{"id":7283,"date":"2024-08-06T19:18:10","date_gmt":"2024-08-06T19:18:10","guid":{"rendered":"https:\/\/longchangextracts.com\/?p=7283"},"modified":"2024-08-06T19:18:10","modified_gmt":"2024-08-06T19:18:10","slug":"big-data-and-pattern-recognition-technology","status":"publish","type":"post","link":"https:\/\/longchangextracts.com\/hu\/big-data-and-pattern-recognition-technology\/","title":{"rendered":"A hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerek min\u0151s\u00e9g\u00e9nek gyors \u00e9rt\u00e9kel\u00e9s\u00e9re ir\u00e1nyul\u00f3 kutat\u00e1s a Big Data \u00e9s a mintafelismer\u00e9si technol\u00f3gia alapj\u00e1n"},"content":{"rendered":"

A hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerek min\u0151s\u00e9g\u00e9nek gyors \u00e9rt\u00e9kel\u00e9s\u00e9re ir\u00e1nyul\u00f3 kutat\u00e1s a Big Data \u00e9s a mintafelismer\u00e9si technol\u00f3gia alapj\u00e1n
\nA hagyom\u00e1nyos k\u00ednai orvosl\u00e1s a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s eg\u00e9szs\u00e9g\u00fcgyi ipar\u00e1nak alappill\u00e9re, \u00e9s a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s min\u0151s\u00e9ge \u00f6sszef\u00fcgg az eg\u00e9szs\u00e9g\u00fcgyi ipar fejl\u0151d\u00e9s\u00e9vel. Jelenleg m\u00e9g mindig vannak probl\u00e9m\u00e1k a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s min\u0151s\u00e9g\u00e9vel, mint p\u00e9ld\u00e1ul a sz\u00e9les v\u00e1laszt\u00e9k, a nagy k\u00fcl\u00f6nbs\u00e9gek, a hossz\u00fa nyomon k\u00f6vet\u00e9si folyamat, a neh\u00e9zkes vizsg\u00e1lati mutat\u00f3k \u00e9s a tudom\u00e1nyos \u00e9rt\u00e9kel\u00e9si m\u00f3dszerek hi\u00e1nya. Ezenk\u00edv\u00fcl a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s min\u0151s\u00e9gellen\u0151rz\u00e9s\u00e9nek folyamat\u00e1ban keletkezett \u00e9s felhalmozott hatalmas mennyis\u00e9g\u0171 nyers adat nem tal\u00e1lt semmilyen mint\u00e1t az adatokb\u00f3l, \u00e9s a hat\u00e9kony felhaszn\u00e1l\u00e1si ar\u00e1ny alacsony, ami a gazdag adatok \u00e9s a szeg\u00e9nyes tud\u00e1s probl\u00e9m\u00e1it eredm\u00e9nyezi. Az \u00c1llamtan\u00e1cs \u00e1ltal 2015-ben kiadott, a nagy adatok fejleszt\u00e9s\u00e9nek el\u0151mozd\u00edt\u00e1s\u00e1ra ir\u00e1nyul\u00f3 cselekv\u00e9si terv el\u0151mozd\u00edt\u00e1sa keret\u00e9ben a modern inform\u00e1ci\u00f3s technol\u00f3gi\u00e1k, p\u00e9ld\u00e1ul a nagy adatintegr\u00e1ci\u00f3, a felh\u0151alap\u00fa sz\u00e1m\u00edt\u00e1stechnika \u00e9s a blokkl\u00e1nc alkalmaz\u00e1sa a hagyom\u00e1nyos k\u00ednai gy\u00f3gyszeripar tudom\u00e1nyos fel\u00fcgyelet\u00e9ben elker\u00fclhetetlen fejl\u0151d\u00e9si trendd\u00e9 v\u00e1lt a j\u00f6v\u0151ben. A hagyom\u00e1nyos k\u00ednai gy\u00f3gyszeripar nagy adataiban rejl\u0151 rejtett mint\u00e1kat \u00e9s szab\u00e1lyokat gyakran nem lehet tapasztalati \u00faton vagy intu\u00edci\u00f3val felfedezni. A