Uru:
- Ikike ime ka njikarịcha na ọrụ nhazi data jiri koodu Python kwadoro AI
- Ikike iji nyochaa koodu AI mepụtara site na nyocha otu, nnwale na ikpe ihu
- Ikike ichekwa data mmepụta nzuzo na ịmanye ụkpụrụ iji AI nwere ọrụ
Na injinịa ụlọ ọrụ mmepụta ihe, a na-enweta ma ọsọ na nkwughachi mgbe a na-eji Python arụ ọrụ ugboro ugboro, ihicha data na ụdị njikarịcha. AI bụ ngwa ngwa kachasị ike maka ide koodu a: nhazi data na pandas, njikarịcha ya na PuLP/SciPy, nleba anya na koodu eserese matplotlib na nkeji. Mana koodu AI anaghị ekwe nkwa na ọdịiche dị n'etiti "yiri ka ọ na-arụ ọrụ" na "ịbụ eziokwu." N'ime nkeji a, anyị na-ejikọta automation injinia kwadoro AI, ịdọ aka ná ntị nke koodu nkwado, yana ụkpụrụ nke ichekwa data mmepụta nzuzo. Nkeji a bụ oyi akwa "tinye na ntụkwasị obi" na nkeji niile gara aga.
Gịnị kpatara Koodu? Ikwugharị na nyocha
Ngụkọ ntuziaka na Excel ga-eme ka ọnwa isii gachara jụọ "Olee ebe ọnụọgụ ahụ si bịa?" enweghị ike ịza ajụjụ a. Koodu, n'aka nke ọzọ, na-edobe ntinye, usoro na mmepụta ihe doro anya ma nwee ike ikwugharị ya. Uru atọ nke ime ngụkọ injinia na koodu: (1) otu nsonaazụ mgbe niile yana otu ntinye, (2) mee ka usoro ọ bụla pụta ìhè na nyocha, (3) na-emelite nsonaazụ ozugbo mgbe ntinye gbanwere.
# Ịtụgharị ngụkọ EOQ ka ọ bụrụ ọrụ a pụrụ ịdabere na ya bubata mathdef eoq (anual_demand, order_cost, hold_cost): """ ọnụ ọgụgụ nke akụ na ụba. Nkeji ga-agbanwe agbanwe: annual_demand [items/ year], order_cost [TL/ order], hold_cost [TL/pieces/year], hold_cost [TL/pieces/year], hold_cost [TL/pieces/year]" na-ejide: "" ma ọ bụrụ na ọ na-akwụ ụgwọ kwa afọ. ValueError ("Ọchịchọ na ọnụ ahịa njide ga-abụ nke ọma") laghachi math.sqrt ((2 * annual_demand * order_cost) / hold_cost) # Nnwale na nsonaazụ ama ama (nkwenye): # D=3600, S=120, H=8 -> ~328,6assert abs(eoq(3600, 3600,) 123) << 0.1 ebipụta (gburugburu (eoq (3600, 120, 8), 1)) #328.6
Ahịrị nkwuputa ebe a dị oke egwu: nwalee megide nsonaazụ amaara ama. Site na ịgbakwunye opekata mpe otu “nnwale nsonaazụ amara” na ọrụ mgbako ọ bụla AI na-ede, ị na-egosi na koodu ahụ na-agbakọ nke ọma.
Ụzọ ziri ezi iji mepụta koodu na AI
Obere ejighị n'aka dị mgbe ị na-ede koodu na AI, ka a pụrụ ịdabere na ya mmepụta.
Ọrụ: Ị bụ ọkachamara na nyocha data Python na engineering engineering. Ọrụ: Dee ọrụ na-ahazi data mmepụta kwa ụbọchị (ụbọchị, emepụtara, ezighi ezi, downtime_minutes) na CSV wee gbakọọ ọnụego nbibi kwa ụbọchị yana ihe mejupụtara OEE. Ihe achọrọ: - jiri pandas, kwado aha kọlụm (nye njehie nwere nghọta ma ọ bụrụ na-efu) - Ọnụ ego efu = ezighi ezi / emepụtara; Chebe megide nkewa site na zero.- Tinye ule (nkwuputa) na nsonaazụ amaara na ọrụ ahụ.- Dee otu na echiche na docstring.Rule: Akwụsịla usoro zoro ezo nke na-ede na netwọk / faịlụ faịlụ. Naanị mee ngụkọta. Kaa akara na nkọwa ebe ị na-ejighị n'aka.
