CN111462853B - Mobile medical system and method based on block chain and 5G communication - Google Patents
Mobile medical system and method based on block chain and 5G communication Download PDFInfo
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Abstract
An ambulatory medical system and method based on blockchain and 5G communication comprises the following steps: the patient detection mobile terminal is used for acquiring patient detection data and transmitting the patient detection data to the cloud server through the 5G network; the cloud server generates a simple diagnosis result according to the detection data, and sends the detection data and the simple diagnosis result to a doctor remote consultation end; the remote consultation end of the doctor displays the detection data and the simple diagnosis result to the consultation doctor, and the consultation doctor judges whether the simple diagnosis result is correct according to the detection data. The simple diagnosis result is intelligently generated according to the detection data and then is sent to the consultation doctor for confirmation through the 5G network, so that the delay of treatment of the patient due to the generation of the wrong diagnosis result occasionally can be avoided; the data transmission time can be reduced by adopting 5G network communication; the patient ID, the detection data, the diagnosis result, the treatment scheme and the timestamp are sent to the block chain together, so that the past medical history of the patient can be inquired through the block chain, and the past medical history of the patient can be prevented from being maliciously tampered because the block chain cannot be tampered.
Description
Technical Field
The invention relates to the field of mobile medical treatment, in particular to a mobile medical treatment system and method based on a block chain and 5G communication.
Background
At present, with the rapid development of medical technology and network technology, mobile medical treatment is in progress. Compared with traditional medical treatment, the mobile medical treatment realizes the optimization of medical resource allocation and utilization, and enables patients, doctors and hospital managers to save more time and worry. Moreover, with the rapid development of artificial intelligence technology, artificial intelligence technology is also beginning to be applied in ambulatory medical treatment, specifically: after a patient detection mobile terminal in the mobile medical system acquires detection data of a patient, the detection data are sent to the cloud server, and the cloud server can quickly generate a simple diagnosis result according to the detection data by adopting an artificial intelligence technology, so that the consultation time of the patient can be greatly shortened. But at the same time, due to the imperfection of the database and the artificial intelligence technology, a wrong simple diagnosis result is occasionally generated, so that the treatment of the patient is delayed.
Meanwhile, most of the existing mobile medical systems adopt 3G or 4G network communication, so that the consultation efficiency of mobile medical treatment is seriously influenced. Moreover, with the continuous development of the blockchain technology in these years, how to apply the blockchain technology to the mobile medical treatment is also an urgent problem to be solved.
Moreover, in the actual operation of the existing mobile medical system, in order to meet the needs of seeing a doctor in each region, a plurality of patient detection mobile terminals are generally designed in the mobile medical system, so that the problem that the service coverage of two adjacent patient detection mobile terminals is repeated often exists, and the medical resources are not uniform.
Finally, with the rapid development of face recognition technology, people begin to apply face recognition technology to medical diagnosis. Specifically, by acquiring facial images of a patient at different periods, the area and the characteristics of facial changes of the patient can be quickly calculated by a face recognition technology and a change detection technology so as to be used for medical diagnosis, but the calculation accuracy of the existing change detection technology is low.
Disclosure of Invention
The invention aims to solve the problems found in the background art and provides an ambulatory medical system and an ambulatory medical method based on a block chain and 5G communication.
In order to achieve the purpose, the invention adopts the following technical scheme: an ambulatory medical system based on blockchain and 5G communication, comprising: the system comprises a patient detection mobile terminal, a cloud server and a doctor remote consultation terminal;
the patient detection mobile terminal is used for collecting detection data of a patient and transmitting the detection data to the cloud server through a 5G network;
the cloud server is used for receiving and storing the detection data, generating a simple diagnosis result according to the detection data, and sending the detection data and the simple diagnosis result to the doctor remote consultation end through a 5G network;
the doctor remote consultation end is used for receiving and displaying the detection data and the simple diagnosis result to a consultation doctor, and the consultation doctor judges whether the simple diagnosis result is correct or not according to the detection data;
if the simple diagnosis result is correct, a consultation doctor sends a confirmation signal and a corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the confirmation signal and the corresponding treatment scheme, the cloud server sends the simple diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the simple diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to a block chain;
if the simple diagnosis result is incorrect, a consultation doctor downloads a new diagnosis result and a corresponding treatment scheme again according to the detection data, and sends the new diagnosis result and the corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the new diagnosis result and the corresponding treatment scheme, the cloud server sends the new diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the new diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to the block chain.
