New energy automobile charging and parking management system based on intelligent load balancing

Abstract

The invention provides a new energy automobile charging and parking management system based on intelligent load balancing, and relates to the technical field of automobile charging and parking management. Comprising an intelligent pulse distribution module, a dynamic parking cooperation module, a neural charging cluster module, a quantum matching optimization module, a vibration guiding feedback module, a dynamic partition acceleration module, a cooperative consciousness network module, a self-adaptive micro-grid module, a prediction interaction cloud distribution module and a prediction interaction cloud guiding module. Through cooperative work of the intelligent pulse distribution module and the quantum matching optimization module, vehicle electric quantity requirements and parking space states can be monitored in real time, charging resources and parking positions can be dynamically distributed, the problem of unbalanced resource utilization in peak hours is effectively avoided, and the use efficiency of the charging piles and the parking spaces is improved. For example, in the peak period of the commercial parking lot, the system preferentially distributes the fast charging position for the low-electric-quantity vehicle and guides other vehicles to the idle parking space, so that the waiting time of the user is remarkably shortened, and the overall user experience is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging and parking management, and specifically to a new energy vehicle charging and parking management system based on intelligent load balancing. Background Art

[0002] With the rapid development of the new energy vehicle industry, the popularity rate of electric vehicles has been continuously increasing, and the demand for charging infrastructure and parking management has been growing day by day. As important supporting facilities for the use of new energy vehicles, the efficiency, intelligence, and resource utilization rate of charging piles and parking lots directly affect the user experience and the sustainable development of urban transportation. However, the current charging and parking management systems still face many challenges in practical applications and are difficult to meet the growing demand and complex scenario requirements. Therefore, how to achieve intelligent allocation of charging resources and parking spaces, improve system efficiency, and optimize the user experience has become an important research direction in the field of new energy vehicles.

[0003] Although the existing charging and parking management technologies have alleviated the problem of resource shortage to a certain extent, there are still the following deficiencies: Problem 1: Traditional charging and parking management mainly relies on manual scheduling or a simple first-come, first-served strategy, lacking an intelligent allocation mechanism. During peak periods, the utilization rates of charging piles and parking spaces are unbalanced, resulting in long waiting times for some users and frequent occurrences of idle resources. For example, during peak hours in commercial parking lots, charging piles may be occupied, and other vehicles cannot charge in a timely manner, affecting the user experience.

[0004] Problem 2: The existing charging systems have insufficient dynamic response capabilities to the power grid load and are difficult to achieve real-time balance. During peak power grid load periods, charging demands may cause local power grid overloads and even lead to unstable power supply. In addition, the traditional systems have low utilization efficiency of renewable energy (such as solar energy) and fail to effectively reduce carbon emissions, restricting the sustainability of the systems. For example, in the night charging scenario in residential parking lots, there is a lack of intelligent scheduling for solar energy storage.

[0005] Problem 3: The current parking guidance technologies mostly rely on single navigation devices and lack multi-dimensional collaborative guidance capabilities. When users are looking for parking spaces, they often face problems such as path congestion or unclear guidance, especially in the short-term parking scenario at airports, where high traffic flow leads to overlapping paths and low parking efficiency. At the same time, the existing systems have limited prediction capabilities for user behavior and parking lot status and are difficult to pre-allocate resources in advance, missing the best opportunity to optimize resource utilization.

[0006] Therefore, a new energy vehicle charging and parking management system based on intelligent load balancing is needed to solve the above problems. Summary of the Invention

[0007] Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides a new energy vehicle charging and parking management system based on intelligent load balancing, which solves the problems in the above background technology.

[0008] Technical Solution

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A new energy vehicle charging and parking management system based on intelligent load balancing, including an intelligent pulse distribution module, a dynamic parking coordination module, a neural charging cluster module, a quantum matching optimization module, a vibration guidance feedback module, a dynamic zoning acceleration module, a collaborative awareness network module, an adaptive microgrid module, a predictive interaction cloud distribution module, and a predictive interaction cloud guidance module; The intelligent pulse distribution module collects vehicle power demand data through a wireless charging grid embedded in the ground, processes the data using ripple harmonic scheduling technology, analyzes vehicle and grid load information using edge computing nodes, fuses the power characteristics and load information using an adaptive equalization network, transmits the fused characteristics to a distributed controller through a high-speed Internet of Things, and controls the charging power according to the priority queue control flow and generates parking guidance data; The dynamic parking coordination module collects vehicle position data through parking lot cameras and radar sensors, processes the data using swarm flow prediction optimization technology, analyzes vehicle flow and residence time information using Internet of Things sensors, fuses the path characteristics and flow information using an adaptive collaboration network, transmits the fused characteristics to a navigation controller through a high-speed Internet of Things, and plans the parking path and allocates charging resources according to the state machine control flow.

[0010] Preferably, the neural charging cluster module collects charging status data, manages the charging mode after processing the data, and generates parking guidance data; the quantum matching optimization module collects parking space status data, matches the parking position and allocates charging resources after processing the data; the vibration guidance feedback module collects vehicle position data, generates a parking guidance signal after processing the data, and connects the charging process; the dynamic zoning acceleration module collects vehicle flow data, optimizes the zoning layout and allocates charging resources after processing the data; the collaborative awareness network module collects user behavior data, manages the parking order and allocates resources after processing the data; the adaptive microgrid module collects energy supply data, schedules the charging power and allocates parking resources after processing the data; the predictive interaction cloud distribution module collects user behavior data, and allocates parking and charging resources after processing the data; the predictive interaction cloud guidance module collects user behavior data, and generates a parking path and a charging guidance signal after processing the data.

[0011] Preferably, the intelligent pulse distribution module collects vehicle power demand data through a built-in power sensor in a wireless charging grid embedded in the ground, receives vehicle position data transmitted by the dynamic parking cooperation module through the high-speed Internet of Things using the Message Queuing Telemetry Transport (MQTT) protocol, and receives charging status data transmitted by the neural charging cluster module through the high-speed Internet of Things using the MQTT protocol; the edge computing node extracts power demand, grid load, and parking space occupancy information, and analyzes the peak load change in the commercial parking lot by combining multi-scale feature analysis; the adaptive equalization network adjusts the distribution weight, fuses the power characteristics and load information, and transmits the fused features to the distributed controller through the high-speed Internet of Things using the MQTT protocol to drive the wireless charging grid to distribute the charging power. The ripple harmonic scheduling technology applies the pulse equalization algorithm, receives power demand, grid load, parking space occupancy, vehicle position, and charging status data, simulates the ripple effect of the energy flow, iteratively adjusts the pulse frequency and power of the charging grid, preferentially allocates high power to low-power vehicles, balances the load through the harmonic adjustment mechanism, and generates a charging distribution plan; the pulse equalization algorithm iterates through the ripple harmonics, surpasses the linear distribution technology, and adapts to the fast charging demand during the peak period of the commercial parking lot; the intelligent pulse distribution module uses a priority queue control flow, triggers the charging distribution according to the power demand ranking, transmits the parking guidance data to the vibration guidance feedback module through the high-speed Internet of Things using the MQTT protocol, drives the ground LED strip to display the guidance line, continuously optimizes the distribution accuracy, and automatically adjusts the parameters to adapt to the grid load fluctuation scenario.

[0012] Preferably, the dynamic parking collaboration module collects vehicle position data through parking lot cameras and radar sensors, receives user behavior data transmitted by the prediction interaction cloud distribution module through a cloud encrypted channel using the Secure Hypertext Transfer Protocol, and parking behavior data transmitted by the collaboration awareness network module through a high-speed Internet of Things using the Constrained Application Protocol; the Internet of Things sensors extract vehicle flow, residence time, and power demand information, and combined with time series analysis, analyze the dynamic changes of short-term parked vehicles in the airport parking lot; the adaptive collaboration network adjusts the path weights, fuses path features and traffic information, and transmits the fused features to the navigation controller through a high-speed Internet of Things using the Constrained Application Protocol to drive the in-vehicle navigation to plan the path; the group flow prediction optimization technology applies a parking collaboration optimization algorithm, receives vehicle position, traffic, residence time, user behavior, and parking behavior data, constructs a virtual flow field to map the vehicle as a gravitational node, iteratively updates the moving trajectory, introduces random perturbations to optimize the path and charging position allocation, and generates a navigation plan; the parking collaboration optimization algorithm transcends traditional path planning techniques through group flow dynamics and adapts to the planning of short-term parked vehicles in the airport parking lot; the dynamic parking collaboration module adopts a state machine control flow, switches the path planning according to the vehicle traffic state, transmits the path data to the quantum matching optimization module through a high-speed Internet of Things using the Constrained Application Protocol, drives the in-vehicle application program and the entrance digital screen to display the path, continuously optimizes the planning accuracy, and automatically adjusts the strategy to adapt to the peak congestion scenario.

[0013] Preferably, the neural charging cluster module collects charging status data through built-in power sensors in interconnected charging piles, receives power allocation data transmitted by the intelligent pulse distribution module through a high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol, and energy supply data transmitted by the adaptive microgrid module through a high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol; the high-speed Internet of Things extracts vehicle power, battery type, and pile occupancy information, and combined with multi-dimensional feature analysis, analyzes the changes in slow charging demand in the residential parking lot; the adaptive collaboration network adjusts and optimizes the weights, fuses the charging status and vehicle information, and transmits the fused features to the distributed controller through a high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol to drive the charging pile to switch the charging mode; the neuron-like topology management technology applies a distributed collaboration algorithm, receives charging status, power, battery type, and energy supply data, constructs an inter-pile topology network, simulates neuron activation to adjust power and priority, and preferentially allocates resources to high-demand vehicles to generate a charging management plan; the distributed collaboration algorithm transcends centralized scheduling techniques through neuron-like topologies and adapts to slow charging battery protection in the residential parking lot; the neural charging cluster module adopts an event-driven control flow, triggers the charging mode switch according to the battery type, transmits the parking guidance data to the vibration guidance feedback module through a high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol, drives the optical signal and the ground projection to display the guidance line, continuously optimizes the management accuracy, and automatically adjusts the parameters to adapt to the diversified battery type scenario.

