HIVEPET COORDINATION ENGINE (HCE) BASED MULTI-LAYER ECOSYSTEM OPERATING SYSTEM AND METHOD

Abstract

This invention relates to an ecosystem operating system that enables the integration of independently operating mobile clients (100), clinic systems (150) and commercial / supply systems (160) under a single central or distributed structure, primarily for pet management and clinical operations.

Description

1 TARIFF 5 HIVEPET COORDINATION ENGINE (HCE) BASED MULTI-LAYER ECOSYSTEM OPERATION SYSTEM AND METHOD TECHNICAL FIELD This invention enables not only the storage of data from different client layers, but also its contextualization. an ecosystem that enables this to be evaluated and transformed into operational processes. It is related to operating systems. The invention specifically applies to mobile clients, clinical systems, and commercial applications within the scope of pet management. an adaptive and multi-layered system that manages data flow between systems by transforming it into events. It relates to systems and methods involving the coordinating motor (HCE). 15 STATE OF THE ART Today, pet management, clinical operations, and commercial supply processes are mostly... They are run through independent systems. These systems are usually mobile. These applications are divided into 20 separate categories: clinical management software, inventory and sales platforms. It consists of structures. This divergence leads to a failure to ensure data integrity between systems and disrupts operational processes. This prevents synchronization. In current solutions, mobile clients only serve as data entry points, without clinical The systems have limited interaction with external data, and commercial systems only interact with inventory and 25 He works in a sales-oriented manner. This structure prevents the creation of a continuous and meaningful flow of data between systems. As a result, the same data is being recreated in different systems, and the data becomes outdated. And decision sharing between systems is not possible. Furthermore, in current systems, data is devoid of contextual relationships and exists as singular records. 30 Data attributes that add meaning to the data, such as time and location relationships, are limited. When evaluated, data alone cannot generate meaning and is used in decision-making processes. It is not available. Existing structures are designed to be data-driven and lack an event-driven operational model. Data 35 The changes cannot be transformed into an instantaneous operational process; the systems trigger behaviors. unable to display, for example, an emergency medical response or automated inventory management Manual intervention is required for these operations. Decision-making mechanisms operate through fixed rules and predefined workflows. Since contextual variables (time, location, role) are not taken into account, the system is adaptive. 40 He / She is unable to behave. 2 Commercial procurement processes, on the other hand, await manual requests from the user and rely on in-system analyses. An automated inventory or supply process cannot be initiated based on this. With increasing data volume, the processing capacities of existing systems are being strained, and security concerns are rising. And reliability mechanisms are inadequate. In conclusion, it brings together different system layers under a single architecture, contextually analyzing data. an advanced 10 that transforms events and can respond adaptively to environmental variables. An ecosystem operating model is needed. THE PURPOSE OF THE INVENTION The primary purpose of the invention is to enable the storage of data from different system components. No, it requires the contextual evaluation of the data in question and its application to operational processes. 15 an integrated coordination structure that enables its transformation (HivePet Coordination Engine - The goal is to create (HCE). This eliminates the disconnect between data generation and transaction generation. Another aim of the invention is to process data not as a passive recording element, but to automate system components. It is about treating it as an active trigger (event) that directs it. 20 Data is viewed not directly, but within contexts such as time, location, user role, and historical data relationships. It is processed by being interpreted in conjunction with context. The system works contextually instead of with fixed rules. An adaptive system with dynamic evaluation capabilities that produces different outputs based on parameters. It offers a structure. Another purpose of the invention is to collect data relating to domestic animals (pets) in partial or different formats. a unique digital entity (Pet Identity) that is constantly updated and expanded regardless of its status. The goal is to combine them under Layer - PIL). However, there are also the Clinical Interaction Layer (CIL) and the Supply Orchestration Layer (SOL). through automated decision-making and feedback processes between medical and commercial processes It is to operate. 30 Another objective of the invention is to coordinate different workloads through an event-based coordination engine. Data processing strategies (real-time, partial) according to their intensity (low, medium, high, very high) (combining, batch processing, prioritization / filtering) that can be dynamically changed and The goal is to provide an adaptive behavioral model that maintains the system's performance. 