Platform tourism resource integration management method and system

Abstract

The application relates to the technical field of tourism creative service, and discloses a platform tourism resource integration integrated management method and system, which comprises the following steps: constructing a space-time dependence graph data model, acquiring dynamic state data, extracting a predetermined time delay and comparing the predetermined time delay with a preset buffer time limit parameter; when the predetermined time delay is greater than the preset buffer time limit parameter, acquiring historical performance data and determining a node time limit adjustment coefficient, sequentially compressing the residence time window of a subsequent associated node, reducing the time window of a non-rigid node to release time redundancy and supplementing the time redundancy to a current blocked link in a reverse direction, and realizing adaptive balance of time deviation; the application converts cascading default risk into topological parameter smoothing operation, avoids external resource replacement, effectively reduces response delay of resource reorganization, and improves overall turnover efficiency of resources.

Description

Technical Field

[0001] This invention relates to the field of tourism creative service technology, specifically to a platform-based integrated management method and system for tourism resources. Background Technology

[0002] Currently, in distributed itinerary design across regions and multiple supply sides, the mainstream resource allocation method mainly retrieves the time and inventory data of independent units such as transportation, accommodation, and scenic spot tickets through heterogeneous network interfaces, and splices the above heterogeneous data into composite itinerary packages according to specific spatiotemporal constraints.

[0003] However, such integration methods heavily rely on the idealized assumption that the physical and spatiotemporal delivery environment is completely normal. When encountering changing conditions such as weather variations or sudden surges in passenger flow, the operating times of transportation vehicles may deviate nonlinearly. The existing system's travel topology is rigidly bound, and even small jumps in upstream timing parameters can directly penetrate the system's inherent buffer redundancy, generating a chain effect of timing conflicts cascading downstream. This can lead to the failure of subsequent accommodation delivery and scenic spot entry windows, resulting in widespread contract defaults and high cross-supply-side cancellation and modification losses. Beyond the limitations of physical resource integration, existing technologies also suffer from fundamental deficiencies in their underlying software scheduling and control methods. For example, Chinese invention patent application CN104517246A discloses a tourism itinerary planning and... The navigation system generates static default itineraries based on pre-stored public transportation data on the server. When it deviates from the original trajectory, it relies on recalculating the route to update the navigation. The underlying logic of the control mechanism is based on an absolutely idealized linear spacetime assumption, treating all itinerary nodes as having rigid, solidified physical boundaries. In real-world scenarios, local delays occur, and the system passively defines timing deviations as hard failures, triggering global link replanning or searching for alternatives. The fundamental flaw lies in failing to penetrate the underlying business attributes of the business entity to explore and quantify the inherent non-rigid experience elasticity of various services. It ignores the objectively existing timing tolerance capability, causing the system to lose its adaptive buffering mechanism that absorbs minor disturbances in situ within the blocked link. When encountering external dynamic disturbances, the central solver is forced to face massive boundary shifts, making it extremely prone to the dilemma of computing power collapse due to reorganization and cross-platform collaborative game losses.

[0004] Therefore, the technical problem to be solved by this invention is how to achieve in-situ closed-loop resolution of local disturbances without changing the overall trip topology, based on the temporal coupling characteristics of heterogeneous resources and the experience elasticity of commercial fulfillment. Summary of the Invention

[0005] To address the problems in the background art, this invention proposes an integrated management method for platform-based tourism resources, comprising the following steps:

[0006] Step S101: Construct a spatiotemporal dependency graph data model of tourism resource service entities and obtain the dynamic state data of each tourism resource service entity in the spatiotemporal dependency graph data model.

[0007] Step S102: When it is identified that the dynamic state data corresponding to any predecessor logic node contains a predetermined time delay, the predetermined time delay is extracted and compared with the preset buffer time limit parameter stored in the corresponding directed edge data structure in the spatiotemporal dependency graph data model.

[0008] Step S103: If the predetermined time delay is less than or equal to the preset buffer time limit parameter, update the admission timestamp of the subsequent associated logical nodes according to the predetermined time delay to maintain the constant topology of the spatiotemporal dependency graph data model; if the predetermined time delay is greater than the preset buffer time limit parameter, obtain the corresponding historical performance data, determine the node time limit adjustment coefficient of each downstream associated logical node according to the ratio of the acceptable actual dwell time to the standard predetermined time in the historical performance data, and compress the dwell time window of the subsequent associated nodes in sequence. Release time redundancy by deducting the time window of non-rigid nodes and reversely supplement it to the current blocked link to complete the adaptive balance of time deviation.

[0009] Preferably, in step S103, compressing the dwell time window of the successor associated node includes the following steps: Step S1031, retrieving the hard constraint labels stored in the successor associated node, and determining that the successor associated node is a rigid spatiotemporal node when a hard constraint label is detected; Step S1032, interrupting the time iteration of the current branch, backtracking to the upstream replaceable node, splitting the spatiotemporal dependency graph data model into a main travel planning branch and a flexible mounting branch and separating them from each other, peeling off the flexible mounting branch when the predetermined time delay penetrates the preset maximum tolerance threshold, and solving the reduced constraint terms of the main travel planning.

