Entanglement space operation method and implementation device

Abstract

The invention relates to the technical field of computer information processing, space structure modeling and system modeling, in particular to an entanglement space calculation method suitable for complex structure relation modeling and state-driven logic operation and an implementation device of the entanglement space calculation method. Specifically, the entanglement space is an abstract data space containing nodes, constraints and channels, the nodes are state units, the constraints define logic relations among the nodes, the channels are state transmission paths, and a multi-level structure can be formed through nesting or coupling. The method comprises the steps of defining a basic unit, constructing an entanglement space, executing state mapping operation and unwrapping operation, and outputting a result. The device comprises a unit definition module, a space construction module, an operation execution module and an output module, and supports constraint conflict detection and state record tracing. According to the method, the problem that a traditional model is difficult to process complex nesting and multiple dependency relationships is solved, unified modeling and dynamic operation are achieved, the method is adaptive to multiple scenes, and the modeling and operation efficiency of a complex system is improved.

Description

Technical Field

[0001] This invention relates to the fields of computer information processing, spatial structure modeling, and system modeling, and particularly to an "entangled space" computation method and its implementation device suitable for complex structural relationship modeling and state-driven logic operations. Specifically, this invention can be widely applied to processes engines, graph structure analysis, knowledge graph reasoning, multidimensional spatial path optimization, complex dependency management, semantic modeling, nested decision systems, and other fields, providing a unified modeling and dynamic computation framework for strongly coupled relationships and nested spatial structures.

[0002] It should be noted that the "entangled space" proposed in this invention is not a geometric space or quantum state space in the traditional sense, but rather an abstract data space constructed through logical rules such as coupling, nesting, and binding. Its core feature is the nonlinear, asymmetric, and multi-constrained mapping relationship between nodes. This spatial structure is widely present in complex system modeling scenarios, such as task scheduling, cross-domain knowledge reasoning, nested process management, spatial path evolution, and system dependency graphs. However, existing computational models, such as vector spaces, tree structures, and graph neural networks, are difficult to directly and efficiently express and compute this type of spatial structure.

[0003] Therefore, the present invention provides a general method for defining, constructing and computing the "entangled space", as well as an implementation device that supports the execution of the method, aiming to solve the bottleneck problem of computational expression in traditional models during multi-layer nesting, multi-path dependence and dynamic evolution processes. Background Technology

[0004] With the widespread application of information systems, the Internet of Things, and intelligent decision-making systems, system structures and data relationships are becoming increasingly complex. Traditional linear spaces or independent data structures, such as vector spaces, matrix spaces, tree diagrams, and directed acyclic graphs (DAGs), are no longer sufficient to describe spatial states or logical relationships characterized by coupling, nesting, and interaction. This is especially true in scenarios such as complex process scheduling, semantic reasoning, dynamic optimization of urban road networks, cross-domain knowledge mapping, and multimodal decision-making, where data objects often exhibit multiple dependencies, bidirectional constraints, nested structures, and nonlinear mapping relationships.

[0005] However, existing spatial computation methods have the following drawbacks: lack of a unified high-coupling spatial structure modeling mechanism; scattered computation rules that cannot adapt to complex evolution processes; limited implementation device efficiency that makes it difficult to support real-time high-dimensional computation or dynamic reasoning; and lack of scalable, heterogeneous compatible structured modeling and dynamic path selection methods.

[0006] Therefore, there is an urgent need to propose a unified modeling method that adapts to complex interactive relationships and nested structures, as well as an efficient computational implementation method. Summary of the Invention

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides a method and apparatus for entanglement space computation, the method comprising: defining entanglement space basic units, constructing entanglement space, performing space computation, and outputting computation results; the apparatus comprises: a unit definition module, a space construction module, a computation module, and an output module.

