A Smart Park Integrated Data Management Method and System

Abstract

This invention discloses a comprehensive data management method and system for smart parks, specifically relating to the field of comprehensive data management technology for smart parks. The method includes acquiring spatial non-contact disturbance signals sensed by environmental disturbance acquisition devices in the smart park; performing disturbance signal acquisition; generating a raw disturbance data set; aligning the time stamps of real physical behavior events recorded within the smart park and performing interference removal and main disturbance extraction operations to form a disturbance contour structure set; and by injecting perceptible disturbance signals during the physical behavior triggering process in the smart park, and based on the disturbance trajectory reconstruction frequency and time sequence verification mechanism, identifying and eliminating pseudo-structured data paths that have not been responded to by physical disturbances, thereby constructing a dynamic data management link oriented towards ensuring authenticity.

Description

Technical Field

[0001] This invention relates to the field of integrated data management technology for smart parks, and more specifically, to a method and system for integrated data management of smart parks. Background Technology

[0002] In the current smart park data management system, behavior auditing relies on centralized collection and unified integration of data from multiple sources such as access control terminals, video acquisition equipment, energy consumption controllers and environmental sensors; each edge device generates structured data, which is transmitted to the central data platform through the network according to preset field specifications, timestamp rules and device identification requirements;

[0003] The existing fusion processing flow is mainly based on field format validation, time continuity judgment and device identifier matching mechanism to complete the alignment and storage of multi-source data; behavior analysis, early warning models and auditing mechanisms generally rely on the consistency of data in field dimensions as the basis of trust.

[0004] This type of fusion and verification method only focuses on the structural characteristics of the data ontology and does not introduce a verification mechanism to confirm whether the data source is actually generated by physical behavior. Attackers can construct data content with a legal format, complete fields, and reasonable timestamps through simulation generation without tampering with the original data or interfering with the device communication protocol, and inject the forged data into the central platform using the existing data upload channel. This forged data has syntactic integrity and logical rationality, and can be treated as real collected data by the system through field verification and time sequence alignment processes.

[0005] During the data fusion and behavior modeling process, the system incorporates both fake and real data into the behavior path analysis and anomaly detection process, forming a mixed behavior trajectory. Because the field structure does not show differences, the log archiving, behavior assessment and risk judgment processes cannot identify the fake part, causing the behavior response to be executed based on fictitious data, the behavior audit path to be distorted, and the system to make incorrect judgments about the real scenario. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a comprehensive data management method and system for smart parks. By injecting perceptible disturbance signals during the physical behavior triggering process of the smart park, and based on the disturbance trajectory reconstruction frequency and time sequence verification mechanism, pseudo-data paths that have not been physically disturbed are identified and eliminated, thereby constructing a dynamic data management link oriented towards authenticity assurance.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a smart park integrated data management method, comprising:

[0008] S1: Acquire spatial non-contact disturbance signals sensed by environmental disturbance acquisition devices in the smart park, perform disturbance signal acquisition, generate a set of original disturbance data, align the time markers of real physical behavior events recorded in the smart park, and perform interference removal and main disturbance extraction operations to form a set of disturbance contour structures.

[0009] S2: Based on the set of disturbance contour structures, after performing node extraction and spatiotemporal order reconstruction operations on the initial correspondence set between real events and disturbance signals, perform spatial location cross mapping and time window overlap calculation, output the set of path segments corresponding to disturbance behavior, perform integrity detection and structure coverage retrieval operations on the set of path segments corresponding to disturbance behavior, and output the candidate set of pseudo-structured path segments.

[0010] S3: Perform trajectory restoration operation on the candidate set of pseudo-path segments and the set of perturbation contour structures, and then perform a joint judgment on authenticity. The behavior path segment is determined to be neither reconstructible nor a path segment that can be inversely reconstructed from the perturbation structure.

[0011] S4: After removing the set of behavior path segments that are determined to be unreconstructable and the set of behavior path segment nodes in the smart park, perform behavior path segment structure reconstruction and connectivity restoration operations to output a set of reliable behavior path segment structures.

[0012] S5: Perform perturbation feature encoding and behavior node synchronization operations on the trusted behavior path segment structure set to generate a perturbation verification signature structure set. After performing perturbation frequency comparison and time phase verification operations on the trusted behavior path segment structure set, perform integrity evaluation operations to generate a dynamically stripped label set.

[0013] In a preferred embodiment, in S1, the spatial non-contact disturbance signal sensed by the environmental disturbance acquisition device in the smart park is acquired, and the disturbance signal acquisition is performed through the spatial non-contact disturbance signal to generate a disturbance raw data set.

[0014] Based on the original set of disturbance data, the time stamps of the records of real physical behavior events in the smart park are aligned, the temporal correspondence between the occurrence nodes of real physical behavior events and disturbance signals is extracted, and the initial correspondence set between real events and disturbance signals is output.

[0015] The initial correspondence set between real events and disturbance signals is subjected to interference removal and main disturbance extraction operations. The disturbance signal features with stable and repeating characteristics are extracted to form a set of disturbance contour structures.

[0016] In a preferred embodiment, in S2, based on the set of disturbance contour structures, node extraction and spatiotemporal sequence reconstruction operations are performed on the initial correspondence set between real events and disturbance signals to form a set of smart park behavior path segment nodes. The set of smart park behavior path segment nodes includes behavior trigger nodes, spatial coordinate positions and trigger type parameters.

[0017] Perform spatial location cross-mapping and time window overlap calculation on the set of disturbance contour structures and the set of smart park behavior path segment nodes to construct the disturbance matching path segment structure and output the set of path segments corresponding to the disturbance behavior.

[0018] Perform integrity checks and structure coverage retrieval operations on the set of path segments corresponding to disturbance behaviors, identify path segments with missing disturbance responses or abnormal disturbance overlap, and output a candidate set of pseudo-structured path segments.

[0019] In a preferred embodiment, S2 further includes the spatial location cross-mapping and time window overlap calculation. Based on the spatial coordinate position and trigger time information contained in each disturbance signal in the disturbance contour structure set, the disturbance signal is sequentially mapped to the behavior trigger node in the smart park behavior path segment node set. By calculating the positional overlap relationship between the spatial coordinates of the disturbance signal and the spatial coordinates of the behavior trigger node, it is determined whether the disturbance signal belongs to the real physical behavior event trigger node in physical space.

