CN114864031A - Data governance cooperative method based on block chain - Google Patents
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Abstract
The invention provides a data governance cooperative method based on a block chain, which comprises the following steps: acquiring target text data; the target text data includes an outpatient case image and an inpatient case image; preprocessing target text data to obtain a binary text picture: identifying characters in the binary text picture to obtain an electronic case; carrying out data cleaning on data in the electronic case to obtain the electronic case after the data cleaning; and storing the electronic case after data cleaning to the block chain. The invention can improve the recognition accuracy of the characters by preprocessing the target text data and then recognizing the characters in the target text data, and in addition, the invention can store the electronic case after the data is cleaned on the block chain, so that the electronic case can not be tampered.
Description
Technical Field
The invention belongs to the technical field of data management, and particularly relates to a block chain-based data management cooperative method.
Background
With the increasing level of medical care, doctors or nurses collect and store a great deal of medical diagnosis data during the course of treatment. Because various departments have a plurality of departments and the informatization levels of the departments are different, the departments are difficult to organically combine with other departments, in the work flows of generation, transmission, processing, storage and the like of medical diagnosis data, because some medical diagnosis data are handwritten, manual error correction is needed when the medical diagnosis data are uploaded, but the error correction process is greatly influenced by the subjectivity of workers, the efficiency is low, and some workers can also possibly falsify the medical diagnosis data.
Disclosure of Invention
The invention aims to provide a data governance cooperative method based on a block chain, and aims to solve the problem of low error correction efficiency of traditional medical diagnosis data.
In order to achieve the purpose, the invention adopts the technical scheme that: a data governance cooperative method based on a block chain comprises the following steps:
step 1: acquiring target text data; the target text data includes an outpatient case image and an inpatient case image;
step 2: preprocessing the target text data to obtain a binary text picture;
and step 3: identifying characters in the binary text picture to obtain an electronic case;
and 4, step 4: carrying out data cleaning on the data in the electronic case to obtain the electronic case after the data cleaning;
and 5: and storing the electronic case after the data washing to a block chain.
Preferably, the step 2: preprocessing the target text data to obtain a binary text picture, wherein the binary text picture comprises the following steps:
step 2.1: carrying out gray level processing on the target text data to obtain a gray level image of the target text data;
step 2.2: denoising the target text data gray level image to obtain denoised target text data;
step 2.3: segmenting the de-noised target text data to obtain a character region gray image and a background region gray image;
step 2.4: and carrying out binarization processing on the character area gray level image to obtain a binarization text picture.
Preferably, said step 2.2: denoising the target text data gray level image to obtain denoised target text data, wherein the denoising comprises the following steps:
denoising the target text data gray scale image by using a gray scale value denoising model to obtain denoised target text data; the gray value denoising model is as follows:
wherein p (x, y) represents the de-noised target text data, q (x, y) represents the gray value at the (x, y) position on the gray map of the target text data, and alpha represents the adjustable factor.
Preferably, said step 2.3: segmenting the de-noised target text data to obtain a text region gray image and a background region gray image, wherein the method comprises the following steps:
step 2.3.1: dividing the de-noised target text data into two groups by adopting a preset gray value;
step 2.3.2: calculating the average gray values in the two groups of images to obtain a first group of average gray values and a second group of average gray values;
step 2.3.3: continuously adjusting the preset gray value to enable the difference value between the first group of average gray values and the second group of average gray values to be maximum;
step 2.3.4: and taking the corresponding gray value with the maximum average gray value difference value as a segmentation value, and segmenting the de-noised target text data by using the segmentation value to obtain a character region gray image and a background region gray image.
Preferably, the difference between the first set of average gray-scale values and the second set of average gray-scale values is calculated by:
wherein d (k) represents a difference value, P 1 Representing the number of pixel points on the first group of images, N representing the total number of pixel points on the de-noised target text data, P 2 Indicating the number of pixels on the second set of images,μ 1 representing a first set of mean gray values, μ 2 Representing the second set of average gray values and mu representing the overall gray average of the target text data.
