Deterministic service rule engine and satellite flash collaborative system based on satellite flash physical layer scheduling, dynamic mapping method, terminal and medium

Abstract

The invention provides a deterministic service rule engine and satellite flash coordination system based on satellite flash physical layer scheduling, a dynamic mapping method, a terminal and a medium, and the system comprises a deterministic rule engine which is used for providing a visual service rule arrangement interface, and compiling an arranged service rule into a scheduling sequence; nodes in the service rules are configured with deterministic time sequence attributes; and the star flash scheduling collaboration layer is connected with the deterministic rule engine and the star flash HDF driving layer, and is used for receiving the scheduling sequence and triggering the star flash HDF driving layer to drive and execute the scheduling sequence based on a star flash microsecond-level synchronous clock. According to the method, deep fusion and dynamic mapping are carried out on upper-layer visual service rules and physical layer scheduling of bottom-layer satellite flash wireless communication, so that a service layer and a physical layer are communicated, and end-to-end microsecond-level deterministic time delay guarantee is realized.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication and real-time control technology, and in particular to a deterministic service rule engine and a star-flash collaborative system based on star-flash physical layer scheduling, a dynamic mapping method, a terminal, and a medium. Background Technology

[0002] With the development of industry and intelligent vehicles, unprecedentedly stringent requirements have been placed on the real-time performance, reliability, and determinism of wireless communication, which has significant shortcomings in meeting microsecond-level end-to-end determinism.

[0003] At the business layer, IoT operating systems and platforms typically provide visual rule engines for quickly building local closed-loop business processes by dragging and dropping nodes. However, upper-layer business execution often relies on general operating system task scheduling mechanisms, which suffer from millisecond-level latency and jitter, failing to meet the demands of extreme real-time scenarios. Meanwhile, at the physical communication layer, StarScan wireless short-range communication offers microsecond-level synchronization and low air interface latency capabilities, and can achieve fine-grained scheduling through a fine-grained time-frequency (TF) resource grid. However, because upper-layer business rule execution and underlying wireless physical layer scheduling are completely separate, even with a fast network, scheduling delays in upper-layer applications and operating system jitter will disrupt end-to-end determinism. This results in a situation where the physical layer is fast, but end-to-end latency remains uncertain, making it difficult to meet the needs of application scenarios extremely sensitive to end-to-end latency and jitter, such as automotive active noise cancellation and high-precision multi-axis motion control.

[0004] Therefore, there is an urgent need for a technical solution that can bridge the gap between the business layer and the physical communication layer and achieve end-to-end microsecond-level deterministic assurance. Summary of the Invention

[0005] The main objective of this invention is to provide a deterministic business rule engine and a StarSpark collaborative system based on StarSpark physical layer scheduling, a dynamic mapping method, a terminal, and a medium, aiming to solve the problem in the prior art where the upper-layer business logic and the lower-layer physical communication are completely separated, and cannot meet the microsecond-level end-to-end deterministic guarantee.

[0006] The technical solution adopted by this invention to solve the technical problem is as follows: In a first aspect, the present invention discloses a deterministic business rule engine and a StarSpark collaborative system based on StarSpark physical layer scheduling, wherein the system comprises: A deterministic rule engine is used to provide a visual interface for orchestrating business rules and to compile the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes. The StarSpark scheduling coordination layer, connected to the deterministic rule engine and the StarSpark HDF driver layer, is used to receive the scheduling sequence and trigger the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

[0007] Optionally, the deterministic rule engine includes a deterministic node library and a rule compiler; The deterministic node library is used to provide nodes with customizable deterministic timing attributes for orchestrating business rules; The rule compiler is used to compile the business rules into scheduling sequences.

[0008] Optionally, the StarSpark scheduling coordination layer includes a scheduling sequence executor; The scheduling sequence executor is aligned with the StarSpark microsecond-level synchronization clock, and is used to receive the scheduling sequence and trigger the StarSpark HDF driver layer to execute the physical layer operation corresponding to the scheduling sequence at the timestamp specified by the scheduling sequence.

[0009] Optionally, the physical layer operation includes: The instruction specifies that the Starflash HDF driver layer collects data on the designated time-frequency resource block; Trigger the CPU to execute the calculation or logical judgment task in the business rule; The instruction specifies that the Starflash HDF driver layer sends control commands on the designated time-frequency resource block.

[0010] Optionally, it also includes a dual-star flash link consisting of a primary star flash link and a backup star flash link; The rule compiler is further configured to compile the business rules into a primary scheduling sequence corresponding to the primary star link and a secondary scheduling sequence corresponding to the backup star link, and to align the primary scheduling sequence and the secondary scheduling sequence using a unified synchronization clock or equivalent time synchronization mechanism.

[0011] Optionally, the scheduling sequence executor is further configured to switch to the backup star link to execute the secondary scheduling sequence when the primary star link fails, provided that the switching time does not exceed a preset switching time.

[0012] Optionally, the StarSpark scheduling coordination layer further includes a resource manager; The resource manager is used to manage the time-frequency resource pool of the StarNet and to perform admission control and resource allocation based on resource requests in the scheduling sequence.

[0013] Optionally, the rule compiler includes a timing analysis unit, a resource request generation unit, and a scheduling sequence generation unit; The timing analysis unit is used to analyze the topology of the business rule and the deterministic timing attributes of all nodes in the business rule to calculate the end-to-end latency requirements and the optimal execution timing of each node, and obtain the timing analysis results. The resource request generation unit is used to generate a resource request for the star flash time-frequency resource based on the data requirements of the nodes in the business rules, and obtain the resource allocation result. The scheduling sequence generation unit is used to generate a scheduling sequence based on the timing analysis results and the resource allocation results.

[0014] Optionally, in a hybrid network comprising wired TSN and wireless strobe, the rule compiler is a hybrid network scheduler; The hybrid network scheduler is used to compile the service rules into scheduling sequences that span wired and wireless domains.

[0015] Optionally, the resource manager is also configured to perform at least one operation in the event of a resource conflict: denying access, reducing non-critical rule resources, splitting rules into multiple periodic executions, or remapping to a spare resource block.

[0016] Optionally, when the StarSpark scheduling coordination layer is configured with an emergency event resource pool, the resource manager is also used to interrupt or adjust the current scheduling sequence when a high-priority event is detected, dynamically generate and execute a temporary emergency scheduling sequence, so as to complete the event reporting and emergency control closed loop on the reserved time and frequency resources of the emergency event resource pool.

[0017] Optionally, the scheduling sequence executor is aligned with a starflash microsecond-level synchronous clock using an interrupt, polling, or hardware timer.

[0018] Optionally, the nodes in the business rules include IO nodes and logical nodes.

[0019] Optionally, the deterministic timing attributes include execution time, maximum completion delay, and priority.

[0020] Optionally, the scheduling sequence is in the format of a gated control list.

[0021] Optionally, the StarSpark scheduling coordination layer is located in the system service layer of the operating system.

[0022] Optionally, when the deterministic rule engine has an AI Agent built in, the deterministic rule engine is also used to analyze the business flow characteristics and network load of the business rules in order to automatically recommend the optimal deterministic timing attribute configuration for the nodes in the business rules, or generate the optimal scheduling sequence.