[0008] A. Define the basic unit of entanglement space: The basic unit includes at least three nodes such as S1, S2, S3, at least three constraints such as constraint 1, constraint 2, and constraint 3, and at least one channel; wherein, nodes S1, S2, and S3 are state units carrying state information, used to represent independent data states or logical units; constraint 1, constraint 2, and constraint 3 are logical relationship rules between nodes, used to define the dependency conditions, state binding relationships, or transition triggering conditions between nodes; the channel is the state transition or data flow path between nodes, used to realize the state transfer between nodes; B. Constructing an entangled space: Nodes S1, S2, and S3 are associated with the constraints to form basic units, and the basic units are nested or coupled to expand into an entangled space with a multi-level structure. In the multi-level structure, the nodes of the lower-level basic units can serve as sub-state units of the upper-level nodes. C. Perform spatial operations: Based on the real-time states of nodes S1, S2, and S3 and the effective states of constraints 1, 2, and 3, perform state mapping operations through channels to transmit the state of the target node to the associated nodes according to the constraint rules; when it is necessary to simplify the operation, perform untangling operations to expand the nested structure containing S1, S2, and S3 into a linear sequence according to the constraint priority. D. Output the operation results: Based on the operation target, output the node status, constraint effective record or channel transmission path to complete the logical operation of the entangled space.

[0009] Preferably, the nodes S1, S2, and S3 serve as state units, and the state information they carry includes static identifiers and dynamic states: static identifiers are inherent attributes of the nodes, used to uniquely distinguish the nodes; dynamic states are parameters that change with the operation process, which can be transmitted between nodes through channels, and the updates of dynamic states must meet the rules of the corresponding constraints.

[0010] Preferably, constraint 1, constraint 2, and constraint 3 are at least one of the following logical relationship rules: A1. Dependency Constraints: Define the preconditions between nodes, such as node S2 can only start state update when the dynamic state of node S1 meets the preset threshold. B1. Binding Constraints: Define the association relationship between node states, such as the dynamic state value of node S3 being the weighted sum of the dynamic state values ​​of nodes S1 and S2; C1. Transition Constraint: Defines the state transmission conditions of the channel. For example, when constraint 2 is in effect, the channel from node S2 to S3 is opened, allowing state transmission.

[0011] Preferably, when constructing the entangled space, the coupling method of the basic units includes: A2. Serial coupling: By using constraints, node S3 of one basic unit is associated with node S1 of another basic unit, forming a chain structure of "node S1-constraint-node S3-constraint-node S1". B2. Nested Coupling: The basic unit containing nodes S1, S2, and S3 is used as the sub-state unit of the upper-level node. The dynamic state of the upper-level node is generated by the node state of the sub-state unit through constraint aggregation.

[0012] Preferably, the state mapping operation specifically involves: when the dynamic state of node S1 changes, if constraint 1 is in effect, the state of S1 is mapped to node S2 through the channel according to the rules of constraint 1, triggering the dynamic state update of S2; the mapping rules include proportional mapping, threshold trigger mapping, or logical judgment mapping.

[0013] Preferably, the specific process of the untangling operation is as follows: based on the priority of constraint 1, constraint 2, and constraint 3, retain the node and channel corresponding to the constraint with the highest priority, and expand the nodes S1, S2, and S3 in the nested structure into a linear sequence according to the "state transmission order". The arrangement order of the nodes in the linear sequence is consistent with the state transmission direction of the channel.

[0014] Preferably, the apparatus for the method is characterized by comprising: A3. Unit Definition Module: Used to define the nodes S1, S2, S3, constraint 1, constraint 2, constraint 3 and channels of the basic unit of entanglement space. Nodes S1, S2 and S3 are configured as state units that store state information, constraints are configured as logical relationship rules between nodes, and channels are configured as state transmission paths. B3, Space Construction Module: Used to associate nodes S1, S2, and S3 through constraints to form basic units, and to extend them into multi-level entangled spaces through coupling, while detecting constraint conflicts between nodes; C3, Operation Module: Used to perform state mapping operation and unwrapping operation. The state mapping operation transmits node state through channels based on constraint rules. The unwrapping operation expands the nested structure according to constraint priority. D3, Output Module: Used to output node status, constraint effective records, or unwrapped linear sequences.

