has three issues per week to explain the solutions of the artificial intelligence industry in detail, bringing AI one step closer to you.
solutions are all selected from the Machine Heart Pro Industry Database.
Solution 1: Mobile unmanned convenience store—Robomamart
Solution introduction :
The developer has developed a mobile unmanned convenience store, which occupies a small area, is low in cost, and is more flexible than traditional stores. The developers load ready-made foods such as groceries and baked goods into autonomous vehicles equipped with refrigeration and heating systems, forming a mobile unmanned convenience store. The convenience store can drive to the buyer's doorstep.
For wholesalers and large retailers that use this mobile convenience store, wholesalers or retailers can retain all customer information on their own when selling without having to hand over customer information to Uber, Postmates, Instacart, etc. (transportation and logistics) companies.
manual selection and delivery of food is very expensive. On-demand customized self-driving store is not only affordable, but also faster and more convenient.
retailers can expand their store footprint at a lower cost without initial capital expenditure.
retailers will be able to use advanced management systems to manage orders, replenish inventory and remote operations, and will have the ability to communicate with customers, store employees and law enforcement through remote operations, and obtain real-time sales and analytical data. Detailed explanation of
solution:
Robomart uses the most advanced automation technology "grab and go" to enable users to shop completely independently. Use autonomous vehicles for delivery services, equipped with the latest cutting-edge technology to charge wireless electric vehicles. Robomart is building software for sensor fusion, control, path planning and obstacle avoidance. Robomart is applying for self-driving vehicle testing license from the California motor vehicle department, working with leading wireless electric vehicle charging station suppliers and using their wireless charging stations as part of Robomar’s products.
This enterprise is a member of the NVIDIA Inception Program program.
Solution 2: Wealth Prediction Robot——Penny
Solution Introduction:
Penny is an intelligent tool created by GBDX, an analysis platform based on DigitalGlobe. By using census data covering New York's high-resolution satellite images to train neural networks, the system's model can discover visual patterns in urban landscapes, such as building shapes, and correlations with income levels of the lot, such as low incomes in lots of parking lots and high incomes in lots of green spaces. Based on these correlations, its artificial intelligence system can achieve predictions on the income of residents in different areas of the city.
researchers also collaborated with data visualization studio Stamen to create an interface that searches for these correlations, which helps to attribution research on income distribution. Detailed explanation of the
solution:
The specific method of training the model is as follows:
. Starting from the income data of the US Census Bureau, the census data around the city is first entered. The census area is then divided into smaller areas to match the satellite image blocks in DigitalGlobe. Coloring these different regions by income level can create a family income map in a city. Green represents the region with the highest annual income quartile (average $71,876 and above), red represents the region with the lowest annual income (average $34,176 and below), and orange represents the middle income levels of $34,176 and $49,904, $49,904 and $71,876, respectively);. Comparing the census data with the DigitalGlobe satellite image, then overlapping the census-based income map created on top of the DigitalGlobe satellite image; . Giving this information to neural networks, using these data layers of the census and satellites to train artificial intelligence to predict average household income in any area of the city. Artificial intelligence models look for patterns in images related to census data. Over time, neural networks have learned what patterns best predict high and low income levels. Then, based on looking at the satellite image, the model can be asked what it thinks the income level is about somewhere.DigitalGlobe operates the world's most advanced commercial image satellites, and GBDX helps developers build new products on DigitalGlobe's high-definition images.
Solution 3: Computational Agronomic Solution Platform - CiBOTechnologies
Solution Introduction: combines software products driven by big data and advanced analysis capabilities, mainly solves the following 6 pain points:
. Farm Service: In order to maintain competitiveness, large farms and companies that provide services to farmers have increased their demand for data utilization. There is currently a lack of tools to integrate large quantities of incoherent data sets from soil samples to income statements;. Agriculture Business: Agribusiness clients have unique needs, limiting factors and opportunities related to their specific environment. Those that best meet their customers’ needs will be able to continue to make profits in the 21st century. The same is true for seed sales and marketing as well as supply chain optimization and product discovery and development. Sustainable development has also become an integral part of agricultural enterprises and will become increasingly important in the coming years. Those who can determine how to combine sustainable operations with profitable and efficient operations will be in a privileged market position; . Sustainable development: Sustainable development is the direction of future agriculture, but there is currently a lack of quantification of the environmental impact on agricultural production; . Insurance: Crop insurance can better manage risks by quantifying yields; 5. Financial services: The vast majority of financial services work on agriculture is based on a statistical understanding of related assets. However, the system of agricultural assets is relatively complex and is related to factors such as farmland potential, weather, and management. Risks need to be better managed to conduct arbitrage and improve systems;
6. Consumer goods: It is difficult to manage global agricultural supply chains. There is a need to provide predictable supply and quality in distributed production systems. In addition, consumers want to understand the source of raw materials production and their impact on the environment.
solution detailed explanation:
uses the following six technologies to achieve six functions in its platform Computational Agronomy:
. Data Fusion: Combined with geospatial science, Continuum DB is a distributed database for spatiotemporal modeling that enables agronomic-related calculations and analysis;. Crop Modeling: BarnCAT (Computational Agronomic Toolkit) is a modeling toolkit for all aspects of plant, soil and agronomic science, helping users answer questions about plants, environment and agriculture. Involved in crop science, plant science, soil science, geospatial science, agronomics, environmental science; . Environmental simulation: WhetherRain is a comprehensive farmland-level random weather generator that uses data science, agronomics and calculations to create detailed weather simulations. involves industries involving crop science, plant science, soil science, geospatial science, agronomics, environmental science and other industries;
. Environmental reconstruction: DirtPatch is an environmental reconstruction platform that combines historical data, mathematical technology and crop science to infer the value of agronomic data. Involves crop science, plant science, soil science, geospatial science, agronomics, environmental science; 5. Simulation of planetary scales: TerraFarm is a large simulation framework that uses lower-level technologies to realize planetary scale agricultural simulation at the daily sub-farmland level. Involves geospatial science, agronomics, environmental science;
6. Derivative view: Emergence is a component that can form insights inside and outside the computing agronomic platform. Aggregation, summary, optimization and visualization of results appear to help solve business problems. Involves geospatial science.