[0020] By combining the recording time of the disturbance signal with the time of the actual physical behavior event corresponding to the trigger node of the actual physical behavior event, the time difference between the two is calculated and a window sliding judgment is performed to confirm whether the disturbance signal is within the allowable time error range of the actual physical behavior event. The mapping results with spatial location intersection and time interval overlap are retained to construct a set of path segments corresponding to the disturbance behavior.

[0021] In a preferred embodiment, in S3, a trajectory restoration operation is performed on the perturbation signal features in the pseudo-path segment candidate set and the perturbation contour structure set to construct a perturbation reconstruction path map;

[0022] Perform disturbance trajectory residual analysis and closure backtesting on the disturbance reconstruction path map, and output the disturbance reconstruction error set;

[0023] Define the set of perturbation reconstruction errors ε:

[0024]

[0025] E k =ω1·R k +ω2·(1-C k )

[0026]

[0027] Where E k P represents the perturbation reconstruction error index for the k-th path segment; k Indicates a pseudo-path segment; ω1 represents the set of all pseudo-path segments in the candidate set of pseudo-path segments; ω2 represents the disturbance trajectory residual offset adjustment parameter; R represents the disturbance propagation closure offset adjustment parameter. k C represents the total residual of the perturbation trajectory for the k-th path segment; k This represents the evaluation index for the closure of the disturbance trajectory of the k-th path segment; This represents a closure measure indicating whether the disturbance trajectory in the k-th path segment is continuously connected at the beginning and end nodes; A jump detection index indicating whether the disturbance intensity in the k-th path segment evolves continuously; This is a verification index indicating whether the overall disturbance energy of the path segment meets the original disturbance intensity distribution boundary. λ represents the closure function of the disturbance; t0 represents the start time of the path segment disturbance signal evolution; t1 represents the end time of the path segment disturbance signal evolution; λ T The percentage of the difference in the temporal evolution of the disturbance trajectory is represented by λ. S λ represents the percentage of spatial path difference in the disturbance trajectory. I α represents the percentage of the difference in the intensity evolution of the disturbance trajectory; α represents the nonlinear penalty exponent of the temporal evolution residual; β represents the nonlinear penalty exponent of the spatial path residual; γ represents the nonlinear penalty exponent of the intensity change residual. This represents the offset residual of the k-th path segment in the time evolution dimension; This represents the offset residual of the k-th path segment in the spatial propagation path dimension; This represents the offset residual of the k-th path segment in the dimension of intensity change trend; dt represents the integral process of the residual of the disturbance trajectory; ε represents the time infinitesimal element of the integral of the residual of the disturbance trajectory; k (t) represents the residual vector of the k-th path segment in terms of time evolution, spatial propagation path, and intensity change trend; Γ k (t) represents the actual evolution trajectory of the disturbance signal of the k-th path segment in the disturbance reconstruction path diagram over time t; Φ k (t) represents the standard characteristic curve of the main disturbance signal for the corresponding path segment in the disturbance profile structure set;

[0028] The dynamic trajectory residual analysis and closure backtracking test operation calculates the difference between the actual evolution trajectory of the disturbance signal in each path segment of the candidate set of pseudo-path segments and the main disturbance signal features in the disturbance contour structure based on the disturbance reconstruction path map. It extracts the offset residuals of the disturbance trajectory in three dimensions: time evolution curve, spatial propagation path and intensity change trend, and forms a set of disturbance trajectory residuals.

[0029] Starting from each node of the path segment of the real physical behavior event in the disturbance reconstruction path diagram, we trace back to the first node of the path segment and retrieve the propagation closure of the disturbance trajectory in the complete path segment. This includes whether the disturbance signal is continuously connected to the first and last nodes of the path segment, whether there are non-physical jump segments in the intensity change, and whether the overall disturbance energy of the path segment meets the boundary conditions of the original disturbance signal intensity distribution. In this way, we can identify whether the disturbance trajectory constitutes a closed, coherent, and reliable evolutionary structure, which is used to output the disturbance reconstruction error set.

[0030] In a preferred embodiment, S3 further includes performing a joint authenticity judgment on the disturbance path segments in the disturbance reconstruction error set, judging whether the disturbance trajectory residual exceeds a preset disturbance trajectory residual threshold and whether the disturbance intensity change exceeds a preset disturbance intensity change threshold, as two judgment conditions;

[0031] If both conditions are met, the segment is determined to be a reconstructive behavior path segment; otherwise, it is determined to be a reverse reconstruction path segment of the disturbed structure.

[0032] In a preferred embodiment, in S4, the set of behavioral path segments determined to be unreconstructable and the set of smart park behavioral path segment nodes are removed to construct a set of path segment stripped label structures.

[0033] Perform behavioral path segment structure reconstruction and connectivity restoration operations on the path segment stripped label structure set, and output a set of trustworthy behavioral path segment structures;

[0034] The behavior path segment structure reconstruction and connectivity restoration operation involves removing the behavior path segments that are deemed unreconstructable from the path segment stripping label structure set, extracting the node structure from the remaining smart park behavior path segment node set, and selecting node combinations that complete the broken connections in the path segment structure based on the temporal and spatial adjacency relationships between nodes to construct a candidate set for reconstruction connections.

[0035] For each distributed fractured abnormal path segment, perform a path reconstruction feasibility calculation on the first and last adjacent nodes to determine whether there is an alternative path structure formed by the combination of the remaining nodes between the first and last adjacent nodes of the distributed fractured abnormal path segment. If there is an alternative path structure that can be reconstructed, perform a path connectivity merging operation. At the same time, perform connectivity verification on the overall path segment network composed of the path segment structure set of real physical behavior events to ensure that the removal operation does not cause path segment isolation or structural damage. Output a set of reliable behavior path segment structures that meet the requirements of temporal continuity, spatial closure and structural accessibility.

[0036] In a preferred embodiment, in S5, perturbation feature encoding and behavior node synchronization operations are performed on the trusted behavior path segment structure set to improve the data security of real physical behavior event records in the smart park and generate a perturbation verification signature structure set.

[0037] The perturbation frequency comparison and time phase verification operation selects the main perturbation signal with repeatability and implantation characteristics from the perturbation contour structure set based on the spatial coordinate position, trigger type parameter and time sequence information of each real physical behavior event trigger node in the trusted behavior path segment structure set, and constructs a perturbation feature encoding template.

[0038] The disturbance feature encoding template is aligned with the corresponding real physical behavior event trigger node by performing timestamp alignment and spatial location binding operations. This ensures that the disturbance signal can be accurately sensed by the environmental disturbance acquisition device when the real physical behavior event is triggered, and that it remains consistent with the main disturbance signal in terms of frequency, amplitude and waveform morphology, and outputs a set of disturbance verification signature structures.