35 40 3 DESCRIPTION OF THE FIGURES 5 Prepared to better understand the structural features and working principles of the invention. Descriptions of the figures are given below: Figure 1: Block 10 showing the general architecture of the multi-layered ecosystem operating system that is the subject of the invention. diagram Figure 2: General flowchart showing the steps involved in transforming data into an event and action. Figure 3: Multilayered relationship between mobile, clinical and commercial layers and the coordination engine. diagram showing the interaction Figure 4: Adaptive behavior model exhibited by the system according to event intensity levels (process 15) (showing strategies) EXPLANATION OF REFERENCE NUMBERS The system components and process steps shown in the figures are numbered below in order: 100: Mobile Client Application 20 110: Data Collection Layer 120: Data Processing Layer 130: Contextual Decision Layer (CDL) 140: Event-Based Coordination Engine (EBCE / HCE) 150: Clinical System Layer 25 160: Commercial System Layer 170: Other Modules and External Systems 200: Obtaining the Data Source 210: Data Normalization Stage 220: Feature Extraction Stage 30 230: Contextual Analysis Stage 240: Event Creation Phase 250: Event Classification and Decision-Making Stage 300: Mobile Client (User Interface) 310: Central Coordination Engine (HCE / EBCE) 35 320: Clinical System Module 330: Commercial (Supply) System Module DETAILED DESCRIPTION OF THE INVENTION This invention has implications for 40 independent fields, primarily in pet management and clinical operations. mobile clients (100), clinical systems (150) and commercial / supply systems (160) are all part of a single system. an ecosystem operating system that enables integration under a centralized or distributed structure It is related. 4 Within the framework of the invention, the system directly processes heterogeneous data from different data sources. 5 instead, a contextual decision layer that analyzes environmental conditions such as time, location, and user. (130) transform into events. The events obtained are processed by the event-based coordination engine (140) using the available data. depending on the intensity, priority and trust criteria, to the relevant target system (clinical or commercial) They are sent as automatic trigger signals. 10 This enables automated procurement and dynamic role authorization without manual user intervention. an adaptive workflow system based on multiple event classification and bidirectional feedback Features and operational decisions are not static, but are made through the learning structure of the system. It shows flexibility. The invention, whose general architecture is shown in Figure 1, is a 15-bit HivePet Coordination Engine (HCE) based device. The ecosystem operating system basically consists of; data collection layer (110), data processing layer (120), It includes a contextual decision layer (130) and an event-based coordination engine (140). core architecture, mobile clients (100), clinical systems (150) and commercial systems (160) This allows numerous independent systems to work in coordination. The core of the invention, the HivePet Coordination Engine (HCE), supports 20 different client layers. It transforms incoming heterogeneous data streams into events, evaluates them contextually, and resulting in a centralized or semi-distributed orchestration that triggers multiple system components. It is a system. It is not a deterministic (fixed, predefined) structure, but depends on contextual variables. They may exhibit different behaviors. 25 As shown in Figure 2, the operation of the system begins with the acquisition of data (200). Data collection Layer (110) can receive data in structured (full) or incomplete (fragmented) formats. As soon as the system receives the data, it puts it through a data normalization process (210) and removes the missing areas It enables the transformation of signals and formats. Then feature extraction (220) and contextual analysis. (230) The process begins. 30 The Contextual Decision Layer (CDL) (130) processes data not just as content; it processes data as when created, along with parameters such as location, user role, and past event relationships. evaluates. Data that has undergone contextual evaluation is not directly processed by the system; it is first processed by a 35 It is transformed into an "Event" (240). Event-Based Coordination Engine (EBCE) (140) processes all data movements occurring in the system. classifies (250) and prioritizes the relevant parts of the system (mobile client (300), clinical system (320), commercial system (330)) triggers. In the invention, animal data is stored in a special digital data layer called "Pet Identity Layer (PIL)". They are gathered under the following headings. The PIL mechanism takes into account whether the incoming data comes from different sources or is partial. 5 Instead of creating a static identity without looking at anything, a constantly expanding and updating digital identity. It produces the model. The data in the system is linked together using the Cross-Layer Data Binding (CLDB) model. In this model... Data is not formed directly, but through contextual relationships between them, creating meaningful chains. This is done, which greatly prevents data duplication. 