A distributed blockchain information technology verification system

Abstract

The present invention discloses a distributed blockchain information technology verification system, comprising a data module, a camera module, and a face recognition module, wherein the camera module is electrically connected to the face recognition module; the data module is used to count and summarize human data information, the camera module is used to scan and shoot conditions in an area, the face recognition module is used to identify human face information and match it with an identity, the data module comprises an identity data statistics module, a priority calculation module, and a priority judgment module, the camera module comprises an identity recognition module and a camera unit, the priority calculation module is electrically connected to the priority judgment module, the identity recognition module is electrically connected to the identity data statistics module, the face recognition module comprises a wake-up module, an image analysis module, an image signal transceiver module, a computing power allocation module, and an identity matching module. The present invention has the characteristics of saving resources and improving efficiency.

Description

Technical Field

[0001] The present invention relates to the field of information verification technology, and specifically to a distributed blockchain information technology verification system. Background Art

[0002] In hospitals, once the identity of medical practitioners is stolen or replaced, it can easily lead to serious medical consequences, so the identity verification of doctors is very necessary. However, doctors' working time is very valuable, and how to save time by adjusting the order of doctor identity verification is an urgent problem to be solved. Therefore, it is necessary to design a distributed blockchain information technology verification system that saves resources and improves efficiency. Summary of the invention

[0003] The purpose of the present invention is to provide a distributed blockchain information technology verification system to solve the problems raised in the above-mentioned background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a distributed blockchain information technology verification system, comprising a data module, a camera module, and a face recognition module, wherein the camera module is electrically connected to the face recognition module;

[0005] The data module is used to count and summarize people's data information, the camera module is used to scan and shoot the conditions in the area, and the face recognition module is used to recognize people's face information and match it with the identity.

[0006] According to the above technical solution, the data module includes an identity data statistics module, a priority calculation module, and a priority judgment module, the camera module includes an identity recognition module and a camera unit, the priority calculation module is electrically connected to the priority judgment module, and the identity recognition module is electrically connected to the identity data statistics module;

[0007] The identity data statistics module is used to store the doctor's identity information and the number of patients registered on the same day. The priority calculation module is used to calculate the doctor's priority index based on the doctor's title and the number of patients registered on the same day. The priority judgment module is used to judge the doctor's priority based on the doctor's priority index. The identity recognition module is used to read the identity information and priority data provided by the doctor. The camera unit is used to photograph the monitoring area.

[0008] According to the above technical solution, the face recognition module includes a wake-up module, an image analysis module, an image signal transceiver module, a computing power allocation module, and an identity matching module. The identity recognition module is electrically connected to the wake-up module, the camera unit is electrically connected to the image analysis module, the computing power allocation module is electrically connected to the image signal transceiver module, and the identity matching module is electrically connected to the identity data statistics module;

[0009] The wake-up module is used to wake up the face recognition module, the image analysis module is used to analyze the face in the captured image, the image signal transceiver module is used to send or receive the face image to be processed, the computing power allocation module is used to allocate the face recognition task, and the identity matching module is used to match the recognized face information with the person's identity information.

[0010] According to the above technical solution, the main working process of the system is:

[0011] S0. Multiple identity recognition modules and face recognition modules are set up in each important area of ​​the hospital to identify and verify the identity information of doctors and patients;

[0012] S1. When no one enters the area, the face recognition module stops working;

[0013] S2. When someone applies to enter an important area, the system reads the doctor's identity information and priority data or the patient's identity information through the identity recognition module and performs face recognition. During face recognition, the higher the doctor's priority, the earlier the face recognition verification is performed;

[0014] S3. After the face recognition task is completed, the face recognition module will not be shut down immediately. When no one enters the area for a period of time, the face recognition module will stop working.

[0015] According to the above technical solution, in the above step S2, the priority calculation rule of the doctor is:

[0016] The higher the professional title of a doctor in the medical system, the more important the doctor is, and the system will give priority to him. The more patients a doctor has registered on the same day, the more patients are waiting to see the doctor, and the system will give priority to him. Therefore, the priority order of doctors is calculated by the professional title of the doctor and the number of patients registered on the same day, as follows:

[0017] X=μp+λυq

[0018] Where X is the priority index. The larger X is, the higher the priority of the doctor. μ is the weight of the hospital grade in the priority index. υ is the weight of the number of patients registered on the same day in the priority index. p is the doctor's title. q is the number of patients registered by this doctor on the same day. λ is the adjustment coefficient.