The cloud server in the mobile medical system intelligently generates a simple diagnosis result according to the detection data, and then sends the simple diagnosis result to the consultation doctor through the 5G network to confirm and provide a treatment scheme according to the symptoms, so that the problem that the treatment of a patient is delayed due to the fact that the cloud server generates wrong diagnosis results occasionally can be avoided; meanwhile, by adopting 5G network communication, the data transmission time can be greatly reduced, so that the remote medical treatment efficiency is improved; moreover, after each patient is subjected to remote consultation, the cloud server packages and sends the patient ID, the detection data, the diagnosis result, the corresponding treatment scheme and the corresponding timestamp to the block chain, so that on one hand, the past medical history of the patient can be quickly and accurately inquired through the block chain during future consultation, and on the other hand, the attribute of the block chain which can not be tampered can be used for preventing a person from maliciously tampering the past medical history of the patient.
Further, the number of the patient detection mobile terminals is at least two, and the cloud server monitors the distance between any two patient detection mobile terminals in real time.
Further, the cloud server monitors the distance between any two of the patient detection mobile terminals in real time, specifically as follows:
any two patient detection mobile terminals are respectively marked as a first patient detection mobile terminal and a second patient detection mobile terminal, the cloud server sets a plaintext set based on a probabilistic public key encryption system and generates system parameters, a public key and a private key, meanwhile, the cloud server sends the public key to the first patient detection mobile terminal and the second patient detection mobile terminal, the first patient detection mobile terminal sets a service coverage area of the first patient detection mobile terminal according to personal configuration and equipment configuration of the first patient detection mobile terminal, the service coverage area is used as a first service coverage area, position information of current service is recorded and is used as first position information, the first service coverage area and the first position information are both based on the plaintext set, the first patient detection mobile terminal encrypts the first position information and the first service coverage area by using a random number and the public key based on a probabilistic public key encryption algorithm to obtain a first position information and a first service coverage area, the first ciphertext is sent to the cloud server, the second patient detection mobile terminal sets the service coverage area according to the personal configuration and the equipment configuration of the second patient detection mobile terminal, the service coverage area is used as a second service coverage area, the position information of the current service is recorded and is used as second position information, the second service coverage area and the second position information are both based on the plaintext set, the second patient detection mobile terminal encrypts the second position information and the second service coverage area by using a random number and a public key based on a probability public key encryption algorithm to obtain a second ciphertext, the second ciphertext is sent to the cloud server, and the cloud server decrypts the first ciphertext and the second ciphertext by using a private key based on the probability public key encryption algorithm after receiving the first ciphertext and the second ciphertext to obtain decryption information, the cloud server calculates a position distance square value between the first patient detection moving end and the second patient detection moving end according to the decryption information, the position distance square value is used as a first calculation value, then the sum of the first service coverage area and the second service coverage area is calculated according to the decryption information and is divided by pi to obtain a second calculation value, then the first calculation value is subtracted from the second calculation value to obtain a third calculation value, then the third calculation value is divided by 2 to obtain a fourth calculation value, then the fourth calculation value is multiplied by pi to obtain a comparison value, finally the comparison value is respectively compared with the first service coverage area and the second service coverage area, when the comparison value is larger than or equal to the larger one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is reasonable, and the cloud server does not send a signal, when the comparison value is smaller than any one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is too small, the cloud server sends a moving signal to the first patient detection moving end and/or the second patient detection moving end, and the first patient detection moving end and/or the second patient detection moving end move in the direction away from each other after receiving the moving signal until the distance between the first patient detection moving end and the second patient detection moving end is reasonable.
The cloud server monitors the distance between any two patient detection mobile terminals in real time, so that the service coverage of two adjacent patient detection mobile terminals can be prevented from being repeated, and medical resources are fully utilized; meanwhile, the cloud server calculates the position distance square value between two adjacent patient detection mobile terminals in a secret manner based on a probability public key encryption algorithm to obtain a first calculated value, then calculates the sum of the first service coverage area and the second service coverage area according to decryption information and divides the sum by pi to obtain a second calculated value, then subtracts the second calculated value from the first calculated value to obtain a third calculated value, then divides the third calculated value by 2 to obtain a fourth calculated value, then multiplies the fourth calculated value by pi to obtain a comparison value, and finally compares the comparison value with the first service coverage area and the second service coverage area respectively to determine the distance between the first patient detection mobile terminal and the second patient detection mobile terminal, so that in the whole calculation process, only the information of the first service coverage area and the second service coverage area needs to be known by utilizing the characteristics of the probability public key encryption algorithm, the first position information and the second position information are not required to be known, so that the first position information and the second position information cannot be disclosed, and the clinic privacy of the clinic patients is guaranteed.
When the patient sees a doctor at every turn, the patient detects and removes the face image that the end gathered the patient, and will face image send to cloud server, when the patient sees a doctor next time, the face image that the patient was gathered when seeing a doctor at every turn is transferred to cloud server, and calculates the facial change result of this patient according to the face image that the face image was gathered when seeing a doctor at every turn.
