CN113033528A - Feedback scale evaluation method, system, device and medium - Google Patents
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Abstract
The invention provides a feedback scale evaluation method, a feedback scale evaluation system, feedback scale evaluation equipment and a feedback scale evaluation medium, wherein the feedback scale evaluation method comprises the following steps: setting a structured grading standard according to a preset feedback scale template; acquiring a scale image corresponding to a feedback scale filled by a user, and acquiring a feature sequence corresponding to text information in the scale image through a text recognition model, wherein the feature sequence comprises a plurality of key features; generating multi-level distribution of the feature sequence according to the structured grading standard, and inputting key features meeting each grading standard into corresponding levels of the multi-level distribution; acquiring multi-level distribution of the same article corresponding to different application objects, acquiring depth information in each multi-level distribution, and outputting an evaluation result according to the change trend of the depth information within preset time; the invention can effectively simplify the processing flow of the feedback meter and improve the processing efficiency.
Description
Technical Field
The invention relates to the field of data processing, in particular to a feedback scale evaluation method, a feedback scale evaluation system, feedback scale evaluation equipment and a feedback scale evaluation medium.
Background
At present, feedback scales aiming at the use condition of products and the like are mostly filled in manually, the amount of collected statistical data is large, useful information collection and arrangement are carried out completely depending on manual work, a large amount of labor cost needs to be consumed, and the efficiency is low. The final result error is large due to the subjectivity of manual statistics, and a unified standardization standard is lacked. In addition, the long period of data feedback will greatly affect normal production activities and increase extra cost.
Disclosure of Invention
In view of the problems in the prior art, the invention provides a feedback meter evaluation method, a feedback meter evaluation system, feedback meter evaluation equipment and a feedback meter evaluation medium, and mainly solves the problem that the existing feedback meter evaluation analysis efficiency is low.
In order to achieve the above and other objects, the present invention adopts the following technical solutions.
A feedback scale assessment method comprising:
setting a structured grading standard according to a preset feedback scale template;
acquiring a scale image corresponding to a feedback scale filled by a user, and acquiring a feature sequence corresponding to text information in the scale image through a text recognition model, wherein the feature sequence comprises a plurality of key features;
generating multi-level distribution of the feature sequence according to the structured grading standard, and inputting key features meeting each grading standard into corresponding levels of the multi-level distribution;
the method comprises the steps of obtaining multi-level distribution of the same article corresponding to different application objects, obtaining depth information in each multi-level distribution, and outputting an evaluation result according to the change trend of the depth information in preset time.
Optionally, the structured ranking criteria comprises: age bracket criteria, no side effects, improved symptoms and/or combinations thereof.
Optionally, the setting of the structured ranking criteria according to the preconfigured feedback scale template comprises:
and determining evaluation parameters in the feedback table template, wherein each evaluation parameter corresponds to one of the structural grading standards.
Optionally, obtaining a scale image corresponding to the feedback scale filled by the user, and obtaining a feature sequence corresponding to text information in the scale image through a text recognition model, includes:
acquiring a gauge image corresponding to a feedback gauge filled by a user through a scanning device or an image acquisition device, and setting a mask image according to the feedback gauge template;
convolving the scale image and the mask image to obtain a sub-image of a region of interest;
and inputting the subimages into a pre-trained text recognition model to obtain a corresponding characteristic sequence.
Optionally, setting the mask image includes:
determining an attention area according to the feedback scale template, wherein the attention area is an attention area corresponding to each level of the structured grading standard;
setting the size of the mask image to be consistent with that of the feedback table template, and shielding the region outside the region of interest in the feedback table template through the mask image.
Optionally, generating a multi-level distribution of the feature sequence according to the structured ranking criterion, and inputting the key features meeting each ranking criterion into a corresponding level of the multi-level distribution, including:
judging whether the key features accord with one grading standard item by item according to the structural grading standard, constructing multi-level distribution by all the key features which accord with the grading standard according to the arrangement sequence of the structural grading standard, and configuring the depth value corresponding to each level of key features in the multi-level distribution.
Optionally, obtaining multi-level distributions of different application objects corresponding to the same article, and obtaining depth information in each multi-level distribution:
and accumulating the depth values of all levels in each multi-level distribution to obtain the depth information corresponding to the multi-level distribution.
A feedback gauge evaluation system comprising:
the standard setting module is used for setting a structured grading standard according to a preset feedback scale template;
the characteristic extraction module is used for acquiring a scale image corresponding to a feedback scale filled by a user and acquiring a characteristic sequence corresponding to text information in the scale image through a text recognition model, wherein the characteristic sequence comprises a plurality of key characteristics;
the multilevel distribution creating module is used for generating multilevel distribution of the feature sequence according to the structural grading standard and inputting the key features meeting each grading standard into the corresponding levels of the multilevel distribution;
and the evaluation module is used for acquiring the multi-level distribution of different application objects corresponding to the same article, acquiring the depth information in each multi-level distribution, and outputting an evaluation result according to the change trend of the depth information in preset time.
