System and methods for dynamic geospatially-referenced cyber-physical infrastructure inventory and asset management

A system and method for dynamic geospatially-referenced cyber-physical infrastructure inventory and asset management in which a wireless computing device is attached to the physical assets in an inventory, wherein each wireless computing device tracks at least one characteristic of the physical asset to which it is attached, such as the location of the physical asset, and periodically transmits an encrypted message to a second computer, which verifies the identification of the wireless computing device and that the contents of the message have not been changed, and updates the record of the physical asset in a database.

BACKGROUND OF THE INVENTION

Field of the Art

The disclosure relates to the field of asset tracking and management, more specifically to the field of crypto-ledger or block chain technology and its uses for managing inventory assets.

Discussion of the State of the Art

Currently, it is possible for corporations and individuals to track certain assets in certain ways, to ensure their safety and ensure valid operation. For example, it is possible to track packages shipped via many shipping corporations, and it is possible and commonplace to have temperature controls and monitoring in certain environments such as libraries and wine cellars. However, the breadth and depth of sensor monitoring with cyber-enabled physical assets is low, and the cost and barrier of entry into such endeavors is usually quite high. What's more, in some situations, again referring to tracking shipping packages, one is relying not on a sensor to report the location of the package in question; rather, one is relying on human operators to reliably report the presence of the object at disparate facilities. Human interaction, and therefore human error, is present in many of these situations and more, such as the issue of contractual obligations involving the physical status of objects or areas, which must be enforced by other humans, and therefore involve unknown variables into the business transaction.

What is needed is a system and methods for autonomous sensor data monitoring of multiple various forms of cyber-physical assets, to improve supply chain risk management, and engaging these assets in the use of self-fulfilling smart contracts.

SUMMARY OF THE INVENTION

Accordingly, the inventor has conceived and reduced to practice, in a preferred embodiment of the invention, a system and methods for dynamic geospatially-referenced cyber-physical infrastructure inventory and asset management. The following non-limiting summary of the invention is provided for clarity and should be construed consistently with embodiments described in the detailed description below.

To solve the problem of assets being unreachable by remote monitoring and smart-contract systems, a system and method have been devised for dynamic geospatially-referenced cyber-physical infrastructure inventory and asset management, including a business operating system, parameter evaluation engine, at least one cyber-physical asset, at least one crypt-ledger, a network, and the ability to represent data in Markov State Models and finite state machines. It is also possible for the system and methods provided herein to be applied to use case of a mobile or stationary processing facility, which may process objects and send status updates on what objects it is processing to an operating system either remotely or locally hosted, for continuous monitoring.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system and methods for dynamic geospatially-referenced cyber-physical infrastructure inventory and asset management.

Definitions

As used herein, a “swimlane” is a communication channel between a time series sensor data reception and apportioning device and a data store meant to hold the apportioned data time series sensor data. A swimlane is able to move a specific, finite amount of data between the two devices. For example a single swimlane might reliably carry and have incorporated into the data store, the data equivalent of 5 seconds worth of data from 10 sensors in 5 seconds, this being its capacity. Attempts to place 5 seconds worth of data received from 6 sensors using one swimlane would result in data loss.

As used herein, a “metaswimlane” is an as-needed logical combination of transfer capacity of two or more real swimlanes that is transparent to the requesting process. Sensor studies where the amount of data received per unit time is expected to be highly heterogeneous over time may be initiated to use metaswimlanes. Using the example used above that a single real swimlane can transfer and incorporate the 5 seconds worth of data of 10 sensors without data loss, the sudden receipt of incoming sensor data from 13 sensors during a 5 second interval would cause the system to create a two swimlane metaswimlane to accommodate the standard 10 sensors of data in one real swimlane and the 3 sensor data overage in the second, transparently added real swimlane, however no changes to the data receipt logic would be needed as the data reception and apportionment device would add the additional real swimlane transparently.