[0039] In a preferred embodiment, S5 further includes performing a perturbation frequency comparison and time phase verification operation on the perturbation verification signature structure set and the trusted behavior path segment structure set, and outputting a perturbation signature mapping map;

[0040] Perform an integrity assessment operation on the perturbation signature map, identify behavioral nodes with missing perturbation signatures or frequency misalignments, mark them as dynamic pseudo-structural behaviors, and generate a set of dynamic stripping labels.

[0041] A smart park integrated data management system includes a disturbance acquisition module, a path mapping and identification module, a reconstruction and determination module, a structure stripping and reconstruction module, and a verification and injection anti-counterfeiting module.

[0042] The disturbance acquisition module is used to acquire spatial non-contact disturbance signals sensed by environmental disturbance acquisition devices in the smart park, perform disturbance signal acquisition, generate a set of original disturbance data, align the time stamps of real physical behavior events recorded in the smart park, and perform interference removal and main disturbance extraction operations to form a set of disturbance contour structures.

[0043] The path mapping and recognition module is based on the set of disturbance contour structures. After performing node extraction and spatiotemporal order reconstruction on the initial correspondence set between real events and disturbance signals, it performs spatial location cross mapping and time window overlap calculation, outputs the set of path segments corresponding to disturbance behavior, performs integrity detection and structure coverage retrieval on the set of path segments corresponding to disturbance behavior, and outputs the candidate set of pseudo-structured path segments.

[0044] The reconstruction judgment module is used to perform trajectory restoration operation on the candidate set of pseudo-path segments and the set of disturbed contour structures, and then perform a joint judgment on authenticity, judging that the behavioral path segments do not have reconstructability.

[0045] The structure stripping and reconstruction module is used to remove the set of behavioral path segments and the set of nodes of the smart park behavioral path segments after performing a removal operation, and then perform behavioral path segment structure reconstruction and connectivity restoration operations to output a set of reliable behavioral path segment structures.

[0046] The verification injection anti-counterfeiting module is used to perform perturbation feature encoding and behavior node synchronization operations on the trusted behavior path segment structure set, generate a perturbation verification signature structure set, and perform perturbation frequency comparison and time phase verification operations in combination with the trusted behavior path segment structure set to perform integrity evaluation operations and generate a dynamically stripped tag set.

[0047] The technical effects and advantages of this invention are as follows:

[0048] 1. This solution introduces a disturbance verification mechanism, which injects non-contact disturbance signals into behavioral nodes and verifies their response characteristics to achieve proactive identification and fabrication removal of multi-source data in smart parks.

[0049] 2. Construct a mapping relationship between the perturbation contour structure and the set of behavior path segment nodes to realize the spatiotemporal binding of perturbation signals and real physical behavior events, and ensure that the behavior path is verifiable;

[0050] 3. Based on the residual of the disturbance trajectory and the backtracking test of closure, identify the structure of path segments that lack real disturbance support, and realize the structural stripping judgment of pseudo-structured path segments;

[0051] 4. By eliminating path segment structures and calculating candidate connections for fracture reconstruction, connectivity restoration operations are performed to avoid structural fractures and information silos in the behavioral path graph caused by the stripping operation;

[0052] 5. Inject a coding template with a unique perturbation feature into the trusted behavior path node, construct a perturbation verification signature structure, and realize the perception binding of the real behavior trigger node;

[0053] 6. Perform frequency comparison and time phase verification operations on the perturbation verification signature structure, construct a perturbation signature mapping graph, identify dynamic forgery behavior, and improve the integrity of anti-counterfeiting of behavior paths. Attached Figure Description

[0054] Figure 1 This is a flowchart outlining the method steps of the present invention;

[0055] Figure 2 This is a schematic diagram of the system module structure of the present invention;

[0056] Figure 3 This is a flowchart of the disturbance observation structure of the present invention;

[0057] Figure 4 This is a flowchart of the path fusion mapping process of the present invention;

[0058] Figure 5 This is a flowchart of the pseudo-path authenticity determination process of the present invention;

[0059] Figure 6 This is a flowchart of the path structure stripping and repair process of the present invention;

[0060] Figure 7 This is a flowchart of the dynamic perturbation signature verification process of the present invention. Detailed Implementation

[0061] 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 scope of protection of the present invention.

[0062] Refer to the instruction manual appendix Figure 1-7 An embodiment of the present invention provides a smart park integrated data management method, comprising:

[0063] S1: Acquire spatial non-contact disturbance signals sensed by environmental disturbance acquisition devices in the smart park, perform disturbance signal acquisition, generate a set of original disturbance data, align the time markers of real physical behavior events recorded in the smart park, and perform interference removal and main disturbance extraction operations to form a set of disturbance contour structures.

[0064] S2: Based on the set of disturbance contour structures, after performing node extraction and spatiotemporal order reconstruction operations on the initial correspondence set between real events and disturbance signals, perform spatial location cross mapping and time window overlap calculation, output the set of path segments corresponding to disturbance behavior, perform integrity detection and structure coverage retrieval operations on the set of path segments corresponding to disturbance behavior, and output the candidate set of pseudo-structured path segments.

[0065] S3: Perform trajectory restoration operation on the candidate set of pseudo-path segments and the set of perturbation contour structures, and then perform a joint judgment on authenticity. The behavior path segment is determined to be neither reconstructible nor a path segment that can be inversely reconstructed from the perturbation structure.

[0066] S4: After removing the set of behavior path segments that are determined to be unreconstructable and the set of behavior path segment nodes in the smart park, perform behavior path segment structure reconstruction and connectivity restoration operations to output a set of reliable behavior path segment structures.

[0067] S5: Perform perturbation feature encoding and behavior node synchronization operations on the trusted behavior path segment structure set to generate a perturbation verification signature structure set. After performing perturbation frequency comparison and time phase verification operations on the trusted behavior path segment structure set, perform integrity evaluation operations to generate a dynamically stripped label set.

[0068] In S1, the spatial non-contact disturbance signals sensed by the environmental disturbance acquisition device in the smart park are acquired. Through the spatial non-contact disturbance signals, disturbance signal acquisition is performed to generate a set of original disturbance data. The disturbance signal acquisition refers to the real-time reception and formatted recording of non-contact disturbance signals such as spatial electromagnetic waves, sound waves or micro-power changes generated when real physical behavior events are triggered in the smart park through the environmental disturbance acquisition device, and the generation of a set of original disturbance data for verification.

