Double-section type parking navigation service fee real-time counting and collecting system and method

Abstract

The invention provides a double-section type parking navigation service fee real-time counting and collecting system and method, and belongs to the technical field of intelligent parking navigage.According to the method, a parking navigation path is divided into an off-site navigation section (S1) and an in-site precise guide section (S2), and when a vehicle passes through a parking lot gate, the S2 section of service fee real-time counting and collecting are triggered through license plate recognition and geo-fence positioning dual verification; and based on the vehicle type coefficient, the time period coefficient and the parking space gap rate, calculating the dynamic service fee through a formula Fee = Base * (1 + alpha) * (1 + beta) * (1 + gamma). The system comprises a vehicle owner terminal system, a gate recognition system and a cloud management system. According to the method, the problems of fuzzy service and charging boundary, lack of dynamic adjustment of a charging mechanism, vulnerability of a charging trigger mechanism and the like in a traditional scheme are solved, and clear service boundary, accurate charging trigger, dynamic pricing strategy and arrearage recovery automation are realized.

Description

Technical Field

[0001] This invention belongs to the field of smart parking navigation technology, specifically relating to a two-stage parking navigation service fee real-time calculation system and method. Background Technology

[0002] In the existing parking navigation service sector, traditional parking navigation systems face numerous technical bottlenecks. On the one hand, the boundary between service and charging is blurred. The commonly used full-course navigation charging model cannot accurately distinguish between the service differences between basic off-site navigation and precise on-site navigation. This often results in users being charged the full amount even though they did not actually use the on-site precise navigation, leading to a high rate of charging disputes. On the other hand, the billing mechanism lacks dynamic adjustment capabilities. The traditional fixed-rate model does not consider differences in vehicle type, time-of-day traffic fluctuations, and the supply and demand relationship of parking spaces. It cannot reasonably cover the service costs of special vehicle types such as trucks, nor can it balance the parking space shortage problem during peak hours through price leverage, resulting in low utilization of parking lot resources and loss of platform revenue. In addition, the charging trigger mechanism has loopholes. Relying on post-departure settlement is prone to bad debt risks, and problems such as GPS positioning drift and license plate recognition errors often lead to incorrect charges. The user experience urgently needs to be improved.

[0003] To address the shortcomings of existing technologies, this invention proposes a dual-segment parking navigation service fee real-time billing system and method. By dividing the navigation path into a free off-site segment and an on-site paid segment, a dual-verification billing mechanism based on gate triggering is constructed. Combined with a multi-factor dynamic pricing model based on vehicle type, time period, and parking space shortage rate, as well as a full-process management system for credit limit freezing and late payment fee collection, this system achieves clear service boundaries, accurate billing triggering, dynamic pricing strategies, and automated arrears collection. It fundamentally solves the problems of numerous billing disputes, high bad debt rates, and insufficient revenue optimization in traditional solutions, providing an innovative technical solution for the field of smart parking navigation. Summary of the Invention

[0004] The purpose of this invention is to provide a two-stage parking navigation service fee real-time billing system and method, which aims to solve the problems in the existing technology where the traditional full-process navigation charging mode cannot distinguish between basic off-site navigation and precise on-site navigation, resulting in users being charged the full amount even if they did not use the precise on-site navigation, leading to a high dispute rate. The fixed rate does not take into account the differences in vehicle type, time period, and parking space supply and demand, and cannot cover the cost of special vehicle types such as trucks. It is also difficult to balance the problem of parking space shortage during peak hours, resulting in resource waste and revenue loss.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for real-time calculation of dual-stage parking navigation service fees, characterized by comprising:

[0007] Dual-segment path segmentation steps: Divide the parking navigation path into an external navigation segment (S1) and an internal fine-guided segment (S2), where S1 is the navigation path before the parking lot entrance and S2 is the precise navigation path inside the parking lot;

[0008] Entry gate trigger billing steps: When a vehicle passes through the parking lot gate, the S2 segment service fee is triggered to be charged in real time through dual verification of license plate recognition and geofence positioning;

[0009] Multi-factor dynamic pricing steps: Based on vehicle type coefficient, time period coefficient and parking space shortage rate, the dynamic service fee is calculated using the formula Fee=Base×(1+α)×(1+β)×(1+γ), where α is the vehicle type coefficient, β is the time period coefficient, and γ is the parking space shortage rate mapping value.

