CN113436712B - Evaluation management system for intelligent medical cloud service platform - Google Patents
Evaluation management system for intelligent medical cloud service platform Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 136
- 238000012216 screening Methods 0.000 claims abstract description 24
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- 238000013210 evaluation model Methods 0.000 claims description 13
- 238000007726 management method Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 3
- 238000005303 weighing Methods 0.000 claims description 3
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- 238000007405 data analysis Methods 0.000 abstract description 3
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
- G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses an evaluation management system for an intelligent medical cloud service platform, and relates to the technical field of intelligent medical treatment. The invention comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring the good evaluation information and the poor evaluation information of each medical system, wherein Phij is used for representing the good evaluation information of a user j on a medical system i, and Pcij is used for representing the poor evaluation information of the user j on the medical system i; and the screening unit is used for screening according to the information acquired by the data acquisition unit to acquire suspicious error information. According to the invention, the screening unit performs screening treatment according to the information acquired by the data acquisition unit to acquire suspicious error information; the user identification unit identifies the user attribute according to the suspicious error information; and the comprehensive evaluation unit analyzes and acquires the comprehensive score of the medical system according to the user attribute identification result, uploads the comprehensive score to the controller, eliminates the information with poor reliability, reduces the influence on data analysis, and improves the evaluation accuracy.
Description
Technical Field
The invention belongs to the technical field of intelligent medical treatment, and particularly relates to an evaluation management system for an intelligent medical treatment cloud service platform.
Background
The medical institution evaluation mechanism is built, so that on one hand, the service behaviors of the medical institutions are standardized, the medical quality is improved, the medical institutions are helped to play advantages to make up the short plates, on the other hand, the medical safety of patients is guaranteed, and the patients are helped to quickly select proper medical institutions.
For example, chinese patent CN111985840a discloses a method, an apparatus and a computer device for evaluating the operation capability of a mechanism, analyzing data in the operation process of the mechanism to be evaluated, determining an evaluation index related to the mechanism to be evaluated from an index library, and the comprehensive calculation formula is as follows: the method for evaluating the operation capability of the mechanism to be evaluated according to the evaluation value is constructed, and a method for evaluating the operation capability of the mechanism to be evaluated in a full dimension is provided for medical institutions such as hospitals, medical insurance fund management and the like, and evaluation results from macroscopic to microscopic and from system to detail are provided; as another example, chinese patent CN109767067a discloses a method for evaluating a hospital based on multiple evaluation dimensions and related products, weighting the multiple risk indexes according to the weight value to obtain a final risk index of the hospital, increasing the manner of evaluating the hospital, and improving the accuracy of hospital evaluation; chinese patent CN110827942a provides a dynamic credit evaluation method and evaluation system based on personal medical behaviors, which promotes the continuously performed person to be evaluated, chinese patent CN111462880a discloses a system and method for medical service evaluation, and improves the practicability of medical service evaluation system.
However, as in the above prior art, much effort is made to investigate how to prompt a user to make an evaluation and to comprehensively analyze the evaluation for medical service evaluation, and analysis of evaluation reliability is lacking, and a solution is now provided.
Disclosure of Invention
The invention aims to provide an evaluation management system for an intelligent medical cloud service platform, which is used for analyzing and acquiring comprehensive scores of a medical system according to user attribute identification results through a data acquisition unit, a screening unit, a user identification unit and a comprehensive evaluation unit, so that the problems in the prior art are solved.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to an evaluation management system for an intelligent medical cloud service platform, which comprises the following components:
the data acquisition unit is used for acquiring the good evaluation information and the poor evaluation information of each medical system, wherein Phij represents the good evaluation information of the user j on the medical system i, and Pcij represents the poor evaluation information of the user j on the medical system i;
the screening unit is used for screening according to the information acquired by the data acquisition unit to acquire suspicious error information;
a user authentication unit for authenticating a user attribute according to the suspicious misinformation;
the comprehensive evaluation unit analyzes and acquires comprehensive scores of the medical system according to the user attribute identification result and uploads the comprehensive scores to the controller;
the controller pushes the composite score of the medical system to the client.