[0069] Based on the original set of disturbance data, the time stamps of the records of real physical behavior events in the smart park are aligned, the temporal correspondence between the occurrence nodes of real physical behavior events and disturbance signals is extracted, and the initial correspondence set between real events and disturbance signals is output.

[0070] Interference removal and main disturbance extraction operations are performed on the initial correspondence set between real events and disturbance signals. Disturbance signal features with stable repetitive characteristics are extracted to form a set of disturbance contour structures. The interference removal and main disturbance extraction operations refer to removing interference components caused by environmental background noise or non-target factors from non-contact disturbance signals, extracting main disturbance signals that recur in multiple identical real physical behavior events and have stable time position and waveform characteristics, constructing a feature basis for determining the authenticity of physical behavior events, and providing disturbance perception support capabilities for data security verification in park scenarios.

[0071] In S2, based on the set of disturbance contour structures, node extraction and spatiotemporal order reconstruction operations are performed on the initial correspondence set between real events and disturbance signals to form a set of smart park behavior path segment nodes. The set of smart park behavior path segment nodes includes behavior trigger nodes, spatial coordinate positions, and trigger type parameters. The node extraction and spatiotemporal order reconstruction operations refer to extracting the trigger position and type information of each real physical behavior event in the smart park based on the initial correspondence set between real events and disturbance signals, and rearranging them according to the temporal order and spatial distribution relationship of the real physical behavior events to construct a set of smart park behavior path segment nodes for mapping and identification.

[0072] Perform spatial location cross-mapping and time window overlap calculation on the set of disturbance contour structures and the set of smart park behavior path segment nodes to construct the disturbance matching path segment structure and output the set of path segments corresponding to the disturbance behavior.

[0073] The integrity detection and structural coverage retrieval operations are performed on the set of path segments corresponding to the disturbance behavior to identify path segments with missing disturbance responses or abnormal disturbance overlap, and output a candidate set of pseudo-structural path segments. The integrity detection and structural coverage retrieval operations refer to determining whether there is a matching relationship between each real physical behavior event trigger node in the set of path segments corresponding to the disturbance behavior and the existence of a disturbance signal. The operation also retrieves whether the disturbance signal forms a continuous spatial and temporal distribution in the set of path segments corresponding to the disturbance behavior, identifies abnormal path segments with missing or broken disturbance responses, and outputs a candidate set of pseudo-structural path segments.

[0074] S2 also includes the spatial location cross-mapping and time window overlap calculation. Based on the spatial coordinate position and trigger time information contained in each disturbance signal in the disturbance contour structure set, the disturbance signal is sequentially mapped to the behavior trigger node in the smart park behavior path segment node set. By calculating the positional overlap relationship between the spatial coordinates of the disturbance signal and the spatial coordinates of the behavior trigger node, it is determined whether the disturbance signal belongs to the real physical behavior event trigger node in physical space.

[0075] By combining the recording time of the disturbance signal with the time of the actual physical behavior event corresponding to the trigger node of the actual physical behavior event, the time difference between the two is calculated and a window sliding judgment is performed to confirm whether the disturbance signal is within the allowable time error range of the actual physical behavior event. The mapping results with spatial location intersection and time interval overlap are retained to construct a set of path segments corresponding to the disturbance behavior, which is used to support the true source tracing of physical behavior path events and the verification of data security and validity.

[0076] It should be noted that in the formula structure involved in this scheme, dimensionless terms can be used as proportional or structural adjustment factors. When combined with quantities with units, they only play a role in numerical scaling and do not introduce new physical dimensions. Therefore, they will not change or confuse the overall unit system. This combination of "dimensionless terms and terms with units" can be understood as a composite structural expression commonly used in mathematical physics modeling. It conforms to the principle of dimensional consistency and has a clear physical interpretation basis.

[0077] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable can form a unified structure through function mapping, ratio combination or normalization adjustment, with clear units and clear meaning. The overall expression conforms to the principle of dimensional consistency and the conventional formula of engineering modeling.

[0078] In this scheme, constants, weights, adjustment factors, threshold parameters, proportional coefficients, etc., are all adjustable control parameters for different application environments. Their values ​​depend on the target equipment configuration, data input characteristics, and performance optimization goals. During the implementation phase, they are converged within a reasonable range through model verification, performance constraints, or engineering calibration. Although these parameters do not have a unique preset value, they have clear adjustment logic and calculation paths. They belong to the deterministic setting process in engineering implementation. The purpose of this setting is to ensure that the scheme is both universally adaptable and reproducible and operable, without affecting its technical clarity and feasibility.

[0079] In S3, the perturbation signal features in the pseudo-path segment candidate set and the perturbation contour structure set are subjected to trajectory restoration operation to construct a perturbation reconstruction path map. The trajectory restoration operation refers to associating and recombining the perturbation signal features in the pseudo-path segment candidate set and the perturbation contour structure set, and reconstructing the continuous evolution trajectory of the perturbation signal in the triggering process of the real physical behavior event according to the change law of time series and spatial location. This is used to determine whether the path segment structure in the pseudo-path segment candidate set that lacks real perturbation support has the basis for the real physical behavior event that can be completely deduced from the perturbation signal features.

[0080] Perform disturbance trajectory residual analysis and closure backtesting on the disturbance reconstruction path map, and output the disturbance reconstruction error set;

[0081] Define the set of perturbation reconstruction errors ε:

[0082]

[0083] E k =ω1·R k +ω2·(1-C k )

[0084]

