Bceg Environmental Remediation Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Bceg Environment Remediation Co., Ltd

Shupeng Li

Yongkang Xie

En Shi

Peng Liu

Yue Hua Zhang

Yang Wang

Xiaowei Song

Fan Chen

Qiang Niu

Lili Guo

Yuewei Yang

Pengcheng Yin

Ying Zhao

Xiaoyu Chen

Zhengyu Qian

Zihao Pan

Fuwang Wang

Shuangchao Cui

Bo Li

Tian Wu

Liang Tang

Lina Wang

Lin Xie

Binbin Xu

Kuan Shi

Zhou Xin

Yu Zhou

Hao Cao

Tuobang Wu

Mingren Hu

Lianghuo Zhang

Ruyue Ma

Jingqiu Li

Yashuai Li

Zhiquan Wu

Yunxiao Wei

Yilan Xing

Dixiang Wang

Jiexi Zhang

Fanyang Bu

Shimin Ruan

Xiaobin Zhang

Penghao Zhao

Haibin Zhang

A resource scheduling method and apparatus, a device, and a storage medium are provided, and relates to the field of computer technology, and in particular to the field of deep learning technology. The method includes: acquiring a graphics processing unit (GPU) topology relationship of a cluster according to GPU connection information of each of computing nodes in the cluster; and in a case where a task request, for applying for a GPU resource, for a target task is received, determining a target computing node of the target task and a target GPU in the target computing node according to the task request and the GPU topology relationship, to complete GPU resource scheduling of the target task. The present disclosure can optimize the resource scheduling.

Resource scheduling method, device, and storage medium

A soil remediation system and a soil remediation method. The soil remediation system comprises a heating unit () and a pumping/injecting device for pumping underground water in soil and injecting a remediation agent into the soil. The soil remediation method comprises the following treatments: heating soil to be remediated; pumping underground water; and injecting a remediation agent.

Soil remediation system and soil remediation method

A method for model production includes acquiring a related operation for model production from a user interface layer of a model production system, and determining a software platform of the model production system; acquiring a model service corresponding to the related operation by invoking an application programming interface (API) corresponding to the related operation, wherein the API is located between the user interface layer and other layer in the model production system; performing the model service by invoking local resources of the software platform with a tool of the software platform adapted to the model service, to generate a target model; and applying the target model in a target usage scene.

Method and system for model production

An apparatus and a method for executing a customized production line using an artificial intelligence development platform, a computing device and a computer readable storage medium are provided. The apparatus includes: a production line executor configured to generate a native form of the artificial intelligence development platform based on a file set, the native form to be sent to a client accessing the artificial intelligence development platform so as to present a native interactive page of the artificial intelligence development platform; and a standardized platform interface configured to provide an interaction channel between the production line executor and the artificial intelligence development platform. The production line executor is further configured to generate an intermediate result by executing processing logic defined in the file set and to process the intermediate result by interacting with the artificial intelligence development platform via the standardized platform interface.

Apparatus and method for executing customized artificial intelligence production line

A method and apparatus for generating a model, and a method and apparatus for recognizing information are provided. An implementation of the method for generating a model includes: acquiring a to-be-converted model, a topology description of the to-be-converted model, and device information of a target device; converting, based on the topology description and the device information, parameters and operators of the to-be-converted model to obtain a converted model applicable to the target device; and generating a deep learning prediction model based on the converted model. This embodiment enables the conversion of an existing model to a deep learning prediction model that can be applied to a target device.

Method and apparatus for generating model, method and apparatus for recognizing information

An in-situ injection of soil and groundwater&#x2014;high pressure rotary jet grouting in-situ remediation system and method. The system has an agent dispensing station, high pressure grouting pump, air compressor, rotary jet grouting drilling machine, second double-pipe water flow joint, automatic lifting mechanism for grouting drill pipe of rotary jet grouting drilling machine, high pressure jet drill pipe, inner tube for high pressure jet triple drill pipe, outer tube for high pressure jet triple drill pipe, agent jet nozzle, an air jet nozzle, cemented carbide block and a drill bit. Parameters are arranged according to the triangle method to ensure that the remediation area is covered within the diffusion radius of the agent. After positioning the GPS measuring point, a guided-boring rig is positioned at the center of the injection point. The high pressure injection remediation uses a double-pipe: using gas and liquid fluids to spread the soil while cutting the soil from bottom to top to achieve thorough mixing of the remediation agent with soil and groundwater; quickly combining laboratory testing to obtain parameters for remediation agent residues, pH values, and contaminant concentrations to verify the in-situ remediation effect and monitor the residual agent.

In-situ injection of soil and groundwater&#x2014;high pressure rotary jet grouting in-situ remediation system and method

A method for determining the diffusion radius of in-situ injection and remediation of contaminated soil and groundwater. According to the triangle method, the hole spacing is perpendicular to the groundwater flow direction, the row spacing is along the groundwater flow direction, and the flow diffusion in the groundwater during the effective time of the remediation agent reaction is considered. Under high pressure rotary injection, the remediation agent and a certain proportion of bromide ions are simultaneously injected into the aquifer as a tracer. The diffusion of the agent is determined by observing the phenomenon of slurry-returning and slurry-channeling of adjacent injection points. After the completion of the injection, the groundwater is quickly sampled in fixed depth, the tracer concentration is quickly detected on site, and the concentration of bromide ions in the groundwater is compared with the background value. Comprehensive determination determines the optimal diffusion radius.

Method for determining diffusion radius of in-situ injection and remediation of contaminated soil and groundwater

An in-situ chemical oxidation high pressure injection optimization remediation method for soil and groundwater. A field test determines the diffusion radius of the injection agent in a typical formation, the maximum single-hole grouting amount per linear meter and the injectability parameter. Density-increased supplementary investigation, soil and groundwater data analysis, is divided by spatial distribution into four concentration main partitions, eight concentration sub-partitions. By optimizing the single-hole grouting amount per linear meter, the preparation concentration of the oxidant, and the parameters of the formulation of each partition, the optimization of the dosing ratio of remediation agent of each partition is achieved. The specific construction performance is to adjust the lifting speed of the high pressure injection drill pipe during the injection process for finally achieve the purpose of reducing the dosing ratio parameters of the agent in the remediation plot.

In-situ chemical oxidation high pressure injection optimization remediation method for soil and groundwater

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Beijing, China

Shupeng Li