[0010] As a preferred embodiment of the present invention, the specific conditions for the gate entry triggering billing step include:

[0011] License plate recognition confidence level > 95%;

[0012] The vehicle's GPS location is within the electronic fence area of ​​the parking lot;

[0013] The gate's inductive loop signal triggers an entry event.

[0014] As a preferred embodiment of the present invention, in the multi-factor dynamic pricing step:

[0015] The vehicle type coefficient α is set as follows: α = 0 for cars and α = 0.8 for trucks;

[0016] The time period coefficient β is set as follows: β = 0.5 during peak hours and β = 0 during off-peak hours;

[0017] The parking space shortage rate γ is calculated as follows: γ = 0.6 × (real-time parking space shortage rate / 100%), where the parking space shortage rate ranges from 0 to 100%.

[0018] As a preferred embodiment of the present invention, a credit recovery step is also included:

[0019] When the account balance is insufficient, the credit limit of the corresponding license plate will be automatically frozen (default 20 yuan);

[0020] If the fee is not paid within 7 days, the outstanding amount and late payment fee will be automatically deducted upon the next entry, and the license plate will be added to the key monitoring list.

[0021] As a preferred embodiment of the present invention, a dual-segment parking navigation service fee real-time billing system is characterized by comprising:

[0022] Vehicle owner terminal system: used to realize dual-segment navigation interaction and payment operations, including:

[0023] A dual-segment navigation interaction module is used to switch between the off-site navigation segment (S1) and the on-site fine guidance segment (S2);

[0024] The payment interaction module is used for real-time deduction, credit limit management, and overdue payment collection.

[0025] Gate recognition system: used for vehicle entry identification and billing triggering, including:

[0026] The license plate recognition processing module is equipped with a 5-megapixel CMOS sensor for recognizing license plates and calculating confidence levels.

[0027] The entry trigger module generates a billing trigger event based on the ground loop signal and geofence positioning.

[0028] Cloud-based management system: used for dual-segment navigation control, dynamic billing, and credit management, including:

[0029] The dual-stage navigation control module includes an off-site path planning unit and an on-site fine-guided engine unit, which generate paths S1 and S2 respectively;

[0030] The dynamic billing engine module includes a parameter acquisition unit and a rate calculation unit, which are used to perform multi-factor dynamic pricing.

[0031] The credit management module includes a credit limit freezing unit and a debt collection execution unit, which are used to manage credit limits and recover overdue fees.

[0032] As a preferred embodiment of the present invention, the system according to claim 5 is characterized in that the rate calculation unit of the dynamic billing engine module supports the following formula:

[0033] Fee=Base×(1+α)×(1+β)×(1+γ), where:

[0034] Base price (0.3 yuan);

[0035] α is the vehicle type coefficient, β is the time period coefficient, and γ is the parking space shortage rate mapping value.

[0036] As a preferred embodiment of the present invention, the recovery execution unit of the credit management module includes:

[0037] The late payment fee calculation unit is used to charge a late payment fee of 2 yuan per instance for overdue fees.

[0038] The key monitoring unit is used to prioritize the identification of license plates with outstanding fees to 99.9%.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. In this solution, the parking navigation path is clearly divided into an off-site navigation segment (S1) and an in-site precision navigation segment (S2) through a two-segment path segmentation step. S1 is a free basic navigation service, and S2 is a paid precision navigation service.