10 Clinical interaction processes are conducted through the Clinical Interaction Layer (CIL). The CIL is a clinical bidirectional data exchange between system (150) and other systems (Commercial (160), Mobile (100)) and manages transaction triggers. Similarly, inventory, supply, and trade transactions within the system are managed by the Supply Orchestration Layer (SOL). It is coordinated through a structure called [name of structure]. 15 For example, when a certain criterion is reached as a result of data analysis from the system, SOL can be activated automatically without any direct request from the user. It is possible to initiate a supply / stock transaction. The system's roles and security mechanisms are outside the standard structure. Dynamic Trust Scoring With the (DTS) structure, the reliability of users, data, or events is continuously updated with parameters up to 20. It is calculated. In addition, thanks to the Multi-Role Interaction Model (MRIM), different roles (individual) can be represented within the system. (user, clinical user, commercial user) different access levels and behavioral patterns It is managed through. Figure 4 shows the Adaptive Event Thresholding (AET) mechanism of the system. The behavioral variations it exhibits have been demonstrated thanks to this. The system automatically determines processing strategies based on the intensity of the events. Low event rate. Transactions are processed in real-time due to high volume. In medium event intensity, closely related events are partially matched and combined (Partial (Aggregation). At high event rates, the system switches to batch processing mode. 30 Under very high data / event loads, the system identifies the most urgent and critical processes algorithmically. It applies a prioritization and filtering system. As shown in Figure 3, the central coordination structure (310), sub-modules (300, 320, 330) Instead of establishing direct matches between them, all routing is done by using events and contexts. He undertakes it. 35 A condition (300) coming from the client to the centre (e.g., a "disease" triggered by a smart collar) The sign ("sign") is analyzed by EBCE (140). This situation triggers the clinical system (320). In accordance with the medical needs that the clinical system will give, The commercial system (330) can be triggered to start the supply of food or medicine. By using a feedback loop at each step, the processes guide the next decision step. It is updated to optimize. As a result, the invention is not just a classic process of managing data. 6 Stepping outside the software, it reads data within context, makes decisions, and automatically orchestrates subsystems. This provides an adaptive device operating architecture. The invention concerns a multi-layered, event-driven orchestration system; primarily for the pet industry. cloud-based servers, healthcare institutions, mobile software infrastructures, veterinary monitoring electronic control mechanisms of systems and commercial / e-commerce supply chains It can be produced directly, implemented by being translated into programming languages, and used in the IT industry for 10 years. It is available for use. HivePet Coordination Engine (HCE) is built on a layered architecture and is modular. It exhibits a structure. The system includes: Data Collection, Data Processing, Contextual Evaluation, Event Generation, It consists of orchestration and execution layers. There is a rigid structure between these layers. There is no dependency, and communication processes are based on an "event" and "context" model. 15 is being carried out. Data collection layer; mobile clients, clinical and commercial systems, sensors and heterogeneous external It consolidates data from data sources. The data entering the system is checked for format, integrity (missing information). (or in installments) and can vary in terms of temporal production, therefore, at the introductory stage No assumption is made regarding a standard structure. 20 Data Normalization Process: Instead of processing the data in its raw form, the system uses a normalization process. This process involves standardizing the data format and identifying missing or incorrect data. Fields are identified and marked, and the reliability score of the data is calculated. Data Processing and Feature Extraction: In this layer, the system separates the data into meaningful components. It divides and creates relational data points. Instead of fixed rules, this process uses 25 It is performed dynamically depending on the nature of the data. The system uses a "multiple data representation" approach to simultaneously process raw, processed, and derived data types. It can store data in different layers. This approach prevents the system from processing the same data repeatedly. This allows it to be used in various application areas without any restrictions. Contextual Evaluation Layer (CDL): Forms the basis of the system's decision support mechanism. 