Smart park management method and system based on multi-system integration

Abstract

The present disclosure provides a smart park management method and system based on multi-system integration, which relates to the technical field of smart park management. The method includes: reading park data, identifying and obtaining park characteristics; generating a park area set with a regional attention mark; sending the regional system identification result and the regional attention mark to the data analysis association network, and establishing a system hidden layer link; configuring an API interface, performing data collaboration of sensors in the park area set, and configuring collaboration nodes; activating associated collaboration nodes according to the system hidden layer link of the corresponding regional system, and obtaining collaboration data; performing regional early warning management through collaboration data and monitoring data of the corresponding regional system. The present disclosure can solve the technical problem in the prior art that the degree of data interconnection in the smart park is low due to the low efficiency of multi-system integration management, achieve the goal of improving the efficiency of multi-system integration management, and achieve the technical effect of improving the degree of data interconnection in the park.

Description

Technical Field

[0001] The present disclosure relates to the technical field of smart park management, and in particular to a smart park management method and system based on multi-system integration. Background Art

[0002] The goal of building a smart park is to achieve intelligent management of enterprises, public facilities, transportation and other aspects within the park through the use of modern information technology, improve the production efficiency and service level of the park, and promote the rapid development of the park economy. At present, the degree of multi-system integration of existing smart parks is low, resulting in a low degree of unified management of the park and data sharing and interconnection, which affects the comprehensive intelligence of the smart park.

[0003] To sum up, the existing technology has the technical problem of low data interconnection level in smart parks due to low efficiency of multi-system integrated management. Summary of the invention

[0004] The present disclosure provides a smart park management method and system based on multi-system integration, which is used to solve the technical problem in the prior art that the degree of data interconnection in the smart park is low due to the low efficiency of multi-system integration management.

[0005] According to the first aspect of the present disclosure, a smart park management method based on multi-system integration is provided, including: establishing digital interaction with the smart park, and reading park data through the digital interaction to identify and obtain park characteristics; using the park characteristics as segmentation data, performing regional segmentation on the smart park, and generating a park area set with a regional attention identifier; performing regional system identification on the park area set, and sending the regional system identification result and the regional attention identifier to the data analysis association network to establish a system hidden layer link; configuring an API interface according to the system hidden layer link, performing data collaboration of sensors in the park area set based on the API interface, and configuring a collaboration node, wherein the collaboration node has a collaboration function and a collaboration position identifier; when any regional system triggers an activation condition, activating the associated collaboration node according to the system hidden layer link of the corresponding regional system to obtain collaboration data; and performing regional early warning management through the collaboration data and the monitoring data of the corresponding regional system.

[0006] According to the second aspect of the present disclosure, a smart park management system based on multi-system integration is provided, including: a park feature acquisition module, the park feature acquisition module is used to establish digital interaction with the smart park, and read park data through the digital interaction to identify and obtain park features; a park area set acquisition module, the park area set acquisition module is used to use the park features as segmentation data to perform area segmentation on the smart park, and generate a park area set with a regional attention mark; a system hidden layer link establishment module, the system hidden layer link establishment module is used to perform regional system identification on the park area set, and send the regional system identification result and the regional attention mark to the data Analyze the associated network and establish the system hidden layer link; a collaborative node configuration module, the collaborative node configuration module is used to configure the API interface according to the system hidden layer link, perform data collaboration of sensors in the park area set based on the API interface, and configure collaborative nodes, wherein the collaborative nodes have collaborative functions and collaborative location identifiers; a collaborative data acquisition module, the collaborative data acquisition module is used to activate the associated collaborative nodes according to the system hidden layer link of the corresponding area system when any area system triggers the activation condition, and obtain collaborative data; a regional early warning management module, the regional early warning management module is used to perform regional early warning management through the collaborative data and the monitoring data of the corresponding area system.

[0007] One or more technical solutions provided in the present disclosure have at least the following technical effects or advantages: according to the method adopted in the present disclosure, digital interaction with the smart park is established, and park data is read through the digital interaction to identify and obtain park characteristics; the smart park is segmented into regions using the park characteristics as segmentation data, and a park region set with a regional attention identifier is generated; the park region set is identified as a regional system, and the regional system identification result and the regional attention identifier are sent to the data analysis association network to establish a system hidden layer link; an API interface is configured according to the system hidden layer link, data coordination of sensors in the park region set is performed based on the API interface, and coordination nodes are configured, wherein the coordination nodes have coordination functions and coordination position identifiers; when any regional system triggers an activation condition, the associated coordination node is activated according to the system hidden layer link of the corresponding regional system to obtain coordination data; regional early warning management is performed through the coordination data and the monitoring data of the corresponding regional system, thereby solving the technical problem of low data interconnection degree of smart parks due to low efficiency of multi-system integrated management in the prior art, achieving the goal of improving the efficiency of multi-system integrated management, and achieving the technical effect of improving the degree of data interconnection degree of parks.

