New energy automobile charging pile orderly management system and method based on WeChat applet

Abstract

The invention relates to the technical field of new energy automobile charging management, in particular to a new energy automobile charging pile orderly management system and method based on a WeChat applet, and the system comprises a multi-dimensional data collection module which is used for collecting the state of a charging pile, parking space occupation and power grid load data; the Internet of Things sensor group is deployed at the charging pile and is used for acquiring electrical parameters such as the state (during charging, idle charging and fault), charging power and voltage of the charging pile in real time; provided is a parking space visual monitoring device. Through combination of an interval tree data structure and a multi-target optimization scheduling model, space-time dimension dynamic management of charging pile resources is realized, the problems of reservation conflicts, resource idleness and the like in a traditional system are effectively solved, the utilization rate of the charging piles is remarkably improved, and an intelligent time period recommendation algorithm is based on user habits and a power grid load state. Users are guided to carry out peak shifting charging, balanced allocation of charging resources and power grid loads is promoted, and optimization of energy utilization efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle charging management, and more specifically to a new energy vehicle charging pile orderly management system and method based on a WeChat applet. Background Art

[0002] With the increasing popularity of new energy vehicles, charging pile management faces numerous pressing challenges. Traditional charging management models face significant technical bottlenecks: First, the lack of real-time sensing and status synchronization prevents users from timely accessing key data such as the idle status and fault information of charging piles. Users often arrive at the site to find charging piles occupied, resulting in inefficient charging pile retrieval and a poor user experience. Second, the reservation mechanism suffers from spatiotemporal scheduling flaws. The existing system can only implement simple time-slot locking and cannot effectively handle conflicts when multiple users make concurrent reservations. Furthermore, the system lacks intelligent scheduling based on factors such as grid load and user usage habits, making it difficult to rationally allocate charging pile resources. Third, the billing strategy is rigid and single, unable to dynamically adjust to peak and valley electricity price fluctuations and grid load changes, increasing charging costs for users while hindering the balanced regulation of grid load.

[0003] Existing charging pile management technology suffers from significant shortcomings: First, resource scheduling algorithms lack multi-objective optimization capabilities. Most systems utilize a single "idle priority" scheduling principle, failing to comprehensively consider multi-dimensional objectives such as user waiting time and grid load balancing. This makes it difficult for scheduling solutions to strike a balance between efficiency and fairness. Second, mechanisms for constraining user behavior are lacking, leaving a lack of effective management measures for violations such as overtime occupancy and the use of fuel vehicles, making it difficult to regulate user behavior. Furthermore, data interaction is limited in dimension, enabling only simple interaction between charging status and order data. It fails to integrate multi-dimensional data such as vehicle type, charging port, and user credit, making it impossible to support intelligent management of integrated charging piles. These technical deficiencies render traditional charging management systems unsuitable for the large-scale deployment of new energy vehicles, necessitating urgent technological innovation to overcome these bottlenecks.

[0004] Therefore, a new energy vehicle charging pile orderly management system and method based on WeChat applet is proposed to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a new energy vehicle charging pile orderly management system and method based on WeChat applet to solve the problems existing in the above-mentioned background technology.

[0006] The present invention provides the following technical solution: an orderly management system for new energy vehicle charging piles based on WeChat mini-program, characterized by comprising:

[0007] A multi-dimensional data acquisition module collects data on charging pile status, parking space occupancy, and grid load. IoT sensors deployed at charging piles collect real-time data on charging pile status (charging, idle, faulty), charging power, voltage, and other electrical parameters. A parking space visual monitoring device uses computer vision technology to identify parking space occupancy and vehicle type (new energy vehicle / fuel vehicle). A grid load monitoring interface obtains real-time load data and electricity price information for the regional power grid.

[0008] The intelligent decision-making engine includes a dynamic reservation conflict detection algorithm, a multi-objective optimization scheduling model, and an intelligent time period recommendation algorithm. The dynamic reservation conflict detection algorithm: Based on a two-dimensional space-time constraint model, it uses an interval tree data structure to detect reservation period conflicts in real time, ensuring that only one reservation is made for the same charging station in the same time period. The multi-objective optimization scheduling model: With the shortest user waiting time, the highest charging station utilization rate, and the balanced grid load as the objective functions, it combines a genetic algorithm to generate the optimal reservation scheduling plan. The intelligent time period recommendation algorithm: Based on the user's historical charging habits, current grid load, and charging station usage predictions, it recommends the optimal charging time for users and supports off-peak charging guidance.

[0009] The credit score management module establishes a multi-dimensional credit evaluation system and links it to appointment priority. This system includes dimensions such as appointment fulfillment rate, number of timeouts, and payment timeliness, and uses the analytic hierarchy process to determine the weight of each dimension. Credit scores are linked to appointment priority, and high-credit users enjoy priority appointments and preferential rates. A credit reward and punishment mechanism is designed to impose appointment restrictions on users who maliciously occupy appointments or repeatedly breach the contract.