A feedback scale evaluation device comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the feedback gauge evaluation method.
A machine-readable medium having stored thereon instructions, which when executed by one or more processors, cause a device to perform the feedback gauge evaluation method.
As described above, the feedback scale evaluation method, system, device, and medium of the present invention have the following advantageous effects.
Carrying out quantization processing on the feedback scale, and quickly acquiring effective information through a structured grading standard; key features in the scale image are automatically identified, statistical association is carried out, and data support is provided for subsequent production improvement; and the trend analysis based on the depth information does not depend on manual work, so that the processing efficiency is improved.
Drawings
Fig. 1 is a schematic flow chart of a feedback scale evaluation method according to an embodiment of the present invention.
FIG. 2 is a block diagram of a feedback gauge evaluation system according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, the present invention provides a feedback scale evaluation method, including steps S01-S04.
In step S01, structured ranking criteria are set according to the pre-configured feedback table template.
In an embodiment, a corresponding feedback scale template may be pre-created for a single item or a class of items. Illustratively, taking the drug "amine extended release capsule" as an example, the corresponding feedback scale template may include the following information: the application time, the application person, sex, age, the existing basic disease, curative effect, side effect, sleep quality, dizziness, tinnitus, fundus conditions (hemorrhage and edema), combined application, name of the person and satisfaction degree. Specifically, the content in the corresponding feedback table, such as whether the indication is improved, may be adjusted according to the actual application requirement.
In an embodiment, the structured grading criteria may include: age bracket criteria, no side effects, improved symptoms and/or combinations thereof. Illustratively, taking a medicine as an example, assuming that the applicable age of the medicine is 8-59 years, the applicable age is taken as an age-segmentation criterion. The specific structuring standard can be set according to the actual application requirements.
In one embodiment, setting the structured ranking criteria according to a preconfigured feedback table template includes:
and determining evaluation parameters in the feedback table template, wherein each evaluation parameter corresponds to one of the structured grading standards. Specifically, still taking "amine sustained-release capsule" as an example, the structured grading standard is sequentially generated according to the arrangement sequence of each information in the feedback scale template, and the determined evaluation parameters of the feedback scale template include: age, existing basic disease, curative effect, side effect, sleep quality, dizziness, tinnitus, fundus conditions (hemorrhage and edema), and combination. The corresponding structuring classification criteria can be expressed as: above the applicable age of 8-59 years, with past history, no obvious curative effect, side effect, poor sleep quality, no improvement of dizziness, no improvement of tinnitus, fundus hemorrhage/edema, and combined medication.
In step S02, a scale image corresponding to the feedback scale filled by the user is obtained, and a feature sequence corresponding to the text information in the scale image is obtained through the text recognition model, where the feature sequence includes a plurality of key features.
In one embodiment, obtaining a scale image corresponding to a feedback scale filled by a user, and obtaining a feature sequence corresponding to text information in the scale image through a text recognition model includes: acquiring a gauge image corresponding to a feedback gauge filled by a user through a scanning device or an image acquisition device, and setting a mask image according to a feedback gauge template; convolving the scale image and the mask image to obtain a sub-image of the attention area; and inputting the subimages into a pre-trained text recognition model to obtain a corresponding characteristic sequence.
In one embodiment, setting a mask image includes: determining an attention area according to a feedback scale template, wherein the attention area is an attention area corresponding to each level of the structured grading standards, namely a feedback information filling area; setting the size of the mask image to be consistent with that of the feedback table template, and shielding the region outside the attention region in the feedback table template through the mask image. Optionally, before the gauge image is convolved with the mask image, the gauge image is normalized to ensure that the dimension of the gauge image matches the mask image, so that the problem of the shift of the mask image attention area caused by the error of the acquired image is reduced.
In an embodiment, the text recognition model may adopt an existing ocr (optical Character recognition) model, and a specific model training process is not described herein. After extracting the key features of each sub-image, the corresponding key features can be further arranged in sequence according to the position sequence of the sub-images in the scale image to form a feature sequence.
In step S03, a multi-level distribution of the feature sequence is generated according to the structured classification criteria, and the key features that meet each of the classification criteria are input into a corresponding level of the multi-level distribution.
In one embodiment, generating a multi-level distribution of the feature sequence according to a structured ranking criterion, inputting key features meeting each ranking criterion into a corresponding level of the multi-level distribution, comprises:
judging whether the key features accord with one grading standard item by item according to the structural grading standard, constructing multi-level distribution by all the key features which accord with the grading standard according to the arrangement sequence of the structural grading standard, and configuring the depth value corresponding to each level key feature in the multi-level distribution.