Ngwa ngwa adịghị ike / Ike ngwa ngwa
Ngwa ngwa adịghị ike:
Dee koodu na-agbakọ arụmọrụ mmepụta.
"Mkpụrụ" bụ enweghị nkọwa, enweghị data schema, enweghị ule. Ọbụlagodi na mmepụta ahụ na-arụ ọrụ, amabeghị ihe ọ na-agbakọ.
Mgbasa ozi siri ike:
Gbakọọ OEE maka CSV na eserese a: kọlụm [planned_time, run_time, ideal_cycle, emepụtara, àgwà]. Nnweta = ọrụ / atụmatụ, Arụmọrụ = (ideal_cycle × emepụtara) / na-agba ọsọ, Ogo = àgwà / emepụtara. OEE bụ ngwaahịa nke atọ a. Weghachite akụrụngwa ọ bụla iche iche, jikwaa nkewa site na efu, tinyekwa nnwale nkwuputa nwere ụkpụrụ amaara ama.
Ngwa ngwa nke abụọ na-enye usoro na eserese ọ bụla n'ụzọ doro anya; Ọ na-ewepụ ohere nke AI na-akọwahie "mkpụrụ" ma nye iwu nkwenye site na nyocha.
Ndepụta nkwenye koodu
Tupu iji koodu AI na mkpebi mmepụta, gafere usoro ndị a:
nzọụkwụ
Ihe ị ga-elele
Nkeji/nha
Nkeji ntinye-mpụta ọ na-agbanwe agbanwe?
Nnwale nsonaazụ amaara
Ọ na-enye ihe atụ aka gbakọọ nke ọma?
Okwu ikpe
Kedu ihe efu, adịghị mma, data efu, otu ahịrị na-eme?
Nlele nleba anya nke arithmetic
Nsonaazụ ọ nwere ike ịpụta n'ime obere nlele?
Mmetụta adịghị ahụ anya
Koodu ahụ ọ na-ehichapụ/eziga data?
Kpachara anya: AI mgbe ụfọdụ na-ewepụta koodu nke "na-arụ ọrụ mana ọ na-ezighi ezi": dịka ọmụmaatụ, ọ na-agbakọ ọnụego mfu na ezighi ezi/mepụtara kama ezighi ezi/ngụkọta, ma ọ bụ na-agbagha pasent na ọnụego. Naanị n'ihi na koodu na-arụ ọrụ na-enweghị njehie apụtaghị na ọ bụ eziokwu. Jide n'aka na iji nsonaazụ amaara nwalee.
Nchekwa na nzuzo data
Data mmepụta bụ ihe nzuzo azụmaahịa: ikike, ụgwọ, ihe ndị ahịa chọrọ, ọnụ ahịa ndị na-ebubata ya. Ịrapagidesi ike n'ihe ndị a n'ime ọrụ AI bụ nnukwu ihe egwu.
- Mechie data: Jiri koodu kama aha ndị ahịa / ndị na-eweta ngwaahịa; Jiri ụkpụrụ nlele dochie ọnụ ahịa nkenke.
- Soro amụma ụlọ ọrụ: Jiri ngwa AI na iwu nhazi data nke ụlọ ọrụ gị kwadoro.
- Rịọ koodu ahụ, mee data na mpaghara: Nwee AI dee koodu, mana jiri ezigbo data mee koodu ahụ na gburugburu nke gị. N'ụzọ dị otú a, nlezianya data adịghị apụ apụ.
- Nyochaa mmepụta: Lelee ma koodu AI na-eziga data na ebe a na-atụghị anya ya (oku netwọk, API mpụga).
NDỤMỌDỤ: Ụkpụrụ nke "mee AI ide koodu, hazie data na mpaghara" na-echebe nzuzo ma gbochie AI ịmepụta nọmba adịgboroja. Ezigbo onye na-edozi ihe / ụlọ akwụkwọ na-agbakọ, AI na-ewu ọkpụkpụ ahụ.