nagy adatelemz\u00e9s gondolkod\u00e1sm\u00f3dj\u00e1ra \u00e9s m\u00f3dszereire kell t\u00e1maszkodni, hogy a gyenge min\u0151s\u00e9g\u0171 \u00e9s t\u00f6red\u00e9kes adatokat, p\u00e9ld\u00e1ul a n\u00f6veked\u00e9si k\u00f6rnyezetet, az \u00fcltet\u00e9st \u00e9s a feldolgoz\u00e1st, a feldolgoz\u00e1st, a k\u00e9miai \u00f6sszet\u00e9tel alakul\u00e1s\u00e1t \u00e9s a gy\u00f3gy\u00e1szati anyagok hagyom\u00e1nyos min\u0151s\u00e9gellen\u0151rz\u00e9s\u00e9t kiv\u00e1l\u00f3 min\u0151s\u00e9g\u0171 \u00e9s nagy \u00e9rt\u00e9k\u0171 s\u0171r\u0171s\u00e9g\u0171 inform\u00e1ci\u00f3v\u00e1 alak\u00edtsuk \u00e1t. Ezut\u00e1n az adatb\u00e1ny\u00e1szat, a g\u00e9pi tanul\u00e1s, a mesters\u00e9ges intelligencia \u00e9s m\u00e1s sz\u00e1m\u00edt\u00e1stechnikai m\u00f3dszerek seg\u00edts\u00e9g\u00e9vel, a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s szakmai ismereteivel \u00e9s meg\u00edt\u00e9l\u00e9s\u00e9vel kombin\u00e1lva, az alkalmaz\u00e1ssal kapcsolatos matematikai modellek hozhat\u00f3k l\u00e9tre, hogy \u00e1tl\u00e1that\u00f3v\u00e1 tegy\u00e9k az entit\u00e1si kapcsolatokat, \u00e9s fontos alapot ny\u00fajtsanak a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s min\u0151s\u00e9g\u00e9nek tudom\u00e1nyos fel\u00fcgyelet\u00e9hez.<\/p>\n

A kemometria gyors fejl\u0151d\u00e9s\u00e9vel \u00e9s alkalmaz\u00e1s\u00e1val az \u00e9lelmiszer- \u00e9s gy\u00f3gyszerbiztons\u00e1gi vizsg\u00e1latokhoz sz\u00fcks\u00e9ges eszk\u00f6z\u00f6k \u00e9s technol\u00f3gi\u00e1k gyorsan fejl\u0151dtek. A kimutat\u00e1si m\u00f3dszerek is fejl\u0151dtek a korai fizikai \u00e9s k\u00e9miai, mikroszk\u00f3pos, kromatogr\u00e1fi\u00e1s \u00e9s t\u00f6megspektrometri\u00e1s (MS) technol\u00f3gi\u00e1kt\u00f3l az \u00e9rint\u00e9smentes k\u00f6zeli infrav\u00f6r\u00f6s spektroszk\u00f3pia (NIRS), a Raman-spektroszk\u00f3pia (RS), a terahertzes (THz) \u00e9s a hiperspektr\u00e1lis k\u00e9palkot\u00f3 (HI) technol\u00f3gi\u00e1kig. Ezek k\u00f6z\u00fcl az ut\u00f3bbi \u00e9vekben fejlesztett\u00e9k ki a NIRS-t, amely a vizsg\u00e1lt anyag k\u00f6zeli infrav\u00f6r\u00f6s spektrum\u00e1ban a hidrog\u00e9ntartalm\u00fa X-H (X=C, N, O) csoportok rezg\u00e9s\u00e9nek \u00e9s forg\u00e1s\u00e1nak kombin\u00e1lt frekvencia- \u00e9s dupl\u00e1z\u00f3d\u00e1si inform\u00e1ci\u00f3it gy\u0171jti \u00f6ssze a min\u0151s\u00e9gi \u00e9s mennyis\u00e9gi elemz\u00e9shez. Sz\u00e9les k\u00f6rben haszn\u00e1lj\u00e1k a mez\u0151gazdas\u00e1gban, a k\u0151olajiparban, a vegyiparban, a doh\u00e1nyiparban \u00e9s az \u00e9lelmiszeriparban, \u00e9s fokozatosan alkalmazz\u00e1k a gy\u00f3gyszeriparban \u00e9s a gy\u00f3gyszer-szab\u00e1lyoz\u00e1sban, nagy lehet\u0151s\u00e9geket k\u00edn\u00e1lva a gy\u00f3gyszerek gyors min\u0151s\u00e9gi \u00e9s mennyis\u00e9gi kimutat\u00e1s\u00e1ra \u00e9s elemz\u00e9s\u00e9re.