[0008] It should be understood that the contents described in this section are not intended to indicate the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 A flowchart of a smart park management method based on multi-system integration provided in an embodiment of the present disclosure;

[0011] Figure 2 A schematic diagram of a process for performing associated activation control of collaborative nodes in a smart park management method based on multi-system integration according to an embodiment of the present disclosure;

[0012] Figure 3 A schematic diagram of the structure of a smart park management system based on multi-system integration provided in an embodiment of the present disclosure.

[0013] Explanation of the reference numerals: park feature acquisition module 11, park area set acquisition module 12, system hidden layer link establishment module 13, collaborative node configuration module 14, collaborative data acquisition module 15, regional early warning management module 16. DETAILED DESCRIPTION

[0014] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0015] Embodiment 1

[0016] The smart park management method based on multi-system integration provided in the embodiment of the present disclosure is referred to Figure 1 Illustratively, the method comprises:

[0017] Establish digital interaction with the smart park, and read park data through the digital interaction to identify and obtain park characteristics;

[0018] Specifically, the smart park is a park to be managed. By establishing a communication connection with the smart park, digital interaction with the smart park is performed. Furthermore, during the digital interaction, the park data is read and obtained, and the park characteristics are obtained according to the park data. The park data may be park parameter data, and the park characteristics obtained according to the park parameter data may be characteristics of the park functions. For example, when the park data includes entertainment facilities, the park characteristics may be identified as a park.

[0019] Using the park characteristics as segmentation data, performing regional segmentation on the smart park, and generating a park area set with regional attention identifiers;

[0020] Specifically, the park characteristics are used as segmentation data for regional division to perform regional segmentation on the smart park. Each park characteristic corresponds to each park area of ​​regional segmentation. For example, when the park characteristics include parks and hospitals, the corresponding park areas in the smart park are segmented. Furthermore, park areas with regional attention marks are generated according to the park characteristics, and a park area set is obtained by combining the park areas. The regional attention mark indicates the importance of the park area. The higher the regional attention, the higher the importance of the corresponding park area. Conversely, the lower the importance.

[0021] Performing regional system identification on the park area set, and sending the regional system identification result and the regional attention identifier to the data analysis association network to establish a system hidden layer link;

[0022] Specifically, the park areas where the park areas are concentrated are systematically identified for their functions, and park areas with similar functions are obtained. The park areas with similar functions are used to establish a regional system to obtain regional system identification results. Furthermore, the data analysis association network is connected for park area association. Among them, the regional system identification results and regional attention identifiers are sent to the data analysis association network, and a systematic hidden layer link is established between the park areas according to the functional role and importance of the park areas. For example, a hidden layer link is established between a park area with entertainment functions and a park area with medical functions, so that injured people in the entertainment park can be sent to the medical park.

[0023] According to the hidden layer link configuration API interface of the system, data coordination of sensors in the park area set is performed based on the API interface, and coordination nodes are configured, wherein the coordination nodes have coordination functions and coordination location identifiers;

[0024] Specifically, according to the hidden layer link of the system, an API interface is configured between the park areas, and data coordination of sensors and data intercommunication of monitoring data within the park area set are performed based on the API interface. A collaborative node is configured within the park area set for data collaboration, wherein the collaborative node has a collaborative function and a collaborative location identifier.

[0025] When any regional system triggers the activation condition, the associated collaborative nodes are activated according to the system hidden layer links of the corresponding regional system to obtain collaborative data;

[0026] Specifically, when any regional system in the park area set triggers an activation condition, the associated collaborative node is activated according to the system hidden layer link of the corresponding regional system that triggers the activation condition, and then the data of the sensor of the corresponding park area with the system hidden layer link is obtained through the API interface, that is, the collaborative data is obtained.

[0027] Regional early warning management is performed through the collaborative data and the monitoring data of the corresponding regional system.

[0028] Specifically, the monitoring data of the corresponding park area is obtained by sensor monitoring based on the sensors in the park area set. The monitoring data of the corresponding park area and the corresponding regional system are linked through the collaborative data and the system hidden layer to perform regional early warning management.

