Intelligent park control method and device based on digital twinning, equipment and storage medium

Abstract

The application discloses a digital-twin-based intelligent park control method and device, equipment and a storage medium, and belongs to the technical field of intelligent parks. The application obtains park data, planning data and a preset model of a target park, generates a park digital model of the target park according to the park data and the preset model, the park data comprises running data and building design data, generates a plurality of management and control strategies of the target park according to the planning data and the park digital model, and determines a target management and control strategy executed by the target park according to a judgment index in the plurality of management and control strategies, so that the management effect of the park is improved.

Description

Technical Field

[0001] This invention relates to the field of smart parks, and more particularly to a smart park control method, apparatus, equipment, and storage medium based on digital twins. Background Technology

[0002] With the development of digitalization, park management systems are now used in both commercial and industrial parks to manage traffic and the operation of various facilities within the park. Currently, park development is often phased, typically including both operational and development areas. Although development areas are often fenced off, they still require the cooperation of operational areas to meet the needs of traffic, water, electricity, and employee living conditions during the development process. Various situations can also affect operational areas. In parks with both operational and development areas, common park management systems sometimes fail to implement the system effectively, resulting in discrepancies between the actual management and control and the actual implementation, leading to poor park management results.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a digital twin-based smart park control method, apparatus, device, and storage medium, aiming to improve the management efficiency of the park. To achieve the above objective, this invention provides a digital twin-based smart park control method, which includes the following steps:

[0005] Acquire park data, planning data, and preset models of the target park, and generate a digital model of the target park based on the park data and the preset models. The park data includes: operation data and architectural design data.

[0006] Multiple management and control strategies for the target park are generated based on the planning data and the park's digital model;

[0007] Based on evaluation indicators, the target control strategy to be implemented in the target park is determined from among multiple control strategies.

[0008] Optionally, the planning data includes: construction time and building data, and the step of generating multiple management and control strategies for the target park based on the planning data and the park digital model includes:

[0009] Based on the construction time, the building data, and the park digital model, determine the prediction model data corresponding to each moment in the construction time;

[0010] Multiple control strategies are determined based on the prediction model data corresponding to each time point.

[0011] Optionally, the predictive model data includes: transportation demand data, facility demand data, human resource demand data, and building external impact data. The step of determining multiple control strategies based on the predictive model data corresponding to each time point includes:

[0012] Multiple park traffic control strategies are generated based on the transportation demand data corresponding to each time point;

[0013] Multiple equipment management strategies are generated based on the facility demand data corresponding to each time point;

[0014] Multiple personnel management strategies are generated based on the human resource demand data corresponding to each time point;

[0015] Multiple comprehensive allocation strategies are generated based on the external impact data of the buildings under construction at each time point;

[0016] Multiple control strategies are generated based on multiple park traffic control strategies, multiple equipment control strategies, multiple personnel control strategies, and multiple comprehensive allocation strategies.

[0017] Optionally, the predictive model data includes: transportation demand data, facility demand data, and human resource demand data. The step of determining the predictive model data of the park digital model corresponding to each moment in the construction time based on the construction time, the building data, and the park digital model includes:

[0018] The construction resources required at each moment are determined based on the construction time and the building data. The construction resources include: construction transportation demand data, construction facility demand data, and construction human resource demand data.

[0019] Based on the digital model of the park, determine the basic transportation demand data, infrastructure demand data, and basic human resource demand data for the operating area;

[0020] The transportation demand data is determined based on the basic transportation demand data and the construction transportation demand data; the facility demand data is determined based on the infrastructure demand data and the construction facility demand data; and the human resource demand data is determined based on the basic human resource demand data and the construction human resource demand data.

[0021] Optionally, the evaluation indicators include policy change indicators of the control strategy, and the step of determining the target control strategy to be implemented in the target park based on the evaluation indicators among multiple control strategies includes:

[0022] Obtain the execution control strategy being implemented in the target park at the current moment;

[0023] The execution control strategy is compared with each of the control strategies to obtain the first indicator value corresponding to multiple strategy change indicators;

[0024] The target control strategy is determined based on multiple values ​​of the first indicator.