[0010] Preferably, the construction of the spatiotemporal dependency graph data model of tourism resource service entities in step S101 includes: taking the aviation nodes, hotel nodes, and scenic spot nodes belonging to the tourism resource service entities as graph nodes in the spatiotemporal dependency graph data model; taking the connection time constraints and spatial transfer constraints between each tourism resource service entity as directed edges in the spatiotemporal dependency graph data model; writing preset buffer time limit parameters and standard access timestamps into the data structure of the directed edges to form a topological data network as the spatiotemporal dependency graph data model.

[0011] Preferably, in step S103, updating the admission timestamp of the subsequent associated logical node includes: using a predetermined time delay as a time shift increment, sequentially accumulating it onto the admission timestamp of the subsequent associated logical node directly connected to any predecessor logical node, thereby correcting the local time deviation while keeping the topological connection relationship of the spatiotemporal dependency graph data model constant.

[0012] Preferably, the method further includes the following human-machine collaborative control steps: Step S501, push the status data obtained after compressing the dwell time window of the subsequent associated node or the replacement node parameters after the alternative route switch to the client in real time; Step S502, present the reconstructed path change information on the client, establish a data interaction flow between the operating subject and the cloud integrated management platform, receive the confirmation instruction fed back by the client, and couple and bind the automatic scheduling strategy with the manual confirmation link.

[0013] Preferably, extracting the predetermined time delay includes: calculating the original deviation between the actual execution timestamp and the standard admission timestamp of any predecessor logical node, obtaining the transmission delay compensation term caused by calculation time and network transmission delay, summing the original deviation and the transmission delay compensation term, and determining the summation result as the predetermined time delay.

[0014] Preferably, obtaining the node time limit adjustment coefficient also includes: lowering the node time limit adjustment coefficient based on the cancellation and rescheduling rate and congestion index in historical orders, and widening the reduction range of the dwell time window of subsequent related nodes.

[0015] Preferably, the solution of the reduced constraint terms for the trunk route planning includes: converting the spatiotemporal dependency graph data model into a dependency chain containing only the core trunk nodes, removing the interaction constraints of the flexible attached branches, and using a multi-objective constraint solver to update the constraint conditions of the simplified trunk route planning and recalculate the state vector.

[0016] Preferably, each step in the method involves distributed calls between the cloud-based integrated management platform and the data interfaces of various independent service providers. These independent service providers include aviation data source providers, hotel data source providers, and scenic area data source providers. The asynchronous event-driven stream processing mechanism between the cloud-based integrated management platform and the data interfaces is used to control the response latency of resource reorganization within the specified time range.

[0017] An integrated management system for platform-based tourism resources is provided, which implements a method for integrated management of platform-based tourism resources. The system includes:

[0018] The graph model construction module is used to construct a spatiotemporal dependency graph data model of tourism resource service entities and obtain the dynamic state data of each tourism resource service entity in the spatiotemporal dependency graph data model.

[0019] The time delay comparison module is connected to the graph model construction module. When the dynamic state data corresponding to any predecessor logic node is found to contain a predetermined time delay, the predetermined time delay is extracted and compared with the predetermined time delay parameter stored in the corresponding directed edge data structure in the spatiotemporal dependency graph data model.

[0020] The time deviation balancing module is connected to the time delay comparison module. When the predetermined time delay is less than or equal to the preset buffer time limit parameter, it updates the admission timestamp of subsequent associated logical nodes according to the predetermined time delay to maintain the constant topology of the spatiotemporal dependency graph data model. When the predetermined time delay is greater than the preset buffer time limit parameter, it obtains the corresponding historical performance data, determines the node time limit adjustment coefficient of each downstream associated logical node based on the ratio of the acceptable actual dwell time to the standard predetermined time in the historical performance data, and sequentially compresses the dwell time window of the subsequent associated nodes. By reducing the time window of non-rigid nodes, time redundancy is released and reversely supplemented to the current blocked link to complete the adaptive balancing of time deviation.

[0021] The beneficial effects of this invention are:

[0022] 1. In the integrated management method for platform tourism resources, to address the susceptible conflicts faced by heterogeneous resources in the delivery sequence, a temporal dependency graph is constructed by extracting the available time boundary of service nodes and the spatial transfer time benchmark value between adjacent nodes. Based on the distance between the expected start time of the subsequent logical node and the end time of the predecessor logical node, an inherent relaxation tolerance parameter is calculated. When an upstream node generates a timestamp lag offset, the offset is directly absorbed in situ within the relaxation tolerance of the corresponding topology link to maintain the overall trip graph structure constant. This transforms the disordered cascading default risk into a closed-loop state smooth adjustment, thereby reducing the scheduling response delay.

[0023] 2. For situations where external lag offsets exceed the inherent buffer limits of the link, the commercial performance elasticity coefficients of all downstream related logical nodes are retrieved. Based on the ratio of acceptable actual dwell time to standard predetermined time in historical performance data, the non-rigid adjustment capability of each node is determined. Local topology folding is then performed on the state vectors of subsequent logical nodes in sequence. Redundancy is released and reversed to the currently blocked link by orderly reducing the time window of elastic nodes. This completes the adaptive settlement of internal time-series debts, avoids frequent triggering of high-overhead external resource replacement actions or full-link cancellation and rescheduling operations, effectively controls the secondary calculation overhead caused by large-scale scheduling, and maintains the integrity of the commercial transaction closed loop.