Obere Case: Na-arụ ọrụ mana koodu na-ezighi ezi
N'ime ụlọ ọrụ mmepụta ihe, onye ọrụ mmepụta ihe Mert nwere AI na-ede edemede nke na-emepụta akụkọ OEE kwa ọnwa. Koodu ahụ na-agba ọsọ nke ọma wee nye OEE mara mma nke 92%. Mana mgbe Mert nwalere ya megide ụbọchị ama ama (ụbọchị o ji aka gbakọọ, nke kwesịrị inwe OEE nke 78%), koodu ahụ ga-alaghachi 92%. Na nyocha, ọ chọpụtara na AI ewepụtala akụrụngwa arụmọrụ na-ezighi ezi (na-eji nkezi okirikiri kama ịdị mma okirikiri). Mgbe emeziri ya, uru ahụ dabara na eziokwu. Koodu ahụ na-arụpụta nsonaazụ na-ezighi ezi na-enweghị mperi ọ bụla. Ihe nkuzi: na-enweghị nnwale na nsonaazụ amara, OEE na-ezighi ezi ga-abanyela na mkpebi njikwa ruo ọnwa ole na ole.
Mmejọ a na-emekarị
- Na-eche "arụ ọrụ = ziri ezi": Ịnabata koodu na-adịghị enye mmejọ dị ka ezi; ọ bụghị nwale na nsonaazụ amara.
- Ikpe ikpe mwụpụ: Ọnọdụ ibipụta dị ka nkewa site na efu, data efu, ntinye adịghị mma na-anwaleghị ha.
- Tapawa data nzuzo: Na-ezigara ọnụ ahịa / data ndị ahịa na AI na-enweghị ịkọwapụta ya.
- Na-emehie AI maka onye na-edozi ihe: Iji mmepụta njikarịcha nke AI na-ekwu "nsonaazụ bụ nke a" dị ka ezigbo mmepụta ihe ngwọta.
- Na-efunahụ auditability: Ịkekọrịta koodu na-enweghị ndetu echiche na nkeji.
Na nchịkọta
- Koodu ahụ na-agbakwụnye nkwugharị na nyocha na akaụntụ injinia; AI na-eme ka koodu a dị ngwa.
- Gosipụta izi ezi site n'ịgbakwunye nkwupụta nwere nsonaazụ amaara na ọrụ mgbako AI ọ bụla.
- Ejila koodu ahụ na mkpebi mmepụta na-agafeghị ya site na nkeji, ikpe ihu na listi nyocha mgbakọ na mwepụ.
- "koodu na-arụ ọrụ" apụtaghị koodu ziri ezi; Ọ nwere ike ịrụ ọrụ na-enweghị ntụpọ ma gbakọọ ezighi ezi.
- Mebie data mmepụta ihe nzuzo, soro amụma ụlọ ọrụ; bipụta koodu ahụ na AI mana hazie data na mpaghara.
Ọrụ ngwa
Họrọ ngụkọ injinia (EOQ, ngwaahịa nchekwa, OEE ma ọ bụ oge ọkọlọtọ). Dee ọrụ Python na AI nke na-eme ngụkọta oge a; Kwado nkeji na docstring yana nsonaazụ amaara site na iji nnwale nkwuputa. Nyocha dị mkpa: nwaa ọrụ ahụ ma ọ dịkarịa ala ikpe atọ (efu, negative, null) wee jiri ya tụnyere ihe atụ ejiri aka gbakọọ; Gopụta na uru amara na nsonaazụ ya ziri ezi, ọbụlagodi na koodu na-agba ọsọ na-enweghị njehie. Nyochaa koodu ahụ site na "nzuzo nzuzo": enwere usoro izipu data? N'ikpeazụ, megharịa ndepụta nkwenye nke ngalaba a ka ọ bụrụ usoro ọrụ nke gị wee mepụta ndepụta nlele.
Ule modul
1. N'ime nrụpụta siri ike, ị jiri AI mepụta maapụ iyi bara uru (VSM). Kedu ihe kacha mma tupu emejuputa atụmatụ a?
- A) Kwado ụkpụrụ AI na nlele ubi (gemba) yana oge okirikiri / ebe obibi ✔
- B) Ịkọba maapụ ozugbo na bọọdụ dị ka AI bụ ihe nlereanya ugbu a
- C) Naanị dozie agba na ụdị igbe nke maapụ ahụ
- D) Ịgbanwe usoro ụdị mkpofu na ibipụta ha
Nkọwa: AI na-ewepụta sketị VSM ezi uche dị na ya dabere na ozi ị na-enye, mana enweghị ike ịhụ oge usoro na mkpofu. Ụkpụrụ bụ isi nke lean bụ 'gemba': a ga-enyocha uru bara uru site na ilele na ịlele n'ọhịa, ma jiri ya mee ihe dị ka ihe ndabere maka mkpebi nkwalite.