[0010] A dynamic billing engine implements tiered billing and overtime progressive billing based on real-time electricity prices and loads. The tiered billing model based on real-time electricity prices and loads: the rate decreases by 30% during off-peak hours (23:00-7:00) and increases by 50% during peak hours (17:00-21:00), with the floating ratio dynamically adjusted based on real-time load fluctuations in the power grid. Overtime progressive billing strategy: the first 30 minutes are charged at 1.5 times the base rate, and after 30 minutes, the rate is doubled, with the rate increasing by 20% for every additional 15 minutes. The system supports point deduction, allowing users to use credit points or charging points to offset part of the fee.

[0011] The WeChat mini-program interactive module provides 3D visualization of charging piles, intelligent matching of vehicles and charging piles, and real-time message push. The 3D visualization of charging piles: Based on WebGL technology, it builds a 3D model of the charging pile parking lot, visually displaying the location, status, and charging interface type of the charging piles. The intelligent matching function of vehicles and charging piles: It automatically obtains the charging interface type of the user's vehicle (via WeChat binding of vehicle information or OCR recognition of the driving license) and selects matching charging piles. The real-time message push system: Using WebSocket long connection technology, it pushes messages such as appointment reminders, charging completion, and timeout warnings, and supports custom message priority.

[0012] The blockchain evidence storage module is used for on-chain storage of key data and execution of smart contracts; a consortium chain architecture is built to store key data such as charging orders, payment records, and credit point changes on-chain to ensure that the data cannot be tampered with; smart contracts are used to automatically execute rules such as overtime billing and point redemption to reduce manual intervention.

[0013] Furthermore, the intelligent decision engine uses an interval tree data structure to implement reservation conflict detection and solves multi-objective optimization scheduling solutions through a genetic algorithm.

[0014] Furthermore, the credit score management module determines the evaluation dimension weights in combination with the hierarchical analysis method, and associates the credit score with the reservation priority and the rate discount.

[0015] Furthermore, the WeChat applet interaction module builds a three-dimensional visualization interface based on WebGL technology, realizes real-time message push through WebSocket, and automatically matches the vehicle charging interface type.

[0016] Furthermore, the blockchain evidence storage module adopts a consortium chain architecture to store charging orders, payment records and credit point changes on the chain, and executes billing rules through smart contracts.

[0017] Furthermore, a method for orderly management of new energy vehicle charging piles based on WeChat mini-programs includes:

[0018] Multimodal binding of user and vehicle information to generate a unique identifier;

[0019] User and vehicle binding process

[0020] Users complete real-name authentication through the WeChat mini-program, which supports facial recognition or ID card OCR recognition;

[0021] Multi-modal vehicle information input: take a photo to identify the driving license to obtain basic vehicle information, manually select the charging port type, and the system automatically generates a unique vehicle identifier;

[0022] Realize intelligent reservation based on 3D interface, trigger dynamic conflict detection and multi-objective optimization scheduling;

[0023] Intelligent appointment and scheduling process

[0024] The mini program uses LBS to obtain the user's location and uses a 3D visualization interface to display the surrounding charging stations, marking the real-time status and estimated idle time.

[0025] The user selects the target charging station, and the system recommends the optimal charging time through a multi-objective optimization model. If the user selects a time period on their own, a dynamic reservation conflict detection algorithm is triggered;

[0026] After the reservation is confirmed, the system sends a reservation instruction to the charging pile and generates a reservation QR code. The user scans the code to unlock the charging pile.

[0027] Real-time monitoring of the charging process, combined with overtime progressive billing and parking space visual recognition;

[0028] Charging and timeout management process

[0029] When charging starts, the system will simultaneously record the start time and electrical parameters of the charging pile and start the charging countdown;

[0030] When the remaining time is less than 15 minutes, a reminder will be sent via WeChat service notification. After charging is completed, if the vehicle has not left, the overtime billing mechanism will be activated and the fee will be calculated according to the progressive rate rules.

[0031] The parking space visual monitoring device detects the type of vehicle in real time. If it is identified as a fuel vehicle, it will automatically send a warning message and report it to the administrator;

[0032] Dynamic billing based on real-time electricity prices and credit points, supporting WeChat payment and blockchain evidence storage;

[0033] The billing engine calculates the payable fee based on charging time, power consumption, real-time electricity price and user credit points, and supports WeChat payment and point deduction combination payment;

[0034] Payment requests are transmitted encrypted via HTTPS and use double signature verification (merchant signature + WeChat payment signature) to prevent amount tampering;

[0035] After the transaction is completed, the blockchain module automatically records the transaction hash value, and users can query the on-chain evidence information through the mini program;

[0036] Multi-dimensional credit score evaluation and reward and punishment mechanisms regulate user behavior;

[0037] After each charging is completed, the system updates the credit score based on the appointment fulfillment, overtime duration, payment timeliness and other dimensions;

[0038] Credit points are evaluated regularly (monthly). High-credit users (points > 90) will enjoy enhanced reservation priority and a 10% discount on rates; low-credit users (points < 60) will be subject to reservation time limits.