[0024] 3. For situations where downstream resource time windows are extremely rigid, a rigid anchor point judgment logic is introduced during the topology reconstruction process. When a downstream node is detected to contain an unchangeable hard constraint label, the translation iteration of the current branch is interrupted, and the process is backtracked to the upstream replaceable node to trigger the overall cutover of the path branch. At the same time, in conjunction with a multi-level topology architecture, the trunk path is separated from the flexible mounting branch. When the offset penetrates the maximum tolerance threshold, the flexible mounting branch is automatically stripped and the state of the trunk node is solved by dimensionality reduction. This forms a hierarchical control chain with adaptive depth and risk blocking capabilities, eliminating the secondary contradiction of the constraint solver falling into infinite iteration caused by large-scale external disturbances. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0026] Figure 1 This is a flowchart of the integrated management method for platform tourism resource integration based on time delay comparison and deviation balancing, as described in this invention.

[0027] Figure 2 This is a structural diagram of the integrated management system for tourism resources on a cloud server and multi-source interactive terminals, as described in this invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0029] A method for integrated management of platform tourism resources includes the following steps:

[0030] Step S101: Construct a spatiotemporal dependency graph data model of tourism resource service entities and obtain the dynamic state data of each tourism resource service entity in the spatiotemporal dependency graph data model.

[0031] Step S102: When it is identified that the dynamic state data corresponding to any predecessor logic node contains a predetermined time delay, the predetermined time delay is extracted and compared with the preset buffer time limit parameter stored in the corresponding directed edge data structure in the spatiotemporal dependency graph data model.

[0032] Step S103: If the predetermined time delay is less than or equal to the preset buffer time limit parameter, update the admission timestamp of the subsequent associated logical nodes according to the predetermined time delay to maintain the constant topology of the spatiotemporal dependency graph data model; if the predetermined time delay is greater than the preset buffer time limit parameter, obtain the corresponding historical performance data, determine the node time limit adjustment coefficient of each downstream associated logical node according to the ratio of the acceptable actual dwell time to the standard predetermined time in the historical performance data, and compress the dwell time window of the subsequent associated nodes in sequence. Release time redundancy by deducting the time window of non-rigid nodes and reversely supplement it to the current blocked link to complete the adaptive balance of time deviation.

[0033] Preferably, in step S103, compressing the dwell time window of the successor associated node includes the following steps: Step S1031, retrieving the hard constraint labels stored in the successor associated node, and determining that the successor associated node is a rigid spatiotemporal node when a hard constraint label is detected; Step S1032, interrupting the time iteration of the current branch, backtracking to the upstream replaceable node, splitting the spatiotemporal dependency graph data model into a main travel planning branch and a flexible mounting branch and separating them from each other, peeling off the flexible mounting branch when the predetermined time delay penetrates the preset maximum tolerance threshold, and solving the reduced constraint terms of the main travel planning.

[0034] Preferably, the construction of the spatiotemporal dependency graph data model of tourism resource service entities in step S101 includes: taking the aviation nodes, hotel nodes, and scenic spot nodes belonging to the tourism resource service entities as graph nodes in the spatiotemporal dependency graph data model; taking the connection time constraints and spatial transfer constraints between each tourism resource service entity as directed edges in the spatiotemporal dependency graph data model; writing preset buffer time limit parameters and standard access timestamps into the data structure of the directed edges to form a topological data network as the spatiotemporal dependency graph data model.

[0035] Preferably, in step S103, updating the admission timestamp of the subsequent associated logical node includes: using a predetermined time delay as a time shift increment, sequentially accumulating it onto the admission timestamp of the subsequent associated logical node directly connected to any predecessor logical node, thereby correcting the local time deviation while keeping the topological connection relationship of the spatiotemporal dependency graph data model constant.

[0036] Preferably, the method further includes the following human-machine collaborative control steps: Step S501, push the status data obtained after compressing the dwell time window of the subsequent associated node or the replacement node parameters after the alternative route switch to the client in real time; Step S502, present the reconstructed path change information on the client, establish a data interaction flow between the operating subject and the cloud integrated management platform, receive the confirmation instruction fed back by the client, and couple and bind the automatic scheduling strategy with the manual confirmation link.

[0037] Preferably, extracting the predetermined time delay includes: calculating the original deviation between the actual execution timestamp and the standard admission timestamp of any predecessor logical node, obtaining the transmission delay compensation term caused by calculation time and network transmission delay, summing the original deviation and the transmission delay compensation term, and determining the summation result as the predetermined time delay.

[0038] Preferably, obtaining the node time limit adjustment coefficient also includes: lowering the node time limit adjustment coefficient based on the cancellation and rescheduling rate and congestion index in historical orders, and widening the reduction range of the dwell time window of subsequent related nodes.

[0039] Preferably, the solution of the reduced constraint terms for the trunk route planning includes: converting the spatiotemporal dependency graph data model into a dependency chain containing only the core trunk nodes, removing the interaction constraints of the flexible attached branches, and using a multi-objective constraint solver to update the constraint conditions of the simplified trunk route planning and recalculate the state vector.