[0023] Secondly, this invention also discloses a method for dynamic mapping of deterministic business rules based on StarSpark physical layer scheduling, wherein the method is applied to the deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling as described above, and the method includes: The deterministic rule engine receives business rules arranged by the user in the visual business rule orchestration interface and compiles the arranged business rules into a scheduling sequence; the nodes in the business rules are configured with deterministic timing attributes. The scheduling sequence is received by the StarSpark scheduling collaboration layer, which is connected to the deterministic rule engine and the StarSpark HDF driver layer, and the scheduling sequence is executed by the StarSpark HDF driver layer based on the StarSpark microsecond-level synchronization clock.

[0024] Thirdly, the present invention discloses a terminal, comprising: a memory, a processor, and a deterministic service rule dynamic mapping program based on StarSpark physical layer scheduling stored in the memory and executable on the processor, wherein the deterministic service rule dynamic mapping program based on StarSpark physical layer scheduling implements the steps of the deterministic service rule dynamic mapping method based on StarSpark physical layer scheduling as described above when executed by the processor.

[0025] Fourthly, the present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program that can be executed to implement the steps of the deterministic business rule dynamic mapping method based on star-flash physical layer scheduling as described above.

[0026] This invention provides a deterministic business rule engine and a StarSpark collaborative system based on StarSpark physical layer scheduling, a dynamic mapping method, a terminal, and a medium. The deterministic business rule engine and StarSpark collaborative system include: a deterministic rule engine, used to provide a visual business rule orchestration interface and compile the orchestrated business rules into a scheduling sequence; the nodes in the business rules are configured with deterministic timing attributes; and a StarSpark scheduling collaboration layer, connected to the deterministic rule engine and the StarSpark HDF driver layer, used to receive the scheduling sequence and trigger the StarSpark HDF driver layer to execute the scheduling sequence based on a StarSpark microsecond-level synchronization clock. Therefore, this invention, through the deterministic rule engine and the StarSpark scheduling collaboration layer, compiles business rules into a scheduling sequence, which is then received by the StarSpark scheduling collaboration layer and executed by the StarSpark HDF driver layer based on a StarSpark microsecond-level synchronization clock, thereby connecting the business layer and the physical layer and achieving end-to-end microsecond-level deterministic latency guarantee. Attached Figure Description

[0027] Figure 1This is a functional principle block diagram of a preferred embodiment of the deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system in this invention; Figure 2 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 3 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 4 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 5 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 6 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 7 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 8 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 9 This is a functional principle block diagram of a specific deterministic business rule engine and star-flash collaborative system based on star-flash physical layer scheduling disclosed in this invention; Figure 10 This is a functional execution flowchart of a specific deterministic business rule engine and a StarSpark collaborative system based on StarSpark physical layer scheduling disclosed in this invention; Figure 11 This is a flowchart of a preferred embodiment of a deterministic business rule dynamic mapping method based on star-flash physical layer scheduling disclosed in this invention; Figure 12 This is a flowchart of a preferred embodiment of a deterministic business rule dynamic mapping method based on star-flash physical layer scheduling disclosed in this invention; Figure 13 This is a flowchart of a preferred embodiment of a deterministic business rule dynamic mapping method based on star-flash physical layer scheduling disclosed in this invention; Figure 14 This is a flowchart of a preferred embodiment of a deterministic business rule dynamic mapping method based on star-flash physical layer scheduling in this invention; Figure 15This is a functional principle block diagram of a preferred embodiment of the terminal in this invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0029] With the development of industry and intelligent vehicles, unprecedentedly stringent requirements have been placed on the real-time performance, reliability, and determinism of wireless communication, which has significant shortcomings in meeting microsecond-level end-to-end determinism.

[0030] At the business layer, IoT operating systems, exemplified by certain OS Meta platforms, provide a visual super rule engine that allows users to build business processes by dragging and dropping logic nodes, enabling rapid development and local closed-loop execution of business logic. However, its execution process is limited by the task scheduling latency and jitter of the operating system, typically in the millisecond range, which cannot meet the requirements of extreme real-time scenarios. For example, in scenarios such as in-vehicle active noise cancellation and high-precision multi-axis motion control, end-to-end business latency must be stable at the tens of microsecond level. However, the existing operating system task scheduling mechanism, combined with the serial mode of network transmission, cannot meet the requirements of tens of microseconds due to its accumulated latency and jitter.

[0031] At the physical communication layer, SparkLink short-range wireless communication possesses microsecond-level synchronization and low air interface latency capabilities, and achieves fine-grained scheduling through a fine-grained time-frequency resource grid (TF), providing the physical foundation for deterministic communication. In other words, as a next-generation short-range wireless communication technology, SparkLink's basic access technology (SLB) provides physical layer capabilities with air interface one-way latency of less than 20μs, reliability greater than 99.999%, and microsecond-level synchronization. Its channel structure employs a fine-grained time-frequency (TF) resource grid to achieve fine-grained scheduling, with a scheduling granularity reaching ultra-short wireless frames of 20.833μs and individual subcarriers.

[0032] However, the execution of the current upper-layer business rule engine is completely disconnected from the scheduling of the underlying communication network. Even with a fast network, scheduling delays in upper-layer applications and operating system jitter will disrupt end-to-end determinism. In other words, the execution of business rules is limited by the non-deterministic scheduling of the operating system, while the microsecond-level deterministic capabilities of the underlying StarNet network cannot be directly utilized by upper-layer businesses. Even with fast physical communication, the latency of the entire end-to-end business process—from sensor triggering to controller decision-making to actuator response—remains millisecond-level and non-deterministic, creating a deterministic gap between upper-layer business logic and underlying physical communication. This prevents a deep, native binding between the execution of upper-layer business logic (business rules) and the deterministic capabilities of underlying physical communication. Therefore, how to directly map upper-layer business rules to physical layer scheduling is a problem that those skilled in the art need to solve.

[0033] Furthermore, the existing communication method adopts a best-effort resource allocation model, that is, when the rule engine needs to send a control command, it simply throws the data packet to the network protocol stack, and the network layer decides when and how to send it. It cannot reserve and allocate the valuable TF resources of StarSpark on demand and in a fine-grained manner for a specific, critical business step (such as an emergency shutdown command), resulting in extensive resource utilization.

[0034] To this end, this application provides a deterministic business rule engine and a StarSpark collaborative system based on StarSpark physical layer scheduling, which can connect the business layer and the physical communication layer to achieve end-to-end microsecond-level deterministic guarantee.

[0035] Please see Figure 1 , Figure 1 This is a functional principle diagram of the deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system in this invention. Figure 1 As shown in the embodiment of the present invention, the deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system include: The deterministic rule engine A1 provides a visual interface for orchestrating business rules and compiles the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes. The StarSpark scheduling coordination layer B2 is connected to the deterministic rule engine A1 and the StarSpark HDF driver layer B3. It is used to receive the scheduling sequence and trigger the StarSpark HDF driver layer B3 to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

[0036] Understandably, the deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system include an enhanced deterministic rule engine A1, a StarSpark scheduling and collaboration layer B2, and a StarSpark HDF driver layer B3. As a deterministic business orchestrator, the deterministic rule engine A1 can not only define logical processes to generate business rules, but also directly understand and orchestrate the underlying StarSpark physical resources. By introducing a deterministic context into the deterministic rule engine A1 and compiling business rules into a scheduling sequence, precise control over the latency of each link in the entire business loop is achieved. This scheduling sequence can be a StarSpark scheduling sequence containing precise timestamps, StarSpark time-frequency resource allocation information, and node execution order.

