Health technology consultation method and system based on picture transmission

Abstract

The invention discloses a health technology consultation method and system based on picture transmission, and relates to the technical field of health consultation, and the method comprises the steps: obtaining a first main test picture, a second main test picture and a third main test picture, training a consultation information pairing model, formulating a first picture processing scheme, a second picture processing scheme, a third picture processing scheme and a fourth picture processing scheme, and carrying out the doctor pairing test of the first main test picture, the second main test picture and the third main test picture. Performing pairwise correlation degree evaluation on a second type of measure schemes I, II and III obtained by testing after pairwise integration of the main test pictures I, II and III, performing a doctor pairing test according to the obtained correlation degree values II, III and IV to be tested, and performing the doctor pairing test after integration of the main test pictures I, II and III. Segmenting an auxiliary test picture from the target picture information, integrating the auxiliary test picture, the first main test picture, the second main test picture and the third main test picture, then carrying out a doctor pairing test, and carrying out health technology consultation service on the target user. According to the health technology consultation method and system based on picture transmission, the work efficiency of consultation service can be improved.

Description

Technical Field

[0001] The present application relates to the field of health consultation technology, and in particular to a health technology consultation method and system based on image transmission. Background Art

[0002] Many medical institutions and health consulting companies provide online consultation services, where users can communicate with doctors or health experts in real time through websites or mobile applications. This method is convenient and fast, and users can get professional advice anytime and anywhere.

[0003] The prior art provides a method of transmitting pictures to match professional doctors for health technology consultation. However, there is currently a problem of low efficiency in the targeted doctor matching process. For example, based on the transmitted picture information, the range of matched doctor information is large, and the matched doctor information is not the final result after strict selection, which makes it impossible for users to make accurate choices, reducing the efficiency of health technology consulting services and user satisfaction. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the present application provides a health technology consultation method and system based on image transmission.

[0005] In a first aspect, the present application provides a health technology consultation method based on image transmission, the method comprising:

[0006] Obtain target image information of the target user, segment the portion of the target image information that mainly contains the symptom condition to obtain a main test image 1, a main test image 2, and a main test image 3, formulate an image processing plan 1, and according to the formulated image processing plan 1, respectively perform a doctor pairing test on the main test image 1, the main test image 2, and the main test image 3 through the trained consultation information pairing model, and comprehensively obtain a first-category doctor consultation allocation result;

[0007] Formulate a second picture processing scheme, and according to the second picture processing scheme, integrate the main test picture 1, the main test picture 2, and the main test picture 3 in pairs, and then evaluate the mutual correlation between the second type of measure scheme 1, the second type of measure scheme 2, and the second type of measure scheme 3 obtained by testing the trained consulting information pairing model to obtain a second correlation degree value to be tested, a third correlation degree value to be tested, and a fourth correlation degree value to be tested;

[0008] According to the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested, a doctor pairing test is performed through the trained consultation information pairing model to comprehensively obtain a second category of doctor consultation allocation result;

[0009] Formulate a third image processing plan, and according to the third image processing plan, integrate the first main test image, the second main test image, and the third main test image, and then perform a doctor pairing test through the trained consultation information pairing model to obtain a third-category doctor consultation assignment result 1;

[0010] Formulate image processing plan four, and according to the image processing plan four, segment the auxiliary test picture from the target image information, integrate the auxiliary test picture, main test picture one, main test picture two and main test picture three, and perform a doctor pairing test through the trained consultation information pairing model to obtain the third category doctor consultation assignment result two, and according to the first category doctor consultation assignment result, the second category doctor consultation assignment result, the third category doctor consultation assignment result one or the third category doctor consultation assignment result two, provide health technology consulting services to the target users, and output the health technology consulting service results.

[0011] Preferably, target image information of the target user is obtained, and a portion of the target image information mainly containing the disease condition is segmented to obtain a main test image 1, a main test image 2, and a main test image 3;

[0012] Obtain all doctor consultation feature information and all registered doctor databases in the historical period, and train a consultation information pairing model based on the doctor consultation feature information and the doctor database;

[0013] Formulate a picture processing plan 1, and according to the picture processing plan 1, input the main test picture 1 into the consultation information matching model to test the picture symptom countermeasure plan to obtain the first type of measure plan 1;

[0014] Input the main test picture 2 into the consulting information pairing model for testing to obtain the first type of measure solution 2;

[0015] Input the main test picture three into the consulting information pairing model for testing to obtain the first type of measure plan three;

[0016] Performing a correlation evaluation on the first type of measure scheme 1, the first type of measure scheme 2 and the first type of measure scheme 3 to obtain a correlation degree value 1 to be tested;

[0017] A correlation determination threshold is preset. When the correlation degree value to be tested is greater than the correlation determination threshold, the first category measure plan one, the first category measure plan two and the first category measure plan three are input into the consultation information matching model to perform a doctor matching test to obtain the first category doctor consultation allocation result.