\nA Bupleurum chinense DC, m\u00e1s n\u00e9ven Narrow leaved Bupleurum B., az Apiaceae csal\u00e1dba tartoz\u00f3 n\u00f6v\u00e9ny A scorzonerifolium Willd. sz\u00e1r\u00edtott gy\u00f6kere K\u00edn\u00e1ban gyakran haszn\u00e1lt \u00f6mlesztett gy\u00f3gyn\u00f6v\u00e9ny. El\u0151sz\u00f6r a \"Shennong Bencao Jing\" c\u00edm\u0171 k\u00f6nyvben jegyezt\u00e9k fel, \u00e9s a legmagasabb rend\u0171 gy\u00f3gyn\u00f6v\u00e9nyek k\u00f6z\u00e9 sorolj\u00e1k. L\u00e1zcsillap\u00edt\u00f3, m\u00e1jnyugtat\u00f3 \u00e9s depresszi\u00f3old\u00f3, valamint a yang qi-t el\u0151seg\u00edt\u0151 hat\u00e1s\u00fa. Az el\u0151bbit \u00e1ltal\u00e1ban \"Beichaihu\", m\u00edg az ut\u00f3bbit \"Nanchaihu\" n\u00e9ven ismerik. Az \u00e9szaki Chaihu-t f\u0151k\u00e9nt Hebei tartom\u00e1nyban, Heilongjiang tartom\u00e1nyban, Liaoning tartom\u00e1nyban stb. \u00e1ll\u00edtj\u00e1k el\u0151, m\u00edg a d\u00e9li Chaihu-t f\u0151k\u00e9nt Liaoning tartom\u00e1nyban, Jilin tartom\u00e1nyban, Bels\u0151-Mong\u00f3lia Auton\u00f3m Ter\u00fcleten stb. termesztik. A Bupleurum chinense eredeti n\u00f6v\u00e9nyfaj\u00e1nak \u00f6sszetetts\u00e9ge miatt mintegy 100 biol\u00f3giai faj tartozik ebbe a nemzets\u00e9gbe. K\u00edn\u00e1ban 36 biol\u00f3giai faj, 17 fajta \u00e9s 7 forma l\u00e9tezik. Ezen t\u00falmen\u0151en a gy\u00f3gy\u00e1szati anyagok min\u0151s\u00e9g\u00e9ben jelent\u0151s k\u00fcl\u00f6nbs\u00e9gek vannak a n\u00f6veked\u00e9si k\u00f6rnyezet v\u00e1ltoz\u00e1sai, a fajt\u00e1k elt\u00e9r\u00e9sei, a feldolgoz\u00e1s \u00e9s a nem gy\u00f3gy\u00e1szati r\u00e9szek hi\u00e1nyos elt\u00e1vol\u00edt\u00e1sa miatt. Jelenleg a K\u00ednai Gy\u00f3gyszerk\u00f6nyv 2015-\u00f6s \u00e9s 2020-as kiad\u00e1sai korl\u00e1tozz\u00e1k a Chaihu nedvess\u00e9g-, hamu-, savban oldhatatlan hamu, kivonat, saikosaponin a \u00e9s d tartalm\u00e1t, ami fontos szerepet j\u00e1tszik a Chaihu min\u0151s\u00e9gellen\u0151rz\u00e9s\u00e9ben. A Q-marker vizsg\u00e1latok azonban meg\u00e1llap\u00edtott\u00e1k, hogy a Bupleurum chinense-ben l\u00e9v\u0151 ill\u00f3olaj, zs\u00edros olaj, triterpenoid szaponinok \u00e9s flavonoidok a l\u00e1zcsillap\u00edt\u00f3, nyugtat\u00f3, f\u00e1jdalomcsillap\u00edt\u00f3, gyullad\u00e1scs\u00f6kkent\u0151 \u00e9s k\u00f6h\u00f6g\u00e9scsillap\u00edt\u00f3 tulajdons\u00e1gainak f\u0151 hat\u00f3anyagai. Jelenleg, b\u00e1r a Bupleurum chinense min\u0151s\u00e9g\u00e9rt\u00e9kel\u00e9s\u00e9ben t\u00f6bb mutat\u00f3 l\u00e9tezik, a vizsg\u00e1lati mutat\u00f3k egyetlenek \u00e9s a hat\u00e1r\u00e9rt\u00e9k t\u00fal alacsony, ami nem felel meg a min\u0151s\u00e9g\u00e9rt\u00e9kel\u00e9s \u00e9s az oszt\u00e1lyoz\u00e1s ig\u00e9nyeinek. Ezenk\u00edv\u00fcl a szab\u00e1lyoz\u00e1si folyamat sor\u00e1n nagy mennyis\u00e9g\u0171 t\u00f6red\u00e9kes nyers adat keletkezett \u00e9s