[0041] This segmentation method clearly defines the boundary between free and paid services, ensuring that charges accurately correspond to the actual service used. For example, users are only charged when they actually enter the parking lot and use the S2 segment service, effectively solving the problem of being charged the full amount even when the in-park guidance is not used in the traditional solution. This significantly reduces the rate of billing disputes from 12 cases per 10,000 times in the traditional solution to 0.7 cases per 10,000 times.

[0042] 2. In this scheme, a multi-factor dynamic pricing step is adopted. Based on the vehicle type coefficient, time period coefficient and parking space shortage rate, the dynamic service fee is calculated by the formula Fee=Base×(1+α)×(1+β)×(1+γ).

[0043] Differentiated cost coverage: Different vehicle coefficients are set for different vehicle types, such as α=0.8 for trucks and α=0 for cars, so that the service fee for trucks is 80% higher than that for cars. This reasonably covers the additional service costs incurred by trucks due to their large space occupation and high navigation difficulty, reflecting the difference in service costs.

[0044] Supply and demand adjustment and revenue enhancement: During peak hours, β=0.5, and the service fee increases by 50%, guiding users to park during off-peak hours. For example, after implementation in a commercial area, traffic flow during peak hours decreased by 22%, and utilization rate during off-peak hours increased by 31%. At the same time, the introduction of the parking space shortage rate γ allows pricing to reflect the real-time supply and demand status. When the parking space shortage rate reaches 100%, the service fee reaches 1.6 times the benchmark price, incentivizing car owners to leave quickly or choose other parking lots. Example data shows that the dynamic pricing mechanism increases the platform's average daily revenue per parking space by 40%, achieving a balance between economic benefits and user experience.

[0045] 3. In this solution, the entry trigger billing step is verified by both license plate recognition and geofence positioning (license plate recognition confidence > 95% and vehicle GPS positioning within the electronic fence range of the parking lot), and combined with the gate's ground induction coil signal to trigger the entry event, thus achieving accurate billing triggering;

[0046] The dual verification mechanism ensures that billing is triggered only when a vehicle actually enters the parking lot, avoiding erroneous charges caused by GPS location drift and license plate recognition errors. At the same time, it changes the traditional post-departure charging to instant deduction upon entry, reducing the bad debt rate from 15.2% to 4.3%, effectively controlling the risk of bad debts. In addition, the charging delay is reduced from 5-10 minutes in the traditional solution to ≤5 seconds, improving the real-time nature and efficiency of charging. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a flowchart illustrating the execution process of the present invention;

[0049] Figure 2 This is a data flow diagram of the dynamic billing engine and credit management of the present invention;

[0050] Figure 3 This is a timing diagram showing the interaction between the system modules of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1

[0053] Please see Figures 1-3 The present invention provides the following technical solutions:

[0054] A method for real-time calculation of dual-stage parking navigation service fees, characterized by comprising:

[0055] Dual-segment path segmentation steps: Divide the parking navigation path into an external navigation segment (S1) and an internal fine-guided segment (S2), where S1 is the navigation path before the parking lot entrance and S2 is the precise navigation path inside the parking lot;

[0056] Entry gate trigger billing steps: When a vehicle passes through the parking lot gate, the S2 segment service fee is triggered to be charged in real time through dual verification of license plate recognition and geofence positioning;

[0057] Multi-factor dynamic pricing steps: Based on vehicle type coefficient, time period coefficient and parking space shortage rate, the dynamic service fee is calculated using the formula Fee=Base×(1+α)×(1+β)×(1+γ), where α is the vehicle type coefficient, β is the time period coefficient, and γ is the parking space shortage rate mapping value.

[0058] In a specific embodiment of the present invention, the first step is a dual-segment path segmentation: the parking navigation path is segmented into an off-site navigation segment (S1) and an in-site precision navigation segment (S2), wherein S1 is the navigation path before the parking lot entrance and S2 is the precision navigation path inside the parking lot.

[0059] Functionality and Service Layers: S1 uses general map navigation (such as Gaode / Baidu) to meet the basic navigation needs of car owners from any starting point to the parking lot entrance. This segment is free to attract users.