[0018] Preferably, a second picture processing scheme is formulated, and according to the second picture processing scheme, the main test picture 1 and the registered picture 2 are integrated and input into the consulting information matching model to test the picture symptom countermeasure scheme to obtain the second type of measure scheme 1;

[0019] The main test picture 2 and the registered picture 3 are integrated and input into the consulting information matching model to test the picture symptom countermeasures to obtain the second type of measure plan 2;

[0020] The main test picture 1 and the registered picture 3 were integrated and input into the consultation information matching model to test the picture symptom countermeasures and obtain the second type of measure plan 3;

[0021] Performing a correlation evaluation on the second-category measure scheme 1 and the second-category measure scheme 2 to obtain a second correlation degree value to be tested;

[0022] Performing a correlation evaluation on the second type of measure scheme 2 and the second type of measure scheme 3 to obtain a correlation degree value 3 to be tested;

[0023] The correlation degree of the second type of measure scheme one and the second type of measure scheme three is evaluated to obtain the correlation degree value four to be tested.

[0024] Preferably, a pre-processed correlation degree value greater than the correlation determination threshold is extracted from the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested;

[0025] According to the pre-processing correlation degree value, two measures are correspondingly extracted from the second-category measure scheme 1, the second-category measure scheme 2 and the second-category measure scheme 3 to obtain a pre-processing measure scheme;

[0026] The pretreatment measure program is input into the consultation information matching model to perform a doctor matching test to obtain the second category doctor consultation allocation result.

[0027] Preferably, edges of the same side of the areas covered by the main test picture 1, the main test picture 2 and the main test picture 3 are connected in a straight line to obtain an edge connection line;

[0028] If the edge connection line is a straight line, a third picture processing plan is formulated. According to the third picture processing plan, the main test picture 1, the main test picture 2 and the main test picture 3 are integrated and input into the consultation information matching model to test the picture symptom countermeasure plan to obtain the third type of measure plan 1;

[0029] The third category of measures is input into the consultation information matching model to perform a doctor matching test to obtain the third category of doctor consultation allocation result one.

[0030] Preferably, if the edge connection line is a non-straight line, a fourth picture processing scheme is formulated, and according to the fourth picture processing scheme, an auxiliary test picture of the part surrounded by the first main test picture, the second main test picture and the third main test picture is segmented from the target picture information;

[0031] The auxiliary test picture, main test picture 1, main test picture 2 and main test picture 3 are integrated and input into the consultation information matching model to test the picture symptom countermeasures to obtain the third type of measure plan 2;

[0032] Input the third category measure plan 2 into the consultation information matching model to perform a doctor matching test to obtain the third category doctor consultation allocation result 2;

[0033] Extracting the doctor information of repeated successful matching from the first category doctor consultation allocation result, the second category doctor consultation allocation result, the third category doctor consultation allocation result one or the third category doctor consultation allocation result two to obtain a comprehensive doctor consultation allocation result;

[0034] According to the comprehensive doctor consultation allocation result, health technology consultation service is provided to the target user, and the health technology consultation service result is output.

[0035] In a second aspect, a health technology consultation system based on image transmission includes:

[0036] The consultation matching plan formulation unit 1 is used to obtain the target image information of the target user, segment the part of the target image information that mainly contains the disease condition to obtain the main test image 1, the main test image 2 and the main test image 3, formulate the image processing plan 1, and according to the formulated image processing plan 1, respectively test the doctor pairing of the main test image 1, the main test image 2 and the main test image 3 through the trained consultation information pairing model, and comprehensively obtain the first category of doctor consultation allocation results;

[0037] The unit for analyzing the relevant information to be tested is used to formulate a second picture processing plan. According to the second picture processing plan, after integrating the main test picture 1, the main test picture 2 and the main test picture 3 in pairs, the second type of measure plan 1, the second type of measure plan 2 and the second type of measure plan 3 obtained by testing the trained consulting information pairing model are evaluated for the mutual correlation between the two to obtain the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested;

[0038] The consultation matching plan formulation unit 2 is used to test the doctor pairing through the trained consultation information pairing model according to the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested, and comprehensively obtain the second category doctor consultation allocation result;

