CN107426682A - A kind of neurosurgery diagnostic control system based on cloud computing - Google Patents

A kind of neurosurgery diagnostic control system based on cloud computing Download PDF

Info

Publication number
CN107426682A
CN107426682A CN201710797257.8A CN201710797257A CN107426682A CN 107426682 A CN107426682 A CN 107426682A CN 201710797257 A CN201710797257 A CN 201710797257A CN 107426682 A CN107426682 A CN 107426682A
Authority
CN
China
Prior art keywords
mrow
msub
module
formula
node
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201710797257.8A
Other languages
Chinese (zh)
Inventor
刘立军
傅楚华
陈波
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Individual
Original Assignee
Individual
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Individual filed Critical Individual
Priority to CN201710797257.8A priority Critical patent/CN107426682A/en
Publication of CN107426682A publication Critical patent/CN107426682A/en
Pending legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/46—Embedding additional information in the video signal during the compression process
    • H04N19/467—Embedding additional information in the video signal during the compression process characterised by the embedded information being invisible, e.g. watermarking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/76—Television signal recording
    • H04N5/91—Television signal processing therefor
    • H04N5/913—Television signal processing therefor for scrambling ; for copy protection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H04W40/14—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality based on stability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations
    • H04W52/46—TPC being performed in particular situations in multi hop networks, e.g. wireless relay networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
    • H04L2209/60—Digital content management, e.g. content distribution
    • H04L2209/608—Watermarking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/001—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using chaotic signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/76—Television signal recording
    • H04N5/91—Television signal processing therefor
    • H04N5/913—Television signal processing therefor for scrambling ; for copy protection
    • H04N2005/91307—Television signal processing therefor for scrambling ; for copy protection by adding a copy protection signal to the video signal
    • H04N2005/91335—Television signal processing therefor for scrambling ; for copy protection by adding a copy protection signal to the video signal the copy protection signal being a watermark
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Quality & Reliability (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention belongs to field of medical technology, discloses a kind of neurosurgery diagnostic control system based on cloud computing, including：Power module, single-chip microcomputer, buzzer, diagnostic module, training module, circuit line, wireless transmitter module and cloud computing module；Power module connects single-chip microcomputer by circuit line；Single-chip microcomputer connects buzzer, diagnostic module, training module and wireless transmitter module respectively by circuit line；Wireless transmitter module connects cloud computing module by wireless signal；Single-chip microcomputer connects cloud computing module by wireless transmitter module.The present invention sets the Video tutorials module in training module to carry out Video tutorials study, and evaluation module can be examined to diagnostic skill, and operation module is trained to diagnoses and treatment operation, lifting treatment technical ability, saves learning cost；If diagnosis is in a bad way simultaneously, other staff can quickly be notified by the buzzer of setting, quickly be treated, lift therapeutic efficiency.