[0019] According to the above technical solution, in the above step S2, the method for calculating the priority weight is as follows:

[0020] During periods when the number of patients in the hospital is relatively high, the weight of the number of doctors' daily registrations in the priority order calculation should be appropriately increased, and the weight of the doctor's professional title level in the priority order calculation should be appropriately reduced;

[0021] During periods when the number of patients in hospitals is relatively small, the weight of the doctor's professional title in the priority calculation should be appropriately increased, and the weight of the number of doctors' daily registrations in the priority calculation should be appropriately reduced.

[0022]

[0023] And μ+υ=100%, we can get

[0024]

[0025] Where x is the total number of advance registrations in the hospital on that day, and x0 is the average total number of advance registrations in the hospital per day.

[0026] According to the above technical solution, the working principle of the computing power allocation module is:

[0027] Since patients come to the hospital less frequently, face recognition is more complicated and takes longer time, while doctors come to the hospital more frequently, face recognition is simpler and takes less time. Therefore, the computing power used for face recognition of patients and doctors is different.

[0028] The system will monitor the number of people in the area through the camera module. The system determines that the number of people wearing white coats is the number of doctors, and the rest are the number of patients. When there are more doctors in the area, the average computing power occupied by each person is less. When there are more patients in the area, the average computing power occupied by each person is more.

[0029] The computing power occupied by each patient for face recognition is M, and the computing power occupied by each doctor for face recognition is N. The number of patients in a certain area is m, and the number of doctors is n. The total computing power occupied is

[0030] Y=mM+nN

[0031] The computing power allocation module allocates characters to each face recognition module according to the computing power required to perform face recognition of characters in each area.

[0032] According to the above technical solution, the main working mode of the face recognition module is:

[0033] The face recognition module enters a dormant state when no one applies to enter an important area. When the identity recognition module detects that someone has applied to enter an important area, the face recognition module is awakened. The module needs to wait for a time t1 to start. When the face recognition module completes the recognition task, in order to save the time required for restarting when someone applies to enter immediately, the face recognition module will wait for a response for a period of time t2, and enter a dormant state after t2 time.

[0034] If a face recognition module is working while other face recognition modules are resting or waiting for a response, the working face recognition module will send the face recognition task to other face recognition modules that are not performing face recognition work, and allocate the face recognition tasks according to their respective computing capabilities, so that multiple face recognitions can perform collaborative analysis.

[0035] According to the above technical solution, the working modes of the face recognition module are divided into the following three types:

[0036] When a certain face recognition module is not working, that is, no one has applied to enter the important area for at least time t2, the calculation function of the face recognition module can be fully allocated to other face recognition modules;

[0037] When a face recognition module is activated, that is, someone has applied to enter an important area, the computing function of the face recognition module has been fully occupied;

[0038] When a face recognition module enters the waiting response mode, that is, within the time t2 after completing the face recognition work, the computing function of its face recognition module can be partially allocated to other face recognition modules.

[0039] According to the above technical solution, the distribution principle of the remaining computing power of the face recognition module is:

[0040] The remaining computing power for real-time processing of other information tasks increases in direct proportion to the time after the task is completed, until the face recognition module is triggered to shut down, and the remaining computing power is its full computing power, specifically

[0041]

[0042] Where A is the computing power for real-time processing of other information tasks, N0 is the overall computing power, N1 is the computing power occupied when waiting for a response, t2 is the pre-set response time, t is the time waiting for a response, t∈[0, t2).

[0043] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by prioritizing doctors, can allow doctors with heavier tasks and stronger abilities to pass face recognition authentication first, thereby improving work efficiency; and the computing power of the face recognition module is allocated according to the number of doctors and patients, which can make full use of computing power resources and avoid waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 It is a schematic diagram of the overall module structure of the present invention; DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] like Figure 1 , the present invention provides a technical solution: a distributed blockchain information technology verification system, including a data module, a camera module, and a face recognition module, wherein the camera module is electrically connected to the face recognition module;

[0048] The data module is used to count and summarize people's data information, the camera module is used to scan and shoot the conditions in the area, and the face recognition module is used to identify people's face information and match it with their identity;

[0049] The data module includes an identity data statistics module, a priority calculation module, and a priority judgment module. The camera module includes an identity recognition module and a camera unit. The priority calculation module is electrically connected to the priority judgment module, and the identity recognition module is electrically connected to the identity data statistics module.