[0029] Among them, this embodiment can solve the technical problem in the prior art that the degree of data interconnection in the smart park is low due to the low efficiency of multi-system integrated management, achieve the goal of improving the efficiency of multi-system integrated management, and achieve the technical effect of improving the degree of data interconnection in the park.

[0030] The method provided in the embodiment of the present disclosure also includes:

[0031] Performing node fitting replication on the collaborative node;

[0032] Performing cluster search with the regional system as the cluster center;

[0033] Performing a search calculation by using the collaborative function and the collaborative position as clustering constraints;

[0034] The clustering is completed according to the search calculation results, and the associated activation control of the collaborative nodes is performed according to the clustering results.

[0035] like Figure 2 As shown, specifically, node fitting replication is performed on the collaborative nodes. Each node can be multi-purpose, but the purpose of replication is to break the constraint of independent use. Further, node fitting replication of the collaborative nodes is an ideal level of replication. In fact, there is only one node, and each node has a multi-purpose effect. When data is sent, data is sent in the order of the multi-purpose effect.

[0036] Furthermore, taking the regional system with similar functions as the cluster center, a cluster search of the park area is performed, and the park areas with similar functions in the park area set are searched and clustered.

[0037] Furthermore, by using the collaborative function and the collaborative position as clustering constraints, cluster search calculation is performed on the park area corresponding to the collaborative node with the collaborative function at each collaborative position to obtain the search calculation result, wherein the search calculation result includes the clustering result.

[0038] Furthermore, clustering is completed according to the clustering result of the search calculation result, the activation order of the collaborative nodes is determined according to the clustering result, and then the associated activation control is performed according to the activation order.

[0039] Among them, performing associated activation control can improve the control efficiency of the multi-system integrated park.

[0040] The method provided in the embodiment of the present disclosure also includes:

[0041] Performing distance impact analysis on the collaborative function to establish a mapping impact factor, wherein the mapping impact factor carries a position direction identifier;

[0042] Performing normalized calculation of the collaborative function and the collaborative position using the mapping influence factor;

[0043] The search calculation is completed based on the normalized calculation result.

[0044] Specifically, the collaborative function represents the functional role of the park area that has a mutual influence. The collaborative function is analyzed for distance influence and a mapping influence factor is established. For example, when the collaborative function is more affected by distance, the mapping influence factor of the collaborative function has a larger value. Furthermore, the mapping influence factor carries a location direction identifier, which is used to calculate the location distance for obtaining the collaborative function.

[0045] Furthermore, according to the values ​​of the mapping influence factors, the collaborative functions and collaborative positions with smaller values ​​of the mapping influence factors are normalized and calculated, thereby obtaining the overall collaborative nodes corresponding to the collaborative system.

[0046] Further, the search calculation is completed according to the normalized calculation result. When the normalized result is obtained, the search calculation is completed, and the search calculation result is obtained to determine the clustering.

[0047] Among them, obtaining normalized calculation results can improve the efficiency of completing the search calculation.

[0048] Further, the search calculation is completed according to the normalized calculation result.

[0049] The method provided in the embodiment of the present disclosure also includes:

[0050] Determine whether the fitting copy result is called. If called, execute the corresponding collaborative node identification;

[0051] According to the clustering results, the data interaction sequence of the identification and coordination nodes is determined and a sequence identifier is generated;

[0052] When the associated collaborative nodes are synchronously activated, data interaction is completed according to the sequence identifier.

[0053] Specifically, it is determined whether the fitting copy result is called, and the judgment calling result is obtained. For example, it is determined whether the fitting copy result is called by obtaining the calling data of the fitting copy result. When the calling data of the fitting copy result exists, it is determined that the fitting copy result is called. When the calling data of the fitting copy result does not exist, it is determined that the fitting copy result is not called.

[0054] Furthermore, if the judgment calling result is that the fitting copy result is called, that is, the fitting copy result is used, then the coordination node identification corresponding to the fitting copy result is executed, and then the identification coordination node of the fitting copy result is called.

[0055] Further, the data interaction sequence of the collaborative nodes is determined based on the collaborative functions and collaborative positions of the clustering results to generate a sequence identifier. For example, the data interaction sequence of the collaborative nodes is determined based on the order of smaller collaborative function differences and closer collaborative positions.

[0056] Further, when the associated collaborative nodes are synchronously activated, data interaction of the associated collaborative nodes is completed according to the sequence identifier.