[0025] Optionally, the step of determining the target control strategy to be implemented in the target park among multiple control strategies based on evaluation indicators includes:

[0026] Extract the feature values ​​corresponding to the preset feature types of each control strategy according to the preset feature types;

[0027] Calculate the second indicator value of the evaluation indicator corresponding to each control strategy based on the aforementioned feature value, and obtain multiple second indicator values;

[0028] The target control strategy is determined based on multiple values ​​of the second indicator.

[0029] Optionally, after the step of generating multiple management and control strategies for the target park based on the planning data and the park digital model, the method further includes:

[0030] Evaluation indicators are determined based on the planning objectives.

[0031] Furthermore, to achieve the above objectives, the present invention also provides a digital twin smart park control device, the digital twin smart park control device comprising:

[0032] The construction module is used to acquire park data, planning data and preset models of the target park, and generate a digital model of the target park based on the park data and the preset model. The park data includes: operation data and architectural design data.

[0033] The analysis module is used to generate multiple management and control strategies for the target park based on the planning data and the park digital model;

[0034] The selection module is used to determine the target control strategy to be implemented in the target park from among multiple control strategies based on evaluation indicators.

[0035] Furthermore, to achieve the above objectives, the present invention also provides a digital twin smart park control device, characterized in that the digital twin smart park control device includes: a memory, a processor, and a digital twin smart park control program stored in the memory and executable on the processor, wherein the digital twin smart park control program is configured to implement the steps of the digital twin smart park control method described in any of the above claims.

[0036] In addition, to achieve the above objectives, the present invention also provides a storage medium, characterized in that the storage medium stores a digital twin smart park control program, which, when executed by a processor, implements the steps of the digital twin smart park control method described above.

[0037] This invention proposes a digital twin-based smart park control method. It generates a digital model of the target park using park data and a preset model, and then generates multiple control strategies for the target park based on the planning data and the digital model. Compared to methods that only control the operating area, this method generates multiple control strategies and determines the target control strategy to be implemented in the target park based on evaluation indicators, thereby improving the effectiveness of target park management. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the structure of a smart park control device based on a digital twin of the hardware operating environment involved in the embodiments of the present invention.

[0039] Figure 2 This is a flowchart illustrating the first embodiment of the digital twin-based smart park control method of the present invention.

[0040] Figure 3 This is a flowchart illustrating the second embodiment of the digital twin-based smart park control method of the present invention.

[0041] Figure 4 This is a flowchart illustrating the third embodiment of the digital twin-based smart park control method of the present invention.

[0042] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0043] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0044] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a digital twin smart park control device for the hardware operating environment involved in the embodiments of the present invention.

[0045] like Figure 1As shown, the digital twin smart park control device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interactive device 1003 may include a display screen and an input unit such as a keyboard. Optionally, the interactive device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0046] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the smart park control equipment of digital twins, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0047] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a digital twin smart park control program.

[0048] exist Figure 1 In the digital twin smart park control device shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with users; the processor 1001 and memory 1005 in the digital twin smart park control device of the present invention can be set in the digital twin smart park control device. The digital twin smart park control device calls the digital twin smart park control program stored in the memory 1005 through the processor 1001 and executes the digital twin smart park control method provided in the embodiment of the present invention.

[0049] This invention provides a digital twin-based smart park control method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a digital twin-based smart park control method according to the present invention.

[0050] In this embodiment, the digital twin-based smart park control method includes:

[0051] Step S1: Obtain the park data, planning data, and preset model of the target park, and generate a digital model of the target park based on the park data and the preset model. The park data includes: operation data and architectural design data.