[0060] S2 utilizes high-precision in-park navigation, combining real-time parking space data to provide precise guidance at the parking space level. This segment is a paid value-added service. Billing isolation: By segmenting the path, free and paid services are clearly distinguished, resolving disputes caused by the blurred boundaries between service and charge in traditional solutions (e.g., charging the full navigation fee even if no parking space is found). System decoupling: S1 relies on third-party map services, while S2 relies on the parking lot's private positioning system. This dual-segmentation reduces system integration complexity and improves scalability.

[0061] II. Entry Gate Trigger Billing Steps: When a vehicle passes through the parking lot gate, the S2 segment service fee is triggered for real-time billing through dual verification of license plate recognition and geofence positioning.

[0062] Functionality: Precise Triggering: A dual verification mechanism (license plate recognition confidence > 95% + vehicle located within the parking lot's electronic fence) ensures that billing is only triggered when the vehicle owner actually enters the parking lot, avoiding erroneous charges. Bad Debt Control: Instant deduction upon vehicle entry transforms the traditional "post-departure charging" into "pre-departure charging," reducing the bad debt rate from 15.2% to 4.3% (based on example data). Service Activation: Automatic activation of the S2 segment navigation service after successful billing, forming a "payment-service" closed loop, improving user experience and service responsiveness.

[0063] III. Multi-factor dynamic pricing steps: Based on vehicle type coefficient, time period coefficient and parking space shortage rate, the dynamic service fee is calculated using the formula Fee=Base×(1+α)×(1+β)×(1+γ), where α is the vehicle type coefficient, β is the time period coefficient, and γ is the parking space shortage rate mapping value.

[0064] Functional Role: Cost Differentiation: Vehicle Type Coefficient α: Trucks (α=0.8) have a service fee 80% higher than cars (α=0) due to their larger space requirements and greater navigation difficulty, reflecting the difference in service costs. Supply and Demand Adjustment: Time Period Coefficient β: Service fees increase by 50% during peak hours (β=0.5), while the base price is maintained during off-peak hours (β=0), guiding users to park during off-peak hours;

[0065] Parking space gap rate γ: When the parking space gap rate reaches 100%, γ = <0.6>, and the service fee increases by 60% based on the benchmark price to balance the supply and demand of parking spaces through the price lever. Revenue optimization: The data of the embodiment shows that the dynamic pricing mechanism increases the average daily revenue of the platform per parking space by 40%, while the user conversion rate only drops by 2.3%, achieving a balance between economic benefits and user experience. Fairness guarantee: The formulaic pricing mechanism eliminates human intervention, and all users are charged based on the same algorithm, reducing the dispute rate from 12 cases per 10,000 times to 0.7 cases per 10,000 times.

[0066] IV. Synergy effect path segmentation of technical features and trigger charging Synergy: The two-segment path provides a clear boundary for trigger charging, and the entrance trigger point becomes the logical segmentation point between the free segment and the charged segment, ensuring the precise correspondence between service and charging. Synergy between trigger charging and dynamic pricing: When entering the gate, data such as vehicle type, time period, and parking space gap rate are obtained synchronously to provide real-time parameters for dynamic pricing, achieving precise charging of "one price per order". Overall technical effect: Through the synergy of three steps, the system realizes a comprehensive optimization of a 72% decrease in the bad debt rate, a reduction of the charging delay from 5 - 10 minutes to ≤ 5 seconds, and a 40% increase in platform revenue, significantly superior to the traditional parking navigation charging scheme.

[0067] Specifically, please refer to Figures 1-3 , and the specific conditions of the entrance trigger charging step include:

[0068] License plate recognition confidence > 95%;

[0069] The vehicle GPS positioning is within the range of the parking lot's electronic fence;

[0070] The ground loop signal of the gate triggers the entrance event.

