Digital twin smart park control method, apparatus and device, and storage medium

Abstract

The invention discloses a digital twin smart park control method, device and equipment and a storage medium, and belongs to the technical field of smart parks. According to the method, park data, planning data and a preset model of a target park are obtained, a park digital model of the target park is generated according to the park data and the preset model, and the park data comprise operation data and building design data; generating a plurality of management and control strategies of the target park according to the planning data and the park digital model; and determining a target management and control strategy executed by the target park in the plurality of management and control strategies according to the evaluation index. The management effect of the park is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart parks, and in particular to a digital twin smart park control method, device, equipment and storage medium. Background Art

[0002] With the development of digitalization, park management systems are now used in commercial parks and industrial parks to manage traffic within the park and the operation of various facilities. At present, the development of parks is often divided into stages. A park often includes: areas that are already in operation and areas under development. Although areas under development are often shielded by fences, areas under development often need to be coordinated with areas that are already in operation to meet the needs of traffic, water, electricity, employee life, etc. during the development process. There will also be various situations that affect areas that are already in operation. In common park management systems, in parks with both areas that are already in operation and areas under development, the management and control of the park will be different from the actual implementation, resulting in poor park management results.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide a digital twin smart park control method, device, equipment and storage medium, aiming to improve the management effect of the park. To achieve the above purpose, the present invention provides a digital twin smart park control method, and the digital twin smart park control method comprises the following steps:

[0005] Acquire park data, planning data and a preset model of a target park, and generate a park digital model of the target park according to the park data and the preset model, wherein the park data includes: operation data and architectural design data;

[0006] Generating a plurality of management and control strategies for the target park according to the planning data and the park digital model;

[0007] A target control strategy to be executed by the target park is determined from among multiple control strategies according to the evaluation indicators.

[0008] Optionally, the planning data includes: construction time and building data, and the step of generating a plurality of management and control strategies of the target park according to the planning data and the park digital model includes:

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

[0010] A plurality of control strategies are determined according to the prediction model data corresponding to each moment.

[0011] Optionally, the prediction model data includes: transportation demand data, facility demand data, human resource demand data and building external impact data, and the step of determining the plurality of control strategies according to the prediction model data corresponding to each moment includes:

[0012] Generate multiple park traffic control strategies according to the transportation demand data corresponding to each moment;

[0013] Generate multiple equipment control strategies according to the facility demand data corresponding to each moment;

[0014] Generate multiple personnel management and control strategies according to the human resource demand data corresponding to each moment;

[0015] Generate multiple comprehensive deployment strategies based on the external impact data of the buildings under construction at each moment;

[0016] A plurality of the control strategies are generated according to a plurality of the park traffic control strategies, a plurality of the equipment control strategies, a plurality of the personnel control strategies and a plurality of the comprehensive deployment strategies.

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

[0018] Determine the construction resources required at each moment according to the construction time and the construction data, the construction resources including: construction transportation demand data, construction facility demand data and construction human resource demand data;

[0019] Determine basic transportation demand data, infrastructure demand data and basic human resource demand data of the operation area according to the digital model of the park;

[0020] The transportation demand data is determined according to the basic transportation demand data and the construction transportation demand data, the facility demand data is determined according to the infrastructure demand data and the construction facility demand data, and the human resource demand data is determined according to the basic human resource demand data and the construction human resource demand data.

[0021] Optionally, the evaluation index includes a policy change index of a control policy, and the step of determining a target control policy to be executed by the target park from among multiple control policies according to the evaluation index includes:

[0022] Get the execution control strategy executed by the target park at the current moment;

[0023] Compare the execution control strategy with each of the control strategies respectively to obtain first indicator values ​​corresponding to multiple strategy change indicators;

[0024] Determine a target control strategy based on multiple first indicator values.

[0025] Optionally, the step of determining the target control strategy to be executed by the target park from among multiple control strategies according to the evaluation index includes:

[0026] Extracting a feature value corresponding to a preset feature type of each of the control strategies according to the preset feature type;

[0027] Calculate the second indicator value of each evaluation indicator corresponding to the control strategy according to the characteristic value to obtain a plurality of the second indicator values;

[0028] Determine a target control strategy based on multiple values ​​of the second indicator.

[0029] Optionally, after the step of generating a plurality of management and control strategies of the target park according to the planning data and the park digital model, the step further includes:

[0030] The evaluation indicators are determined according to the planning objectives.