2. Kedu ụdị metrik maka ịtụle izi ezi nke ụdị amụma amụma chọrọ na-ekwu maka ebumnuche a kpọmkwem?
- A) Cp na Cpk
- B) MAPE, MAE na RMSE ✔
- C) OEE na MTBF
- D) Ogologo oge na WIP
Nkọwa: MAPE (pụtara njehie pasent zuru oke), MAE na RMSE bụ metrik na-atụ njehie amụma; Ọ na-enye gị ohere inyocha izi ezi nke ihe nlereanya site n'iji amụma atụnyere ihe achọrọ n'ezie. Cp/Cpk bụ ikike usoro yana OEE bụ metrik arụmọrụ akụrụngwa.
3. Na ngụkọta oge reorder (ROP), AI nyere gị uru. Kedu ihe ị ga-ebu ụzọ mee iji nsonaazụ ya n'enweghị nsogbu?
- A) Gbanye nsonaazụ ahụ wee tinye ya ozugbo na ERP
- B) Jụọ AI 'ị ji n'aka?' ma nwee afọ ojuju na azịza 'ee'
- C) Bipụta usoro, ntinye na nkeji eji ma megharịa mgbako ahụ na aka / jiri koodu ✔
- D) Tọgharịa ngwaahịa nchekwa kpamkpam
Nkọwa: Ọ na-arụ ọrụ na usoro ROP = nkezi ihe achọrọ × oge ndu + ngwaahịa nchekwa. Ọ dị mkpa iji nyochaa nsonaazụ AI na-enye site n'ibipụta n'ụzọ doro anya usoro ọ na-eji na ntinye (ọnụego achọrọ, oge ndu, ngwaahịa nchekwa) na ịtụgharị ya na aka / na koodu; ma ọ bụghị ya, unit ma ọ bụ mgbakọ mgbakọ na mwepụ agaghị achọpụta.
4. Kedu ihe na-achị oge nhazi kacha nso (SPT) na-akawanye mma na nhazi oge ọrụ?
- A) Ọ na-emegharị oge nhazi naanị
- B) Na-abawanye ọnụ ọgụgụ igwe
- C) Na-ekwe nkwa na-ebelata oriri ume site na ọkara
- D) Na-eme ka ibelata oge ịgba ọsọ na nkezi latency ✔
Nkọwa: Usoro SPT (Oge nhazi oge kachasị dị mkpirikpi) na-achọ ibelata oge ịgba ọsọ na nkezi latency site n'iweta obere ọrụ n'ihu. Agbanyeghị, ọ nwere ike iyigharị ogologo ọrụ mgbe niile; Ọ bụrụ na ụbọchị nnyefe dị oke egwu, iwu dịka EDD dabara adaba. Nhọrọ nke usoro iwu kwesịrị ekwesị site na ebumnuche.
5. Kedu ihe ọ pụtara ma ọ bụrụ na uru Cpk dị ala karịa Cp na SPC?
- A) Nkezi usoro esila n'ụlọ ọrụ ndidi (nsogbu na-akpachi anya) ✔
- B) Ọdịiche nke usoro ahụ bụ efu
- C) Ọ na-egosi na ọnụ ọgụgụ nke samples bụ nnukwu
- D) Gosiri na eserese njikwa adịghị mkpa
Nkọwa: Cp bụ nrịbama nrịbama nke na-eburu n'uche nha nke usoro a gbasaa na obosara ndidi, yana Cpk bụ ndenye nke ọma nke na-eburu n'uche ngbanwe nke nkezi n'ihe gbasara etiti ndidi. Ọ bụrụ na Cpk dị ala karịa Cp, ọ na-egosi na usoro ahụ adịghị n'etiti (nkezi na-apụ na ebumnuche); Ọ bụ ezie na mgbasa ahụ bụ otu ihe ahụ, enwere nsogbu n'etiti.
6. Ị na-eji usoro AI nyere mgbe ị na-agbakọ oge ọkọlọtọ na oge ọmụmụ. Kedu n'ime ndị a na-enweta oge ọkọlọtọ nke ọma?
- A) Oge ọkọlọtọ = Oge a na-ahụ anya, oke na tempo anaghị etinye n'uche
- B) Oge ọkọlọtọ = Oge nkịtị × (1 + òkè ruru); Oge nkịtị = Oge echekwara × ijeụkwụ ✔
- C) Oge ọkọlọtọ = Oge a na-ahụ anya ÷ ọnụọgụ igwe
- D) Oge ọkọlọtọ bụ mgbe niile oge onye ọrụ kacha ngwa ngwa
Nkọwa: Gbakọọ dị ka oge ọkọlọtọ = Oge nkịtị × (1 + Oke oke); A na-achọta oge nkịtị site na oge a na-ahụ anya × Tempo (ọnụọgụ). Ọ bụ iwu na ịtinye mbak (izu ike, nkeonwe, igbu oge). AI nwere ike inye usoro a, mana tempo na òkè ụkpụrụ ga-enyocharịrị site na nleba anya na amụma ụlọ ọrụ.