Description

A kind of neurosurgery diagnostic control system based on cloud computing
Technical field
The invention belongs to field of medical technology, more particularly to it is a kind of based on the neurosurgery of cloud computing diagnosis control system System.
Background technology
Neurosurgery is cured mainly due to the disease of the nervous system such as brain, spinal cord caused by wound, such as cerebral hemorrhage bleeding Threat to life is measured, traffic accident causes brain wound, or brain to have oncothlipsis to need operative treatment etc..Neurosurgery be with perform the operation be main Means, a Clinical Surgery for curing central nervous system (brain, spinal cord), peripheral nervous system and automatic nervous system disease are special Section.Using Surgical method research the nervous system disease surgical intervention concept, have benefited from early stage human anatomy, physiology, The achievement of the preclinical medicines such as pathological anatomy, Pathological Physiology and experimental surgery, particularly volume infarct cerebral theory, clinical god Through systems inspection, asepsis and the foundation for anaesthetizing art, the surgical intervention to the nervous system disease has hope and scientific basis.So And existing neural diagnostic systems, if training study needs another purchase equipment, spend cost high；Simultaneously during diagnosis if Other staff can not be notified in time by being in a bad way, and easily caused treatment delay and delayed the state of an illness.
In summary, the problem of prior art is present be：Existing neural diagnostic systems, if training study needs another purchase to set It is standby, spend cost high；Easily cause and control if other staff can not be notified in time by being in a bad way during diagnosis simultaneously Treat delay and delay the state of an illness.
The content of the invention
The problem of existing for prior art, the invention provides a kind of neurosurgery diagnosis control based on cloud computing System.
The present invention is achieved in that a kind of neurosurgery diagnostic control system based on cloud computing, described to be based on cloud The neurosurgery of calculating is included with diagnostic control system：
The power module connects single-chip microcomputer by circuit line；The single-chip microcomputer connects buzzer respectively by circuit line, Diagnostic module, training module and wireless transmitter module；The wireless transmitter module connects cloud computing module by wireless signal；Institute State single-chip microcomputer and cloud computing module is connected by wireless transmitter module；
The computational methods of the power of the authorization data transmission of the wireless transmitter module include：
Step 1, the collection of relaying to be selected is made to be combined into R={ SR1,ST2, select via node r ∈ R；
Step 2, calculate the signal to noise ratio for the link that PT and each via node r is formed And obtain
Step 3, calculate the set R={ SR to be selected of via node1,ST2With PR form link signal to noise ratioWherein
Step 4, compareWithSize；
Step 5, ifSelection can obtain single relaying r of the end-to-end spectrum efficiency of maximumopt； In the first stage, authorized user's transmitting terminal PT is with powerBroadcast the message sp, cognitive user ST1With powerTo SR1Send data s1；If the via node of selection is SR1, SR1S is recovered respectivelypWith s1；If in selection After node be ST2, ST2To spReceive, SR1To s1Receive；In second stage, roptWith powerAuthorized user's number is forwarded to PR According to sp, ST2With powerTo SR2Send data s2, roptAssist the transmission power of main user data transmissionMeter Calculation is as follows,
Step 6, ifTwo relaying SR of selection1And ST2；In the first stage, authorized user launches PT is held with transmit powerTo cognitive user broadcast message sp, cognitive user ST1With powerTo SR1Hair Send data s1, ST2Recover spAnd eliminate and come from ST1Interference, SR1S is recovered respectivelypWith s1, SR2To spReceive；In second-order Section, SR1With ST2Respectively with powerWithTo PR forwarding authorized user's data sp, ST2With powerTo SR2Send data s2, SR2Need to eliminate and come from SR1With ST2Interference, ST2Sending method is designed, it is sent s2PR is not produced Raw interference；SR1And ST2Assist the general power of main user data transmissionIt is calculated as follows：
Each relay and be for the power of authorization data transmission
The diagnostic module, is connected with single-chip microcomputer, for being diagnosed to patients' neural；
The cloud computing module, and wireless transmitter module wireless connection, for being connect single-chip microcomputer by wireless transmitter module Enter on a large amount of distributed computers, high-strength computing capability, safeguard protection service, quick network service clothes are provided to single-chip microcomputer Business；
The training module is connected by circuit line and connects Video tutorials module, evaluation module and operation module respectively；
The training module is by reception signal data R (x), according to formula r (x)=sign (Re (R (x)))+j*sign (Im (R (x))) the result r (x) mapped out to reception signal reality imaginary part by sign bit is obtained, then by local training sequence data C (k), profit Obtained with formula c (k)=sign (Re (C (k)))+j*sign (Im (C (k))) to training sequence data reality imaginary part by symbol bit mapping The result c (k) gone out, formula is utilized according to obtained r (x) and c (k) Timing slip estimation function is generated, N=2* (NFFT+CP) represents the length of associated window and local sequence in formula, and x, which is represented, slides phase Close the original position of window；