[0052] The park data here includes: operational data of the areas already in operation within the target park and design data of completed buildings within the park. Preferably, the actual building data of completed buildings within the park can be obtained through on-site inspection, and the design data can be corrected based on the actual building data. The planning data here includes planning data of undeveloped areas within the target park. Optionally, the planning data may also include planning data of areas associated with the target park, such as planning data of residential land adjacent to the target park. It should be noted that the difficulty of obtaining planning data for undeveloped areas within the target park and planning data for adjacent residential land may differ, as may the amount of data. Optionally, data from various design software, such as 3ds Max, Cinema 4D, Industry Foundation Classes, and Rhino 3D, can be imported using the Unreal Engine 5 Datasmith plugin. The preset model includes a three-dimensional model space and a running data model. The three-dimensional model space includes the land space of the target park and preset roads outside the target park. By importing architectural design data and constructing a three-dimensional architectural model of the target park based on the virtualized micropolygon geometry system of Unreal Engine 5, and importing the running data into the running data model, a digital model of the target park is generated.

[0053] Step S2: Generate multiple management and control strategies for the target park based on the planning data and the park digital model;

[0054] The control strategies here have several different dimensions, including: traffic control, equipment control, and personnel control within the park. Traffic control includes restrictions on vehicles entering and exiting the park, such as speed limits, time restrictions, vehicle type restrictions, vehicle quantity restrictions, and no-parking zone settings. In some embodiments, the control strategies specifically include: restricting trucks to travel only within a predetermined area and time limit; allowing buses to enter the park directly; and limiting the number of trucks within the target park. Equipment control includes the maintenance cycle of public equipment and the intensity of equipment operation monitoring. In some embodiments, for example, adjusting the monitoring cycle of the sprinkler system around the construction site, adjusting the monitoring cycle of electrical equipment, and adjusting the cleaning cycle of public restrooms. In some embodiments, personnel control within the park includes requirements for entering the park and requirements for entering office areas. For example, when a construction site is connected to the park and the number of construction workers is below a first threshold, access cards are provided to construction workers, requiring them to use these cards to enter the park. When a building under construction is connected to the park, and the number of construction workers in the building exceeds the second threshold, the construction workers can directly enter the park. The above example is a specific implementation of the control strategy. Specifically, multiple control strategies can be generated.

[0055] Step S3: Determine the target control strategy to be implemented in the target park from among multiple control strategies based on the evaluation indicators.

[0056] The evaluation indicators here include: control difficulty indicators, control cost indicators, and strategy change indicators. It should be noted that different control strategies correspond to different values ​​for these indicators. For example, regarding personnel control, the control strategy of allowing construction workers to directly enter the park when the number of workers is below a certain threshold, versus requiring workers to use access cards, and versus requiring workers to use access cards to enter the park, differ in both control difficulty and control cost indicators. Furthermore, the value of the strategy change indicator varies depending on the control strategy currently in effect: allowing workers to directly enter the park versus requiring workers to use access cards.

[0057] In this embodiment, a digital model of the target park is generated using the park data and the preset model. Based on the planning data and the digital model, multiple control strategies for the target park are generated. Compared to methods that only control the operating area, multiple control strategies can be generated. The target control strategy to be implemented in the target park is determined from among the multiple control strategies according to the evaluation indicators, thereby improving the effectiveness of target park management.

[0058] Furthermore, based on the first embodiment, a second embodiment of the digital twin-based smart park control method of the present invention is proposed. In this embodiment, reference is made to... Figure 3 The planning data includes: construction time and building data. The step of generating multiple management and control strategies for the target park based on the planning data and the park digital model includes:

[0059] Step S21: Determine the prediction model data corresponding to each moment in the construction time based on the construction time, the building data, and the park digital model;

