Smart parking lot management method and cloud platform based on information sharing

Abstract

The present invention relates to the field of smart parking lot management, and in particular to a smart parking lot management method and cloud platform based on information sharing, including collecting parking lot related data information through sensors and detection equipment installed in the parking lot, integrating and processing, uploading to the information sharing platform, and establishing a parking space demand prediction neural network model. Vehicles entering the parking lot are classified according to type, and the allocation ratio of various types of parking spaces in the parking lot is determined according to the parking lot demand prediction, and the parking space reservation policy is adjusted. The dynamic parking lot utilization rate is determined according to the parking space demand prediction; the parking lot management optimization index is determined according to the allocation ratio of various types of parking spaces in the parking lot and the parking lot utilization rate; and the parking lot energy consumption and safety inspection frequency are adjusted. According to the adjusted parking lot energy consumption and safety inspection frequency, a parking lot operation supervision model is established to monitor and manage the parking lot operation.

Description

Technical Field

[0001] The present invention relates to the field of smart parking lot management, and in particular to a smart parking lot management method and a cloud platform based on information sharing. Background Art

[0002] With the development of cities and the continuous increase in the number of cars, parking has become an increasingly serious problem. At the same time, it is even more difficult to achieve efficient parking and maximize resources. In addition, there are many disadvantages in the traditional parking lot management method, such as: opaque parking space information, inefficient vehicle search for parking spaces, and uneven use of parking lot resources.

[0003] Although with the emergence of new generation technologies such as artificial intelligence, the traditional parking lot management method has been optimized to a certain extent, the existing technology still has the following problems: the parking space demand is not accurately grasped, resulting in the inability to determine the allocation ratio of various types of parking spaces in the parking lot in real time, resulting in waste of resources and energy consumption; at the same time, the parking space reservation policy is not optimized, resulting in a decrease in parking lot operating costs; there is a lack of timely feedback, and the key parameters in the parking lot operation are not adjusted according to the parking lot management optimization indicators, resulting in unreasonable resource allocation and the inability to form the sustainable development of smart parking lots.

[0004] Therefore, a smart parking lot management method and cloud platform based on information sharing is needed. Summary of the invention

[0005] The information sharing-based smart parking lot management method and cloud platform provided by the present invention aim to improve the management efficiency of parking lots, optimize the utilization of parking resources, improve the user parking experience, and promote the intelligent development of urban transportation.

[0006] The technical solution of the present invention is specifically as follows:

[0007] The smart parking lot management method based on information sharing includes the following steps:

[0008] Step S1. Collect parking lot-related data and information through sensors and detection equipment installed in the parking lot, integrate and process them, upload them to the information sharing platform, and establish a parking space demand prediction neural network model;

[0009] Step S2. Classify vehicles entering the parking lot by type using survey statistics, and determine the allocation ratio of various types of parking spaces in the parking lot according to the parking lot demand forecast; and adjust the parking space reservation policy according to the allocation of various types of parking spaces;

[0010] Step S3. Determine the dynamic parking lot utilization rate according to the parking space demand forecast; at the same time, determine the parking lot management optimization index according to the allocation ratio of various parking spaces in the parking lot and the parking lot utilization rate;

[0011] Step S4. Adjust the parking lot energy consumption and safety inspection frequency through the parking lot management optimization index;

[0012] Step S5: Based on the adjusted parking lot energy consumption and safety inspection frequency, a parking lot operation supervision model is established to monitor and manage the parking lot operation.

[0013] Further, step S1 specifically includes:

[0014] The parking space demand prediction neural network model extracts features of preprocessed parking lot related information on the information sharing platform, uses the obtained feature data as training samples, inputs the sample data into the parking space demand prediction neural network model, and outputs the final parking space demand prediction result; the parking space demand prediction neural network model includes an input gate, an update gate, a forget gate, and an output gate.

[0015] Further, step S2 specifically includes:

[0016] Set The level corresponding to the vehicle type is , ,in, Indicates Vehicle type characteristic attributes; Indicates The weight of each feature attribute; Represents a constant used to adjust the level division scale; if , then the vehicle is determined to be a first-level vehicle; if , then the vehicle is determined to be a second-level vehicle; if , the vehicle is determined to be a third-level vehicle.

[0017] Further, step S2 specifically includes:

[0018] According to the parking lot demand forecast results, calculate the The allocation ratio of each type of parking space is , ,in, Indicates The predicted demand for parking spaces of different types, Represents the total predicted demand for various types of parking spaces.