Further, each user can only make one piece of evaluation information to the same medical system every time of visit, and the screening unit performs screening processing according to the information acquired by the data acquisition unit, and the method for acquiring suspicious error information comprises the following steps:
step one: optionally a user;
step two: acquiring the evaluation times of the user selected in the first step and the medical system in the same day, if the evaluation times are more than 3, marking all evaluation information corresponding to the user as suspicious error information, otherwise, entering the third step;
step three: acquiring the number of times of evaluation of the user and the medical system in X1 days, if the number of times of evaluation is larger than X2, marking all evaluation information corresponding to the user as suspicious error information, otherwise, marking all evaluation information corresponding to the user as normal information;
step four: repeating the first to third steps until screening treatment is completed on all the evaluation information;
wherein X1 and X2 are preset values, and X2 is more than or equal to X1.
Further, for the suspicious error information in the second step, the method for identifying the user attribute by the user identification unit according to the suspicious error information is as follows:
step S01: optionally selecting a user corresponding to the false information, and acquiring the distance between the parameter and the evaluated medical system in the same day of the user;
step S02: acquiring the time of the visit corresponding to the user from each medical system;
step S03: calculating an activity value Bp according to a difference Tp between the times of the user's visits to the two medical systems and a distance Sp between the two medical systems that the user is participating in and evaluating in the same day, p=2, 3, …, n;
step S04: if Bp is more than or equal to X3, the corresponding user is marked as a fake evaluation user, otherwise, the corresponding user is a normal user;
step S05: repeating the steps S01-S04 until all the user attributes are identified;
wherein X3 is a preset value.
Further, for the suspicious error information in the third step, the method for identifying the user attribute by the user identification unit according to the suspicious error information is as follows:
step SS01: optionally selecting a user corresponding to the false information, and acquiring corresponding department information and character information and picture information of each evaluation of the user when the user participates in the evaluation of the medical system in X1 day;
step SS02: judging the type of department information corresponding to the user, if the number of the types of departments is greater than X4, marking the corresponding user as a pseudo-evaluation user, otherwise, entering into step SS03;
step SS03: identifying the user attribute according to the coincidence ratio of the character information and the picture information evaluated by the user each time;
repeating steps SS01-SS03 until all user attributes are identified;
wherein X4 is a preset value.
Further, in the step SS03, the step of identifying the user attribute according to the overlap ratio of the text information and the picture information evaluated by the user each time is as follows:
step S31: judging the coincidence degree of character information evaluated each time according to the duplication checking rule, and marking the coincidence degree as L1;
step S32: acquiring a picture pixel value used by a user for each evaluation;
step S33: calculating a suspected value Y according to L1, the total number m of evaluation of a user and a medical system in X1 days and the evaluation number G of pictures with the same pixel value in each evaluation;
step S34: if the suspected value Y is more than or equal to X5, the corresponding user is marked as a fake evaluation user, otherwise, the user is a normal user;
wherein X5 is a preset value.
Further, the specific method for analyzing and acquiring the comprehensive score of the medical system by the comprehensive evaluation unit according to the user attribute identification result is as follows:
firstly, eliminating all evaluation information corresponding to a pseudo evaluation user, and taking the evaluation information of a normal user as an analysis basis;
and secondly, calculating the comprehensive score of the medical system according to the evaluation model.
Further, the evaluation model is specifically:
determining the evaluation model as four stages: the system comprises an advanced layer, a reference layer, a dynamic layer and a subbase layer;
the evaluation model includes: target scoring, evaluation scoring, and distributing weight values for the same.
Further, the high-level layer is a target evaluation index of the medical system, the reference layer is an overall index of the medical system for evaluation target planning, the dynamic layer is a subdivision index of the overall index made on the reference layer, and the base layer is a primary index of the medical system.
Further, the target scoring weight is 30%; the target scoring is to score the indexes of the advanced layer, the reference layer, the dynamic layer and the subbase layer;
if the targets are all completed, counting 100 minutes, weighing and counting 30 minutes;
if the target is not completed, counting 0 points, and counting 0 points after weighting;
if the targets are 2, counting 50 points, and counting 15 points after weighting;
if the targets are 3, counting 60 points, and counting 18 points after weighting;
the scoring weight of the evaluation is 70%, the score is weighted and then is counted for 14, the score is weighted and then is counted for 20, and the score is counted for-14.