[0050] The identity data statistics module is used to store the identity information of the doctor and the number of patients registered on the same day. The priority calculation module is used to calculate the priority index of the doctor according to the doctor's title and the number of patients registered on the same day. The priority judgment module is used to judge the priority of the doctor according to the doctor's priority index. The identity recognition module is used to read the identity information and priority data provided by the doctor. The camera unit is used to take pictures of the monitoring area.

[0051] The face recognition module includes a wake-up module, an image analysis module, an image signal transceiver module, a computing power allocation module, and an identity matching module. The identity recognition module is electrically connected to the wake-up module, the camera unit is electrically connected to the image analysis module, the computing power allocation module is electrically connected to the image signal transceiver module, and the identity matching module is electrically connected to the identity data statistics module;

[0052] The wake-up module is used to wake up the face recognition module, the image analysis module is used to analyze the face in the captured image, the image signal transceiver module is used to send or receive the face image to be processed, the computing power allocation module is used to allocate the face recognition task, and the identity matching module is used to match the recognized face information with the person's identity information;

[0053] The main workflow of the system is:

[0054] S0. Multiple identity recognition modules and face recognition modules are set up in each important area of ​​the hospital to identify and verify the identity information of doctors and patients;

[0055] S1. When no one enters the area, the face recognition module stops working;

[0056] S2. When someone applies to enter an important area, the system reads the doctor's identity information and priority data or the patient's identity information through the identity recognition module and performs face recognition. During face recognition, the higher the doctor's priority, the earlier the face recognition verification is performed;

[0057] S3. After the face recognition task is completed, the face recognition module will not be shut down immediately. When no one enters the area for a period of time, the face recognition module will stop working;

[0058] Through the above steps, doctors are prioritized, so that doctors with heavier tasks and stronger capabilities can pass the face recognition authentication first, which improves work efficiency. The computing power of the face recognition module is allocated according to the number of doctors and patients, which can make full use of computing power resources and avoid waste.

[0059] In the above step S2, the doctor's priority calculation rule is:

[0060] The higher the professional title of a doctor in the medical system, the more important the doctor is, and the system will give priority to him. The more patients a doctor has registered on the same day, the more patients are waiting to see the doctor, and the system will give priority to him. Therefore, the priority order of doctors is calculated by the professional title of the doctor and the number of patients registered on the same day, as follows:

[0061] X=μp+λυq

[0062] Where X is the priority index. The larger X is, the higher the priority of the doctor. μ is the weight of the hospital grade in the priority index. υ is the weight of the number of patients registered on the same day in the priority index. p is the doctor's title. q is the number of patients registered by this doctor on the same day. λ is the adjustment coefficient.

[0063] In the above step S2, the method for calculating the priority weight is as follows:

[0064] During periods when the number of patients in the hospital is relatively high, the weight of the number of doctors' daily registrations in the priority order calculation should be appropriately increased, and the weight of the doctor's professional title level in the priority order calculation should be appropriately reduced;

[0065] During periods when the number of patients in hospitals is relatively small, the weight of the doctor's professional title in the priority calculation should be appropriately increased, and the weight of the number of doctors' daily registrations in the priority calculation should be appropriately reduced.

[0066]

[0067] And μ+υ=100%, we can get

[0068]

[0069] Where x is the total number of advance registrations in the hospital on that day, and x0 is the average daily total number of advance registrations in the hospital;

[0070] The working principle of the computing power allocation module is:

[0071] Since patients come to the hospital less frequently, face recognition is more complicated and takes longer time, while doctors come to the hospital more frequently, face recognition is simpler and takes less time. Therefore, the computing power used for face recognition of patients and doctors is different.

[0072] The system will monitor the number of people in the area through the camera module. The system determines that the number of people wearing white coats is the number of doctors, and the rest are the number of patients. When there are more doctors in the area, the average computing power occupied by each person is less. When there are more patients in the area, the average computing power occupied by each person is more.