The acquisition methods of timing slip estimation function：
Training sequence is mapped by sign bit, and using result as local sequence, the data received flow into slip successively In window, the data in sliding window are subjected to conjugation related operation by sign bit and local sequence, obtained on sliding window start bit The timing slip estimation function value put；
The procedural representation of computing is：First according to the sign bit information of reception signal reality imaginary data, formula r (x) is utilized =sign (Re (R (x)))+j*sign (Im (R (x))), map receiving data, wherein R (x) represents reception signal, Re () represents to take the value of real part of complex data, and Im () represents to take the imaginary values of complex data, and sign () represents to take data Sign bit, it is that -1, r (x) is to take symbol to reception signal reality imaginary part less than 0 output result if it is 1 that data, which are more than 0 output result, The result mapped out after number, there are four kinds of numerical value ± 1 ± j, then utilize formula c (k)=sign (Re (C (k)))+j*sign (Im (C (k))) training sequence is mapped, wherein C (k) represents local training sequence, and c (k) is to local sequence reality imaginary data The complex result gone out by sign bit information MAP, there are four kinds of numerical value ± 1 ± j；Finally according to formulaTiming slip estimation function is asked for, wherein, F (x) Timing slip estimation function value is represented, N=2* (NFFT+CP) represents the length of associated window and local sequence；
The Video tutorials module, is connected with training module, for providing instructional video, helps staff to carry out video Teaching；
The image watermark method of the Video tutorials module specifically includes following steps：
Using 64 × 64 black and white picture as picture to be embedded, using 256 × 256 black and white picture as carrier picture, If f (m, n) and g (m, n) are respectively the gray value of corresponding diagram picture point, m and n are the positive integer less than 256；
Step 1, fractional order chaotic signal is generated, select parameter μ and ν, mapped using following fractional order Logistic：
Produce a number of components rank chaotic signal x (1) ..., x (n), x (0) are taken as key；
Step 2, watermark is made, utilizes chaotic signal x (1) ..., x (n) and information f (m, n) chaos of original graph picture point Scramble obtains the gray value c (m, n) of new images, and new images are the encrypted image of watermark picture；
Step 3, embedded watermark, according to the information g of the gray value c (m, n) of the point of encrypted image and carrier image point (m, N), watermark is embedded in using weighted average calculation mode, encrypted watermark picture gray value c (m, n) is added to carrier image gray value g On (m, n), numerical result isUsing as the gray value of final embedded watermarking images, to calculate：
The result after watermark is embedded in using d (m, n) as carrier image；
Step 4, watermark is extracted, using the calculation of step 3, calculate the gray value c (m, n) of encrypted watermark；
Step 5, watermarking images are decrypted, according to the gray value c (m, n) of encrypted image, and the key chain obtained by using x (0) x*(1),...,x*(n) XOR calculating is carried out, decrypted image can be obtained；
The evaluation module, is connected with training module, for providing on-line examination, and scoring, lifts staff Understanding to diagnoses and treatment correlation technique, and mistake in understanding is found in time, right a wrong；
The operation module, is connected with training module, for providing operating platform to staff.
Further, the link stability and energy hybrid model of the method for repairing route of the cloud computing module：
Internet of Things topological structure regards the network model G=(V, E) of a non-directed graph as, and wherein V represents a group node, E tables Show the side collection of one group of connecting node, P (u, v)={ P0,P1,P2,L,PnBe all possible paths between node u and node v collection Close, PiIt is node u and v possible path, selects egress u to node v optimal path,
The formula of link stability and residue energy of node is as follows：
Wherein, EisAnd Ei0For the dump energy and gross energy of node i, EthFor the energy threshold of node；
Link stability formula and residue energy of node formula change into the optimization formula of a totality, and the formula provides two Individual important parameter (w1And w2), shown in its expression formula such as formula (6)：
Wherein w1And w2The coefficient of setting between node energy and link stationary value, w1+w2=1；
The maximum of the target summation is taken, is represented with formula below (7)：
MRFact(Pi)=max { RFact (P1),RFact(P2),L RFact(Pn)} (7)
Node when receiving data packet information, according to formula (3) and formula (4) calculate respectively outgoing link stationary value and The dump energy of node, optimal path then is chosen using formula (7), to complete the selected of route.