Further, the assessment model also includes treatment scores including a cure score Z1, a time-consuming score Z2, a spending score Z3, a service score Z4; the target score was 30% and the evaluation score was 60% based on the treatment score of 10%.
The invention has the following beneficial effects:
the screening unit performs screening processing according to the information acquired by the data acquisition unit to acquire suspicious error information; the user identification unit identifies the user attribute according to the suspicious error information; and the comprehensive evaluation unit analyzes and acquires the comprehensive score of the medical system according to the user attribute identification result, uploads the comprehensive score to the controller, eliminates the information with poor reliability, reduces the influence on data analysis, and improves the evaluation accuracy.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of an evaluation management system for an intelligent medical cloud service platform 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.
In the description of the present invention, it should be understood that the terms "upload," "transfer," and the like are used for descriptive purposes only and for simplifying the description, and are not intended to indicate or imply that the components or elements being referred to must have a particular orientation, be constructed and operate in a particular orientation, and are not to be construed as limiting the invention.
Referring to fig. 1, the present invention is an evaluation management system for an intelligent medical cloud service platform, including:
the data acquisition unit is used for acquiring the good evaluation information and the poor evaluation information of each medical system, wherein Phij represents the good evaluation information of the user j on the medical system i, and Pcij represents the poor evaluation information of the user j on the medical system i;
the screening unit is used for screening according to the information acquired by the data acquisition unit to acquire suspicious error information;
a user authentication unit for authenticating a user attribute according to the suspicious misinformation;
the comprehensive evaluation unit analyzes and acquires comprehensive scores of the medical system according to the user attribute identification result and uploads the comprehensive scores to the controller;
the controller pushes the comprehensive scores of the medical system to the client, and the user can check the comprehensive strength ranking of the medical institutions and select the medical institutions which are most suitable for the user according to the situation of the user.
As an embodiment of the present invention, preferably, each user can only make one piece of evaluation information for the same medical system at each visit, and the screening unit performs screening processing according to the information collected by the data collecting unit, and the method for obtaining suspicious error information includes:
step one: optionally a user;
step two: acquiring the evaluation times of the user selected in the first step and the medical system in the same day, if the evaluation times are more than 3, marking all evaluation information corresponding to the user as suspicious error information, otherwise, entering the third step; as an embodiment of the present invention, preferably, for the suspicious error information in the second step, the method for identifying the user attribute by the user identifying unit according to the suspicious error information includes:
step S01: optionally selecting a user corresponding to the false information, and acquiring the distance between the user and the medical system (referred to as a hospital) under evaluation in the same day;
step S02: acquiring the time of the visit corresponding to the user from each medical system;
step S03: calculating an activity value Bp according to a difference Tp between the times of the user's visits to the two medical systems and a distance Sp between the two medical systems that the user is participating in and evaluating in the same day, p=2, 3, …, n;
step S04: if Bp is more than or equal to X3, the corresponding user is marked as a fake evaluation user, otherwise, the corresponding user is a normal user;
step S05: repeating the steps S01-S04 until all the user attributes are identified;
wherein X3 is a preset value.
Step three: acquiring the number of times of evaluation of the user and the medical system in X1 days, if the number of times of evaluation is larger than X2, marking all evaluation information corresponding to the user as suspicious error information, otherwise, marking all evaluation information corresponding to the user as normal information; as an embodiment of the present invention, preferably, for the suspicious error information in the third step, the method for identifying the user attribute by the user identifying unit according to the suspicious error information includes:
step SS01: optionally selecting a user corresponding to the false information, and acquiring corresponding department information and character information and picture information of each evaluation of the user when the user participates in the evaluation of the medical system in X1 day;
step SS02: judging the type of department information corresponding to the user, if the number of the types of departments is greater than X4, marking the corresponding user as a pseudo-evaluation user, otherwise, entering into step SS03;
step SS03: identifying the user attribute according to the coincidence ratio of the character information and the picture information evaluated by the user each time;
repeating steps SS01-SS03 until all user attributes are identified;
wherein X4 is a preset value.
Step four: repeating the first to third steps until screening treatment is completed on all the evaluation information;
wherein X1 and X2 are preset values, and X2 is more than or equal to X1.
