{"id":1189,"date":"2025-11-29T03:37:19","date_gmt":"2025-11-29T03:37:19","guid":{"rendered":"https:\/\/thermovisio.es\/?page_id=1189"},"modified":"2026-01-11T15:53:34","modified_gmt":"2026-01-11T13:53:34","slug":"ai-diagnostics","status":"publish","type":"page","link":"https:\/\/thermovisio.es\/ee\/ai-diagnostics\/","title":{"rendered":"AI Diagnostics"},"content":{"rendered":"\n
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MI un ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s diagnostika<\/strong><\/strong><\/h2>\n\n\n\n
MI un ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s diagnostika uzlabo tradicion\u0101lo termogr\u0101fiju ar automatiz\u0113tu boj\u0101jumu noteik\u0161anu un progres\u012bvu rakstu atpaz\u012b\u0161anu. M\u016bsu mode\u013ci analiz\u0113 term\u0101los att\u0113lus, RGB fotogr\u0101fijas un metadatus, lai klasific\u0113tu defektus, kvantific\u0113tu riskus un prognoz\u0113tu attei\u010du tendences.
Apvienojot datu kopas un viedos algoritmus, m\u0113s nodro\u0161in\u0101m \u0101tr\u0101kus, konsekvent\u0101kus un \u013coti prec\u012bzus diagnostikas rezult\u0101tus, kas atbalsta proakt\u012bvu uztur\u0113\u0161anu un datos balst\u012btu l\u0113mumu pie\u0146em\u0161anu.<\/div>\n<\/div>\n\n\n\n
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Sazin\u0101ties<\/span><\/span><\/path><\/svg><\/span><\/span><\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Ko m\u0113s analiz\u0113jam<\/strong><\/strong><\/h4>\n\n\n\n

M\u016bsu MI mode\u013ci izv\u0113rt\u0113 term\u0101los raksturlielumus, vizu\u0101l\u0101s neatbilst\u012bbas, elektriskos rakstus, \u0113nojumu, modu\u013cu degrad\u0101ciju, karstos punktus un telpisk\u0101s korel\u0101cijas vis\u0101 PV lauk\u0101.<\/p>\n<\/div>\n\n\n\n

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Mode\u013ci un precizit\u0101te<\/strong><\/strong><\/h4>\n\n\n\n

Ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s sp\u0113j noteikt smalkas anom\u0101lijas, kuras cilv\u0113ka acij var pasl\u012bd\u0113t gar\u0101m \u2014 \u012bpa\u0161i lielos saules parkos. T\u0101 palielina precizit\u0101ti, samazina inspekciju laiku un nodro\u0161ina nemain\u012bgu kvalit\u0101ti atk\u0101rtot\u0101s p\u0101rbaud\u0113s.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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K\u0101p\u0113c ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s uzlabo inspekciju<\/strong><\/strong><\/h4>\n\n\n\n

Saules elektrostaciju termogr\u0101fisk\u0101 inspekcija nodro\u0161ina prec\u012bzu, neinvaz\u012bvu metodi saules PV instal\u0101ciju tehnisk\u0101 st\u0101vok\u013ca un veiktsp\u0113jas nov\u0113rt\u0113\u0161anai. Izmantojot ar droniem ieg\u016btu augstas iz\u0161\u0137irtsp\u0113jas term\u0101lo att\u0113lveido\u0161anu, m\u0113s identific\u0113jam:<\/p>\n\n\n\n

<\/path><\/svg>karstos punktus<\/span><\/div>
<\/path><\/svg>boj\u0101tus modu\u013cus<\/span><\/div>
<\/path><\/svg>virk\u0146u atteices<\/span><\/div>
<\/path><\/svg>citas sl\u0113ptas anom\u0101lijas<\/span><\/div><\/div>\n\n\n\n
pirms t\u0101s p\u0101raug d\u0101rg\u0101s d\u012bkst\u0101v\u0113s.
\u0160is pakalpojums pal\u012bdz akt\u012bvu \u012bpa\u0161niekiem uztur\u0113t optim\u0101lu efektivit\u0101ti, pagarin\u0101t iek\u0101rtu kalpo\u0161anas laiku un samazin\u0101t ekspluat\u0101cijas zudumus, nodro\u0161inot savlaic\u012bgu boj\u0101jumu atkl\u0101\u0161anu un skaidras diagnostikas atzi\u0146as.<\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n\n\n\n
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Rezult\u0101tu piem\u0113ri<\/strong><\/strong><\/h4>\n\n\n\n