Advantages of the present invention and good effect are：The system sets the Video tutorials module in training module to be regarded Frequency study course learns, and evaluation module can be examined to diagnostic skill, and operation module is trained to diagnoses and treatment operation, is lifted Technical ability is treated, saves learning cost；If diagnosis is in a bad way simultaneously, can quickly be notified by the buzzer of setting Other staff, are quickly treated, and lift therapeutic efficiency.The image watermark method of the present invention, it is discrete mixed using fractional order Ignorant signal enters line shuffle to watermarking images information, then the watermarking images after scramble are embedded into carrier image to reach picture property right The purpose of protection, compared with the cipher mode using common chaotic maps, it is safer.
Brief description of the drawings
Fig. 1 is provided in an embodiment of the present invention based on the neurosurgery of cloud computing diagnostic control system structural representation.
In figure：1st, power module；2nd, single-chip microcomputer；3rd, buzzer；4th, diagnostic module；5th, training module；6th, circuit line；7th, regard Frequency study course module；8th, evaluation module；9th, operation module；10th, wireless transmitter module；11st, cloud computing module.
Embodiment
In order to further understand the content, features and effects of the present invention, hereby enumerating following examples, and coordinate accompanying drawing Describe in detail as follows.
The structure of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in figure 1, be somebody's turn to do the neurosurgery based on cloud computing is included with diagnostic control system：Power module 1, single-chip microcomputer 2, Buzzer 3, diagnostic module 4, training module 5；Circuit line 6；Wireless transmitter module 10 and cloud computing module 11；Power module 1 is logical Oversampling circuit line 6 connects single-chip microcomputer 2；Single-chip microcomputer 2 connects buzzer 3, diagnostic module 4 and training module 5 respectively by circuit line 6； Training module 5 connects Video tutorials module 7, evaluation module 8 and operation module 9 respectively by circuit line 6；Wireless transmitter module 10 Cloud computing module 11 is connected by wireless signal；Single-chip microcomputer 2 connects cloud computing module 11 by wireless transmitter module 10.
Diagnostic module 4, it is connected with single-chip microcomputer 2, for being diagnosed to patients' neural.
Video tutorials module 7, it is connected with training module 5, for providing instructional video, helps staff to carry out video religion Learn.
Evaluation module 8, it is connected with training module 5, for providing on-line examination, and scoring, lifts staff couple The understanding of diagnoses and treatment correlation technique, and mistake in understanding is found in time, right a wrong.
Operation module 9, it is connected with training module 5, for providing operating platform to staff.
Cloud computing module 11, and the wireless connection of wireless transmitter module 10, for by wireless transmitter module 10 by single-chip microcomputer 2 Access on a large amount of distributed computers, high-strength computing capability, safeguard protection service, quick network service are provided to single-chip microcomputer 2 Service.
The computational methods of the power of the authorization data transmission of wireless transmitter module include：
Step 1, the collection of relaying to be selected is made to be combined into R={ SR1,ST2, select via node r ∈ R；
Step 2, calculate the signal to noise ratio for the link that PT and each via node r is formed And obtain
Step 3, calculate the set R={ SR to be selected of via node1,ST2With PR form link signal to noise ratioWherein
Step 4, compareWithSize；
Step 5, ifSelection can obtain single relaying r of the end-to-end spectrum efficiency of maximumopt； In the first stage, authorized user's transmitting terminal PT is with powerBroadcast the message sp, cognitive user ST1With powerTo SR1Send data s1；If the via node of selection is SR1, SR1S is recovered respectivelypWith s1；If in selection After node be ST2, ST2To spReceive, SR1To s1Receive；In second stage, roptWith powerAuthorized user's number is forwarded to PR According to sp, ST2With powerTo SR2Send data s2, roptAssist the transmission power of main user data transmissionMeter Calculation is as follows,
Step 6, ifTwo relaying SR of selection1And ST2；In the first stage, authorized user launches PT is held with transmit powerTo cognitive user broadcast message sp, cognitive user ST1With powerTo SR1Hair Send data s1, ST2Recover spAnd eliminate and come from ST1Interference, SR1S is recovered respectivelypWith s1, SR2To spReceive；In second-order Section, SR1With ST2Respectively with powerWithTo PR forwarding authorized user's data sp, ST2With powerTo SR2Send data s2, SR2Need to eliminate and come from SR1With ST2Interference, ST2Sending method is designed, it is sent s2PR is not produced Raw interference；SR1And ST2Assist the general power of main user data transmissionIt is calculated as follows：
Each relay and be for the power of authorization data transmission
Training module is connected by circuit line and connects Video tutorials module, evaluation module and operation module respectively；
The training module is by reception signal data R (x), according to formula r (x)=sign (Re (R (x)))+j*sign (Im (R (x) the result r (x) mapped out to reception signal reality imaginary part by sign bit)) is obtained, then by local training sequence data C (k), is utilized Formula c (k)=sign (Re (C (k)))+j*sign (Im (C (k))) obtains mapping out training sequence data reality imaginary part by sign bit Result c (k), formula is utilized according to obtained r (x) and c (k) Timing slip estimation function is generated, N=2* (NFFT+CP) represents the length of associated window and local sequence in formula, and x, which is represented, slides phase Close the original position of window；
The acquisition methods of timing slip estimation function：