[0039] Provides a credit repair mechanism where users can restore their credit by accumulating points through continuous normal use.

[0040] Furthermore, in the intelligent reservation step, the system recommends the optimal charging time based on the user's historical habits and grid load through a combination of collaborative filtering and a rule engine.

[0041] Furthermore, the timeout management step adopts a time-space dual detection mechanism to determine the vehicle departure status through the change of charging pile status and parking space visual recognition.

[0042] Furthermore, the billing formula of the dynamic billing step includes a real-time electricity price coefficient, a load fluctuation coefficient and a credit point discount factor, and the overtime billing is progressively increased at 1.5 times the rate for the first 30 minutes.

[0043] Furthermore, the credit score evaluation adopts the formula S=α×R(booking)+β×T(time)+γ×P(payment)+δ×V(vehicle), and the dimension weights α, β, γ, and δ are determined by the hierarchical analysis method.

[0044] Beneficial effects of the present invention:

[0045] 1. Intelligent upgrade of charging pile resource scheduling and utilization

[0046] By combining an interval tree data structure with a multi-objective optimization scheduling model, dynamic management of charging pile resources across time and space is achieved, effectively resolving issues such as reservation conflicts and idle resources in traditional systems and significantly improving charging pile utilization. An intelligent time period recommendation algorithm, based on user habits and grid load conditions, guides users to charge during off-peak hours, promoting a balanced allocation of charging resources and grid load, and optimizing energy efficiency.

[0047] 2. Breakthrough Improvement in User Interaction Experience and Operational Efficiency

[0048] A WebGL-based 3D visualization interface for finding charging piles provides users with an intuitive display of charging pile locations and status, significantly reducing search time. The intelligent vehicle-pile matching function automatically selects charging ports through feature vector analysis, eliminating ineffective charging pile searches. A real-time messaging system, combined with visual parking monitoring technology, provides proactive reminders and status monitoring throughout the charging process, effectively reducing user wait time and operational costs.

[0049] 3. Intelligent Innovation in Billing Mechanisms and User Behavior Management

[0050] The dynamic billing engine combines real-time electricity prices, grid load, and user credit points to create a flexible tiered billing model. This optimizes user charging costs while providing operators with a more scientific revenue management solution. The combination of a progressive overtime billing strategy and a multi-dimensional credit evaluation system, by linking credit points with reservation priority, effectively regulates user behavior and fosters a virtuous interactive "credit-service" ecosystem.

[0051] 4. Systematic Breakthrough in Data Security and System Reliability

[0052] Using a consortium blockchain architecture and smart contract technology, key data such as charging orders and payment records are stored on-chain, ensuring data immutability and automated process execution. The combination of a distributed system architecture and multiple security mechanisms (such as HTTPS encryption and dual-signature verification) ensures system stability and transaction security in high-concurrency scenarios, meeting financial-grade data security standards.

[0053] 5. Comprehensive expansion of industry application value and social benefits

[0054] Through automated scheduling and intelligent management, the system significantly reduces the labor costs and management complexity of charging pile operations. It also effectively reduces the phenomenon of non-new energy vehicles occupying space and wasting resources through technical means. Its standardized architecture, compatible with multiple scenarios, provides a replicable technical solution for the large-scale deployment of new energy charging networks, promotes the upgrading of charging infrastructure towards intelligence and standardization, and contributes to the healthy development of the new energy vehicle industry ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will be further described below with reference to the accompanying drawings.

[0056] Figure 1 This is a system block diagram of an orderly management system for new energy vehicle charging piles based on WeChat applet of the present invention.

[0057] Figure 2 This is a flow chart of the method for orderly managing new energy vehicle charging piles based on WeChat applet of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] As an embodiment of the present invention:

[0060] See also Figure 1 As shown, a new energy vehicle charging pile orderly management system based on WeChat applet is characterized by including:

[0061] A multi-dimensional data acquisition module collects data on charging pile status, parking space occupancy, and grid load. IoT sensors deployed at charging piles collect real-time data on charging pile status (charging, idle, faulty), charging power, voltage, and other electrical parameters. A parking space visual monitoring device uses computer vision technology to identify parking space occupancy and vehicle type (new energy vehicle / fuel vehicle). A grid load monitoring interface obtains real-time load data and electricity price information for the regional power grid.

[0062] Using the ACS758LCB-050B current sensor (measurement range ±50A, resolution 1.5625mA) and the LV25-P voltage sensor (measurement range 0-300V, accuracy ±0.1%), the current, voltage, charging gun plug-in status and other parameters are collected at a 500ms cycle. The state vector is generated: P(t) = [I(t), V(t), S(t), T(t)], where I(t) is the current (A), V(t) is the voltage (V), S(t) is the state code (0 = idle, 1 = charging, 2 = fault), and T(t) is the temperature (°C). This data is pushed to the backend via the MQTT protocol (QoS = 1).