[0040] Preferably, each step in the method involves distributed calls between the cloud-based integrated management platform and the data interfaces of various independent service providers. These independent service providers include aviation data source providers, hotel data source providers, and scenic area data source providers. The asynchronous event-driven stream processing mechanism between the cloud-based integrated management platform and the data interfaces is used to control the response latency of resource reorganization within the specified time range.

[0041] A platform-based integrated management system for tourism resources, comprising:

[0042] The graph model construction module is used to construct a spatiotemporal dependency graph data model of tourism resource service entities and obtain the dynamic state data of each tourism resource service entity in the spatiotemporal dependency graph data model.

[0043] The time delay comparison module is connected to the graph model construction module. When the dynamic state data corresponding to any predecessor logic node is found to contain a predetermined time delay, the predetermined time delay is extracted and compared with the predetermined time delay parameter stored in the corresponding directed edge data structure in the spatiotemporal dependency graph data model.

[0044] The time deviation balancing module is connected to the time delay comparison module. When the predetermined time delay is less than or equal to the preset buffer time limit parameter, it updates the admission timestamp of subsequent associated logical nodes according to the predetermined time delay to maintain the constant topology of the spatiotemporal dependency graph data model. When the predetermined time delay is greater than the preset buffer time limit parameter, it obtains the corresponding historical performance data, determines the node time limit adjustment coefficient of each downstream associated logical node based on the ratio of the acceptable actual dwell time to the standard predetermined time in the historical performance data, and sequentially compresses the dwell time window of the subsequent associated nodes. By reducing the time window of non-rigid nodes, time redundancy is released and reversely supplemented to the current blocked link to complete the adaptive balancing of time deviation.

[0045] Example 1: In a cross-regional tourism resource collaborative processing environment, the technical solution of this invention achieves stable fulfillment of itineraries under complex physical conditions by integrating heterogeneous data from multiple supply sides and optimizing temporal logic. It constructs a spatiotemporal dependency graph data model of tourism resource service entities, extracting airlines, hotels, and scenic spots as graph nodes, and using directed edges to connect time constraints and spatial transfer constraints. In this spatiotemporal dependency graph data model, each directed edge stores corresponding preset buffer time limit parameters and standard access timestamps. When the cloud-based integrated management platform receives flight dynamic signals transmitted by the airline data source provider through an asynchronous event-driven stream processing mechanism, it parses the actual fulfillment timestamp of the preceding logic node and records the actual fulfillment... The difference between the timestamp and the standard access timestamp is determined as the predetermined time delay. To address the objective data synchronization deviation in distributed heterogeneous networks, the system pre-establishes a communication delay benchmark. The platform integrates the management terminal and sends synchronization test messages containing the initial sending timestamp to the terminals of each data source provider. It receives response messages with the receiving timestamp and calculates the time difference between the sending and receiving timestamps. Half of the difference is established as the basic transmission delay parameter. The system extracts the basic transmission delay parameter within a continuous preset period and calculates the moving average. The moving average is set as the transmission delay compensation term. The system automatically obtains the transmission delay compensation term, sums the original deviation with the transmission delay compensation term, and accurately quantifies the timing offset value of the predecessor logic node.

[0046] When the predetermined time delay is less than or equal to the preset buffer time limit parameter stored in the directed edge data structure, the system uses the predetermined time delay as a time shift increment, sequentially accumulating it to the admission timestamps of subsequent associated logical nodes. This maintains the topological structure of the spatiotemporal dependency graph data model and achieves lossless smooth absorption of local temporal deviations. When the predetermined time delay is greater than the preset buffer time limit parameter, the cloud-based integrated management platform calls the historical performance data stored in the backend. Based on the ratio of the acceptable actual dwell time to the standard predetermined time in this historical performance data, it determines the node time limit adjustment coefficient for each downstream associated logical node. Specifically, the cloud... The integrated management platform pre-cleans data using a sliding statistical algorithm based on time windows, filtering out invalid fulfillment records caused by force majeure events such as server failures or extreme weather. It extracts a set of valid orders that did not lead to subsequent trip defaults and for which there were no user complaints. The platform calculates the probability density function of the actual dwell time at each node within this set, and quantifies and defines the dwell time threshold where the cumulative probability distribution reaches an 85% confidence interval as the aforementioned acceptable actual dwell time. This provides an unambiguous and precise benchmark parameter for coefficient calculation. Based on the fundamental statistical probability control principle, the processing unit uses the following mathematical model to calculate the node time limit adjustment coefficient: , where variables The value range of the node time limit adjustment coefficient is greater than 0 and less than or equal to 1. This represents the average actual dwell time of nodes in historical orders retrieved from the backend database for completion of delivery. This represents the scheduling duration of the node in the current standard pre-order; The percentage of customers who voluntarily cancel or change their bookings within a historical period is a dimensionless decimal. The system collects real-time visitor congestion index data through an independent service provider interface. If the data is greater than or equal to 1, the system's internal dimensionless mapping module preprocesses the average actual dwell time and scheduling time (which have time attributes). This preprocessing divides these values ​​by a globally agreed-upon baseline time constant, reducing them to pure numerical constants representing only duration multiples. These constants are then used in conjunction with the dimensionless cancellation / rescheduling ratio and congestion index, eliminating the risk of cross-dimensional calculations at the physical level and ensuring that the final output adjustment coefficient is always a pure proportional value. The system then multiplies the original dwell time window by The reduced target time window is calculated, enabling the reduction of the cloud-based graph time window to be realized simultaneously at the physical fulfillment level. The platform's integrated management terminal automatically issues a sequence of voucher change instructions to the corresponding service provider through the data interface based on the target time window length. The instruction sequence forcibly switches the user's bound global standard tour ticket to an electronic ticket voucher for a limited tour area and a fast-track, queue-free passage. By objectively reducing physical time redundancy through physical rights and tour paths, the system automatically compresses the dwell time windows of subsequent related nodes according to the node's time limit adjustment coefficient. Time redundancy is released by reducing the time windows of non-rigid nodes, and the released time redundancy is then used to supplement the currently blocked link, completing the adaptive balance of time deviation.