Further, the face change result of the patient calculated according to the face image acquired at each visit is specifically as follows:
calculating normalization parameters of face images collected before and after the patient visits, performing normalization and face image color correction on the face images collected before and after the patient visits to obtain processed face images collected before and after the patient visits, constructing a convolutional neural network model, wherein the convolutional neural network model comprises a face image change characteristic increment coding unit and a multi-module geometric constraint solving unit, the face image change characteristic increment coding unit comprises a module corresponding to the collection of the early-stage face images and a module corresponding to the collection of the later-stage face images, the module for collecting the early-stage face images comprises three modules which are sequentially connected, each module comprises three convolutional layers and a pooling layer, the module for collecting the later-stage face images also comprises three modules which are sequentially connected, and each sub-module comprises three convolutional layers and a pooling layer, the method comprises the steps of subtracting pooled features of all modules before and after a visit to obtain differential features, wherein the multi-module geometric constraint solving unit comprises three modules, the first module is used for early-stage face edge calculation, the second module is used for face change region calculation, the third module is used for later-stage face edge calculation, the first module and the third module share a parameter structure, the structures of the first module and the third module and the structure of the second module interact through a cost function to realize effective constraint of a final change detection result through geometric structure information, the face image change feature incremental coding unit before and after the visit is connected with the multi-module geometric constraint solving unit through the differential features as input, the cost function comprises a change detection classification cost function, an edge geometric cost function and a geometric change cost function, and a constructed convolutional neural network model is trained, and the processed facial images collected before and after the patient is in a visit are input into the trained convolutional neural network model, and the facial change result of the patient is obtained by calculation,
the change detection classification cost function a (bz, jz) is:
A(bz,jz)＝-yz×bz×log(Tanh(jz))-(1-bz)×log(1-Tanh(jz))
in the formula, bz represents a change value of a face image of a patient, jz represents a characteristic value calculated after a solver, Tanh (·) is a hyperbolic function, and yz represents a balance factor of an unchanged area and a changed area;
the edge geometric cost function A (bz)by,jzby) Comprises the following steps:
in the formula, bzbyAnd jzbyRespectively representing the real value of the edge of the face of the patient and the calculated value of the edge of the face of the patient,
representing the edge geometric loss corresponding to the jth group of output features of the first module and the third module;
the geometric change cost function
Comprises the following steps:
in the formula,
the result of calculating edges for the face image of the previous patient,
and solving an edge result of structural parameter sharing calculation for the later-stage patient face image and the earlier-stage patient face image.
The convolutional neural network model with dense connection of the change features and geometric constraint is constructed by extracting two layers of the network from the geometric constraint and the change features of the face of the patient, so that the accuracy and the stability of the extraction of the change features of the face of the patient can be effectively improved; meanwhile, the repeatability of the facial change characteristics of the patient is improved by adopting a dense connection structure, and the extraction of the change of the geometric information can be realized by adopting the geometric information as multi-module constraint; moreover, the repeatability of the facial change characteristics of the patient under the constraint condition of geometric information change is further improved by adopting the integral cost function calculation integrating the change detection classification cost function, the edge geometric cost function and the geometric change cost function.
Further, according to the change detection classification cost function, the edge geometric cost function and the geometric change cost function, the integral cost function of the multi-module geometric constraint is obtained as follows:
an ambulatory medical method based on blockchain and 5G communication comprises the following steps:
the detection data of the patient are collected through the patient detection mobile terminal, and the detection data are transmitted to the cloud server through the 5G network;
the cloud server receives and stores the detection data, generates a simple diagnosis result according to the detection data, and sends the detection data and the simple diagnosis result to the doctor remote consultation end through a 5G network;
the doctor remote consultation end receives and displays the detection data and the simple diagnosis result to a consultation doctor, and the consultation doctor judges whether the simple diagnosis result is correct or not according to the detection data;
if the simple diagnosis result is correct, a consultation doctor sends a confirmation signal and a corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the confirmation signal and the corresponding treatment scheme, the cloud server sends the simple diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the simple diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to a block chain;
if the simple diagnosis result is incorrect, a consultation doctor downloads a new diagnosis result and a corresponding treatment scheme again according to the detection data, and sends the new diagnosis result and the corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the new diagnosis result and the corresponding treatment scheme, the cloud server sends the new diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the new diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to the block chain.