Solution 4: Data Analysis Tool - Warren
Solution Introduction:
uses leading cloud search and filtering technology, and computer programs automatically capture and collect Internet financial data, policy events, news announcements and other information in 7x24, update them simultaneously with mainstream financial media, and establish a powerful financial database. Integrate various cutting-edge technologies to apply them to the field of financial analysis, and evolve complex financial data analysis that originally required massive professional knowledge and a lot of time and energy into self-service Q&A. There is a huge library of concept themes, providing data support for financial analysis.It can update the latest prices in real time, automatically upload the daily limit data, and based on the sector information, company information, company historical daily limit data and current market performance. It can answer complex financial market questions, such as various data, stock trends, etc., and can answer about 1 million English questions about the impact of global events on stock prices. With a powerful artificial intelligence system as support, the logical analysis and judgment function is automatically improved through users' selection of search results and the continuous collection and improvement of financial data. Detailed explanation of the
solution:
. Let the functions of the software imitate the workflow of financial engineers as much as possible. This includes collecting, integrating and preliminary analysis of various related historical data;. Use the correlation analysis method of big data to calculate thousands of variables to find meaningful association relationships and patterns; . For specific events that occur in real time, use the corresponding correlation modes and algorithm logic established by the model to simulate and calculate, so as to predict the trend of a certain type of stock or a specific stock over a period of time; . Compare the prediction results with the actual occurrence results, and adjust the patterns accordingly accordingly according to their success and failure probability until the prediction probability is very close to the actual result; 5. Store this successful module in the entire cloud computing model and classify it for trend prediction of specific events in the future.
Solution 5: Log Analysis and Management Tool
Solution Introduction:
provides a software solution for processing big data. Its highly scalable distributed system architecture design supports dozens of TB of new data every day. Real-time and timely alarms are provided for specific baselines, fixed thresholds, dynamic baselines, etc., and send daily, weekly, and monthly reports through email, WeChat , telephone, remote interface, etc. Provides search boxes that can be programmed with SPL to implement valid indexing of logs. Through SPL statistics, or the quick statistics menu, create various visual effects such as pie charts, bar charts, line charts, maps, etc. to assist users in tracking events and highlight key KPI status to highlight exceptions.
The traditional operation and maintenance and data analysis methods are difficult to centrally manage due to the complex log data, and it is even more impossible to conduct correlation analysis. By auditing of network equipment and security equipment, logs can allow users to:
. Monitor network equipment in real-time health through logging means to effectively supplement the shortcomings of network management software;. Meet national level protection requirements, and collect and store network equipment and security equipment logs; . Automatically output daily security daily/weekly/monthly reports to provide work efficiency for security operation and maintenance personnel; . Through analysis of security equipment logs, security logs and attack traceability analysis can be effectively realized, network security management can be strengthened, and network security levels can be provided. In addition, in 2015, FortScale survey reported that 85% of data breaches were caused by internal threats, so user behavior audits (UBAs) at all links of the intranet are becoming increasingly important. And the software can assist in defense by analyzing content user behavior. Detailed explanation of
solution:
. Log Easy provides desensitization function to log data, and the downloaded data is also desensitized. Log Easy supports the full life cycle management of logs, supports the configuration of life cycles of different types of logs, supports index backup, supports interface log recovery, supports full-text retrieval, and facilitates users to implement: ) Meets the requirements of the Network Security Law;) Meets the log query requirements of the regulatory department; ) Implements data life cycle management, providing both plain text data queries and desensitized data queries, which can not only realize real-time data fast searches, but also realize historical data restoration searches. . Log Easy can realize log audit of network equipment and security equipment. ) Network equipment audit. Real-time monitoring of logs of all network equipment: including the log volume trends and log level distribution of each network device. Through IP address and log level filtering, you can coordinate the statistics and query the event trends of a certain abnormal network device. The log levels and log details are respectively, so as to facilitate and quickly locate faults; ) Firewall log audit.Provides log level, event code, and five-tuple (source address, destination address, source port, destination port, access protocol) for users to search and filter. Users can quickly trace logs based on source IP, log level, etc. Output the firewall log five-tuple inspection daily report. By analyzing the firewall logs, identify suspicious scan source IP information, as well as the scanned suspicious destination IP and destination port information; ) Real-time attack profile analysis of IPS log audit In addition, there are attack detailed analysis, email attack analysis, SQL injection attack analysis, Web attack analysis, etc.; ) Security device log analysis Scenario log can easily implement hundreds of security device event statistics rules, such as malware access information statistics, including malware source IP distribution, malware destination IP distribution, malware service distribution, malware name, service, number of events and percentages, etc. Each statistics can be customized. not only realizes security behavior audit, but also assists in intranet operation and maintenance analysis, and achieves defense against intranet security. It is an important part of the Security Operation Center (SOC).
