构建多元供给格局。在强化政府部门兜底保障作用的同时，也要支持社会力量以多元供给满足群众 需求。比如，随着人口老龄化程度加深，不少经营主体参与到提升老年人公共服务中来，养老驿站、老 年大学、父母食堂等设施的建立，有效补齐了服务供给短板。人们对公共服务的期许已经从"有没有"转 向"好不好"，多元化社会力量的加入，将有力推动公共服务实现高品质、多样化升级，从而更好满足人 们对美好生活的期待。(本文来源：经济日报 作者：张晓) 要树立底线思维。提高公共服务水平的过程，是不断解决群众急难愁盼问题的过程，这是一项循序 渐进的长期任务。在这一过程中，要优先加强普惠性、基础性、兜底性民生建设，解决好群众最关心最 直接最现实的利益问题，尤其要在针对儿童、老年人、残疾人、农民工、失业人群等弱势群体的帮扶上 做实做细，在增强基本公共服务均衡性可及性上再上水平。 积极回应群众期盼。公共服务的对象是群众，要实现公共服务供给与需求的适配，就要坚持以群众 的诉求为出发点和落脚点。要充分尊重群众的主体地位，将群众的参与权、知情权、管理权和监督权落 到实处，从而实现公共服务内容与群众实际需求的同频共振。一方面，要聚焦基层，通过听证会、座谈 会 ...

虹洋热电作为徐圩新区的公用热源点，为石化产业基地内的多家企业提供热能。自2011年建设至今，虹 洋热电总投资超98亿元，已有10炉9机的建成投运，总供热能力为3428.5吨/小时，是徐圩新区最大的清 洁能源热电联产项目，也是徐圩新区首个热电联产项目。 本报讯(毕建宇)近日，江苏省工业和信息化厅公布《2025年江苏省先进级智能工厂名单》，连云港虹洋 热电有限公司凭借在智能制造和数字化转型方面的突出成效成功入选，成为全省能源行业智能化建设的 示范企业。 近年来，虹洋热电积极响应国家"双碳"战略，以"数据驱动、智能赋能"为核心，全面推进热电厂智改数 转网联工作。"我们积极采用前沿技术，如人工智能、大数据、物联网、数字孪生、仿真等，构建智能 化的生产管理体系，建立统一的数据中台架构，解决数据分散采集和标准不统一的问题，实现全厂级的 数据互联互通，全面提升了热电厂的安全性和环保水平。"连云港虹洋热电有限公司相关负责人说。 "智慧虹洋"一期智能化项目的建设，成功开发了煤场控制及皮带巡检智能系统；借助UWB(超宽带)技术 构建智能化安全管理体系；引入一系列高效的节能设备和技术，进一步降低能耗……近年来，虹洋热电 不断创新， ...

市场前景值得期待 近日，国家金融监督管理总局（以下简称"金融监管总局"）发布了今年前4个月健康险原保险保费收入 （以下简称"保费收入"）情况。数据显示，今年前4个月，保险公司合计实现健康险保费收入4557亿 元，同比增长4.06%。其中，财险公司健康险保费收入同比增长8.47%，人身险公司健康险保费收入同 比增长2.39%。 受访专家表示，随着市场康养需求的提升、消费者对保险产品认知的深化以及保险产品预定利率的下 调，具有保障功能的健康险更容易获得消费者的认同，因此增速较快。展望未来，护理险、失能险等健 康险的市场潜力有望逐步释放。 保费收入稳健增长 健康险，是指由保险公司对被保险人因健康原因或者医疗行为的发生给付保险金的保险，主要包括医疗 保险、疾病保险、失能收入损失保险、护理保险以及医疗意外保险等。 今年以来，在保险公司特别是人身险公司保费收入增长整体放缓的背景下，健康险保费收入同比仍保持 较为稳健的增长。 金融监管总局披露的最新数据显示，今年前4个月，保险公司合计实现健康险保费收入4557亿元，同比 增长4.06%。其中，财险公司健康险保费收入为1302亿元，同比增长8.47%，在其经营的各险种中保费 ...