[0019] Further, step S2 specifically includes:

[0020] if , then define the allocation ratio of this type of parking spaces as a high allocation ratio level; if , then define the allocation ratio of this type of parking spaces as the medium allocation ratio level; if , then define the allocation ratio of this type of parking spaces as a low allocation ratio level; , , , Represent the level thresholds respectively.

[0021] Further, step S2 specifically includes:

[0022] Adjust parking space reservation policy according to the allocation of various parking spaces; when in the high allocation ratio level, the reservation time limit ;in, Indicates the base booking duration; Indicates adjustment factor; booking fee limit ;in, Indicates the base booking duration; represents the adjustment coefficient;

[0023] When in the medium allocation ratio tier, the booking duration limit ; Booking fee limit ;

[0024] When in a low allocation tier, there are limits on the length of bookings ;in, Indicates the base booking duration; Indicates adjustment factor; booking fee limit ;in, Indicates the base booking duration; Represents the adjustment coefficient.

[0025] Further, step S3 specifically includes:

[0026] Introducing historical correction parameters , , calculate the dynamic parking lot utilization rate , as shown below:

[0027] in, Indicates the parking lot utilization rate in the previous time period; The initial utilization rate of the parking lot during the current time period;

[0028] The utilization rate of the first parking lot is preset to The utilization rate of the second parking lot is , , the dynamic parking lot utilization rate Respectively and Make a comparison to determine parking lot utilization.

[0029] Further, step S3 specifically includes:

[0030] in, represents the parking lot management optimization index; represents the weight coefficient, which indicates the importance of different types of parking spaces in management adjustment;

[0031] The first preset comparison indicator , the second preset comparison indicator ,in, , compare the parking lot management optimization index with the first preset comparison index and the second preset comparison index, and adjust the parking lot energy consumption and safety inspection frequency.

[0032] Further, in step S4, the energy consumption of the parking lot is calculated , ;in, Indicates the time period of the record; Indicated in Obtain energy consumption information inside the parking lot; represents the adjustment parameter; Indicates the energy efficiency coefficient of parking equipment; Indicates the working capacity of parking equipment;

[0033] when When the parking lot energy consumption is adjusted , ,in, Represents the first parking lot management optimization index The influence coefficient on energy consumption adjustment; When the parking lot energy consumption is adjusted , , Represents the second parking lot management optimization index The influence coefficient on energy consumption adjustment; When the parking lot energy consumption is adjusted , , Represents the third parking lot management optimization index The influence coefficient on energy consumption adjustment;

[0034] Indicates the basic safety inspection frequency, represents the control coefficient of the parking lot management optimization process; if , then set the safety inspection frequency to the first safety inspection frequency ,but , represents the first safety sensitivity coefficient; if , then set the safety inspection frequency to the second safety inspection frequency ,but , represents the third safety sensitivity factor; if , then set the safety inspection frequency to the third safety inspection frequency ,but , Represents the third safety sensitivity factor.

[0035] The smart parking lot management cloud platform based on information sharing includes the following contents:

[0036] Parking space information collection module, parking lot management center module, parking space demand prediction module, vehicle classification module, parking space allocation module, parking space reservation adjustment module, parking lot dynamic utilization rate module, optimization adjustment module, parking lot operation supervision module;

[0037] Parking space information collection module, used to collect relevant information of each parking space in the parking lot in real time;

[0038] The parking lot management center module, as the core of the entire system, is used to centrally manage and monitor the operation of the parking lot and coordinate the work of each module;

[0039] The parking space demand prediction module predicts the demand for parking spaces in the future based on historical data;

[0040] The vehicle classification module is used to classify vehicles entering the parking lot in order to manage parking lot resources;

[0041] The parking space allocation module determines the allocation ratio of various types of parking spaces in the parking lot based on real-time data and demand forecasts, and optimizes the utilization of parking resources;

[0042] The parking space reservation adjustment module adjusts the parking space reservation policy according to the allocation of various parking spaces and makes flexible adjustments based on actual conditions;

[0043] The parking lot dynamic utilization rate module monitors the parking lot utilization rate in real time and provides data support to managers;

[0044] The optimization and adjustment module determines the parking lot management optimization index according to the allocation ratio of various parking spaces in the parking lot and the parking lot utilization rate, optimizes and adjusts the parking lot energy consumption and safety inspection frequency, and improves the overall efficiency and utilization rate of the parking lot; the optimization and adjustment module includes the parking lot energy consumption adjustment module and the safety inspection frequency adjustment module;

[0045] The parking lot operation supervision module provides comprehensive supervision functions, establishes a parking lot operation supervision model, and monitors and manages parking lot operations.