Training sequence is mapped by sign bit, and using result as local sequence, the data received flow into slip successively In window, the data in sliding window are subjected to conjugation related operation by sign bit and local sequence, obtained on sliding window start bit The timing slip estimation function value put；
The procedural representation of computing is：First according to the sign bit information of reception signal reality imaginary data, formula r (x) is utilized =sign (Re (R (x)))+j*sign (Im (R (x))), map receiving data, wherein R (x) represents reception signal, Re () represents to take the value of real part of complex data, and Im () represents to take the imaginary values of complex data, and sign () represents to take data Sign bit, it is that -1, r (x) is to take symbol to reception signal reality imaginary part less than 0 output result if it is 1 that data, which are more than 0 output result, The result mapped out after number, there are four kinds of numerical value ± 1 ± j, then utilize formula c (k)=sign (Re (C (k)))+j*sign (Im (C (k))) training sequence is mapped, wherein C (k) represents local training sequence, and c (k) is to local sequence reality imaginary data The complex result gone out by sign bit information MAP, there are four kinds of numerical value ± 1 ± j；Finally according to formulaTiming slip estimation function is asked for, wherein, F (x) Timing slip estimation function value is represented, N=2* (NFFT+CP) represents the length of associated window and local sequence.
The image watermark method of the Video tutorials module specifically includes following steps：
Using 64 × 64 black and white picture as picture to be embedded, using 256 × 256 black and white picture as carrier picture, If f (m, n) and g (m, n) are respectively the gray value of corresponding diagram picture point, m and n are the positive integer less than 256；
Step 1, fractional order chaotic signal is generated, select parameter μ and ν, mapped using following fractional order Logistic：
Produce a number of components rank chaotic signal x (1) ..., x (n), x (0) are taken as key；
Step 2, watermark is made, utilizes chaotic signal x (1) ..., x (n) and information f (m, n) chaos of original graph picture point Scramble obtains the gray value c (m, n) of new images, and new images are the encrypted image of watermark picture；
Step 3, embedded watermark, according to the information g of the gray value c (m, n) of the point of encrypted image and carrier image point (m, N), watermark is embedded in using weighted average calculation mode, encrypted watermark picture gray value c (m, n) is added to carrier image gray value g On (m, n), numerical result isUsing as the gray value of final embedded watermarking images, to calculate：
The result after watermark is embedded in using d (m, n) as carrier image；
Step 4, watermark is extracted, using the calculation of step 3, calculate the gray value c (m, n) of encrypted watermark；
Step 5, watermarking images are decrypted, according to the gray value c (m, n) of encrypted image, and the key chain obtained by using x (0) x*(1),...,x*(n) XOR calculating is carried out, decrypted image can be obtained.
The link stability and energy hybrid model of the method for repairing route of cloud computing module：
Internet of Things topological structure regards the network model G=(V, E) of a non-directed graph as, and wherein V represents a group node, E tables Show the side collection of one group of connecting node, P (u, v)={ P0,P1,P2,L,PnBe all possible paths between node u and node v collection Close, PiBe node u andvPossible path, select egress u to node v optimal path,
The formula of link stability and residue energy of node is as follows：
Wherein, EisAnd Ei0For the dump energy and gross energy of node i, EthFor the energy threshold of node；
Link stability formula and residue energy of node formula change into the optimization formula of a totality, and the formula provides two Individual important parameter (w1And w2), shown in its expression formula such as formula (6)：
Wherein w1And w2The coefficient of setting between node energy and link stationary value, w1+w2=1；
The maximum of the target summation is taken, is represented with formula below (7)：
MRFact(Pi)=max { RFact (P1),RFact(P2),L RFact(Pn)} (7)
Node when receiving data packet information, according to formula (3) and formula (4) calculate respectively outgoing link stationary value and The dump energy of node, optimal path then is chosen using formula (7), to complete the selected of route.
The operation principle of the present invention：
Startup power supply module 1, is powered, and single-chip microcomputer 2 is manipulated to diagnostic module 4, training module 5 and buzzer 3； Staff is diagnosed by diagnostic module 4 to patient, if be in a bad way during diagnosis, single-chip microcomputer 2 can open in time Dynamic buzzer 3 is alarmed, and notifies other staff；Between at one's leisure, staff can pass through regarding in training module 5 Frequency study course module 7 can carry out Video tutorials study, and evaluation module 8 can be examined to diagnostic skill, and operation module 9 is to examining Disconnected treatment operation is trained, and lifting treatment technical ability, saves learning cost.
It is described above to be only the preferred embodiments of the present invention, any formal limitation not is made to the present invention, Every technical spirit according to the present invention belongs to any simple modification made for any of the above embodiments, equivalent variations and modification In the range of technical solution of the present invention.