[0037] It should be noted that, see Figure 2 As shown, the deterministic rule engine A1 can be deployed on the Meta platform in development mode, while the StarSpark scheduling coordination layer B2 and the StarSpark HDF driver layer B3 can be deployed on the operating system. Specifically, the StarSpark scheduling coordination layer B2 can be deployed in the operating system's system service layer, in runtime mode. Furthermore, the StarSpark scheduling coordination layer B2 is tightly coupled with the deterministic rule engine A1 and the underlying StarSpark HDF driver layer B3. The HDF framework provides a channel for connecting upper-layer services and lower-layer drivers. As a next-generation wireless short-range communication technology, StarSpark's driver and SDK will inevitably undergo deep adaptation with the operating system. Therefore, adding a dedicated backend for StarSpark scheduling within the rule engine is entirely feasible. The StarSpark HDF driver refers to a driver program written in accordance with the HDF framework specification, used to control and manage StarSpark hardware devices. It allows the system to operate remote StarSpark modules as if they were local hardware. Device location can be distinguished by setting the `remote` or `local` attribute in the function parameters. It can mask differences in underlying communication protocols (such as NFC, Bluetooth, WiFi, UWB, or StarSpark), supporting multi-link redundancy and fast switching, thereby improving connection stability and user experience in driver-car connectivity scenarios. In driver-car connectivity applications, the StarSpark HDF driver supports seamless transitions between services such as navigation and music. For example, when a user switches from their phone to the car's infotainment system, services can automatically migrate via a soft bus mechanism without re-establishing a connection. Relying on the unified driver architecture of the HDF framework, StarSpark hardware can efficiently collaborate with other devices.

[0038] It should also be noted that the nodes in the business rules can include IO nodes and logical nodes, and the deterministic timing attributes can include execution time, maximum completion latency, and priority. Specifically, execution time indicates that execution must be performed in the Nth microsecond of the scheduling cycle, maximum completion latency indicates that the time from triggering to completion must not exceed X microseconds, and priority indicates the priority of the deterministic task.

[0039] In this embodiment, see Figure 3As shown, when the deterministic rule engine A1 has a built-in AI Agent, it can also analyze the business flow characteristics and network load of business rules to automatically recommend the optimal deterministic timing attribute configuration for nodes in the business rules, or generate the optimal scheduling sequence. In essence, by configuring the AI ​​Agent (i.e., the intelligent agent) in the deterministic rule engine A1, it can automatically recommend the optimal deterministic timing attribute configuration for nodes in the compiled business rules by analyzing business flow characteristics and network load, and even directly generate the optimal scheduling sequence, thus achieving intelligent scheduling.

[0040] As can be seen, in this embodiment, the business rules are compiled into a scheduling sequence through the deterministic rule engine and the StarSpark scheduling coordination layer. The StarSpark scheduling coordination layer receives the scheduling sequence and triggers the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock. This connects the business layer and the physical layer, achieving end-to-end microsecond-level deterministic latency guarantee. The latency of the entire business process is reduced from the millisecond-level, nondeterministic nature of traditional solutions to a deterministic level that can be stably controlled within tens of microseconds. This meets the most stringent real-time application requirements of industrial and automotive applications, achieving microsecond-level end-to-end determinism. It realizes deep integration of upper-layer business logic and lower-layer physical communication, enabling the on-demand, fine-grained allocation and reservation of valuable wireless resources, greatly improving resource utilization efficiency.

[0041] In some specific embodiments, see Figure 3 As shown, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling include: a deterministic rule engine A1, which provides a visual business rule orchestration interface and compiles the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes; and a StarSpark scheduling collaboration layer B2, which connects to the deterministic rule engine A1 and the StarSpark HDF driver layer B3, for receiving the scheduling sequences and triggering the StarSpark HDF driver layer B3 to execute the scheduling sequences based on the StarSpark microsecond-level synchronization clock. Specifically, the deterministic rule engine A1 may include a deterministic node library and a rule compiler; the deterministic node library provides nodes with customizable deterministic timing attributes for orchestrating business rules; and the rule compiler compiles the business rules into scheduling sequences.

[0042] Understandably, the deterministic node library of the deterministic rule engine A1 adds deterministic timing attribute panels for key IO nodes and logic nodes (such as "reading sensors," "sending control commands," and "conditional judgments"), allowing developers to configure strict timing constraints for these nodes. In other words, the deterministic node library, as a toolset for declaring deterministic business intent, enables developers to precisely define the real-time requirements of each step in the business process. It provides visual nodes with deterministic timing attributes. The rule compiler, by parsing these nodes and their deterministic timing attributes, can determine how many underlying resources (such as the time-frequency blocks of a star flash) are requested for each business step and how to orchestrate them into an execution sequence accurate to microseconds. In high-level business logic orchestration, the introduction of microsecond-level timing constraints and physical resource requirements, along with other deterministic attributes, allows developers to deploy highly deterministic industrial and automotive applications in a business-friendly manner. The deterministic node library provides upper-layer developers with a visual programming module with deterministic timing attributes, while providing the lower-layer rule compiler with a semantic dictionary that compiles business logic into precise timing and resource requirements. This allows users to achieve highly deterministic applications without worrying about the complex details of underlying clock synchronization and physical layer scheduling. They can simply declare their timing requirements in a business-friendly way within a familiar visual rule engine, greatly reducing the development threshold.

[0043] Furthermore, in some specific embodiments, the rule compiler may specifically include a timing analysis unit, a resource request generation unit, and a scheduling sequence generation unit; wherein, the timing analysis unit is used to analyze the topology of the business rule and the deterministic timing attributes of all nodes in the business rule to calculate the end-to-end latency requirements and the optimal execution timing of each node, and obtain the timing analysis results; the resource request generation unit is used to generate resource requests for star flash time-frequency resources according to the data requirements of the nodes in the business rule, and obtain resource allocation results; the scheduling sequence generation unit is used to generate a scheduling sequence based on the timing analysis results and the resource allocation results.

[0044] Understandably, when a user saves or deploys a business rule containing deterministic timing attributes, the rule compiler automatically performs timing analysis, resource request generation, and scheduling sequence generation operations. This involves parsing the business rule's topology and the timing constraints of all nodes, calculating the end-to-end latency requirements and optimal execution timing for each step of the entire business rule (i.e., the rule flow), generating resource reservation requests for the underlying StarSpark TF resources based on the data volume and timing requirements of each IO node, and then compiling the entire rule flow into a scheduling sequence containing precise timestamps, resource block allocation, and node execution order. The specific compilation algorithm of this rule compiler can be to transform the graphical business logic and timing constraints into a precise GCL (Gate Control List) or similar mathematical model and algorithm.

[0045] In other words, the rule compiler compiles business rules into a scheduling sequence that includes precise timestamps, StarSpark time-frequency resource allocation, and node execution order. The StarSpark scheduling coordination layer B2 synchronizes with the StarSpark global clock and strictly executes this scheduling sequence, bypassing the nondeterministic scheduling of the operating system. This overturns the traditional OS layered model and establishes a deterministic orchestration and compilation path from the upper-layer visualized business logic to the lower-layer wireless communication physical resources (StarSpark TF grid). It realizes a direct cross-layer mapping from business rules to physical layer scheduling, breaking the boundaries of the traditional OSI seven-layer model. That is, by treating each logical node and each execution step in the deterministic rule engine A1 as an atomic task that can be orchestrated on StarSpark's microsecond-level clock and resource grid, it achieves a direct cross-layer mapping from business rules to physical layer scheduling.