[0063] The parking visual monitoring device uses a 2-megapixel high-definition camera and uses the YOLOv8 target detection model (mAP@0.5=92%) to identify parking space occupancy and vehicle type, and outputs the confidence vector: V detect =[p ev , p ice , p empty ], when p ev >0.7 is considered as new energy vehicle, p ice >0.7 is considered a fuel vehicle.

[0064] Grid load monitoring interface: obtains real-time electricity price curve E(t) and load factor L(t) through Modbus TCP protocol, where:

[0065]

[0066] When L(t)>1.2, a peak warning is triggered.

[0067] The intelligent decision-making engine includes a dynamic reservation conflict detection algorithm, a multi-objective optimization scheduling model, and an intelligent time period recommendation algorithm. The dynamic reservation conflict detection algorithm: Based on a two-dimensional space-time constraint model, it uses an interval tree data structure to detect reservation period conflicts in real time, ensuring that only one reservation is made for the same charging station in the same time period. The multi-objective optimization scheduling model: With the shortest user waiting time, the highest charging station utilization rate, and the balanced grid load as the objective functions, it combines a genetic algorithm to generate the optimal reservation scheduling plan. The intelligent time period recommendation algorithm: Based on the user's historical charging habits, current grid load, and charging station usage predictions, it recommends the optimal charging time for users and supports off-peak charging guidance.

[0068] Dynamic reservation conflict detection algorithm: Based on the time-space two-dimensional constraint model, the interval tree data structure is used to store reservation records (triplets R = (t start , t end , ID charger )) The following algorithm is used to achieve millisecond-level conflict detection with a time complexity of O(logn+k):

[0069]

[0070] Multi-objective optimization scheduling model: taking user waiting time T wait Shortest, charging pile utilization rate U charger Highest, grid load balance L grid The optimal objective function is:

[0071]

[0072] Among them, w1=0.4, w2=0.3, w3=0.3 (determined by the hierarchical analysis method), solved by genetic algorithm, the encoding method is binary, the crossover probability is 0.8, and the mutation probability is 0.01.

[0073] Intelligent time period recommendation algorithm: Calculates the recommendation degree based on user historical habits, grid load and charging pile predictions:

[0074] R(t i )=w1×P avail (t i )+w2×C price (t i )+w3×S sim (t i )

[0075] Among them S sim (t i ) is the user's historical habit similarity, calculated using cosine similarity:

[0076]

[0077] The credit score management module establishes a multi-dimensional credit evaluation system and links it to appointment priority. This system includes dimensions such as appointment fulfillment rate, number of timeouts, and payment timeliness, and uses the analytic hierarchy process to determine the weight of each dimension. Credit scores are linked to appointment priority, and high-credit users enjoy priority appointments and preferential rates. A credit reward and punishment mechanism is designed to impose appointment restrictions on users who maliciously occupy appointments or repeatedly breach the contract.

[0078] Evaluation dimensions and weights: The weight vector W = [0.43, 0.30, 0.20, 0.07 T , corresponding to the appointment fulfillment rate R(booking), overtime penalty coefficient T(time), payment timeliness rate P(payment), and vehicle compliance V(vehicle).

[0079] Point calculation formula: S = α × R (booking) + β × T (time) + γ × P (payment) + δ × V (vehicle)

[0080] The timeout penalty coefficient T(time) = 100-50×(1-e -0·05×Δt ), Δt is the timeout number of minutes.

[0081] Reward and punishment mechanism: high-credit users (S>90) will have their reservation priority coefficient multiplied by 1.5 and receive a 10% discount on the fee; low-credit users (S<60) will be prohibited from making reservations during peak hours.

[0082] A dynamic billing engine implements tiered billing and overtime progressive billing based on real-time electricity prices and loads. The tiered billing model based on real-time electricity prices and loads: the rate decreases by 30% during off-peak hours (23:00-7:00) and increases by 50% during peak hours (17:00-21:00), with the floating ratio dynamically adjusted based on real-time load fluctuations in the power grid. Overtime progressive billing strategy: the first 30 minutes are charged at 1.5 times the base rate, and after 30 minutes, the rate is doubled, with the rate increasing by 20% for every additional 15 minutes. The system supports point deduction, allowing users to use credit points or charging points to offset part of the fee.

[0083] Implement tiered billing and overtime progressive billing based on real-time electricity prices and loads:

[0084] Tiered billing model: The rate is reduced by 30% during off-peak hours (23:00-7:00) and increased by 50% during peak hours (17:00-21:00), and is dynamically adjusted based on the load factor:

[0085] E final (t) = E(t) × (1 + β × (L(t) - 1))

[0086] Where β=0.5, the load adjustment range is ±30%.

