Digital Logbook
Unlock Efficiency and Streamline Operations with
our Digital Logbook Solution
Features Of The Digital Logbook
Features Of The Digital Logbook
Web-based Platform
Our Digital Logbook is accessible anytime, anywhere. Whether you prefer on- premises or cloud deployment, our platform provides the flexibility to suitHighlighting how Digital Thread saves time and resources by eliminating manual data entry and transfer, allowing employees to focus on more value-added tasks your specific needs.
Customizable Checklists
Tailor your checklists to fit your unique requirements. With our platform, you can easily create and configure checklists, define the order of checklist items, and provide additional instructions for each step.
Digital Recording of Information
Say goodbye to stacks of paper logs. Our solution enables seamless digital recording of information, streamlining the process and eliminating the risks associated with paper-based systems.
Checklist Approval Workflow
Ensure accuracy and compliance with our checklist approval workflow. Define the appropriate review and approval processes, enhancing accountability and control.
Instant Access to Information
Retrieve your data in an instant. Our platform offers real-time availability of recorded information, empowering you with quick access for analysis, reporting, and audits.
Benefits of Digital Logbook
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Digital Logbook Operation Rules
Functional Administrator
- Defines production lines and workshops
- Defines systems users and roles
- Configures different masters and checklists
Operator
- Views schedule
- Files checklist
- Views filled checklist
Line Incharge
- Approves filled check by operator
- Files checklist for production lines
- Monitors summary and reports
- Defines checklist-schedule for workstations
Process Incharge
- Configures different masters and checklist.
- Defines checklist-schedules.
- Files checklist for plant if applicable.
- Monitors reports and expectations from filled checklist.
Technical Features
Digital Logbook Implementation
POPULAR NEWS
Featured Posts
Intelligent Vision in the Mining Industry
In the year 1996, Martin Cruz Smith wrote on his novel, Rose, the following lines: “Then there was the whole concept of coal mining, which is a culture unto itself, the most dangerous occupation in the world, and which draws and develops a certain kind of man.” The occupation of mining, be it coal or gold, has remained one of the least sought-after professions in the world. Considering the hazards and the hard-work it brings, it is of no wonder that it was always well suited for a certain breed of men. Hazards are a common occurrence under the mines. Many factors contribute to the danger in the mining industry. They can be: Now that technology is growing rapidly bringing new solutions to the forefront, it has smartened up the safety, efficiency, productivity-related concerns in the mining industry. Artificial intelligence has been introduced in this sector some years ago, but AI could only sort out the repetitive work. There is a necessity for a system that can expand the scope of automation in the mining industry. What if there is an automated supervising authority that tracks, traces, records, predicts, and keeps overall watchful surveillance on every mining section? Well, an artificial intelligence-powered intelligent vision-based system could be a deciding factor in this regard. Here is how intelligent vision-based system could revolutionize the way the mining industry operates: Automated miner tracker A crucial aspect of such an intelligent system would be acting as “machine supervisors”. That means the computer vision can capture snapshots and videos with the help of smart cameras, reducing the need for human supervisors to oversee mining tasks. The shots and videos would be stored, analyzed later to monitor every mine worker, and implement necessary steps to improve productivity. Such vision solutions could help in the evaluation of a worker’s activity from day to day and monitoring various trajectories of people to strengthen security and process-related concerns. Tracking mining equipment and automation Vision-based intelligent tracking can track scheduled task status, real-time production, map the operator and equipment location, predict productivity based on analyzing underground mining sites, and even churn out safety instructions. Seems far-fetched but this is the future. Threat detection This is a grave concern in the mining industry. Any sort of miscalculated step, a mishap, or deliberate activity could put lives in jeopardy. A vision-based system could negate that by determining outside threats such as any callous action that puts men and machines in danger, detecting any unusual vehicle activity, or even catching unscrupulous personnel or presence of weapons/firearms. The immediate call would be notified automatically to the security team or police force. Smart database The most prominent facet of intelligent vision is its immense database and its ability to read, process data captured. It can collect and churn out useful data and save them in a database server. The data is processed into various data inputs like MySQL, pdf, or excel as desired by the mining team. The system can automate data pipeline processes and form a readable human-friendly interface. This improves data quality and transparency. The biggest catch is, it would save a lot of time and reduce human interference. Identification and classification of raw materials This is where intelligent vision could be a real game-changer. Instead of spending hours and days in manually reviewing image samples, the implementation of such solutions would fasten up the identification and classification of rock/ore samples. A highly trained AI vision could even determine the sort of compounds discovered just by evaluating drilling data. The next step would be labeling incoming compound samples just by scanning images or running stored information. This segment of intelligent vision has the potential to overhaul the mining industry. Miscellaneous Here are a few more ways computer-based vision system could change how mining operations are conducted: Powered by data and AI, the computer-based intelligent vision has immense potential to change the mining game forever. It is to be noted that the above-mentioned possibilities are not the only ones to consider and there could be more uses of such intelligent solutions. Smart collaboration makes smart results and the industry has ample amount of scope to deliver just that.