[0046] Beneficial effects: 1. The present invention can accurately predict parking demand by establishing a parking demand prediction neural network model, so that parking lot managers can make adjustments in advance and optimize parking space allocation; based on intelligent prediction and management, parking lots can more effectively utilize parking space resources, avoid congestion and waste of resources, and improve the utilization rate and efficiency of parking lots. At the same time, parking lot managers can formulate more accurate management strategies, adjust parking space allocation and pricing strategies according to real-time needs, and improve the scientificity and effectiveness of management decisions.

[0047] 2. The present invention can manage the parking positions of vehicles by classifying them according to vehicle types, avoid mutual interference or safety hazards caused by vehicles of different types, and improve the safety of parking lots; determine the allocation ratio of various parking spaces according to the parking space demand situation, so as to more effectively utilize parking space resources and avoid certain types of vehicles occupying too many parking spaces of a specific type, thereby improving the overall parking space utilization efficiency; adjust the parking space reservation policy according to the allocation of various parking spaces, so that parking lot managers can formulate reservation policies more scientifically, adjust parking space reservation rules according to actual needs, improve management efficiency and decision-making accuracy, and at the same time, due to the optimization of parking space reservation policies, it can reduce unnecessary waste of resources and energy consumption, reduce parking lot operating costs, and achieve effective utilization and conservation of resources.

[0048] 3. In the present invention, parking lot managers can dynamically adjust the utilization rate of parking spaces according to actual needs to avoid idle or overused resources, thereby optimizing the utilization efficiency of parking lot resources; dynamically managing the utilization rate of parking spaces can effectively reduce the waiting time of vehicles in the parking lot and improve the operation efficiency of the parking lot. According to the allocation ratio of various parking spaces in the parking lot and the utilization rate of the parking lot, it is ensured that all types of parking spaces in the parking lot can be fully utilized to improve the overall utilization rate of the parking lot; the management optimization indicators determined based on the allocation ratio and utilization rate of various types of parking spaces in the parking lot can help parking lot managers to allocate and manage resources more scientifically, improve management efficiency, and optimize the operation effect of the parking lot.

[0049] 4. The present invention can optimize the energy efficiency of parking lot equipment, reduce energy waste, and lower the overall energy consumption of parking lots by calculating the energy consumption of parking lots and adjusting it according to management optimization indicators; optimizing parking lot energy consumption can reduce the operating costs of parking lots, save energy expenses, and improve economic benefits, thereby achieving effective cost control and management. By adjusting the frequency of safety inspections, the safety and management level of parking lots can be improved, potential safety hazards can be discovered and resolved in a timely manner, and the safety of vehicles and personnel in parking lots can be guaranteed. Adjusting the energy consumption and safety inspection frequency of parking lots based on parking lot management optimization indicators improves the management efficiency of parking lots, makes management more intelligent and refined, optimizes resource allocation, and promotes parking lot management towards a more intelligent, efficient, and sustainable direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the intelligent parking lot management method based on information sharing of the present invention;

[0051] Figure 2 This is a schematic diagram of the module composition of the smart parking lot management cloud platform based on information sharing of the present invention;

[0052] Figure 3 It is a schematic diagram of the optimization and adjustment module of the present invention. DETAILED DESCRIPTION

[0053] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should also 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.

[0054] See attached Figure 1 This embodiment provides a smart parking lot management method based on information sharing, including the following steps:

[0055] S1. Through the sensors and detection equipment installed in the parking lot, the relevant data and information of the parking lot are collected, integrated and processed, and uploaded to the information sharing platform to establish a parking space demand prediction neural network model.

[0056] Sensors and monitoring equipment are installed in each parking space, including geomagnetic sensors, video monitoring equipment, and environmental monitoring sensors. Specifically, geomagnetic sensors can accurately detect whether a parking space is occupied, and video monitoring equipment can not only detect the occupancy status of parking spaces, but also identify license plate numbers. These sensors transmit parking space status information, parking lot entrance and exit traffic, payment settlement information, equipment maintenance information, etc. to the server of the parking lot management center in real time.

[0057] The server of the parking lot management center integrates and processes the parking lot related information and uploads it to the information sharing platform. The information sharing platform can be a network platform based on cloud computing, which can be connected with multiple information hubs, including community information centers, surrounding public service centers, and surrounding commercial and entertainment centers. The information sharing platform also interacts with external traffic information systems, map navigation applications, etc. to obtain information such as surrounding traffic flow and road construction.