Compared with the prior art, the invention has the advantages that:
1. the cloud server in the mobile medical system intelligently generates a simple diagnosis result according to the detection data, and then sends the simple diagnosis result to the consultation doctor through the 5G network to confirm and provide a treatment scheme according to the symptoms, so that the problem that the treatment of a patient is delayed due to the fact that the cloud server generates wrong diagnosis results occasionally can be avoided;
2. by adopting 5G network communication, the data transmission time can be greatly reduced, thereby improving the efficiency of remote medical treatment;
3. after the remote consultation of each patient is finished, the cloud server packs and sends the patient ID, the detection data, the diagnosis result, the corresponding treatment scheme and the corresponding timestamp to the block chain, so that on one hand, the past medical history of the patient can be quickly and accurately inquired through the block chain during the future consultation, and on the other hand, the attribute of the block chain which can not be tampered can be used for preventing a person from maliciously tampering the past medical history of the patient;
4. the cloud server monitors the distance between any two patient detection mobile terminals in real time, so that the service coverage of two adjacent patient detection mobile terminals can be prevented from being repeated, and medical resources are fully utilized;
5. the cloud server secretly calculates a position distance square value between two adjacent patient detection mobile terminals based on a probability public key encryption algorithm to obtain a first calculated value, then calculates the sum of a first service coverage area and a second service coverage area according to decryption information and divides the sum by pi to obtain a second calculated value, then subtracts the second calculated value from the first calculated value to obtain a third calculated value, then divides the third calculated value by 2 to obtain a fourth calculated value, multiplies the fourth calculated value by pi to obtain a comparison value, and finally compares the comparison value with the first service coverage area and the second service coverage area respectively to determine the distance between the first patient detection mobile terminal and the second patient detection mobile terminal, so that in the whole calculation process, only the information of the first service coverage area and the second service coverage area needs to be known by utilizing the characteristics of the probability public key encryption algorithm, the first position information and the second position information do not need to be known, so that the first position information and the second position information cannot be disclosed, and the treatment privacy of the patient is guaranteed;
6. the convolutional neural network model with dense connection of the change features and geometric constraint is constructed by extracting two layers of the network from the geometric constraint and the change features of the face of the patient, so that the accuracy and the stability of the extraction of the change features of the face of the patient can be effectively improved;
7. the repeatability of the facial change characteristics of the patient is improved by adopting a densely connected structure, and the extraction of the change of the geometric information can be realized by adopting the geometric information as multi-module constraint;
8. the repeatability of the facial change characteristics of the patient under the constraint condition of geometric information change is further improved by adopting the integral cost function calculation integrating three cost functions of the change detection classification cost function, the edge geometric cost function and the geometric change cost function.
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Fig. 1 is a schematic diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
referring to fig. 1, the present embodiment provides an ambulatory medical system based on blockchain and 5G communication, including: the system comprises a patient detection mobile terminal, a cloud server and a doctor remote consultation terminal;
the patient detection mobile terminal is used for collecting detection data of a patient and transmitting the detection data to the cloud server through a 5G network;
the cloud server is used for receiving and storing the detection data, generating a simple diagnosis result according to the detection data, and sending the detection data and the simple diagnosis result to the doctor remote consultation end through a 5G network;
the doctor remote consultation end is used for receiving and displaying the detection data and the simple diagnosis result to a consultation doctor, and the consultation doctor judges whether the simple diagnosis result is correct or not according to the detection data;
if the simple diagnosis result is correct, a consultation doctor sends a confirmation signal and a corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the confirmation signal and the corresponding treatment scheme, the cloud server sends the simple diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the simple diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to a block chain;
if the simple diagnosis result is incorrect, a consultation doctor downloads a new diagnosis result and a corresponding treatment scheme again according to the detection data, and sends the new diagnosis result and the corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the new diagnosis result and the corresponding treatment scheme, the cloud server sends the new diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the new diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to the block chain.
The cloud server in the mobile medical system intelligently generates a simple diagnosis result according to the detection data, and then sends the simple diagnosis result to the consultation doctor through the 5G network to confirm and provide a treatment scheme according to the symptoms, so that the problem that the treatment of a patient is delayed due to the fact that the cloud server generates wrong diagnosis results occasionally can be avoided; meanwhile, by adopting 5G network communication, the data transmission time can be greatly reduced, so that the remote medical treatment efficiency is improved; moreover, after each patient is subjected to remote consultation, the cloud server packages and sends the patient ID, the detection data, the diagnosis result, the corresponding treatment scheme and the corresponding timestamp to the block chain, so that on one hand, the past medical history of the patient can be quickly and accurately inquired through the block chain during future consultation, and on the other hand, the attribute of the block chain which can not be tampered can be used for preventing a person from maliciously tampering the past medical history of the patient.