[0046] As can be seen, in this embodiment, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling include a deterministic rule engine and a StarSpark scheduling collaboration layer. The deterministic rule engine includes a deterministic node library and a rule compiler. The rule compiler in the deterministic rule engine compiles business rules into scheduling sequences, which are then received by the StarSpark scheduling collaboration layer. Based on the StarSpark microsecond-level synchronization clock, the StarSpark HDF driver layer is triggered to execute the scheduling sequence, thereby connecting the business layer and the physical layer and achieving end-to-end microsecond-level deterministic latency guarantee. This reduces the latency of the entire business process from the millisecond-level, nondeterministic nature of traditional solutions to a deterministic level that can be stably controlled within tens of microseconds, meeting the most stringent real-time application requirements of industry and automotive. It achieves microsecond-level end-to-end determinism, realizing deep integration of upper-layer business logic and lower-layer physical communication. It can allocate and reserve valuable wireless resources on demand and in a refined manner, greatly improving resource utilization efficiency.

[0047] In some specific embodiments, see Figure 4As shown, in a hybrid network containing wired TSN (Time-Sensitive Networking) and wireless SN, the rule compiler can be a hybrid network scheduler. This hybrid network scheduler compiles business rules into scheduling sequences that span both wired and wireless domains. It is understood that in a hybrid network containing both wired TSN and wireless SN, the rule compiler can be upgraded to a hybrid network scheduler. This scheduler can generate a unified scheduling sequence spanning both wired and wireless domains, ensuring end-to-end deterministic latency when data flows between different networks, and achieving converged scheduling with the TSN network. TSN defines deterministic scheduling mechanisms for wired Ethernet (such as time-aware shapers). This invention applies the core ideas of TSN (such as time gating and resource reservation) to a business logic-driven scenario in the wireless domain through deep hardware and software integration, solving the non-deterministic problem caused by operation scheduling.

[0048] In some specific embodiments, see Figure 5 As shown, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling include: a deterministic rule engine A1, which provides a visual business rule orchestration interface and compiles the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes; and a StarSpark scheduling collaboration layer B2, connected to the deterministic rule engine A1 and the StarSpark HDF driver layer B3, which receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the scheduling sequences based on the StarSpark microsecond-level synchronization clock. Specifically, the deterministic rule engine A1 may include a deterministic node library and a rule compiler; the deterministic node library provides nodes with customizable deterministic timing attributes for orchestrating business rules; and the rule compiler compiles the business rules into scheduling sequences. Specifically, the StarSpark scheduling collaboration layer B2 may include a scheduling sequence executor; the scheduling sequence executor, aligned with the StarSpark microsecond-level synchronization clock, receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the physical layer operations corresponding to the scheduling sequences at the timestamp specified in the scheduling sequences.

[0049] It should be noted that the scheduling sequence executor can be aligned with the StarSpark microsecond-level synchronous clock using interrupts, polling, or hardware timers. The scheduling sequence executor and the StarSpark HDF driver interact at the microsecond level through the underlying interface. Furthermore, the scheduling sequence executor directly drives the underlying hardware execution by bypassing the operating system task scheduler. Physical layer operations can specifically include: instructing the StarSpark HDF driver layer B3 to collect data on a specified time-frequency resource block; triggering the CPU to execute calculation or logical judgment tasks in the business rules; and instructing the StarSpark HDF driver layer B3 to send control commands on the specified time-frequency resource block.

[0050] Understandably, the scheduling sequence executor is the core of deterministic execution. It receives the scheduling sequence generated by the rule compiler. The scheduling sequence executor is strictly aligned with the StarSpark microsecond-level synchronization clock, which is provided by the GCS global clock source. Like a metronome, the scheduling sequence executor triggers the StarSpark HDF driver layer B3 to execute corresponding actions at precise times specified in the scheduling sequence. For example, at time T1, it instructs the StarSpark HDF driver to collect data from sensor A on the specified TF resource block; at time T2, it triggers the CPU to execute the logical judgment part of the business rule; and at time T3, it instructs the StarSpark HDF driver to send control instructions to executor B on the specified TF resource block. In this way, the general, non-deterministic task scheduling of the operating system is bypassed, allowing the execution of the entire business process to be completely driven by the underlying, deterministic StarSpark clock. By using a scheduling sequence executor synchronized with the StarSpark microsecond-level synchronization clock to precisely control the execution timing of each sensing, calculation, and control step in the business process, microsecond-level end-to-end business determinism is achieved.

[0051] As can be seen, in this embodiment, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling include a deterministic rule engine and a StarSpark scheduling collaboration layer. The deterministic rule engine includes a deterministic node library and a rule compiler, while the StarSpark scheduling collaboration layer includes a scheduling sequence executor. The rule compiler in the deterministic rule engine compiles the compiled business rules into a scheduling sequence, which is then received by the scheduling sequence executor in the StarSpark scheduling collaboration layer. Based on the StarSpark microsecond-level synchronization clock, the StarSpark HDF driver layer is triggered to execute the scheduling sequence, thereby connecting the business layer and the physical layer. This achieves end-to-end microsecond-level deterministic latency guarantee, reducing the latency of the entire business process from the millisecond-level, non-deterministic nature of traditional solutions to a deterministic level that can be stably controlled within tens of microseconds. This meets the most stringent real-time application requirements in industry and automotive, achieving microsecond-level end-to-end determinism. It realizes deep integration of upper-layer business logic and lower-layer physical communication, enabling on-demand, fine-grained allocation and reservation of valuable wireless resources, greatly improving resource utilization efficiency.

[0052] In some specific embodiments, see Figure 6As shown, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling include: a deterministic rule engine A1, which provides a visual business rule orchestration interface and compiles the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes; and a StarSpark scheduling collaboration layer B2, connected to the deterministic rule engine A1 and the StarSpark HDF driver layer B3, which receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the scheduling sequences based on the StarSpark microsecond-level synchronization clock. Specifically, the deterministic rule engine A1 may include a deterministic node library and a rule compiler; the deterministic node library provides nodes with customizable deterministic timing attributes for orchestrating business rules; and the rule compiler compiles the business rules into scheduling sequences. The StarSpark scheduling collaboration layer B2 may include a scheduling sequence executor; the scheduling sequence executor, aligned with the StarSpark microsecond-level synchronization clock, receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the physical layer operations corresponding to the scheduling sequences at the timestamp specified in the scheduling sequences. The StarSpark scheduling coordination layer B2 may further include a resource manager; wherein the resource manager is used to manage the time-frequency resource pool of the StarSpark network and to perform admission control and resource allocation based on resource requests in the scheduling sequence.

[0053] Understandably, the StarSpark scheduling coordination layer B2 includes a scheduling sequence executor and a resource manager. The resource manager is responsible for managing the TF resource pool of the entire StarSpark network. The resource manager receives resource requests from the rule compiler and performs unified admission control and resource allocation through scheduling algorithms and admission control policies used to resolve resource conflict between multiple deterministic rules, thereby resolving resource conflicts between multiple deterministic rules.

[0054] Furthermore, the resource manager is specifically used to perform at least one operation when resource conflicts occur: denying access, reducing non-critical rule resources, splitting rules into multiple periodic executions, or remapping to spare resource blocks.