Report Industry Investment Rating - Not provided in the content Core Viewpoints of the Report - The report systematically studies the construction method of futures automated trading strategies, emphasizing the core advantages of automated trading in efficiency, discipline, and data processing, and points out that successful strategy construction requires developers to have comprehensive capabilities such as market cognition, programming skills, and psychological qualities. It also provides a complete risk control framework and a gradual implementation plan from simulation to live trading, and believes that AI - driven and compliance - transparent will be the main future development directions [3]. Summary by Relevant Catalogs 1. Big Data Era's Automated Trading Revolution 1.1 Market Background and Development Status of Automated Trading - In the era of big data and AI, the proportion of automated trading in the global foreign exchange market is rising. The daily average trading volume of the global foreign exchange market is $7.5 trillion, with 65% of transactions conducted electronically. Barclays plans to increase the proportion of automated spot foreign exchange transactions. Automated trading improves efficiency, reduces manual intervention, and has a significant speed advantage over manual trading, with an average execution delay of 300 - 500 milliseconds for manual trading and less than 5 milliseconds for automated systems [6]. 1.2 Core Advantage Analysis of Automated Trading - Automated trading has discipline advantages as it follows preset rules without being affected by emotions, avoiding behavior biases like over - trading after consecutive losses. It can also monitor multiple markets and thousands of varieties 24/7. In terms of data processing, modern quantitative systems can process TB - level market data daily, providing a basis for trading decisions [7]. 2. Core Competency System for Building Automated Trading 2.1 Market Cognition and Market Judgment Ability - Developers need multi - dimensional professional capabilities, including understanding of variety characteristics, participant structures, and price formation mechanisms. For example, trading crude oil futures requires knowledge of OPEC policies, geopolitical factors, and inventory data, as well as technical analysis skills [8]. 2.2 Programming and Quantitative Analysis Skills Requirements - Python is the industry - standard programming language, and statistical modeling involves advanced techniques such as time - series analysis and machine learning. For instance, a simple mean - reversion strategy may need ADF tests and Z - score standardization [9]. 2.3 Psychological Quality and Risk Management Ability - Psychological quality is crucial. During strategy development, developers face a 3 - 6 - month trial - and - error period, and in live trading, they need to maintain emotional stability. Professional traders often establish psychological training mechanisms [10]. 3. Tool Selection and Platform Evaluation 3.1 Comparison of Mainstream Automated Trading Platforms - There are three types of automated trading tools: retail - level platforms (e.g., MT5, TradingView), professional - level platforms (e.g., Infinite Easy, MultiCharts), and institutional - level systems (e.g., QuantConnect, AlgoTrader), each with different features [11]. 3.2 Data Interface and Execution Efficiency Evaluation - The quality of data interfaces affects strategy performance. The CTP interface of SHFE can process over 5000 orders per second, and the penetration - style regulatory interface balances data richness and compliance. Different platforms have different order round - trip times (RTT), and developers should choose tools according to strategy types [12]. 4. Strategy Development Process and Practice Guide 4.1 Methodology and Trap Avoidance of Historical Backtesting - Strategy development starts with historical backtesting. Reliable backtesting needs to address issues like survivorship bias, look - ahead bias, and slippage. Backtesting has limited reference value for high - frequency strategies [13]. 4.2 Construction Principles of Risk Control System - A complete risk control module includes fund management, position control, circuit - breaker mechanisms, and exception handling. It should be tested under extreme market conditions, and the risk control system needs continuous optimization in live trading [14]. 5. Live Trading Deployment and Continuous Optimization 5.1 Key Transition from Simulation to Live Trading - It is recommended to use a three - stage transition method: 3 - month simulation trading, 1 - month trial with 10% of live - trading funds, and then gradually increase the position to the target level [15]. 5.2 Wrong - Order Handling and System Monitoring Mechanism - The wrong - order handling system should have multi - level protection, including syntax checking, rationality verification, and emergency processing. A complete log system should be established to record order life cycles for strategy optimization [16]. 6. Typical Case Analysis and Strategy Evolution 6.1 Implementation Path of Market - Maker Strategy - A complete market - maker system includes order - book analysis, quote generation, and risk - hedging modules. For copper futures, factors such as the price difference between SHFE and LME copper and spot premium/discount need to be considered. The income from market - making is gradually decreasing, and higher requirements are placed on speed and strategy [17]. 6.2 Modern Evolution of Trend - Following Strategy - Traditional double - moving - average strategies are being replaced by LSTM - based waveform prediction models. For example, adding a volatility - adaptive mechanism to the iron ore futures breakout system can increase the return - risk ratio by over 15% [18]. 7. Conclusion and Outlook 7.1 Double - Edged Sword Characteristic of Automated Trading - Automated trading has both advantages in execution efficiency and scale and risks such as technical failures and strategy homogenization. Developers should maintain awe of the market and establish a human - machine collaboration mechanism [19]. 7.2 Future Development Directions and Technological Trends - The future development directions of automated trading are AI - driven, multi - modal integration, and compliance - transparent. Individual developers are advised to start with simple rule - based strategies and continuously learn and adapt [20].