[0087] Overtime progressive billing strategy:

[0088]

[0089] Final cost:

[0090]

[0091] The WeChat mini-program interactive module provides 3D visualization of charging piles, intelligent matching of vehicles and charging piles, and real-time message push. The 3D visualization of charging piles: Based on WebGL technology, it builds a 3D model of the charging pile parking lot, visually displaying the location, status, and charging interface type of the charging piles. The intelligent matching function of vehicles and charging piles: It automatically obtains the charging interface type of the user's vehicle (via WeChat binding of vehicle information or OCR recognition of the driving license) and selects matching charging piles. The real-time message push system: Using WebSocket long connection technology, it pushes messages such as appointment reminders, charging completion, and timeout warnings, and supports custom message priority.

[0092] Provides 3D visualization for pile location, intelligent matching of vehicles and piles, and real-time message push:

[0093] 3D visualization interface: A parking lot model is built based on WebGL technology (three.js library). Charging station status is distinguished by material color (idle - RGB (0, 255, 0), occupied - RGB (255, 0, 0)), and LOD hierarchical rendering is supported.

[0094] Intelligent matching of vehicle and charging pile: Obtain vehicle model through OCR recognition of driving license, query interface matching table (such as "Tesla Model 3" corresponds to "CCS2" interface), and filter results:

[0095] chargerList={c∣c.interfaceType==vehicle.interfaceType}

[0096] Real-time message push: Using WebSocket long connection, the heartbeat packet interval is 30 seconds, the message format includes priority (1-3 levels), and high-priority messages trigger system notifications.

[0097] The blockchain evidence storage module is used for on-chain storage of key data and execution of smart contracts; a consortium chain architecture is built to store key data such as charging orders, payment records, and credit point changes on-chain to ensure that the data cannot be tampered with; smart contracts are used to automatically execute rules such as overtime billing and point redemption to reduce manual intervention.

[0098] Used for key data on-chain storage and smart contract execution:

[0099] Consortium chain architecture: uses Hyperledger Fabric 2.5, 3 consensus nodes (operator, regulator, third party), PBFT consensus algorithm, and a block generation interval of 5 seconds.

[0100] Smart contract example (overtime billing):

[0101]

[0102] Specifically, the intelligent decision engine uses an interval tree data structure to implement reservation conflict detection and solves multi-objective optimization scheduling solutions through a genetic algorithm.

[0103] The termination condition of the genetic algorithm is that the fitness growth is less than 1% for 50 consecutive generations or 200 generations of iterations. The fitness function is

[0104] Specifically, the credit score management module combines the hierarchical analysis method to determine the weights of evaluation dimensions and associates credit scores with reservation priorities and rate discounts.

[0105] AHP judgment matrix example:

[0106]

[0107] Maximum eigenvalue λ max =4.053, consistency ratio CR=0.02<0.1.

[0108] Specifically, the WeChat mini-program interaction module builds a three-dimensional visualization interface based on WebGL technology, implements real-time message push through WebSocket, and automatically matches the vehicle charging interface type.

[0109] The number of WebGL model faces is controlled within 5000, loaded in GLTF format, and supports dynamic updating of charging pile status materials.

[0110] Specifically, the blockchain evidence storage module adopts a consortium chain architecture to store charging orders, payment records, and credit point changes on the chain, and executes billing rules through smart contracts.

[0111] Transaction hash calculation method:

[0112] H = SHA256 (chargerId || timestamp || amount || nonce), which is tamper-proof through ECDSA signature

[0113] See Figure 2 As shown, a method for orderly management of new energy vehicle charging piles based on WeChat applet includes:

[0114] Multimodal binding of user and vehicle information to generate a unique identifier;

[0115] User and vehicle binding process

[0116] Users complete real-name authentication through the WeChat mini-program, which supports facial recognition or ID card OCR recognition;

[0117] Multi-modal vehicle information input: take a photo to identify the driving license to obtain basic vehicle information, manually select the charging port type, and the system automatically generates a unique vehicle identifier;

[0118] Real-name authentication: Call the WeChat face authentication interface (liveness detection pass rate 99.7%) or ID card OCR (recognition accuracy >98%).

[0119] Vehicle information input: Use the OCR function on the driving license to extract the license plate number, vehicle model, etc., manually select the charging port type, and generate a unique identifier:

[0120] VID=MD5(plateNo||vin||interfaceType)

[0121] Realize intelligent reservation based on 3D interface, trigger dynamic conflict detection and multi-objective optimization scheduling;

[0122] Intelligent appointment and scheduling process

[0123] The mini program uses LBS to obtain the user's location and uses a 3D visualization interface to display the surrounding charging stations, marking the real-time status and estimated idle time.