[0014] Preferably, the quantum matching optimization module collects parking space status data through sensors built in the parking space, receives path data transmitted by the dynamic parking cooperation module through the high-speed Internet of Things using the Constrained Application Protocol, and resource allocation data transmitted by the prediction interaction cloud allocation module through the cloud encryption channel using the Hypertext Transfer Protocol Secure; the sensor network extracts the location, power, and occupancy information of the parking space, and combines high-dimensional feature analysis to analyze the fast charging space matching requirements of commercial parking lots; the adaptive matching network adjusts the matching weight, fuses the vehicle requirements and the parking space status, and transmits the fused features to the navigation controller through the high-speed Internet of Things using the Constrained Application Protocol to drive the in-vehicle navigation to match the parking location; the quantum state flow matching technology applies the quantum flow matching algorithm, receives vehicle requirements, parking space status, path, and resource allocation data, maps them into high-dimensional vectors, iteratively analyzes the matching probability, introduces the interference effect to optimize the allocation, and generates a matching scheme; the quantum flow matching algorithm adapts to the fast charging space matching of commercial parking lots by means of quantum state flow, surpassing the genetic algorithm technology; the quantum matching optimization module uses a priority queue to control the flow, triggers the matching according to the vehicle requirements sorting, transmits the matching data through the high-speed Internet of Things using the Constrained Application Protocol to the vibration guidance feedback module, drives the in-vehicle application and the entrance digital screen to display the matching location, continuously optimizes the matching accuracy, and automatically adjusts the strategy to adapt to the parking space shortage scenario.

[0015] Preferably, the vibration guidance feedback module collects vehicle location data through ground vibration sensors, receives matching data transmitted by the quantum matching optimization module through the high-speed Internet of Things using the Constrained Application Protocol, and path data transmitted by the dynamic parking cooperation module through the high-speed Internet of Things using the Constrained Application Protocol; the sensor network extracts the target parking space status information, and combines spatial feature analysis to analyze the parking path requirements during the peak period of the shopping mall parking lot; the adaptive guidance network adjusts the guidance weight, fuses the vehicle location and the parking space information, and transmits the fused features to the tactile controller through the low-latency wireless network using the ZigBee protocol to drive the steering wheel to generate vibration signals; the ripple propagation guidance technology applies the vibration guidance algorithm, receives vehicle location, parking space status, path, and matching data, maps the parking space as a wave source, generates dynamic vibration signals, and adjusts the ripple direction in real time to guide the vehicle to avoid congestion and generate a guidance scheme; the vibration guidance algorithm adapts to the guidance during the peak period of the shopping mall parking lot by means of ripple propagation, surpassing the Global Positioning System navigation technology; the vibration guidance feedback module uses a state machine to control the flow, switches the guidance signal according to the vehicle location status, transmits the guidance data through the high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol to the dynamic partition acceleration module, drives the ground light-emitting diode light strip to display the path, continuously optimizes the guidance accuracy, and automatically adjusts the strategy to adapt to complex parking scenarios.

[0016] Preferably, the dynamic partition acceleration module collects vehicle flow data through the parking lot entrance sensor, receives the flow data transmitted by the dynamic parking cooperation module through the high-speed Internet of Things in the Constrained Application Protocol, and the parking behavior data transmitted by the cooperative awareness network module through the high-speed Internet of Things in the Constrained Application Protocol; the Internet of Things sensor extracts the partition load and parking space status information, and combines multi-scale feature analysis to analyze the resource requirements of the short-term parking area in the public parking lot; the adaptive partition network adjusts the partition weight, fuses the flow data and the load information, and transmits the fused features to the layout controller through the high-speed Internet of Things in the Message Queuing Telemetry Transport Protocol to drive the projection device to display the partition layout; the fluid sculpture optimization technology applies the partition optimization algorithm, receives the flow, load, parking space status, and parking behavior data, constructs a flow field, iteratively adjusts the partition boundary, preferentially allocates resources to high-flow areas, and the virtual sculpture mechanism optimizes the boundary smoothness to generate a partition plan; the partition optimization algorithm surpasses the static partition technology through fluid sculpture and adapts to the optimization of the short-term parking area in the public parking lot; the dynamic partition acceleration module adopts an event-driven control flow, triggers partition adjustment according to the flow change, transmits the partition data to the neural charging cluster module through the high-speed Internet of Things in the Message Queuing Telemetry Transport Protocol, drives the ground projection and navigation screen to display the partition boundary, continuously optimizes the layout accuracy, and automatically adjusts the strategy to adapt to the peak-hour traffic scenario.

[0017] Preferably, the cooperative awareness network module collects user behavior data through in-vehicle terminals and user devices, receives the guidance data transmitted by the predictive interaction cloud guidance module through the cloud encryption channel in the Secure HyperText Transfer Protocol, and the vehicle status data transmitted by the dynamic parking cooperation module through the high-speed Internet of Things in the Constrained Application Protocol; the high-speed Internet of Things extracts vehicle power, location, and parking habit information, and combines behavior feature analysis to analyze the group order requirements during the peak period in the airport parking lot; the adaptive cooperation network adjusts and optimizes the weight, fuses the user behavior and vehicle status, and transmits the fused features to the order controller through the high-speed Internet of Things in the Constrained Application Protocol to drive the voice assistant to generate order management instructions; the manifold mapping optimization technology applies the group cooperation algorithm, receives user behavior, vehicle status, and load data, constructs high-dimensional manifold mapping data, iteratively adjusts resources and order, preferentially allocates resources to high-demand vehicles, and distributed federated learning optimizes the chaos degree to generate an order management plan; the group cooperation algorithm surpasses the traditional federated learning technology through manifold mapping and adapts to the order management during the peak period in the airport parking lot; the cooperative awareness network module adopts a priority queue control flow, triggers order management according to the user behavior priority, transmits the order data to the dynamic partition acceleration module through the high-speed Internet of Things in the Constrained Application Protocol, drives the voice prompt and ground projection to display the guidance line, continuously optimizes the order accuracy, and automatically adjusts the strategy to adapt to the abnormal user behavior scenario.

[0018] Preferably, the adaptive microgrid module collects energy supply data through sensors built in solar panels and energy storage devices, receives power demand data transmitted by the intelligent pulse distribution module through the high-speed Internet of Things using the Message Queuing Telemetry Transport (MQTT) protocol, and receives charging status data transmitted by the neural charging cluster module through the high-speed Internet of Things using the MQTT protocol; the sensor network extracts solar output, energy storage status, and grid load information, and combines multi-dimensional feature analysis to analyze the change in slow charging demand at night in residential parking lots; the adaptive equalization network adjusts the distribution weight, integrates energy supply and power demand, and transmits the integrated features to the energy controller through the high-speed Internet of Things using the MQTT protocol to drive the charging pile to schedule power; The ecological weaving scheduling technology applies a microgrid scheduling algorithm, receives solar energy, energy storage, grid load, power demand, and charging status data, constructs an energy node network, iteratively adjusts the distribution ratio, gives priority to using energy storage and solar energy during peak periods, and optimizes carbon emissions through a virtual balancing mechanism to generate an energy scheduling plan; the microgrid scheduling algorithm transcends linear scheduling technology through ecological weaving and adapts to the optimization of slow charging at night in residential parking lots; the adaptive microgrid module uses a state machine to control the flow, switches the scheduling mode according to the energy supply state, and transmits energy data to the neural charging cluster module through the high-speed Internet of Things using the MQTT protocol to drive the application to display the energy source, continuously optimize the scheduling accuracy, and automatically adjust the strategy to adapt to the solar fluctuation scenario; the predictive interaction cloud distribution module collects user behavior data through the cloud server and receives traffic data transmitted by the dynamic parking cooperation module through the high-speed Internet of Things using the Constrained Application Protocol (CoAP); the cloud computing network extracts traffic flow and parking lot status information, combines time series feature analysis to analyze the change in fast charging bay demand in commercial parking lots; the adaptive prediction network adjusts the distribution weight, integrates user behavior and traffic data, and transmits the integrated features to the interaction controller through a cloud encryption channel using the Secure Hypertext Transfer Protocol (HTTPS) to drive the cloud server to allocate fast charging resources; The predicted interaction cloud guidance module collects user behavior data through a cloud server and receives the matching data transmitted by the quantum matching optimization module via the Constrained Application Protocol over a high-speed Internet of Things. The cloud computing network extracts traffic flow and parking lot status information, and combines behavior feature analysis to analyze the changing demand for short-term parking paths in the airport parking lot. The adaptive guidance network adjusts the guidance weights, integrates user behavior and matching data, and transmits the integrated features to the interaction controller via a secure Hypertext Transfer Protocol through a cloud encryption channel to drive the application program to generate paths. The time flow prediction technology applies a prediction optimization algorithm, receives user behavior, traffic flow, parking lot status, and matching data, constructs time flow field mapping data, simulates the status for 30 minutes, predicts vehicle arrivals and resource requirements, the allocation module dynamically pre-allocates fast charging resources, and the guidance module optimizes short-term parking paths to generate allocation and guidance plans. The prediction optimization algorithm, through time flow prediction, surpasses time series analysis technology and adapts to the reservation of fast charging positions in commercial parking lots and the optimization of short-term parking paths in airport parking lots. The predicted interaction cloud allocation module adopts an event-driven control flow, triggers resource allocation based on user behavior, and transmits the allocation data to the quantum matching optimization module via the Constrained Application Protocol over a high-speed Internet of Things. The predicted interaction cloud guidance module adopts a state machine control flow, switches the guidance signal according to the path status, and transmits the guidance data to the collaborative awareness network module via the Constrained Application Protocol over a high-speed Internet of Things. The two modules drive the application program and the entrance digital screen to display the allocation results and paths, continuously optimize the prediction accuracy, and automatically adjust the strategy to adapt to the traffic peak scenario.