Preferably, the step 4: the data cleaning of the data in the electronic case is carried out to obtain the electronic case after the data cleaning, and the method comprises the following steps:
step 4.1: performing pinyin text conversion on characters in the electronic case to obtain pinyin characters of the electronic case;
step 4.2: detecting the relevance of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model to obtain a first error correction result;
step 4.3: detecting the relevance of the pinyin characters of each electronic case and pinyin characters of a second preset verification target field by adopting a second error correction model to obtain a second error correction result;
step 4.4: and performing data cleaning on characters in the electronic case according to the first error correction result and the second error correction result to obtain the data-cleaned electronic case.
Preferably, the step 4.2: detecting the relevance of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model to obtain a first error correction result, wherein the first error correction result comprises the following steps:
step 4.2.1: obtaining a first error correction value of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model; wherein the first error correction model is:
wherein S is i The ith character element (i is more than or equal to 0 and less than or equal to M, M represents the length of the character string S) of any character string S in the pinyin characters of the electronic case, T j J is more than or equal to 0 and is less than or equal to N, and N represents the length of the character string T;
step 4.2.2: obtaining a first error correction result according to the first error correction value; wherein the first error correction value is the value of the matrix D (i, j) at the (N + 1) th row and column element of the M +1 th row.
Preferably, said step 4.2.2: obtaining a first error correction result according to the first error correction value, including:
the formula is adopted:
obtaining a first error correction result; wherein l d Indicating the spelling distance.
Preferably, the step 4.3: detecting the relevance of each electronic case pinyin character and a second preset verification target field pinyin character by adopting a second error correction model to obtain a second error correction result, wherein the second error correction result comprises the following steps:
step 4.3.1: obtaining a second error correction value of each electronic case pinyin character and a second preset target field pinyin character by adopting a second error correction model; wherein the second error correction model is:
wherein S is i The ith character element (i is more than or equal to 0 and less than or equal to M, M represents the length of the character string S) of any character string S in the pinyin characters of the electronic case, T j J is more than or equal to 0 and is less than or equal to N, and N represents the length of the character string T;
step 4.3.2: obtaining a second error correction result according to the second error correction value; wherein the second error correction value is the value of the matrix L (i, j) at the (N + 1) th row and column element of the M +1 th row.
Preferably, said step 4.3.2: obtaining a second error correction result according to the second error correction value, including:
the formula is adopted:
obtaining a second error correction result; wherein l cs Representing the second error correction value.
The block chain-based data governance collaborative method provided by the invention has the beneficial effects that: compared with the prior art, the method has the advantages that the target text data are preprocessed, then the characters in the target text data are recognized, the recognition accuracy of the characters can be improved, in addition, the electronic case after the data are cleaned is stored on the block chain, and the electronic case can not be tampered.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions 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 it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a block chain-based data governance coordination method according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention aims to provide a data governance cooperative method based on a block chain, and aims to solve the problem of low error correction efficiency of traditional medical diagnosis data.
Referring to fig. 1, to achieve the above object, the technical solution adopted by the present invention is: a data governance cooperative method based on a block chain comprises the following steps:
step 1: acquiring target text data; the target text data comprises outpatient case images and inpatient case images;
and 2, step: preprocessing the target text data to obtain a binary text picture;
further, the step 2 comprises:
step 2.1: carrying out gray level processing on the target text data to obtain a gray level image of the target text data;
step 2.2: denoising the target text data gray level image to obtain denoised target text data;
in an embodiment of the present invention, step 2.2 comprises:
denoising the target text data gray scale image by using a gray scale value denoising model to obtain denoised target text data; the gray value denoising model is as follows:
wherein p (x, y) represents the de-noised target text data, q (x, y) represents the gray value at the (x, y) position on the gray map of the target text data, and alpha represents the adjustable factor.
According to the invention, the image is subjected to smooth denoising processing by using the gray value denoising model, so that the noise in the image can be filtered under the condition of keeping the original information of the image as much as possible, the image is clearer, and the character recognition is convenient.