[0071] In this embodiment: 1. License plate recognition confidence > 95%: Anti-misrecognition mechanism: By setting a high confidence threshold (> 95%), it avoids misrecognition caused by scenarios such as license plate damage and insufficient light (such as misjudging "Beijing A12345" as "Beijing A12346"), ensuring that the billing object precisely corresponds to the actual entering vehicle and reducing subsequent credit recovery disputes.

[0072] Data reliability guarantee: Combining a 5 - megapixel CMOS sensor and an AI recognition chip (such as the same neural network model as Tesla FSD), it improves the recognition accuracy from 82% of the traditional scheme to 99.1%, providing basic data support for charging triggering.

[0073] Embodiment association: When the confidence output by the gate recognition module is 96%, the system determines it as a valid recognition; if the confidence is only <85%>, it triggers the manual review process (such as Figure 2 the T9 node in

[0074] 2. Vehicle GPS location is within the electronic fence of the parking lot: Spatial boundary verification: The actual area of ​​the parking lot is delineated by GIS geofencing technology (such as Baidu Map Geofence API) to prevent vehicles outside the parking lot (such as those just passing by the parking lot) from accidentally triggering billing, thus resolving the dispute of "GPS drift leading to charging" in the traditional solution (in one case, the car owner was mistakenly charged 300 meters away from the parking lot, and the dispute rate reached 23%).

[0075] Service scenario matching: The S2 segment precision guidance service is only activated when the vehicle actually enters the physical area of ​​the parking lot, ensuring the logical closed loop of "pay to enjoy service" and avoiding resource waste (28% of navigation requests in the traditional solution do not actually enter the parking lot).

[0076] Example Implementation: The radius of the parking lot's electronic fence is set to 50 meters (based on the gate's location coordinates). When the distance between the vehicle's GPS coordinates and the gate's coordinates is less than 50 meters, it is determined to be "within the fence," triggering the billing process (e.g., Figure 2 (Acquire parking space data at the T3 node).

[0077] 3. Gate inductive loop signal triggers entry event: Physical trigger confirmation: The inductive loop detects the physical signal of the vehicle passing through (inductance change > threshold), forming a "logical + physical" dual verification with license plate recognition and GPS positioning to prevent virtual signal forgery (such as hackers simulating license plate recognition data) and improve system security (a parking lot once suffered a single-day loss of 32,000 yuan due to a virtual signal attack).

[0078] Time-synchronization control: Ground sensor signals serve as timestamps for "actual vehicle entry," ensuring strict synchronization between billing triggering and vehicle entry actions. This reduces the tolling delay from 5-10 minutes in traditional methods to ≤5 seconds (e.g., Figure 2 (Real-time deduction at the T7 node).

[0079] Example: The inductive loop is buried 3 meters in front of the gate. When the front wheel of a vehicle passes over the loop, a pulse signal is generated and synchronized to the billing system. If the time difference between the signal and the license plate recognition is less than 200ms, it is considered a valid trigger.

[0080] Please refer to the details. Figures 1-3 In the multi-factor dynamic pricing step:

[0081] The vehicle type coefficient α is set as follows: α = 0 for cars and α = 0.8 for trucks;

[0082] The time period coefficient β is set as follows: β = 0.5 during peak hours and β = 0 during off-peak hours;

[0083] The parking space shortage rate γ is calculated as follows: γ = 0.6 × (real-time parking space shortage rate / 100%), where the parking space shortage rate ranges from 0 to 100%.

[0084] In this embodiment: 1. Vehicle type coefficient α: Car α = 0, truck α = 0.8: Service cost differentiation: Trucks have larger body size (occupying an average of 2.3 car parking spaces) and larger turning radius (40% larger than cars), resulting in higher complexity of in-field precise route planning (the algorithm's computational load increases by 67%). The coefficient α = 0.8 makes the service fee for trucks 80% higher than that for cars, covering the additional service costs (data from a logistics park shows that the cost of truck navigation service is 2.1 times that of cars).