[0019] Beneficial effects

[0020] The present invention provides a new energy vehicle charging and parking management system based on intelligent load balancing. It has the following beneficial effects: 1. Through the collaborative work of the intelligent pulse allocation module and the quantum matching optimization module, the present invention can real-time monitor the vehicle power demand and parking space status, dynamically allocate charging resources and parking positions, effectively avoid the problem of uneven resource utilization during peak periods, and improve the utilization efficiency of charging piles and parking spaces. For example, during the peak period of a commercial parking lot, the system preferentially allocates fast charging positions to low-power vehicles and guides other vehicles to idle parking spaces at the same time, significantly shortening the user waiting time and optimizing the overall user experience. By using the adaptive equilibrium network and the quantum flow matching algorithm to analyze the power, location, and parking lot status data, the system can automatically identify the best mode of resource allocation, reducing the phenomenon of resource idleness and user queuing.

[0021] 2. Through the integrated application of the adaptive microgrid module and the neural charging cluster module, the present invention achieves dynamic balance of the grid load and efficient utilization of renewable energy, significantly enhancing the sustainability of the system. During peak grid load periods, the system adjusts the charging power through ecological weaving scheduling technology, preferentially using solar energy and energy storage devices for power supply to avoid overloading of local grids. For example, in the slow charging scenario at a residential parking lot at night, the system intelligently schedules the energy storage device for power supply, reducing grid dependence and simultaneously lowering carbon emissions. By using the neuron-like topology management technology to analyze the charging status and energy supply data, the system can dynamically switch the charging mode, protect the battery health, and further improve the charging efficiency and the environmental friendliness of the system.

[0022] 3. Through the collaborative action of the dynamic parking coordination module, the vibration guidance feedback module, and the predictive interaction cloud guidance module, the system achieves multi-dimensional collaborative guidance and resource pre-allocation, greatly improving the parking efficiency and user experience. The system uses group flow prediction optimization technology and ripple propagation guidance technology to real-time plan the parking path and generate dynamic guidance signals, avoiding problems such as path congestion and unclear guidance. For example, in the short-term parking scenario at an airport, the system predicts high traffic flow and pre-allocates parking spaces, guiding vehicles to park quickly through vibration signals and ground lights, significantly improving the parking efficiency. By using time flow prediction technology and adaptive guidance network to analyze user behavior and parking lot status, the system can pre-allocate resources in advance and optimize the path, reducing resource waste and ensuring smooth operation during peak periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is the specific flowchart of the present invention; Figure 2 is the simulation diagram of the analysis of the change of electrical characteristic parameters of the present invention; Figure 3 is the parking path diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Specific Embodiment 1: As Figures 1-3As shown in the figure, the new energy vehicle charging and parking management system based on intelligent load balancing includes a data acquisition module, a data processing module, a fire risk assessment module, a multi-dimensional analysis module, an early warning module, a multi-source data fusion module, a post-analysis module, and a self-learning mechanism module. Each module is closely connected through a data stream and a cooperation mechanism to form a closed-loop application, providing accurate and efficient charging and parking solutions for the characteristics of different parking lot scenarios (such as commercial, residential, airport parking lots, etc.). The detailed design of the system modules is as follows: The ripple harmonic scheduling technology is a charging power distribution technology based on energy fluctuation simulation. This technology analyzes the spatio-temporal distribution of power demand and grid load to generate dynamic pulse signals to adjust the output power of the charging grid. The core steps are as follows: First, the wireless charging grid is equipped with electromagnetic sensors with an accuracy of ±5%. It detects the battery power of the vehicles parked on it every second through electromagnetic induction, with the unit being a percentage. The data acquisition format is JSON, for example, {"battery percentage": 15, "charging rate": 20.5}. Then, the edge computing node uses a microprocessor ARMCortex-A53 with 2GB of memory to receive the power demand data and the grid load data (real-time power, with the unit being kilowatts). The edge computing node uses Fourier transform to decompose the load fluctuation, extracts the harmonic components with a frequency range of 0.1 - 10 Hz, and calculates the priority of vehicles with a battery level below 20%. Subsequently, the pulse equalization algorithm is applied. This algorithm receives data such as power demand, grid load (peak value of 1000 kW), parking space occupancy (represented by a boolean value), vehicle position (presented in coordinates, such as (10, 20)), and charging status (current, with the unit being amperes), simulates the ripple effect of the energy flow, with the amplitude range being 5 - 50 kW, and iteratively adjusts the charging grid pulse frequency (initial value of 5 Hz, step size of 0.5 Hz) and power (range of 10 - 50 kW), preferentially allocating high power, such as 50 kW, to low-battery vehicles. Finally, the distributed controller uses an Inteli5 processor with 128GB of storage, fuses the battery characteristics (remaining battery percentage, charging rate) and load information (grid output power, peak capacity), and transmits the fused characteristics to the charging grid through a 5G network with a bandwidth of 100 Mbps and a high-speed Internet of Things based on the MQTT protocol, driving it to output the adjusted pulse power, and generating parking guidance data (presented as a coordinate sequence), which is displayed as a guidance line through a ground-mounted LED strip with a length of 10 meters and a power consumption of 5 W / m at a flashing frequency of 5 times per second.

[0026] The group flow prediction optimization technology is a path planning technology based on the collective behavior of vehicles. By simulating the flow trajectories of vehicles in the parking lot, it predicts the flow distribution and optimizes the parking path. The specific steps are as follows: First, the parking lot camera has a resolution of 1080p and a frame rate of 30 frames per second, which can identify the license plate and position of the vehicle (coordinates, such as (10, 20)). The radar sensor measures the distance with an accuracy of ±0.1 meters and updates every 0.5 seconds to collect path features (current position, target parking space coordinates). Then, the IoT sensor supporting the CoAP protocol counts the number of vehicles entering the parking lot per minute (unit: vehicles / minute) and the residence time (unit: minutes), generating traffic information, such as 5 vehicles per minute and an average residence time of 15 minutes. Next, the parking cooperation optimization algorithm is applied. This algorithm receives data such as vehicle position, traffic, residence time, user behavior (reservation time, unit: minutes), and parking behavior (historical residence duration), constructs a virtual flow field, regards the vehicle as a gravitational node, whose mass is measured by the residence time, calculates the trajectory vector, with the direction angle ranging from 0 to 360 degrees, introduces a random perturbation with an amplitude of ±5 meters to avoid local congestion, and updates the trajectory with a 0.1-second step size and 100 iterations. Finally, the navigation controller uses an ARM Cortex-A72 processor, fuses the path features and traffic information, transmits the fused features to the in-vehicle navigation through Wi-Fi6 with a high-speed IoT transmission delay of 10 milliseconds, plans the path according to the state machine control flow (switches the path when the traffic density is higher than 5 vehicles / minute) (presented as a coordinate sequence, such as [(10, 20), (30, 40), (50, 60)]), drives the in-vehicle navigation to display the path, the entrance digital screen has a refresh rate of 1 time per second, displays the arrow direction, and simultaneously allocates charging resources (charging position number, such as "A12").

[0027] The neuron-like topology management technology is a charging mode management technology that dynamically adjusts power distribution by constructing a topological network among charging piles. The specific process is as follows: First, the interconnected charging pile is equipped with a power sensor with an accuracy of ±1%, which detects the current (in amperes) and voltage (in volts), records once per second, and collects charging status data in the format of {"current": 10, "voltage": 220}. Then, through the high-speed Internet of Things using the MQTT protocol, it receives the power distribution data (power, in kilowatts) transmitted by the intelligent pulse distribution module and the energy supply data (electricity, in kilowatt-hours) transmitted by the adaptive microgrid module, and extracts the vehicle power (expressed as a percentage), battery type (divided into lithium batteries or lead-acid batteries), and pile occupancy information (expressed as a boolean value). Next, applying the distributed collaborative algorithm, it constructs an inter-pile topology network with charging piles as nodes and distance (in meters) as edges, simulates neuron activation with the sigmoid activation function, and if the power is below 30%, adjusts the power to the slow charging mode with a power of 10 kilowatts. Finally, the distributed controller fuses the charging status (current, voltage) and vehicle information (power, battery type), transmits the fusion features to the charging pile through the high-speed Internet of Things, drives it to switch modes, with 30 kilowatts for fast charging and 10 kilowatts for slow charging, generates parking guidance data, and displays the guidance line through ground projection (presented as a green straight line with a length of 10 meters).

[0028] The quantum state flow matching technology is a parking space matching technology that optimizes the parking position allocation through high-dimensional vector analysis. Its operation steps are as follows: First, the parking space is equipped with an infrared sensor that updates the detection of the occupancy status (expressed as a boolean value) once per second and collects parking space status data in the format of {"position": (50, 60), "power": 30, "occupancy": 0}. Then, the sensor network receives the path data (presented as a coordinate sequence) transmitted by the dynamic parking collaboration module and the resource allocation data (fast charging position number) transmitted by the prediction interaction cloud allocation module, and extracts the parking space position (coordinates), power (kilowatts), and occupancy information. Next, applying the quantum flow matching algorithm, it maps the vehicle requirements (power demand, stay time) and the parking space status to a 10-dimensional high-dimensional vector, iteratively analyzes the matching probability with a step size of 0.01, ranging from 0 to 1, introduces the interference effect, and the weight adjustment range is ±0.1 to optimize the allocation. Finally, the navigation controller fuses the vehicle requirements and the parking space status, transmits the fusion features to the in-vehicle navigation through the high-speed Internet of Things using the CoAP protocol, matches the parking position according to the priority queue control flow (short stay time first), drives the in-vehicle application to display the position (coordinates), and the entrance digital screen displays the number, such as "A12".

[0029] The ripple propagation guidance technology is a parking guidance technology that guides the vehicle to the target parking space through vibration signals. Its specific steps are as follows: First, the ground vibration sensor detects changes in vehicle tire pressure with an accuracy of ±2%, sampling every 0.2 seconds to collect vehicle position data (presented in coordinates). Then, it receives the matching data (parking space coordinates) transmitted by the quantum matching optimization module and the path data (coordinate sequence) transmitted by the dynamic parking collaboration module, and extracts the target parking space status (occupancy, distance). Next, the vibration guidance algorithm is applied to map the parking space as a wave source with an amplitude of 5 Pascals, generate a dynamic vibration signal with a frequency of 10 Hz, and adjust the ripple direction in real time with an angle range of 0-360 degrees. Finally, the tactile controller transmits the fusion features to the steering wheel through a low-latency wireless network (using the ZigBee protocol with a delay of 5 milliseconds), driving it to generate vibrations with an intensity of 0.5 Newtons, and the ground LED light strip flashes blue to display the path.