[0057] Among them, data interaction of associated collaborative nodes is completed to improve the efficiency of multi-system integration.

[0058] The method provided in the embodiment of the present disclosure also includes:

[0059] The computational effort is evaluated based on the clustering results, and the number of processing centers is distributed based on the evaluation results;

[0060] According to the quantity distribution results, the processing centers are randomly distributed in the clustering results, and a cluster search of the processing centers is performed;

[0061] When any round of cluster search is completed, the processing center is adaptively updated within the cluster results;

[0062] The data of the collaborative nodes are preprocessed according to the clustering results of the processing center, and the data preprocessing results are sent to the corresponding regional system.

[0063] Specifically, the computational amount determined by the collaborative nodes is evaluated according to the clustering results to obtain the evaluation results. Further, the number of computations of the processing centers is determined according to the computational amount in the evaluation results, that is, the number of computing power requirements of the processing centers. Further, according to the evaluation results, the number of processing centers is distributed according to the distribution of computing power requirements.

[0064] Further, the processing centers are distributed according to the number distribution results in the cluster clustering results, wherein the number of processing centers is randomly distributed. Further, a cluster search of the processing centers is performed to update the positions of the processing centers.

[0065] Furthermore, the cluster search is performed iteratively, and after any round of cluster search is completed, the position of the processing center in the cluster result is updated adaptively. After the iterative cluster search is completed, the position of the processing center in the cluster result is updated adaptively.

[0066] Furthermore, data preprocessing of the collaborative nodes is performed according to the clustering results of the processing center, and the data preprocessing results are sent to the corresponding regional system. The amount of calculation of the regional system is reduced, and distributed computing is then performed. Among them, the efficiency of park management can be improved according to the obtained data preprocessing results.

[0067] The method provided in the embodiment of the present disclosure also includes:

[0068] When an early warning is generated in any area, an early warning interval is allocated based on the early warning value, and the associated area and the early warning area are activated as a trigger state;

[0069] The network parameters of the early warning network are reconstructed in the trigger state, and continuous early warning monitoring of the region is performed based on the reconstruction result.

[0070] Specifically, when an early warning is generated in any area, it indicates that the collaborative data of the area and the monitoring data values ​​of the corresponding area system are abnormal. A warning interval is allocated based on the warning value, and the associated areas and warning areas in the park area set corresponding to the warning value are activated as a trigger state for controlling the warning value.

[0071] Further, the network parameters of the early warning network are reconstructed in a triggered state to reduce the early warning value, and continuous early warning monitoring of the region is performed based on the reconstruction result until the early warning value is reduced. The network parameters of the early warning network are reconstructed to improve the efficiency of park control.

[0072] The method provided in the embodiment of the present disclosure also includes:

[0073] Evaluate the stability of data interaction on the API interface and weight the stability evaluation results according to the importance of the data;

[0074] Filter the weighted calculation results according to the comprehensive evaluation threshold, establish a backup communication network, and configure the conversion trigger threshold;

[0075] When the conversion trigger threshold is activated, the corresponding API interface is converted to the backup communication network to complete the data interaction.

[0076] Specifically, the stability of data interaction of the API interface is evaluated to obtain a stability evaluation result. For example, the stability of data interaction of the API interface is judged by judging the integrity of data acquisition. Further, the stability evaluation result is weighted according to the importance of the data to obtain a weighted calculation result. Among them, the weight ratio of the weighted calculation of the data importance and the stability evaluation result is obtained by a technician in this field according to the actual situation. For example, 6:4.

[0077] Furthermore, the weighted calculation results are screened according to the comprehensive evaluation threshold, the weighted calculation results that meet the comprehensive evaluation threshold are extracted, the extracted weighted calculation results are combined to establish a backup communication network, and a conversion trigger threshold is configured for the backup communication network.

[0078] Furthermore, when the conversion trigger threshold is activated, the corresponding API interface is converted to a backup communication network to complete data interaction, thereby improving data interaction efficiency.