[0039] The consultation matching plan formulation unit 3 is used to formulate the picture processing plan 3. According to the picture processing plan 3, the main test picture 1, the main test picture 2 and the main test picture 3 are integrated and then the doctor pairing test is performed through the trained consultation information pairing model to obtain the third category doctor consultation allocation result 1;

[0040] The comprehensive matching unit for health consultation is used to formulate picture processing plan four. According to picture processing plan four, the auxiliary test picture is segmented from the target picture information, and the auxiliary test picture, main test picture one, main test picture two and main test picture three are integrated and then tested for doctor pairing through the trained consultation information pairing model to obtain the third category doctor consultation allocation result two. According to the first category doctor consultation allocation result, the second category doctor consultation allocation result, the third category doctor consultation allocation result one or the third category doctor consultation allocation result two, health technology consulting services are provided to the target users, and the health technology consulting service results are output.

[0041] Compared with the prior art, the present invention has the following characteristics and beneficial effects:

[0042] By first identifying the disease area of ​​the target image information, the main disease area part of the image is separately segmented, and four image processing schemes are formulated respectively to achieve differential analysis of the diversity of the segmented main test image 1, main test image 2 and main test image 3, that is, different analysis methods of the four schemes are processed respectively. The image processing scheme 1, the image processing scheme 2 and the image processing scheme 3 are all differential information analyses within the scope of the three main test images, and the image processing scheme 4, in addition to the three main test images, also requires the integrated analysis of the auxiliary test images, so as to increase the processing of the correlation between multiple information, and take into account more comprehensively. Whether the surrounding information of the main test image has a certain degree of correlation, the rigor of the information analysis process of the four image processing schemes is enhanced, and finally the four types of matched doctors screened out by the four image processing schemes are comprehensively screened out to find the doctors who are matched repeatedly, which greatly reduces the traditional technology of only directly matching the target image information with the overall image information, resulting in a large mismatch problem in the final matched doctor object, and the problem of no further information essence screening. Through the above-mentioned method, the doctor consultation assignment results are indirectly verified many times, which improves the efficiency of the entire health technology consulting service and the efficient use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flowchart of a health technology consultation method based on picture transmission mainly embodied in this embodiment.

[0044] Figure 2 This is a structural block diagram of a health technology consultation system based on image transmission mainly embodied in this embodiment. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below in conjunction with the following examples.

[0046] Reference Figure 1, a health technology consultation method based on picture transmission, the method comprising the following steps:

[0047] S1. Obtain the target image information of the target user, segment the part with the main disease condition in the target image information to obtain the main test image 1, the main test image 2 and the main test image 3, formulate the image processing plan 1, and according to the formulated image processing plan 1, respectively perform the doctor pairing test on the main test image 1, the main test image 2 and the main test image 3 through the trained consultation information pairing model, and comprehensively obtain the first category of doctor consultation allocation results.

[0048] S2. Formulate picture processing plan 2. According to picture processing plan 2, integrate main test picture 1, main test picture 2 and main test picture 3 in pairs, and then evaluate the mutual correlation between second category measure plan 1, second category measure plan 2 and second category measure plan 3 obtained by testing the trained consulting information pairing model to obtain correlation degree value 2 to be tested, correlation degree value 3 to be tested and correlation degree value 4 to be tested.

[0049] S3. Perform a doctor pairing test based on the second, third and fourth correlation degree values ​​to be tested through the trained consultation information pairing model to comprehensively obtain the second category doctor consultation allocation result.

[0050] S4. Formulate image processing plan three. According to image processing plan three, integrate main test picture one, main test picture two and main test picture three, and then perform doctor pairing test through the trained consultation information pairing model to obtain third-category doctor consultation allocation result one.

[0051] S5. Formulate image processing plan four. According to image processing plan four, segment the auxiliary test picture from the target image information, integrate the auxiliary test picture, main test picture one, main test picture two and main test picture three, and perform doctor pairing test through the trained consultation information pairing model to obtain third-category doctor consultation assignment result two; according to the first-category doctor consultation assignment result, the second-category doctor consultation assignment result, the third-category doctor consultation assignment result one or the third-category doctor consultation assignment result two, provide health technology consulting services to the target users and output the health technology consulting service results.