Conceptual Architecture

FIG. 1is a diagram of an exemplary architecture of a system for the capture and storage of time series data from sensors with heterogeneous reporting profiles according to a preferred aspect of the invention. In this embodiment, a plurality of sensor devices110a-nstream data to a collection device, in this case a web server acting as a network gateway115. These sensors110a-ncan be of several forms, some non-exhaustive examples being: physical sensors measuring humidity, pressure, temperature, orientation, and presence of a gas; or virtual such as programming measuring a level of network traffic, memory usage in a controller, and number of times the word “refill” is used in a stream of email messages on a particular network segment, to name a small few of the many diverse forms known to the art. In the embodiment, the sensor data is passed without transformation to the data management engine120, where it is aggregated and organized for storage in a specific type of data store125designed to handle the multidimensional time series data resultant from sensor data. Raw sensor data can exhibit highly different delivery characteristics. Some sensor sets may deliver low to moderate volumes of data continuously. It would be infeasible to attempt to store the data in this continuous fashion to a data store as attempting to assign identifying keys and the to store real time data from multiple sensors would invariably lead to significant data loss. In this circumstance, the data stream management engine120would hold incoming data in memory, keeping only the parameters, or “dimensions” from within the larger sensor stream that are pre-decided by the administrator of the study as important and instructions to store them transmitted from the administration device112. The data stream management engine120would then aggregate the data from multiple individual sensors and apportion that data at a predetermined interval, for example, every 10 seconds, using the timestamp as the key when storing the data to a multidimensional time series data store over a single swimlane of sufficient size. This highly ordered delivery of a foreseeable amount of data per unit time is particularly amenable to data capture and storage but patterns where delivery of data from sensors occurs irregularly and the amount of data is extremely heterogeneous are quite prevalent. In these situations, the data stream management engine cannot successfully use strictly single time interval over a single swimlane mode of data storage. In addition to the single time interval method the invention also can make use of event based storage triggers where a predetermined number of data receipt events, as set at the administration device112, triggers transfer of a data block consisting of the apportioned number of events as one dimension and a number of sensor ids as the other. In the embodiment, the system time at commitment or a time stamp that is part of the sensor data received is used as the key for the data block value of the value-key pair. The invention can also accept a raw data stream with commitment occurring when the accumulated stream data reaches a predesigned size set at the administration device112.

It is also likely that that during times of heavy reporting from a moderate to large array of sensors, the instantaneous load of data to be committed will exceed what can be reliably transferred over a single swimlane. The embodiment of the invention can, if capture parameters pre-set at the administration device112, combine the data movement capacity of two or more swimlanes, the combined bandwidth dubbed a metaswimlane, transparently to the committing process, to accommodate the influx of data in need of commitment. All sensor data, regardless of delivery circumstances are stored in a multidimensional time series data store125which is designed for very low overhead and rapid data storage and minimal maintenance needs to sap resources. The embodiment uses a key-value pair data store examples of which are Riak, Redis and Berkeley DB for their low overhead and speed, although the invention is not specifically tied to a single data store type to the exclusion of others known in the art should another data store with better response and feature characteristics emerge. Due to factors easily surmised by those knowledgeable in the art, data store commitment reliability is dependent on data store data size under the conditions intrinsic to time series sensor data analysis. The number of data records must be kept relatively low for the herein disclosed purpose. As an example one group of developers restrict the size of their multidimensional time series key-value pair data store to approximately 8.64×104records, equivalent to 24 hours of 1 second interval sensor readings or 60 days of 1 minute interval readings. In this development system the oldest data is deleted from the data store and lost. This loss of data is acceptable under development conditions but in a production environment, the loss of the older data is almost always significant and unacceptable. The invention accounts for this need to retain older data by stipulating that aged data be placed in long term storage. In the embodiment, the archival storage is included130. This archival storage might be locally provided by the user, might be cloud based such as that offered by Amazon Web Services or Google or could be any other available very large capacity storage method known to those skilled in the art.