[0015] Preferably, the unit definition module further includes an attribute configuration submodule, used to configure the dynamic state types of nodes S1, S2, and S3 and the effective conditions of constraints 1, 2, and 3.

[0016] Preferably, the constraint conflict detection of the space construction module is specifically as follows: when node S1 is associated with both constraint 1 and constraint 2, it is determined to be a conflict, and the conflict is resolved by reducing the effective weight of the lower priority constraint.

[0017] Preferably, the calculation module further includes a status recording submodule, which is used to record in real time the dynamic status change time of nodes S1, S2, and S3, the effective / ineffective time of constraints 1-3, and the status transmission record of the channel. The record is used for tracing the calculation process.

[0018] This device can be deployed on edge computing devices, embedded smart terminals, FPGA hardware platforms, and cloud computing environments, adapting to heterogeneous computing needs.

[0019] Compared with existing technologies, the present invention has the following advantages: it provides a unified spatial description and computation mechanism for complex interactive systems; the computation rules are adaptive and evolvable, supporting complex nested dependency structures; it has high computational efficiency and is compatible with edge devices, embedded systems, FPGAs, GPU / TPU cloud environments; it supports multimodal data input, multi-path decision-making, and multi-scale spatial structures; and it has a built-in visual tracking and debugging interface, which facilitates the monitoring of complex logic processes and the tracing of anomalies. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The diagram shows the structure of the entangled space unit. S1, S2, and S3 are nodes representing state units. "Constraint 1", "Constraint 2", and "Constraint 3" are the logical relationships or dependencies between nodes. "Channel" represents the state transition or data flow path.

[0022] Figure 2 This is a schematic diagram of the computing device architecture. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0024] The implementation device of the present invention completes the construction and operation of entanglement space through modular collaboration. The core modules include an input construction module, a space construction module, and an operation execution module, which include an entanglement mapping engine, an unentanglement optimizer, a path selector, a state management module, and an output control module.

[0025] The connections between modules are as follows: The input construction module transforms multimodal data into structured information and transmits it to the space construction module; the space construction module generates entangled units and multi-level spaces based on the "node-constraint-channel" ternary structure, and simultaneously sends the initial state to the state management module; the operation execution module obtains the real-time state from the state management module, realizes state propagation through the entanglement mapping engine, simplifies the structure through the untangling optimizer, and determines the optimal path through the path selector, and feeds back the operation results to the state management module for updating; the output control module extracts the results from the state management module and transforms them into visualized or standardized output. The operating status of all modules is synchronized in real time to the fault monitoring submodule to ensure timely intervention in case of anomalies.

[0026] To clearly understand the technical solution of this application, the technical terms involved in the prior art and the embodiments of this application are explained below: Entangled Unit: A group of data nodes with multiple constraints and state interaction mechanisms; the core constituent unit.

[0027] Untangling operation: Maps a multidimensional nested coupled structure to a linearly operable or graph-like structure.

[0028] Coupled path: The link of data, state, and logic transfer paths within the entangled space.

[0029] Channel priority: A path selection metric calculated based on parameters such as state weight, path cost, and time constraints.

[0030] Entangled Mapping Engine: The core module for implementing node state mapping, path evolution, and decoupling of nested structures.

[0031] Existing spatial computation methods rely on highly coupled spatial structure modeling mechanisms, lack uniformity, and have scattered computational rules that cannot adapt to complex evolution processes. Furthermore, due to the limited efficiency of the implementation devices, they are unable to support real-time high-dimensional computation or dynamic reasoning, and lack scalable, heterogeneous compatible structured modeling and dynamic path selection methods.

[0032] To address the aforementioned issues, this application provides a computational method and implementation apparatus for performing entanglement space computations.

