6results about How to "Improve distribution structure" patented technology

The invention discloses a precise temperature control and energy comprehensive management device for an injection molding machine, the device is arranged on a machine barrel of the injection molding machine, the device comprises a temperature difference power generation system, a semiconductor refrigeration system and a cooling system, the temperature difference power generation system comprises a plurality of temperature difference power generation sheets and is used for controlling the temperature of a high-temperature heating section of the machine barrel and absorbing waste heat for power generation; the plurality of thermoelectric power generation sheets are connected in series to the voltage stabilizing circuit to supply power to a PLC of the injection molding machine; the semiconductor refrigeration system comprises a plurality of semiconductor refrigeration sheets and is used for controlling the temperature of the low-temperature heating section of the machine barrel; the cooling system is used for maintaining the constant temperature difference between the cold and hot ends of the thermoelectric power generation sheet and improving the refrigeration efficiency of the semiconductor refrigeration sheet. The device has the advantages of being simple in structure, safe, reliable, long in service life, free of moving parts, free of noise, environmentally friendly and the like, continuous and precise control over the temperature of the high-temperature section of the machine barrel can be achieved by controlling the heat dissipating capacity of the cold end of the thermoelectric power generation piece, and the temperature of the low-temperature section of the machine barrel can be precisely controlled by changing the magnitude of current supplied to the semiconductor chilling plate.

The invention relates to the technical field of solid-state batteries, in particular to a modified composite solid electrolyte and a preparation method thereof. The preparation method comprises the following steps: S1, mixing a PVDF-HPF copolymer, PEO, LTFSI and a solvent to obtain a solution A; s2, mixing an ionic liquid, an inorganic lithium conducting material, silicon carbide and the solution A, and emulsifying to obtain a pouring liquid; s3, pouring the pouring liquid into a mold with a substrate to serve as a solid electrolyte precursor solution; and S4, drying the solid electrolyte precursor solution until the surface is dry, inverting the mold in a microwave field for microwave heating, and then drying the residual solvent to obtain the modified composite solid electrolyte. According to the preparation method, PEO / PVDF-HPF is adopted as a polymer matrix, silicon carbide is added into the polymer matrix, and the polymer matrix is polymerized into a film under microwaves, so that the problem of non-uniform heat transmission in the preparation process can be improved; meanwhile, by adding the ionic liquid, the lithium conductivity of the composite electrolyte can be improved, and the interface impedance is reduced.

The invention provides a flow battery electrode frame assembly, a galvanic pile unit and a galvanic pile, the flow battery electrode frame assembly comprises an outer frame body, the middle part of the outer frame body is a first hollow structure for embedding a first electrode, the upper and lower parts of the front and back surfaces of the outer frame body are provided with electrolyte flow channel grooves, and the four corners of the outer frame body are respectively provided with electrolyte flow holes communicated with the corresponding electrolyte flow channel grooves; a plurality of first distribution runners communicated with the electrolyte runner groove are arranged on the front surface of the outer frame body and close to the edge of the first hollow structure; a film pressing frame mounting groove is formed in the reverse side of the outer frame body and surrounds the edge of the first hollow structure; the middle part of the film pressing frame is a second hollow structure for embedding a second electrode, and one surface of the film pressing frame is provided with a plurality of second distribution runners; and after the film pressing frame is embedded in the film pressing frame mounting groove, the second distribution flow channel is connected and communicated with the electrolyte flow channel groove on the side where the second distribution flow channel is located. According to the technical scheme, the area ratio of the electrode reaction region can be ensured to the maximum extent, and the reaction efficiency of the flow battery is improved.

The present invention discloses an abnormal data monitoring method and system based on artificial intelligence, which relates to the field of data monitoring technology. The method generates a standardized feature matrix by real-time monitoring and preprocessing data from different sources, effectively reduces data noise and scale differences between features, and analyzes the deviation of each sample relative to the overall distribution, and combines the critical value of the chi-square distribution table to make abnormal judgments on the samples, thereby ensuring the accuracy of abnormal data detection. Then, artificial intelligence technology is used to enhance the abnormal samples, and by introducing distribution constraints, several groups of supplementary abnormal samples are dynamically generated, while ensuring that the generated samples are close to the real distribution, and then by identifying and screening outliers in the abnormal group, the unreasonable samples introduced by noise or deviation in the enhancement process are further reduced, thereby improving the quality of the abnormal group data.

The utility model discloses a cylindrical grinding machine main shaft, including the axle body, the shell, the inner cover, the outer cover, the air curtain hole, the drain hole, the axle body is coaxially loaded in the shell, the both ends of shell are covered with the inner cover, the outer cover is stacked with the inner cover outside, be provided with first water retaining ring on the axle body, the inner cover is provided with the integrative second water retaining ring, the second water retaining ring installs in the port inner wall of shell, first water retaining ring, second water retaining ring mutually cooperate and form the zigzag water retaining joint, the inner cover is provided with the air curtain hole in the outer end port of water retaining joint, the outer cover is cooperated in the axle body outer peripheral surface and forms the water inlet joint. Through above -mentioned mode, the utility model provides a cylindrical grinding machine main shaft, forms the quick drainage channel through the air curtain hole and the drain hole, and realizes directional control and arranges simultaneously with air seal groove and guide angle, and the bearing distribution structure is optimized to the grinding wheel load simultaneously, thereby significantly improved the waterproof sealing performance and running stability of main shaft whole.

The invention discloses an abnormal data monitoring method and system based on artificial intelligence, and relates to the technical field of data monitoring. According to the method, data from different sources are monitored and preprocessed in real time, a standardized feature matrix is generated, the scale difference between data noise and features is effectively reduced, and the accuracy of abnormal data monitoring is improved. And by analyzing the deviation condition of each sample relative to the overall distribution and combining the critical value of the chi-square distribution table, abnormal judgment is performed on the samples, so that the detection accuracy of abnormal data is ensured. Then, the artificial intelligence technology is used for enhancing the abnormal samples, distribution constraints are introduced, a plurality of groups of supplementary abnormal samples are dynamically generated, meanwhile, it is ensured that the generated samples are close to real distribution, outliers in the abnormal groups are recognized and screened out, unreasonable samples introduced due to noise or deviation in the enhancement process are further reduced, and the enhancement accuracy is improved. And the quality of the abnormal group data is improved.