[0124] The user selects the target charging station, and the system recommends the optimal charging time through a multi-objective optimization model. If the user selects a time period on their own, a dynamic reservation conflict detection algorithm is triggered;

[0125] After the reservation is confirmed, the system sends a reservation instruction to the charging pile and generates a reservation QR code. The user scans the code to unlock the charging pile.

[0126] Real-time monitoring of the charging process, combined with overtime progressive billing and parking space visual recognition;

[0127] LBS positioning: Call WeChat wx.getLocation() interface, accuracy 10 meters, charging pile distance calculation:

[0128]

[0129] (The coefficient converts the difference in latitude and longitude to kilometers)

[0130] Time period recommendation: Combined with the ARIMA (1,1,1) model to predict power grid load:

[0131] L(t)=φ1L(t-1)+θ1∈(t-1)+∈(t)

[0132] Charging and timeout management process

[0133] When charging starts, the system will simultaneously record the start time and electrical parameters of the charging pile and start the charging countdown;

[0134] When the remaining time is less than 15 minutes, a reminder will be sent via WeChat service notification. After charging is completed, if the vehicle has not left, the overtime billing mechanism will be activated and the fee will be calculated according to the progressive rate rules.

[0135] The parking space visual monitoring device detects the type of vehicle in real time. If it is identified as a fuel vehicle, it will automatically send a warning message and report it to the administrator;

[0136] Remaining time prediction: Using Kalman filter algorithm, state equation:

[0137]

[0138] where x k =[remaining power, charging rate], Δt=5 minutes.

[0139] Timeout detection logic:

[0140]

[0141] Dynamic billing based on real-time electricity prices and credit points, supporting WeChat payment and blockchain evidence storage;

[0142] The billing engine calculates the payable fee based on charging time, power consumption, real-time electricity price and user credit points, and supports WeChat payment and point deduction combination payment;

[0143] Payment requests are transmitted encrypted via HTTPS and use double signature verification (merchant signature + WeChat payment signature) to prevent amount tampering;

[0144] After the transaction is completed, the blockchain module automatically records the transaction hash value, and users can query the on-chain evidence information through the mini program;

[0145] Cost calculation example: charging 20kWh, basic electricity price 1.2 yuan / kWh, load factor 1.1, overtime 25 minutes, credit points 85 points:

[0146] E final =1.2×(1+0.5×0.1)=1.26 yuan / kWh

[0147]

[0148] Blockchain transaction structure:

[0149]

[0150]

[0151] Multi-dimensional credit score evaluation and reward and punishment mechanisms regulate user behavior;

[0152] After each charging is completed, the system updates the credit score based on the appointment fulfillment, overtime duration, payment timeliness and other dimensions;

[0153] Credit points are evaluated regularly (monthly). High-credit users (points > 90) will enjoy enhanced reservation priority and a 10% discount on rates; low-credit users (points < 60) will be subject to reservation time limits.

[0154] Provides a credit repair mechanism where users can restore their credit by accumulating points through continuous normal use.

[0155] Points update formula:

[0156] S new =0.8×S old +0.2×S current

[0157] For example, the historical score is 80 and the current score is 75, then S new =79

[0158] Regular evaluation: updated monthly, high credit users (>90 points) will have their appointment priority increased, low credit users (<60 points) will have their appointment time limited

[0159] Specifically, in the intelligent reservation step, the system recommends the optimal charging time based on the user's historical habits and grid load through a combination of collaborative filtering and rule engine.

[0160] Historical habit analysis generates a time period preference vector H = [h1,h2,...,h 96 ], (one period every 15 minutes).

[0161] Specifically, the timeout management step adopts a dual-time and space detection mechanism to judge the vehicle departure status through the change of charging pile status and parking space visual recognition.

[0162] Dual detection conditions: Charging has been completed for more than 30 minutes and the parking space is still occupied, or it is identified as being occupied by a fuel vehicle.

[0163] Specifically, the billing formula of the dynamic billing step includes the real-time electricity price coefficient, load fluctuation coefficient and credit point discount factor. The overtime billing is progressively increased at 1.5 times the rate for the first 30 minutes.

[0164] The load fluctuation coefficient β is automatically adjusted according to L(t), and a rate reduction is triggered when L(t)<0.8.

[0165] Specifically, the credit score evaluation adopts the formula S = α × R (booking) + β × T (time) + γ × P (payment) + δ × V (vehicle), and the dimension weights α, β, γ, and δ are determined through the hierarchical analysis method.

[0166] Vehicle compliance V (vehicle): New energy vehicles get 100 points, and fuel vehicles get 0 points.

[0167] Further implementation methods supplement

[0168] Specific implementation details of the intelligent decision engine

[0169] Interval tree insertion and maintenance algorithm:

[0170] When a new reservation request is generated, the system updates the interval tree through the following steps:

[0171] Create a new node newNode = (t_start, t_end, chargerId), where the max value is initialized to newNode.end;

[0172] Start traversing from the root node. If newNode.start>=currentNode.end, insert into the right subtree; if newNode.end<=currentNode.start, insert into the left subtree;

[0173] After insertion, update the max value of all nodes on the path upward to max(currentNode.max,newNode.end);

[0174] This algorithm ensures the balance of the tree structure, avoids degeneration into a linked list structure, and guarantees the efficiency of subsequent queries.