[0033] The calculation method of this invention is specifically implemented through the following steps: A1. Entanglement Space Construction: The input construction module encodes scene data into nodes, i.e., state units; constraints, i.e. logical rules; and channels, i.e. transmission paths. The space construction module combines them into entanglement units, which are also the core constituent units, and forms multi-level entanglement spaces through coupling rules, such as subspaces nested within parent nodes.

[0034] A2. State Mapping Operation: The entanglement mapping engine, based on constraint rules, transmits the state of a node to related nodes through channels, realizing the dynamic propagation of the state, such as the task progress triggering the task's start conditions.

[0035] A3. Untangling operation: The untangling optimizer expands the multi-dimensional nested entangled space into a linear sequence or simplified graph according to the constraint priority, removes redundant structures, and reduces computational complexity.

[0036] A4. Path selection operation: The path selector is based on channel priority and needs to combine parameters such as state weight and path cost to select the optimal path from the coupled paths. It supports dynamic adjustment, such as switching to a backup path in case of an anomaly.

[0037] A5. Result Output and Status Update: The output control module converts the calculation results into scene-executable instructions, and the status management module synchronously updates the real-time status of nodes, constraints and channels, forming a closed loop.

[0038] To facilitate understanding by those skilled in the art, the present invention provides the following three specific embodiments: a process dependency graph optimization method based on entanglement space, entanglement space modeling and path optimization for urban road networks, and an entanglement space inference module on an embedded device.

[0039] It should be noted that the above-mentioned process dependency graph optimization method based on entanglement space aims to transform complex nested process dependencies into an entanglement space structure, generate the optimal execution sequence through untangling operations, and improve task scheduling efficiency by dynamically adjusting the subtask allocation path in real time.

[0040] For example, the electronic product assembly process includes four tasks: component quality inspection (P1), motherboard soldering (P2), casing assembly (P3), and complete machine testing (P4). These tasks have nested dependencies: P2 depends on P1 and requires 80% of the components to be qualified; P4 depends on both P2 and P3 and requires both to be completed. Furthermore, P2 and P3 share assembly, but P2 takes priority in case of resource conflicts. Therefore, the optimal execution sequence needs to be generated through entanglement space operations to reduce waiting time.

[0041] Based on A1, an entanglement space needs to be constructed, specifically: the input construction module receives process data, including: The nodes include: S1: P1 status including the percentage of qualified parts and remaining time; S2: P2 status including welding progress and assembly station occupancy status; S3: P3 status including assembly progress and assembly station application status; and S4: P4 status including test preparation progress.

[0042] Constraints include: Constraint 1: S1 qualification rate ≥ 80%, S2 can be started; Constraint 2: S2 progress = 100% and S3 progress = 100%, S4 can be started; Constraint 3: S2 occupies the assembly station, S3 enters waiting.

[0043] Channels include: Channel 1: S1-S2, transmitting the pass / fail percentage; Channel 2: S2-S4, transmitting the completion signal; Channel 3: S3-S4, transmitting the completion signal; Channel 4: S2-S3, transmitting the assembly station occupancy status.

[0044] The spatial construction module combines the above elements into entangled units and defines the parent node S. 总 The overall assembly schedule is associated with child nodes through aggregation constraints, forming a nested structure of "subspace-parent node".

[0045] Based on A2, a state mapping operation is required, specifically: a. In the initial state, the pass rate of S1 is 0%, and S2, S3, and S4 have not been started. 总 =0%, at the 60th minute, the qualified rate of S1 reaches 85%, and the constraint is triggered through channel 1, and S2 starts. At this time, the assembly station is occupied, and the progress of S2 starts to update from 0%.

[0046] b. At the 90th minute, the progress of S2 reaches 50%. At this time, it pauses and waits for parts. The "assembly station occupied" status is transmitted through channel 4. S3 enters the waiting state and the application status is marked as "queuing".

[0047] c. At the 120th minute, S2 reaches 100% progress, releases the assembly station, triggers constraint 3 through channel 4, starts S3, occupies the assembly station, and simultaneously transmits a "complete signal" to S4 through channel 2.