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Computer Vision – The past and the present
Artificial Intelligence or in short AI happens to be that one game-changer that has all the means to alter the world of technology and innovation in gigantic proportions. Here, we are looking into one of the most powerful and captivating facets of AI which is Computer Vision, something that has permeated in our lives but most of us do not have a clue about it. Computer vision involves mimicking the intricacy of the human visual system by machine and identifying and processing images and videos in the same way that humans do. Credits go to advancements and innovations in artificial intelligence, deep learning, and neural networks, which has propelled the field across multiple successive levels and even surpassed humans in tasks like detection or labeling objects. Data generation is another driving factor behind such a rapid growth of computer vision, as we generate large amounts of data every day. The Past Although computer vision has tonnes of applications today which are growing exponentially, it first appeared in the 1950s. After almost two decades of experiments, it was in the 1970s when it witnessed commercial usage which was to distinguish between typed and handwritten text. So, what prompted such an experiment? It stems from a question related to Neuroscience which is how do our brains work. Let us climb up from the basic purpose of computer vision. It is all about recognizing similar features. Hence for a machine to understand such pictorial data, it needs to feed on thousands to millions of images. The next step is to run them through specifically made software processes, or algorithms, that prompts the computer to clamp down and identify similar patterns in all the entities. Quite revolutionary for its time but it was a painstaking task and required lots of coding and other efforts by the creators and their operators. Also, this was before the advent of deep learning, so the scope of computer vision was limited. Let us take the case of facial recognition, and here is the conventional way to do that: Remember, in the early days, there was little automation involved and hence everything needed to be executed manually. The Present The advent of deep learning presented a different approach to fixing computer vision issues. It removed the aspect of manually coding every single rule into the application. It introduced specific features, like small sets of applications that can detect and identify particular attributes in images, like its dimensions. The features then apply algorithms such as logistic regression, linear regression, decision trees, etc to identify patterns, classify images and detect objects in them. Machine learning made it quick and it took considerably less amount of time to decipher an image. Today, we have ultra-fast processors and chips with related hardware coupled with a reliable, fast internet, and of course, the cloud servers, making the entire process extremely fast. The advantage of merging cloud with computer vision is that there is always a ready-to-use repository with millions of processed images, hence this enables us to build upon previous work rather than starting from zero. As the AI industry keeps on evolving, tasks that used to take a week take 5 minutes today. Some computer vision for real-world applications takes microseconds today. Uses of intelligent vision Face recognition Facial recognition is probably the best application to come out of computer vision. Facial recognition involves algorithms that identify facial features in images and runs them through the massive repository of stored images to compare. Facial recognition first found its niche in the police department and other law enforcement bodies who use this technology to identify criminals and suspects in video recordings. Now facial recognition is found in handsets and other consumer devices that authenticate owner identities. Social media uses facial recognition to identify and verify users. Augmented reality Augmented reality or AR is a branch of technology that allows commercial computing devices to project upon and embed virtual entities on real-life features. Computer vision embedded in AR detects real-world objects to identify them as displayed on the device screen and place a computer-generated unreal entity. Self-driving cars Self-driving cars are a recent phenomenon and they are making a lot of news lately. Undoubtedly, it has been made possible by the advancements in computer vision, which is an integral part of self-driving vehicles. Computer vision helps self-driving cars to be aware of their vicinity. The smart cameras fixed on these cars capture real-time footage from different angles and feed it to the plugged-in repository. The captured footages are processed to ascertain different factors on the roads such as traffic signals, other cars, and pedestrians, lying objects, roadblocks, and road features. The self-driving car simultaneously steers its way through the roads, while avoiding hindrances, and safely making its way towards its endpoint. Self-driving is still in the build-up stage and only time will decide how far it evolves. Industrial fleet management This is where intelligent computer vision finds a significant placement. From identifying and tracking all the automotive vehicles in a fleet along with their operators to monitor the routes taken, pit-stops, operator behavior, prediction of circumstance, streamlining the supply chain, reducing manual labor costs; the industrial field has benefitted immensely from computer vision and it is an ever-evolving process. Computer vision has put a benchmark on the world of technology and certainly is an astounding feature to come out of AI. As AI keeps on evolving at a rapid pace, computer vision is becoming increasingly mainstream in our daily lives. What is more amazing is that AI is yet to reach its prime and computer vision is just the tip of the iceberg for whatever that is about to come.