Step 2.3: segmenting the de-noised target text data to obtain a character region gray image and a background region gray image;
further, step 2.3 comprises:
step 2.3.1: dividing the de-noised target text data into two groups by adopting a preset gray value;
step 2.3.2: calculating the average gray values in the two groups of images to obtain a first group of average gray values and a second group of average gray values;
step 2.3.3: continuously adjusting the preset gray value to enable the difference value between the first group of average gray values and the second group of average gray values to be maximum; wherein, the difference calculation formula of the first group of average gray values and the second group of average gray values is as follows:
wherein d (k) represents a difference value, P 1 Representing the number of pixel points on the first group of images, N representing the total number of pixel points on the de-noised target text data, P 2 Representing the number of pixels, mu, in the second set of images 1 Representing a first set of mean gray values, μ 2 Representing the second set of average gray values and mu representing the overall gray average of the target text data.
Step 2.3.4: and taking the corresponding gray value with the maximum average gray value difference value as a segmentation value, and segmenting the de-noised target text data by using the segmentation value to obtain a character region gray image and a background region gray image.
The invention segments the image based on the idea of histogram, can obtain the optimal gray value segmentation value as a whole according to the probability of the gray value distribution of the image, and can strip the gray image of the background area together with the noise by utilizing the gray value segmentation value to segment the image, so that the outline and the texture of the character area are clearer.
Step 2.4: and carrying out binarization processing on the character area gray level image to obtain a binarization text picture.
And step 3: identifying characters in the binary text picture to obtain an electronic case;
and 4, step 4: carrying out data cleaning on the data in the electronic case to obtain the electronic case after the data cleaning;
because the electronic case is extracted from the outpatient case image and the inpatient case image, and many outpatient case images and inpatient case images are manually filled by doctors or nurses, the electronic case can be wrongly filled or the handwritten characters can be wrongly recognized by a machine, and the invention provides an error correction method for checking whether the electronic case has errors. The specific error correction idea is as follows:
further, step 4 comprises:
step 4.1: performing pinyin text conversion on characters in the electronic case to obtain pinyin characters of the electronic case;
step 4.2: detecting the relevance of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model to obtain a first error correction result;
in an embodiment of the present invention, step 4.2 comprises:
step 4.2.1: obtaining a first error correction value of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model; wherein the first error correction model is:
wherein S is i The ith character element (i is more than or equal to 0 and less than or equal to M, M represents the length of the character string S) of any character string S in the pinyin characters of the electronic case, T j J is more than or equal to 0 and is less than or equal to N, and N represents the length of the character string T;
step 4.2.2: obtaining a first error correction result according to the first error correction value; wherein the first error correction value is the value of the matrix D (i, j) at the (N + 1) th row and column element of the M +1 th row.
In practical application, the invention firstly needs to construct a matrix D of M +1 × N +1, where the first row of the matrix D represents each character of the character string S, the first column represents each character of the character string T, the second row represents the initialization value of each character in the character string S, the second column represents the initialization value of each character of the character string T, and the initialization values of the other matrix elements are all 0. Then, calculating the values of elements in a matrix D according to the first error correction model, where the values of the elements in the matrix are determined by the upper left element, the left element and the upper element of the element to be calculated, and between two character strings, if the corresponding characters in the two character strings in the matrix are the same, the similarity of the corresponding characters is equal to the value of the upper left element, otherwise the similarity of the corresponding characters is equal to the minimum value of the upper left element, the left element and the upper element plus l, such as the element D (1,1) in the matrix D, and the corresponding characters are the same, so that D (1,1) ═ D (0,0) ═ 0 is known according to the formula, and when the corresponding characters are different, such as the element D (1,2) in the matrix D, so that D (1,2) ═ min (D (0, l), D (0,2), D (,1)) +1 ═ 0+1 ═ 1, similarly, the values of other elements in the matrix D can be obtained according to a formula, and finally, the value of the lower right corner element in the matrix D, that is, the value of the (M + 1) th row and the (N + 1) th column element is the first error correction value.