[0048] The untangling operation described in A3 is as follows: The untangling optimizer expands the nested structure according to the constraint priority, i.e., constraint 2 > constraint 1 > constraint 3, filters redundant states, such as the intermediate qualified percentage of S1, and retains the core nodes and channels; and generates a linear execution sequence: S1 (0-120 minutes) - S2 (60-120 minutes, parallel second half of S1) - S3 (120-180 minutes) - S4 (after 180 minutes, receive the completion signals of S2 and S3).

[0049] Based on the path selection operation described in A4, specifically: at the 100th minute, S2 pauses due to a part defect and is expected to wait for 30 minutes, during which the assembly station is idle; the path selector calculates the channel priority, and the "resource utilization rate" of the S3 starting path is higher than that of the waiting path. At this time, "temporarily start S3" is selected, and the adjusted sequence is: S3 occupies the assembly station from 100 to 130 minutes, and S2 reuses the assembly station after 130 minutes, reducing the total time from 240 minutes to 210 minutes.

[0050] In this embodiment, the device performs and its effects as follows: in the computation execution module, the entanglement mapping engine updates its state every 10 minutes, the unentanglement optimizer generates a sequence in less than 5 seconds, and the path selector responds to abnormal adjustments in less than 10 seconds; the output control module generates a Gantt chart and a resource usage table, showing that after optimization, "the assembly table idle time has been reduced from 60 minutes to 30 minutes", and the total task time has been reduced by 12.5%.

[0051] It should be noted that the above-mentioned entangled spatial modeling and path optimization based on urban road networks aims to map the urban road network into entangled spatial nodes, define the constraints of road segment states and intersection flow conditions, and define passageways as travel paths. Based on dynamic mapping calculations and path selection mechanisms, multi-traffic flow coordination and path congestion avoidance optimization are achieved.

[0052] For example, a city's road network includes "main road A, secondary road B, and branch road C" and intersections X (where A intersects B) and Y (where B intersects C). The following issues need to be addressed: road segment traffic conditions are affected by real-time traffic flow; congestion occurs when traffic flow at intersection A exceeds 3000 vehicles / hour; the traffic light cycle at intersection X is set at 40 seconds for green and 60 seconds for red; private cars and buses need differentiated routes, with buses prioritizing the use of the dedicated lane for road B. Entanglement space computation is needed to achieve congestion avoidance and multi-traffic flow coordination.

[0053] Based on A1, an entanglement space needs to be constructed. Specifically, the input construction module receives process data, including: road segment length, traffic sensor data, and traffic light timing. Nodes, or state units, include: S1: Main road A, with real-time traffic flow and speed; S2: Secondary road B, with real-time traffic flow and dedicated lane status; S3: Intersection X, with traffic light status and remaining green light time; S4: Branch road C, with real-time traffic flow and traffic restrictions.

[0054] Constraints include: Constraint 1: S3 green light and remaining time greater than or equal to 15 seconds - S1-S2 channel opens; Constraint 2: S1 traffic > 3000 vehicles / hour - S1-S2 channel priority is reduced by 60%; Constraint 3: Bus request - S2 dedicated lane opens, S2-Y channel priority is increased to the highest.

[0055] Channels include: Channel 1: S1-S2, via X, associated with constraint 1 and constraint 2; Channel 2: S2-C, via Y, associated with constraint 3; Channel 3: S1-C, via XY, as an alternative.

[0056] The spatial construction module combines the above elements into entangled units and defines the parent node. This refers to the regional traffic status, which is linked to child nodes through aggregation constraints to form a multi-level space.

[0057] Based on A2, a state mapping operation is required, specifically: a. During the morning rush hour at 8:00, the traffic flow in S1 is 2500 vehicles / hour, and the traffic is smooth. The green light in S3 has 30 seconds remaining - constraint 1 takes effect, and the priority of channel 1 is 1.0.

[0058] b. S2 traffic = 1000 vehicles / hour, channel 2 priority = 1.0; at 8:15, S1 traffic = 3200 vehicles / hour, at which point congestion-constraint 2 takes effect, and channel 1 priority drops to 0.4.