halmoz\u00f3dott fel, amelyet nem haszn\u00e1ltak fel teljes m\u00e9rt\u00e9kben, \u00e9s hi\u00e1nyzik a szisztematikus integr\u00e1ci\u00f3. Ez\u00e9rt a nagy adatok h\u00e1tter\u00e9n alapulva ez a tanulm\u00e1ny a Bupleurum chinense-t veszi p\u00e9ld\u00e1nak, \u00f6sszegy\u0171jti a Bupleurum chinense gy\u00f3gy\u00e1szati anyagok 130 t\u00e9tel\u00e9nek k\u00fcl\u00f6nb\u00f6z\u0151 vizsg\u00e1lati adatait, amelyeket a k\u00f6zpontunk a K\u00ednai Gy\u00f3gyszerk\u00f6nyv 2015-\u00f6s kiad\u00e1s\u00e1nak felhaszn\u00e1l\u00e1s\u00e1val tesztelt, \u00e9s a NIRS elemz\u00e9si technol\u00f3gi\u00e1t, a k\u00e9miai szt\u00f6chiometri\u00e1t \u00e9s a matematikai modellez\u00e9si m\u00f3dszereket haszn\u00e1lja a Bupleurum chinense gy\u00f3gy\u00e1szati anyagok k\u00fcl\u00f6nb\u00f6z\u0151 vizsg\u00e1lati adatainak mintafelismer\u00e9si kutat\u00e1s\u00e1ra. Gyors nyomonk\u00f6vethet\u0151s\u00e9gi elemz\u00e9si modellt, tartalmi el\u0151rejelz\u00e9si modellt, \u00e1tfog\u00f3 \u00e9rt\u00e9kel\u00e9si \u00e9s oszt\u00e1lyoz\u00e1si adatb\u00e1zist hozunk l\u00e9tre a Bupleurum chinense gy\u00f3gy\u00e1szati anyagok sz\u00e1m\u00e1ra, szisztematikusan \u00e9rt\u00e9kelj\u00fck a Bupleurum chinense gy\u00f3gy\u00e1szati anyagok min\u0151s\u00e9g\u00e9t, \u00e9s \u00faj \u00f6tleteket ny\u00fajtunk a k\u00ednai gy\u00f3gy\u00e1szati anyagok min\u0151s\u00e9g\u00e9nek tudom\u00e1nyos fel\u00fcgyelet\u00e9hez.<\/p>\n

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\nA n\u00e9metorsz\u00e1gi \"Ipar 4.0\"-ra hivatkozva K\u00edna hagyom\u00e1nyos k\u00ednai gy\u00f3gyszeripara jelenleg az Ipar 2.0 szintj\u00e9n van, amely viszonylag lemaradt. A Sigma gap a term\u00e9k teljes\u00edtm\u00e9nye \u00e9s a fogyaszt\u00f3i elv\u00e1r\u00e1sok k\u00f6z\u00f6tti szakad\u00e9kra utal. A Six Sigma menedzsment m\u00f3dszerben (a 6 Sigma egy c\u00e9l) a k\u00ednai hagyom\u00e1nyos k\u00ednai gy\u00f3gyszeripar jelenleg a 2,0~3,0 Sigma szinten van. A fenti szakad\u00e9k oka, hogy a term\u00e9kek nehezen nyomon k\u00f6vethet\u0151k, t\u00fals\u00e1gosan f\u00fcggnek a v\u00e9gterm\u00e9k tesztel\u00e9s\u00e9t\u0151l, \u00e9s hi\u00e1nyzik a folyamatok meg\u00e9rt\u00e9se. 2018 \u00e9s 2020 k\u00f6z\u00f6tt a k\u00ednai hagyom\u00e1nyos gy\u00f3gyszerek sz\u00far\u00f3pr\u00f3baszer\u0171 ellen\u0151rz\u00e9s\u00e9nek hibaar\u00e1nya j\u00f3val magasabb, mint a hagyom\u00e1nyos k\u00ednai szabadalmi gy\u00f3gyszerek\u00e9, valamint az egyszer\u0171 k\u00e9sz\u00edtm\u00e9nyek\u00e9 \u00e9s a k\u00e9miai gy\u00f3gyszerek\u00e9. A hagyom\u00e1nyos k\u00ednai orvosl\u00e1s f\u0151zetdarabjainak min\u0151s\u00e9ge a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s klinikai biztons\u00e1goss\u00e1g\u00e1nak \u00e9s hat\u00e9konys\u00e1g\u00e1nak alapja, \u00e9s \u00f6sszef\u00fcgg