[0058] The parking space information collection module continuously collects parking space status information, that is, the parking space occupancy within a certain period of time, and sends the updated information to the parking lot management center server at a certain interval, for example, 10 seconds. The parking lot management center server performs preliminary screening and preprocessing on the received information, such as removing erroneous data, and then uploads the accurate parking space information, including parking lot location, number of vacant parking spaces, charging standards, etc., to the information sharing platform in a unified data format.

[0059] Through deep learning, a parking space demand prediction neural network model is established, and a gate function is introduced. The gate function includes an input gate, an update gate, a forget gate, and an output gate. The pre-processed parking lot related information is extracted from the information sharing platform using existing technology, and the obtained feature data is used as a training sample. The sample data is input into the parking space demand prediction neural network model, where: , Indicated in The input features of .

[0060] Will Input into the parking space demand prediction neural network model, and set the initial state as The input gate controls the degree to which new information enters the cell state of the neural network, that is, the input gate will filter out the information related to the parking space demand and needs to be remembered according to the characteristics of the input data at the current moment. The specific process is as follows:

[0061] in, represents the activation value of the input gate; represents the initial weight; represents a constant coefficient; represents the input gate neuron weight; Indicates the hidden state at the previous moment The weight to the input gate; represents the input gate bias; Represents the activation function.

[0062] The update gate is used to control the extent to which the old cell state is updated, that is, whether to rely more on the previous cell state or update the cell state based on the current input and the hidden state of the previous moment; the specific process is as follows:

[0063] in, represents the activation value of the update gate; express The state of the moment; represents the update gate neuron weight; represents the update gate bias; represents the control parameters; represents the stability coefficient; represents the time response coefficient; Indicated in The weight corresponding to the moment; Indicates the determination threshold.

[0064] The forget gate determines how much old information is forgotten from the cell state, that is, to remove old factors that no longer affect the current parking demand over time. The specific process is as follows:

[0065] in, Represents the activation value of the forget gate; represents the weight of the forget gate neuron; represents the forget gate bias; Represents a dynamic factor.

[0066] The output gate controls how the information in the cell state is output to the hidden state. The specific process is as follows:

[0067] in, represents the activation value of the output gate; represents the activation function; represents the output gate neuron weight; Represents the output gate bias.

[0068] Finally, connect a fully connected layer to hide the state Map to the prediction results, and then output the final parking space demand prediction results .

[0069] The present invention can accurately predict parking demand by establishing a parking demand prediction neural network model, so that parking lot managers can make adjustments in advance and optimize parking allocation; based on intelligent prediction and management, parking lots can more effectively use parking resources, avoid congestion and waste of resources, and improve the utilization rate and efficiency of parking lots. At the same time, parking lot managers can formulate more accurate management strategies, adjust parking allocation and pricing strategies according to real-time needs, and improve the scientificity and effectiveness of management decisions.

[0070] S2. Use survey statistics to classify vehicles entering the parking lot by type, and determine the allocation ratio of various types of parking spaces in the parking lot based on the parking space demand forecast; and adjust the parking space reservation policy based on the allocation of various types of parking spaces.

[0071] The vehicles entering the parking lot are classified by type through investigation and statistics. In the embodiment of the present invention, the parking lot parking spaces are divided according to the vehicle type. The specific process is as follows:

[0072] Set The level corresponding to the vehicle type is ,in, In the embodiment of the present invention, the vehicle type is defined as The characteristic attributes are The corresponding weights are ; Define parking space levels as ,in, Indicates Vehicle type characteristic attributes; Indicates The weight of each feature attribute; Represents a constant used to adjust the scale of hierarchical division; in the calculation In the embodiment of the present invention, three characteristic attributes of the vehicle are considered to divide the levels, including: vehicle length, vehicle wheelbase, and vehicle height.

[0073] if , then the vehicle is determined to be a first-tier vehicle, mainly small vehicles, such as ordinary cars, mini cars, etc.; if , then the vehicle is determined to be a second-tier vehicle, mainly medium-sized vehicles, such as SUVs and some medium-sized MPVs; if , the vehicle is determined to be a third-tier vehicle, mainly large vehicles, such as buses, trucks, etc.

[0074] Specifically, the weights are determined based on industry experience or historical data of parking lot operations, and parking lot management experts, automotive engineering experts, etc. are asked to evaluate the importance of each feature attribute based on their professional knowledge and then determine the weights, which is not specifically limited in this embodiment.