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Smart camera network – yesterday and today
The last decade saw a remarkable expansion of smart video surveillance networks. There is a worldwide commotion about smart video cameras that use facial recognition technology to record and track every single move the public makes. Governments in some countries have installed millions of surveillance cameras in every nook and corner in a bid to track any crime or mishap. Companies and even private homes are plugging in their cameras into police networks. In addition to that, the meteoric advancements made in the artificial intelligence (AI) field have brought in new edge and capabilities to harness security surveillance potentialities. The emergence of all-seeing smart camera networks is a ground-breaking development that aims to help and support law enforcement agencies to track the suspected, keep records, solve a crime and make the society a decent place to live in. Although law enforcement agencies have been using CCTV cameras since as far as the ’90s, there has been a sharp rise in smart cameras and sensors past few years. What is the reason for such emergence though of a smart video surveillance system? To understand the phenomenon, we have to take a walk through its history, the prime players that led to such a massive development among cameras. The early years In the initial years i.e. in their first decade since introduction, the humble CCTV cameras were low-resolution devices that recorded onto tapes. They were mostly deployed by businesses or city authorities to film a specific area of interest. Although CCTVs saw their place in public, it was not widespread like how it is today. Collecting footages from camera records would take hours before coming into a fruitful solution. In the late 1990s, a video surveillance system stepped up a level with the first internet-enabled surveillance camera being introduced to the market, which converted moving images and recorded clips into digital data. The video management system or VMS was introduced later which organized and allocated video information into databases. Although it is possible to plug-in large numbers of surveillance cameras into a VMS, it remains an expensive task. As a result, city councils came up with a unique idea, which was urging businesses and privately owned residences to install their cameras into the law enforcement server. Now that the number of cameras on police networks are thousands in number and only a small team of operators to watch them, law enforcement agencies face a new issue of concluding such a vast array of captured footage. This is where analytics came in. Video analytics is a revolutionary aspect that came around 2006, which worked on gathering all the footage, aggregating the images, and summarizing long videos. Video analytics itself came with three specific intents: Behavior recognition: It includes capabilities like violence detection, emotion recognition, fall detection, fire, and smoke detection, erratic behavior, loitering, jaywalking, freeway toll fare evasion, and even lie detection. Object recognition: It includes capabilities like recognizing faces, cars, weapons, animals, fires, and other things, as well as human characteristics like age, gender, and hair color. Anomalous behavior recognition: This field of detection works by focusing and recording a fixed area for a specific period and determining the circumstances to be deemed normal for that scene. If the camera spots something unusual, it flags the incident for attention. Video analytics system capabilities range from searching and analyzing throughout recorded footage or real-time streams to highlighting individuals or objects as it traverses a smart camera network. Video analytics covers a wide array of information retrieved from smart camera networks and converts them into meaningful data. The recent years CCTV surveillance has emerged into a multibillion-dollar industry that encompasses a wide range of industry verticals. It served as a major overhaul in police and law enforcement, found its place in health care facilities and educational institutions, became an integral part of any retail or commercial setup. CCTV surveillance and monitoring gave rise to a specific set of vendors who excel in providing such services. It incorporates and drives the whole ecosystem of tech companies such as Amazon, IBM, Microsoft, Cisco, and Verizon who have started to merge such networks with other tech verticals like broadband connectivity, cloud services, or video surveillance software. The UK has over 6 million video surveillance cameras installed throughout the country as of 2020 With the private and public sector eyeing for an expansion of video surveillance, smart cameras network has become a new cash cow. It is predicted that there will be something around 50 billion cameras in the world in the next 30 years. Although video surveillance started as a staple in law enforcement, private firms have started diversifying and driving such systems and/or products towards the commercial sector. The retail industry benefitted a lot from this system in particular, as they could now monitor queue length, amount of footfall, analyze shopping patterns, and chalk out floor layouts as per their convenience. Both public and private sectors agree on the further implementation of smart video surveillance. In the United States, smart camera networks are just emerging, and with the advent of “Internet-of-things” or IoT, the smart vision surveillance network is amplified into many folds to give precise and as a required outcome. Moreover, their installation and applications are being facilitated by almost every tech industry out there. In future cameras, networks will be required to respond to any given situation autonomously or with minimal human interaction.
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