As an embodiment of the present invention, preferably, in step SS03, the step of identifying the user attribute according to the overlap ratio of the text information and the picture information evaluated by the user each time includes:
step S31: judging the coincidence degree of character information evaluated each time according to the duplication checking rule, and marking the coincidence degree as L1;
step S32: acquiring a picture pixel value used by a user for each evaluation;
step S33: calculating a suspected value Y according to L1, the total number m of evaluation of a user and a medical system in X1 days and the evaluation number G of pictures with the same pixel value in each evaluation;
step S34: if the suspected value Y is more than or equal to X5, the corresponding user is marked as a fake evaluation user, otherwise, the user is a normal user;
wherein X5 is a preset value.
As an embodiment of the present invention, preferably, the specific method for analyzing and obtaining the comprehensive score of the medical system by the comprehensive evaluation unit according to the user attribute identification result is as follows:
firstly, eliminating all evaluation information corresponding to a pseudo evaluation user, and taking the evaluation information of a normal user as an analysis basis;
and secondly, calculating the comprehensive score of the medical system according to the evaluation model.
As an embodiment provided by the present invention, preferably, the evaluation model is specifically:
determining the evaluation model as four stages: the system comprises an advanced layer, a reference layer, a dynamic layer and a subbase layer;
the evaluation model includes: target scoring, evaluation scoring, and distributing weight values for the same.
As an embodiment of the present invention, preferably, the high-level layer is a target evaluation index of the medical system, the reference layer is an overall index of the medical system planned for the evaluation target, the dynamic layer is a subdivision index of the overall index made on the reference layer, and the sub-base layer is a primary index of the medical system.
As one embodiment provided by the present invention, preferably, the target scoring weight is 30%; the target scoring is to score the indexes of the advanced layer, the reference layer, the dynamic layer and the subbase layer;
if the targets are all completed, counting 100 minutes, weighing and counting 30 minutes;
if the target is not completed, counting 0 points, and counting 0 points after weighting;
if the targets are 2, counting 50 points, and counting 15 points after weighting;
if the targets are 3, counting 60 points, and counting 18 points after weighting;
the scoring weight of the evaluation is 70%, the score is weighted and then is counted for 14, the score is weighted and then is counted for 20, and the score is counted for-14.
As an embodiment provided by the present invention, preferably, the evaluation model further includes a treatment score, where the treatment score includes a cure score Z1, a time-consuming score Z2, a cost score Z3, and a service score Z4; the target score was 30% and the evaluation score was 60% based on the treatment score of 10%.
An evaluation management system for an intelligent medical cloud service platform is used for acquiring suspicious error information through screening processing according to information acquired by a data acquisition unit by a screening unit; the user identification unit identifies the user attribute according to the suspicious error information; the comprehensive evaluation unit analyzes and acquires the comprehensive score of the medical system according to the user attribute identification result, uploads the comprehensive score to the controller, eliminates the information with poor reliability, reduces the influence on data analysis, improves the evaluation accuracy, and helps patients to quickly select proper medical institutions.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. An evaluation management system for an intelligent medical cloud service platform, comprising:
the data acquisition unit is used for acquiring the good evaluation information and the poor evaluation information of each medical system, wherein Phij represents the good evaluation information of the user j on the medical system i, and Pcij represents the poor evaluation information of the user j on the medical system i;
the screening unit is used for screening according to the information acquired by the data acquisition unit to acquire suspicious error information;
a user authentication unit for authenticating a user attribute according to the suspicious misinformation;
the comprehensive evaluation unit analyzes and acquires comprehensive scores of the medical system according to the user attribute identification result and uploads the comprehensive scores to the controller;
the controller pushes the comprehensive score of the medical system to the client;
each user can only make one piece of evaluation information to the same medical system in each visit, and the screening unit performs screening treatment according to the information acquired by the data acquisition unit, and the method for acquiring suspicious error information comprises the following steps:
step one: optionally a user;
step two: acquiring the evaluation times of the user selected in the first step and the medical system in the same day, if the evaluation times are more than 3, marking all evaluation information corresponding to the user as suspicious error information, otherwise, entering the third step;