In one embodiment, taking the aforementioned "amine sustained release capsule" as an example, the structuration grading criteria are: above the applicable age of 8-59 years, with past history, no obvious curative effect, side effect, poor sleep quality, no improvement of dizziness, no improvement of tinnitus, fundus hemorrhage/edema, and combined medication. Two existing feedback tables are denoted as a and B, respectively. The signature sequences obtained from table a are represented as: the traditional Chinese medicine composition is 25 years old, has no existing basic diseases, good curative effect, no side effect, poor sleep quality, dizziness improvement, no obvious improvement on tinnitus, no hemorrhage/edema on eyeground and no combined medicine. The signature sequence corresponding to table B can be expressed as: 40 years old, hypertension/diabetes, no obvious curative effect, tachypnea/accelerated heartbeat, poor sleep quality, dizziness and improvement, tinnitus improvement and fundus edema, and is used together with the medicine a. Then, the hierarchical distribution corresponding to the scale a is obtained by comparing the hierarchical distribution with the structured hierarchical standard item by item, and can be expressed as: poor sleep quality-tinnitus is not improved significantly; the multi-level distribution for gauge B may be represented as: hypertension/diabetes-no obvious curative effect-tachypnea/accelerated heartbeat-poor sleep quality-fundus edema-drug combination with a.
In step S04, multi-level distributions of different application objects corresponding to the same item are obtained, depth information in each multi-level distribution is obtained, and an evaluation result is output according to a variation trend of the depth information within a preset time.
In one embodiment, multi-level distributions of different application objects corresponding to the same article are obtained, and depth information in each multi-level distribution is obtained: and accumulating the depth values of all levels in each multi-level distribution to obtain the depth information corresponding to the multi-level distribution. For example, the depth value corresponding to each level in the multi-level distribution may be set to 1. The depth information of the gauge a is 2 and the depth information of the gauge B is 6 in step S03. In another embodiment, the depth value may also be set according to the weight ratio of the key feature, and if the side effect has a greater influence on the drug evaluation, a greater depth value is configured for the corresponding key feature. The feedback scales of the same medicine in one month or three months can be sorted to generate corresponding multi-level distribution, a distribution curve of multi-level distribution depth information is generated, and the change trend of the depth information is judged according to the distribution curve. In another embodiment, a threshold segmentation line may be set, and if the depth information curve is below the threshold segmentation line, the feedback result is considered to be within a normal range; and if the depth information curve is above the threshold segmentation line, judging that the result is abnormal, and feeding back the corresponding key features to a medicine production party so as to make adjustment and improvement on pertinence. Optionally, one or more multi-level distributions beyond the threshold dividing line range may be recorded, and the original feedback scale image may be obtained and output to the drug manufacturer. The data screening is carried out by the method, and the data processing amount is reduced.
Referring to fig. 2, the present embodiment provides a feedback scale evaluation system for implementing the feedback scale evaluation method in the foregoing method embodiments. Since the technical principle of the system embodiment is similar to that of the method embodiment, repeated description of the same technical details is omitted.
In one embodiment, a feedback gauge evaluation system includes:
the standard setting module 10 is used for setting a structured grading standard according to a preset feedback scale template;
the feature extraction module 11 is configured to obtain a scale image corresponding to a feedback scale filled by a user, and obtain a feature sequence corresponding to text information in the scale image through a text recognition model, where the feature sequence includes a plurality of key features;
a multi-level distribution creating module 12, configured to generate a multi-level distribution of the feature sequence according to the structured classification standard, and input a key feature meeting each classification standard into a corresponding level of the multi-level distribution;
and the evaluation module 13 is configured to acquire multi-level distributions of different application objects corresponding to the same article, acquire depth information in each multi-level distribution, and output an evaluation result according to a change trend of the depth information within a preset time.
The standard setting module 10 is used to assist in executing step S01 described in the foregoing method embodiments; the feature extraction module 11 is configured to perform step S02 described in the foregoing method embodiment; the multi-level distribution creation module 12 is configured to perform step S03 described in the foregoing method embodiment; the evaluation module 13 is configured to perform step S04 described in the previous method embodiment.
The embodiment of the present application further provides a feedback scale evaluation device, and the device may include: one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of fig. 1. In practical applications, the device may be used as a terminal device, and may also be used as a server, where examples of the terminal device may include: the mobile terminal includes a smart phone, a tablet computer, an electronic book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop, a vehicle-mounted computer, a desktop computer, a set-top box, an intelligent television, a wearable device, and the like.