[0060] It should be noted that resource requirements vary at different stages of construction. In this embodiment, the construction time is generally obtained from the construction schedule plan. The construction stages in a typical construction schedule plan include: foundation construction, main structure construction, decoration, mechanical and electrical equipment installation, and final acceptance. Based on the construction schedule plan, the construction arrangements and resource requirements for each construction moment within the construction period are determined. Different stages will have different impacts on the target park. The foundation construction stage requires significant manpower and material resources due to earthwork excavation, pile foundation construction, and other steps, potentially obstructing traffic flow within the target park. Therefore, it is necessary to determine the predictive model data using the park's digital model. Different 3D building construction models are rendered at different times based on the building data. Optionally, specifically, the execution method involves extracting construction progress data from the construction schedule plan, normalizing the construction progress data, and then adjusting the data of the park's digital model at each moment based on the construction progress data, thereby determining the predictive model data corresponding to each moment in the construction time.

[0061] Step S22: Determine multiple control strategies based on the prediction model data corresponding to each time point.

[0062] In this embodiment, the control strategy can be a preset control strategy associated with the predictive model data. In other embodiments, multiple control strategies can be determined by the administrator based on the predictive model data corresponding to each time point. Alternatively, some control strategies can be determined by reading the preset control strategy associated with the predictive model data, while other control strategies can be determined by the administrator based on the predictive model data corresponding to each time point.

[0063] In this embodiment, the prediction model data corresponding to each moment in the construction time is determined by the construction time, the building data, and the digital model of the park. Multiple control strategies are determined based on the prediction model data corresponding to each moment. This effectively determines a variety of different control strategies based on the construction process, improves the ability of the control strategies to adapt to the actual situation of the target park, and effectively avoids the situation where the implementation of subsequent control strategies does not match the actual situation of the park.

[0064] Furthermore, the predictive model data includes: transportation demand data, facility demand data, human resource demand data, and building external impact data. The step of generating multiple control strategies based on the predictive model data corresponding to each time point includes:

[0065] Multiple park traffic control strategies are generated based on the transportation demand data corresponding to each time point;

[0066] Multiple equipment management strategies are generated based on the facility demand data corresponding to each time point;

[0067] Multiple personnel management strategies are generated based on the human resource demand data corresponding to each time point;

[0068] Multiple comprehensive allocation strategies are generated based on the external impact data of the buildings under construction at each time point;

[0069] Multiple control strategies are generated based on multiple park traffic control strategies, multiple equipment control strategies, multiple personnel control strategies, and multiple comprehensive allocation strategies.

[0070] It should be noted that the transportation demand data, facility demand data, and human resource demand data here all refer to the demand data of the target park. For example, human resource demand data includes the number of security guards, cleaning staff, construction workers, and equipment maintenance personnel needed within the park; transportation demand data can include data on vehicles entering the park and traffic congestion within the park; the comprehensive allocation strategy here refers to the functional adjustment of the areas affected by buildings under construction, such as adjusting the office areas of affected companies within the target park so that they can work in areas with less impact, for example, relocating common offices.

[0071] Furthermore, the predictive model data includes: transportation demand data, facility demand data, and human resource demand data. The step of determining the predictive model data of the park's digital model for each moment in the construction time, based on the construction time, the building data, and the park's digital model, includes:

[0072] The construction resources required at each moment are determined based on the construction time and the building data. The construction resources include: construction transportation demand data, construction facility demand data, and construction human resource demand data.

[0073] Based on the digital model of the park, determine the basic transportation demand data, infrastructure demand data, and basic human resource demand data for the operating area;

[0074] The transportation demand data is determined based on the basic transportation demand data and the construction transportation demand data; the facility demand data is determined based on the infrastructure demand data and the construction facility demand data; and the human resource demand data is determined based on the basic human resource demand data and the construction human resource demand data.

[0075] In this embodiment, while determining the construction transportation demand data, construction facility demand data, and construction human resource demand data of the construction resources of the building under construction, the basic transportation demand data, infrastructure demand data, and basic human resource demand data are also determined. This enables the accurate calculation of the transportation demand data, facility demand data, and human resource demand data, thereby improving the accuracy of subsequent determination of control strategies.