7. AI kwuru na ọ nyere ihe ngwọta 'kachasị mma' maka nsogbu ụgbọ okporo ígwè (VRP). Kedu nyocha kacha zie ezie?
- A) Na-ezigara ndị ọkwọ ụgbọala ụzọ ozugbo n'ihi na AI na-ekwu 'kachasị mma'
- B) Kwado ngwọta megide ikike, windo oge na ihe mgbochi ọnụ ahịa ✔
- C) Naanị belata ọnụ ọgụgụ nkwụsị ma bipụta
- D) Ilele map anya na ileghara ihe mgbochi anya
Nkọwa: AI nwere ike mebie ihe mgbochi (ikike ụgbọ ala, windo oge, oge ịnya ụgbọ ala) ma ọ bụ wepụta ihe ngwọta dị elu dị ka 'kachasị mma'. Ngwọta; Ekwesịrị ịlele ya megide ikike, mpio oge na ngụkọta anya/mkpachi ọnụ ahịa, ma ọ bụrụ na ọ ga-ekwe omume, mepụtagharị ma jiri ya tụnyere onye na-edozi ya.
8. OEE (Overall Equipment Effectiveness) bụ ngwaahịa nke akụkụ atọ?
- A) Achọrọ, ngwaahịa na ọnụ ahịa
- B) Cp, Cpk na sigma
- C) Uru, ịrụ ọrụ na ịdị mma ✔
- D) Takt, okirikiri na nnyefe oge
Nkọwa: OEE = Nnweta × Arụmọrụ × Ogo. Nnweta na-egosipụta oge ọdịda, arụmọrụ na-egosipụta mfu ọsọ ọsọ, yana ịdị mma na-egosipụta ngwaahịa na-adịghị mma. Ọ bụrụ na AI na-enye uru OEE, ọ dị mkpa iji hụ na a na-agbakọ akụkụ atọ a ma gbasaa iche iche.
9. Kedu usoro nkwenye kachasị dị oke egwu mgbe ị na-akọwapụta mmepụta nke ịme anwansị omume?
- A) Na-akwado nkesa ntinye na echiche na ezigbo data ✔
- B) Mbelata oge ọrụ ma mee mkpebi n'otu ọnọdụ
- C) Ịkwalite palette agba nke eserese
- D) Na-ezobe mkpụrụ osisi na-enweghị usoro na-eme ka nsonaazụ ya bụrụ nke a na-apụghị imegharị
Nkọwa: Mwepụta ịme anwansị dị mma naanị ka echiche ntinye. Nkesa, oge mbata, oge ọrụ na ihe dị n'ụdị kwesịrị iji tụnyere ma kwado ya na ezigbo data; Tụkwasị na nke a, ekwesịrị ịlele na edozila ihe nlereanya ahụ nke ọma (nkwenye). Ma ọ bụghị ya, 'ihe mkpofu, pụọ'.
10. Kedu ihe dị mkpa tupu iji edemede njikarịcha Python emepụtara AI na mkpebi mmepụta?
- A) Itinye nsonaazụ ya ozugbo ebe koodu na-arụ ọrụ na-enweghị njehie
- B) Nnwale site na nsonaazụ amaara ama, na-akwado ya na nlele otu yana ikpe ihu yana ichedo nzuzo data ✔
- C) Mbelata aha agbanwe agbanwe wee hichapụ nkọwa
- D) Na-ebulite edemede na data mmepụta niile na ọrụ ọha
Nkọwa: Koodu AI nwere ike ịnwe ntụgharị otu na-ezighi ezi, mmachi na-ezighi ezi, ma ọ bụ nsonaazụ na-ezighi ezi na ikpe ihu. Koodu; A ga-enyocha ntinye ule obere nwere nsonaazụ amaara site na njikwa otu/nha nha yana ikpe ihu, na enweghị ikike izipu data mmepụta ihe nzuzo na ọrụ mpụga na-enweghị ikike. E gosipụtara nsonaazụ ya site na ikpe injinia.