[0030] Fluid sculpture optimization technology is a partition layout optimization technology that adjusts partition boundaries through flow fields. The specific implementation steps are as follows: First, the laser sensor at the entrance of the parking lot counts vehicles with an accuracy of ±1 vehicle, updates every second, and collects flow data. Then, it receives the flow data (vehicles / minute) transmitted by the dynamic parking collaboration module and the parking behavior data (stay time) transmitted by the collaborative awareness network module, extracts the partition load (expressed as a percentage of occupancy) and the parking space status (expressed as a Boolean value). Next, the partition optimization algorithm is applied to construct the flow field, with the speed vector as the unit of meters / second, iteratively adjust the partition boundary with a step size of 1 meter, and perform 50 iterations in total, giving priority to allocating resources to high-flow areas (more than 5 vehicles / minute). Finally, the layout controller fuses the flow data and load information, and transmits the fusion features to the projection device through the high-speed Internet of Things using the MQTT protocol, driving it to display the partition line (presented as a red dotted line), and the navigation screen displays the area number, such as "Area B".

[0031] Manifold mapping optimization technology is a parking order management technology that optimizes resource allocation through high-dimensional manifolds. The specific operation process is as follows: First, the on-board terminal records the parking time in minutes, and the user device uploads the reservation data in the form of a timestamp, synchronizes every minute, and collects user behavior data. Then, the guidance data (path coordinates) transmitted by the predictive interactive cloud guidance module and the vehicle status data (power, location) transmitted by the dynamic parking collaboration module are received to extract parking habits (expressed as the number of frequent short stops). Next, the group collaboration algorithm is applied to construct a 5-dimensional high-dimensional manifold, adjust the resource priority with a step size of 0.1 and 100 iterations, and optimize the chaos through distributed federated learning, with a target value below 0.5. Finally, the order controller integrates user behavior and vehicle status, and transmits the fused features to the voice assistant through the high-speed Internet of Things using the CoAP protocol, driving it to play instructions, such as "Please drive on the right", and the ground projection displays the guide line.

[0032] The ecological weaving scheduling technology is an energy scheduling technology that optimizes the charging power through the energy node network. The core steps are as follows: First, the solar panel sensor detects the light intensity with an accuracy of ±5% and the unit of W / m², and the energy storage device records the electricity quantity with the unit of kWh to collect the energy supply data. Then, it receives the electricity demand data (expressed as a percentage) transmitted by the intelligent pulse distribution module and the charging status data (voltage) transmitted by the neural charging cluster module, and extracts the solar output (kW) and the energy storage status (expressed as a percentage). Next, it applies the microgrid scheduling algorithm to construct the energy node network with nodes being solar panels and energy storage devices, iteratively adjusts the allocation ratio with a step size of 5% for a total of 20 iterations, and preferentially uses the energy storage during the peak period (when the grid load exceeds 800 kW). Finally, the energy controller fuses the energy supply and the electricity demand, transmits the fusion characteristics to the charging pile through the high-speed Internet of Things using the MQTT protocol to drive its scheduling power in the range of 10 - 50 kW, and the application shows the energy source and presents it by icon switching.

[0033] The time flow prediction technology is a resource allocation and path guidance technology that predicts vehicle demand through the time flow field. The specific steps are as follows: First, the cloud server is deployed using AWS and updates and receives the user reservation time every 5 minutes with the unit of minutes to collect the user behavior data. Then, the prediction interaction cloud allocation module receives the traffic flow data (vehicles / minute) transmitted by the dynamic parking coordination module, and the prediction interaction cloud guidance module receives the matching data (parking space number) transmitted by the quantum matching optimization module, and extracts the traffic flow (vehicles / hour) and the parking lot status (expressed as the occupancy rate). Next, it applies the prediction optimization algorithm to construct the time flow field with a time window of 30 minutes, predicts the vehicle arrival (number of vehicles) and the resource demand (number of fast charging positions), the allocation module dynamically pre-allocates the fast charging resources and reserves 10 positions, and the guidance module optimizes the short-term parking path (presented as a coordinate sequence). Finally, the interaction controller transmits the fusion characteristics to the cloud server through the cloud encryption channel (using HTTPS and TLS1.3 encryption), the allocation module drives the entrance digital screen to display the fast charging position number, and the guidance module drives the application to display the navigation line.

[0034] The system consists of ten modules, which are connected through the data flow and the cooperation mechanism to form a closed-loop application.

[0035] The intelligent pulse distribution module collects vehicle power demand data through electromagnetic sensors, and the data format is JSON. At the same time, it receives the vehicle position data (presented in coordinates) from the dynamic parking cooperation module and the charging status data (current) from the neural charging cluster module. Its calculation process is to use Fourier transform to extract harmonics with a frequency range of 0.1 - 10 Hz, fuse the power characteristics (expressed as a percentage) and load information (in kilowatts), sort through a priority queue according to the power percentage, with vehicles with a power below 20% having priority, generate a charging power after fusion, such as 50 kW, and parking guidance data (presented as a coordinate sequence), and transmit it to the vibration guidance feedback module through the MQTT protocol to drive the LED strip to display the guidance line.

[0036] The dynamic parking cooperation module collects vehicle position data through cameras and radars, and receives the user behavior data (reservation time) from the prediction interaction cloud distribution module and the parking behavior data (stay time) from the cooperation awareness network module. Its calculation process is to use particle swarm optimization to generate a trajectory vector, with the direction angle range of 0 - 360 degrees, fuse the path characteristics (coordinates) and traffic information (vehicles / minute), control the flow according to the state machine, adjust the path when the traffic density is higher than 5 vehicles / minute, generate path data (presented as a coordinate sequence) after fusion, and transmit it to the quantum matching optimization module through the CoAP protocol to drive the in-vehicle navigation to display the path.

[0037] The neural charging cluster module collects charging status data through power sensors, and receives the power distribution data (in kilowatts) from the intelligent pulse distribution module and the energy supply data (in kilowatt-hours) from the adaptive microgrid module. The calculation process is to simulate neuron activation through the sigmoid function, fuse the charging status (current, voltage) and vehicle information (battery type), trigger slow charging when the battery type is lithium battery according to event-driven, generate a charging mode instruction after fusion, such as 10 kW slow charging, and transmit it to the vibration guidance feedback module through the MQTT protocol to drive the ground projection to display the guidance line.

[0038] The quantum matching optimization module collects parking space status data through infrared sensors, and receives the path data from the dynamic parking cooperation module and the resource allocation data (fast charging position number) from the prediction interaction cloud distribution module. The calculation process is to perform 10-dimensional high-dimensional vector matching, fuse the vehicle demand (power demand) and parking space status (power), sort through a priority queue according to the stay time, generate matching data (parking space coordinates) after fusion, and transmit it to the vibration guidance feedback module through the CoAP protocol to drive the in-vehicle application to display the position.

[0039] The vibration guidance feedback module collects vehicle position data through vibration sensors, and receives the matching data from the quantum matching optimization module and the path data from the dynamic parking cooperation module. The calculation process is based on the ripple propagation model with an amplitude of 5 Pascals, fusing the vehicle position (coordinates) and parking space information (distance). According to the state machine, it switches when the position deviation exceeds 5 meters. After fusion, a vibration signal is generated with a frequency of 10 Hertz, which is transmitted to the dynamic partition acceleration module through the MQTT protocol to drive the vibration of the steering wheel and the display of the light strip.

[0040] The dynamic partition acceleration module collects traffic flow data through laser sensors, and receives the traffic flow data from the dynamic parking cooperation module and the parking behavior data from the collaborative awareness network module. The calculation process is to construct a traffic flow field with the velocity vector as the unit, fusing the traffic flow data (vehicles per minute) and the load information (represented by the occupancy rate). According to event-driven, it adjusts when the traffic flow surges, that is, increases by 2 vehicles per minute. After fusion, partition data (boundary coordinates) is generated, which is transmitted to the neural charging cluster module through the MQTT protocol to drive the projection device to display the partition line.

[0041] The collaborative awareness network module collects user behavior data through in-vehicle terminals and user devices, and receives the guidance data (path coordinates) from the predictive interaction cloud guidance module and the vehicle status data from the dynamic parking cooperation module. The calculation process is to perform high-dimensional manifold mapping with a dimension of 5, fusing the user behavior (reservation time) and the vehicle status (battery level). It is sorted according to parking habits through a priority queue, with frequent short stops taking precedence. After fusion, an order management instruction (presented as voice text) is generated, which is transmitted to the dynamic partition acceleration module through the CoAP protocol to drive the voice assistant to play the instruction.

[0042] The adaptive microgrid module collects energy supply data through solar panel sensors, and receives the power demand data from the intelligent pulse distribution module and the charging status data from the neural charging cluster module. The calculation process is to optimize the energy node network with an allocation ratio step of 5%, fusing the energy supply (in kilowatt-hours) and the power demand (represented as a percentage). According to the state machine, when the energy storage state is higher than 50%, the energy storage is preferentially used. After fusion, a power scheduling instruction, such as 20 kilowatts, is generated, which is transmitted to the neural charging cluster module through the MQTT protocol to drive the charging pile to adjust the power.

[0043] The predictive interaction cloud allocation module collects user behavior data through the cloud server and receives the traffic flow data from the dynamic parking cooperation module. The calculation process is to perform time flow field prediction with a time window of 30 minutes, fusing the user behavior (reservation time) and the traffic flow data (vehicles per hour). According to event-driven, it is triggered and updated every 5 minutes based on the arrival time. After fusion, resource allocation data (fast charging position number) is generated, which is transmitted to the quantum matching optimization module through the CoAP protocol to drive the entrance digital screen to display the number.