30 This layer, which creates the data, processes it not in isolation, but in relation to time, location, user role, and historical data relationships. and evaluates it along with contextual parameters such as system status. Context-Based Parsing: The same data input can be parsed differently depending on contextual variables. It can be interpreted in various ways. Contextual Conflict Management: In situations where multiple contexts overlap, the system; 35 by choosing one of the following methods: prioritizing, postponing, canceling, or combining It manages the mechanism. Event Generation Layer: This layer transforms raw data into "event" form. The system processes individual data, It can generate repetitive, chained, simultaneous, or delayed event types. Multiple datasets By combining them into a single event, the processing load is optimized and decision 40 The accuracy rate of these mechanisms is increased. 7 Orchestration Layer (EBCE): Where events are evaluated, prioritized, and appropriately selected. It is the central point to which components are directed. The layer that operates on the principle of dynamic routing; the fixed layer... Instead of a single flow, it selects routes appropriate to the context of the event. Delayed processing and parallel triggering. Thanks to their skills, they manage complex processes. Application Layer and Bidirectional Interaction: The application layer interacts with mobile and clinical systems. It provides end-to-end interaction. The system not only sends data but also provides feedback. It collects process data through its mechanism and updates itself after each operation, preparing for the next one. It has an autonomous structure that optimizes the decision cycle. Adaptive Behavior and Synchronization: The system considers user role, data density, and processing latency. It dynamically updates its behavior patterns based on variables such as these. Thanks to "Threshold Adaptation," performance indicators are based on operational values ​​instead of fixed values. It modifies according to needs. Interlayer synchronization is achieved not through direct connections, This is achieved through a multifaceted architecture based on the flow of events and context. System Variations: HivePet Coordination Engine (HCE) adapts to different operational scales and needs. It has a flexible structure that can provide: 20 Centralized Architecture: Where all processes are managed through a single server, with maximum control. It is a model that is at that level. Distributed Architecture: Workload is spread across nodes, making it fault-tolerant. It is a model. Hybrid Architecture: Performance and security where critical decisions are centralized and routine operations are handled locally. 25 It is a structure focused on balance. Adaptive Density Management: Instantaneous processing, partial merging, or response to event density. It can switch to batch processing modes. Delayed Processing: Data is processed after a specific period of time in situations where increased decision clarity is required. It has a hold option. 30 Multiple System Triggering: An event can trigger multiple systems simultaneously, in parallel, sequentially, or conditionally. It is the ability to provide timely warnings. The HivePet Coordination Engine (HCE) facilitates coordination between different operational layers. In order to provide this, various application scenarios have been developed. These scenarios ensure the modularity of the system. and embodies its flexible structure. 35 Scenario - Clinical Process Triggering The system receives and processes user-generated data. Contextual analysis layer. When data from (CDL) is identified as having a clinical need, the relevant clinical systems It is triggered automatically. The process involves transmitting the clinical procedure result back to the system and the data. It is completed with the update. 40 8 Scenario - Automatic Supply 5 The system monitors inventory levels and consumption rates by analyzing periodic data streams. Previously If the defined parameters are exceeded, the commercial module is triggered and the order is cancelled. The process is initiated. Post-process tracking is performed to manage the supply cycle end-to-end. Scenario - Event Set Processing In cases where a large amount of the same or similar types of data is generated, the system organizes this data into discrete 10-bit systems. Instead of processing them in a structured way, it combines them into a meaningful "set of events". This method eliminates unnecessary elements. While preventing processing load, it allows for higher performance from a larger dataset. It allows for accurate decision-making. Scenario - Adaptive Decision The system allows the same data input to be processed at different times or under different contextual conditions 15 times. It does not produce a standard response if this occurs. Contextual Evaluation Layer It analyzes the current situation through this process and dynamically makes the most appropriate decision based on the current data. It derives as follows. Scenario - Multilayered Interaction User data collected via mobile platforms is incorporated into a multi-layered flow by the system. 20 In this process, while the diagnosis or analysis is triggered by the clinical layer, the commercial layer simultaneously... It is activated for operational support (e.g., resource booking or stock updating). The process The resulting feedback is added to the data pool to optimize the system's future decisions. is included. 30 35