a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s eg\u00e9szs\u00e9g\u00fcgyi ipar\u00e1nak fejl\u0151d\u00e9s\u00e9vel. K\u00fcl\u00f6n\u00f6sen fontos a hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerk\u00e9sz\u00edtm\u00e9nyek fel\u00fcgyelet\u00e9nek \u00faj m\u00f3dszereinek kutat\u00e1sa \u00e9s a hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerk\u00e9sz\u00edtm\u00e9nyek min\u0151s\u00e9g\u00e9nek tudom\u00e1nyos fel\u00fcgyelete. A hagyom\u00e1nyos k\u00ednai gy\u00f3gy\u00e1szat min\u0151s\u00e9g\u00e9t sz\u00e1mos t\u00e9nyez\u0151 befoly\u00e1solja, mint p\u00e9ld\u00e1ul a fajta, az \u00e9l\u0151hely, a betakar\u00edt\u00e1s, a feldolgoz\u00e1s, a t\u00e1rol\u00e1s stb., amelyek bizonyos fok\u00fa \u00f6sszetetts\u00e9ggel rendelkeznek, de vannak elker\u00fclhetetlen bels\u0151 \u00f6sszef\u00fcgg\u00e9sek is. A tudom\u00e1nyos fel\u00fcgyelet el\u00e9r\u00e9se \u00e9rdek\u00e9ben integr\u00e1lni kell az interdiszciplin\u00e1ris technol\u00f3gi\u00e1t, az informatik\u00e1t \u00e9s a nagy adattudom\u00e1nyt. Az elm\u00falt \u00e9vekben K\u00edna sokat dolgozott a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s min\u0151s\u00e9gellen\u0151rz\u00e9s\u00e9n, de m\u00e9g mindig nem tud megfelelni az egyre magasabb min\u0151s\u00e9gellen\u0151rz\u00e9si k\u00f6vetelm\u00e9nyeknek. Ezzel kapcsolatban egyes tanulm\u00e1nyok javasolt\u00e1k a hagyom\u00e1nyos k\u00ednai gy\u00f3gy\u00e1szat min\u0151s\u00e9gi markereinek (Q-markerek) koncepci\u00f3j\u00e1t, amelyek ir\u00e1nyt adnak a hagyom\u00e1nyos k\u00ednai gy\u00f3gy\u00e1szat min\u0151s\u00e9g\u00e9nek \u00e9rt\u00e9kel\u00e9s\u00e9hez. A hagyom\u00e1nyos k\u00ednai gy\u00f3gy\u00e1szat min\u0151s\u00e9g\u00e9nek \u00e1tfog\u00f3 \u00e9rt\u00e9kel\u00e9se azonban t\u00f6bb Q-markert foglal mag\u00e1ban, \u00e9s a k\u00fcl\u00f6nb\u00f6z\u0151 Q-markerek hozz\u00e1j\u00e1rul\u00e1sa a hagyom\u00e1nyos k\u00ednai gy\u00f3gy\u00e1szat min\u0151s\u00e9g\u00e9hez is elt\u00e9r\u0151, \u00edgy hi\u00e1nyzik egy tudom\u00e1nyos \u00e1tfog\u00f3 \u00e9rt\u00e9kel\u00e9si m\u00f3dszer. Ez a k\u00eds\u00e9rlet az Fq \u00e1tfog\u00f3 \u00e9rt\u00e9kel\u00e9si index koncepci\u00f3j\u00e1t javasolja a hagyom\u00e1nyos k\u00ednai gy\u00f3gy\u00e1szat sz\u00e1m\u00e1ra, a K\u00ednai Gy\u00f3gyszerk\u00f6nyv jelenlegi \u00e9rt\u00e9kel\u00e9si rendszer\u00e9vel kombin\u00e1lva. A nagy adatelemz\u00e9s eredm\u00e9nyei alapj\u00e1n az egyes \u00e9szlel\u00e9si indexek Rij-adatai dimenzi\u00f3tlanok, \u00e9s a vari\u00e1ci\u00f3s egy\u00fctthat\u00f3 m\u00f3dszer\u00e9t haszn\u00e1lj\u00e1k a hagyom\u00e1nyos k\u00ednai gy\u00f3gyszer min\u0151s\u00e9g\u00e9rt\u00e9kel\u00e9si index Wi s\u00falykoefficiens\u00e9nek kisz\u00e1m\u00edt\u00e1s\u00e1nak optimaliz\u00e1l\u00e1s\u00e1ra, elker\u00fclve a szak\u00e9rt\u0151i