[0044] The predictive interaction cloud guidance module collects user behavior data and receives the matching data from the quantum matching optimization module. The calculation process involves optimizing the time flow field with a random flow amplitude of ±5 meters, fusing the user behavior (arrival time) and the matching data (parking space number), and according to the state machine, switching when the path is congested, i.e., the occupancy rate exceeds 80%. After fusion, path data (presented as a coordinate sequence) is generated and transmitted to the collaborative awareness network module through the CoAP protocol to drive the application to display the navigation line. Specific Embodiment 2: As Figures 1-3 shown below, the key algorithms mentioned in Embodiment 1 are analyzed in detail, including their core mathematical formulas and explanations: Pulse Equalization Algorithm: The mathematical formula is as follows:

[0046] where : The charging power of the i-th vehicle at time t (unit: kilowatt); : The remaining battery percentage of the i-th vehicle (range: 0 - 1); : The maximum output power of the charging grid (value: 50 kilowatts); : The grid load (unit: kilowatt, range: 0 - 1000); : The load regulation coefficient (value: 0.001, basis: for every 1 kilowatt increase in load, the power decreases by 0.1%); : The harmonic adjustment factor (range: 0.8 - 1.2, based on the harmonic components extracted by Fourier transform); : The pulse frequency (unit: hertz); : The initial frequency (value: 5 hertz, basis: typical fast charging response time); : The harmonic amplitude coefficient (value: 0.5, basis: the amplitude of load fluctuation); : The load fluctuation period (value: 10 seconds, basis: the statistical period of grid load); : The harmonic order (value: 5, basis: the first 5 harmonics are sufficient to characterize the fluctuation).

[0047] Parking Collaboration Optimization Algorithm: The mathematical formula is as follows:

[0048] where : The trajectory vector of the -th vehicle (unit: meter / second); : The gravitational mass of the -th vehicle (value: the number of minutes of stay, such as 15); : Vehicle and The position coordinates (unit: meters, e.g., (10, 20)); : The distance between vehicles (unit: meters); Random perturbation coefficient (value: 0.1, basis: to avoid local congestion); Random perturbation vector (range: ±5 meters, uniformly distributed); Time step (value: 0.1 seconds, basis: real-time update frequency).

[0049] Distributed collaborative algorithm: The mathematical formula is as follows:

[0050] Where : The power distribution of the th charging pile (unit: kilowatts); : Total available power (value: 100 kilowatts, basis: cluster capacity); : The charging demand of the th vehicle (value: 1 - , range 0 - 1); : Weight (value: 0.5 - 1, basis: battery type, lithium battery 1, lead-acid 0.5); Sigmoid activation function; : The number of charging piles (value: 10, basis: the scale of the residential parking lot).

[0051] Quantum flow matching algorithm: The mathematical formula is as follows:

[0052] Where : The matching degree between vehicle i and parking space j; : Vehicle demand vector (10-dimensional, e.g., [0.15, 15,...], battery level, stay time); : Parking space status vector (10-dimensional, e.g., [30, 0,...], power, occupancy); : Inner product; : Matching probability (range: 0 - 1); : Interference coefficient (value: 0.1, basis: to optimize the matching accuracy); : Interference term (value: ±0.1, randomly generated); D: Vector dimension (value: 10, basis: feature diversity); N: The number of parking spaces (value: 20, basis: the scale of the commercial parking lot).

[0053] Vibration guidance algorithm: The mathematical formula is as follows:

[0054] where vibration signal intensity (unit: Newton); : amplitude (value: 0.5 Newton, basis: human perception threshold); ; ripple direction (unit: radian, range: 0 - 2π); : vehicle position (e.g., (15, 25)); : parking space position (e.g., (50, 60)).

[0055] Partition optimization algorithm: The mathematical formula is as follows:

[0056] where : flow field strength (unit: dimensionless); : flow contribution of the th vehicle (value: 1 vehicle / minute); : vehicle position (e.g., (10, 20)); the th partition boundary coordinate (unit: meter); adjustment step size (value: 1 meter, basis: partition accuracy); flow field gradient.

[0057] Group collaboration algorithm: The mathematical formula is as follows:

[0058] where : order priority of the th vehicle (range: 0 - 1); : user behavior characteristics (value: number of short stops, e.g., 3); : weight (value: 0.8, basis: short stop priority); : chaos penalty coefficient (value: 0.1, basis: order optimization); : chaos degree (unit: m²); : actual position; : target position; : number of vehicles (value: 50, basis: airport peak period).

[0059] Microgrid scheduling algorithm: The mathematical formula is as follows:

[0060] where : Energy storage distribution power (unit: kilowatt); : Solar output (value: 20 kilowatts, basis: light intensity); : Grid power (value: 500 kilowatts, basis: load); : Total demand (value: 100 kilowatts, basis: cluster demand); : Remaining energy in energy storage (value: 50 kilowatt-hours, initial value); : Available energy in the grid (value: 1000 kilowatt-hours, basis: capacity); : Time step (value: 1 hour).

[0061] Prediction optimization algorithm: The mathematical formula is as follows:

[0062] Among them : Resource demand after 30 minutes (unit: vehicles); : Historical flow (value: 5 vehicles / minute, basis: real-time data); : Decay coefficient (value: 0.1, basis: time impact); : Random error (range: ±2 vehicles, uniform distribution); : Predistributed resources (unit: fast charging positions); : Current demand (value: 10 vehicles); : Total demand (value: 50 vehicles); : Historical window (value: 5 minutes, basis: short-term prediction). Specific embodiment three: Such as Figures 1-3 As shown below, the following is a specific use case in the new energy vehicle charging and parking management system based on intelligent load balancing: Case one: Fast charging during peak hours in a commercial parking lot Scenario description: Time: 9:00 - 11:00 am, April 13, 2025.

[0064] Location: Parking lot of a large city shopping center.

[0065] Background: The parking lot has 50 parking spaces, 20 of which are equipped with fast charging piles (power 30 - 50 kilowatts). During peak hours, there are 50 vehicles per hour, and 80% of them need fast charging, with a grid load of approximately 800 kilowatts.

[0066] User demand: A driver enters the parking lot driving an electric vehicle (lithium battery) with only 15% battery charge and hopes to charge quickly and park in a space near the mall entrance.

[0067] System working process: Intelligent Pulse Allocation Module: The ground wireless charging grid detects that the vehicle's battery level is 15% through electromagnetic sensors and generates a data packet. The edge computing node analyzes the grid load (800 kW) and the parking space occupancy (10 vacant spaces), uses the ripple harmonic scheduling technology to preferentially allocate high power to low-battery vehicles, and calculates a charging power of 24 kW. The module generates guiding coordinates (from the entrance to the target parking space) and sends them to the vibration guiding feedback module through the MQTT protocol. Dynamic Parking Collaboration Module: The parking lot cameras and radar sensors capture the vehicle's position at the entrance, and the IoT sensors record the traffic flow as 8 vehicles per minute. The system combines the cloud reservation data (arriving at 10:00) to predict peak-hour congestion. The parking collaboration optimization algorithm regards the vehicle as a node in a virtual flow field, plans a congestion-avoiding path, transmits it to the quantum matching optimization module, and displays it on the in-vehicle navigation screen.

[0068] Quantum Matching Optimization Module: The parking space sensor confirms that parking space "A12" (30 kW, vacant) is available. The system matches "A12" according to the vehicle's fast-charging requirement and 30-minute stay time, and sends the result to the vibration guiding feedback module through the CoAP protocol. The entrance digital screen displays "A12".

[0069] Vibration Guiding Feedback Module: The ground vibration sensor tracks the vehicle's position, combines the coordinates of "A12", generates a vibration signal of 10 Hz, and drives the steering wheel to vibrate slightly through the ZigBee protocol to prompt a right turn. The ground LED light strip flashes blue to outline the path to "A12". Predictive Interaction Cloud Allocation Module: The cloud server receives reservation information and traffic data, predicts that 20 fast-charging spaces will be needed in the next 30 minutes, reserves 10 (including "A12"), and notifies the quantum matching optimization module through the HTTPS protocol. Collaborative Awareness Network Module: Monitors the driver's behavior (often comes to the mall for short stops), preferentially arranges fast-charging spaces, and maintains the order of the parking lot. Neural Charging Cluster Module: Confirms that the charging pile at "A12" is activated and allocates 24 kW of fast charging. Adaptive Microgrid Module: Coordinates the power grid and energy storage to ensure stable power supply during peak hours. Dynamic Partitioning Acceleration Module: Adjusts the parking lot partitions and preferentially allocates more parking spaces to the fast-charging area. Predictive Interaction Cloud Guidance Module: Optimizes the path planning to avoid congested areas.

[0070] Result: The vehicle parks at "A12" within 8 minutes, charges at 24 kW, and the battery level rises to 50% after 30 minutes. The driver easily finds the parking space through the in-vehicle navigation, ground lights, and steering wheel vibration. The system balances the grid load, preferentially satisfies low-battery vehicles, reduces waiting time, and keeps the parking lot running efficiently.

[0071] Case 2: Slow Charging at Night in a Residential Parking Lot to Protect the Battery Scenario Description: Time: From 20:00 to 6:00 the next day on April 13, 2025.

[0072] Location: The underground parking lot of a suburban residential community.

[0073] Background: There are 30 parking spaces in the parking lot and 10 slow charging piles (with a power of 10 - 15 kilowatts). 20 vehicles are parked at night, and 90% of them stay for more than 4 hours. The solar output is 20 kilowatts, and the energy storage device has 50 kilowatt-hours.

[0074] User requirement: A resident drives an electric vehicle (lithium battery) with 30% battery power into the parking lot, hopes to slow charge at night to protect the battery, and plans to leave at 7:00 in the morning.