[0052] Specifically, the target image information is firstly identified for the disease area, so as to separately segment the main disease area part of the image, and four image processing schemes are respectively formulated to realize the differential analysis of the diversity of the segmented main test image 1, main test image 2 and main test image 3, that is, the four schemes are processed in different analysis methods, among which the image processing scheme 1, the image processing scheme 2 and the image processing scheme 3 are all differential information analyses performed within the scope of the three main test images, while the image processing scheme 4 requires the integrated analysis of the auxiliary test images in addition to the three main test images, so as to increase the processing of the correlation between multiple information, and conduct a more comprehensive consideration. Taking into account the influence of whether the surrounding information of the main test image has a certain degree of correlation, the rigor of the information analysis process of the four image processing schemes is enhanced, and finally the four types of matched doctors screened out by the four image processing schemes are comprehensively screened out to find the doctors who are matched repeatedly. This greatly reduces the traditional technology of only directly matching the target image information with the overall image information, resulting in a large mismatch problem in the final matched doctor object, and the problem of no further information essence screening. Through the above-mentioned method, the doctor consultation assignment results are indirectly verified many times, which improves the efficiency of the entire health technology consulting service and the efficient use of resources.

[0053] The specific step S1 includes the following sub-steps:

[0054] The target image information of the target user is obtained, and the part mainly containing the disease condition in the target image information is segmented to obtain the main test image one, the main test image two and the main test image three.

[0055] Obtain all doctor consultation feature information and all registered doctor databases in the historical period, and train a consultation information matching model based on the doctor consultation feature information and doctor database.

[0056] Formulate picture processing plan one, and according to picture processing plan one, input the main test picture one into the consultation information matching model to test the picture symptom countermeasure plan to obtain the first type of measure plan one.

[0057] Input the main test picture 2 into the consulting information pairing model for testing to obtain the first type of measure plan 2.

[0058] Input the main test picture three into the consulting information matching model for testing to obtain the first type of measure plan three.

[0059] The correlation degree of the first-category measure scheme 1, the first-category measure scheme 2 and the first-category measure scheme 3 is evaluated to obtain a correlation degree value of one to be tested.

[0060] A correlation determination threshold is preset. When the correlation degree value to be tested is greater than the correlation determination threshold, the first category measure plan one, the first category measure plan two and the first category measure plan three are input into the consultation information matching model to perform a doctor matching test to obtain the first category doctor consultation allocation result.

[0061] Specifically, such as target image information (if it is image A), main test image one, main test image two and main test image three (for example, if there is a text label on the image to identify which diseases are there, then the main disease area image is intercepted according to the key terms in the text label, or identified by comparing historical sample disease images, if they are A, B, C respectively), doctor consultation feature information (including all historical consultation The image information sent by users, and the corresponding matched doctors and the targeted explanation information-measures provided by the doctors), doctor database (refers to all doctors who have registered information on the consultation platform, that is, after registration, users can receive subsequent one-to-one health technology consulting services), consultation information matching model (that is, establishing a comparison table 1 between the user-transmitted image and the doctor consultation feature information, and establishing a comparison table 2 between the doctor consultation feature information and the doctor database, which are respectively used for subsequent analysis of the target image information and matching reference processing), the first type of measures plan one (such as image A is matched with multiple doctors, and the matched multiple doctors provide detailed explanations for the disease conditions in this image A: for example, the cause of the disease, what symptoms there are, the inducing conditions and other feature information Explanation , and provide targeted treatment measures for the symptoms: such as taking medicine for a certain symptom: what medicine to take, lifestyle adjustment: exercise or Chinese medicine adjustment, etc., timely medical treatment, attention to other specific parts that are likely to induce this symptom for timely inspection, etc. If the test obtains several measures and steps, the most repeated measures and steps are extracted, if it is a1), the first-class measures plan two and the first-class measures plan three (the explanation is the same as the first-class measures plan one, no further explanation is given here, if they are b1 and c1 respectively), the correlation degree value to be tested is one (if it is 60%, that is, the auxiliary between a1, b1, and c1 The average correlation degree of the effects: the correlation degree of a1-b1, the correlation degree of a1-c1, the correlation degree of b1-c1, the average value of the three correlation degrees, such as c1 and b1 promote the efficiency of a1, which can be assisted by establishing a knowledge association tree map among a1, b1 and c1 for evaluation), preset correlation judgment threshold (if it is 50%), the first category of doctor consultation allocation results (that is, matching one or more specific doctors from the doctor database who include the three plans a1, b1 and c1 in the historical consultation matching information, if there are multiple doctors: Y1, Y2, Y3, Y4).

[0062] The specific step S2 includes the following sub-steps:

[0063] Formulate picture processing plan 2. According to picture processing plan 2, integrate the main test picture 1 and the registered picture 2 and input them into the consultation information matching model to test the picture disease countermeasure plan to obtain the second type of measure plan 1.

[0064] The main test picture 2 and the registered picture 3 were integrated and input into the consultation information matching model to test the picture disease countermeasures plan to obtain the second type of measure plan 2.

[0065] The main test picture one and the registered picture three are integrated and input into the consultation information matching model to test the picture disease countermeasures plan to obtain the second type of measure plan three.