[0085] Where E k P represents the perturbation reconstruction error index for the k-th path segment; k Indicates a pseudo-path segment; ω1 represents the set of all pseudo-path segments in the candidate set of pseudo-path segments; ω1 represents the disturbance trajectory residual offset adjustment parameter, which is used to adjust the proportion of the degree of offset of the disturbance trajectory in the total error calculation in the three dimensions of time evolution curve, spatial propagation path and intensity change trend; ω2 represents the disturbance propagation closure offset adjustment parameter, which is used to adjust the contribution of whether the disturbance signal constitutes a complete propagation closure structure in the path segment to the total error calculation; R k C represents the total residual of the perturbation trajectory for the k-th path segment; k This represents the evaluation index for the closure of the disturbance trajectory of the k-th path segment; This represents a closure measure indicating whether the disturbance trajectory in the k-th path segment is continuously connected at the beginning and end nodes; A jump detection index indicating whether the disturbance intensity in the k-th path segment evolves continuously; This is a verification index indicating whether the overall disturbance energy of the path segment meets the original disturbance intensity distribution boundary. The perturbation closure function is represented by λ, where a higher value indicates stronger closure; t0 represents the start time of the path segment perturbation signal evolution; t1 represents the end time of the path segment perturbation signal evolution; λ T The percentage of the difference in the temporal evolution of the disturbance trajectory is represented by λ. S λ represents the percentage of spatial path difference in the disturbance trajectory. I α represents the percentage of the difference in the intensity evolution of the disturbance trajectory; α represents the nonlinear penalty exponent of the temporal evolution residual; β represents the nonlinear penalty exponent of the spatial path residual; γ represents the nonlinear penalty exponent of the intensity change residual. This represents the offset residual of the k-th path segment in the time evolution dimension; This represents the offset residual of the k-th path segment in the spatial propagation path dimension; This represents the offset residual of the k-th path segment in the dimension of intensity change trend; dt represents the integral process of the residual of the disturbance trajectory; ε represents the time infinitesimal element of the integral of the residual of the disturbance trajectory; k (t) represents the residual vector of the k-th path segment in terms of time evolution, spatial propagation path, and intensity change trend; Γ k (t) represents the actual evolution trajectory of the disturbance signal of the k-th path segment in the disturbance reconstruction path diagram over time t; Φ k (t) represents the standard characteristic curve of the main disturbance signal for the corresponding path segment in the disturbance profile structure set;

[0086] The dynamic trajectory residual analysis and closure backtracking test operation calculates the difference between the actual evolution trajectory of the disturbance signal in each path segment of the candidate set of pseudo-path segments and the main disturbance signal features in the disturbance contour structure based on the disturbance reconstruction path map. It extracts the offset residuals of the disturbance trajectory in three dimensions: time evolution curve, spatial propagation path and intensity change trend, and forms a set of disturbance trajectory residuals.

[0087] Starting from each node of the path segment of the real physical behavior event in the disturbance reconstruction path diagram, we trace back to the first node of the path segment and retrieve the propagation closure of the disturbance trajectory in the complete path segment. This includes whether the disturbance signal is continuously connected to the first and last nodes of the path segment, whether there are non-physical jump segments in the intensity change, and whether the overall disturbance energy of the path segment meets the boundary conditions of the original disturbance signal intensity distribution. In this way, we can identify whether the disturbance trajectory constitutes a closed, coherent, and reliable evolutionary structure, which is used to output the disturbance reconstruction error set.

[0088] S3 also includes a joint judgment on the authenticity of the disturbance path segments in the disturbance reconstruction error set, judging whether the disturbance trajectory residual exceeds the preset disturbance trajectory residual threshold and whether the disturbance intensity change exceeds the preset disturbance intensity change threshold, as two judgment conditions;

[0089] If both conditions are met, it is determined that there is no reconstructible behavior path segment; otherwise, it is determined that there is a reverse reconstruction path segment of the disturbance structure, providing logical criteria for the traceability consistency and data security consistency of the real physical behavior event record.

[0090] In S4, the set of behavioral path segments that are determined to be unreconstructable and the set of smart park behavioral path segment nodes are removed by a removal operation, and a set of path segment stripping label structures is constructed. The removal operation refers to removing the behavioral path segments that are determined to be unreconstructable from the set of smart park behavioral path segment nodes, and generating a structured label for each removed path segment. This label is used to perform path segment connectivity restoration and trusted reconstruction in the overall path segment network structure composed of the set of real physical behavioral event path segments.

[0091] Perform behavioral path segment structure reconstruction and connectivity restoration operations on the path segment stripped label structure set, and output a set of trustworthy behavioral path segment structures;

[0092] The behavior path segment structure reconstruction and connectivity restoration operation involves removing the behavior path segments that are deemed unreconstructable from the path segment stripping label structure set, extracting the node structure from the remaining smart park behavior path segment node set, and selecting node combinations that complete the broken connections in the path segment structure based on the temporal and spatial adjacency relationships between nodes to construct a candidate set for reconstruction connections.

[0093] For each distributed fractured abnormal path segment, the first and last adjacent nodes are used to perform a path reconstruction feasibility calculation. It is determined whether there is an alternative path structure formed by the combination of the remaining nodes between the first and last adjacent nodes of the distributed fractured abnormal path segment. If there is an alternative path structure, a path connectivity merging operation is performed. At the same time, the connectivity of the overall path segment network composed of the path segment structure set of real physical behavior events is verified to ensure that the removal operation does not cause path segment isolation or structural damage. The output is a set of credible behavior path segment structures that meet the requirements of temporal continuity, spatial closure and structural reachability, providing a structural basis for behavior authenticity determination and data security verification.

[0094] In S5, perturbation feature encoding and behavior node synchronization operations are performed on the trusted behavior path segment structure set to improve the data security of real physical behavior event records in the smart park and generate a perturbation verification signature structure set.

[0095] The perturbation frequency comparison and time phase verification operation selects the main perturbation signal with repeatability and implantation characteristics from the perturbation contour structure set based on the spatial coordinate position, trigger type parameter and time sequence information of each real physical behavior event trigger node in the trusted behavior path segment structure set, and constructs a perturbation feature encoding template.

[0096] The disturbance feature encoding template is aligned with the corresponding real physical behavior event trigger node by performing timestamp alignment and spatial location binding operations. This ensures that the disturbance signal can be accurately sensed by the environmental disturbance acquisition device when the real physical behavior event is triggered, and that it remains consistent with the main disturbance signal in terms of frequency, amplitude and waveform. The disturbance verification signature structure set is output for active identification of behavior authenticity and judgment of pseudo-path elimination.

[0097] The perturbation feature coding templates include spread spectrum frequency hopping perturbation coding templates and low frequency amplitude modulation acoustic wave injection perturbation coding templates;

[0098] In this scheme, if the spread spectrum frequency hopping type disturbance coding template is applied to the disturbance feature coding template, a narrowband electromagnetic signal with frequency hopping disturbance characteristics can be injected based on the temporal sequence and spatial location of each real physical behavior event triggering node in the trusted behavior path segment structure set, and the frequency configuration binding operation in the disturbance profile structure is performed, so that the disturbance verification signature structure has uniqueness and non-forgeability in the spectrum space, ensuring that the environmental disturbance acquisition device can stably sense the disturbance characteristics at the corresponding real physical behavior event triggering node;

[0099] In this scheme, when a low-frequency amplitude-modulated acoustic wave injection type perturbation coding template is applied to a perturbation feature coding template, an amplitude-modulated audio mode with obvious distinction from the target environment background acoustic contour can be selected based on the repetitive main perturbation signal in the perturbation contour structure. Spatial sound source binding and time synchronization coding operations are performed on each behavior trigger node in the credible behavior path segment structure set. During the triggering process of real physical behavior events, a perturbation verification signature structure is generated through non-contact sound field perception to ensure that the perturbation response of the real physical behavior event path has reversibility and pseudo-structure discernibility.