[0031] In addition, to achieve the above-mentioned purpose, the present invention also provides a digital twin smart park control device, the digital twin smart park control device comprising:

[0032] A construction module, used to obtain park data, planning data and a preset model of a target park, and generate a park digital model of the target park according to the park data and the preset model, wherein the park data includes: operation data and architectural design data;

[0033] An analysis module, used for generating a plurality of management and control strategies of the target park according to the planning data and the park digital model;

[0034] A selection module is used to determine a target control strategy to be executed by the target park from among multiple control strategies according to evaluation indicators.

[0035] In addition, to achieve the above-mentioned purpose, 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, and the digital twin smart park control program is configured to implement the steps of the digital twin smart park control method described in any one of the above items.

[0036] In addition, to achieve the above-mentioned purpose, the present invention also provides a storage medium, characterized in that a digital twin smart park control program is stored on the storage medium, and when the digital twin smart park control program is executed by the processor, the steps of the digital twin smart park control method described in any one of the above items are implemented.

[0037] The present invention proposes a digital twin smart park control method, which generates a park digital model of the target park through the park data and the preset model, and generates multiple management and control strategies of the target park based on the planning data and the park digital model. Compared with the method of only controlling the operating area, multiple management and control strategies can be generated, and the target management and control strategy to be executed by the target park can be determined from the multiple management and control strategies according to the evaluation indicators, thereby improving the management effect of the target park. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a structural schematic diagram of a smart park control device of a digital twin of a hardware operating environment involved in an embodiment of the present invention;

[0039] Figure 2 It is a flow chart of the first embodiment of the digital twin smart park control method of the present invention;

[0040] Figure 3 It is a flow chart of the second embodiment of the digital twin smart park control method of the present invention;

[0041] Figure 4 It is a flow chart of the third embodiment of the digital twin smart park control method of the present invention;

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

[0043] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0044] Reference Figure 1 , Figure 1 A schematic diagram of the structure of a smart park control device that is a digital twin of the hardware operating environment involved in an embodiment 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. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The interactive device 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional interactive device 1003 may also be connected to the communication bus through a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. 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 in does not constitute a limitation on the smart park control device of the digital twin, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0047] like Figure 1 As shown, the memory 1005 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 the 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, and 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 by the embodiment of the present invention.

[0049] The embodiment of the present invention provides a digital twin smart park control method, referring to Figure 2 , Figure 2 This is a flow chart of the first embodiment of a digital twin smart park control method of the present invention.

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

[0051] Step S1, acquiring park data, planning data and a preset model of a target park, and generating a park digital model of the target park according to the park data and the preset model, wherein the park data includes: operation data and architectural design data;

[0052] The park data here include: the operation data of the area where the target park has been put into operation and the design data of the buildings that have been completed in the park. Preferably, the actual construction data of the buildings that have been completed in the park can be detected on site, and the design data can be corrected according to the actual construction data. The planning data here include the planning data of the unfinished development area in the target park. Optionally, the planning data can also include the planning data of the area associated with the target park, for example: the planning data of the residential land adjacent to the target park. It should be noted that the difficulty of obtaining the planning data of the unfinished development area in the target park and the planning data of the residential land adjacent to the target park may not be the same, and there is also a difference in the amount of data. Optionally, based on the Unreal 5 Engine Datasmith plug-in, data from a variety of different design software are imported, such as: 3ds Max, Cinema 4D, Industry Foundation Classes, Rhino 3D, etc. The preset model includes a three-dimensional model space and an operation data model. The three-dimensional model space includes the plot space of the target park and the preset roads outside the target park. The architectural design data is imported, and the three-dimensional architectural model of the target park is constructed according to the virtualized micro-polygon geometry system of Unreal 5. The operation data is imported into the operation data model, thereby generating a digital model of the target park.

[0053] Step S2, generating multiple management and control strategies for the target park according to the planning data and the park digital model;

[0054] The control strategy here specifically has multiple different dimensions, including: the traffic control dimension of the park, the control dimension of equipment, the control dimension of personnel in the park, etc. The traffic control dimension here includes: restrictions on vehicles entering and leaving the park, including: speed limit, restricted travel time, restricted vehicle type, limited number of vehicles in the park, and no-parking area settings. In some embodiments, the control strategy specifically includes: controlling trucks to drive within a limited time and only within a preset traffic area, buses can enter the park directly, and limit the number of trucks in the target park. The control dimension of the equipment here includes: the maintenance cycle of public equipment and the monitoring intensity of equipment operation. In some embodiments, for example: adjusting the monitoring cycle of the enclosure spray system outside the building under construction, adjusting the monitoring cycle of power equipment, and the cleaning cycle of public toilets. In some embodiments, the personnel control dimension in the park includes: requirements for entering the park and requirements for entering the office area, for example: when the building under construction is connected to the park, and the number of construction personnel in the building under construction is less than the first number threshold, the construction personnel are configured with access cards, and the construction personnel are required to use access cards to enter the park. When the building under construction is connected to the park, and the number of construction workers in the building under construction exceeds the second threshold, the construction workers can directly enter the park. The above examples are specific implementation examples of the control strategy. Specifically, multiple control strategies can be generated.