[0055] As can be seen, in this embodiment of the application, the deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system include a deterministic rule engine and a StarSpark scheduling collaboration layer. The deterministic rule engine includes a deterministic node library and a rule compiler, and the StarSpark scheduling collaboration layer includes a resource manager and a scheduling sequence executor. The rule compiler in the deterministic rule engine compiles the generated business rules into scheduling sequences. The resource manager in the StarSpark scheduling coordination layer manages the StarSpark network's time-frequency resource pool and performs admission control and resource allocation based on resource requests in the scheduling sequences. The scheduling sequence executor in the StarSpark scheduling coordination layer receives the scheduling sequence and triggers the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock. This connects the business layer and the physical layer, achieving end-to-end microsecond-level deterministic latency guarantee. It reduces the latency of the entire business process from the millisecond-level, nondeterministic nature of traditional solutions to a deterministic level that can be stably controlled within tens of microseconds, meeting the most stringent real-time application requirements of industrial and automotive applications. It achieves microsecond-level end-to-end determinism, realizing deep integration of upper-layer business logic and lower-layer physical communication. It can allocate and reserve valuable wireless resources on demand and in a fine-grained manner, greatly improving resource utilization efficiency.

[0056] In some specific embodiments, see Figure 7 As shown, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling include: a deterministic rule engine A1, which provides a visual business rule orchestration interface and compiles the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes; and a StarSpark scheduling collaboration layer B2, connected to the deterministic rule engine A1 and the StarSpark HDF driver layer B3, which receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the scheduling sequences based on the StarSpark microsecond-level synchronization clock. Specifically, the deterministic rule engine A1 may include a deterministic node library and a rule compiler; the deterministic node library provides nodes with customizable deterministic timing attributes for orchestrating business rules; and the rule compiler compiles the business rules into scheduling sequences. The StarSpark scheduling collaboration layer B2 may include a scheduling sequence executor; the scheduling sequence executor, aligned with the StarSpark microsecond-level synchronization clock, receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the physical layer operations corresponding to the scheduling sequences at the timestamp specified in the scheduling sequences. The StarSpark scheduling coordination layer B2 may further include a resource manager; the resource manager is used to manage the time-frequency resource pool of the StarSpark network and to perform admission control and resource allocation according to resource requests in the scheduling sequence; the StarSpark HDF driver layer B3 contains a StarSpark HDF driver; the StarSpark HDF driver is used to drive the execution of corresponding physical layer operations of the hardware physical layer.

[0057] As can be seen, in this embodiment of the application, the deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system include a deterministic rule engine and a StarSpark scheduling collaboration layer. The deterministic rule engine includes a deterministic node library and a rule compiler, and the StarSpark scheduling collaboration layer includes a resource manager and a scheduling sequence executor. The rule compiler in the deterministic rule engine compiles the generated business rules into scheduling sequences. The resource manager in the StarSpark scheduling coordination layer manages the StarSpark network's time-frequency resource pool and performs admission control and resource allocation based on resource requests in the scheduling sequences. The scheduling sequence executor in the StarSpark scheduling coordination layer receives the scheduling sequence and triggers the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock. This connects the business layer and the physical layer, achieving end-to-end microsecond-level deterministic latency guarantee. It reduces the latency of the entire business process from the millisecond-level, nondeterministic nature of traditional solutions to a deterministic level that can be stably controlled within tens of microseconds, meeting the most stringent real-time application requirements of industrial and automotive applications. It achieves microsecond-level end-to-end determinism, realizing deep integration of upper-layer business logic and lower-layer physical communication. It can allocate and reserve valuable wireless resources on demand and in a fine-grained manner, greatly improving resource utilization efficiency.

[0058] For example, see Figure 8 As shown, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling include: a deterministic rule engine A1, which provides a visual business rule orchestration interface and compiles the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes; and a StarSpark scheduling collaboration layer B2, connected to the deterministic rule engine A1 and the StarSpark HDF driver layer B3, which receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the scheduling sequences based on the StarSpark microsecond-level synchronization clock. Specifically, the deterministic rule engine A1 may include a deterministic node library and a rule compiler; the deterministic node library provides nodes with customizable deterministic timing attributes for orchestrating business rules; and the rule compiler compiles the business rules into scheduling sequences. The StarSpark scheduling collaboration layer B2 may include a scheduling sequence executor; the scheduling sequence executor, aligned with the StarSpark microsecond-level synchronization clock, receives the scheduling sequences and triggers the StarSpark HDF driver layer B3 to execute the physical layer operations corresponding to the scheduling sequences at the timestamp specified in the scheduling sequences. The StarSpark scheduling coordination layer B2 may further include a resource manager; the resource manager is used to manage the time-frequency resource pool of the StarSpark network and to perform admission control and resource allocation according to resource requests in the scheduling sequence; the StarSpark HDF driver layer B3 contains a StarSpark HDF driver; the StarSpark HDF driver is used to drive the StarSpark chip and TF resources in the hardware physical layer.

[0059] For example, see Figure 10As shown, users can orchestrate deterministic business rules on the Mate platform, which is equipped with the deterministic rule engine A1. The rule compiler in the deterministic rule engine A1 automatically performs timing analysis, resource request generation, and scheduling sequence generation operations on the business rule, compiling the entire rule process into a scheduling sequence containing precise timestamps, resource block allocation, and node execution order. The scheduling sequence is then sent to the StarSpark scheduling coordination layer B2. The resource manager in the StarSpark scheduling coordination layer B2 manages the time-frequency resource pool of the StarSpark network and performs admission control and resource allocation based on the resource requests in the scheduling sequence. The scheduling sequence executor is strictly aligned with the StarSpark microsecond-level synchronization clock. At time T1, the scheduling sequence executor instructs the StarSpark HDF driver to collect sensor data on the specified TF resource block; at time T2, it triggers the CPU to execute the logical judgment part of the business rule; and at time T3, it instructs the StarSpark HDF driver to send control commands to the executor on the specified TF resource block.

[0060] In some specific embodiments, when the StarSpark scheduling coordination layer B2 is configured with a contingency resource pool, the resource manager is also specifically used to interrupt or adjust the current scheduling sequence when a high-priority event is detected, dynamically generating and executing a temporary emergency scheduling sequence to complete the event reporting and emergency control closed loop on the reserved time-frequency resources of the contingency resource pool. It can be understood that, in addition to pre-compiled static scheduling sequences, an event-driven dynamic scheduling mechanism can also interrupt or adjust the current scheduling sequence when a high-priority event is detected, dynamically generating and executing a temporary emergency scheduling sequence. That is, a portion of TF resources is reserved for contingency reporting; when a high-priority event occurs, the current regular scheduling is interrupted, and the StarSpark scheduling coordination layer B2 dynamically generates and executes a temporary, high-priority emergency scheduling sequence.

[0061] In some specific embodiments, the deterministic business rule engine and the StarSpark collaborative system based on StarSpark physical layer scheduling may further include a dual-StarSpark link consisting of a primary StarSpark link and a backup StarSpark link. Furthermore, the rule compiler may be specifically used to compile business rules into a primary scheduling sequence corresponding to the primary StarSpark link and a secondary scheduling sequence corresponding to the backup StarSpark link, and to align the primary and secondary scheduling sequences using a unified synchronization clock or equivalent time synchronization mechanism. The scheduling sequence executor may also be specifically used to switch to the backup StarSpark link to execute the secondary scheduling sequence when the primary StarSpark link fails, provided that the switchover time does not exceed a preset time limit.