[0059] c. S3 red light has 40 seconds remaining - lane 1 is closed; at 8:20, the bus makes a request - constraint 3 takes effect, lane S2 opens, and lane 2's priority is increased to 2.0; S3 green light has 20 seconds remaining - lane 1 opens, with a priority of 0.4.

[0060] The untangling operation described in A3 is as follows: For the private car's requirement of "from starting point A to ending point C", the untangling optimizer filters low-priority channels. Channel 1 is excluded due to its priority of 0.4. The effective path channels 1 and 2 are retained, with channel 1 as the backup and channel 2 as the primary selection.

[0061] The simplified core route is as follows: Main Road A - Secondary Road B, Dedicated Lane - Branch Road C, with a "Congestion Warning": S1 is currently congested, it is recommended to take Lane 2.

[0062] Based on the path selection operation described in A4, it is specifically as follows: a. Private car route: The route selector is based on "shortest travel time", specifically: channel priority = 1 / estimated time, select "channel 2: BC", estimated time is 18 minutes.

[0063] b. Bus route: Based on "dedicated lane priority", that is, lane priority = dedicated lane status × 1.5, select "lane 2: use dedicated lane B", the estimated time is 10 minutes, which is 44% faster than private cars.

[0064] c. Dynamic adjustment: At 8:25, the S1 traffic volume dropped to 2800 vehicles / hour - the priority of Channel 1 was restored to 0.8, and the private car route was automatically switched to "Channel 1 - Channel 2", with the time reduced to 15 minutes.

[0065] In this embodiment, the device performs and its effects as follows: the spatial construction module distributes the processing of 500 road segment nodes and completes spatial initialization within 5 minutes; in the computation execution module, the entanglement mapping engine updates the state every 30 seconds, the path selector supports parallel planning for 1000 vehicles, and the time taken by a single vehicle is less than 20ms; the output control module generates a navigation route with lane-level guidance and a traffic situation map, showing that "the average time taken by private cars is reduced by 16.7%, and the on-time rate of buses is increased to 92%".

[0066] It should be noted that the entanglement space inference module on the aforementioned embedded device aims to embed an entanglement space inference engine into a smart terminal, supporting decision-making tasks based on multimodal data such as text, images, and voice, and achieving real-time response and adaptive resource scheduling through dynamic path selection.

[0067] For example, a smart bracelet needs to achieve "sports and health monitoring," integrating three types of data: accelerometer to monitor exercise type, heart rate sensor to monitor heart rate value, and voice input module to monitor user commands. Functions include: heart rate > 110 bpm and exercise type = running - push rest reminder; heart rate > 130 bpm and voice message includes "discomfort" - trigger emergency contact. Due to the limitations of the bracelet's resources, lightweight computing is required.

[0068] Based on A1, an entanglement space needs to be constructed. Specifically, the input construction module receives sensor data through a hardware interface. Nodes, or state units, include: S1: Heart rate, with current value and abnormal state; S2: Exercise type, with type and confidence level; and S3: Voice command, with keywords and urgency level.

[0069] Constraints include: Constraint 1: S1 > 110 times / min and S2 = running - triggers "rest reminder"; Constraint 2: S1 > 130 times / min and S3 keyword = "discomfort" - triggers "emergency contact".

[0070] Channels include: Channel 1: S1-S2, which transmits abnormal heart rate status; Channel 2: S1-S3, which transmits severe abnormal heart rate status.

[0071] The spatial construction module adopts a single-level structure with no parent node. It preloads entangled units into the wristband ROM and uses an "on-demand activation" strategy for state updates. For example, S1 collects data every 5 seconds, while S2 and S3 are only woken up when S1 is abnormal.

[0072] Based on A2, state mapping operations are required, and low-power optimization must be met, specifically: a. Normal state: S1=85 times / min, normal - channels 1 and 2 are closed, S2 and S3 are in sleep mode, and wake up once every 30 seconds.