[0066] The correlation degree of the second type of measure scheme one and the second type of measure scheme two is evaluated to obtain the correlation degree value two to be tested.

[0067] The correlation degree of the second type of measure scheme two and the second type of measure scheme three is evaluated to obtain the correlation degree value three to be tested.

[0068] The correlation degree of the second type of measure scheme one and the second type of measure scheme three is evaluated to obtain the correlation degree value four to be tested.

[0069] Specifically, if the main test image 1 and the registered image 2 are integrated (i.e., A and B are simultaneously input into the model for analysis, this method is to determine whether there is a correlation between the symptoms in A and B. If so, the subsequent test matching scheme will be different from the above a1 or b1 or c1. If not, the subsequent test matching scheme will be similar to the above a1 or b1 or c1 to a large extent), the second type of measure scheme 1, the second type of measure scheme 2 and the second type of measure scheme 3 (all the same as the above The explanation of Category 1 measure plan one is the same and will not be elaborated here. If they are a2, b2, c2 respectively), the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested (if they are 50%, 65%, 45% respectively, they are all the same as the explanation of the above-mentioned correlation degree value one to be tested. The difference is that the second correlation degree value to be tested here is the correlation degree between a2-b2. Similarly, the third correlation degree value to be tested is the correlation degree between b2-c2, and the fourth correlation degree value to be tested is the correlation degree between a2-c2).

[0070] The specific step S3 includes the following sub-steps:

[0071] A preprocessing correlation degree value greater than the correlation determination threshold is extracted from the second correlation degree value to be tested, the third correlation degree value to be tested, and the fourth correlation degree value to be tested.

[0072] According to the pre-processing correlation degree value, two measure schemes are correspondingly extracted from the second-category measure scheme one, the second-category measure scheme two and the second-category measure scheme three to obtain the pre-processing measure scheme.

[0073] The pretreatment measures were input into the consultation information matching model to test the doctor matching and obtain the second category of doctor consultation allocation results.

[0074] Specifically, such as the pre-treatment correlation degree value (if it is the correlation degree value three to be tested), the pre-treatment measure plan (ie b2 and c2), the second type of doctor consultation allocation result (ie matching from the doctor database one or more specific doctors whose historical consultation matching information includes the two plans b2 and c2, if there are multiple doctors: Y0, Y1, Y2, Y4, Y6).

[0075] The specific step S4 includes the following sub-steps:

[0076] The edges of the same side of the areas covered by the main test picture 1, the main test picture 2 and the main test picture 3 are connected by straight lines to obtain edge connection lines.

[0077] If the edge connection line is a straight line, then formulate picture processing plan three. According to picture processing plan three, main test picture one, main test picture two and main test picture three are integrated and input into the consultation information matching model to test the picture symptom countermeasure plan to obtain the third type of measure plan one.

[0078] The third category of measures was input into the consultation information matching model to test the doctor matching and obtain the third category of doctor consultation allocation result one.

[0079] Specifically, edge connection lines (such as the most prominent edge position points on the left and right sides of A, B, and C, left side: position point 1, position point 2, position point 3, right side: position point 4, position point 5, position point 6, connect the three left positions linearly, if straight line L1 is obtained, connect the three right positions linearly, if straight line L2 is obtained), if the edge connection line is a straight line (that is, L1 and L2 are both straight lines, indicating that there is no asymptomatic area surrounded by the coverage area of ​​A, B, and C, then the correlation detection step between the asymptomatic area and the symptom area can be omitted, reducing the cumbersomeness of the analysis process and reducing the interference between information), third category measure plan one (the same as the explanation of the second category measure plan one, the second category measure plan two, and the second category measure plan three mentioned above, no further explanation is given here, if it is f1), third category doctor consultation allocation result one (that is, match one or more specific doctors whose historical consultation matching information includes the f1 plan from the doctor database, if there are multiple doctors: Y1, Y2, Y4, Y5, Y7).

[0080] The specific step S5 includes the following sub-steps:

[0081] If the edge connection line is a non-straight line, a fourth image processing scheme is formulated. According to the fourth image processing scheme, an auxiliary test image of the part surrounded by the first, second and third main test images is segmented from the target image information.

[0082] The auxiliary test picture, main test picture one, main test picture two and main test picture three were integrated and input into the consultation information matching model to test the picture symptom countermeasures plan to obtain the third type of measure plan two.

[0083] The third category measure plan 2 is input into the consultation information matching model to test the doctor matching and obtain the third category doctor consultation allocation result 2.