[0075] Based on the parking space demand forecast of the parking lot, the allocation ratio of each type of parking space in the parking lot is determined; and the parking space reservation policy is adjusted according to the allocation of each type of parking space.

[0076] Output of the neural network based on parking space demand prediction , ,in, Indicates The predicted demand for various types of parking spaces is ; Definition The allocation ratio of each type of parking space is , ,in, .

[0077] if , then define the allocation ratio of this type of parking spaces as a high allocation ratio level;

[0078] if , then define the allocation ratio of this type of parking spaces as the medium allocation ratio level;

[0079] if , then define the allocation ratio of this type of parking spaces as a low allocation ratio level.

[0080] In an embodiment of the present invention, , , , They represent the level thresholds respectively. According to the actual situation of the parking lot, the specific level thresholds are set. , , , .

[0081] The parking space reservation policy is adjusted according to the allocation of various types of parking spaces. For parking space types with low allocation ratios, the reservation time can be limited or the reservation fee can be increased to balance the use requirements of different types of parking spaces. In the embodiment of the present invention, the specific process is as follows:

[0082] When you are in the high allocation ratio tier, due to the large number of parking spaces, you can relax the reservation restrictions, reduce the reservation fee, and increase the reservation duration; the details are as follows:

[0083] Booking duration limit ;in, Indicates the base booking duration; represents the adjustment coefficient;

[0084] Booking fee restrictions ;in, Indicates the base booking duration; represents the adjustment coefficient;

[0085] When in the middle allocation tier, maintain a moderate booking policy that is neither too lenient nor too strict, balancing booking costs and booking duration; as shown below:

[0086] Booking duration limit ; Booking fee limit ;

[0087] When in the low allocation ratio tier, due to the small number of parking spaces, you can limit the reservation time and increase the reservation fee to balance the supply and demand relationship; the details are as follows:

[0088] Booking duration limit ;in, Indicates the base booking duration; represents the adjustment coefficient;

[0089] Booking fee restrictions ;in, Indicates the base booking duration; In the embodiment of the present invention, when calculating the reservation duration limit, the calculation result can be approximated to an integer.

[0090] The present invention can manage the parking positions of vehicles by classifying them according to vehicle types, avoid mutual interference or safety hazards caused by vehicles of different types, and improve the safety of parking lots; by determining the allocation ratio of various parking spaces according to parking space demand conditions, parking space resources can be used more effectively, and certain types of vehicles can be avoided from excessively occupying specific types of parking spaces, thereby improving the overall parking space utilization efficiency; by adjusting the parking space reservation policy according to the allocation conditions of various parking spaces, parking lot managers can formulate reservation policies more scientifically, adjust parking space reservation rules according to actual needs, and improve management efficiency and decision-making accuracy. At the same time, due to the optimization of parking space reservation policies, unnecessary resource waste and energy consumption can be reduced, the operating costs of parking lots can be reduced, and effective utilization and conservation of resources can be achieved.

[0091] A reasonable parking space allocation ratio can reduce the time users spend looking for parking spaces, alleviate congestion in parking lots, improve user parking experience, and increase user satisfaction.

[0092] S3. Determine the dynamic parking lot utilization rate based on the parking space demand forecast; at the same time, determine the parking lot management optimization index based on the allocation ratio of various types of parking spaces in the parking lot and the parking lot utilization rate.

[0093] In the embodiment of the present invention, the total number of parking spaces in the parking lot is defined as , divided into time periods, respectively , for each time period The predicted parking space demand is At the same time, for each time period Set a weight , must meet ; Calculate the initial utilization rate of the parking lot in the current time period ,

[0094] Introducing historical correction parameters , , calculate the dynamic parking lot utilization rate , as shown below:

[0095] in, Indicates the parking lot utilization rate in the previous time period.

[0096] The utilization rate of the first parking lot is preset to The utilization rate of the second parking lot is , , the dynamic parking lot utilization rate Respectively and A comparison is made to determine the parking lot utilization rate, as shown below:

[0097] if , it means that the parking lot utilization rate is the first parking lot utilization rate. At this time, the parking lot is close to saturation or has been saturated, and corresponding management measures need to be taken;

[0098] if , it means that the parking lot utilization rate is the second parking lot utilization rate, the parking lot utilization is at a medium level, and the parking spaces are neither largely idle nor close to saturation;

[0099] if , it means that the parking lot utilization rate is the third parking lot utilization rate, the parking lot utilization rate is very low, and there are a large number of vacant spaces.