The number of the mobile ends for patient detection is at least two, the cloud server monitors any two of the distances between the mobile ends for patient detection in real time, and the method specifically comprises the following steps:
any two patient detection mobile terminals are respectively marked as a first patient detection mobile terminal and a second patient detection mobile terminal, the cloud server sets a plaintext set based on a probabilistic public key encryption system and generates system parameters, a public key and a private key, meanwhile, the cloud server sends the public key to the first patient detection mobile terminal and the second patient detection mobile terminal, the first patient detection mobile terminal sets a service coverage area of the first patient detection mobile terminal according to personal configuration and equipment configuration of the first patient detection mobile terminal, the service coverage area is used as a first service coverage area, position information of current service is recorded and is used as first position information, the first service coverage area and the first position information are both based on the plaintext set, the first patient detection mobile terminal encrypts the first position information and the first service coverage area by using a random number and the public key based on a probabilistic public key encryption algorithm to obtain a first position information and a first service coverage area, the first ciphertext is sent to the cloud server, the second patient detection mobile terminal sets the service coverage area according to the personal configuration and the equipment configuration of the second patient detection mobile terminal, the service coverage area is used as a second service coverage area, the position information of the current service is recorded and is used as second position information, the second service coverage area and the second position information are both based on the plaintext set, the second patient detection mobile terminal encrypts the second position information and the second service coverage area by using a random number and a public key based on a probability public key encryption algorithm to obtain a second ciphertext, the second ciphertext is sent to the cloud server, and the cloud server decrypts the first ciphertext and the second ciphertext by using a private key based on the probability public key encryption algorithm after receiving the first ciphertext and the second ciphertext to obtain decryption information, the cloud server calculates a position distance square value between the first patient detection moving end and the second patient detection moving end according to the decryption information, the position distance square value is used as a first calculation value, then the sum of the first service coverage area and the second service coverage area is calculated according to the decryption information and is divided by pi to obtain a second calculation value, then the first calculation value is subtracted from the second calculation value to obtain a third calculation value, then the third calculation value is divided by 2 to obtain a fourth calculation value, then the fourth calculation value is multiplied by pi to obtain a comparison value, finally the comparison value is respectively compared with the first service coverage area and the second service coverage area, when the comparison value is larger than or equal to the larger one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is reasonable, and the cloud server does not send a signal, when the comparison value is smaller than any one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is too small, the cloud server sends a moving signal to the first patient detection moving end and/or the second patient detection moving end, and the first patient detection moving end and/or the second patient detection moving end move in the direction away from each other after receiving the moving signal until the distance between the first patient detection moving end and the second patient detection moving end is reasonable.
The cloud server monitors the distance between any two patient detection mobile terminals in real time, so that the service coverage of two adjacent patient detection mobile terminals can be prevented from being repeated, and medical resources are fully utilized; meanwhile, the cloud server calculates the position distance square value between two adjacent patient detection mobile terminals in a secret manner based on a probability public key encryption algorithm to obtain a first calculated value, then calculates the sum of the first service coverage area and the second service coverage area according to decryption information and divides the sum by pi to obtain a second calculated value, then subtracts the second calculated value from the first calculated value to obtain a third calculated value, then divides the third calculated value by 2 to obtain a fourth calculated value, then multiplies the fourth calculated value by pi to obtain a comparison value, and finally compares the comparison value with the first service coverage area and the second service coverage area respectively to determine the distance between the first patient detection mobile terminal and the second patient detection mobile terminal, so that in the whole calculation process, only the information of the first service coverage area and the second service coverage area needs to be known by utilizing the characteristics of the probability public key encryption algorithm, the first position information and the second position information are not required to be known, so that the first position information and the second position information cannot be disclosed, and the clinic privacy of the clinic patients is guaranteed.
When the patient sees a doctor at every turn, the patient detects and removes the face image that the end gathered the patient, and will face image send to cloud server, when the patient next sees a doctor, the face image that the face image was gathered when the cloud server was transferred to this patient at every turn, and calculate this patient's facial change result according to the face image that gathers when seeing a doctor at every turn, specifically as follows:
calculating normalization parameters of face images collected before and after the patient visits, performing normalization and face image color correction on the face images collected before and after the patient visits to obtain processed face images collected before and after the patient visits, constructing a convolutional neural network model, wherein the convolutional neural network model comprises a face image change characteristic increment coding unit and a multi-module geometric constraint solving unit, the face image change characteristic increment coding unit comprises a module corresponding to the collection of the early-stage face images and a module corresponding to the collection of the later-stage face images, the module for collecting the early-stage face images comprises three modules which are sequentially connected, each module comprises three convolutional layers and a pooling layer, the module for collecting the later-stage face images also comprises three modules which are sequentially connected, and each sub-module comprises three convolutional layers and a pooling layer, the method comprises the steps of subtracting pooled features of all modules before and after a visit to obtain differential features, wherein the multi-module geometric constraint solving unit comprises three modules, the first module is used for face edge calculation in the early stage, the second module is used for face change region calculation, the third module is used for face edge calculation in the