Wherein, the step 4.2.2: obtaining a first error correction result according to the first error correction value, including:
the formula is adopted:
obtaining a first error correction result; wherein l d Denotes a first error correction value, and max (M, N) denotes a maximum value of the corresponding string length.
Step 4.3: detecting the relevance of the pinyin characters of each electronic case and pinyin characters of a second preset verification target field by adopting a second error correction model to obtain a second error correction result;
further, the step 4.3 comprises:
step 4.3.1: obtaining a second error correction value of each electronic case pinyin character and a second preset target field pinyin character by adopting a second error correction model; wherein the second error correction model is:
wherein S is i The ith character element (i is more than or equal to 0 and less than or equal to M, M represents the length of the character string S) of any character string S in the pinyin characters of the electronic case, T j J is more than or equal to 0 and is less than or equal to N, and N represents the length of the character string T;
step 4.3.2: obtaining a second error correction result according to the second error correction value; wherein the second error correction value is the value of the matrix L (i, j) at the (N + 1) th row and column element of the M +1 th row.
It should be noted that the second error correction value of the present invention is similar to the calculation process of the second error correction value, and is not described herein again.
Further, the step 4.3.2 comprises:
the formula is adopted:
obtaining a second error correction result; wherein l cs Representing the second error correction value.
Step 4.4: and performing data cleaning on characters in the electronic case according to the first error correction result and the second error correction result to obtain the data-cleaned electronic case.
It should be noted that, in the present invention, first, a first set of corresponding character strings whose first error correction result is greater than a first preset value and a second set of corresponding character strings whose second error correction result is greater than a second preset value need to be obtained; then, the union of the first set and the second set is used as error data, and the corresponding electronic case is removed based on the error data, so that filling errors or recognition errors of handwritten characters by a machine can be basically removed, and the electronic case is more real and reliable.
And 5: and storing the electronic case after the data cleaning to a block chain.
The medical record of the patient is stored in the block chain network, the authenticity of the medical record data of the patient can be ensured by using the tamper resistance of the block chain network, and medical staff can diagnose according to the medical record of the patient.
According to a specific embodiment of the present invention, the present invention discloses the following technical effects:
the invention can improve the recognition accuracy of the characters by preprocessing the target text data and then recognizing the characters in the target text data, and in addition, the invention can store the electronic case after the data is cleaned on the block chain, so that the electronic case can not be tampered.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A data governance cooperative method based on a block chain is characterized by comprising the following steps:
step 1: acquiring target text data; the target text data comprises outpatient case images and inpatient case images;
step 2: preprocessing the target text data to obtain a binary text picture;
and step 3: identifying characters in the binary text picture to obtain an electronic case;
and 4, step 4: carrying out data cleaning on the data in the electronic case to obtain the electronic case after the data cleaning;
and 5: and storing the electronic case after the data cleaning to a block chain.
2. A data governance cooperative method based on a block chain is characterized in that the step 2: preprocessing the target text data to obtain a binary text picture, wherein the binary text picture comprises the following steps:
step 2.1: carrying out gray level processing on the target text data to obtain a gray level image of the target text data;
step 2.2: denoising the target text data gray level image to obtain denoised target text data;
step 2.3: segmenting the de-noised target text data to obtain a character region gray image and a background region gray image;
step 2.4: and carrying out binarization processing on the character area gray level image to obtain a binarization text picture.
3. The cooperative data governance method based on blockchain according to claim 2, wherein the step 2.2: denoising the target text data gray level image to obtain denoised target text data, wherein the denoising comprises:
denoising the target text data gray scale image by using a gray scale value denoising model to obtain denoised target text data; the gray value denoising model is as follows:
wherein p (x, y) represents the de-noised target text data, q (x, y) represents the gray value at the (x, y) position on the gray map of the target text data, and alpha represents the adjustable factor.