Reliably capturing and storing sensor data as well as providing for longer term, offline, storage of the data, while important, is only an exercise without methods to repetitively retrieve and analyze most likely differing but specific sets of data over time. The invention provides for this requirement with a robust query language that both provides straightforward language to retrieve data sets bounded by multiple parameters, but to then invoke several transformations on that data set prior to output. In the embodiment isolation of desired data sets and transformations applied to that data occurs using pre-defined query commands issued from the administration device112and acted upon within the database by the structured query interpreter135. Below is a highly simplified example statement to illustrate the method by which a very small number of options that are available using the structured query interpreter135might be accessed.

Here “data_spec” might be replaced by a list of individual sensors from a larger array of sensors and each sensor in the list might be given a human readable identifier in the format “sensor AS identifier”. “unit” allows the researcher to assign a periodicity for the sensor data such as second (s), minute (m), hour (h). One or more transformational filters, which include but a not limited to: mean, median, variance, standard deviation, standard linear interpolation, or Kalman filtering and smoothing, may be applied and then data formatted in one or more formats examples of with are text, JSON, KML, GEOJSON and TOPOJSON among others known to the art, depending on the intended use of the data.

FIG. 2is a diagram of an exemplary architecture of a business operating system200according to a preferred aspect. Client access to the system205both for system control and for interaction with system output such as automated predictive decision making and planning and alternate pathway simulations, occurs through the system's highly distributed, very high bandwidth cloud interface210which is application driven through the use of the Scala/Lift development environment and web interaction operation mediated by AWS ELASTIC BEANSTALK™, both used for standards compliance and ease of development. Much of the business data analyzed by the system both from sources within the confines of the client business, and from cloud-based sources, also enter the system through the cloud interface210, data being passed to the analysis and transformation components of the system, the directed computational graph module255, high volume web crawling module215and multidimensional time series database220. The directed computational graph retrieves one or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a number of physical sensors, web-based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and human input device information. Within the directed computational graph, data may be split into two identical streams, wherein one sub-stream may be sent for batch processing and storage while the other sub-stream may be reformatted for transformation pipeline analysis. The data is then transferred to general transformer service260for linear data transformation as part of analysis or decomposable transformer service250for branching or iterative transformations that are part of analysis. The directed computational graph255represents all data as directed graphs where the transformations are nodes and the result messages between transformations edges of the graph. These graphs which contain considerable intermediate transformation data are stored and further analyzed within graph stack module245. High volume web crawling module215uses multiple server hosted preprogrammed web spiders to find and retrieve data of interest from web-based sources that are not well tagged by conventional web crawling technology. Multiple dimension time series database module220receives data from a large plurality of sensors that may be of several different types. The module is designed to accommodate irregular and high volume surges by dynamically allotting network bandwidth and server processing channels to process the incoming data. Data retrieved by the multidimensional time series database220and the high volume web crawling module215may be further analyzed and transformed into task optimized results by the directed computational graph255and associated general transformer service250and decomposable transformer service260modules.