[0076] Furthermore, based on the first or second embodiment, a third embodiment of the digital twin-based smart park control method of the present invention is proposed. In this embodiment, reference is made to... Figure 4 The evaluation indicators include policy change indicators of the control strategy. The step of determining the target control strategy to be implemented in the target park based on the evaluation indicators among multiple control strategies includes:

[0077] Step S31: Obtain the execution control strategy being implemented in the target park at the current moment;

[0078] Step S32: Compare the execution control strategy with each of the control strategies to obtain the first indicator value corresponding to multiple strategy change indicators;

[0079] Step S33: Determine the target control strategy based on multiple values ​​of the first indicator.

[0080] Compared to other types of evaluation indicators, the applicant recognizes that in the process of park management, if the control strategy changes significantly or frequently, it will significantly affect the enterprises and personnel in the park, leading to dissatisfaction. Therefore, it is necessary to obtain the current execution control strategy and compare it with each of the aforementioned control strategies to determine the degree of change in the control strategy, thereby obtaining the first indicator value. The target control strategy is determined based on the first indicator value. In other embodiments, the target control strategy can also be determined based on the first indicator value and other indicators besides the strategy change indicator in the evaluation indicators.

[0081] In this embodiment, the execution control strategy of the target park at the current moment is obtained, and the execution control strategy is compared with each of the control strategies to obtain the first indicator values ​​corresponding to multiple strategy change indicators. The target control strategy is determined based on the multiple first indicator values, which improves the management effect of the target park while avoiding significant changes in the control strategy.

[0082] Furthermore, based on any of the above embodiments, a fourth embodiment of the digital twin-based smart park control method of the present invention is proposed. In this embodiment, the step of determining the target control strategy to be executed by the target park among multiple control strategies based on evaluation indicators includes:

[0083] Extract the feature values ​​corresponding to the preset feature types of each control strategy according to the preset feature types;

[0084] Calculate the second indicator value of the evaluation indicator corresponding to each control strategy based on the aforementioned feature value, and obtain multiple second indicator values;

[0085] The target control strategy is determined based on multiple values ​​of the second indicator.

[0086] Specifically, in the third embodiment, the evaluation index here may include the strategy change index, and the value of the second index here includes: the value of the first index.

[0087] The comprehensive indicator data corresponding to each control strategy is obtained by weighting all the values ​​of the second indicator for each control strategy; the target control strategy is determined based on the comprehensive indicator data.

[0088] In this embodiment, feature values ​​corresponding to the preset feature types of each control strategy are extracted by preset feature types. Second indicator values ​​of the evaluation indicators corresponding to each control strategy are calculated based on the feature values ​​to obtain multiple second indicator values. The target control strategy is determined based on the multiple second indicator values, thereby effectively improving the management effect of the obtained target control strategy.

[0089] Furthermore, after the step of generating multiple management and control strategies for the target park based on the planning data and the park digital model, the method further includes:

[0090] Evaluation indicators are determined based on the planning objectives.

[0091] Evaluation indicators are determined based on different planning objectives. In other embodiments, evaluation indicators may also be set by managers.

[0092] Furthermore, this invention also proposes a digital twin-based smart park control device, which includes:

[0093] The construction module is used to acquire park data, planning data and preset models of the target park, and generate a digital model of the target park based on the park data and the preset model. The park data includes: operation data and architectural design data.

[0094] The analysis module is used to generate multiple management and control strategies for the target park based on the planning data and the park digital model;

[0095] The selection module is used to determine the target control strategy to be implemented in the target park from among multiple control strategies based on evaluation indicators.

[0096] Furthermore, this invention also proposes a digital twin smart park control device, which includes: a memory, a processor, and a digital twin smart park control program stored in the memory and executable on the processor. The digital twin smart park control program is configured to implement the steps of any of the above-described embodiments of the digital twin smart park control method.