[0100] Determine the parking lot management optimization index based on the allocation ratio of various parking spaces in the parking lot and the parking lot utilization rate , the specific process is as follows:

[0101] in, Represents the weight coefficient, which indicates the importance of different types of parking spaces in management adjustments.

[0102] In the present invention, parking lot managers can dynamically adjust the utilization rate of parking spaces according to actual needs to avoid idle or overused resources, thereby optimizing the utilization efficiency of parking lot resources; dynamically managing the utilization rate of parking spaces can effectively reduce the waiting time of vehicles in the parking lot and improve the operation efficiency of the parking lot. According to the allocation ratio of various parking spaces in the parking lot and the utilization rate of the parking lot, it is ensured that all types of parking spaces in the parking lot can be fully utilized to improve the overall utilization rate of the parking lot; the management optimization index determined based on the allocation ratio and utilization rate of various types of parking spaces in the parking lot can help parking lot managers to allocate and manage resources more scientifically, improve management efficiency, and optimize the operation effect of the parking lot.

[0103] S4. Adjust parking lot energy consumption and safety inspection frequency through parking lot management optimization indicators.

[0104] Define the first preset comparison indicator , the second preset comparison indicator ,in, , comparing the parking lot management optimization index with the first preset comparison index and the second preset comparison index, and adjusting the parking lot energy consumption;

[0105] Calculating Parking Lot Energy Consumption , the specific process is as follows:

[0106] in, represents the energy consumption of parking lot; Indicates the time period of the record; Indicated in Obtain energy consumption information inside the parking lot; represents the adjustment parameter; Indicates the energy efficiency coefficient of parking equipment; Indicates the working capacity of parking equipment.

[0107] In the embodiment of the present invention, when When the parking lot energy consumption is adjusted , ,in, Represents the first parking lot management optimization index The influence coefficient on energy consumption adjustment;

[0108] when When the parking lot energy consumption is adjusted , , Represents the second parking lot management optimization index The influence coefficient on energy consumption adjustment;

[0109] when When the parking lot energy consumption is adjusted , , Represents the third parking lot management optimization index The influence coefficient on energy consumption adjustment.

[0110] Compare the parking lot management optimization index with the first preset comparison index and the second preset comparison index, and adjust the safety inspection frequency; define, Indicates the basic safety inspection frequency, It represents the control coefficient of the parking lot management optimization process. The specific process is as follows:

[0111] if , then set the safety inspection frequency to the first safety inspection frequency ,but ; represents the first safety sensitivity coefficient;

[0112] if , then set the safety inspection frequency to the second safety inspection frequency ,but ; represents the third safety sensitivity coefficient;

[0113] if , then set the safety inspection frequency to the third safety inspection frequency ,but ; Represents the third safety sensitivity factor.

[0114] The present invention can optimize the energy utilization efficiency of parking lot equipment, reduce energy waste, and reduce the overall energy consumption of parking lots by calculating the energy consumption of parking lots and adjusting it according to management optimization indicators; optimizing the energy consumption of parking lots can reduce the operating costs of parking lots, save energy expenses, and improve economic benefits, thereby achieving effective cost control and management. By adjusting the frequency of safety inspections, the safety and management level of parking lots can be improved, potential safety hazards can be discovered and resolved in a timely manner, and the safety of vehicles and personnel in parking lots can be guaranteed. Adjusting the energy consumption and safety inspection frequency of parking lots based on parking lot management optimization indicators improves the management efficiency of parking lots, makes management more intelligent and refined, and optimizes resource allocation.

[0115] S5. According to the obtained parking lot energy consumption and safety inspection frequency, a parking lot operation supervision model is established to monitor and manage the parking lot operation.

[0116] In the embodiment of the present invention, the comprehensive monitoring value is introduced The specific process is as follows:

[0117] in, Indicates the correction factor in the comprehensive monitoring process; represents the evaluation function, which is used to evaluate the operation of the parking lot; Represents a constant value.

[0118] Parking lot management and operation personnel use comprehensive monitoring values Make judgments, further optimize management strategies, identify potential problems and opportunities, provide managers with a comprehensive basis for decision-making, improve the service quality and efficiency of parking lots, and thus enhance user experience and satisfaction.

[0119] The present invention can carry out refined management and analysis of parking lot operation data through the parking lot operation supervision model, improve management level, optimize resource allocation, and thus realize refined management of parking lot operation; and can timely detect abnormal situations and issue early warnings, which helps to prevent the occurrence of potential problems. The data analysis results based on the supervision model can provide data support for parking lot managers, help them make more scientific and reasonable decisions, and promote the development of parking lot operations in a more intelligent and data-driven direction.

