Alibaba Cloud Agriculture China Translated from Chinese

Chongcheng Technology (冲程科技)

Real-time Analytics · Risk Management

Chongcheng Technology needed a digital platform that could standardize agricultural operations across fragmented fields and connect sensors, equipment, imagery, and lab data into one decision system. Traditional agriculture had very low digital penetration, and the company also needed secure cloud infrastructure for large, distributed agricultural data and customer-facing applications.

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digital agriculture currently accounts for only about 1% of the sector

smart aquaculture scenarios reduced feed cost 20%

smart aquaculture scenarios reduced labor cost 80%

Reported impact

Business outcome

Chongcheng created a production cloud platform for digital agriculture and smart-orchard operations instead of treating each farm deployment as a one-off project. The same cloud foundation now supports sensor collection, AI analysis, traceability, and security controls, while downstream customers have seen material labor and feed-cost improvement in digital farming scenarios.

Solution

Chongcheng Technology built its CCtech industrial-internet PaaS platform and downstream agricultural SaaS applications on Alibaba Cloud to support smart orchards and other digital-farming scenarios. The platform combines an industrial IoT middle layer, big-data layer, AI compute layer, expert knowledge base, and user center so sensor feeds, farm machinery, image-recognition outputs, and partner-lab models can be collected and analyzed in one place. That data is then used to drive intelligent sensing, warning, decision-making, and visualized results for orchard monitoring, planting management, product traceability, and agricultural operations. To make the platform production ready, Chongcheng uses Alibaba Cloud databases, load balancing, NAT gateway, elastic public IP, and security services such as DSC, WAF, and Content Security. This gave the company a cloud base for smart-orchard deployments, traceability systems, and broader intelligent-agriculture SaaS delivery across mountain and hill farming environments.

Data flow

Sensor readings, farm-equipment data, image-recognition outputs, and lab-originated base models enter the CCtech PaaS platform, where the data is aggregated, analyzed, and converted into visualized recommendations. Those outputs feed smart-orchard, traceability, aquaculture, and other agricultural SaaS applications used by growers and operators.

Solution architecture

4 components · 4 layers

Serving
- Server Load Balancer (SLB) Distributes traffic for Chongcheng's cloud applications and field-service endpoints.
Orchestration
- CCtech Industrial Internet PaaS Platform Combines IoT, big-data, AI-compute, and expert-knowledge services for digital agriculture.
Governance
- Data Security Center Helps secure agricultural and farm-operational data with automated threat detection and governance.
Networking
- NAT Gateway Provides controlled network access for cloud-based agricultural workloads.

Architecture clues

Chongcheng separates platform concerns into IoT, big data, AI compute, knowledge, and user-center layers instead of building a monolithic farm app.
Security services are part of the agricultural platform because farm and production data is treated as a protected operational asset.
The platform mixes real-time sensor and image data with partner laboratory models before generating visual outputs.

Evidence from the source

Alibaba Cloud provides the database, load balancing, NAT gateway, EIP, and security services that keep Chongcheng's agricultural workloads stable and secure in production. (original: 阿里云数据库产品向冲程提供高性能读能力... 冲程也配备了阿里云的负载均衡搭配NAT网关、弹性公网IP... 数据安全中心、Web应用防火墙、内容安全等安全产品。)

Chongcheng said its platform fuses IoT, big data, cloud computing, 5G, and AI to create digital perception, intelligent decision-making, precise operation, and smart agricultural management. (original: 融合IoT、大数据、云计算、5G、AI等前沿信息技术及系统解决方案，构建数字化感知、智能化决策、精准化作业、智慧化管理的农业生产方式。)

Its CCtech PaaS includes an industrial IoT center, industrial big-data center, AI-compute center, expert knowledge base, and industry user center, and handles sensor, machinery, and image-recognition data for orchards. (original: “冲程CCtech产业互联网PaaS平台”主要包含产业物联网中台、产业大数据中台、AI计算中台、专家知识库以及产业用户中心... 种植现场的一些传感器，农机，设备接口和系统的对接，以及监测果园时的图像识别，都需要通过冲程自研的PaaS来完成。)

Transparency Evidence & methodology 100/100 reference value · 88% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
3 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence88%

Source authority20/25
The main evidence is an official vendor-published customer story.
Claim traceability25/25
12 of 12 key fields link to a named source.
Direct evidence15/20
3 direct source quotes retained.
Date provenance10/10
The publication date comes from the source page or publisher.
Source transparency8/10
1 source reference document publisher, URL, authority, retrieval, and supported fields.
Implementation specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Alibaba Cloud
专精特新｜看见专精特新新潜力| "5G+AI"智慧果园，冲程科技数字赋能大农业_企业成长服务中心客户故事_客户案例_客户故事_案例-阿里云

First-party
https://enterprise.aliyun.com/customer/zhihuiguoyuan1

Source type
Vendor-published customer story
Supports
- Customer
- Region
- Company size
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes

Date evidence

Published on source page Jun 7, 2022 · Alibaba Cloud · First-party
Open date source

Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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