[0047] During the compression of the dwell time window, the system retrieves hard constraint labels stored in the subsequent associated nodes. If a hard constraint label is detected, the system determines that the subsequent associated node is a rigid spatiotemporal node, interrupts the time iteration of the current branch, and backtracks to the upstream replaceable node. When performing topology splitting and reorganization, the system scans the current blocked links according to the breadth-first traversal algorithm of graph theory. The processing unit extracts the node data structure attribute weight values ​​and marks them as core resource nodes of cross-regional transportation fixed accommodation facilities, locking and connecting them to construct the main itinerary plan. The system extracts attribute weight values ​​that are lower than the preset retention threshold and classifies and aggregates the nodes into flexible attached branches. When the stripping trigger condition is determined, the processing unit compares the predetermined time delay with the set maximum tolerance threshold. If numerical penetration occurs, the system cuts off the logical connection arc between the flexible attached branch and the main itinerary plan. After severing the association, the system calls the refund and change application interface to send inventory release order cancellation messages to the service provider terminals contained in the flexible attached branch. The system splits the spatiotemporal dependency graph data model into the main itinerary. The process planning and flexible loading branches are separated. When the predetermined time delay exceeds the preset maximum tolerance threshold, the flexible loading branch is stripped away, and only the dependency chain of the core backbone node is retained. The multi-objective constraint solver updates the constraints of the simplified backbone process planning and recalculates the state vector to form a deterministic reconfiguration scheduling instruction. In this solution stage, the multi-objective constraint solver takes minimizing the cumulative lag time difference of the global process chain and minimizing the external resource replacement cost as the joint objective function. The hard physical business hours boundary of the associated service provider system is used as the absolute constraint parameter. The branch and bound search algorithm is used to iteratively approximate the solution in the locally reduced spatiotemporal solution space. The iteration terminates when the difference in the objective function calculation of two consecutive cycles converges to one-thousandth of the accuracy, thereby outputting the optimal alternative state vector solution. The reconfigured state data or alternative process route parameters are pushed to the client in real time, presenting path change information on the client and receiving confirmation instructions through the client feedback. The automatic scheduling strategy is coupled and bound with the manual confirmation link to complete the closed-loop adjustment of the entire process.

[0048] Example 2: This experiment constructs a computational environment simulating cross-regional tourism resource fulfillment to verify the resource reconstruction performance of the aforementioned spatiotemporal dependency graph data model under sudden dynamic disturbances. The experimental platform is configured with a 3.2GHz processor and 64GB of memory, employing computational fluid dynamics to process discrete spatiotemporal order data streams. It simulates a complex tourism resource network containing 1000 nodes and 5000 directed edges. During the initialization phase, based on the time window distribution characteristics of the historical fulfillment database, standard access timestamps for each node are set. To simulate link instability in real industrial scenarios, normally distributed power frequency interference harmonics are actively injected into the signal source. The signal-to-noise ratio is... The value is set to 20dB to characterize the raw data noise caused by network transmission delay and computation time. The physical basis for this signal modulation method is that the time-series random lag fluctuation law caused by fiber optic Ethernet data packets during congestion queuing and the voltage sampling drift characteristics caused by high-frequency white noise under the constraint of 20dB signal-to-noise ratio in electrical circuits are highly isomorphic in terms of probability density distribution and power spectral density curves at the mathematical level. This allows the network delay in the macroscopic dimension to be scientifically characterized by electrical parameters. The setting of this parameter is based on the statistical analysis of the average response time of heterogeneous data sources. By balancing real-time performance and computational load, 200ms is selected as the sampling period.