[0073] b. Exercise status: S1=115 times / min, abnormal - channel 1 activated, wake up S2 - S2 recognizes "exercise type = running, confidence level 95%" - constraint 1 takes effect - generate "rest reminder" signal.

[0074] c. Emergency state: S1=135 times / min, serious abnormality - channel 2 activated, wake up S3 - S3 analyzes the voice "a little uncomfortable", keyword = "uncomfortable" - constraint 2 takes effect - generate "emergency contact" signal.

[0075] Based on the untangling operation described in A3, specifically: the untangling optimizer adopts a "rule-first" strategy, directly outputting the results according to the logic of constraints 1 and 2, without retaining intermediate states, such as the historical motion type of S2; Furthermore, it needs to be compatible with embedded resources, and the output format needs to be simplified to "instruction code + trigger reason", such as "rest prompt", and the data volume needs to be less than 512 bytes.

[0076] Based on the path selection operation described in A4, it is specifically as follows: a. Low power scenario, power <15%: Because voice parsing consumes a lot of power, the path selector turns off the voice parsing of S3 and only retains the inference of S1 and S2, i.e., constraint 1, to prioritize basic monitoring.

[0077] b. Typical scenario: Constraint 1 and Constraint 2 are run simultaneously, with a response time of <300ms, which satisfies real-time interaction.

[0078] In this embodiment, the device performs and its effects as follows: the input construction module uses hardware-accelerated coding to avoid CPU usage; the operation execution module is optimized using C language, with inference time <100ms; the output control module drives the screen to display prompts or trigger communication, improving battery life by 30% and achieving a health event recognition accuracy of 90%.

[0079] The implementation of the three scenarios described above demonstrates that this invention, through a unified modeling framework of "node-constraint-channel," can flexibly adapt to complex relationships in different fields such as processes, transportation, and health. The mapping, untangling, and path selection operational logic can achieve dynamic optimization and, through lightweight design, adapt to different devices from the cloud to the edge. The supporting devices, through modular collaboration, ensure efficient execution from data input to result output. Whether improving process efficiency, optimizing traffic scheduling, or realizing embedded intelligent reasoning, its practicality in complex system modeling and computation is evident.

[0080] This embodiment is only a preferred example of the present invention. Any modifications or equivalent substitutions made within the core framework of "entanglement unit construction - mapping operation - unentanglement operation - path selection" should be included within the protection scope of the present invention.

[0081] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for entangled space computation, characterized in that, Includes the following steps: A1. Define the basic unit of entanglement space: The basic unit of entanglement space includes at least three nodes such as S1, S2, S3, at least three constraints such as constraint 1, constraint 2, and constraint 3, and at least one channel; wherein, nodes S1, S2, and S3 are state units carrying state information, used to represent independent data states or logical units; constraint 1, constraint 2, and constraint 3 are logical relationship rules between nodes, used to define the dependency conditions, state binding relationships, or transition triggering conditions between nodes; the channel is the state transition or data flow path between nodes, used to realize the state transfer between nodes; A2. Constructing an entangled space: Nodes S1, S2, and S3 are associated with the constraints to form basic units, and the basic units are nested or coupled to expand into an entangled space with a multi-level structure. In the multi-level entangled space, the nodes of the lower-level basic units can serve as the sub-state units of the upper-level nodes. A3. Perform spatial operations: Based on the real-time states of nodes S1, S2, and S3 and the effective states of constraints 1, 2, and 3, perform state mapping operations through channels to transmit the state of the target node to the associated nodes according to the constraint rules; when it is necessary to simplify the operation, perform untangling operations to expand the nested structure containing S1, S2, and S3 into a linear sequence according to the constraint priority. A4. Output the operation results: Based on the operation target, output the node status, constraint effective record or channel transmission path to complete the logical operation of the entangled space.