[0073] The computing power occupied by each patient for face recognition is M, and the computing power occupied by each doctor for face recognition is N. The number of patients in a certain area is m, and the number of doctors is n. The total computing power occupied is

[0074] Y=mM+nN

[0075] The computing power allocation module will allocate people to each face recognition module according to the computing power required to perform face recognition of each area;

[0076] The main working mode of the face recognition module is:

[0077] The face recognition module enters a dormant state when no one applies to enter an important area. When the identity recognition module detects that someone has applied to enter an important area, the face recognition module is awakened. The module needs to wait for a time t1 to start. When the face recognition module completes the recognition task, in order to save the time required for restarting when someone applies to enter immediately, the face recognition module will wait for a response for a period of time t2, and enter a dormant state after t2 time.

[0078] If a face recognition module is working while other face recognition modules are resting or waiting for a response, the working face recognition module will send the face recognition task to other face recognition modules that are not working on face recognition, and distribute the face recognition tasks according to their respective computing capabilities, so that multiple face recognition modules can perform collaborative analysis;

[0079] The working modes of the face recognition module are divided into the following three modes:

[0080] When a certain face recognition module is not working, that is, no one has applied to enter the important area for at least time t2, the calculation function of the face recognition module can be fully allocated to other face recognition modules;

[0081] When a face recognition module is activated, that is, someone has applied to enter an important area, the computing function of the face recognition module has been fully occupied;

[0082] When a face recognition module enters the waiting response mode, that is, within the time t2 after completing the face recognition work, the computing function of its face recognition module can be partially allocated to other face recognition modules;

[0083] The distribution principle of the remaining computing power of the face recognition module is:

[0084] The remaining computing power for real-time processing of other information tasks increases in direct proportion to the time after the task is completed, until the face recognition module is triggered to shut down, and the remaining computing power is its full computing power, specifically

[0085]

[0086] Where A is the computing power for real-time processing of other information tasks, N0 is the overall computing power, N1 is the computing power occupied when waiting for a response, t2 is the pre-set response time, t is the time waiting for a response, t∈[0, t2).

[0087] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0088] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A distributed blockchain information technology verification system, characterized by: It includes a data module, a camera module, and a face recognition module, wherein the camera module is electrically connected to the face recognition module; The data module is used to count and summarize the data information of people, the camera module is used to scan and shoot the conditions in the area, and the face recognition module is used to recognize the face information of people and match them with their identities; The data module includes an identity data statistics module, a priority calculation module, and a priority judgment module. The camera module includes an identity recognition module and a camera unit. The priority calculation module is electrically connected to the priority judgment module, and the identity recognition module is electrically connected to the identity data statistics module. The identity data statistics module is used to store the identity information of the doctor and the number of patients registered on the same day, the priority calculation module is used to calculate the priority index of the doctor according to the doctor's title and the number of patients registered on the same day, the priority judgment module is used to judge the priority of the doctor according to the doctor's priority index, the identity recognition module is used to read the identity information and priority data provided by the doctor, and the camera unit is used to shoot the monitoring area; The face recognition module includes a wake-up module, an image analysis module, an image signal transceiver module, a computing power allocation module, and an identity matching module. The identity recognition module is electrically connected to the wake-up module, the camera unit is electrically connected to the image analysis module, the computing power allocation module is electrically connected to the image signal transceiver module, and the identity matching module is electrically connected to the identity data statistics module; The wake-up module is used to wake up the face recognition module, the image analysis module is used to analyze the face in the captured image, the image signal transceiver module is used to send or receive the face image to be processed, the computing power allocation module is used to allocate the face recognition task, and the identity matching module is used to match the recognized face information with the person's identity information; The main workflow of the system is: S0. Multiple identity recognition modules and face recognition modules are set up in each important area of ​​the hospital to identify and verify the identity information of doctors and patients; S1. When no one enters the area, the face recognition module stops working; S2. When someone applies to enter an important area, the system reads the doctor's identity information and priority data or the patient's identity information through the identity recognition module and performs face recognition. During face recognition, the higher the doctor's priority, the earlier the face recognition verification is performed; S3. After the face recognition task is completed, the face recognition module will not be shut down immediately. When no one enters the area for a period of time, the face recognition module will stop working.