Core Viewpoint - Unisoc Co., Ltd. is preparing for a secondary listing on the Hong Kong Stock Exchange, driven by the increasing demand for computing power from its DeepSeek large model, positioning itself as a leading provider of digital and AI solutions in the ICT sector [1][2]. Company Overview - Unisoc is a subsidiary of Tsinghua Unigroup, originally listed on the Shenzhen Stock Exchange in November 1999, and is part of a larger group that includes multiple listed companies in both A-shares and H-shares [1]. - The company ranks third in China's digital infrastructure market and second in both the networking and computing/storage infrastructure markets, according to Frost & Sullivan [1]. Business Model and Revenue - Unisoc offers a comprehensive range of digital solutions, integrating cloud computing, big data, AI, IoT, cybersecurity, and edge computing, which supports various industries in their digital transformation [2]. - The company has four major subsidiaries, with H3C contributing the most to its revenue, recognized as a leading manufacturer of AI servers and Ethernet switches [2]. Financial Performance - Unisoc's revenue has shown steady growth, with reported revenues of approximately 737.52 billion RMB, 775.38 billion RMB, and 790.24 billion RMB for the years 2022, 2023, and 2024, respectively [3]. - The digital solutions segment has become the main revenue driver, accounting for 62.7%, 68.4%, and 70.5% of total revenue in the same years [3][4]. Profitability - The company's net profits from continuing operations were approximately 37.42 billion RMB, 36.85 billion RMB, and 19.82 billion RMB for 2022, 2023, and 2024, respectively, with a declining gross margin from 19.8% to 16.0% over the same period [5]. - Despite a drop in profit for 2024 due to increased costs and reduced margins, the company maintains a strong cash position with 73.17 billion RMB in cash and cash equivalents by the end of 2024 [5]. Market Trends - The global digital solutions market has been growing steadily, projected to increase from 1.5 trillion USD in 2020 to 2.6 trillion USD by 2024, with a compound annual growth rate (CAGR) of 14.1% [7]. - The market is expected to reach 4.8 trillion USD by 2029, with a CAGR of 12.7% from 2024 to 2029, driven by advancements in cloud computing, AI, and other technologies [7]. Competitive Landscape - The company faces increasing competition from major players like Huawei and ZTE, particularly in the telecommunications sector, and must navigate challenges from rising self-developed hardware by cloud service providers [11]. - Unisoc's IPO proceeds are intended for R&D, strategic investments, and global market expansion to strengthen its competitive position [11]. Future Outlook - The successful listing on the Hong Kong Stock Exchange is anticipated to elevate the company's market presence, with potential for strong long-term value driven by technological barriers and favorable policies [12].