[0075] System working process: Neural charging cluster module: The charging pile sensor detects 30% battery power and records the current and voltage. The system receives the power data from the pulse distribution module and the energy information from the microgrid module, confirms the lithium battery type, and uses a distributed collaborative algorithm to allocate 11 kilowatts of slow charging power to avoid battery overheating. Generates guiding coordinates and sends them to the vibration guiding feedback module via the MQTT protocol, projecting a green guiding line on the ground. Adaptive microgrid module: The solar sensor confirms a 20-kilowatt output, and the energy storage device shows 50 kilowatt-hours. The system preferentially uses the energy storage, allocates 80% of the power to the vehicle, reducing the dependence on the power grid. Notifies the charging cluster via the MQTT protocol, and the in-vehicle application shows "Charging with energy storage". Intelligent pulse distribution module: Detects the low load at night (200 kilowatts) and the slow charging requirement of the vehicle, supports the allocation of 11 kilowatts, and transmits the location data to the parking coordination module. Dynamic parking coordination module: The sensor locates the vehicle at the entrance, with low night traffic (2 vehicles per hour), plans a simple path to the slow charging space "B5", and sends it to the quantum matching optimization module via the CoAP protocol. Quantum matching optimization module: Confirms that "B5" is vacant and suitable for slow charging, matches according to the long stay time, and sends the coordinates to the vibration guiding feedback module. Vibration guiding feedback module: Tracks the vehicle's position, projects a green path to "B5", slightly vibrates the steering wheel to guide the driver, and ensures accuracy via the ZigBee protocol. Collaborative awareness network module: Records the habit of the resident often parking at night, preferentially allocates slow charging spaces, and keeps the parking lot quiet and orderly. Predictive interaction cloud allocation module: Predicts the low demand at night and reserves enough slow charging spaces. Dynamic zoning acceleration module: Optimizes the layout of the slow charging area to ensure sufficient resources at night. Predictive interaction cloud guiding module: Provides a path suggestion to directly reach "B5".

[0076] Result: The vehicle parks at "B5" within 5 minutes, slow charges at 11 kilowatts, and the battery power reaches 90% by 6:00 the next day, protecting the battery health. The resident sees the energy storage power supply on the in-vehicle application and feels environmentally friendly and worry-free. The system efficiently uses the energy storage, reduces the power grid pressure, and ensures the smooth charging of all night-time vehicles.

[0077] Case 3: Efficient Coordination of Short-Term Airport Parking Scenario Description: Time: 3:00 - 6:00 PM, April 13, 2025.

[0078] Location: Parking Lot of a Certain International Airport.

[0079] Background: The parking lot has 100 parking spaces, 30 of which are equipped with charging piles (a mix of fast and slow chargers). During peak hours, 60% of the vehicles stay for less than 30 minutes, the traffic flow is 10 vehicles per minute, and the grid load is 600 kilowatts.

[0080] User Requirement: A driver is dropping off a passenger and is driving an electric vehicle (lead-acid battery) with 40% battery charge. The driver hopes to quickly charge the vehicle and park in a space close to the terminal building for 20 minutes.

[0081] System Workflow: Dynamic Parking Coordination Module: The entrance camera and radar lock the vehicle's position, and the IoT sensors record the high traffic flow (10 vehicles per minute) and short-stay trend. Combining with the 3:15 passenger drop-off reservation data in the cloud, the system predicts congestion near the terminal building, plans a path to avoid the crowd, and sends it to the Quantum Matching Optimization Module via the CoAP protocol, which is then displayed on the in-vehicle navigation. Quantum Matching Optimization Module: The sensors confirm that Parking Space "C8" (15 kilowatts, vacant) is close to the terminal building, matches its short-stay and charging requirements, and notifies the Vibration Guidance Feedback Module via the CoAP protocol. The entrance screen shows "C8".

[0082] Intelligent Pulse Allocation Module: Detects 40% battery charge and lead-acid battery type, allocates 12 kilowatts of power, balances the 600-kilowatt grid load, and sends guidance data to "C8" via the MQTT protocol. Vibration Guidance Feedback Module: The sensors track the vehicle, generate a 10-hertz vibration signal to prompt a left turn, and the amber LED strip lights up the path to ensure precise guidance via the ZigBee protocol. Cooperative Awareness Network Module: Identifies the short-stay pattern of the driver's frequent passenger drop-offs, preferentially allocates parking spaces close to the terminal building, plays a voice prompt "Turn left and go to C8" via the in-vehicle system, and maintains order during peak hours. Predictive Interactive Cloud Guidance Module: Analyzes the traffic flow and reservations, predicts an increase in short-stay vehicles, optimizes the path to directly reach "C8", and notifies the Cooperative Awareness Network Module via the HTTPS protocol. Neural Charging Cluster Module: Adjusts the charging pile at "C8" to 12 kilowatts, suitable for rapid charging of lead-acid batteries, and starts charging. Adaptive Microgrid Module: Ensures that the power grid stably supports charging during peak hours. Dynamic Partitioning Acceleration Module: Preferentially allocates parking spaces to the short-stay area to optimize the traffic flow. Predictive Interactive Cloud Allocation Module: Reserves short-stay charging spaces to meet peak demand.

[0083] Results: The vehicle parks at "C8" within 4 minutes and charges at 12 kW. After 20 minutes, the battery level increases to 50%, which is sufficient for the return journey. The driver is guided to park quickly through voice, vibration, and lights and leaves promptly after dropping off passengers. The system efficiently manages high traffic, maintains the turnover of parking spaces at the terminal building, and operates in an orderly manner.

[0084] Summary: These three cases demonstrate the strong adaptability of the system in commercial, residential, and airport scenarios, meeting the requirements of fast charging, battery protection, and short-term parking respectively. Specific Embodiment 4: As Figures 1-3 shown below is the detailed hardware composition and hardware description of each module in Embodiment 1: Intelligent Pulse Distribution Module: The wireless charging grid covers the parking lot ground and is equipped with electromagnetic sensors that detect the vehicle's battery level every second; Battery level sensor: Hall effect sensor, embedded in the coil, to monitor current and voltage in real time. Edge computing node: ARM Cortex-A53 processor, running the ripple harmonic scheduling algorithm, analyzing the grid load to calculate the priority of low-battery vehicles, and fusing battery level and load data. Distributed controller: Intel i5-8500T processor, receiving the fused features, driving the output power of the charging grid, and transmitting the guiding coordinates through MQTT.

[0086] Ground LED strip: RGB LED strip, displaying guiding lines with a flashing frequency of 5 Hz to indicate the path. High-speed Internet of Things module: 5G module, realizing MQTT data transmission, connecting the edge node and the controller, with a latency of 10 ms, supporting Wi-Fi 6, a power consumption of 2W, and a coverage of 500m.

[0087] Hardware collaboration: The battery level sensor collects data. After being processed by the edge node, the controller adjusts the power of the charging grid and drives the LED strip, and the 5G module ensures real-time communication.

[0088] Dynamic Parking Collaboration Module: Parking lot camera: 1080p high-definition camera, capturing the vehicle's license plate and position; Radar sensor: Millimeter-wave radar. Measuring the vehicle's distance every 0.5 seconds to generate path feature data; Internet of Things sensor: Traffic counter, counting the number of vehicles per minute (5 - 10 vehicles) and the parking duration (5 - 60 minutes), supporting the CoAP protocol. Navigation controller: ARM Cortex-A72 processor, running the parking collaboration optimization algorithm, fusing path and traffic data to plan the path. In-vehicle navigation display: 7-inch touch screen, displaying the path and charging positions (such as "A12"). Entrance digital screen: 55-inch LED display, showing path arrows and parking space numbers. High-speed Internet of Things module: Wi-Fi 6 router, transmitting path data, connecting sensors and controllers.

[0089] Hardware Collaboration: The camera and radar collect position data, the IoT sensors provide traffic data, the navigation controller plans the route, the display screens and in-vehicle screens guide the driver, and Wi-Fi 6 ensures fast transmission.

[0090] Neural Charging Cluster Module: Interconnected Charging Pile: Smart charging pile, with built-in power sensors that detect current (0 - 50A) and voltage (220V), and support fast / slow charging switching. Power Sensor: Current transformer, records the charging status per second and supports MQTT transmission. Distributed Controller: Intel i5 - 8500T processor, runs distributed collaborative algorithms to adjust power and integrate battery type and power data. Ground Projection Device: Laser projector, displays green guiding lines and receives MQTT data. High-Speed IoT Module: 5G module transmits charging status and guiding data with a latency of 10ms. Hardware Collaboration: The charging pile sensors collect data, the controller optimizes power distribution, the projection device displays the route, and the 5G module enables real-time communication.

[0091] Quantum Matching Optimization Module: Parking Space Sensor: Infrared sensor, detects the parking space status per second (such as {"position":(50,60), "power":30,"occupied":0}) and supports CoAP transmission. Power consumption is 1W and the coverage is 10m. Navigation Controller: ARM Cortex-A72 processor (quad-core, 1.8GHz, 4GB RAM). Runs quantum flow matching algorithm to match parking spaces (such as "A12") and integrates vehicle requirements and status. Supports Wi-Fi 6 with a power consumption of 15W. In-Vehicle Application Terminal: Embedded display screen (8 inches, 1280x720 resolution). Displays the matched parking space number and receives data via CoAP. Power consumption is 12W and the operating temperature is -10°C to 50°C. Entrance Digital Screen: 55-inch LED screen (LG 55SVH7F, 4K resolution). Displays the parking space number (such as "A12") with a refresh rate of 1Hz. Power consumption is 180W, IP65 protection, and brightness of 1500 nits. High-Speed IoT Module: Wi-Fi 6 module (Intel AX200, latency 10ms). Transmits matched data (CoAP / HTTPS), covers 200m, and power consumption is 3W. Hardware Collaboration: The sensors detect the parking space status, the controller calculates the match, the display screens and in-vehicle terminals guide the driver, and Wi-Fi 6 ensures efficient transmission.

[0092] Vibration Guiding Feedback Module: Ground vibration sensor: Piezoelectric sensor, detects tire pressure changes every 0.2 seconds and outputs position coordinates. Power consumption is 2mW, coverage is 5m. Tactile controller: The microcontroller drives the steering wheel through ZigBee. Steering wheel vibration module: Linear vibration motor (Nidec C1026, 0.5 - 1N). Receives signals to guide the driver (e.g., turn left). Power consumption is 3W, lifespan is 20000 hours. Ground LED strip: RGB LED strip (5m / section, power consumption 5W / m). Displays a blue path with a blinking frequency of 5Hz. IP65 waterproof, brightness is 800 lumens.

[0093] Low-latency wireless module: ZigBee module (TI CC2652R, latency 5ms). Transmits guiding data, coverage is 100m, power consumption is 1W. Hardware cooperation: The vibration sensor locates the vehicle, the tactile controller generates signals, the vibration motor and the LED strip guide the driver, and ZigBee ensures low latency.