The present application further provides a machine-readable medium, where one or more modules (programs) are stored in the medium, and when the one or more modules are applied to a device, the device may execute instructions (instructions) included in the feedback table evaluation method in fig. 1 according to the present application. The machine-readable medium can be any available medium that a computer can store or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Referring to fig. 3, the present embodiment provides a device 80, and the device 80 may be a desktop device, a laptop computer, a smart phone, or the like. In detail, the device 80 comprises at least, connected by a bus 81: a memory 82 and a processor 83, wherein the memory 82 is used for storing computer programs, and the processor 83 is used for executing the computer programs stored in the memory 82 to execute all or part of the steps of the foregoing method embodiments.
The above-mentioned system bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In summary, according to the feedback scale evaluation method, system, device and medium of the present invention, only structured hierarchical standard setting needs to be performed on the feedback scale template, so that required data information can be quickly obtained from a large number of feedback scales, and the processing efficiency is greatly improved; the change trend is analyzed through the depth information, the risk is timely evaluated, meanwhile, data screening can be carried out, the feedback scale exceeding the threshold segmentation line is subjected to targeted analysis, the data processing amount is reduced, and the problem is easier to locate; the data processing process is simplified, and the operation is convenient. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A feedback gauge evaluation method, comprising:
setting a structured grading standard according to a preset feedback scale template;
acquiring a scale image corresponding to a feedback scale filled by a user, and acquiring a feature sequence corresponding to text information in the scale image through a text recognition model, wherein the feature sequence comprises a plurality of key features;
generating multi-level distribution of the feature sequence according to the structured grading standard, and inputting key features meeting each grading standard into corresponding levels of the multi-level distribution;
the method comprises the steps of obtaining multi-level distribution of the same article corresponding to different application objects, obtaining depth information in each multi-level distribution, and outputting an evaluation result according to the change trend of the depth information in preset time.
2. The feedback gauge evaluation method of claim 1, wherein the structured ranking criteria comprises: age bracket criteria, no side effects, improved symptoms and/or combinations thereof.
3. The feedback meter evaluation method according to claim 1, wherein setting the structured ranking criteria according to the preconfigured feedback meter template comprises:
and determining evaluation parameters in the feedback table template, wherein each evaluation parameter corresponds to one of the structural grading standards.
4. The feedback scale evaluation method according to claim 1, wherein obtaining a scale image corresponding to the feedback scale filled by a user, and obtaining a feature sequence corresponding to text information in the scale image through a text recognition model, comprises:
acquiring a gauge image corresponding to a feedback gauge filled by a user through a scanning device or an image acquisition device, and setting a mask image according to the feedback gauge template;
convolving the scale image and the mask image to obtain a sub-image of a region of interest;
and inputting the subimages into a pre-trained text recognition model to obtain a corresponding characteristic sequence.
5. The feedback meter evaluation method according to claim 4, wherein setting the mask image includes:
determining an attention area according to the feedback scale template, wherein the attention area is an attention area corresponding to each level of the structured grading standard;
setting the size of the mask image to be consistent with that of the feedback table template, and shielding the region outside the region of interest in the feedback table template through the mask image.
6. The feedback scale evaluation method according to claim 1, wherein a multi-level distribution of the feature sequence is generated based on the structured ranking criterion, and the entering of the key features meeting each of the ranking criteria into a corresponding level of the multi-level distribution comprises:
judging whether the key features accord with one grading standard item by item according to the structural grading standard, constructing multi-level distribution by all the key features which accord with the grading standard according to the arrangement sequence of the structural grading standard, and configuring the depth value corresponding to each level of key features in the multi-level distribution.
7. The feedback scale evaluation method according to claim 1 or 6, wherein multi-level distributions of different application objects corresponding to the same item are obtained, and depth information in each multi-level distribution is obtained:
and accumulating the depth values of all levels in each multi-level distribution to obtain the depth information corresponding to the multi-level distribution.
8. A feedback gauge evaluation system, comprising:
the standard setting module is used for setting a structured grading standard according to a preset feedback scale template;
the characteristic extraction module is used for acquiring a scale image corresponding to a feedback scale filled by a user and acquiring a characteristic sequence corresponding to text information in the scale image through a text recognition model, wherein the characteristic sequence comprises a plurality of key characteristics;
the multilevel distribution creating module is used for generating multilevel distribution of the feature sequence according to the structural grading standard and inputting the key features meeting each grading standard into the corresponding levels of the multilevel distribution;
and the evaluation module is used for acquiring the multi-level distribution of different application objects corresponding to the same article, acquiring the depth information in each multi-level distribution, and outputting an evaluation result according to the change trend of the depth information in preset time.
9. A feedback gauge evaluating apparatus, comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of any of claims 1-7.
10. A machine-readable medium having stored thereon instructions, which when executed by one or more processors, cause an apparatus to perform the method of any of claims 1-7.
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