[0084] The doctor information of repeated successful matching is extracted from the first category doctor consultation allocation result, the second category doctor consultation allocation result, the third category doctor consultation allocation result one or the third category doctor consultation allocation result two to obtain the comprehensive doctor consultation allocation result.

[0085] Based on the comprehensive doctor consultation allocation results, health technology consulting services are provided to target users and the health technology consulting service results are output.

[0086] Specifically, if the edge connection line is a non-straight line (if one of the straight lines in L1 and L2 is an arc, it means that there is a symptom-free area surrounded by the coverage area of ​​A, B, and C. At this time, the symptom impact correlation between the symptom-free area and the A, B, and C areas can be considered. The symptom-free area is the auxiliary test image D. Because it is very likely that A, B, and C affect the disease condition in the D area, or D increases the occurrence of the symptom condition of A, B, and C, it is necessary to conduct a combined analysis of D, that is, consider the correlation effect of D), the third type of measure plan 2 (if it is f2), the third type of doctor consultation allocation result 2 (that is, match one or more doctors who include the f2 plan in the historical consultation matching information from the doctor database, if there are multiple doctors: Y1, Y2, and Y4), comprehensive doctor consultation allocation result (that is, the three professional doctors Y1, Y2, and Y4), health technology consultation service result (that is, the information of the three doctors Y1, Y2, and Y4 is sent to the user end, providing the user with the option of selecting one of the doctors for one-to-one consultation).

[0087] A health technology consultation system based on image transmission, by applying a health technology consultation method based on image transmission as described above, comprises a consultation matching scheme formulation unit 1, a correlation information analysis unit to be tested, a consultation matching scheme formulation unit 2, a consultation matching scheme formulation unit 3 and a health consultation comprehensive matching unit, with reference to Figure 2, obtain the target image information of the target user through the consultation matching scheme formulation unit 1, segment the part of the target image information that mainly contains the disease conditions to obtain the main test image 1, the main test image 2 and the main test image 3, formulate the image processing scheme 1, and according to the formulated image processing scheme 1, respectively test the doctor pairing of the main test image 1, the main test image 2 and the main test image 3 through the trained consultation information pairing model, and comprehensively obtain the first type of doctor consultation allocation result; formulate the image processing scheme 2 through the to-be-tested associated information analysis unit, and according to the image processing scheme 2, integrate the main test image 1, the main test image 2 and the main test image 3 in pairs, and then conduct a pairwise mutual correlation evaluation of the second type of measure scheme 1, the second type of measure scheme 2 and the second type of measure scheme 3 obtained by the trained consultation information pairing model to obtain the to-be-tested associated degree value 2, the to-be-tested associated degree value 3 and the to-be-tested associated degree value 4; through the consultation matching scheme formulation unit 2, according to the to-be-tested associated degree value 2, the to-be-tested associated degree value 3 and the to-be-tested associated degree value 4 The degree of connection value four is tested for doctor pairing through the trained consultation information pairing model, and the second category doctor consultation allocation result is obtained comprehensively; the picture processing plan three is formulated through the consultation matching plan formulation unit three, and according to the picture processing plan three, the main test picture one, the main test picture two and the main test picture three are integrated, and then the doctor pairing test is performed through the trained consultation information pairing model to obtain the third category doctor consultation allocation result one; the picture processing plan four is formulated through the health consultation comprehensive matching unit, and according to the picture processing plan four, the auxiliary test picture is segmented from the target picture information, and the auxiliary test picture, the main test picture one, the main test picture two and the main test picture three are integrated, and then the doctor pairing test is performed through the trained consultation information pairing model to obtain the third category doctor consultation allocation result two; according to the first category doctor consultation allocation result, the second category doctor consultation allocation result, the third category doctor consultation allocation result one or the third category doctor consultation allocation result two, health technology consulting services are provided to the target users, and the health technology consulting service results are output.