[0120] See attached Figure 2 , this embodiment provides a smart parking lot management cloud platform based on information sharing, including the following contents:

[0121] Parking space information collection module, parking lot management center module, parking space demand prediction module, vehicle classification module, parking space allocation module, parking space reservation adjustment module, parking lot dynamic utilization rate module, optimization adjustment module, parking lot operation supervision module;

[0122] The parking space information collection module is used to collect the status information of each parking space in the parking lot in real time, including whether the parking space is vacant, the type of vehicle, etc.

[0123] The parking lot management center module, as the core of the entire system, is used to centrally manage and monitor the operation of the parking lot and coordinate the work of each module;

[0124] The parking space demand forecasting module predicts the demand for parking spaces in the future based on historical data, helping to optimize parking space allocation and make preparations in advance;

[0125] The vehicle classification module is used to classify vehicles entering the parking lot so as to manage parking lot resources more efficiently;

[0126] The parking space allocation module determines the allocation ratio of various types of parking spaces in the parking lot based on real-time data and demand forecasts, and optimizes the utilization of parking resources;

[0127] The parking space reservation adjustment module adjusts the parking space reservation policy according to the allocation of various parking spaces and makes flexible adjustments based on actual conditions;

[0128] The parking lot dynamic utilization module monitors the parking lot utilization in real time and provides data support to managers so that they can make timely adjustments and decisions;

[0129] See attached Figure 3 , an optimization and adjustment module determines the parking lot management optimization index according to the allocation ratio of various parking spaces in the parking lot and the parking lot utilization rate, optimizes and adjusts the parking lot energy consumption and safety inspection frequency, and improves the overall efficiency and utilization rate of the parking lot; the optimization and adjustment module includes a parking lot energy consumption adjustment module and a safety inspection frequency adjustment module;

[0130] The parking lot operation supervision module provides comprehensive supervision functions, including energy consumption monitoring, safety inspections, operation data analysis, etc., to help managers achieve refined management and decision-making.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

Claims

1. A smart parking lot management method based on information sharing, characterized in that: The following steps are involved: Step S1. Collect parking lot-related data and information through sensors and detection equipment installed in the parking lot, integrate and process them, upload them to the information sharing platform, and establish a parking space demand prediction neural network model; Step S2. Classify vehicles entering the parking lot by type using survey statistics, and determine the allocation ratio of various types of parking spaces in the parking lot based on the parking space demand forecast; and adjust the parking space reservation policy according to the allocation of various types of parking spaces; Step S3. Determine the dynamic parking lot utilization rate according to the parking space demand forecast; at the same time, determine the parking lot management optimization index according to the allocation ratio of various parking spaces in the parking lot and the parking lot utilization rate; in, represents the parking lot management optimization index; represents the weight coefficient; Indicates The allocation ratio of each type of parking space, ; Indicates dynamic parking lot utilization rate; the first preset comparison indicator , the second preset comparison indicator ,in, , compare the parking lot management optimization index with the first preset comparison index and the second preset comparison index, and adjust the parking lot energy consumption and safety inspection frequency; Step S4. Adjust the parking lot energy consumption and safety inspection frequency through the parking lot management optimization index; Parking lot energy consumption ;in, Indicates the time period of the record; Indicated in Obtain energy consumption information inside the parking lot; represents the adjustment parameter; Indicates the energy efficiency coefficient of parking equipment; Indicates the working capacity of parking equipment; when When the parking lot energy consumption is adjusted , ,in, Represents the first parking lot management optimization index The influence coefficient on energy consumption adjustment; When the parking lot energy consumption is adjusted , , Represents the second parking lot management optimization index The influence coefficient on energy consumption adjustment; When the parking lot energy consumption is adjusted , , Represents the third parking lot management optimization index The influence coefficient on energy consumption adjustment; Indicates the basic safety inspection frequency, represents the control coefficient of the parking lot management optimization process; if , then set the safety inspection frequency to the first safety inspection frequency ,but , represents the first safety sensitivity coefficient; if , then set the safety inspection frequency to the second safety inspection frequency ,but , represents the third safety sensitivity factor; if , then set the safety inspection frequency to the third safety inspection frequency ,but , represents the third safety sensitivity coefficient; Step S5: Based on the adjusted parking lot energy consumption and safety inspection frequency, a parking lot operation supervision model is established to monitor and manage the parking lot operation.