[0175] Genetic algorithm parameter configuration for multi-objective optimization scheduling:

[0176] Population size: 100 individuals (each individual represents a scheduling plan);

[0177] Chromosome length: number of charging piles × number of time periods (e.g. 50 charging piles × 96 time periods = 4800 bits);

[0178] Selection operator: Roulette wheel selection (fitness proportional selection), retaining the top 20% of excellent individuals to directly enter the next generation;

[0179] The crossover probability is dynamically adjusted during the iteration process: the initial value is 0.8, and it is reduced to 0.6 when there is no fitness improvement for 10 consecutive generations to avoid premature convergence.

[0180] Real-time electricity price linkage mechanism of dynamic billing engine

[0181] Grid load-electricity price mapping table:

[0182]

[0183] Note: The load factor is calculated in real time as current load / transformer rated load and is updated every 15 minutes.

[0184] Points deduction rules:

[0185] 100 credit points = 1 RMB (can be used to deduct electricity bills);

[0186] A single charge can be deducted up to 20% of the order amount;

[0187] Points deduction takes precedence over credit discount (if a user meets the requirements of both points deduction and 10% discount rate, the points will be deducted first and then the discount will be calculated).

[0188] Performance optimization of WeChat mini-program interaction module

[0189] LOD (Level of Detail) technology for 3D scenes:

[0190] Close-up (distance < 50 meters): Loads the complete 3D model (more than 5000 faces) and displays the charging port details.

[0191] Mid-ground (50-200 meters): Load a simplified model (1000-2000 polygons) and only display the main body of the charging pile;

[0192] Distant view (>200 meters): Load an icon model (number of faces <100) and use color blocks to represent the status; control the rendering level through the WebGL renderOrder property to ensure that the near-view model is rendered first.

[0193] Fuzzy query mechanism for vehicle-pile matching: When an error occurs in the OCR recognition of the driving license (for example, the vehicle model is identified as "BYD Han EV" but is actually "BYD Han DM"), the system optimizes the matching through the following strategies:

[0194] Extract car model keywords (such as "BYD" and "Han") and query all car models containing the keywords in the database;

[0195] Sort by interface compatibility (e.g., the "GB / T20234.3" interface is compatible with more than 90% of domestically produced new energy vehicles);

[0196] Recommends the three most likely interface types and allows users to manually correct them.

[0197] Details of the consensus mechanism of the blockchain evidence storage module

[0198] PBFT (Practical Byzantine Fault Tolerance) algorithm process:

[0199] Pre-preparation phase: After receiving the transaction, the master node generates a block proposal PRE-PREPARE message, which includes the block number n, view number v, and transaction data hash d;

[0200] Preparation phase: After the replica node verifies the legitimacy of the proposal, it broadcasts a PREPARE message with its own signature.

[0201] Commit phase: When a node receives 2f+1 valid PREPARE messages (f is the number of allowed faulty nodes, f=1 in this system), it broadcasts a COMMIT message.

[0202] Consensus reached: After receiving 2f+1 COMMIT messages, the node writes the block into the local ledger.

[0203] Priority strategy for data on-chain:

[0204] High priority: charging order creation, payment completion, charging pile failure alarm (online delay < 5 seconds);

[0205] Medium priority: credit score changes, appointment record updates (on-chain delay <30 seconds);

[0206] Low priority: user operation logs, system announcements (batch upload to the chain, once every 10 minutes).

[0207] Exception handling mechanism of the credit points management module

[0208] Credit Points Dispute Appeal Process:

[0209] The user submits a complaint through the mini program and uploads evidence (such as a photo of the completed charge and a payment receipt);

[0210] System automatic verification:

[0211] If it is a timeout error (e.g., charging is not completed in time due to a charging pile failure), the points will be corrected directly;

[0212] If it is an identification error (such as misjudgment of a fuel vehicle), manual review will be triggered;

[0213] After manual review, points will be restored according to the following formula:

[0214]

[0215] Where ΔS is the dispute deduction score, t appeal Time taken for appeal (hours). If it exceeds 72 hours, 50% will be restored.

[0216] The step-by-step mechanism of credit repair:

[0217] No default for 30 consecutive days: +5 points;

[0218] 90 consecutive days without default: +15 points, unlocking the right to make reservations during peak hours;

[0219] Actively report and verify charging station failure: +2 points each time (maximum 10 points per month).

[0220] System security architecture design

[0221] Data transmission encryption:

[0222] Mini Program communicates with the server using TLS 1.3 and EV (Extended Validation) certificates.