2. A distributed blockchain information technology verification system according to claim 1, characterized in that: In the above step S2, the doctor's priority calculation rule is: The higher the professional title of a doctor in the medical system, the more important the doctor is, and the system will give priority to him. The more patients a doctor has registered on the same day, the more patients are waiting to see the doctor, and the system will give priority to him. Therefore, the priority order of doctors is calculated by the professional title of the doctor and the number of patients registered on the same day, as follows: X=μp+λυq Where X is the priority index. The larger X is, the higher the priority of the doctor. μ is the weight of the hospital grade in the priority index. υ is the weight of the number of patients registered on the same day in the priority index. p is the doctor's title. q is the number of patients registered by this doctor on the same day. λ is the adjustment coefficient.

3. A distributed blockchain information technology verification system according to claim 2, characterized in that: In the above step S2, the method for calculating the priority weight is as follows: During periods when the number of patients in hospitals is relatively high, it is appropriate to increase the salaries of doctors. The weight of the number of daily registrations in the priority order calculation should be appropriately reduced, and the weight of the doctor's professional title level in the priority order calculation should be appropriately reduced; During periods when the number of patients in hospitals is relatively small, the weight of the doctor's professional title in the priority calculation should be appropriately increased, and the weight of the number of doctors' daily registrations in the priority calculation should be appropriately reduced. And μ+υ=100%, we can get Where x is the total number of advance registrations in the hospital on that day, and x0 is the average total number of advance registrations in the hospital per day.

4. A distributed blockchain information technology verification system according to claim 3, characterized in that: The working principle of the computing power allocation module is: Since patients come to the hospital less frequently, face recognition is more complicated and takes longer time, while doctors come to the hospital more frequently, face recognition is simpler and takes less time. Therefore, the computing power used for face recognition of patients and doctors is different. The system will monitor the number of people in the area through the camera module. The system determines that the number of people wearing white coats is the number of doctors, and the rest are the number of patients. When there are more doctors in the area, the average computing power occupied by each person is less. When there are more patients in the area, the average computing power occupied by each person is more. The computing power occupied by each patient for face recognition is M, and the computing power occupied by each doctor for face recognition is N. The number of patients in a certain area is m, and the number of doctors is n. The total computing power occupied is Y=mM+nN The computing power allocation module allocates characters to each face recognition module according to the computing power required to perform face recognition of characters in each area.

5. A distributed blockchain information technology verification system according to claim 4, characterized in that: The main working mode of the face recognition module is: The face recognition module enters a dormant state when no one applies to enter an important area. When the identity recognition module detects that someone has applied to enter an important area, the face recognition module is awakened. The module needs to wait for a time t1 to start. When the face recognition module completes the recognition task, in order to save the time required for restarting when someone applies to enter immediately, the face recognition module will wait for a response for a period of time t2, and enter a dormant state after t2 time. If a face recognition module is working while other face recognition modules are resting or waiting for a response, the working face recognition module will send the face recognition task to other face recognition modules that are not performing face recognition work, and allocate the face recognition tasks according to their respective computing capabilities, so that multiple face recognitions can perform collaborative analysis.

6. A distributed blockchain information technology verification system according to claim 5, characterized in that: The working modes of the face recognition module are divided into the following three modes: When a certain face recognition module is not working, that is, no one has applied to enter the important area for at least time t2, the calculation function of the face recognition module can be fully allocated to other face recognition modules; When a face recognition module is activated, that is, someone has applied to enter an important area, the computing function of the face recognition module has been fully occupied; When a face recognition module enters the waiting response mode, that is, within the time t2 after completing the face recognition work, the computing function of its face recognition module can be partially allocated to other face recognition modules.

7. A distributed blockchain information technology verification system according to claim 6, characterized in that: The distribution principle of the remaining computing power of the face recognition module is: The remaining computing power for real-time processing of other information tasks increases in direct proportion to the time after the task is completed, until the face recognition module is triggered to shut down, and the remaining computing power is its full computing power, specifically Where A is the computing power for real-time processing of other information tasks, N0 is the overall computing power, N1 is the computing power occupied when waiting for a response, t2 is the pre-set response time, t is the time waiting for a response, t∈[0, t2).

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