[0100] S5 also includes performing perturbation frequency comparison and time phase verification operations on the perturbation verification signature structure set and the trusted behavior path segment structure set, outputting a perturbation signature mapping map. The perturbation frequency comparison and time phase verification operations refer to extracting the injection frequency parameters and timestamp information for each perturbation feature in the perturbation verification signature structure set, and simultaneously extracting the actual perceived frequency and actual perceived time sequence from the perturbation perception record corresponding to each real physical behavior event time trigger node in the trusted behavior path segment structure set; by performing item-by-item comparison between the frequency parameters of the perturbation verification signature structure and the actual perceived frequency, identifying whether the frequency matches within the specified spectrum range, and performing phase calibration on the timestamp information, calculating the phase difference between the perturbation signal injection time and the actual perceived time for each real physical behavior event trigger node, and verifying whether it is within the preset phase tolerance range; retaining the pairing relationship between behavior nodes with consistent frequency comparison and time phase error within the allowable range and perturbation signatures, outputting a perturbation signature mapping map containing frequency and time calibration results, used for dynamic authenticity assessment and pseudo-path identification;

[0101] An integrity assessment operation is performed on the perturbation signature map to identify behavioral nodes with missing perturbation signatures or frequency misalignments. These nodes are marked as dynamic pseudo-behaviors and a dynamic stripping label set is generated. The integrity assessment operation involves checking the perturbation signature injection status of each real physical behavioral event triggering node in the perturbation signature map to identify abnormal nodes with missing perturbation features or perturbation frequency offsets. These nodes are then marked as dynamic pseudo-behaviors to ensure data security during the dynamic acquisition and storage of behavioral data.

[0102] A smart park integrated data management system includes a disturbance acquisition module, a path mapping and identification module, a reconstruction and determination module, a structure stripping and reconstruction module, and a verification and injection anti-counterfeiting module.

[0103] The disturbance acquisition module is used to acquire spatial non-contact disturbance signals sensed by environmental disturbance acquisition devices in the smart park, perform disturbance signal acquisition, generate a set of original disturbance data, align the time stamps of real physical behavior events recorded in the smart park, and perform interference removal and main disturbance extraction operations to form a set of disturbance contour structures.

[0104] The path mapping and recognition module is based on the set of disturbance contour structures. After performing node extraction and spatiotemporal order reconstruction on the initial correspondence set between real events and disturbance signals, it performs spatial location cross mapping and time window overlap calculation, outputs the set of path segments corresponding to disturbance behavior, performs integrity detection and structure coverage retrieval on the set of path segments corresponding to disturbance behavior, and outputs the candidate set of pseudo-structured path segments.

[0105] The reconstruction judgment module is used to perform trajectory restoration operation on the candidate set of pseudo-path segments and the set of disturbed contour structures, and then perform a joint judgment on authenticity, judging that the behavioral path segments do not have reconstructability.

[0106] The structure stripping and reconstruction module is used to remove the set of behavioral path segments and the set of nodes of the smart park behavioral path segments after performing a removal operation, and then perform behavioral path segment structure reconstruction and connectivity restoration operations to output a set of reliable behavioral path segment structures.

[0107] The verification injection anti-counterfeiting module is used to perform perturbation feature encoding and behavior node synchronization operations on the trusted behavior path segment structure set, generate a perturbation verification signature structure set, and perform perturbation frequency comparison and time phase verification operations in combination with the trusted behavior path segment structure set to perform integrity evaluation operations and generate a dynamically stripped tag set.

[0108] It should be noted that, including but not limited to: the current data authenticity identification mechanism in smart parks mainly relies on single representation methods such as image recognition, sensor stream data or access control records. Although it has the advantage of visualization, it lacks underlying verification methods for the behavior triggering process. This makes it easy for behavioral data to suffer from pseudo-injection and authenticity mismatch in simulated operation, delayed response or malicious intervention scenarios, which seriously weakens the decision-making basis and response accuracy of the smart park monitoring and scheduling system.

[0109] This solution proposes a smart park data authenticity management mechanism based on disturbance verification and pseudo-structure stripping. By constructing a dynamic sensing link with spatial non-contact disturbance signals as the core, it introduces an integrated verification process including main disturbance signal extraction, path consistency verification, and disturbance signature embedding. This ensures that the path structure has responsive correspondence, the disturbance mapping has traceability, and the behavior expression has anti-counterfeiting identification capability. It fundamentally constructs a set of park data authenticity protection methods covering the entire chain of collection, mapping, judgment, and re-identification.

[0110] This scheme includes a perturbation observation structure phase:

[0111] The environmental disturbance acquisition device can sense non-contact disturbance signals caused by real physical behavior events in the space in real time. These non-contact disturbance signals are time-annotated and aligned with the real physical behavior events recorded in the smart park. The disturbance contour structure set is constructed through interference removal and feature extraction operations.

[0112] This perturbation observation structure stage is used to generate perturbation feature structures that can characterize real physical behavior, laying the original data foundation for path mapping and authenticity verification;

[0113] This solution includes path mapping and pseudo-structure identification stages:

[0114] Using the set of perturbation contour structures as a reference, the behavior trigger nodes of real events are extracted from both temporal and spatial dimensions and reconstructed into a set of smart park behavior path segment nodes. The perturbation structure and path nodes are mapped in both spatial location and temporal window, and continuity and coverage retrieval are performed to identify path segments that have not formed effective perturbation mappings and generate a set of pseudo-structured path segment candidates.

[0115] The path mapping and pseudo-structure identification stage screens out possible pseudo-structure behavior clues by judging whether there are abnormal paths lacking perturbation signals in the current behavior path segment.

[0116] This solution includes a phase for reconstructing closure determination:

[0117] The candidate set of pseudo-path segments and the set of disturbance contour structures are used to reconstruct the trajectory, construct the disturbance reconstruction path map, and perform trajectory residual analysis and closure backtracking test on the disturbance reconstruction path map to determine whether the disturbance trajectory constitutes a credible closed propagation structure in the three dimensions of time, space and intensity change. The disturbance reconstruction error set is extracted, and the path segment is determined to be reconstructable based on whether the residual value and intensity change exceed the threshold.