2. The intelligent parking lot management method based on information sharing according to claim 1 is characterized in that: The step S1 specifically includes: The parking space demand prediction neural network model extracts features of preprocessed parking lot related information on the information sharing platform, uses the obtained feature data as training samples, inputs the sample data into the parking space demand prediction neural network model, and outputs the final parking space demand prediction result; the parking space demand prediction neural network model includes an input gate, an update gate, a forget gate, and an output gate.

3. The intelligent parking lot management method based on information sharing according to claim 1 is characterized in that: The step S2 specifically includes: Set The level corresponding to the vehicle type is , ,in, Indicates Vehicle type The vehicle types have characteristic attributes; Indicates The weight of each feature attribute; Represents a constant used to adjust the level division scale; if , then the vehicle is determined to be a first-level vehicle; if , then the vehicle is determined to be a second-level vehicle; if , the vehicle is determined to be a third-level vehicle.

4. The intelligent parking lot management method based on information sharing according to claim 1 is characterized in that: The step S2 specifically includes: According to the parking lot demand forecast results, calculate the The allocation ratio of each type of parking space is , ,in, Indicates The predicted demand for parking spaces of different types, Represents the total predicted demand for various types of parking spaces.

5. The intelligent parking lot management method based on information sharing according to claim 4 is characterized in that: The step S2 specifically includes: if , then define the allocation ratio of this type of parking spaces as a high allocation ratio level; if , then define the allocation ratio of this type of parking spaces as the medium allocation ratio level; if , then define the allocation ratio of this type of parking spaces as a low allocation ratio level; , , , Represent the level thresholds respectively.

6. The intelligent parking lot management method based on information sharing according to claim 5 is characterized in that: The step S2 specifically includes: Adjust parking space reservation policy according to the allocation of various parking spaces; when in the high allocation ratio level, the reservation time limit ;in, Indicates the base booking duration; Indicates adjustment factor; booking fee limit ;in, Indicates the base booking duration; represents the adjustment coefficient; When in the medium allocation ratio tier, the booking duration limit ; Booking fee limit ; When in a low allocation tier, there are limits on the length of bookings ;in, Indicates the base booking duration; Represents the adjustment factor; Booking fee limit ;in, Indicates the base booking duration; Represents the adjustment coefficient.

7. The intelligent parking lot management method based on information sharing according to claim 1 is characterized in that: The step S3 specifically includes: Introducing historical correction parameters , , calculate the dynamic parking lot utilization rate , as shown below: in, Indicates the parking lot utilization rate in the previous time period; Indicates the preliminary utilization rate of the parking lot in the current time period; The utilization rate of the first parking lot is preset to The utilization rate of the second parking lot is , , the dynamic parking lot utilization rate Respectively and Make a comparison to determine parking lot utilization.

8. A smart parking lot management cloud platform based on information sharing, applied to the smart parking lot management method based on information sharing as claimed in claim 1, characterized in that: Includes the following: Parking space information collection module, parking lot management center module, parking space demand prediction module, vehicle classification module, parking space allocation module, parking space reservation adjustment module, parking lot dynamic utilization rate module, optimization adjustment module, parking lot operation supervision module; The parking space information collection module is used to collect relevant information of each parking space in the parking lot in real time; The parking lot management center module, as the core of the entire system, is used to centrally manage and monitor the operation of the parking lot and coordinate the work of each module; The parking space demand prediction module predicts the demand for parking spaces in the future based on historical data; The vehicle classification module is used to classify vehicles entering the parking lot so as to manage parking lot resources; The parking space allocation module determines the allocation ratio of various types of parking spaces in the parking lot based on real-time data and demand forecasts, and optimizes the utilization of parking lot resources; The parking space reservation adjustment module adjusts the parking space reservation policy according to the allocation of various types of parking spaces and makes flexible adjustments based on actual conditions; The parking lot dynamic utilization rate module monitors the parking lot utilization rate in real time and provides data support to managers; The optimization and adjustment module determines the parking lot management optimization index according to the allocation ratio of various parking spaces in the parking lot and the parking lot utilization rate, optimizes and adjusts the parking lot energy consumption and safety inspection frequency, and improves the overall efficiency and utilization rate of the parking lot; the optimization and adjustment module includes a parking lot energy consumption adjustment module and a safety inspection frequency adjustment module; The parking lot operation supervision module provides comprehensive supervision functions, establishes a parking lot operation supervision model, and monitors and manages parking lot operations.

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