[0049] The experiment consisted of two sample groups: the first group was the experimental group using the technical solution of this invention, and the second group was the control group using existing technology to handle disturbances through end-to-end cancellation and rebooking methods. Under simulated disturbance conditions, the time deviation of the aviation nodes was set as gradient values ​​of 10 min, 30 min, 60 min, and 120 min to test the system performance indicators under different intensities of disturbance. The experimental data showed that when the aviation node generated a time deviation of 10 min, the predetermined time delay calculated by the experimental group was less than the preset buffer time limit parameter, which was set to 15 min. The system directly triggered the local timing offset correction logic, updating the admission timestamp through vector addition. The average reconstruction latency of the experimental group was controlled within 45ms, and the resource replacement mechanism was not triggered. Due to the lack of parameterized interaction, the control group directly triggered the chain refund and change assessment of subsequent hotel and scenic spot nodes, with a processing time of 12.5s. As the deviation increased to 60 minutes, the reservation time delay exceeded the preset buffer time limit parameter. The experimental group started the dwell time window compression mechanism. The node time limit adjustment coefficient determined by the experimental group based on historical performance data was 0.85, and the system compressed the time window sequentially. The dwell time window of subsequent associated nodes was reduced, and the time window of non-rigid nodes was deducted to release time redundancy. The released time redundancy was then used to supplement the current blocked link. This successfully maintained the average response time of the reconstruction command at 350ms. The resource delivery success rate of the experimental group was 98.5%, while the resource delivery success rate of the control group dropped to 82.3% due to resource locking conflicts that caused some nodes to fail to deliver. Further testing showed that when the deviation reached 120 minutes, the experimental group triggered the hard constraint label detection mechanism, determined that the node was a rigid spatiotemporal node, and used a multi-objective constraint solver for topology reconstruction. In the reconstruction state vector output by the experimental group, the temporal dependency deviation between nodes was limited to the standard error range, proving that the mechanism still has stability under extreme conditions. When the upper limit threshold was exceeded, the experimental data showed a performance degradation trend. In particular, the system computing power consumption increased exponentially with the number of reconstructed nodes, verifying the basis for setting the preset maximum tolerance threshold. The experimental results confirmed that the response delay of resource reorganization can be reduced through dynamic balancing of the spatiotemporal dependency graph data model and hard constraint backtracking reconstruction, effectively mitigating the disturbance of complex tourism itineraries.

[0050] Example 3: A current method for handling implicit negative event record injection in tourism resource integration is applied to a tourism resource management system containing multi-service provider order flows. The platform integration management terminal receives dynamic status data from airline, hotel, and scenic spot nodes, constructs a spatiotemporal dependency graph data model, and stores the spatiotemporal dependency graph data model in the associated storage space of the cloud server. During the integration of tourism resources, if the system receives abnormal order records identified as implicit negative events, such as hotel room overbooking status or logical conflicts triggered by airline overbooking, the cloud integration management platform triggers the implicit negative event record injection mechanism. Specifically, at the physical level, this injection... The entry mechanism refers to encapsulating the captured implicit negative events into virtual data packets containing the original node identifier and the expected time difference, and loading them into a sandbox verification model independent of the main transaction database via a memory message queue. This sandbox is used to construct a shadow verification topology parallel to the current spatiotemporal dependency graph, ensuring that potential congestion or out-of-bounds risks are pre-calculated across the entire chain without polluting the real order data. The platform integration management terminal determines the corresponding node and subsequent link transmission path of the negative event in the spatiotemporal dependency graph data model, and calculates the impact weight of the implicit negative event record on the global trip fulfillment risk based on the defined negative event intensity assessment model.

[0051] The weighting of this influence is determined based on a nonlinear regression model using historical order volatility characteristics, and its calculation formula is as follows: ,in, The weighting of the impact of negative event records on trip fulfillment risk. The preset historical error normalization coefficient, This represents the number of affected order records involved in the current implicit negative event. This represents the total number of associated order records in the current spatiotemporal dependency graph data model. Let be the time delay sensitivity constant. This is the disturbance attenuation coefficient. This refers to the lag time of the predecessor node caused by implicit negative events. The preset buffer time limit parameters for the corresponding nodes include the three basic constants: historical error normalization coefficient, time delay sensitivity constant, and disturbance attenuation coefficient. These are empirical values ​​obtained by using the full-dimensional abnormal allocation logs accumulated by the platform over the past three years as the training data base during the R&D phase. They are solidified after long-term backward convergence training of the real offline fulfillment failure rate and the corresponding input bias using a multivariate nonlinear fitting tool. They are not temporarily customized by the system.

[0052] The cloud-based integrated management platform is based on the calculated Numerical values ​​automatically retrieve alternative fulfillment strategies from the associated node's space. When the value exceeds the preset risk trigger threshold, the system will trigger a response based on the resource change cost index. Compressible time redundancy The ratio is used to dynamically compress the dwell time window or perform path replacement on subsequent nodes. To ensure absolute self-consistency in the physical dimension, the system introduces a risk time equivalent constant here, multiplying the dimensionless influence weight value by this equivalent constant. This accurately reduces the dimensionality of the abstract risk assessment index and projects it onto the concrete time axis, transforming it into a risk coverage requirement with a time dimension. This ensures that subsequent comparison operations based on numerical magnitude are completely equivalent in both physical essence and logical level. Less than Based on the risk coverage requirements of the mapping, the system triggers automatic isolation of implicit negative event records, marking the records as unavailable and removing them from the spatiotemporal dependency graph data model. Simultaneously, it sends an anomaly alarm to the management terminal through a preset interface protocol. This automatic isolation logic is achieved by monitoring the data fluctuation characteristics before and after the injection of implicit negative events. Specifically, when the variance of a key indicator in the data stream deviates from the baseline value by 15% within 50ms after injection, the platform's integrated management terminal identifies the anomaly and triggers a preset logic reset command. This reverts the resource access pointer of the system's physical execution mechanism to the last stable state before injection. Through these actions, the system achieves immediate cancellation of implicit negative event records while ensuring data storage consistency.