[0088] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A health technology consultation method based on picture transmission, characterized in that: The following steps are involved: Obtain target image information of the target user, segment the portion of the target image information that mainly contains the symptom condition to obtain a main test image 1, a main test image 2, and a main test image 3, formulate an image processing plan 1, and according to the formulated image processing plan 1, respectively perform a doctor pairing test on the main test image 1, the main test image 2, and the main test image 3 through the trained consultation information pairing model, and comprehensively obtain a first-category doctor consultation allocation result; Formulate a second picture processing scheme, and according to the second picture processing scheme, integrate the main test picture 1, the main test picture 2, and the main test picture 3 in pairs, and then evaluate the mutual correlation between the second type of measure scheme 1, the second type of measure scheme 2, and the second type of measure scheme 3 obtained by testing the trained consulting information pairing model to obtain a second correlation degree value to be tested, a third correlation degree value to be tested, and a fourth correlation degree value to be tested; According to the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested, a doctor pairing test is performed through the trained consultation information pairing model to comprehensively obtain a second category of doctor consultation allocation result; Formulate a third image processing plan, and according to the third image processing plan, integrate the first main test image, the second main test image, and the third main test image, and then perform a doctor pairing test through the trained consultation information pairing model to obtain a third-category doctor consultation assignment result 1; Formulate image processing plan four, and according to the image processing plan four, segment the auxiliary test picture from the target image information, integrate the auxiliary test picture, main test picture one, main test picture two and main test picture three, and perform a doctor pairing test through the trained consultation information pairing model to obtain the third category doctor consultation assignment result two, and according to the first category doctor consultation assignment result, the second category doctor consultation assignment result, the third category doctor consultation assignment result one or the third category doctor consultation assignment result two, provide health technology consulting services to the target users, and output the health technology consulting service results.

2. A health technology consultation method based on picture transmission according to claim 1, characterized in that: The target image information of the target user is obtained, the part of the target image information mainly containing the symptom condition is segmented to obtain the main test image 1, the main test image 2 and the main test image 3, the image processing plan 1 is formulated, and according to the formulated image processing plan 1, the main test image 1, the main test image 2 and the main test image 3 are respectively tested for doctor pairing through the trained consultation information pairing model, and the steps of comprehensively obtaining the first category of doctor consultation allocation results are specifically as follows: Obtain target image information of a target user, and segment a portion of the target image information mainly containing the disease condition to obtain a main test image 1, a main test image 2, and a main test image 3; Obtain all doctor consultation feature information and all registered doctor databases in the historical period, and train a consultation information pairing model based on the doctor consultation feature information and the doctor database; Formulate a picture processing plan 1, and according to the picture processing plan 1, input the main test picture 1 into the consultation information matching model to test the picture symptom countermeasure plan to obtain the first type of measure plan 1; Input the main test picture 2 into the consulting information pairing model for testing to obtain the first type of measure solution 2; Input the main test picture three into the consulting information pairing model for testing to obtain the first type of measure plan three; Performing a correlation evaluation on the first type of measure scheme 1, the first type of measure scheme 2 and the first type of measure scheme 3 to obtain a correlation degree value 1 to be tested; A correlation determination threshold is preset. When the correlation degree value to be tested is greater than the correlation determination threshold, the first category measure plan one, the first category measure plan two and the first category measure plan three are input into the consultation information matching model to perform a doctor matching test to obtain the first category doctor consultation allocation result.

3. A health technology consultation method based on picture transmission according to claim 2, characterized in that: Formulate a second picture processing scheme, and according to the second picture processing scheme, integrate the main test picture 1, the main test picture 2, and the main test picture 3 in pairs, and then evaluate the mutual correlation between the second type of measure scheme 1, the second type of measure scheme 2, and the second type of measure scheme 3 obtained by testing the trained consulting information pairing model to obtain the second correlation degree value to be tested, the third correlation degree value to be tested, and the fourth correlation degree value to be tested, specifically: Formulate a second picture processing plan, and according to the second picture processing plan, integrate the main test picture 1 and the registered picture 2 and input them into the consulting information matching model to test the picture symptom countermeasure plan to obtain the second type of measure plan 1; The main test picture 2 and the registered picture 3 are integrated and input into the consulting information matching model to test the picture symptom countermeasures to obtain the second type of measure plan 2; The main test picture 1 and the registered picture 3 were integrated and input into the consultation information matching model to test the picture symptom countermeasures and obtain the second type of measure plan 3; Performing a correlation evaluation on the second-category measure scheme 1 and the second-category measure scheme 2 to obtain a second correlation degree value to be tested; Performing a correlation evaluation on the second type of measure scheme 2 and the second type of measure scheme 3 to obtain a correlation degree value 3 to be tested; The correlation degree of the second type of measure scheme one and the second type of measure scheme three is evaluated to obtain the correlation degree value four to be tested.

4. A health technology consultation method based on picture transmission according to claim 3, characterized in that: The steps of testing doctor pairing by using the trained consultation information pairing model according to the second, third and fourth correlation degree values ​​to be tested, and comprehensively obtaining the second category doctor consultation allocation result are specifically as follows: Extracting a pre-processed correlation degree value greater than the correlation determination threshold from the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested; According to the pre-processing correlation degree value, two measures are correspondingly extracted from the second-category measure scheme 1, the second-category measure scheme 2 and the second-category measure scheme 3 to obtain a pre-processing measure scheme; The pretreatment measure program is input into the consultation information matching model to perform a doctor matching test to obtain the second category doctor consultation allocation result.

