumulated times larger than a time threshold value to a manager.
2. The automated data management method according to claim 1, wherein all of the usage operations generated in time sequence from the time when the user enters the first usage operation to wake up the intelligent diagnostic device until the intelligent diagnostic device starts the AI service engine in each of the feature operation records constitute a target usage operation sequence for developing the feature analysis for each of the users who generate the feature operation records.
3. The automated data management method based on the AI engine of claim 1, wherein the step S200 includes:
step S201: respectively acquiring an operation interface to which each use operation belongs in a target use operation sequence corresponding to each characteristic operation record; capturing the same using operation of the corresponding operation interface in each target using operation sequence in sequence; in each target use operation sequence, sequentially taking two use operations with the same operation interface and the closest sequence as endpoints of a section of characteristic operation sequence, and intercepting to obtain a plurality of characteristic operation sequences; acquiring a using operation set contained in each characteristic operation sequence; the characteristic operation sequences with the same operation interface corresponding to the endpoints belong to one type of characteristic operation sequences;
step S202: if the j-th type characteristic operation sequence with the operation interface X corresponding to the end point exists, the i-th segment characteristic operation sequence, wherein the end point with the earlier sequence of the i-th segment characteristic operation sequence is a using operation a, and the end point with the later sequence is a using operation b; extracting a use operation set contained in the ith characteristic operation sequence, acquiring a use operation total number N corresponding to the use operation set, and collecting operation interface information corresponding to each use operation in the use operation set to obtain an interface information set S1;
step S203: when the j-th characteristic operation sequence with the operation interface X corresponding to the end point is not provided with the i+1th characteristic operation sequence, extracting all the using operations after the using operation b in the corresponding target using operation sequence, recording the total number of the using operations after the using operation b as M, if M < N, collecting operation interface information corresponding to the M using operations after the using operation b, obtaining an interface information set S2, and if M is more than or equal to N, collecting operation interface information of the N using operations which sequentially appear after the using operation b, and obtaining an interface information set S2; when an ith+1th section of characteristic operation sequence exists in a jth class of characteristic operation sequence with an operation interface X corresponding to an endpoint, extracting a use operation set contained in the ith+1th section of characteristic operation sequence, and collecting operation interface information corresponding to each use operation in the use operation set to obtain an interface information set S2;
step S204: calculating a characteristic value U=N [ (S1N) S2)/(S1U S2) ] corresponding to the ith section of characteristic operation sequence, and reserving the ith section of characteristic operation sequence as a characteristic operation sequence corresponding to the occurrence of trial-and-error behavior of a user in a corresponding characteristic operation record when U is smaller than a threshold value; and when U is larger than a threshold value, eliminating the ith section of characteristic operation sequence.
4. The automated data management method based on the AI engine of claim 3, wherein the step S300 includes:
step S301: acquiring an average speed V of a user operation, a total number R of characteristic operation sequences and a minimum use operation item K required from an operation page before an AI service engine is started to an operation page for acquiring relevant diagnosis navigation guidance in each characteristic operation record in corresponding intelligent diagnosis guiding equipment; extracting all historical visit records with the time interval within a threshold range from the current time for the user generating the characteristic operation records, wherein the total number of the historical visit records is L; acquiring an average visit interval time Tr presented between all of the historical visit records;
step S302: calculating a user portrait characteristic value p=v×r×k+tr×l for a user who generates the characteristic operation records.
5. The automated data management method based on the AI engine of claim 1, wherein the step S400 includes:
step S401: acquiring an optimal travel route recommended by an AI service engine for each characteristic user, setting a travel time length threshold based on the optimal travel route, and extracting the actual travel route of the characteristic user when a monitoring camera away from the end point of the optimal travel route still does not capture the corresponding characteristic user when the intelligent diagnosis guiding equipment is full of the time length threshold from the characteristic user, and retrieving video monitoring information of a medical institution;
step S402: and comparing the actual travelling path with the optimal travelling path, locking all position information of the travelling path deviation of the characteristic user, and marking all guiding signboards which can assist in guiding the user to the optimal travelling path within the range of each position information threshold value.
6. An automated data management system applied to an automated data management method based on an AI engine as claimed in any one of claims 1 to 5, wherein the system comprises a characteristic operation record extraction management module, a characteristic operation sequence screening management module, a characteristic user capturing management module, a guiding signpost mark processing module and an early warning feedback management module;
the characteristic operation record extraction management module is used for monitoring the operation record of the user who uses each intelligent diagnosis guiding device arranged at each area position in the medical institution; setting a target diagnosis guiding service item as a user use operation record for acquiring relevant diagnosis guiding navigation and is set as a characteristic operation record;
the characteristic operation sequence screening management module is used for extracting target use operation sequences for characteristic analysis of user development from each characteristic operation record respectively; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target using operation sequence, calculating the characteristic value of each characteristic operation sequence, and screening the characteristic operation sequence for each characteristic operation record based on the characteristic value;
the characteristic user capturing management module is used for calculating user portrait characteristic values for the users corresponding to the characteristic operation records according to the characteristic operation sequence distribution conditions in the characteristic operation records and the use operation rules presented by the users; setting a characteristic threshold value, and locking a characteristic user larger than the characteristic threshold value;
the guiding signpost marking processing module is used for acquiring the optimal travel route recommended by the AI service engine for each characteristic user, and finishing marking processing on the related guiding signpost arranged in the medical institution according to the deviation condition of the actual travel route of the corresponding characteristic user and the optimal travel route;
the early warning feedback management module is used for setting period duration, accumulating the marking times of each guiding signpost in each period duration, and feeding back the guiding signpost with accumulated times larger than a time threshold to a manager.
7. The automated data management system of claim 6, wherein the feature operation sequence screening management module comprises a feature operation sequence interception management unit, a feature value calculation management unit;
the characteristic operation sequence intercepting and managing unit is used for extracting target use operation sequences for characteristic analysis of user development from the characteristic operation records respectively; intercepting a characteristic operation sequence which is generated when the user is preliminarily matched with the trial-and-error behavior in each target use operation sequence;
the characteristic value calculation management unit is used for calculating characteristic values of each characteristic operation sequence and screening the characteristic operation sequences for each characteristic operation record based on the characteristic values.
8. The automated data management system of claim 6, wherein the feature user capture management module comprises a user portrait feature value calculation unit, a feature user capture unit;
the user portrait characteristic value calculation unit is used for calculating user portrait characteristic values for the users corresponding to the characteristic operation records according to the characteristic operation sequence distribution conditions in the characteristic operation records and the use operation rules presented by the users;
the feature user capturing unit is used for setting a feature threshold value and locking feature users larger than the feature threshold value.
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