M\u0113s izmantojam objektu noteik\u0161anas, segment\u0101cijas, anom\u0101liju noteik\u0161anas un klasifik\u0101cijas mode\u013cus, kas apm\u0101c\u012bti ar nozares specifisk\u0101m datu kop\u0101m. Precizit\u0101te tiek nep\u0101rtraukti uzlabota, izmantojot atgriezenisk\u0101s saites ciklus un re\u0101l\u0101s pasaules datu integr\u0101ciju.<\/p>\n<\/div>\n<\/div>\n <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_gspb_post_css":"#gspb_row-id-gsbp-f54e2ab{justify-content:space-between;margin-top:0;margin-bottom:0;display:flex;flex-wrap:wrap;padding-top:60px}#gspb_row-id-gsbp-f54e2ab>.gspb_row__content{display:flex;justify-content:space-between;margin:0 auto;width:100%;flex-wrap:wrap;row-gap:40px}div[id^=gspb_col-id]{box-sizing:border-box;position:relative}body.gspb-bodyfront #gspb_row-id-gsbp-f54e2ab>.gspb_row__content{width:var(--theme-container-width, 1200px);max-width:var(--theme-normal-container-max-width, 1200px)}#gspb_col-id-gsbp-45da077.gspb_row__col--8{width:66.66666666666667%}@media (max-width:575.98px){#gspb_col-id-gsbp-45da077.gspb_row__col--8{width:100%}}#gspb_col-id-gsbp-45da077.gspb_row__col--8{flex-direction:column}body #gspb_col-id-gsbp-45da077.gspb_row__col--8{display:flex;flex-direction:column}.gspb_container-id-gsbp-178d18d,.gspb_container-id-gsbp-d49d578{flex-direction:column;box-sizing:border-box}#gspb_container-id-gsbp-1330d8c.gspb_container>p:last-of-type,#gspb_container-id-gsbp-178d18d.gspb_container>p:last-of-type,#gspb_container-id-gsbp-26194c9.gspb_container>p:last-of-type,#gspb_container-id-gsbp-ac836e4.gspb_container>p:last-of-type,#gspb_container-id-gsbp-ce60644.gspb_container>p:last-of-type,#gspb_container-id-gsbp-d49d578.gspb_container>p:last-of-type{margin-bottom:0}#gspb_container-id-gsbp-178d18d.gspb_container,#gspb_container-id-gsbp-d49d578.gspb_container{position:relative}#gspb_container-id-gsbp-178d18d.gspb_container{padding-top:25px}#gspb_button-id-gsbp-b7e853b{display:flex;justify-content:flex-start}#gspb_button-id-gsbp-b7e853b .gspb-buttonbox-text{display:flex;flex-direction:column}#gspb_button-id-gsbp-b7e853b>.gspb-buttonbox{box-sizing:border-box}#gspb_button-id-gsbp-b7e853b>.gspb-buttonbox>.gspb-buttonbox-textwrap>.gspb-buttonbox-icon{display:flex}#gspb_button-id-gsbp-b7e853b>.gspb-buttonbox .gspb-buttonbox-icon svg,#gspb_button-id-gsbp-b7e853b>.gspb-buttonbox .gspb-buttonbox-icon svg path{fill:currentColor!important}#gspb_button-id-gsbp-b7e853b>.gspb-buttonbox>.gspb-buttonbox-textwrap{display:flex;flex-direction:row;align-items:center}#gspb_button-id-gsbp-b7e853b .gspb-buttonbox-icon{margin-left:13px}#gspb_button-id-gsbp-b7e853b>.gspb-buttonbox{margin-top:25px}#gspb_col-id-gsbp-4ac55fb.gspb_row__col--4{width:33.333333333333336%}@media (max-width:575.98px){#gspb_col-id-gsbp-4ac55fb.gspb_row__col--4{width:100%}}#gspb_col-id-gsbp-4ac55fb.gspb_row__col--4{background-size:cover;background-repeat:no-repeat;background-position:45% 29%;background-image:url(https:\/\/thermovisio.es\/wp-content\/uploads\/2025\/12\/thermovisio-ai-diagnostics-neural-network-anomaly-detection.png)}#gspb_col-id-gsbp-4ac55fb.gspb_row__col--4:hover{background-size:cover}#gspb_col-id-gsbp-4ac55fb.gspb_row__col--4,#gspb_col-id-gsbp-4ac55fb.gspb_row__col--4>.gspb_backgroundOverlay,#gspb_container-id-gsbp-26194c9.gspb_container>.gspb_backgroundOverlay,#gspb_container-id-gsbp-ac836e4.gspb_container>.gspb_backgroundOverlay,#gspb_container-id-gsbp-ce60644.gspb_container>.gspb_backgroundOverlay{border-top-left-radius:12px;border-top-right-radius:12px;border-bottom-right-radius:12px;border-bottom-left-radius:12px}#gspb_row-id-gsbp-30814f4{justify-content:space-between;display:flex;flex-wrap:wrap;margin-top:50px;margin-bottom:0}#gspb_row-id-gsbp-30814f4>.gspb_row__content{display:flex;justify-content:space-between;margin:0 auto;width:100%;row-gap:40px;flex-wrap:wrap}#gspb_col-id-gsbp-26287bc.gspb_row__col--6{width:calc(50% - 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