[0079] Embodiment 2

[0080] Based on the same inventive concept as the smart park management method based on multi-system integration in the aforementioned embodiment, Figure 3 As shown, the present disclosure also provides a smart park management system based on multi-system integration, the system comprising:

[0081] A park feature acquisition module 11, which is used to establish digital interaction with the smart park, and read park data through the digital interaction to identify and obtain park features;

[0082] A park area set acquisition module 12, wherein the park area set acquisition module 12 is used to perform area segmentation on the smart park using the park characteristics as segmentation data, and generate a park area set with a regional attention mark;

[0083] A system hidden layer link establishment module 13, the system hidden layer link establishment module 13 is used to perform regional system identification on the park area set, and send the regional system identification result and the regional attention identifier to the data analysis association network to establish a system hidden layer link;

[0084] A collaborative node configuration module 14, the collaborative node configuration module 14 is used to configure an API interface according to the hidden layer link of the system, perform data collaboration of sensors in the park area set based on the API interface, and configure collaborative nodes, wherein the collaborative nodes have collaborative functions and collaborative location identifiers;

[0085] A collaborative data acquisition module 15, wherein the collaborative data acquisition module 15 is used to activate the associated collaborative nodes according to the system hidden layer links of the corresponding regional system and acquire collaborative data when an activation condition is triggered by any regional system;

[0086] The regional early warning management module 16 is used to perform regional early warning management through the collaborative data and the monitoring data of the corresponding regional system.

[0087] Furthermore, the system further comprises:

[0088] A node fitting replication module, wherein the node fitting replication module is used to perform node fitting replication on the collaborative node;

[0089] A cluster search execution module, the cluster search execution module is used to perform cluster search with the regional system as the cluster center;

[0090] A search calculation execution module, the search calculation execution module is used to perform search calculation by using the collaborative function and the collaborative position as clustering constraints;

[0091] The associated activation control module is used to complete clustering according to the search calculation results, and perform associated activation control of the collaborative nodes according to the clustering results.

[0092] Furthermore, the system further comprises:

[0093] A mapping impact factor establishment module, the mapping impact factor establishment module is used to perform distance impact analysis on the collaborative function and establish a mapping impact factor, wherein the mapping impact factor carries a position direction identifier;

[0094] A normalization calculation module, the normalization calculation module is used to perform normalization calculation of the collaborative function and the collaborative position using the mapping influence factor;

[0095] A search calculation module is used to complete the search calculation according to the normalized calculation result.

[0096] Furthermore, the system further comprises:

[0097] A corresponding collaborative node identification execution module, wherein the corresponding collaborative node identification execution module is used to determine whether the fitting replication result is called, and if called, execute the corresponding collaborative node identification;

[0098] A sequence identifier generation module, which is used to determine the data interaction sequence of the identification collaboration nodes according to the clustering result and generate a sequence identifier;

[0099] A data interaction module is used to complete data interaction according to the sequence identifier when the associated collaborative nodes are synchronously activated.

[0100] Furthermore, the system further comprises:

[0101] A quantity distribution module, which is used to evaluate the amount of calculation according to the clustering results and to distribute the quantity of processing centers based on the evaluation results;

[0102] A cluster search module, the cluster search module is used to randomly distribute the processing centers in the cluster results according to the quantity distribution results, and perform cluster search of the processing centers;

[0103] An adaptive position updating module, wherein the adaptive position updating module is used to adaptively update the position of the processing center within the clustering result after any round of clustering search is completed;

[0104] The data preprocessing result sending module is used to perform data preprocessing of the collaborative node according to the clustering result of the processing center, and send the data preprocessing result to the corresponding regional system.

[0105] Furthermore, the system further comprises:

[0106] A warning interval allocation module, which is used to allocate a warning interval based on the warning value when a warning is generated in any area, and activate the associated area and the warning area to a trigger state;

[0107] A continuous early warning monitoring module is used to reconstruct the network parameters of the early warning network in the trigger state, and perform continuous early warning monitoring of the area based on the reconstruction result.

[0108] Furthermore, the system further comprises:

[0109] A stability evaluation module, which is used to evaluate the stability of data interaction of the API interface and perform weighted calculation on the stability evaluation results according to the importance of the data;

[0110] A conversion trigger threshold configuration module, the conversion trigger threshold configuration module is used to screen the weighted calculation results according to the comprehensive evaluation threshold, establish a backup communication network, and configure the conversion trigger threshold;

[0111] The API interface conversion module is used to convert the corresponding API interface into a backup communication network to complete data interaction when the conversion trigger threshold is activated.

[0112] The specific example of the smart park management method based on multi-system integration in the aforementioned embodiment 1 is also applicable to the smart park management system based on multi-system integration in this embodiment. Through the aforementioned detailed description of the smart park management method based on multi-system integration, those skilled in the art can clearly understand the smart park management system based on multi-system integration in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method description.

[0113] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of this disclosure can be achieved, and this document is not limited here.