[0094] Dynamic partition acceleration module: Entrance sensor: Laser counter, counts the traffic flow per second and supports MQTT transmission. Internet of Things sensor: LoRa module, extracts partition load and supports CoAP transmission. Power consumption is 2W, battery life is 3 years. Layout controller: ARM Cortex-A53 processor, runs the partition optimization algorithm to adjust the boundaries. Projection device: DLP projector, displays red partition lines, resolution is 1080p.

[0095] Navigation screen: 43-inch display screen, displays the partition number (such as "Area B"), power consumption is 150W, brightness is 1000 nits. High-speed Internet of Things module: 5G module, transmits partition data, latency is 10ms, power consumption is 2W.

[0096] Hardware cooperation: The laser sensor collects the traffic flow, the controller optimizes the partition, the projection device and the navigation screen display the boundaries, and the 5G module ensures real-time performance.

[0097] Collaborative awareness network module: In-vehicle terminal: Embedded processor, records the parking time (such as 15 minutes) and supports CoAP transmission. Power consumption is 10W, with a 4G module.

[0098] User device interface: Wi-Fi hotspot (Qualcomm QCA6174). Receives the reservation time (timestamp) and uploads it through HTTPS. Power consumption is 3W, coverage is 50m.

[0099] Order controller: Intel i5-8265U processor (4 cores, 1.6GHz, 32GB storage). Runs the group collaboration algorithm to generate instructions (such as "Drive on the right"). Supports 5G, power consumption is 30W.

[0100] Voice Assistant: Smart Speaker (TI PCM1864, 4-channel ADC). Plays voice prompts and receives CoAP data. Power consumption is 5W and volume is 80dB.

[0101] Floor Projection Device: Laser Projector (Optoma ZH406, 4000 lumens). Displays guiding lines, resolution is 1080p, and power consumption is 140W.

[0102] High-speed IoT Module: Wi-Fi 6 Module (Intel AX201, latency 10ms). Transmits order data, coverage is 200m, and power consumption is 3W.

[0103] Hardware Collaboration: The in-vehicle terminal and user device collect behaviors, the controller optimizes order, the speaker and projector guide the driver, and Wi-Fi 6 ensures communication.

[0104] Adaptive Microgrid Module: Solar Panel: Monocrystalline Silicon Photovoltaic Panel (JinkoSolar, 500W per panel, efficiency 22%). Built-in light sensor (accuracy ±5%), output is 20 - 30kW. IP68 protection, lifespan is 25 years. Energy Storage Device: Lithium Battery Pack (CATL, 50kWh, voltage 400V). Records energy storage status (0 - 100%), supports MQTT transmission. Power consumption is 50W (standby), cycle life is 5000 times. Grid Sensor: Power Meter (Schneider PM5560, accuracy ±0.5%). Monitors load (0 - 1000kW), supports real-time data upload. Power consumption is 10W, operating temperature is -25°C to 70°C. Energy Controller: Intel i5-8500T Processor (6 cores, 2.1GHz, 64GB storage). Runs microgrid scheduling algorithm, allocates power (such as 20kW energy storage). Supports 5G, power consumption is 40W. Charging Pile Interface: Smart Distributor (Delta EVCC, 10 - 50kW). Executes power scheduling and receives MQTT instructions. Power consumption is 20W, IP55 protection. High-speed IoT Module: 5G Module (Quectel RM500Q, bandwidth 100Mbps). Transmits energy data, latency is 10ms, power consumption is 2W. Hardware Collaboration: The solar panel and energy storage device collect data, the controller schedules power, the distributor adjusts the charging pile output, and the 5G module ensures real-time performance.

[0105] Predictive Interaction Cloud Allocation Module: Cloud Server: AWS EC2 instance (c5.4xlarge, 16 cores, 32GB RAM). Receives reservation data (updated every 5 minutes), runs the time series prediction algorithm, and allocates fast charging positions. Bandwidth is 1Gbps, power consumption is 300W. Interaction Controller: ARM Cortex-A72 processor (quad-core, 1.8GHz, 4GB RAM). Integrates user behavior and traffic data to drive allocation instructions. Supports Wi-Fi6, power consumption is 15W. Entrance Digital Screen: 55-inch LED screen (Samsung QM55R, 4K resolution). Displays fast charging position numbers (e.g., 8), refresh rate is 1Hz. Power consumption is 200W, IP65 protection. High-Speed IoT Module: Wi-Fi 6 router (Netgear Nighthawk AX12, latency 10ms). Transmits allocation data (CoAP / HTTPS), coverage is 200m, power consumption is 20W. Hardware Collaboration: The server predicts demand, the controller generates allocation schemes, the digital screen displays the results, and Wi-Fi 6 ensures fast communication.

[0106] Prediction Interaction Cloud Guidance Module: Cloud Server: AWS EC2 instance (m5.2xlarge, 8 cores, 16GB RAM). Receives user behavior and matching data, optimizes the path (e.g., [(30,40), (50,60)]). Bandwidth is 500Mbps, power consumption is 200W. Interaction Controller: ARM Cortex-A72 processor (quad-core, 1.8GHz, 4GB RAM). Integrates behavior and matching data to generate navigation lines. Supports Wi-Fi 6, power consumption is 15W. In-Vehicle Application Terminal: 8-inch display screen (1280x720 resolution). Displays navigation lines and receives data via CoAP. Power consumption is 12W, operating temperature is -10°C to 50°C. Entrance Digital Screen: 55-inch LED screen (LG 55SVH7F, 4K resolution). Displays path guidance, refresh rate is 1Hz. Power consumption is 180W, IP65 protection. High-Speed IoT Module: Wi-Fi 6 module (Intel AX201, latency 10ms). Transmits guidance data (CoAP / HTTPS), coverage is 200m, power consumption is 3W. Hardware Collaboration: The server optimizes the path, the controller generates guidance instructions, the in-vehicle terminal and the digital screen display the path, and Wi-Fi 6 ensures efficient transmission.

[0107] The above hardware components cover the functional requirements of all modules, from data collection (sensors, cameras) to processing (processors, controllers), output (display screens, light strips, projectors), and then to communication (5G, Wi-Fi 6, ZigBee), ensuring the efficient operation of the system in commercial, residential, and airport scenarios.

[0108] Among them, attached Figure 3It is a parking path simulation diagram. The red asterisk represents the parking space, and each colored path represents the trajectory of a vehicle moving from the starting position to the target parking space.

[0109] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a reference structure" does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0110] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A new energy vehicle charging and parking management system based on intelligent load balancing, characterized in that: It includes an intelligent pulse distribution module, a dynamic parking coordination module, a neural charging cluster module, a quantum matching optimization module, a vibration guidance feedback module, a dynamic partition acceleration module, a collaborative awareness network module, an adaptive microgrid module, a predictive interaction cloud distribution module, and a predictive interaction cloud guidance module; The intelligent pulse distribution module collects vehicle power demand data through a wireless charging grid embedded in the ground, processes the data using ripple harmonic scheduling technology, analyzes vehicle and grid load information using edge computing nodes, fuses power characteristics and load information using an adaptive equalization network, transmits the fused features to a distributed controller through a high-speed Internet of Things, and allocates charging power and generates parking guidance data according to the priority queue control flow; The dynamic parking coordination module collects vehicle position data through parking lot cameras and radar sensors, processes the data using swarm flow prediction optimization technology, analyzes vehicle flow and residence time information using Internet of Things sensors, fuses path features and flow information using an adaptive collaborative network, transmits the fused features to a navigation controller through a high-speed Internet of Things, and plans a parking path and allocates charging resources according to the state machine control flow.

2. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 1, characterized in that: The neural charging cluster module collects charging status data, manages the charging mode after processing the data, and generates parking guidance data; The quantum matching optimization module collects parking space status data, matches the parking position and allocates charging resources after processing the data; the vibration guidance feedback module collects vehicle position data, generates a parking guidance signal and connects the charging process after processing the data; the dynamic partition acceleration module collects vehicle flow data, optimizes the partition layout and allocates charging resources after processing the data; the collaborative awareness network module collects user behavior data, manages the parking order and allocates resources after processing the data; the adaptive microgrid module collects energy supply data, schedules the charging power and allocates parking resources after processing the data; the predictive interaction cloud distribution module collects user behavior data, and allocates parking and charging resources after processing the data; the predictive interaction cloud guidance module collects user behavior data, and generates a parking path and a charging guidance signal after processing the data.

3. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 1, characterized in that: The intelligent pulse distribution module collects vehicle power demand data through a built-in power sensor in a wireless charging grid embedded in the ground, receives vehicle position data transmitted by the dynamic parking coordination module through a high-speed Internet of Things using the Message Queuing Telemetry Transport protocol, and charging status data transmitted by the neural charging cluster module through a high-speed Internet of Things using the Message Queuing Telemetry Transport protocol; The edge computing node extracts power demand, grid load, and parking space occupancy information, combines multi-scale feature analysis, and analyzes the load change during peak hours in commercial parking lots; the adaptive equalization network adjusts the allocation weights, fuses the power characteristics and load information, and transmits the fused features to the distributed controller through the high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol, driving the wireless charging grid to allocate charging power. The ripple harmonic scheduling technology applies the pulse equalization algorithm, receives power demand, grid load, parking space occupancy, vehicle position, and charging status data, simulates the ripple effect of the energy flow, iteratively adjusts the pulse frequency and power of the charging grid, preferentially allocates high power to low-battery vehicles, balances the load through the harmonic adjustment mechanism, and generates a charging allocation plan; the pulse equalization algorithm iterates through the ripple harmonics, surpasses the linear allocation technology, and adapts to the fast charging demand during peak hours in commercial parking lots; the intelligent pulse distribution module uses a priority queue to control the flow, triggers the charging allocation according to the power demand sorting, transmits the parking guidance data to the vibration guidance feedback module through the high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol, drives the ground LED strip to display the guidance line, continuously optimizes the allocation accuracy, and automatically adjusts the parameters to adapt to the grid load fluctuation scenario.

4. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 1, wherein: The dynamic parking collaboration module collects vehicle position data through the parking lot cameras and radar sensors, receives user behavior data transmitted by the prediction interaction cloud allocation module through the secure hypertext transfer protocol via the cloud encryption channel, and parking behavior data transmitted by the collaboration awareness network module through the high-speed Internet of Things using the Constrained Application Protocol; the Internet of Things sensors extract vehicle flow, stay time, and power demand information, combines time series analysis, and analyzes the dynamic changes of short-term parked vehicles in airport parking lots. The adaptive collaboration network adjusts the path weights, fuses the path characteristics and flow information, and transmits the fused features to the navigation controller through the high-speed Internet of Things using the Constrained Application Protocol, driving the in-vehicle navigation to plan the path; the group flow prediction optimization technology applies the parking collaboration optimization algorithm, receives vehicle position, flow, stay time, user behavior, and parking behavior data, constructs a virtual flow field to map the vehicle as a gravitational node, iteratively updates the movement trajectory, introduces random perturbations to optimize the path and charging position allocation, and generates a navigation plan. The parking collaboration optimization algorithm surpasses the traditional path planning technology through group flow dynamics and adapts to the planning of short-term parked vehicles in parking lots.

5. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 2, wherein: The neural charging cluster module collects charging status data through the built-in power sensors of the interconnected charging piles, receives the power allocation data transmitted by the intelligent pulse distribution module through the high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol, and the energy supply data transmitted by the adaptive microgrid module through the high-speed Internet of Things using the Message Queuing Telemetry Transport Protocol; the high-speed Internet of Things extracts vehicle power, battery type, and pile occupancy information, combines multi-dimensional feature analysis, and analyzes the change of slow charging demand in residential parking lots. The adaptive collaborative network adjusts and optimizes the weights, fuses the charging state and vehicle information, and transmits the fused features to the distributed controller via the high-speed Internet of Things using the Message Queuing Telemetry Transport protocol to drive the charging pile to switch the charging mode; the neuron-like topology management technology applies a distributed collaborative algorithm to receive charging state, battery level, battery type, and energy supply data, constructs an inter-pile topology network, simulates neuron activation to adjust power and priority, and preferentially allocates resources to high-demand vehicles to generate a charging management plan; the distributed collaborative algorithm adapts to the slow charging battery protection in the parking lot through the neuron-like topology.

6. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 2, characterized in that: The quantum matching optimization module collects parking space status data through sensors built into the parking space, receives path data transmitted by the dynamic parking collaboration module via the high-speed Internet of Things using the Constrained Application Protocol, and resource allocation data transmitted by the prediction interaction cloud allocation module via the cloud encryption channel using the Hypertext Transfer Protocol Secure; the sensor network extracts parking space location, power, and occupancy information, and combines high-dimensional feature analysis to analyze the fast charging space matching requirements in commercial parking lots. The adaptive matching network adjusts the matching weights, fuses vehicle requirements and parking space status, and transmits the fused features to the navigation controller via the high-speed Internet of Things using the Constrained Application Protocol to drive the in-vehicle navigation to match the parking location; the quantum state flow matching technology applies the quantum flow matching algorithm to receive vehicle requirements, parking space status, path, and resource allocation data, maps them into high-dimensional vectors, iteratively analyzes the matching probability, introduces the interference effect to optimize the allocation, and generates a matching plan; the quantum flow matching algorithm adapts to the fast charging space matching in the parking lot through the quantum state flow.

7. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 2, characterized in that: The vibration guidance feedback module collects vehicle location data through ground vibration sensors, receives matching data transmitted by the quantum matching optimization module via the high-speed Internet of Things using the Constrained Application Protocol, and path data transmitted by the dynamic parking collaboration module via the high-speed Internet of Things using the Constrained Application Protocol; the sensor network extracts the target parking space status information, and combines spatial feature analysis to analyze the parking path requirements during peak periods in shopping mall parking lots. The adaptive guidance network adjusts the guidance weights, fuses vehicle location and parking space information, and transmits the fused features to the tactile controller via a low-latency wireless network using the ZigBee protocol to drive the steering wheel to generate vibration signals; the ripple propagation guidance technology applies the vibration guidance algorithm to receive vehicle location, parking space status, path, and matching data, maps the parking space as a wave source, generates dynamic vibration signals, and adjusts the ripple direction in real time to guide the vehicle to avoid congestion and generate a guidance plan.

8. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 2, wherein: The dynamic partition acceleration module collects vehicle flow data through sensors at the parking lot entrance, receives traffic flow data transmitted by the dynamic parking collaboration module via the high-speed Internet of Things using the Constrained Application Protocol, and parking behavior data transmitted by the collaborative awareness network module via the high-speed Internet of Things using the Constrained Application Protocol; the Internet of Things sensors extract partition load and parking space status information, and combine multi-scale feature analysis to analyze the resource requirements in the short-term parking area of public parking lots. The adaptive partition network adjusts the partition weights, fuses traffic data and load information, and transmits the fused features to the layout controller via the High-Speed Internet of Things (IoT) using the Message Queuing Telemetry Transport (MQTT) protocol to drive the projection device to display the partition layout; the Fluidic Sculpture Optimization Technology applies a partition optimization algorithm, receives traffic, load, parking space status, and parking behavior data, constructs a traffic flow field, iteratively adjusts the partition boundaries, preferentially allocates resources to high-traffic areas, and the Virtual Sculpture Mechanism optimizes the boundary smoothness to generate a partition plan.

9. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 2, characterized in that: The Cooperative Awareness Network Module collects user behavior data through in-vehicle terminals and user devices, receives guidance data transmitted by the Prediction Interaction Cloud Guidance Module via a secure hypertext transfer protocol (HTTPS) through a cloud encryption channel, and vehicle status data transmitted by the Dynamic Parking Collaboration Module via the High-Speed IoT using the Constrained Application Protocol (CoAP); the High-Speed IoT extracts vehicle battery level, location, and parking habit information, and combines behavioral feature analysis to analyze the group order requirements during peak periods in the airport parking lot; The Adaptive Cooperative Network adjusts and optimizes the weights, fuses user behavior and vehicle status, and transmits the fused features to the Order Controller via the High-Speed IoT using the Constrained Application Protocol (CoAP) to drive the voice assistant to generate order management instructions; the Manifold Mapping Optimization Technology applies a group collaboration algorithm, receives user behavior, vehicle status, and load data, constructs high-dimensional manifold mapping data, iteratively adjusts resources and order, preferentially allocates resources to high-demand vehicles, and Distributed Federated Learning optimizes the chaos level to generate an order management plan.

10. The new energy vehicle charging and parking management system based on intelligent load balancing according to claim 2, characterized in that: The Adaptive Microgrid Module collects energy supply data through sensors built into solar panels and energy storage devices, receives power demand data transmitted by the Intelligent Pulse Allocation Module via the High-Speed IoT using the Message Queuing Telemetry Transport (MQTT) protocol, and charging status data transmitted by the Neural Charging Cluster Module via the High-Speed IoT using the Message Queuing Telemetry Transport (MQTT) protocol; the sensor network extracts solar output, energy storage status, and grid load information, and combines multi-dimensional feature analysis to analyze the changing slow charging demands at night in the residential parking lot; the Adaptive Equalization Network adjusts and allocates weights, fuses energy supply and power demand, and transmits the fused features to the Energy Controller via the High-Speed IoT using the Message Queuing Telemetry Transport (MQTT) protocol to drive the charging pile to adjust the power; The described ecological weaving scheduling technology applies a microgrid scheduling algorithm, receives data on solar energy, energy storage, grid load, electricity demand, and charging status, constructs an energy node network, iteratively adjusts the allocation ratio, preferentially uses energy storage and solar energy during peak periods, optimizes carbon emissions through a virtual balancing mechanism, and generates an energy scheduling plan; the microgrid scheduling algorithm transcends linear scheduling technology through ecological weaving and adapts to the optimization of slow charging at residential parking lots at night; the adaptive microgrid module uses a state machine to control the flow, switches the scheduling mode according to the energy supply status, transmits energy data through a high-speed Internet of Things to the neural charging cluster module using the Message Queuing Telemetry Transport Protocol, drives the application program to display the energy source, continuously optimizes the scheduling accuracy, and automatically adjusts the strategy to adapt to the solar energy fluctuation scenario; the prediction interaction cloud allocation module collects user behavior data through a cloud server and receives traffic data transmitted by the dynamic parking collaboration module through the high-speed Internet of Things using the Constrained Application Protocol; the cloud computing network extracts traffic flow and parking lot status information, combines time series feature analysis, and analyzes the change in the demand for fast charging positions in commercial parking lots; the adaptive prediction network adjusts the allocation weight, fuses user behavior and traffic data, and transmits the fused features through a cloud encryption channel to the interaction controller using the Secure Hypertext Transfer Protocol, driving the cloud server to allocate fast charging resources; The prediction interaction cloud guidance module collects user behavior data through a cloud server and receives matching data transmitted by the quantum matching optimization module through the high-speed Internet of Things using the Constrained Application Protocol; the cloud computing network extracts traffic flow and parking lot status information, combines behavior feature analysis, and analyzes the change in the demand for short-stop paths in airport parking lots; The adaptive guidance network adjusts the guidance weight, fuses user behavior and matching data, and transmits the fused features through a cloud encryption channel to the interaction controller using the Secure Hypertext Transfer Protocol, driving the application program to generate a path; the time flow prediction technology applies a prediction optimization algorithm, receives user behavior, traffic, parking lot status, and matching data, constructs time flow field mapping data, simulates the status for 30 minutes, predicts vehicle arrival and resource demand, the allocation module dynamically pre-allocates fast charging resources, and the guidance module optimizes the short-stop path, generating an allocation and guidance plan; the prediction optimization algorithm transcends time series analysis technology through time flow prediction and adapts to the reservation of fast charging positions in commercial parking lots and the optimization of short-stop paths in airport parking lots; The prediction interaction cloud allocation module uses event-driven control flow, triggers resource allocation according to user behavior, and transmits the allocation data through the high-speed Internet of Things to the quantum matching optimization module using the Constrained Application Protocol; The prediction interaction cloud guidance module uses a state machine to control the flow, switches the guidance signal according to the path status, and transmits the guidance data through the high-speed Internet of Things to the collaborative awareness network module using the Constrained Application Protocol; the two modules drive the application program and the entrance digital screen to display the allocation result and the path, continuously optimize the prediction accuracy, and automatically adjust the strategy to adapt to the traffic peak scenario.

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