Claims (2)

  1. A kind of 1. neurosurgery diagnostic control system based on cloud computing, it is characterised in that the nerve based on cloud computing Surgery is included with diagnostic control system：
    The power module connects single-chip microcomputer by circuit line；The single-chip microcomputer connects buzzer respectively by circuit line, diagnosis Module, training module and wireless transmitter module；The wireless transmitter module connects cloud computing module by wireless signal；The list Piece machine connects cloud computing module by wireless transmitter module；
    The computational methods of the power of the authorization data transmission of the wireless transmitter module include：
    Step 1, the collection of relaying to be selected is made to be combined into R={ SR1,ST2, select via node r ∈ R；
    Step 2, calculate the signal to noise ratio for the link that PT and each via node r is formedAnd Obtain
    Step 3, calculate the set R={ SR to be selected of via node1,ST2With PR form link signal to noise ratioWherein
    Step 4, compareWithSize；
    Step 5, ifSelection can obtain single relaying r of the end-to-end spectrum efficiency of maximumopt； In one stage, authorized user's transmitting terminal PT is with powerBroadcast the message sp, cognitive user ST1With power To SR1Send data s1；If the via node of selection is SR1, SR1S is recovered respectivelypWith s1；If selection via node for ST2, ST2To spReceive, SR1To s1Receive；In second stage, roptWith powerTo PR forwarding authorized user's data sp, ST2 With powerTo SR2Send data s2, roptAssist the transmission power of main user data transmissionIt is calculated as follows,
    <mrow> <msubsup> <mi>P</mi> <mi>r</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msub> <mi>P</mi> <mi>T</mi> </msub> <mfrac> <mrow> <mo>|</mo> <mo>|</mo> <msup> <mrow> <mo>&amp;lsqb;</mo> <msubsup> <mi>f</mi> <mi>r</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> </msubsup> <mo>&amp;rsqb;</mo> </mrow> <mi>H</mi> </msup> <msub> <mi>g</mi> <mrow> <mi>P</mi> <mi>T</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> <mrow> <mo>|</mo> <mo>|</mo> <msub> <mi>g</mi> <mrow> <mi>r</mi> <mo>,</mo> <mi>R</mi> <mi>R</mi> </mrow> </msub> <msubsup> <mi>p</mi> <mi>r</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <mi>p</mi> </msub> <mo>)</mo> </mrow> </msubsup> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> </mrow> </mfrac> <mo>\</mo> <mo>*</mo> <mi>M</mi> <mi>E</mi> <mi>R</mi> <mi>G</mi> <mi>E</mi> <mi>F</mi> <mi>O</mi> <mi>R</mi> <mi>M</mi> <mi>A</mi> <mi>T</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
    Step 6, ifTwo relaying SR of selection1And ST2；In the first stage, authorized user's transmitting terminal PT With transmit powerTo cognitive user broadcast message sp, cognitive user ST1With powerTo SR1Send number According to s1, ST2Recover spAnd eliminate and come from ST1Interference, SR1S is recovered respectivelypWith s1, SR2To spReceive；In second stage, SR1 With ST2Respectively with powerWithTo PR forwarding authorized user's data sp, ST2With powerTo SR2Send Data s2, SR2Need to eliminate and come from SR1With ST2Interference, ST2Sending method is designed, it is sent s2Interference is not produced to PR； SR1And ST2Assist the general power of main user data transmissionIt is calculated as follows：
    Each relay and be for the power of authorization data transmission
    The diagnostic module, is connected with single-chip microcomputer, for being diagnosed to patients' neural；
    The cloud computing module is big for being accessed single-chip microcomputer by wireless transmitter module with wireless transmitter module wireless connection Measure on distributed computer, high-strength computing capability, safeguard protection service, quick network communication services are provided to single-chip microcomputer；
    The training module is connected by circuit line and connects Video tutorials module, evaluation module and operation module respectively；
    The training module is by reception signal data R (x), according to formula r (x)=sign (Re (R (x)))+j*sign (Im (R (x) the result r (x) mapped out to reception signal reality imaginary part by sign bit)) is obtained, then by local training sequence data C (k), profit Obtain reflecting training sequence data reality imaginary part by sign bit with formula c (k)=sign (Re (C (k)))+j*sign (Im (C (k))) The result c (k) of injection, formula is utilized according to obtained r (x) and c (k) Timing slip estimation function is generated, N=2* (NFFT+CP) represents the length of associated window and local sequence in formula, and x, which is represented, slides phase Close the original position of window；
    The acquisition methods of timing slip estimation function：
    Training sequence is mapped by sign bit, and using result as local sequence, the data received are flowed into sliding window successively, Data in sliding window are subjected to conjugation related operation by sign bit and local sequence, obtain determining on sliding window original position Hour offset estimation function value；
    The procedural representation of computing is：First according to the sign bit information of reception signal reality imaginary data, using formula r (x)= Sign (Re (R (x)))+j*sign (Im (R (x))), map receiving data, wherein R (x) represents reception signal, Re () Expression takes the value of real part of complex data, and Im () represents to take the imaginary values of complex data, and sign () represents to take the symbol of a data Number position, it is that -1, r (x) is to take symbol to reception signal reality imaginary part less than 0 output result if it is 1 that data, which are more than 0 output result, The result mapped out afterwards, there are four kinds of numerical value ± 1 ± j, then utilize formula c (k)=sign (Re (C (k)))+j*sign (Im (C (k))) training sequence is mapped, wherein C (k) represents local training sequence, c (k) be to local sequence reality imaginary data by The complex result that sign bit information MAP goes out, there are four kinds of numerical value ± 1 ± j；Finally according to formulaTiming slip estimation function is asked for, wherein, F (x) Timing slip estimation function value is represented, N=2* (NFFT+CP) represents the length of associated window and local sequence；
    The Video tutorials module, is connected with training module, for providing instructional video, helps staff to carry out video religion Learn；
    The image watermark method of the Video tutorials module specifically includes following steps：