4. The cooperative data governance method based on blockchain according to claim 2, wherein said step 2.3: segmenting the de-noised target text data to obtain a text region gray image and a background region gray image, wherein the method comprises the following steps:
step 2.3.1: dividing the de-noised target text data into two groups by adopting a preset gray value;
step 2.3.2: calculating the average gray values in the two groups of images to obtain a first group of average gray values and a second group of average gray values;
step 2.3.3: continuously adjusting the preset gray value to enable the difference value between the first group of average gray values and the second group of average gray values to be maximum;
step 2.3.4: and taking the corresponding gray value with the maximum average gray value difference value as a segmentation value, and segmenting the de-noised target text data by using the segmentation value to obtain a character region gray image and a background region gray image.
5. The data governance collaborative method based on a blockchain according to claim 4, wherein the difference calculation formula between the first set of mean gray values and the second set of mean gray values is:
wherein d (k) represents a difference value, P 1 Representing the number of pixel points on the first group of images, N representing the total number of pixel points on the de-noised target text data, P 2 Representing the number of pixels, mu, in the second set of images 1 Representing a first set of mean gray values, μ 2 Representing the second set of average gray values and mu representing the overall gray average of the target text data.
6. The cooperative data governance method based on blockchain according to claim 1, wherein said step 4: the data cleaning of the data in the electronic case is carried out to obtain the electronic case after the data cleaning, and the method comprises the following steps:
step 4.1: performing pinyin text conversion on characters in the electronic case to obtain pinyin characters of the electronic case;
step 4.2: detecting the relevance of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model to obtain a first error correction result;
step 4.3: detecting the relevance of the pinyin characters of each electronic case and pinyin characters of a second preset verification target field by adopting a second error correction model to obtain a second error correction result;
step 4.4: and performing data cleaning on characters in the electronic case according to the first error correction result and the second error correction result to obtain the data-cleaned electronic case.
7. The cooperative data governance method based on blockchain according to claim 6, wherein said step 4.2: detecting the relevance of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model to obtain a first error correction result, wherein the first error correction result comprises the following steps:
step 4.2.1: obtaining a first error correction value of each electronic case pinyin character and a first preset target field pinyin character by adopting a first error correction model; wherein the first error correction model is:
wherein S is i The ith character element (i is more than or equal to 0 and less than or equal to M, M represents the length of the character string S) of any character string S in the pinyin characters of the electronic case, T j J is more than or equal to 0 and is less than or equal to N, and N represents the length of the character string T;
step 4.2.2: obtaining a first error correction result according to the first error correction value; wherein the first error correction value is the value of the matrix D (i, j) at the (N + 1) th row and column element of the M +1 th row.
8. The cooperative data governance method based on blockchain according to claim 7, wherein said step 4.2.2: obtaining a first error correction result according to the first error correction value, including:
the formula is adopted:
obtaining a first error correction result; wherein l d Representing a first error correction value.
9. The cooperative data governance method based on blockchain according to claim 6, wherein said step 4.3: detecting the relevance of each electronic case pinyin character and a second preset verification target field pinyin character by adopting a second error correction model to obtain a second error correction result, wherein the second error correction result comprises the following steps:
step 4.3.1: obtaining a second error correction value of each electronic case pinyin character and a second preset target field pinyin character by adopting a second error correction model; wherein the second error correction model is:
wherein S is i The ith character element (i is more than or equal to 0 and less than or equal to M, M represents the length of the character string S) of any character string S in the pinyin characters of the electronic case, T j J is more than or equal to 0 and is less than or equal to N, and N represents the length of the character string T;
step 4.3.2: obtaining a second error correction result according to the second error correction value; wherein the second error correction value is the value of the matrix L (i, j) at the (N + 1) th row and column element of the M +1 th row.
10. The cooperative data governance method based on blockchain according to claim 9, wherein said step 4.3.2: obtaining a second error correction result according to the second error correction value, including:
the formula is adopted:
obtaining a second error correction result; wherein l cs Representing the second error correction value.
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