s\u00falyoz\u00e1s szubjekt\u00edv preferenci\u00e1j\u00e1t, \u00e9s objekt\u00edven t\u00fckr\u00f6zve az egyes indexek relat\u00edv fontoss\u00e1g\u00e1t a hagyom\u00e1nyos k\u00ednai gy\u00f3gyszer min\u0151s\u00e9g\u00e9rt\u00e9kel\u00e9s\u00e9ben. A hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerek min\u0151s\u00e9g\u00e9nek \u00e1tfog\u00f3 \u00e9rt\u00e9kel\u00e9s\u00e9re \u00e9s oszt\u00e1lyoz\u00e1s\u00e1ra alkalmas gyors \u00e9s tudom\u00e1nyos \u00e9rt\u00e9kel\u00e9si m\u00f3dszer j\u00f6tt l\u00e9tre, amely \u00faj megk\u00f6zel\u00edt\u00e9st biztos\u00edt a hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerek min\u0151s\u00e9g\u00e9nek tudom\u00e1nyos fel\u00fcgyelet\u00e9hez.
\nA tudom\u00e1nyos fel\u00fcgyelet a gy\u00f3gyszerbiztons\u00e1g, a hat\u00e9konys\u00e1g \u00e9s a min\u0151s\u00e9g ellen\u0151rizhet\u0151s\u00e9g\u00e9nek garanci\u00e1ja. A hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerek min\u0151s\u00e9g\u00e9nek tudom\u00e1nyos fel\u00fcgyelet\u00e9nek nemcsak a hagyom\u00e1nyos tapasztalatokat, a jelenlegi \u00e9rt\u00e9kel\u00e9si rendszereket \u00e9s a fejlett technikai m\u00f3dszereket kell kombin\u00e1lnia, hanem modern inform\u00e1ci\u00f3s technol\u00f3gi\u00e1t \u00e9s statisztikai m\u00f3dszereket is be kell vezetnie. A nagy mennyis\u00e9g\u0171 adat kinyer\u00e9s\u00e9vel \u00e9s elemz\u00e9s\u00e9vel \u00e1tfog\u00f3 \u00e9rt\u00e9kel\u00e9si m\u00f3dszert \u00e9s oszt\u00e1lyoz\u00e1si szabv\u00e1nyokat kell kidolgozni a hagyom\u00e1nyos k\u00ednai gy\u00f3gyszerek min\u0151s\u00e9g\u00e9re vonatkoz\u00f3an. A mintafelismer\u00e9si technol\u00f3gia az \"integrit\u00e1s\" \u00e9s a \"hom\u00e1lyoss\u00e1g\" jellemz\u0151ivel rendelkezik, \u00e9s az azonos\u00edt\u00e1s \u00e1tfog\u00f3 \u00e9s sz\u00e1mszer\u0171s\u00edthet\u0151 eszk\u00f6ze. Jelenleg \u00f6sszhangban van a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s jellemz\u0151ivel, \u00e9s j\u00f3l t\u00fckr\u00f6zi a hagyom\u00e1nyos k\u00ednai orvosl\u00e1s hiteless\u00e9g\u00e9t. Az FQ egy fejl\u0151d\u0151 rendszer, amely a nagy mennyis\u00e9g\u0171 adat folyamatos felhalmoz\u00e1s\u00e1val \u00e9s a Q-markerek bevezet\u00e9s\u00e9vel tov\u00e1bb fog fejl\u0151dni \u00e9s javulni, \u00e9s jobban alkalmazhat\u00f3 a hagyom\u00e1nyos k\u00ednai gy\u00f3gy\u00e1szat min\u0151s\u00e9g\u00e9nek tudom\u00e1nyos fel\u00fcgyelet\u00e9re.<\/p>","protected":false},"excerpt":{"rendered":"

Research on Rapid Evaluation of Traditional Chinese Medicine Quality Based on Big Data and Pattern Recognition Technology Traditional Chinese medicine is the mainstay of the traditional Chinese medicine health industry, and the quality of traditional Chinese medicine is related to the development of the health industry. 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