step three: acquiring the number of times of evaluation of the user and the medical system in X1 days, if the number of times of evaluation is larger than X2, marking all evaluation information corresponding to the user as suspicious error information, otherwise, marking all evaluation information corresponding to the user as normal information;
step four: repeating the first to third steps until screening treatment is completed on all the evaluation information; wherein X1 and X2 are preset values, and X2 is more than or equal to X1;
aiming at the suspicious error information in the second step, the user identification unit identifies the user attribute according to the suspicious error information by the following method:
step S01: optionally selecting a user corresponding to the false information, and acquiring the distance between the parameter and the evaluated medical system in the same day of the user;
step S02: acquiring the time of the visit corresponding to the user from each medical system;
step S03: calculating an activity value Bp according to a difference Tp between the times of the user's visits to the two medical systems and a distance Sp between the two medical systems that the user is participating in and evaluating in the same day, p=2, 3, …, n;
step S04: if Bp is more than or equal to X3, the corresponding user is marked as a fake evaluation user, otherwise, the corresponding user is a normal user;
step S05: repeating the steps S01-S04 until all the user attributes are identified;
wherein X3 is a preset value;
aiming at the suspicious error information in the step three, the user identification unit identifies the user attribute according to the suspicious error information, and the method comprises the following steps:
step SS01: optionally selecting a user corresponding to the false information, and acquiring corresponding department information and character information and picture information of each evaluation of the user when the user participates in the evaluation of the medical system in X1 day;
step SS02: judging the type of department information corresponding to the user, if the number of the types of departments is greater than X4, marking the corresponding user as a pseudo-evaluation user, otherwise, entering into step SS03;
step SS03: identifying the user attribute according to the coincidence ratio of the character information and the picture information evaluated by the user each time;
repeating steps SS01-SS03 until all user attributes are identified;
wherein X4 is a preset value;
the comprehensive evaluation unit analyzes and acquires the comprehensive score of the medical system according to the user attribute identification result, and the specific method comprises the following steps:
firstly, eliminating all evaluation information corresponding to a pseudo evaluation user, and taking the evaluation information of a normal user as an analysis basis;
and secondly, calculating the comprehensive score of the medical system according to the evaluation model.
2. The evaluation management system for intelligent medical cloud service platform according to claim 1, wherein in the step SS03, the step of identifying the user attribute according to the coincidence degree of the text information and the picture information of each evaluation of the user is:
step S31: judging the coincidence degree of character information evaluated each time according to the duplication checking rule, and marking the coincidence degree as L1;
step S32: acquiring a picture pixel value used by a user for each evaluation;
step S33: calculating a suspected value Y according to L1, the total number m of evaluation of a user and a medical system in X1 days and the evaluation number G of pictures with the same pixel value in each evaluation;
step S34: if the suspected value Y is more than or equal to X5, the corresponding user is marked as a fake evaluation user, otherwise, the user is a normal user;
wherein X5 is a preset value.
3. The evaluation management system for an intelligent medical cloud service platform according to claim 2, wherein the evaluation model is specifically:
determining the evaluation model as four stages: the system comprises an advanced layer, a reference layer, a dynamic layer and a subbase layer;
the evaluation model includes: target scoring, evaluation scoring and distributing weight values for the target scoring and the evaluation scoring;
the high-level layer is a target evaluation index of the medical system, the reference layer is an overall index of the medical system for planning the evaluation target, the dynamic layer is a subdivision index of the overall index made on the reference layer, and the underlayment layer is a primary index of the medical system.
4. The assessment management system for an intelligent medical cloud service platform according to claim 3, wherein:
the target scoring weight is 30%; the target scoring is to score the indexes of the advanced layer, the reference layer, the dynamic layer and the subbase layer;
if the targets are all completed, counting 100 minutes, weighing and counting 30 minutes;
if the target is not completed, counting 0 points, and counting 0 points after weighting;
if the targets are 2, counting 50 points, and counting 15 points after weighting;
if the targets are 3, counting 60 points, and counting 18 points after weighting;
the scoring weight of the evaluation is 70%, the score is weighted and then is counted for 14, the score is weighted and then is counted for 20, and the score is counted for-14.
5. The assessment management system for a smart healthcare cloud service platform of claim 3, wherein the assessment model further comprises a treatment score comprising a cure score Z1, a time-consuming score Z2, a spending score Z3, a service score Z4; the target score was 30% and the evaluation score was 60% based on the treatment score of 10%.
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