Investment Rating - The report does not explicitly provide an investment rating for the smart retail industry Core Insights - The smart retail industry is characterized by the integration of AI, big data, and IoT technologies to enhance consumer experience and operational efficiency, shifting from a product-centric to a consumer-centric model [12][13][27] - The future development trends of smart retail include internal system integration, instant delivery services, personalized recommendations, and catering to the aging population [6][18] Summary by Sections Research Purpose & Summary - The report aims to systematically outline the practical paths, development history, policy environment, application scenarios, and future trends of smart retail [2] Smart Retail Practice Path - The core principle of smart retail is to utilize emerging technologies to gain insights into consumer habits, predict consumption trends, and guide production, focusing on demand forecasting and store analysis [4][15] - Key applications include smart stores and instant retail, which aim to optimize supply chains and enhance consumer experiences through data integration [4][6] Smart Retail Industry Overview - Smart retail is defined as the reconstruction of the retail industry through AI, big data, and IoT, focusing on consumer experience rather than just products [12][13] - The evolution of China's retail industry has progressed through traditional retail, e-commerce, new retail, and now smart retail, with significant technological integration [18][19] Policy Environment - Since 2020, the government has introduced various policies to encourage the smart transformation of retail facilities and the application of technological achievements in consumer sectors, aiming to establish a modern retail system by 2029 [21] Application of Smart Retail - Smart stores serve as the core carriers of smart retail, utilizing technologies like facial recognition and AI to enhance customer interaction and operational efficiency [25][27] - Instant retail, as a significant branch of smart retail, leverages technology and local supply to provide rapid delivery services, with a strong growth trend in lower-tier markets [28][32] Industry Application by Sector - The report highlights various sectors such as supermarkets and restaurants, emphasizing the need for data-driven consumer insights and the integration of online and offline channels to enhance customer engagement and operational efficiency [34][35]

Core Viewpoint - The expansion of service consumption is essential for driving economic growth and improving people's livelihoods, emphasizing the need for a balanced approach that integrates current and long-term goals, as well as supply and demand considerations [1][10]. Group 1: Importance of Service Consumption - Service consumption connects the economy and people's livelihoods, covering various sectors such as dining, accommodation, and healthcare, and is crucial for enhancing living standards and optimizing consumption structure [2][3]. - As China's GDP per capita exceeds $13,000, service consumption is rapidly growing, with a significant increase in residents' willingness to spend on services that meet their spiritual needs [2][7]. - In Q1 2024, service consumption accounted for 43.4% of total household consumption, reflecting its growing importance and potential [2][7]. Group 2: Economic and Employment Impact - Service consumption promotes economic quality improvement and supports multiple industries, creating a virtuous cycle of demand and supply [3][4]. - The service sector is the largest employment reservoir, with 48.1% of jobs in 2023, significantly higher than primary and secondary industries, thus broadening employment opportunities [4][9]. Group 3: Policy and Market Expansion - The National Development and Reform Commission has implemented policies to stimulate service consumption, focusing on innovation and digital transformation to meet consumer needs [6][10]. - By 2024, the service consumption market is projected to grow significantly, with per capita service spending increasing from 5,246 yuan in 2013 to 13,000 yuan in 2024, marking a 148% growth [7][8]. Group 4: Future Trends and Challenges - The demand for high-quality, diverse services in education, healthcare, and cultural tourism is rapidly increasing, with significant potential in sectors like elderly care and home services [8][9]. - Despite the positive trends, challenges such as insufficient quality service supply and a less favorable consumption environment remain, necessitating further efforts to enhance service quality and market openness [10][12].