later stage, the first module and the third module share a parameter structure, the structures of the first module and the third module and the structure of the second module interact through a cost function to realize effective constraint of a final change detection result through geometric structure information, the face image change feature incremental coding unit before and after the visit is connected with the multi-module geometric constraint solving unit through the differential features as input, the cost function comprises a change detection classification cost function, an edge geometric cost function and a geometric change cost function, and the overall cost function through the multi-module geometric constraint adopts a model-free optimization algorithm, the face image acquisition module is used for training the convolutional neural network model, after training is completed, training fitting parameters of the convolutional neural network model are obtained, processed face images acquired before and after the patient visits a doctor are input into the trained convolutional neural network model, the face change result of the patient is obtained through calculation, only the second module participates in calculation of the face change area of the patient, and meanwhile, the calculation result of the face image geometrical structure of the patient in the later period before and after the patient visits the doctor is output through the first module and the third module; wherein,
the change detection classification cost function a (bz, jz) is:
A(bz,jz)＝-yz×bz×log(Tanh(jz))-(1-bz)×log(1-Tanh(jz))
in the formula, bz represents a change value of a face image of a patient, jz represents a characteristic value calculated after a solver, Tanh (·) is a hyperbolic function, and yz represents a balance factor of an unchanged area and a changed area;
the edge geometric cost function A (bz)by,jzby) Comprises the following steps:
in the formula, bzbyAnd jzbyRepresenting true values of edges of patient faces and edges of patient faces, respectivelyThe calculated value is calculated by calculating the value of,
representing the edge geometric loss corresponding to the jth group of output features of the first module and the third module;
the geometric change cost function
Comprises the following steps:
in the formula,
the result of calculating edges for the face image of the previous patient,
calculating an edge result of structural parameter sharing calculation for the later-stage patient face image and the earlier-stage patient face image;
and detecting a classification cost function, an edge geometric cost function and a geometric change cost function according to the change to obtain an integral cost function of the multi-module geometric constraint, wherein the integral cost function is as follows:
the convolutional neural network model with dense connection of the change features and geometric constraint is constructed by extracting two layers of the network from the geometric constraint and the change features of the face of the patient, so that the accuracy and the stability of the extraction of the change features of the face of the patient can be effectively improved; meanwhile, the repeatability of the facial change characteristics of the patient is improved by adopting a dense connection structure, and the extraction of the change of the geometric information can be realized by adopting the geometric information as multi-module constraint; moreover, the repeatability of the facial change characteristics of the patient under the constraint condition of geometric information change is further improved by adopting the integral cost function calculation integrating the change detection classification cost function, the edge geometric cost function and the geometric change cost function.
The embodiment also provides an ambulatory medical method based on the blockchain and 5G communication, which comprises the following steps:
the detection data of the patient are collected through the patient detection mobile terminal, and the detection data are transmitted to the cloud server through the 5G network;
the cloud server receives and stores the detection data, generates a simple diagnosis result according to the detection data, and sends the detection data and the simple diagnosis result to the doctor remote consultation end through a 5G network;
the doctor remote consultation end receives and displays the detection data and the simple diagnosis result to a consultation doctor, and the consultation doctor judges whether the simple diagnosis result is correct or not according to the detection data;
if the simple diagnosis result is correct, a consultation doctor sends a confirmation signal and a corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the confirmation signal and the corresponding treatment scheme, the cloud server sends the simple diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the simple diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to a block chain;
if the simple diagnosis result is incorrect, a consultation doctor downloads a new diagnosis result and a corresponding treatment scheme again according to the detection data, and sends the new diagnosis result and the corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the new diagnosis result and the corresponding treatment scheme, the cloud server sends the new diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the new diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to the block chain.
Claims (5)
1. An ambulatory medical system based on blockchain and 5G communication, comprising: the system comprises a patient detection mobile terminal, a cloud server and a doctor remote consultation terminal;
the patient detection mobile terminal is used for collecting detection data of a patient and transmitting the detection data to the cloud server through a 5G network;
the cloud server is used for receiving and storing the detection data, generating a simple diagnosis result according to the detection data, and sending the detection data and the simple diagnosis result to the doctor remote consultation end through a 5G network;
the doctor remote consultation end is used for receiving and displaying the detection data and the simple diagnosis result to a consultation doctor, and the consultation doctor judges whether the simple diagnosis result is correct or not according to the detection data;
if the simple diagnosis result is correct, a consultation doctor sends a confirmation signal and a corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the confirmation signal and the corresponding treatment scheme, the cloud server sends the simple diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the simple diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to a block chain;
if the simple diagnosis result is incorrect, a consultation doctor downloads a new diagnosis result and a corresponding treatment scheme again according to the detection data, and sends the new diagnosis result and the corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the new diagnosis result and the corresponding treatment scheme, the cloud server sends the new diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the new diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packed together and sent to a block chain;
the number of the patient detection mobile ends is at least two, and the cloud server monitors the distance between any two patient detection mobile ends in real time;
the cloud server monitors any two of the intervals between the mobile ends for patient detection in real time, and the method specifically comprises the following steps:
any two patient detection mobile terminals are respectively marked as a first patient detection mobile terminal and a second patient detection mobile terminal, the cloud server sets a plaintext set based on a probabilistic public key encryption system and generates system parameters, a public key and a private key, meanwhile, the cloud server sends the public key to the first patient detection mobile terminal and the second patient detection mobile terminal, the first patient detection mobile terminal sets a service coverage area of the first patient detection mobile terminal according to personal configuration and equipment configuration of the first patient detection mobile terminal, the service coverage area is used as a first service coverage area, position information of current service is recorded and is used as first position information, the first service coverage area and the first position information are both based on the plaintext set, the first patient detection mobile terminal encrypts the first position information and the first service coverage area by using a random number and the public key based on a probabilistic public key encryption algorithm to obtain a first position information and a first service coverage area, the first ciphertext is sent to the cloud server, the second patient detection mobile terminal sets the service coverage area according to the personal configuration and the equipment configuration of the second patient detection mobile terminal, the service coverage area is used as a second service coverage area, the position information of the current service is recorded and is used as second position information, the second service coverage area and the second position information are both based on the plaintext set, the second patient detection mobile terminal encrypts the second position information and the second service coverage area by using a random number and a public key based on a probability public key encryption algorithm to obtain a second ciphertext, the second ciphertext is sent to the cloud server, and the cloud server decrypts the first ciphertext and the second ciphertext by using a private key based on the probability public key encryption algorithm after receiving the first ciphertext and the second ciphertext to obtain decryption information, the cloud server calculates a position distance square value between the first patient detection moving end and the second patient detection moving end according to the decryption information, the position distance square value is used as a first calculation value, then the sum of the first service coverage area and the second service coverage area is calculated according to the decryption information and is divided by pi to obtain a second calculation value, then the first calculation value is subtracted from the second calculation value to obtain a third calculation value, then the third calculation value is divided by 2 to obtain a fourth calculation value, then the fourth calculation value is multiplied by pi to obtain a comparison value, finally the comparison value is respectively compared with the first service coverage area and the second service coverage area, when the comparison value is larger than or equal to the larger one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is reasonable, and the cloud server does not send a signal, when the comparison value is smaller than any one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is too small, the cloud server sends a moving signal to the first patient detection moving end and/or the second patient detection moving end, and the first patient detection moving end and/or the second patient detection moving end move in the direction away from each other after receiving the moving signal until the distance between the first patient detection moving end and the second patient detection moving end is reasonable.
2. The mobile medical system based on blockchain and 5G communication of claim 1, wherein the patient detection mobile terminal acquires a face image of the patient at each visit of the patient and sends the face image to the cloud server, and the cloud server retrieves the face image acquired at each visit of the patient at the next visit of the patient and calculates a face change result of the patient according to the face image acquired at each visit.
3. The system of claim 2, wherein the face change of the patient is calculated from the face image acquired at each visit as follows:
calculating normalization parameters of face images collected before and after the patient visits, performing normalization and face image color correction on the face images collected before and after the patient visits to obtain processed face images collected before and after the patient visits, constructing a convolutional neural network model, wherein the convolutional neural network model comprises a face image change characteristic increment coding unit and a multi-module geometric constraint solving unit, the face image change characteristic increment coding unit comprises a module corresponding to the collection of the early-stage face images and a module corresponding to the collection of the later-stage face images, the module for collecting the early-stage face images comprises three modules which are sequentially connected, each module comprises three convolutional layers and a pooling layer, the module for collecting the later-stage face images also comprises three modules which are sequentially connected, and each sub-module comprises three convolutional layers and a pooling layer, the method comprises the steps of subtracting pooled features of all modules before and after a visit to obtain differential features, wherein the multi-module geometric constraint solving unit comprises three modules, the first module is used for early-stage face edge calculation, the second module is used for face change region calculation, the third module is used for later-stage face edge calculation, the first module and the third module share a parameter structure, the structures of the first module and the third module and the structure of the second module interact through a cost function to realize effective constraint of a final change detection result through geometric structure information, the face image change feature incremental coding unit before and after the visit is connected with the multi-module geometric constraint solving unit through the differential features as input, the cost function comprises a change detection classification cost function, an edge geometric cost function and a geometric change cost function, and a constructed convolutional neural network model is trained, inputting the processed face images collected before and after the patient is in a visit into the trained convolutional neural network model, and calculating to obtain a face change result of the patient; wherein,
the change detection classification cost function a (bz, jz) is:
A(bz,jz)＝-yz×bz×log(Tanh(jz))-(1-bz)×log(1-Tanh(jz))
in the formula, bz represents a change value of a face image of a patient, jz represents a characteristic value calculated after a solver, Tanh (·) is a hyperbolic function, and yz represents a balance factor of an unchanged area and a changed area;
the edge geometric cost function A (bz)by,jzby) Comprises the following steps:
in the formula, bzbyAnd jzbyRespectively representing the real value of the edge of the face of the patient and the calculated value of the edge of the face of the patient,
representing the edge geometric loss corresponding to the jth group of output features of the first module and the third module;
the geometric change cost function
Comprises the following steps:
in the formula,
the result of calculating edges for the face image of the previous patient,
and solving an edge result of structural parameter sharing calculation for the later-stage patient face image and the earlier-stage patient face image.