    Using 64 × 64 black and white picture as picture to be embedded, using 256 × 256 black and white picture as carrier picture, if f (m, n) and g (m, n) are respectively the gray value of corresponding diagram picture point, and m and n are the positive integer less than 256；
    Step 1, fractional order chaotic signal is generated, select parameter μ and ν, mapped using following fractional order Logistic：
    <mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>x</mi> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>+</mo> <mfrac> <mi>&amp;mu;</mi> <mrow> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <mi>v</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mfrac> <mrow> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mi>j</mi> <mo>+</mo> <mi>v</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>&amp;Gamma;</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>-</mo> <mi>j</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mi>x</mi> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>x</mi> <mo>(</mo> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> <mo>)</mo> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    Produce a number of components rank chaotic signal x (1) ..., x (n), x (0) are taken as key；
    Step 2, watermark is made, utilizes chaotic signal x (1) ..., x (n) and information f (m, n) Chaotic Scrambling of original graph picture point The gray value c (m, n) of new images is obtained, new images are the encrypted image of watermark picture；
    Step 3, embedded watermark, according to the gray value c (m, n) of the point of encrypted image and the information g (m, n) of carrier image point, is adopted Watermark is embedded in weighted average calculation mode, encrypted watermark picture gray value c (m, n) is added to carrier image gray value g (m, n) On, numerical result isUsing as the gray value of final embedded watermarking images, to calculate：
    <mrow> <mi>d</mi> <mrow> <mo>(</mo> <mi>m</mi> <mo>,</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <mi>mod</mi> <mrow> <mo>(</mo> <mover> <mi>g</mi> <mo>~</mo> </mover> <mo>(</mo> <mrow> <mi>m</mi> <mo>,</mo> <mi>n</mi> </mrow> <mo>)</mo> <mo>,</mo> <mn>256</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    The result after watermark is embedded in using d (m, n) as carrier image；
    Step 4, watermark is extracted, using the calculation of step 3, calculate the gray value c (m, n) of encrypted watermark；
    Step 5, watermarking images are decrypted, according to the gray value c (m, n) of encrypted image, and the key chain x obtained by using x (0)* (1),...,x*(n) XOR calculating is carried out, decrypted image can be obtained；
    The evaluation module, is connected with training module, and for providing on-line examination, and scoring, staff is to examining for lifting The understanding of disconnected treatment correlation technique, and mistake in understanding is found in time, right a wrong；
    The operation module, is connected with training module, for providing operating platform to staff.
  2. 2. the neurosurgery diagnostic control system based on cloud computing as claimed in claim 1, it is characterised in that the cloud meter Calculate the link stability and energy hybrid model of the method for repairing route of module：
    Internet of Things topological structure regards the network model G=(V, E) of a non-directed graph as, and wherein V represents a group node, and E represents one The side collection of group connecting node, P (u, v)={ P0,P1,P2,L,PnBe all possible paths between node u and node v set, Pi It is node u and v possible path, selects egress u to node v optimal path,
    The formula of link stability and residue energy of node is as follows：
    <mrow> <msub> <mi>F</mi> <mn>1</mn> </msub> <mo>=</mo> <munder> <mo>&amp;Pi;</mo> <mrow> <mi>e</mi> <mo>&amp;Element;</mo> <msub> <mi>P</mi> <mi>i</mi> </msub> </mrow> </munder> <mi>L</mi> <mi>S</mi> <mrow> <mo>(</mo> <mi>e</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mi>F</mi> <mn>2</mn> </msub> <mo>=</mo> <munder> <mo>&amp;Pi;</mo> <mrow> <mi>e</mi> <mo>&amp;Element;</mo> <msub> <mi>P</mi> <mi>i</mi> </msub> </mrow> </munder> <mi>C</mi> <mi>e</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mi>C</mi> <mi>e</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msub> <mi>E</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> <msub> <mi>E</mi> <mrow> <mi>i</mi> <mn>0</mn> </mrow> </msub> </mfrac> <mo>,</mo> <msub> <mi>E</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> <mo>&gt;</mo> <msub> <mi>E</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
    Wherein, EisAnd Ei0For the dump energy and gross energy of node i, EthFor the energy threshold of node；
    Link stability formula and residue energy of node formula change into the optimization formula of a totality, and the formula provides two weights Want parameter (w1And w2), shown in its expression formula such as formula (6)：
    <mrow> <mtable> <mtr> <mtd> <mrow> <mi>R</mi> <mi>F</mi> <mi>a</mi> <mi>c</mi> <mi>t</mi> <mrow> <mo>(</mo> <msub> <mi>P</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <msub> <mi>F</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <msub> <mi>F</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <msub> <mi>w</mi> <mn>1</mn> </msub> <munder> <mo>&amp;Pi;</mo> <mrow> <mi>e</mi> <mo>&amp;Element;</mo> <msub> <mi>P</mi> <mi>i</mi> </msub> </mrow> </munder> <mi>L</mi> <mi>S</mi> <mrow> <mo>(</mo> <mi>e</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>w</mi> <mn>2</mn> </msub> <munder> <mo>&amp;Pi;</mo> <mrow> <mi>e</mi> <mo>&amp;Element;</mo> <msub> <mi>P</mi> <mi>i</mi> </msub> </mrow> </munder> <mi>C</mi> <mi>e</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
    Wherein w1And w2The coefficient of setting between node energy and link stationary value, w1+w2=1；
    The maximum of the target summation is taken, is represented with formula below (7)：
    MRFact(Pi)=max { RFact (P1),RFact(P2),L RFact(Pn)} (7)
    Node calculates the stationary value and node of outgoing link according to formula (3) and formula (4) respectively when receiving data packet information Dump energy, then optimal path is chosen using formula (7), to complete the selected of route.
CN201710797257.8A 2017-09-06 2017-09-06 A kind of neurosurgery diagnostic control system based on cloud computing Pending CN107426682A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710797257.8A CN107426682A (en) 2017-09-06 2017-09-06 A kind of neurosurgery diagnostic control system based on cloud computing