[0097] Furthermore, this invention also proposes a storage medium storing a digital twin smart park control program, which, when executed by a processor, implements the steps of any of the above-described embodiments of the digital twin smart park control method.

[0098] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0099] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0101] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A digital twin-based smart park control method, characterized in that, The digital twin-based smart park control method includes the following steps: Acquire park data, planning data, and preset models of the target park, and generate a digital model of the target park based on the park data and the preset models. The park data includes: operation data and architectural design data. Multiple management and control strategies for the target park are generated based on the planning data and the park's digital model; Based on evaluation indicators, determine the target control strategy to be implemented in the target park from among multiple control strategies; The planning data includes: construction time and building data. The step of generating multiple management and control strategies for the target park based on the planning data and the park digital model includes: Based on the construction time, the building data, and the park digital model, determine the prediction model data corresponding to each moment in the construction time; Multiple control strategies are determined based on the prediction model data corresponding to each time point; The predictive model data includes: transportation demand data, facility demand data, human resource demand data, and building external impact data. The step of determining multiple control strategies based on the predictive model data corresponding to each time point includes: Multiple park traffic control strategies are generated based on the transportation demand data corresponding to each time point; Multiple equipment management strategies are generated based on the facility demand data corresponding to each time point; Multiple personnel management strategies are generated based on the human resource demand data corresponding to each time point; Multiple comprehensive allocation strategies are generated based on the external impact data of the buildings under construction at each time point; Multiple control strategies are generated based on multiple park traffic control strategies, multiple equipment control strategies, multiple personnel control strategies, and multiple comprehensive allocation strategies; The predictive model data includes: transportation demand data, facility demand data, and human resource demand data. The step of determining the predictive model data of the park digital model corresponding to each moment in the construction time based on the construction time, the building data, and the park digital model includes: Based on the construction time and the building data, the construction resources required at each moment are determined. The construction resources include: construction transportation demand data, construction facility demand data, and construction human resource demand data. Based on the digital model of the park, determine the basic transportation demand data, infrastructure demand data, and basic human resource demand data for the operating area; The transportation demand data is determined based on the basic transportation demand data and the construction transportation demand data; the facility demand data is determined based on the infrastructure demand data and the construction facility demand data; and the human resource demand data is determined based on the basic human resource demand data and the construction human resource demand data. The evaluation indicators include policy change indicators of the control strategy. The step of determining the target control strategy to be implemented in the target park based on the evaluation indicators among multiple control strategies includes: Obtain the execution control strategy being implemented in the target park at the current moment; The execution control strategy is compared with each of the control strategies to obtain the first indicator value corresponding to multiple strategy change indicators; The target control strategy is determined based on multiple values ​​of the first indicator.

2. The smart park control method based on digital twins as described in claim 1, characterized in that, Following the step of generating multiple management and control strategies for the target park based on the planning data and the park digital model, the method further includes: Evaluation indicators are determined based on the stated planning objectives.

3. A digital twin-based smart park control device, characterized in that, The digital twin smart park control device is used to implement the digital twin smart park control method as described in any one of claims 1 to 2, and the digital twin smart park control device includes: The construction module is used to acquire park data, planning data and preset models of the target park, and generate a digital model of the target park based on the park data and the preset model. The park data includes: operation data and architectural design data. The analysis module is used to generate multiple management and control strategies for the target park based on the planning data and the park digital model; The selection module is used to determine the target control strategy to be implemented in the target park from among multiple control strategies based on evaluation indicators.

4. A digital twin-based smart park control device, characterized in that, The digital twin smart park control device includes: a memory, a processor, and a digital twin smart park control program stored in the memory and executable on the processor, wherein the digital twin smart park control program is configured to implement the steps of the digital twin smart park control method as described in any one of claims 1 to 2.

5. A storage medium, characterized in that, The storage medium stores a digital twin smart park control program, which, when executed by a processor, implements the steps of the digital twin smart park control method as described in any one of claims 1 to 2.

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