2. The method for entanglement space computation according to claim 1, characterized in that, The state information carried by nodes S1, S2, and S3 includes static identifiers and dynamic states: static identifiers are inherent attributes of the nodes, used to uniquely distinguish the nodes; dynamic states are parameters that change with the operation process, which can be transmitted between nodes through channels, and the update of dynamic states must meet the corresponding constraint rules.

3. The method for entanglement space computation according to claim 1, characterized in that, Constraint 1, Constraint 2, and Constraint 3 are at least one of the following logical relation rules: B1. Dependency Constraints: Define the preconditions between nodes, such as node S2 can only start state update when the dynamic state of node S1 meets the preset threshold. B2. Binding Constraints: Define the association relationship between node states, such as the dynamic state value of node S3 being the weighted sum of the dynamic state values ​​of nodes S1 and S2; B3. Transition Constraint: Defines the state transmission conditions of the channel. For example, when constraint 2 is in effect, the channel from node S2 to S3 is opened, allowing state transmission.

4. The method for entanglement space computation according to claim 1, characterized in that, When constructing the entangled space, the coupling methods of the basic units include: C1. Serial coupling: By using constraints, node S3 of one basic unit is associated with node S1 of another basic unit, forming a chain structure of "node S1-constraint-node S3-constraint-node S1". C2. Nested Coupling: The basic unit containing nodes S1, S2, and S3 is used as the sub-state unit of the upper-level node. The dynamic state of the upper-level node is generated by the node state of the sub-state unit through constraint aggregation.

5. The method for entanglement space computation according to claim 1, characterized in that, The state mapping operation is specifically as follows: when the dynamic state of node S1 changes, if constraint 1 is in effect, the state of S1 is mapped to node S2 through the channel according to the rule of constraint 1, triggering the dynamic state update of S2; the mapping rule includes proportional mapping, threshold trigger mapping or logical judgment mapping.

6. The method for entanglement space computation according to claim 1, characterized in that, The specific process of the untangling operation is as follows: based on the priority of constraint 1, constraint 2, and constraint 3, retain the node and channel corresponding to the constraint with the highest priority, and expand the nodes S1, S2, and S3 in the nested structure into a linear sequence according to the "state transmission order". The arrangement order of the nodes in the linear sequence is consistent with the state transmission direction of the channel.

7. An apparatus for implementing entangled space operations, specifically a method for entangled space operations according to any one of claims 1-6, characterized in that, include: D1, Unit Definition Module: Used to define the nodes S1, S2, S3, constraint 1, constraint 2, constraint 3 and channels of the basic unit of entanglement space. Nodes S1, S2 and S3 are configured as state units that store state information, constraints are configured as logical relationship rules between nodes, and channels are configured as state transmission paths. D2, Space Construction Module: Used to associate nodes S1, S2, and S3 through constraints to form basic units, and to extend them into multi-level entangled spaces through coupling, while detecting constraint conflicts between nodes; D3, Operation Execution Module: Used to perform state mapping operations and unwrapping operations. The state mapping operation transmits node states through channels based on constraint rules, and the unwrapping operation expands the nested structure according to constraint priority. D4, Output Module: Used to output node status, constraint effective records, or unwrapped linear sequences.

8. The apparatus for implementing entanglement space operations according to claim 7, characterized in that, The unit definition module also includes an attribute configuration submodule, which is used to configure the dynamic state types of nodes S1, S2, and S3 and the effective conditions of constraints 1, 2, and 3.

9. The apparatus for implementing entanglement space operations according to claim 7, characterized in that, The constraint conflict detection of the spatial construction module is as follows: when node S1 is associated with both constraint 1 and constraint 2, it is determined to be a conflict, and the conflict is resolved by reducing the effective weight of the lower priority constraint.

10. The apparatus for implementing entanglement space operations according to claim 7, characterized in that, The computation execution module also includes a status recording submodule, which is used to record in real time the dynamic status change time of nodes S1, S2, and S3, the effective / ineffective time of constraints 1-3, and the status transmission record of the channel. The records are used for tracing the computation process.

Images