[0062] As can be seen, in this embodiment, by constructing a deterministic rule engine and a StarSpark scheduling coordination layer, business rules are compiled into scheduling sequences, which are then received by the StarSpark scheduling coordination layer. Based on the StarSpark microsecond-level synchronization clock, the StarSpark HDF driver layer is triggered to execute the scheduling sequence, thereby connecting the business layer and the physical layer and achieving end-to-end microsecond-level deterministic latency guarantee. This reduces the latency of the entire business process from the millisecond-level, nondeterministic nature of traditional solutions to a deterministic level that can be stably controlled within tens of microseconds, meeting the most stringent real-time application requirements of industry and automotive. It achieves microsecond-level end-to-end determinism, realizing deep integration of upper-layer business logic and lower-layer physical communication. It can allocate and reserve valuable wireless resources on demand and in a refined manner, greatly improving resource utilization efficiency.

[0063] For example, in a practical application scenario of an in-vehicle active noise cancellation system, an ANC controller connects microphones (sensors) and speakers (actuators) at multiple locations within the vehicle via a star-studded interface. When defining business rules, the user compiles a rule on the Meta platform, which deploys the deterministic rule engine A1, by dragging and dropping. This rule periodically (e.g., every 50µs) reads the noise signal from the microphone array, processes it using an inverse noise cancellation algorithm, and immediately sends the calculated inverse sound wave signal to the corresponding speaker. The user configures the "read microphone" node with a period of 50µs and the "send sound wave" node with a maximum completion delay of 15µs.

[0064] Then, when the rule compiler in the deterministic rule engine A1 starts, it analyzes the business rule to generate a scheduling sequence, namely: Cycle0, Time 0-5µs: On the StarScan uplink resource block R1, trigger all microphones to perform data acquisition and transmission.

[0065] Cycle0, Time 6-10µs: Triggers the CPU to execute the computation task of "inverting noise reduction algorithm".

[0066] Cycle0, Time 11-15µs: On the Starflash downlink resource block R2, the calculation results are concurrently transmitted to all speakers.

[0067] Cycle0, Time 16-49µs: Idle or used for non-real-time business.

[0068] Cycle1, Time 50-55µs: Repeat the operation of the previous cycle.

[0069] The scheduling sequence is then loaded into the StarScan scheduling coordination layer B2 in the ANC controller. The scheduling sequence executor in StarScan scheduling coordination layer B2 is aligned with the StarScan microsecond-level synchronization clock. At 0µs of each cycle, it precisely triggers microphone data acquisition instead of waiting for an OS thread to be woken up. At 11µs, it precisely triggers speaker signal transmission, completing the entire end-to-end service loop from noise acquisition to anti-phase sound wave emission. The latency is stably controlled within 15µs with extremely low jitter, thus solving the millisecond-level nondeterministic latency problem caused by operating system scheduling. This meets the requirements of applications extremely sensitive to end-to-end latency and jitter, such as in-vehicle active noise cancellation and high-precision multi-axis motion control. Two ANC controllers, one primary and one backup, can also be deployed with redundancy achieved through a dual-StarScan network, further ensuring system reliability.

[0070] In one embodiment, such as Figure 11 As shown, the present invention also provides a method for dynamic mapping of deterministic business rules based on StarSpark physical layer scheduling, applied to the aforementioned deterministic business rule engine based on StarSpark physical layer scheduling and StarSpark collaborative system. The method includes: Step S11: Receive the business rules arranged by the user in the visual business rule arrangement interface through the deterministic rule engine, and compile the arranged business rules into a scheduling sequence; the nodes in the business rules are configured with deterministic timing attributes.

[0071] In this embodiment, a deterministic rule engine processes the visualized business rules, compiling the timing constraints and resource requirements of business rules with deterministic timing attributes into a scheduling sequence containing precise timestamps, resource allocation information, and node execution order.

[0072] Step S12: Receive the scheduling sequence through the StarSpark scheduling collaboration layer connected to the deterministic rule engine and the StarSpark HDF driver layer, and trigger the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

[0073] In this embodiment, the StarSpark scheduling coordination layer in the system interacts with the deterministic rule engine and the StarSpark HDF driver layer, and receives the scheduling sequence issued by the deterministic rule engine. Since the StarSpark scheduling coordination layer is strictly aligned with the StarSpark microsecond-level synchronization clock, it triggers the StarSpark HDF driver layer to drive and execute the scheduling sequence.

[0074] As can be seen, in this embodiment of the invention, the system receives business rules arranged by the user in the visual business rule orchestration interface provided by the deterministic rule engine, and configured with deterministic timing constraints; compiles the business rules into a scheduling sequence, which defines the execution timestamps of each step in the business process and the StarSpark time-frequency resource blocks occupied; loads the scheduling sequence into the StarSpark scheduling coordination layer, which is synchronized with the StarSpark microsecond-level synchronous clock, and strictly follows the timestamps in the scheduling sequence to trigger the StarSpark HDF driver layer to drive the execution of the scheduling sequence, thereby realizing the deep integration and dynamic mapping of the upper-layer visual business rules and the lower-layer StarSpark wireless communication physical layer scheduling, connecting the business layer and the physical layer, and achieving end-to-end microsecond-level deterministic latency guarantee.

[0075] See Figure 12 As shown, this embodiment of the invention discloses a specific method for dynamic mapping of deterministic business rules based on StarSpark physical layer scheduling. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution. This method is applied to the aforementioned deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system, including: Step S21: The rule compiler in the deterministic rule engine receives the business rules arranged by the user in the visual business rule arrangement interface, and compiles the arranged business rules into a scheduling sequence; the nodes in the business rules are configured with deterministic timing attributes.

[0076] In this embodiment, a deterministic rule engine processes visualized business rules. When the deterministic rule engine includes a deterministic node library and a rule compiler, the rule compiler specifically compiles the timing constraints and resource requirements of business rules with deterministic timing attributes into a scheduling sequence containing precise timestamps, resource allocation information, and node execution order. The deterministic node library, as a toolset for declaring deterministic business intents, allows developers to precisely define the real-time requirements of each step in the business process and provides visualized nodes with deterministic timing attributes. The rule compiler compiles the entire rule process into a scheduling sequence containing precise timestamps, resource block allocation, and node execution order.

[0077] Step S22: Receive the scheduling sequence through the StarSpark scheduling collaboration layer connected to the deterministic rule engine and the StarSpark HDF driver layer, and trigger the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

[0078] In this embodiment, the StarSpark scheduling coordination layer in the system interacts with the deterministic rule engine and the StarSpark HDF driver layer, and receives the scheduling sequence issued by the deterministic rule engine. Since the StarSpark scheduling coordination layer is strictly aligned with the StarSpark microsecond-level synchronization clock, it triggers the StarSpark HDF driver layer to drive and execute the scheduling sequence.

[0079] See Figure 13 As shown, this embodiment of the invention discloses a specific method for dynamic mapping of deterministic business rules based on StarSpark physical layer scheduling. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution. This method is applied to the aforementioned deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system, including: Step S31: The rule compiler in the deterministic rule engine receives the business rules arranged by the user in the visual business rule arrangement interface, and compiles the arranged business rules into a scheduling sequence; the nodes in the business rules are configured with deterministic timing attributes.