[0055] Step S3: determining the target control strategy to be executed by the target park from among multiple control strategies according to the evaluation index.

[0056] The evaluation indicators here include: control difficulty index, control cost index and strategy change index. It should be noted that for different control strategies, the corresponding control difficulty index value, control cost index value and strategy change index value. For example: for personnel control, when the number of construction personnel is lower than the first number threshold, the control strategy that construction personnel can directly enter the park and the control strategy that configures access control cards for construction personnel and requires construction personnel to use access control cards to enter the park are different in control difficulty index and control cost index. In addition, the control strategy executed at the current moment determines the control strategy that construction personnel can directly enter the park and the control strategy that configures access control cards for construction personnel and requires construction personnel to use access control cards to enter the park. The corresponding strategy change index values ​​are determined respectively.

[0057] In this embodiment, a digital model of the target park is generated through the park data and the preset model, and multiple management and control strategies of the target park are generated based on the planning data and the park digital model. Compared with the method of only controlling the operating area, multiple management and control strategies can be generated, and the target management and control strategy to be executed by the target park can be determined from the multiple management and control strategies based on the evaluation indicators, thereby improving the management effect of the target park.

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

[0059] Step S21, determining the prediction model data corresponding to each moment in the construction time according to the construction time, the building data and the park digital model;

[0060] It should be noted that during the construction of the building under construction, resource requirements at different stages will be different. In this embodiment, the construction time is generally obtained in the construction schedule planning. The construction stages in the general construction schedule include: foundation construction stage, main structure construction stage, decoration and decoration stage, electromechanical equipment installation stage, and completion acceptance stage. According to the construction schedule, the construction arrangements and corresponding required resources corresponding to each construction moment in the construction time period are determined. Different stages will have different impacts on the target park. The foundation construction stage requires a large amount of manpower and material resources because it involves earth excavation, pile foundation construction and other steps. There is a situation where the traffic channel in the target park is occupied, so it is necessary to determine the prediction model data through the digital model of the park. Different three-dimensional models of building construction are rendered at different times according to the building data. Optionally, the execution method is specifically to extract the construction progress data in the construction schedule table, and after normalizing the construction progress data, adjust the data corresponding to the digital model of the park at each time according to the construction progress data, so as to determine the prediction model data corresponding to each time in the construction time.

[0061] Step S22, determining a plurality of control strategies according to the prediction model data corresponding to each moment.

[0062] In this embodiment, the control strategy here can be to read the preset control strategy associated with the prediction model data. In other embodiments, the management personnel can also determine multiple control strategies based on the prediction model data corresponding to each moment. It is also possible that part of the control strategy is determined by reading the preset control strategy associated with the prediction model data, and the other part of the control strategy is determined by the management personnel based on the prediction model data corresponding to each moment.

[0063] In this embodiment, the prediction model data corresponding to each moment in the construction time is determined through the construction time, the building data and the digital model of the park, and multiple control strategies are determined according to the prediction model data corresponding to each moment, so that a variety of different control strategies can be effectively determined based on the construction process, thereby improving the ability of the control strategy to adapt to the actual situation of the target park, and effectively avoiding the situation where the implementation of subsequent control strategies does not match the actual situation of the park.

[0064] Furthermore, the prediction model data includes: transportation demand data, facility demand data, human resource demand data and building external impact data, and the step of generating a plurality of control strategies according to the prediction model data corresponding to each moment includes:

[0065] Generate multiple park traffic control strategies according to the transportation demand data corresponding to each moment;

[0066] Generate multiple equipment control strategies according to the facility demand data corresponding to each moment;

[0067] Generate multiple personnel management and control strategies according to the human resource demand data corresponding to each moment;

[0068] Generate multiple comprehensive deployment strategies based on the external impact data of the buildings under construction at each moment;

[0069] A plurality of the control strategies are generated according to a plurality of the park traffic control strategies, a plurality of the equipment control strategies, a plurality of the personnel control strategies and a plurality of the comprehensive deployment 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, the human resource demand data includes the demand number of security guards, cleaning staff, construction staff, and equipment maintenance staff in the park. The transportation demand data may include data on vehicles entering the park and traffic congestion data in the park. The comprehensive deployment strategy here refers to functional adjustments to areas affected by buildings under construction, for example, adjusting the office areas of affected companies in the target park to work in areas with less impact, for example, adjusting the location of public offices.