4. The ambulatory medical system based on blockchain and 5G communication according to claim 3, wherein the overall cost function of the multi-module geometric constraint is obtained by detecting a classification cost function, an edge geometric cost function and a geometric change cost function according to the change, wherein the overall cost function is:
5. an ambulatory medical method based on blockchain and 5G communication is characterized by comprising the following steps:
the method comprises the steps that detection data of a patient are collected through a patient detection mobile terminal, and the detection data are transmitted to a cloud server through a 5G network;
the cloud server receives and stores the detection data, generates a simple diagnosis result according to the detection data, and sends the detection data and the simple diagnosis result to a doctor remote consultation end through a 5G network;
the doctor remote consultation end receives and displays the detection data and the simple diagnosis result to a consultation doctor, and the consultation doctor judges whether the simple diagnosis result is correct or not according to the detection data;
if the simple diagnosis result is correct, a consultation doctor sends a confirmation signal and a corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the confirmation signal and the corresponding treatment scheme, the cloud server sends the simple diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the simple diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packaged together and sent to a block chain;
if the simple diagnosis result is incorrect, a consultation doctor downloads a new diagnosis result and a corresponding treatment scheme again according to the detection data, and sends the new diagnosis result and the corresponding treatment scheme to the cloud server through the doctor remote consultation end, after the cloud server receives the new diagnosis result and the corresponding treatment scheme, the cloud server sends the new diagnosis result and the corresponding treatment scheme to the patient detection mobile end, and meanwhile, the patient ID, the detection data, the new diagnosis result, the corresponding treatment scheme and the corresponding timestamp are packed together and sent to a block chain;
the number of the patient detection mobile ends is at least two, and the cloud server monitors the distance between any two patient detection mobile ends in real time;
the cloud server monitors any two of the intervals between the mobile ends for patient detection in real time, and the method specifically comprises the following steps:
any two patient detection mobile terminals are respectively marked as a first patient detection mobile terminal and a second patient detection mobile terminal, the cloud server sets a plaintext set based on a probabilistic public key encryption system and generates system parameters, a public key and a private key, meanwhile, the cloud server sends the public key to the first patient detection mobile terminal and the second patient detection mobile terminal, the first patient detection mobile terminal sets a service coverage area of the first patient detection mobile terminal according to personal configuration and equipment configuration of the first patient detection mobile terminal, the service coverage area is used as a first service coverage area, position information of current service is recorded and is used as first position information, the first service coverage area and the first position information are both based on the plaintext set, the first patient detection mobile terminal encrypts the first position information and the first service coverage area by using a random number and the public key based on a probabilistic public key encryption algorithm to obtain a first position information and a first service coverage area, the first ciphertext is sent to the cloud server, the second patient detection mobile terminal sets the service coverage area according to the personal configuration and the equipment configuration of the second patient detection mobile terminal, the service coverage area is used as a second service coverage area, the position information of the current service is recorded and is used as second position information, the second service coverage area and the second position information are both based on the plaintext set, the second patient detection mobile terminal encrypts the second position information and the second service coverage area by using a random number and a public key based on a probability public key encryption algorithm to obtain a second ciphertext, the second ciphertext is sent to the cloud server, and the cloud server decrypts the first ciphertext and the second ciphertext by using a private key based on the probability public key encryption algorithm after receiving the first ciphertext and the second ciphertext to obtain decryption information, the cloud server calculates a position distance square value between the first patient detection moving end and the second patient detection moving end according to the decryption information, the position distance square value is used as a first calculation value, then the sum of the first service coverage area and the second service coverage area is calculated according to the decryption information and is divided by pi to obtain a second calculation value, then the first calculation value is subtracted from the second calculation value to obtain a third calculation value, then the third calculation value is divided by 2 to obtain a fourth calculation value, then the fourth calculation value is multiplied by pi to obtain a comparison value, finally the comparison value is respectively compared with the first service coverage area and the second service coverage area, when the comparison value is larger than or equal to the larger one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is reasonable, and the cloud server does not send a signal, when the comparison value is smaller than any one of the first service coverage area and the second service coverage area, the distance between the first patient detection moving end and the second patient detection moving end is too small, the cloud server sends a moving signal to the first patient detection moving end and/or the second patient detection moving end, and the first patient detection moving end and/or the second patient detection moving end move in the direction away from each other after receiving the moving signal until the distance between the first patient detection moving end and the second patient detection moving end is reasonable.
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