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710797257.8A CN107426682A (en) 2017-09-06 2017-09-06 A kind of neurosurgery diagnostic control system based on cloud computing

Publications (1)

Publication Number Publication Date
CN107426682A true CN107426682A (en) 2017-12-01

Family

ID=60432751

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710797257.8A Pending CN107426682A (en) 2017-09-06 2017-09-06 A kind of neurosurgery diagnostic control system based on cloud computing

Country Status (1)

Country Link
CN (1) CN107426682A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109537791A (en) * 2018-11-01 2019-03-29 湖南城市学院 A kind of dismountable modularization indoor decoration Combined type suspended ceiling structure

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103986648A (en) * 2014-05-06 2014-08-13 安徽理工大学 Internet-of-Things route repairing method based on link stability and energy sensing
CN104008519A (en) * 2014-03-09 2014-08-27 吴国成 Image watermarking method based on fractional order chaotic mapping and weighted average
CN104125190A (en) * 2014-08-18 2014-10-29 西安电子科技大学 OFDM (orthogonal frequency division multiplexing) system symbol timing synchronization realizing method suitable for low-signal-to-noise-ratio channel environments
CN104202790A (en) * 2014-09-01 2014-12-10 西安电子科技大学 Power self-adaptation based MIMO-CCRN bottleneck effect elimination method
CN105788390A (en) * 2016-04-29 2016-07-20 吉林医药学院 Medical anatomy auxiliary teaching system based on augmented reality
CN106296511A (en) * 2016-08-19 2017-01-04 上海梅斯医药科技有限公司 A kind of virtual diagnosis and therapy system
CN106530865A (en) * 2017-01-17 2017-03-22 昆明医科大学 Remote medical education system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104008519A (en) * 2014-03-09 2014-08-27 吴国成 Image watermarking method based on fractional order chaotic mapping and weighted average
CN103986648A (en) * 2014-05-06 2014-08-13 安徽理工大学 Internet-of-Things route repairing method based on link stability and energy sensing
CN104125190A (en) * 2014-08-18 2014-10-29 西安电子科技大学 OFDM (orthogonal frequency division multiplexing) system symbol timing synchronization realizing method suitable for low-signal-to-noise-ratio channel environments
CN104202790A (en) * 2014-09-01 2014-12-10 西安电子科技大学 Power self-adaptation based MIMO-CCRN bottleneck effect elimination method
CN105788390A (en) * 2016-04-29 2016-07-20 吉林医药学院 Medical anatomy auxiliary teaching system based on augmented reality
CN106296511A (en) * 2016-08-19 2017-01-04 上海梅斯医药科技有限公司 A kind of virtual diagnosis and therapy system
CN106530865A (en) * 2017-01-17 2017-03-22 昆明医科大学 Remote medical education system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109537791A (en) * 2018-11-01 2019-03-29 湖南城市学院 A kind of dismountable modularization indoor decoration Combined type suspended ceiling structure

Similar Documents

Publication Publication Date Title
Jiang et al. Toward practical privacy-preserving processing over encrypted data in IoT: an assistive healthcare use case
CN106296511A (en) A kind of virtual diagnosis and therapy system
Novak et al. Future directions for meningitis surveillance and vaccine evaluation in the meningitis belt of sub-Saharan Africa
Avgousti et al. Cardiac ultrasonography over 4G wireless networks using a tele‐operated robot
Mollenkopf et al. Is it time to include point‐of‐care ultrasound in general surgery training? A review to stimulate discussion
Dumphy et al. Family nurse practitioner students’ perceptions of readiness and transition into advanced practice
CN105099850A (en) Intelligent internet of vehicle and things monitoring system based on frequency spectrum sensing and monitoring method thereof
CN113160944A (en) Medical image sharing method based on block chain
CN107426682A (en) A kind of neurosurgery diagnostic control system based on cloud computing
CN111403029A (en) Information processing method and device for improving evaluation quality
CN109948348A (en) Medical block chain technology-based identity authentication system and use method thereof
Widmer et al. Tele-echocardiography in paediatrics
WO2018115492A1 (en) Medical viewing certificates for mobile devices
Bhattarai et al. An integrated secure efficient computing architecture for embedded and remote ECG diagnosis
Giakoumaki et al. Using digital watermarking to enhance security in wireless medical image transmission
Galeotti et al. Merit of test: Perspective of information economics
CN115526226A (en) Time sequence feature-based federal distillation small sample fault diagnosis method
JP2023511394A (en) Method and server for providing intestinal microbiota analysis results
Uemura et al. Novel, high‐definition 3‐D endoscopy system with real‐time compression communication system to aid diagnoses and treatment between hospitals in T hailand
Gonzalez-Baixauli et al. Eliciting non-functional requirements interactions using the personal construct theory
Istrate et al. Training and Social Awareness for Increasing Organ Donation in the European Union and Neighbouring Countries: EUDONORGAN
Wang et al. Design and application of a novel telemedicine system jointly driven by multinetwork integration and remote control: Practical experience from PLAGH, China
Huijbregts Evidence-based diagnosis and treatment of the painful sacroiliac joint
CN103718228B (en) Messaging device and information processing method
CN116936078B (en) Traditional Chinese medicine pre-inquiry collection management system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20171201