[0118] This reconstruction closure judgment stage identifies, through mathematical backtracking, whether there are behavioral paths that only forge trigger nodes without the support of real disturbance trajectories, thus structurally stripping away irreversible pseudo-structured data;

[0119] This solution includes path stripping and connectivity restoration phases:

[0120] Path segments that do not have the ability to reconstruct by disturbance are removed, and a set of path segment stripped label structures is generated. The temporal and spatial adjacency relationships are analyzed from the set of nodes of the remaining smart park behavior path segments. Replaceable connection structures are constructed and connectivity is restored. A set of trustworthy behavior path segments with complete structure and trustworthy support is output.

[0121] The path stripping and connectivity restoration phase is used to complete the structural isolation of pseudo-path segments and ensure the integrity of the smart park behavioral data structure in terms of physical connectivity and behavioral logic.

[0122] This solution includes a dynamic disturbance anti-counterfeiting mechanism stage:

[0123] Based on the trusted behavior path segment structure set, a perturbation verification signature is injected into each real behavior event triggering node, and its spatial location and timestamp are bound to form a perturbation verification signature structure set. Perturbation frequency comparison and time phase verification are performed to construct a perturbation signature mapping graph. Behavior nodes with missing perturbation signatures or misaligned frequencies are evaluated for integrity and marked, and a dynamically stripped label set is output.

[0124] This dynamic disturbance anti-counterfeiting mechanism embeds real behavior path nodes into actively identifiable disturbance signals, enabling the authenticity of current behavior to be determined through non-contact sensing during the operation of the smart park in the future, thereby constructing a dynamic anti-counterfeiting mechanism with feedforward verification capabilities.

[0125] The above description is merely 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 smart park integrated data management method, characterized in that, Comprise: S1: acquire the space non-contact disturbance signal perceived by the environment disturbance collection device in the smart park, perform disturbance signal collection, generate disturbance original data set, align the time label of the real physical behavior event record in the smart park, and perform interference removal and main disturbance extraction operation, form disturbance profile structure set; S2: based on the disturbance profile structure set, after performing node extraction and spatio-temporal sequence reconstruction operation on the initial corresponding relationship set of real events and disturbance signals, perform spatial position cross mapping and time window overlap calculation, output disturbance behavior corresponding path segment set, perform integrity detection and structure coverage retrieval operation on the disturbance behavior corresponding path segment set, output pseudo-constructed path segment candidate set; S3: after performing trajectory restoration operation on the pseudo-constructed path segment candidate set and the disturbance profile structure set, perform reality joint judgment, judge the behavior path segment not to have reconstruction and disturbance structure inverse reconstruction path segment; S4: after performing the exclusion operation on the behavior path segment set determined not to have reconstruction and the smart park behavior path segment node set, perform behavior path segment structure reconstruction and connectivity recovery operation, output credible behavior path segment structure set; S5: perform disturbance feature coding and behavior node synchronization operation on the credible behavior path segment structure set, generate disturbance verification signature structure set, perform disturbance frequency comparison and time phase verification operation on the credible behavior path segment structure set, and perform integrity evaluation operation, generate dynamic stripping label set.

2. The smart park comprehensive data management method according to claim 1, characterized in that: In S1, the space non-contact disturbance signal perceived by the environment disturbance collection device in the smart park is acquired, and the disturbance signal collection is performed through the space non-contact disturbance signal to generate the disturbance original data set; Based on the disturbance original data set, the time label of the real physical behavior event record in the smart park is aligned, the time sequence corresponding relationship between the real physical behavior event occurrence node and the disturbance signal is extracted, and the initial corresponding relationship set of real events and disturbance signals is output; Perform interference removal and main disturbance extraction operation on the initial corresponding relationship set of real events and disturbance signals, extract disturbance signal features with repetition characteristics, and form disturbance profile structure set.