[0053] Example 4: In the initial deployment of the tourism resource management system, the system needs to perform benchmark calibration on the response stability of each independent service entity to address the impact of differences in computing power among service providers in different regions on integrated fulfillment. Based on the historical response data of each node, the cloud-based integrated management platform establishes a response performance calibration procedure. The system statistically analyzes the average response latency and latency fluctuation of each node, defining them as the benchmark response time. With response stability index Before the system officially went live, the platform integrated management terminal sent a series of synchronization handshake commands to the terminals of each service provider and collected the timestamps of the command sending. Response timestamp from service provider's terminal The transmission delay compensation term for each node is calculated. .

[0054] When the system identifies a risk of order conflicts in the tourism resource integration network, it needs to perform resource rearrangement according to preset logical judgment criteria. The system monitors and records the physical fulfillment progress of all active orders in real time, quantifying the fulfillment status of each order into a state vector. ,in, Indicates the first The system sets a performance risk threshold based on the current fulfillment rate of each tourism resource node. This threshold is derived based on the statistical distribution function of historical default samples, when the state variable value of a certain node... The system will initiate an early warning and perform resource redundancy calls when the following triggering conditions are met: ,in, For the first The instantaneous default probability of each node. The preset risk trigger threshold, For the first The instantaneous volatility coefficient of each node is determined by the local topological connectivity of the temporal dependency graph in which the node currently resides. The system normalizes the instantaneous default probability of each node and identifies high-risk nodes as current weak points in resource integration. When the calculation results show... When the risk trigger threshold is exceeded, the cloud-based integrated management platform automatically locks the access path of the damaged resource node and retrieves the configuration parameters of the homogeneous replacement node from the backup resource pool. The system uses resource switching logic based on preset buffer time limit parameters to map the logical connection of the damaged node to the replacement node, completing the dynamic migration of resource load. The entire migration process is recorded as an immutable operation log, ensuring that the data flow link from anomaly identification to resource rearrangement has engineering auditability, and realizing the robust operation of tourism resources in the face of sudden disturbances.

[0055] Example 5: In the deployment phase of the tourism resource integration system, to address the inconsistencies in computing performance and response latency among different service provider terminals, the system executes a standardized benchmark calibration procedure. The platform integration management terminal sends a series of timestamped synchronization handshake commands to measure the physical response latency of aviation nodes, hotel nodes, and scenic spot nodes, and defines the average response latency of each node as the benchmark response time. Simultaneously, the response delay fluctuation amplitude of the node within the preset observation period is calculated and defined as the response stability index. The system constructs a benchmark parameter matrix containing the physical performance parameters of each node through the above calibration process. This matrix serves as the weight criterion for path selection in the subsequent tourism resource reconstruction process.

[0056] When determining the trigger strategy for resource reconfiguration, the system employs a multi-dimensional weighted optimization algorithm to handle order conflicts and defines a comprehensive function for resource optimization objectives. The calculation formula is as follows: ,in, The resource optimization objective function is... This is the time weighting coefficient for travel deviation. To reconstruct the absolute deviation values ​​of the time points before and after the trip, This is the weighting coefficient for resource switching costs. To mitigate the direct economic costs of resource node switching, the system's internal priority determination procedure follows these steps: The system sets the timing deviation value weight of the core backbone nodes of the process to the highest level to ensure process continuity; provided that timing continuity is satisfied, based on... The values ​​are sorted in ascending order among the candidate replacement nodes, and then selected. The smallest alternative node that satisfies the hard constraints is taken as the objective solution; when the calculated When the numerical value changes by less than 1% in three consecutive iterations, the system determines that the solver has reached a steady state and issues a locking command to the corresponding resource control terminal. This optimization process... and The numerical values ​​are calibrated by simulating the system overhead under different disturbance intensities through offline pre-experimentation to ensure that the solver can output deterministic scheduling results within 500ms during peak periods of tourism resource circulation.

[0057] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for integrated management of platform tourism resources, characterized in that, Includes the following steps: Step S101: Construct a spatiotemporal dependency graph data model of tourism resource service entities and obtain the dynamic state data of each tourism resource service entity in the spatiotemporal dependency graph data model. Step S102: When it is identified that the dynamic state data corresponding to any predecessor logic node contains a predetermined time delay, the predetermined time delay is extracted and compared with the preset buffer time limit parameter stored in the corresponding directed edge data structure in the spatiotemporal dependency graph data model. Step S103: If the predetermined time delay is less than or equal to the preset buffer time limit parameter, update the admission timestamp of the subsequent related logical nodes according to the predetermined time delay to maintain the constant topological structure of the spatiotemporal dependency graph data model. If the predetermined time delay exceeds the preset buffer time limit parameter, the corresponding historical performance data is obtained. Based on the ratio of the acceptable actual dwell time to the standard predetermined time in the historical performance data, the node time limit adjustment coefficient of each downstream associated logical node is determined, and the dwell time window of the subsequent associated nodes is compressed in sequence. By reducing the time window of non-rigid nodes, time redundancy is released and reversely supplemented to the current blocked link to complete the adaptive balance of time deviation.