5. A health technology consultation method based on picture transmission according to claim 4, characterized in that: Formulate a third picture processing plan, and according to the third picture processing plan, integrate the first main test picture, the second main test picture, and the third main test picture, and then perform a doctor pairing test through the trained consultation information pairing model to obtain the third category doctor consultation allocation result one, specifically: Connecting the same side edges of the areas covered by the main test picture 1, the main test picture 2 and the main test picture 3 to obtain an edge connection line; If the edge connection line is a straight line, a third picture processing plan is formulated. According to the third picture processing plan, the main test picture 1, the main test picture 2 and the main test picture 3 are integrated and input into the consultation information matching model to test the picture symptom countermeasure plan to obtain the third type of measure plan 1; The third category of measures is input into the consultation information matching model to perform a doctor matching test to obtain the third category of doctor consultation allocation result one.

6. A health technology consultation method based on picture transmission according to claim 5, characterized in that: Formulate a fourth image processing scheme, segment the auxiliary test image from the target image information according to the fourth image processing scheme, integrate the auxiliary test image, the first main test image, the second main test image, and the third main test image, and then perform a doctor pairing test through the trained consultation information pairing model to obtain a third-category doctor consultation assignment result 2; provide health technology consultation services to the target user according to the first-category doctor consultation assignment result, the second-category doctor consultation assignment result, the third-category doctor consultation assignment result 1, or the third-category doctor consultation assignment result 2, and output the health technology consultation service results, specifically: If the edge connection line is a non-straight line, a fourth picture processing scheme is formulated, and according to the fourth picture processing scheme, an auxiliary test picture of a part surrounded by the first main test picture, the second main test picture and the third main test picture is segmented from the target picture information; The auxiliary test picture, main test picture 1, main test picture 2 and main test picture 3 are integrated and input into the consultation information matching model to test the picture symptom countermeasures to obtain the third type of measure plan 2; Input the third category measure plan 2 into the consultation information matching model to perform a doctor matching test to obtain the third category doctor consultation allocation result 2; Extracting the doctor information of repeated successful matching from the first category doctor consultation allocation result, the second category doctor consultation allocation result, the third category doctor consultation allocation result one or the third category doctor consultation allocation result two to obtain a comprehensive doctor consultation allocation result; According to the comprehensive doctor consultation allocation result, health technology consultation service is provided to the target user, and the health technology consultation service result is output.

7. A health technology consultation system based on image transmission, characterized in that: The system is used to implement a health technology consultation method based on picture transmission as described in any one of claims 1 to 6, comprising: The consultation matching plan formulation unit 1 is used to obtain the target image information of the target user, segment the part of the target image information that mainly contains the disease condition to obtain the main test image 1, the main test image 2 and the main test image 3, formulate the image processing plan 1, and according to the formulated image processing plan 1, respectively test the doctor pairing of the main test image 1, the main test image 2 and the main test image 3 through the trained consultation information pairing model, and comprehensively obtain the first category of doctor consultation allocation results; The unit for analyzing the relevant information to be tested is used to formulate a second picture processing plan. According to the second picture processing plan, after integrating the main test picture 1, the main test picture 2 and the main test picture 3 in pairs, the second type of measure plan 1, the second type of measure plan 2 and the second type of measure plan 3 obtained by testing the trained consulting information pairing model are evaluated for the mutual correlation between the two to obtain the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested; The consultation matching plan formulation unit 2 is used to test the doctor pairing through the trained consultation information pairing model according to the second correlation degree value to be tested, the third correlation degree value to be tested and the fourth correlation degree value to be tested, and comprehensively obtain the second category doctor consultation allocation result; The consultation matching plan formulation unit 3 is used to formulate the picture processing plan 3. According to the picture processing plan 3, the main test picture 1, the main test picture 2 and the main test picture 3 are integrated and then the doctor pairing test is performed through the trained consultation information pairing model to obtain the third category doctor consultation allocation result 1; The comprehensive matching unit for health consultation is used to formulate picture processing plan four. According to picture processing plan four, the auxiliary test picture is segmented from the target picture information, and the auxiliary test picture, main test picture one, main test picture two and main test picture three are integrated and then tested for doctor pairing through the trained consultation information pairing model to obtain the third category doctor consultation allocation result two. According to the first category doctor consultation allocation result, the second category doctor consultation allocation result, the third category doctor consultation allocation result one or the third category doctor consultation allocation result two, health technology consulting services are provided to the target users, and the health technology consulting service results are output.

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