Results of the transformative analysis process may then be combined with further client directives, additional business rules and practices relevant to the analysis and situational information external to the already available data in the automated planning service module230which also runs powerful predictive statistics functions and machine learning algorithms to allow future trends and outcomes to be rapidly forecast based upon the current system derived results and choosing each a plurality of possible business decisions. Using all available data, the automated planning service module230may propose business decisions most likely to result is the most favorable business outcome with a usably high level of certainty. Closely related to the automated planning service module in the use of system derived results in conjunction with possible externally supplied additional information in the assistance of end user business decision making, the business outcome simulation module225coupled with the end user facing observation and state estimation service240allows business decision makers to investigate the probable outcomes of choosing one pending course of action over another based upon analysis of the current available data. For example, the pipelines operations department has reported a very small reduction in crude oil pressure in a section of pipeline in a highly remote section of territory. Many believe the issue is entirely due to a fouled, possibly failing flow sensor, others believe that it is a proximal upstream pump that may have foreign material stuck in it. Correction of both of these possibilities is to increase the output of the effected pump to hopefully clean out it or the fouled sensor. A failing sensor will have to be replaced at the next maintenance cycle. A few, however, feel that the pressure drop is due to a break in the pipeline, probably small at this point, but even so, crude oil is leaking and the remedy for the fouled sensor or pump option could make the leak much worse and waste much time afterwards. The company does have a contractor about 8 hours away or could rent satellite time to look but both of those are expensive for a probable sensor issue, significantly less than cleaning up an oil spill though and then with significant negative public exposure. These sensor issues have happened before and the business operating system200has data from them, which no one really studied due to the great volume of columnar figures, so the alternative courses225,240of action are run. The system, based on all available data predicts that the fouled sensor or pump are unlikely the root cause this time due to other available data and the contractor is dispatched. She finds a small breach in the pipeline. There will be a small cleanup and the pipeline needs to be shutdown for repair but multiple tens of millions of dollars have been saved. This is just one example of a great many of the possible use of the business operating system, those knowledgeable in the art will easily formulate more.

FIG. 3is a diagram of an exemplary architecture of an automated planning service module and related modules300according to an embodiment of the invention. Seen here is a more detailed view of the automated planning service module230as depicted inFIG. 2. The module functions by receiving business decision or business venture candidates as well as relevant currently available related data and any campaign analysis modification commands through a client interface305. The module may also be used provide transformed data or run parameters to the action outcome simulation module225to seed a simulation prior to run or to transform intermediate result data isolated from one or more actors operating in the action outcome simulation module225, during a simulation run. Significant amounts of supporting information such as, but not restricted to current business conditions, infrastructure, ongoing venture status, financial status, market conditions, and world events which may impact the current decision or venture that have been collected by the business operating system as a whole and stored in such data stores as the multidimensional times series database220, the analysis capabilities of the directed computational graph module255and web-based data retrieval abilities of the high volume web crawler module215all of which may be stored in one or more data stores320,325may also be used during simulation of alternative business decision progression, which may entail such variables as, but are not limited to implementation timing, method to end changes, order and timing of constituent part completion or impact of choosing another goal instead of an action currently under analysis.

Contemplated actions may be broken up into a plurality of constituent events that either act towards the fulfillment of the venture under analysis or represent the absence of each event by the discrete event simulation module311which then makes each of those events available for information theory based statistical analysis312, which allows the current decision events to be analyzed in light of similar events under conditions of varying dis-similarity using machine learned criteria obtained from that previous data; results of this analysis in addition to other factors may be analyzed by an uncertainty estimation module313to further tune the level of confidence to be included with the finished analysis. Confidence level would be a weighted calculation of the random variable distribution given to each event analyzed. Prediction of the effects of at least a portion of the events involved with a business venture under analysis within a system as complex as anything from the microenvironment in which the client business operates to more expansive arenas as the regional economy or further, from the perspective of success of the client business is calculated in dynamic systems extraction and inference module314, which use, among other tools algorithms based upon Shannon entropy, Hartley entropy and mutual information dependence theory.

Of great importance in any business decision or new business venture is the amount of business value that is being placed at risk by choosing one decision over another. Often this value is monetary but it can also be competitive placement, operational efficiency or customer relationship based, for example: the may be the effects of keeping an older, possibly somewhat malfunctioning customer relationship management system one more quarter instead of replacing it for $14 million dollars and a subscription fee. The automated planning service module has the ability predict the outcome of such decisions per value that will be placed at risk using programming based upon the Monte Carlo heuristic model316which allows a single “state” estimation of value at risk. It is very difficult to anticipate the amount of computing power that will be needed to complete one or more of these business decision analyses which can vary greatly in individual needs and often are run with several alternatives concurrently. The invention is therefore designed to run on expandable clusters315, in a distributed, modular, and extensible approach, such as, but not exclusively, offerings of Amazon's AWS. Similarly, these analysis jobs may run for many hours to completion and many clients may be anticipating long waits for simple “what if” options which will not affect their business operations in the near term while other clients may have come upon a pressing decision situation where they need alternatives as soon as possible. This is accommodated by the presence of a job queue that allows analysis jobs to be implemented at one of multiple priority levels from low to urgent. In case of a change in more hypothetical analysis jobs to more pressing, job priorities can also be changed during run without loss of progress using the priority based job queue318.