2. The integrated management method for platform tourism resources according to claim 1, characterized in that, In step S103, compressing the dwell time window of the successor associated node includes the following steps: Step S1031, retrieving the hard constraint labels stored in the successor associated node, and determining that the successor associated node is a rigid spatiotemporal node when a hard constraint label is detected; Step S1032, interrupting the time iteration of the current branch, backtracking to the upstream replaceable node, splitting the spatiotemporal dependency graph data model into the main travel planning and the flexible mounting branch and separating them from each other, peeling off the flexible mounting branch when the predetermined time delay penetrates the preset maximum tolerance threshold, and solving the reduced constraint terms of the main travel planning.

3. The integrated management method for platform tourism resources according to claim 1, characterized in that, The construction of the spatiotemporal dependency graph data model of tourism resource service entities in step S101 includes: taking the aviation nodes, hotel nodes, and scenic spot nodes belonging to the tourism resource service entities as graph nodes in the spatiotemporal dependency graph data model; taking the connection time constraints and spatial transfer constraints between each tourism resource service entity as directed edges in the spatiotemporal dependency graph data model; and writing the preset buffer time limit parameters and standard access timestamps into the data structure of the directed edges to form a topological data network as the spatiotemporal dependency graph data model.

4. The integrated management method for platform tourism resources according to claim 1, characterized in that, In step S103, updating the admission timestamp of subsequent associated logical nodes includes: using a predetermined time delay as a time shift increment, sequentially accumulating it onto the admission timestamp of subsequent associated logical nodes directly connected to any predecessor logical node, thereby correcting local time deviations while keeping the topological connection relationship of the spatiotemporal dependency graph data model constant.

5. The integrated management method for platform tourism resources according to claim 1, characterized in that, The method also includes the following human-machine collaborative control steps: Step S501, push the status data obtained after compressing the dwell time window of the subsequent associated node or the replacement node parameters after the alternative route switching to the client in real time; Step S502: Present the reconstructed path change information on the client, establish a data interaction flow between the operating entity and the cloud-based integrated management platform, receive confirmation instructions from the client, and couple and bind the automatic scheduling strategy with the manual confirmation process.

6. The integrated management method for platform tourism resources according to claim 1, characterized in that, Extracting the predetermined time delay includes: calculating the original deviation between the actual execution timestamp and the standard admission timestamp of any predecessor logical node, obtaining the transmission delay compensation term caused by calculation time and network transmission delay, summing the original deviation and the transmission delay compensation term, and determining the summation result as the predetermined time delay.

7. The integrated management method for platform tourism resources according to claim 1, characterized in that, Obtaining the node time limit adjustment coefficient also includes: lowering the node time limit adjustment coefficient based on the cancellation and rescheduling rate and congestion index in historical orders, and expanding the scope of reduction for the dwell time window of subsequent related nodes.

8. The integrated management method for platform tourism resources according to claim 2, characterized in that, The process of reducing constraints in the main trunk travel planning includes: converting the spatiotemporal dependency graph data model into a dependency chain containing only the core trunk nodes, removing the interaction constraints of the flexible mounting branches, and using a multi-objective constraint solver to update the constraints of the simplified main trunk travel planning and recalculate the state vector.

9. The integrated management method for platform tourism resources according to claim 1, characterized in that, Each step in the method involves distributed calls between the cloud-based integrated management platform and the data interfaces of various independent service providers, including aviation data providers, hotel data providers, and scenic area data providers. The asynchronous event-driven stream processing mechanism between the cloud-based integrated management platform and the data interfaces is used to control the response latency of resource reorganization within the specified time range.

10. A platform-based integrated tourism resource management system, used to implement the platform-based integrated tourism resource management method as described in claim 1, characterized in that, The system includes: The graph model construction module is used to construct a spatiotemporal dependency graph data model of tourism resource service entities and obtain the dynamic state data of each tourism resource service entity in the spatiotemporal dependency graph data model. The time delay comparison module is connected to the graph model construction module. When the dynamic state data corresponding to any predecessor logic node is found to contain a predetermined time delay, the predetermined time delay is extracted and compared with the predetermined time delay parameter stored in the corresponding directed edge data structure in the spatiotemporal dependency graph data model. The time deviation balancing module is connected to the time delay comparison module. When the predetermined time delay is less than or equal to the preset buffer time limit parameter, it updates the admission timestamp of subsequent associated logical nodes according to the predetermined time delay to maintain the constant topology of the spatiotemporal dependency graph data model. When the predetermined time delay is greater than the preset buffer time limit parameter, it obtains the corresponding historical performance data, determines the node time limit adjustment coefficient of each downstream associated logical node based on the ratio of the acceptable actual dwell time to the standard predetermined time in the historical performance data, and sequentially compresses the dwell time window of the subsequent associated nodes. By reducing the time window of non-rigid nodes, time redundancy is released and reversely supplemented to the current blocked link to complete the adaptive balancing of time deviation.

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