[0114] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A smart park management method based on multi-system integration, characterized in that: The method comprises: Establish digital interaction with the smart park, and read park data through the digital interaction to identify and obtain park characteristics; Using the park characteristics as segmentation data, the smart park is segmented into regions, and a park region set with a regional attention mark is generated, wherein the regional attention mark indicates the importance of the park region, and the higher the regional attention, the higher the importance of the corresponding park region; Performing regional system identification on the park area set, and sending the regional system identification result and the regional attention identifier to the data analysis association network to establish a system hidden layer link; According to the hidden layer link configuration API interface of the system, data coordination of sensors in the park area set is performed based on the API interface, and coordination nodes are configured, wherein the coordination nodes have coordination functions and coordination location identifiers; When any regional system triggers the activation condition, the associated collaborative nodes are activated according to the system hidden layer links of the corresponding regional system to obtain collaborative data; Regional early warning management is performed through the collaborative data and the monitoring data of the corresponding regional system.

2. The method according to claim 1, characterized in that The method further comprises: Perform node fitting replication on the collaborative node, wherein the node fitting replication refers to breaking the constraint of independent use of the node so that each node has a multi-use effect; Performing cluster search with the regional system as the cluster center; Performing a search calculation by using the collaborative function and the collaborative position as clustering constraints; The clustering is completed according to the search calculation results, and the associated activation control of the collaborative nodes is performed according to the clustering results.

3. The method according to claim 2, characterized in that The method further comprises: Performing distance impact analysis on the collaborative function to establish a mapping impact factor, wherein the mapping impact factor carries a position direction identifier; Performing normalized calculation of the collaborative function and the collaborative position using the mapping influence factor; The search calculation is completed based on the normalized calculation result.

4. The method according to claim 2, characterized in that The method further comprises: Determine whether the fitting copy result is called. If called, execute the corresponding collaborative node identification; According to the clustering results, the data interaction sequence of the identification and coordination nodes is determined and a sequence identifier is generated; When the associated collaborative nodes are synchronously activated, data interaction is completed according to the sequence identifier.

5. The method according to claim 2, characterized in that The method further comprises: The computational effort is evaluated based on the clustering results, and the number of processing centers is distributed based on the evaluation results; According to the quantity distribution results, the processing centers are randomly distributed in the clustering results, and a cluster search of the processing centers is performed; When any round of cluster search is completed, the processing center is adaptively updated within the cluster results; The data of the collaborative nodes are preprocessed according to the clustering results of the processing center, and the data preprocessing results are sent to the corresponding regional system.

6. The method according to claim 1, characterized in that The method further comprises: When an early warning is generated in any area, an early warning interval is allocated based on the early warning value, and the associated area and the early warning area are activated as a trigger state; The network parameters of the early warning network are reconstructed in the trigger state, and continuous early warning monitoring of the region is performed based on the reconstruction result.

7. The method according to claim 1, characterized in that The method further comprises: Evaluate the stability of data interaction on the API interface and weight the stability evaluation results according to the importance of the data; Filter the weighted calculation results according to the comprehensive evaluation threshold, establish a backup communication network, and configure the conversion trigger threshold; When the conversion trigger threshold is activated, the corresponding API interface is converted to the backup communication network to complete the data interaction.

8. The smart park management system based on multi-system integration is characterized by: A method for managing a smart park based on multi-system integration according to any one of claims 1 to 7, the system comprising: A park feature acquisition module, which is used to establish digital interaction with the smart park, and read park data through the digital interaction to identify and obtain park features; A park area set acquisition module, the park area set acquisition module is used to use the park characteristics as segmentation data, perform regional segmentation on the smart park, and generate a park area set with a regional attention mark, the regional attention mark indicates the importance of the park area, the higher the regional attention, the higher the corresponding park area importance; A system hidden layer link establishment module, the system hidden layer link establishment module is used to perform regional system identification on the park area set, and send the regional system identification result and the regional attention identifier to the data analysis association network to establish a system hidden layer link; A collaborative node configuration module, the collaborative node configuration module is used to configure the API interface according to the hidden layer link of the system, perform data collaboration of sensors in the park area set based on the API interface, and configure collaborative nodes, wherein the collaborative nodes have collaborative functions and collaborative location identifiers; A collaborative data acquisition module, wherein when an activation condition is triggered by any regional system, the collaborative data acquisition module is used to activate the associated collaborative nodes according to the system hidden layer links of the corresponding regional system to acquire collaborative data; A regional early warning management module is used to perform regional early warning management through the collaborative data and the monitoring data of the corresponding regional system.

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