3. The smart park comprehensive data management method according to claim 2, characterized in that: In S2, based on the disturbance profile structure set, the node extraction and spatio-temporal sequence reconstruction operation are performed on the initial corresponding relationship set of real events and disturbance signals to form the smart park behavior path segment node set, which includes behavior trigger node, spatial coordinate position and trigger type parameter; Perform spatial position cross mapping and time window overlap calculation on the disturbance profile structure set and the smart park behavior path segment node set to construct disturbance matching path segment structure, output disturbance behavior corresponding path segment set; Perform integrity detection and structure coverage retrieval operation on the disturbance behavior corresponding path segment set, identify the path segment with missing disturbance response or disturbance overlap anomaly, and output pseudo-constructed path segment candidate set. 4.The method of claim 3, wherein: S2 further comprises the spatial position cross mapping and time window overlap calculation, which maps each disturbance signal in the disturbance profile structure set to the behavior trigger node in the behavior path segment node set by using the spatial coordinate position and trigger time point information contained in each disturbance signal, and determines whether the disturbance signal belongs to the real physical behavior event trigger node in the physical space by calculating the position coincidence relationship between the spatial coordinate of the disturbance signal and the spatial coordinate of the behavior trigger node; The time difference between the disturbance signal recording time and the real physical behavior event time corresponding to the real physical behavior event trigger node is calculated, and the window sliding judgment is performed to determine whether the disturbance signal is within the allowed time error range of the real physical behavior event, and the mapping results with spatial position cross fitting and time interval overlap are reserved to construct the disturbance behavior corresponding path segment set. 5.The method of claim 4, wherein: In S3, the trajectory restoration operation is performed on the pseudo path segment candidate set and the disturbance signal features in the disturbance profile structure set to construct the disturbance reconstruction path graph; The disturbance trajectory residual analysis and closure backtracking operation is performed on the disturbance reconstruction path graph to output the disturbance reconstruction error set; The disturbance reconstruction error set is defined as: E k = ω1·R k + ω2·(1-C k ) wherein E k denotes the perturbation reconstruction error indicator of the kth path segment; P k denotes the pseudo-constructed path segment; denotes the set of all pseudo-constructed path segments in the candidate set; ω1 denotes the perturbation trajectory residual deviation degree control parameter; ω2 denotes the perturbation propagation closure deviation degree control parameter; R k denotes the total amount of perturbation trajectory residual of the kth path segment; C k denotes the perturbation trajectory closure evaluation indicator of the kth path segment; denotes the closure measure of whether the perturbation trajectory in the kth path segment is continuously connected at the head and tail nodes; denotes the jump detection indicator of whether the perturbation intensity in the kth path segment continuously evolves; denotes the verification indicator of whether the overall perturbation energy of the path segment satisfies the original perturbation intensity distribution boundary; denotes the perturbation closure function; t0 denotes the starting time point of the path segment perturbation signal evolution; t1 denotes the ending time point of the path segment perturbation signal evolution; λ T denotes the proportion setting amount of the perturbation trajectory time evolution difference; λ S denotes the proportion setting amount of the perturbation trajectory spatial path difference; λ I denotes the proportion setting amount of the perturbation trajectory intensity evolution difference; α denotes the nonlinear penalty index of the time evolution residual; β denotes the nonlinear penalty index of the spatial path residual; γ denotes the nonlinear penalty index of the intensity change residual; denotes the deviation residual of the kth path segment in the time evolution dimension; denotes the deviation residual of the kth path segment in the spatial propagation path dimension; denotes the deviation residual of the kth path segment in the intensity change trend dimension; denotes the perturbation trajectory residual integration process; dt denotes the time infinitesimal of the perturbation trajectory residual integration; ε k (t) represents the residual vector of the kth path segment in the time evolution dimension, the spatial propagation path dimension, and the intensity variation trend dimension; Γ k (t) represents the actual evolution trajectory of the disturbance signal of the kth path segment in the perturbed reconstructed path graph over time t; Φ k (t) represents the standard characteristic curve of the main disturbance signal of the corresponding path segment in the disturbance profile structure set; The disturbance trajectory residual analysis and closure backtracking operation calculates the difference between the actual evolution trajectory of each path segment disturbance signal in the pseudo path segment candidate set and the main disturbance signal features in the disturbance profile structure based on the disturbance reconstruction path graph, extracts the offset residual of the disturbance trajectory in the time evolution curve, spatial propagation path and intensity change trend, and forms the disturbance trajectory residual set; Each node of the real physical behavior event path segment in the disturbance reconstruction path graph is taken as the starting point to backtrack to the path segment head node, and the propagation closure of the disturbance trajectory in the complete path segment is searched, including whether the disturbance signal is continuously connected to the path segment head and tail nodes, whether the intensity change has a jump section, and whether the overall disturbance energy of the path segment meets the original disturbance signal intensity distribution boundary condition, so as to identify whether the disturbance trajectory constitutes a credible evolution structure, which is used to output the disturbance reconstruction error set. 6.The method of claim 5, wherein: S3 further comprises a realness joint judgment on the disturbance path segment in the disturbance reconstruction error set, which judges whether the disturbance trajectory residual exceeds the preset disturbance trajectory residual threshold and whether the disturbance intensity change exceeds the preset disturbance intensity change threshold as two judgment conditions; If both conditions meet the respective judgment conditions, it is determined that the behavior path segment does not have reconstruction, otherwise it is determined that the disturbance structure inverse reconstruction path segment. 7.The method of claim 6, wherein: In S4, the behavior path segment set determined not to have reconstruction is executed with the path segment node set in the smart park behavior path segment to construct the path segment stripping label structure set. The behavior path segment structure reconstruction and connectivity recovery operation is performed on the path segment stripped label structure set, and a trusted behavior path segment structure set is output; The behavior path segment structure reconstruction and connectivity recovery operation is performed on the path segment stripped label structure set, and a trusted behavior path segment structure set is output; The behavior path segment structure reconstruction and connectivity recovery operation is performed on the path segment stripped label structure set, and a trusted behavior path segment structure set is output; 8. The intelligent garden comprehensive data management method of claim 7, wherein: In S5, the disturbance feature coding and behavior node synchronization operation is performed on the trusted behavior path segment structure set to improve the data security of the intelligent garden real physical behavior event record, and a disturbance verification signature structure set is generated; The disturbance frequency comparison and time phase verification operation selects a main disturbance signal with repetitive and implanted features in the disturbance profile structure set based on the spatial coordinate position, trigger type parameter and time sequence information of each real physical behavior event trigger node in the trusted behavior path segment structure set, constructs a disturbance feature coding template, and performs timestamp alignment and spatial position binding operations on the disturbance feature coding template and the corresponding real physical behavior event trigger node to ensure that the disturbance signal can be accurately perceived by the environment disturbance acquisition device when the real physical behavior event is triggered, and the frequency, amplitude and waveform form are consistent with the main disturbance signal, and a disturbance verification signature structure set is output.

9. The intelligent garden comprehensive data management method of claim 8, wherein: S5 also includes performing a disturbance frequency comparison and time phase verification operation on the disturbance verification signature structure set and the trusted behavior path segment structure set, and outputting a disturbance signature mapping diagram; The integrity evaluation operation is performed on the disturbance signature mapping diagram to identify behavior nodes with missing disturbance signatures or frequency misplacement, mark them as dynamic pseudo-structure behaviors, and generate a dynamic stripping label set.

10. An intelligent garden comprehensive data management system comprising an intelligent garden comprehensive data management method according to claim 9, comprising a disturbance acquisition module, a path mapping identification module, a reconstruction determination module, a structure stripping reconstruction module, and a verification injection anti-fake module, wherein: ​ The disturbance collection module is configured to acquire a spatial non-contact disturbance signal sensed by an environmental disturbance collection device in the smart park, perform disturbance signal collection, generate a disturbance original data set, align a time label of a real physical behavior event record in the smart park, and perform interference removal and main disturbance extraction operations to form a disturbance profile structure set; The path mapping identification module is configured to perform node extraction and spatio-temporal sequence reconstruction operations on an initial corresponding relationship set of real events and disturbance signals based on the disturbance profile structure set, perform spatial position cross mapping and time window overlap calculation, output a disturbance behavior corresponding path segment set, perform integrity detection and structure coverage retrieval operations on the disturbance behavior corresponding path segment set, and output a pseudo-constructed path segment candidate set; The reconstruction judgment module is configured to perform trajectory restoration operations on the pseudo-constructed path segment candidate set and the disturbance profile structure set, and then perform a joint reality judgment to determine whether the behavior path segment is reconstructable; The structure stripping reconstruction module is configured to perform exclusion operations on the behavior path segment set determined as not having reconstructability and a smart park behavior path segment node set, and then perform behavior path segment structure reconstruction and connectivity recovery operations to output a trusted behavior path segment structure set; The verification injection anti-fake module is configured to perform disturbance feature encoding and behavior node synchronization operations on the trusted behavior path segment structure set, generate a disturbance verification signature structure set, perform disturbance frequency comparison and time phase verification operations in combination with the trusted behavior path segment structure set, perform integrity evaluation operations, and generate a dynamic stripping label set.

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