智能远程配电系统材料供需情况汇算数据
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通过数据检测，各智能与非智能电器在一体化智能制造过程中整体数据的产生与分配情况，实时撑控数据制造效率和规避低效率制造风险。撑握数据从研发到制造，从创新到实用，整体数据相互演绎，相互联动，新质智能用材数据重组统一规划，统一管理。汇算数据提质创新再分配，增设库存缺补货预警，实时监测数据分类搭配情况。让用户在设备选型、设备优化、节能降耗和设备维护等方面做出科学高效选择，从而提升用户在智能化一体化增进节能效率和提高经济效益。新质智能可广泛应用在各智能家居，别墅，宾馆，酒店，工业机电，及各场站，高楼，岛屿，桥梁，城市景观，交通，电力，水利，军工等智能一体化新建或旧改新等领域；1，C3为表格中预采购数量；D3为记录在某一时间段里各技术智能制造自行产生数据和分配各类智能用材数据时间；2，利用RANDBETWEEN(C3*%,C3*%)公式计算：E3主件制造值,占C3中15—20%；F3自主研发值,占C3中35-45%；G3物联网创新值,占C3中20—25%；H3为新质技术加工值,占C3中5—10%；I3(3—6%)为在C3中的损耗值；3，制造有效值：(E3+F3+G3+H3+I3)-I3=J3=C3*90%，J3为智能新质产品数据；4，制造值效率监测：各制造值与新质产品J3和预采购数C3的90%值存在逻辑关系：当(E3+F3+G3+H3+I3)-I3-C3*90%值大于或等于“0”时，L列效率监测显示为“优”；小于“0”时，L列效率监测显示为“低”，5，统计A类M3(30-35%)、B类N3(25-30%)、C类O3(20-25%)、D类P3(10-15%)、E类Q3(5-10%)分别在J3中各用材数据，分配按各自比值数据相互自行调节；6，新产品数据减各类智能用材数据为库存数据SUM(K3:O3)-J3；S3单元格中函数设小于“0”时“需补货”预警；
Through data detection, this dataset records the generation and distribution of overall data for both intelligent and non-intelligent electrical appliances during integrated intelligent manufacturing processes, enabling real-time monitoring of data manufacturing efficiency and avoidance of low-efficiency manufacturing risks. It also enables mastery of the mutual deduction and linkage of overall data throughout the lifecycle from R&D to manufacturing, and from innovation to practical application, supporting unified planning and management of restructured data for new-type intelligent materials. It conducts data reconciliation to improve quality, innovate and redistribute data, adds inventory restocking warning functions, and monitors the classified matching of data in real time. This allows users to make scientific and efficient decisions in equipment selection, optimization, energy conservation and consumption reduction, and equipment maintenance, thereby helping users enhance energy-saving efficiency and economic benefits through intelligent integration. New-type intelligent technologies can be widely applied in various fields of intelligent integration new construction or renovation, including smart homes, villas, guesthouses, hotels, industrial electromechanical equipment, various stations, high-rise buildings, islands, bridges, urban landscapes, transportation, power, water conservancy, military industry, etc. 1. Cell C3 represents the pre-purchased quantity in the table; Cell D3 records the time during which each intelligent manufacturing technology independently generates data and allocates data for various types of intelligent materials within a specific time period; 2. Use the formula `RANDBETWEEN(C3*%, C3*%)` to calculate: - E3: Main component manufacturing value, accounting for 15–20% of C3 - F3: Independent R&D value, accounting for 35–45% of C3 - G3: IoT innovation value, accounting for 20–25% of C3 - H3: New-type technology processing value, accounting for 5–10% of C3 - I3 (3–6%): Loss value, accounting for 3–6% of C3; 3. Manufacturing valid value: `(E3 + F3 + G3 + H3 + I3) - I3 = J3 = C3 * 90%`, where J3 represents the data of intelligent new-type products; 4. Manufacturing value efficiency monitoring: There is a logical relationship between each manufacturing value, the new-type product data J3, and 90% of the pre-purchased quantity C3: - When `(E3 + F3 + G3 + H3 + I3) - I3 - C3 * 90% >= 0`, the efficiency monitoring in column L will display "Excellent"; - When the value is < 0, the efficiency monitoring in column L will display "Low"; 5. Statistics on material data for Category A (M3, 30–35%), Category B (N3, 25–30%), Category C (O3, 20–25%), Category D (P3, 10–15%), and Category E (Q3, 5–10%) within J3, with allocation automatically adjusted based on their respective ratio data; 6. Inventory data = new product data minus various intelligent material data, i.e., `SUM(K3:O3) - J3`; Set a function in cell S3 to trigger a "Restock Needed" warning when the value is < 0.