Structured plan analysis result data may be stored in either a general purpose automated planning engine executing Action Notation Modeling Language (ANML) scripts for modeling which can be used to prioritize both human and machine-oriented tasks to maximize reward functions over finite time horizons317or through the graph-based data store245, depending on the specifics of the analysis in complexity and time run.

The results of analyses may be sent to one of two client facing presentation modules, the action outcome simulation module225or the more visual simulation capable observation and state estimation module240depending on the needs and intended usage of the data by the client.

FIG. 4is a system diagram illustrating connections between core components of the invention for geo-locating and tracking the status of cyber-physical assets, according to a preferred aspect. A business operating system410operates an optimization engine411, parametric evaluation engine412, and uses abstract data representations413including Markov State Models (MSM)414and abstract representations of finite state machines415to read, modify, and generally operate on data. A business operating system410such as this is connected to a network450, which may be an intranet, the internet, a local area connection, or any one of many other configurations of networks. Also connected to this network450is at least one database420, which holds information including a crypto-ledger421, an implementation of a blockchain data construct, which will be expounded upon in later figures. Connected to a network450is at least one cyber-physical asset430,440, which may hold any number of sensors or data according to a specific implementation, and have geoJSON431,441data with which to record their geo-physical location. A cyber-physical asset430,440may be a delivery crate with a possible plurality of sensors and computers embedded or attached to the crate in some way, or may be an object inside a mundane crate such as a piece of research equipment which may communicate with a business operating system410during transit, or may be a stationary object such as research equipment, computer systems, and more, which are capable of sending status updates at least consisting of geoJSON431,441information regarding their geophysical location over a network450. A business operating system may use a Markov State Model (MSM)414as a tool for data representation of the states of cyber-physical assets which send status updates in this way, and may or may not reduce a MSM to a finite state machine representation415with or without stochastic elements, according to a preferred aspect. These data representations413are useful for visualizing and analyzing current, previous, and possible future states of assets430,440connected to an operating system410over a network450.

FIG. 5is a method diagram illustrating key steps in the communication between cyber-physical assets430,440and remote servers running a business operating system410, according to a preferred aspect. Any relevant sensors or sensing equipment and software must be installed on the asset510first, before relevant data can be sent to a business operating system410. Such sensors may include a variety of implementations, including temperature sensors, GPS tracking software, accelerometers, or any other sensors and accompanying hardware and software as needed or desired by the user upon implementation of this system. The cyber-physical asset430,440will maintain, as part of their software involvement in the system, a private key, and the requisite software for a crypto-ledger421implementation520using blockchain technology. Blockchain technology is essentially a method for secure message sending between network connected devices, often used for the purposes of transaction ledgers and smart contracts, using asymmetric encryption. The cyber-physical asset will be in communication with a business operating system410either continuously or at set intervals530, depending on individual implementations, according to a preferred aspect. During these communications, the asset will, using the asymmetric encryption in blockchain crypto-ledgers, send status updates based on any sensors installed on the asset530. A business operating system that receives these updates will then verify them with previous status updates in databases540to ensure that the updates received are legitimate, and not forged or from a dubious source. If the public key, or signature, or contents of the encrypted message are not able to be verified properly, the ledger held in at least one database is not updated560. If they are properly verified and indicate they are from the real asset and indicate a legitimate status update, any databases which hold a copy of the crypto-ledger421are updated with the new status of the asset550. It will be apparent to one skilled in the art that additional uses for an update verification process may be that partial updates (for example, with certain pieces of data not sent to the server in the status update) may be used, and with this partial observability, missing data between status updates may be inferred using machine learning techniques. It is possible to implement a rules engine for this purpose, to determine what rules to apply for inference of missing data, depending on the implementation of the system.