[0223] Charging station and backend communication: MQTT protocol uses AES-256 encryption, and the message body is signed with JSON Web Token (JWT);

[0224] Communication between blockchain nodes: TLS two-way authentication is used, and the node certificate is issued by the consortium chain CA.

[0225] Anti-replay attack measures:

[0226] All interface requests contain a random number nonce and a timestamp. The server verifies that the difference between the timestamp and the current time is less than 5 minutes.

[0227] The payment request generates a unique order number, orderId, in the format of YYYYMMDDHHMMSS+6-digit random number. Duplicate requests will be rejected directly.

[0228] System expansion and compatibility design

[0229] Charging pile protocol adaptation layer:

[0230] Supports multiple charging pile communication protocols (GB / T28181, IEC61850, OCPP1.6), and realizes protocol conversion through the adapter module:

[0231]

[0232] When adding a new charging pile type, you only need to implement the corresponding adapter interface without modifying the core business logic.

[0233] Multi-platform compatible solution:

[0234] WeChat Mini Program: The main entrance, supporting 99% of WeChat users;

[0235] H5 webpage: Accessed through WeChat's built-in browser, with the same functions as the mini-program;

[0236] Alipay / Baidu Mini Program: Reserved interfaces allow for rapid porting of core functions through the adaptation layer.

[0237] It should be noted that all data collected in this application is collected with the consent and authorization of the user, and the use of the data is legal and compliant, and the use and processing of the data complies with the relevant laws, regulations and standards of the relevant regions. The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formula are set by those skilled in the art based on actual conditions.

[0238] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An orderly management system for new energy vehicle charging piles based on WeChat applet, characterized in that: include: Multi-dimensional data acquisition module, used to collect charging pile status, parking space occupancy and grid load data; Intelligent decision-making engine, including dynamic reservation conflict detection algorithm, multi-objective optimization scheduling model and intelligent time slot recommendation algorithm; Credit score management module, establishing a multi-dimensional credit evaluation system and linking it to appointment priority; Dynamic billing engine, which implements tiered billing and overtime progressive billing based on real-time electricity prices and load; WeChat mini-program interactive module, providing 3D visualization for finding piles, intelligent matching of vehicles and piles, and real-time message push; The blockchain evidence storage module is used for on-chain storage of key data and execution of smart contracts.

2. The orderly management system for new energy vehicle charging piles based on WeChat applet according to claim 1 is characterized in that: The intelligent decision engine uses an interval tree data structure to detect reservation conflicts and solves multi-objective optimization scheduling solutions through a genetic algorithm.

3. The orderly management system for new energy vehicle charging piles based on WeChat applet according to claim 1 is characterized in that: The credit score management module determines the evaluation dimension weights in combination with the hierarchical analysis method, and associates the credit score with the reservation priority and the rate discount.

4. The orderly management system for new energy vehicle charging piles based on WeChat applet according to claim 1, characterized in that: The WeChat applet interaction module builds a three-dimensional visualization interface based on WebGL technology, implements real-time message push through WebSocket, and automatically matches the vehicle charging interface type.

5. The orderly management system for new energy vehicle charging piles based on WeChat applet according to claim 1, characterized in that: The blockchain evidence storage module adopts a consortium chain architecture to store charging orders, payment records and credit point changes on the chain, and executes billing rules through smart contracts.

6. A method for orderly management of new energy vehicle charging piles based on WeChat applet, characterized in that: include: Multimodal binding of user and vehicle information to generate a unique identifier; Realize intelligent reservation based on 3D interface, trigger dynamic conflict detection and multi-objective optimization scheduling; Real-time monitoring of the charging process, combined with overtime progressive billing and parking space visual recognition; Dynamic billing based on real-time electricity prices and credit points, supporting WeChat payment and blockchain evidence storage; Multi-dimensional credit score evaluation and reward and punishment mechanisms regulate user behavior.

7. A method for orderly managing new energy vehicle charging piles based on WeChat applet according to claim 6, characterized in that: In the intelligent reservation step, the system recommends the optimal charging time based on the user's historical habits and grid load through a combination of collaborative filtering and a rule engine.

8. A method for orderly managing new energy vehicle charging piles based on WeChat applet according to claim 6, characterized in that: The timeout management step adopts a time-space dual detection mechanism to determine the vehicle departure status through the change of charging pile status and parking space visual recognition.

9. A method for orderly managing new energy vehicle charging piles based on WeChat applet according to claim 6, characterized in that: The billing formula of the dynamic billing step includes the real-time electricity price coefficient, the load fluctuation coefficient and the credit point discount factor. The overtime billing is progressively increased at 1.5 times the rate for the first 30 minutes.

10. A method for orderly managing new energy vehicle charging piles based on WeChat applet according to claim 6, characterized in that: The credit score evaluation adopts the formula S=α×R(booking)+β×T(time)+γ×P(payment)+δ×V(vehicle), and the dimension weights α, β, γ, and δ are determined by the hierarchical analysis method.

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