Claims

9 REQUIREMENTS 5 1. The invention describes a system that gathers information from multiple sources and integrates these processes into operational procedures. It is a transformative multi-layered coordination system, characterized by its features; - Heterogeneous data from mobile clients (100), clinical systems (150) and commercial external modules (160) At least one data collection layer (110) containing the stream, 10 - a data processing layer that organizes the format of the collected data and extracts its distinctive features. (120), - processed data with parameters such as time of acquisition, location, user role, and historical data relationship. at least one contextual decision layer that generates dynamic decisions by evaluating (130), - transforming data streams from mere information into triggering event units, 15 classifies, prioritizes, and processes the relevant target system based on workload and context. an event-based coordination engine (140) that automatically directs module (150, 160) It includes.

2. The system in claim 1 is characterized by the contextual decision layer (130) within it being the same. A data point is evaluated based on multiple different environmental parameters (time, role, location). It has a rule base that allows it to produce different action outcomes by operating in different contexts. It is the fact that.

3. The system described in Claim 1 is characterized by its ability to process incomplete, unstructured, or improperly organized animal data. Instead of creating a static identity, it exists independently of time-dependent data entries. Digital data 25 produces a unique digital identity that is constantly updated and expanded with the available data. It has a structure that enables its unification.

4. The system in Claim 1 is characterized by its ability to exchange data with clinical systems (150), only central structure (140) not only transmits data but also ensures the preservation of the transaction context bidirectional processing triggers and feedback between the clinical system (150) and the clinical system. It includes a contact layer (CIL). 30 5. The system in Request 1 is characterized by manual operation performed by the client or directly by the user. event signals automatically generated by the system (140) without a transaction request In line with this, the supplier actively initiated the stock, supply and commercial purchasing processes with a firm decision. It includes an orchestration layer (SOL).

6. The system described in Claim 1 is characterized by; individual, clinical and commercial users within the same system. 35 Instead of perceiving the types as a single type, the system behavior and data are dynamically adjusted according to each type. a configuration that includes a multi-role interaction model (MRIM) that generates access and authorization levels It is having.

7. The system described in Claim 1 is characterized by its ability to maintain the reliability of incoming data and generated events on a constant basis. Instead of a rule list, 40 constantly updated features such as visibility, access history, and transaction authorization. Characterized by a dynamic confidence scoring (DTS) structure that rates parameters. It is done. 5 8. The system in Claim 1 is characterized by its real-time response depending on system density and workload. processing, partial merging, batch processing, or prioritization and filtering modes. an adaptive thresholding mechanism (ATM) that shapes incident operations by transitioning It includes.

9. An incident management and event taking place in the event-based coordination engine (140) in Claim 1. It is a routing method and its characteristic is; - Obtaining raw data from different and heterogeneous data sources (100, 150, 160) (200), - Normalization of the received data in accordance with the system format (210), - Feature and type extraction from normalized data (220), 15 - Time, location and behavior of extracted data features in the contextual decision layer (130) taking into account the relationships, the contextual analysis process (230) - contextually meaningful data is transformed into an event within the system. creation (240), - Classifying the generated events and making decisions according to their importance and intensity level. 20 being put into the mechanism (250) - depending on the decision taken, on the relevant clinical (150), commercial (160) or mobile (100) layers automatic triggering of the appropriate system action It includes the steps of the process.

10. The method in Request 9 is characterized by the following: 25 obtained after the action is triggered. the results can be replicated if the same or similar events are created later. a process feedback to the system to be read by the evaluation algorithm It includes the steps involved in the process.

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