[0080] In this embodiment, a deterministic rule engine processes visualized business rules. When the deterministic rule engine includes a deterministic node library and a rule compiler, the rule compiler specifically compiles the timing constraints and resource requirements of business rules with deterministic timing attributes into a scheduling sequence containing precise timestamps, resource allocation information, and node execution order. The deterministic node library, as a toolset for declaring deterministic business intents, allows developers to precisely define the real-time requirements of each step in the business process and provides visualized nodes with deterministic timing attributes. The rule compiler compiles the entire rule process into a scheduling sequence containing precise timestamps, resource block allocation, and node execution order.

[0081] Step S32: Receive the scheduling sequence through the scheduling sequence executor in the StarSpark scheduling collaboration layer connected to the deterministic rule engine and the StarSpark HDF driver layer, and trigger the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

[0082] In this embodiment, when the StarSpark scheduling coordination layer includes a scheduling sequence executor, the scheduling sequence executor is aligned with the StarSpark microsecond-level synchronization clock. Specifically, the scheduling sequence executor receives the scheduling sequence and triggers the StarSpark HDF driver layer to execute the physical layer operation corresponding to the scheduling sequence at the timestamp specified in the scheduling sequence. The physical layer operation may specifically include: instructing the StarSpark HDF driver layer to collect data on a specified time-frequency resource block; triggering the CPU to execute calculation or logical judgment tasks in the business rules; and instructing the StarSpark HDF driver layer to send control commands on a specified time-frequency resource block. The scheduling sequence executor is the core of deterministic execution. It receives the scheduling sequence generated by the rule compiler. It can be understood that the scheduling sequence executor is strictly aligned with the StarSpark microsecond-level synchronization clock, and at the precise time specified in the scheduling sequence, the scheduling sequence executor triggers the StarSpark HDF driver layer to execute the corresponding action.

[0083] As can be seen, in this embodiment of the invention, the business rules arranged by the user in the visual business rule orchestration interface provided by the deterministic rule engine and configured with deterministic timing constraints are received. Specifically, the business rules are compiled into a scheduling sequence by the rule compiler in the deterministic rule engine. The scheduling sequence defines the execution timestamp of each step in the business process and the StarSpark time-frequency resource block occupied. The scheduling sequence is loaded into the StarSpark scheduling coordination layer. The scheduling sequence executor in the StarSpark scheduling coordination layer is synchronized with the StarSpark microsecond-level synchronization clock and strictly follows the timestamp in the scheduling sequence to trigger the StarSpark HDF driver layer to drive the execution of the scheduling sequence. This achieves deep integration and dynamic mapping between the upper-layer visual business rules and the lower-layer StarSpark wireless communication physical layer scheduling, connects the business layer and the physical layer, and achieves end-to-end microsecond-level deterministic latency guarantee. In other words, by constructing a deterministic rule engine and a StarSpark scheduling collaboration layer, business rules are compiled into scheduling sequences containing precise timestamps, resource allocation information, and node execution order. These sequences are then driven by a scheduling sequence executor synchronized with StarSpark's microsecond-level clock. This connects the business layer and the physical layer, enabling unified scheduling of the perception-decision-execution business process. It is suitable for extreme real-time scenarios such as in-vehicle active noise reduction and precision control in intelligent manufacturing, and solves the problems of operating system scheduling jitter and inefficient resource utilization.

[0084] See Figure 14 As shown, this embodiment of the invention discloses a specific method for dynamic mapping of deterministic business rules based on StarSpark physical layer scheduling. Compared with the previous embodiment, this embodiment further explains and optimizes the technical solution. This method is applied to the aforementioned deterministic business rule engine based on StarSpark physical layer scheduling and the StarSpark collaborative system, including: Step S41: The rule compiler in the deterministic rule engine receives the business rules arranged by the user in the visual business rule arrangement interface, and compiles the arranged business rules into a scheduling sequence; the nodes in the business rules are configured with deterministic timing attributes.

[0085] In this embodiment, a deterministic rule engine processes visualized business rules. When the deterministic rule engine includes a deterministic node library and a rule compiler, the rule compiler specifically compiles the timing constraints and resource requirements of business rules with deterministic timing attributes into a scheduling sequence containing precise timestamps, resource allocation information, and node execution order. The deterministic node library, as a toolset for declaring deterministic business intents, allows developers to precisely define the real-time requirements of each step in the business process and provides visualized nodes with deterministic timing attributes. The rule compiler compiles the entire rule process into a scheduling sequence containing precise timestamps, resource block allocation, and node execution order.

[0086] Step S42: The time-frequency resource pool of the StarSpark network is managed by the resource manager in the StarSpark scheduling coordination layer connected to the deterministic rule engine and the StarSpark HDF driver layer. Admission control and resource allocation are performed according to the resource requests in the scheduling sequence. The scheduling sequence is received by the scheduling sequence executor in the StarSpark scheduling coordination layer, and the StarSpark HDF driver layer is triggered to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

[0087] In this embodiment, when the StarSpark scheduling coordination layer includes a resource manager and a scheduling sequence executor, the resource manager manages the StarSpark network's time-frequency resource pool and performs admission control and resource allocation based on resource requests in the scheduling sequence, resolving resource conflicts between multiple deterministic rules. The scheduling sequence executor is aligned with the StarSpark microsecond-level synchronization clock, and specifically receives the scheduling sequence. At the timestamp specified in the scheduling sequence, it triggers the StarSpark HDF driver layer to execute the physical layer operation corresponding to the scheduling sequence. The physical layer operation may specifically include: instructing the StarSpark HDF driver layer to collect data on a specified time-frequency resource block; triggering the CPU to execute calculation or logical judgment tasks in the business rules; and instructing the StarSpark HDF driver layer to send control commands on a specified time-frequency resource block. The scheduling sequence executor is the core of deterministic guarantee execution. It receives the scheduling sequence generated by the rule compiler. Understandably, the resource manager is responsible for managing the entire StarSpark network's TF resource pool. The resource manager receives resource requests from the rule compiler and uses a scheduling algorithm and admission control strategy to resolve resource conflict between multiple deterministic rules, performing unified admission control and resource allocation to resolve resource conflicts between multiple deterministic rules. The scheduling sequence executor is strictly aligned with StarSpark's microsecond-level synchronization clock. At the precise moment specified by the scheduling sequence, the scheduling sequence executor triggers the StarSpark HDF driver layer to execute the corresponding action.

[0088] As can be seen, in this embodiment of the invention, the business rules arranged by the user in the visual business rule orchestration interface provided by the deterministic rule engine and configured with deterministic timing constraints are received. Specifically, the business rules are compiled into a scheduling sequence by the rule compiler in the deterministic rule engine. The scheduling sequence defines the execution timestamp of each step in the business process and the occupied StarSpark time-frequency resource blocks. The scheduling sequence is loaded into the StarSpark scheduling coordination layer. The StarSpark scheduling coordination layer includes a resource manager and a scheduling sequence executor, which are used for TF time-frequency resource admission and allocation, and triggering data acquisition, calculation and control command transmission at a specified time, respectively. This bypasses the jitter of general operating system task scheduling, realizes end-to-end microsecond-level deterministic latency guarantee from perception to decision to execution, realizes deep integration and dynamic mapping between the upper-layer visual business rules and the physical layer scheduling of the lower-layer StarSpark wireless communication, connects the business layer and the physical layer, and realizes end-to-end microsecond-level deterministic latency guarantee.