FIG. 6is a method diagram illustrating key steps in a business operating system410interacting with data received from cyber-physical assets430,440in databases420to verify updates in a cryptographic ledger421, according to a preferred aspect. Any asset must generate a public and private key610in accordance with the specifications of asymmetric encryption, which are known technologies in the art. An asset must prepare an update620, which may mean formatting data received from any installed sensors, performing any relevant calculations or modifications to raw data, and preparing any network devices for sending the data across a network450. The cyber-physical asset430,440must sign any update with its private key630, which encrypts the update in a way that only the private or public keys can be used to decrypt. The asset, when connected to a network450, may send the prepared and encrypted update to any “nodes” or computer systems running a business operating system410, to be verified before being added onto the ledger421,640. Any nodes running a business operating system410will attempt to verify the asset status update650, before then verifying with the ledger held in at least one database420and any other relevant nodes or computer systems with such a business operating system410that the asset update is legitimate, valid, and shall be added to the ledger of status updates from the asset660. It is possible to implement this system and method in an ongoing identification and authentication service, for continuous updates, rather than discrete authentication and verification for discrete updates.

FIG. 7is a method diagram illustrating several steps in the use of smart contracts combined with cyber-physical assets, according to a preferred aspect. Such smart contracts are possible as a result of implementing blockchain technology to not only keep track of and verify entries in crypto-ledgers421, but to store and execute distributed programs, for the purposes of self-enforcing contracts, known as smart contracts. In this implementation, a smart contract is implemented with a domain-specific-language (DSL) which may be provided by a vendor of the system or specified by a user of the system710. A DSL may be thought of as a custom programming language, and may, depending on the implementation, also be an otherwise unmodified implementation of a programming language, according to a preferred aspect. Conditions for smart contracts in this system may be based on the past, present, or future status of cyber-physical assets monitored by the system720. Upon completion of whatever conditions are programmed into a smart contract, the contract program executes, which may perform any number of tasks that may be programmed into a computer, including withdrawal of funds, depositing of funds, messages sent across a network450, or other similar results of an executed program730, according to a preferred aspect. These parametrically-triggered remuneration contracts may be versatile and diverse in their implementation according to the needs of the consumer.

FIG. 8is a method diagram illustrating key steps in the function of a parametric evaluation engine412, according to a preferred aspect. A parametric evaluation engine412may query at least one database420for a ledger421containing previous or current status updates of at least one cyber-physical asset430,440,810. This query may be performed across a network450from a business operating system410run on a computer system and may take the form of any database query format, including NOSQL™ databases such as MONGODB™, or SQL™ databases including MICROSOFT SQL SERVER™ and MYSQL™ databases, depending on the desired database implementation in the system, according to a preferred aspect. Asset status histories may be returned to a parametric evaluation engine412, which may be listed to a user of the engine, in a basic user interface which allows the listing and searching of such asset status update histories820. Asset statuses may be viewed over time as a history rather than listed separately, if desired, for the purpose of noting and examining trends in an asset's status830, according to an aspect.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (“ASIC”), or on a network interface card.

Although the system shown inFIG. 9illustrates one specific architecture for a computing device10for implementing one or more of the inventions described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number of processors13may be used, and such processors13may be present in a single device or distributed among any number of devices. In one embodiment, a single processor13handles communications as well as routing computations, while in other embodiments a separate dedicated communications processor may be provided. In various embodiments, different types of features or functionalities may be implemented in a system according to the invention that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).