杭州地处钱塘江、京杭运河、杭甬运河交汇枢纽，拥有航道里程2026公里，通航水域589.1平方公 里。5月29日，在市十四届人大常委会第二十七次会议上，《杭州市水上交通条例（草案）》（下称 《条例（草案）》）提交审议，旨在以地方立法为水运复兴、文旅融合等提供法治保障。 杭州在1998年出台了《杭州水上交通管理条例》《杭州市水上交通事故处理条例》两部地方性法 规，并先后制定了三部政府规章。近年来，相关上位法多次修改，对水上交通管理提出新要求，现行水 上地方性法规已不能适应构建现代化内河航运体系的发展需要。 《条例（草案）》坚持问题导向，结合杭州实际、发挥杭州优势，就上位法规定进行细化，同时针 对水上停靠点和水上活动区域管理要求进行明确。 《条例（草案）》不少细则颇具"杭州味"。例如，明确提出运用物联网、大数据、云计算、人工智 能等技术，提升水上交通管理智能化水平；健全相关信用管理制度，提升水上交通监管能力水平等。同 时为满足"后亚运"时期市民高涨的"亲水热情"，对水上停靠点和水上活动区域管理进行明确。例如，明 确主管部门应当将水上停靠点布局纳入港区规划；通航市域内使用竹筏、橡皮艇、摩托艇等开展漂流、 游乐等水上活 ...

经济观察网讯 6月3日，据上海市经济信息化委官网，为加快构建本市现代化产业体系，促进产业融合 发展，发挥工业服务业对产业升级的赋能作用，上海市经济信息化委制定了《2025年上海市促进产业高 质量发展（工业服务业发展）项目申报指南》，支持范围如下： （原标题：2025年上海市促进产业高质量发展（工业服务业发展）项目申报指南发布，支持范围包括 AI等） 1、支持工业供应链高质量发展 （1）加强数智化技术运用。支持企业开展物流核心算法研发，探索AI大模型等在仓储管理、线路规 划、智能配载等场景的应用。 （2）强化工业物流场景建设。支持企业进一步拓展数智化平台的物流实时调度、仓配一体、可视化跟 踪追溯等功能。 （3）提升全球物流服务能力。重点支持国际物流企业数智化平台建设，实现国内、国际工业物流全链 路协同。 来源：本网综述 （1）提升工业供应链整体解决能力。支持企业建设服务产品矩阵,打造覆盖研发设计、采购（销售）管 理、生产排期、履约交付、售后维保服务于一体的全生命周期解决方案。聚焦人工智能、大数据、区块 链等新技术，支持企业加大软、硬件投入，开展AI本地计算集群部署、云服务器租赁、设备购置，构 建产业级多模态数据 ...

内容概要：近年来，随着国民收入水平不断提高，消费者对珠宝首饰的需求逐渐增加，不仅注重产品的 实用性，更追求品牌、设计和个性化，电子商务平台为消费者提供了丰富多样的珠宝选择，满足了不同 层次消费者的需求，此外，随着互联网的普及和电子商务的发展，消费者越来越习惯在线上购物，尤其 是年轻一代消费者，他们更倾向于通过网络购买珠宝首饰，享受便捷的购物体验，在此背景下，我国珠 宝电子商务市场迅速崛起，据中国珠宝玉石首饰行业协会数据显示，2023年我国珠宝电子商务零售额达 3397.5亿元，同比增长26.21%，2024年，在我国商品零售市场增速放缓的不利背景下，金银珠宝作为非 必需品，与整体市场走势同频共振，其降幅居所统计的全部商品类别的首位，据中国珠宝玉石首饰行业 协会数据显示，2024年中国珠宝电子商务零售额降至2982.6亿元。 上市企业：曼卡龙（300945）、迪阿股份（301177）、老凤祥（600612）、中国黄金（600916）、航民 股份（600987）、菜百股份（605599）、豫园股份（600655）、萃华珠宝（002731）、周生生 （00116.HK）、潮宏基（002345）、明牌珠宝（00257 ...