[0071] Furthermore, the prediction model data includes: transportation demand data, facility demand data and human resource demand data, and the step of determining the prediction model data of the digital model of the park corresponding to each moment in the construction time according to the construction time, the building data and the digital model of the park includes:

[0072] Determine the construction resources required at each moment according to the construction time and the construction data, the construction resources including: construction transportation demand data, construction facility demand data and construction human resource demand data;

[0073] Determine basic transportation demand data, infrastructure demand data and basic human resource demand data of the operation area according to the digital model of the park;

[0074] The transportation demand data is determined according to the basic transportation demand data and the construction transportation demand data, the facility demand data is determined according to the infrastructure demand data and the construction facility demand data, and the human resource demand data is determined according to 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 in 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, so that the transportation demand data, facility demand data and human resource demand data can be accurately calculated, thereby improving the accuracy of the subsequent determination of the management and control strategy.

[0076] Further, based on the first embodiment or the second embodiment, a third embodiment of the digital twin smart park control method of the present invention is proposed. In this embodiment, referring to Figure 4 The evaluation index includes a strategy change index of the control strategy, and the step of determining the target control strategy to be executed by the target park from multiple control strategies according to the evaluation index includes:

[0077] Step S31, obtaining the execution control strategy executed by the target park at the current moment;

[0078] Step S32, comparing the execution control strategy with each of the control strategies respectively, to obtain first indicator values ​​corresponding to a plurality of strategy change indicators;

[0079] Step S33, determining a target control strategy according to multiple first indicator values.

[0080] Compared with other types of evaluation indicators, the applicant realizes that in the process of park management, in the actual management process, if the control strategy changes greatly or changes frequently, it will significantly affect the enterprises and personnel in the park, thereby leading to dissatisfaction. Therefore, it is necessary to obtain the execution control strategy at the current moment, and compare it with each of the control strategies to determine the degree of change of the control strategy, and then obtain the first indicator value, and determine the target control strategy 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 other than the strategy change indicator in the evaluation indicators.

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

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

[0083] Extracting a feature value corresponding to a preset feature type of each of the control strategies according to the preset feature type;

[0084] Calculate the second indicator value of each evaluation indicator corresponding to the control strategy according to the characteristic value to obtain a plurality of the second indicator values;

[0085] Determine a target control strategy based on multiple values ​​of the second indicator.

[0086] Specifically, based on the third embodiment, the evaluation index here may include a strategy change index, and the second index value here includes: the first index value;

[0087] A weighted calculation is performed based on all the second indicator values ​​corresponding to each of the control strategies to obtain comprehensive indicator data corresponding to each of the control strategies; and a target control strategy is determined based on the comprehensive indicator data.

[0088] In this embodiment, the characteristic value corresponding to the preset characteristic type of each of the control strategies is extracted by the preset characteristic type, and the second indicator value of the evaluation indicator corresponding to each of the control strategies is calculated according to the characteristic value to obtain multiple second indicator values, and the target control strategy is determined according to the multiple second indicator values, thereby effectively improving the management effect of the obtained target control strategy.

[0089] Furthermore, after the step of generating a plurality of management and control strategies of the target park according to the planning data and the park digital model, the step further includes:

[0090] The evaluation indicators are determined according to the planning objectives.

[0091] The evaluation index is determined according to different planning objectives. In other embodiments, the evaluation index may also be set by management personnel.

[0092] In addition, an embodiment of the present invention further provides a digital twin smart park control device, the digital twin smart park control device comprising:

[0093] A construction module, used to obtain park data, planning data and a preset model of a target park, and generate a park digital model of the target park according to the park data and the preset model, wherein the park data includes: operation data and architectural design data;

[0094] An analysis module, used for generating a plurality of management and control strategies of the target park according to the planning data and the digital model of the park;

[0095] A selection module is used to determine a target control strategy to be executed by the target park from among multiple control strategies according to evaluation indicators.

[0096] In addition, an embodiment of the present 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, and the digital twin smart park control program is configured to implement the steps of an embodiment of the digital twin smart park control method described in any one of the above items.