[0089] For example, in the visualized business rules, deterministic attributes are configured for at least some nodes to generate business rules carrying deterministic context; the topology dependencies and deterministic attributes of the business rules are parsed, timing analysis is performed to obtain node-level timing plans, and resource requests for StarSpark TF time-frequency resources are generated based on the data requirements of I / O nodes; admission control and resource allocation are performed on the resource requests to obtain resource allocation results; a scheduling sequence aligned with StarSpark's microsecond-level synchronization clock is generated based on the timing plan and resource allocation results, and the scheduling sequence is sent to the scheduling sequence executor; the scheduling sequence executor triggers acquisition, calculation, and transmission actions at the time specified in the scheduling sequence based on StarSpark's microsecond-level synchronization clock, so that the business loop is completed within microsecond-level timing constraints, thereby achieving end-to-end deterministic latency guarantee. Specifically, the timing analysis calculates the earliest start time (EST), latest finish time (LFT), and relaxation amount (Slack) for each node, and determines the execution order based on Slack and priority; resource allocation may specifically include mapping resource requests to StarSpark's ultra-short radio frame granularity and / or single subcarrier granularity time-frequency resource grids.

[0090] Figure 15 A schematic diagram of the structure of a terminal provided in an embodiment of this application. The terminal may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0091] When the processor 502 executes the program, it implements the deterministic business rule dynamic mapping method based on star flash physical layer scheduling provided in the above embodiments.

[0092] Furthermore, the terminal also includes: Communication interface 503 is used for communication between memory 501 and processor 502.

[0093] The memory 501 is used to store computer programs that can run on the processor 502.

[0094] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0095] If the memory 501, processor 502, and communication interface 503 are implemented independently, they can be interconnected via a bus to communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one line is used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0096] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0097] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0098] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for dynamic mapping of deterministic business rules based on star-flash physical layer scheduling.

[0099] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0100] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus or device (such as a computer-based system, a processor-included system or other system that can read and execute instructions from and from an instruction execution system, apparatus or device).

[0102] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0103] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A deterministic business rule engine and a StarSpark collaborative system based on StarSpark physical layer scheduling, characterized in that, The system includes: A deterministic rule engine is used to provide a visual interface for orchestrating business rules and to compile the orchestrated business rules into scheduling sequences; the nodes in the business rules are configured with deterministic timing attributes. The StarSpark scheduling coordination layer, connected to the deterministic rule engine and the StarSpark HDF driver layer, is used to receive the scheduling sequence and trigger the StarSpark HDF driver layer to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

2. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 1, characterized in that, The deterministic rule engine includes a deterministic node library and a rule compiler; The deterministic node library is used to provide nodes with customizable deterministic timing attributes for orchestrating business rules; The rule compiler is used to compile the business rules into scheduling sequences.

3. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 2, characterized in that, The Star Flash scheduling coordination layer includes a scheduling sequence executor; The scheduling sequence executor is aligned with the StarSpark microsecond-level synchronization clock, and is used to receive the scheduling sequence and trigger the StarSpark HDF driver layer to execute the physical layer operation corresponding to the scheduling sequence at the timestamp specified by the scheduling sequence.

4. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 3, characterized in that, The physical layer operations include: The instruction specifies that the Starflash HDF driver layer collects data on the designated time-frequency resource block; Trigger the CPU to execute the calculation or logical judgment task in the business rule; The instruction specifies that the Starflash HDF driver layer sends control commands on the designated time-frequency resource block.

5. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 3, characterized in that, It also includes a dual-star flash link consisting of a primary star flash link and a backup star flash link; The rule compiler is further configured to compile the business rules into a primary scheduling sequence corresponding to the primary star link and a secondary scheduling sequence corresponding to the backup star link, and to align the primary scheduling sequence and the secondary scheduling sequence using a unified synchronization clock or equivalent time synchronization mechanism.

6. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 5, characterized in that, The scheduling sequence executor is also used to switch to the backup star link to execute the secondary scheduling sequence when the primary star link fails, provided that the switching time does not exceed a preset switching time.

7. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 1, characterized in that, The StarScan scheduling coordination layer also includes a resource manager; The resource manager is used to manage the time-frequency resource pool of the StarNet and to perform admission control and resource allocation based on resource requests in the scheduling sequence.

8. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 2, characterized in that, The rule compiler includes a timing analysis unit, a resource request generation unit, and a scheduling sequence generation unit; The timing analysis unit is used to analyze the topology of the business rule and the deterministic timing attributes of all nodes in the business rule to calculate the end-to-end latency requirements and the optimal execution timing of each node, and obtain the timing analysis results. The resource request generation unit is used to generate a resource request for the star flash time-frequency resource based on the data requirements of the nodes in the business rules, and obtain the resource allocation result. The scheduling sequence generation unit is used to generate a scheduling sequence based on the timing analysis results and the resource allocation results.

9. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 2, characterized in that, In a hybrid network that includes wired TSN and wireless strobe, the rule compiler is a hybrid network scheduler; The hybrid network scheduler is used to compile the service rules into scheduling sequences that span wired and wireless domains.

10. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 7, characterized in that, The resource manager is also used to perform at least one operation in the event of a resource conflict: denying access, reducing non-critical rule resources, splitting rules into multiple cycles for execution, or remapping to a spare resource block.

11. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 7, characterized in that, When the StarSpark scheduling coordination layer is configured with an emergency event resource pool, the resource manager is also used to interrupt or adjust the current scheduling sequence when a high-priority event is detected, dynamically generate and execute a temporary emergency scheduling sequence, so as to complete the event reporting and emergency control closed loop on the reserved time and frequency resources of the emergency event resource pool.

12. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 3, characterized in that, The scheduling sequence executor is aligned with a starflash microsecond-level synchronous clock using interrupts, polling, or hardware timers.

13. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 1, characterized in that, The nodes in the business rules include IO nodes and logical nodes.

14. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 1, characterized in that, The deterministic timing attributes include execution time, maximum completion delay, and priority.

15. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 1, characterized in that, The scheduling sequence is in the format of a gated control list.

16. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to claim 1, characterized in that, The StarSpark scheduling coordination layer is located in the system service layer of the operating system.

17. The deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to any one of claims 1 to 16, characterized in that, When the deterministic rule engine has an AI Agent built in, it is also used to analyze the business flow characteristics and network load of the business rules in order to automatically recommend the optimal deterministic timing attribute configuration for the nodes in the business rules, or generate the optimal scheduling sequence.

18. A method for dynamic mapping of deterministic business rules based on star-flash physical layer scheduling, characterized in that, The method applied to the deterministic business rule engine and StarSpark collaborative system based on StarSpark physical layer scheduling according to any one of claims 1 to 17, the method comprising: The deterministic rule engine receives business rules arranged by the user in the visual business rule orchestration interface and compiles the arranged business rules into a scheduling sequence; the nodes in the business rules are configured with deterministic timing attributes. The scheduling sequence is received by the StarSpark scheduling collaboration layer, which is connected to the deterministic rule engine and the StarSpark HDF driver layer, and the StarSpark HDF driver layer is triggered to execute the scheduling sequence based on the StarSpark microsecond-level synchronization clock.

19. A terminal, characterized in that, include: The system includes a memory, a processor, and a deterministic dynamic mapping program for business rules based on StarSpark physical layer scheduling, which is stored in the memory and can run on the processor. When the processor executes the deterministic dynamic mapping program for business rules based on StarSpark physical layer scheduling, it implements the steps of the deterministic dynamic mapping method for business rules based on StarSpark physical layer scheduling as described in claim 18.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that can be executed to implement the steps of the deterministic business rule dynamic mapping method based on star-flash physical layer scheduling as described in claim 18.

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