[0097] In addition, an embodiment of the present invention also proposes a storage medium, on which a digital twin smart park control program is stored. When the digital twin smart park control program is executed by a processor, the steps of the embodiment of the digital twin smart park control method described in any one of the above items are implemented.

[0098] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0099] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

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

[0101] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A digital twin smart park control method, characterized in that: The digital twin smart park control method comprises the following steps: Acquire park data, planning data and a preset model of a target park, and generate a park digital model of the target park according to the park data and the preset model, wherein the park data includes: operation data and architectural design data; Generating a plurality of management and control strategies for the target park according to the planning data and the park digital model; A target control strategy to be executed by the target park is determined from among multiple control strategies according to the evaluation indicators.

2. The digital twin smart park control method according to claim 1, characterized in that: The planning data includes: construction time and building data, and the step of generating multiple management and control strategies of the target park according to the planning data and the park digital model includes: Determine the prediction model data corresponding to each moment in the construction time according to the construction time, the building data and the park digital model; A plurality of control strategies are determined according to the prediction model data corresponding to each moment.

3. The digital twin smart park control method according to claim 2, characterized in that: The prediction model data includes: transportation demand data, facility demand data, human resource demand data and building external impact data. The step of determining a plurality of control strategies according to the prediction model data corresponding to each moment includes: Generate multiple park traffic control strategies according to the transportation demand data corresponding to each moment; Generate multiple equipment control strategies according to the facility demand data corresponding to each moment; Generate multiple personnel management and control strategies according to the human resource demand data corresponding to each moment; Generate multiple comprehensive deployment strategies based on the external impact data of the buildings under construction at each moment; A plurality of the control strategies are generated according to a plurality of the park traffic control strategies, a plurality of the equipment control strategies, a plurality of the personnel control strategies and a plurality of the comprehensive deployment strategies.

4. The digital twin smart park control method according to claim 2, characterized in that: The prediction model data includes: transportation demand data, facility demand data and human resource demand data. The step of determining the prediction model data of the digital model of the park corresponding to each moment in the construction time according to the construction time, the building data and the digital model of the park includes: Determine the construction resources required at each moment according to the construction time and the construction data, the construction resources including: construction transportation demand data, construction facility demand data and construction human resource demand data; Determine basic transportation demand data, infrastructure demand data and basic human resource demand data of the operation area according to the digital model of the park; The transportation demand data is determined according to the basic transportation demand data and the construction transportation demand data, the facility demand data is determined according to the infrastructure demand data and the construction facility demand data, and the human resource demand data is determined according to the basic human resource demand data and the construction human resource demand data.

5. The digital twin smart park control method according to claim 1, characterized in that: The evaluation index includes a strategy change index of the management and control strategy, and the step of determining the target management and control strategy to be executed by the target park from among multiple management and control strategies according to the evaluation index includes: Get the execution control strategy executed by the target park at the current moment; Compare the execution control strategy with each of the control strategies respectively to obtain first indicator values ​​corresponding to multiple strategy change indicators; Determine a target control strategy based on multiple first indicator values.

6. The digital twin smart park control method according to claim 1, characterized in that: The step of determining the target control strategy to be executed by the target park from among the multiple control strategies according to the evaluation index comprises: Extracting a feature value corresponding to a preset feature type of each of the control strategies according to the preset feature type; Calculate the second indicator value of each evaluation indicator corresponding to the control strategy according to the characteristic value to obtain a plurality of the second indicator values; Determine a target control strategy based on multiple values ​​of the second indicator.

7. The digital twin smart park control method according to any one of claims 1 to 6, characterized in that: After the step of generating a plurality of control strategies for the target park according to the planning data and the park digital model, the method further includes: The evaluation indicators are determined according to the planning objectives.

8. A digital twin smart park control device, characterized in that: The digital twin smart park control device includes: A construction module, used to obtain park data, planning data and a preset model of a target park, and generate a park digital model of the target park according to the park data and the preset model, wherein the park data includes: operation data and architectural design data; An analysis module, used for generating a plurality of management and control strategies of the target park according to the planning data and the park digital model; A selection module is used to determine a target control strategy to be executed by the target park from among multiple control strategies according to evaluation indicators.

9. 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. 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 7.

10. A storage medium, characterized in that: The storage medium stores a digital twin smart park control program, and when the digital twin smart park control program is executed by the processor, the steps of the digital twin smart park control method as described in any one of claims 1 to 7 are implemented.

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