[0085] Resource utilization regulation: By using price levers to guide trucks to enter the parking lot during off-peak hours (after implementing dynamic pricing, the truck entry rate during peak hours decreased by 19%), parking lot congestion is alleviated (in the traditional solution, truck entry leads to a 35% decrease in average traffic efficiency).

[0086] Example calculation: Car base fee is 0.3 yuan, α = 0, so Fee = 0.3 × 1 = 0.3 yuan; Truck α = 0.8, Fee = 0.3 × 1.8 = 0.54 yuan (excluding time period and gap rate factors).

[0087] 2. Time Period Coefficient β: Peak β = 0.5, Off-Peak β = 0: Traffic Balance Adjustment: During peak hours (such as weekdays 7:00-9:00, 17:00-19:00), β = 0.5 is set, and the service fee is increased by 50% to guide users to choose off-peak hours for entry (after implementation in a certain commercial area, peak hour traffic flow decreased by 22%, and off-peak utilization increased by 31%).

[0088] Service premium rationalization: During peak hours, the system concurrency is high (3.8 times that of off-peak hours), which increases server resource consumption. The β coefficient reflects the "service premium during periods of tight supply and demand". At the same time, the price filters high-priority users (users willing to pay the premium can get a smoother navigation experience).

[0089] Example: During the evening rush hour at 17:30, which falls under the period of β=0.5, if the parking space shortage rate is 70% (γ=0.42), the toll for trucks is 0.3×1.8×1.5×1.42=1.15 yuan (as per Example 1 in the instruction manual), which is 110% higher than that during off-peak hours.

[0090] 3. Parking space shortage rate γ = 0.6 × (real-time shortage rate / 100%): Dynamic pricing based on supply and demand: The parking space shortage rate (remaining parking spaces / total parking spaces × 100%) is linearly mapped to γ ​​(0 ~ 0.6). When the shortage rate is 100% (no vacant spaces), γ = 0.6, and the service fee reaches 1.6 times the benchmark price, which incentivizes car owners to leave quickly or choose other parking lots, thus alleviating the "parking space shortage" phenomenon (after implementation in a parking lot at a certain airport, the average parking time was shortened from 47 minutes to 32 minutes).

[0091] Real-time data support: Real-time parking space data is obtained through ultrasonic sensors in the parking lot (updated every 10 seconds) or video surveillance, and is refreshed every 2 minutes to ensure that the pricing reflects the current supply and demand status (in the traditional fixed price scheme, car owners often have disputes because the parking space is shown to be available but there is actually no space, accounting for 37%).

[0092] Example calculation: When the parking space shortage rate is 70%, γ = 0.6 × 0.7 = 0.42. After being superimposed with vehicle type and time period factors, dynamic pricing is achieved (such as the calculation of peak hour fees for trucks).

[0093] Please refer to the details. Figures 1-3 It also includes credit recovery steps:

[0094] When the account balance is insufficient, the credit limit of the corresponding license plate will be automatically frozen (default 20 yuan);

[0095] If the fee is not paid within 7 days, the outstanding amount and late payment fee will be automatically deducted upon the next entry, and the license plate will be added to the key monitoring list.

[0096] In this embodiment: 1. Credit freeze mechanism when balance is insufficient

[0097] Scenario: A small car enters the parking lot with only 0.2 yuan in the account (0.354 yuan needs to be paid).

[0098] Module coordination: Gate recognition system: The license plate recognition processing module outputs a confidence level of 97%, and the entry trigger module confirms that the GPS is within the fence and generates a billing event;

[0099] Cloud management system: The dynamic billing engine calculates Fee = 0.354 yuan. The payment interaction module detects that the balance is insufficient and triggers the credit limit freeze unit of the credit management module, which automatically freezes the credit limit of the license plate by 20 yuan.

[0100] Vehicle owner terminal: Push notification "Credit limit has been frozen, late payment fee will be waived if payment is made within 7 days".

[0101] 2. Procedures for recovering overdue payments

[0102] Scenario: A truck enters the parking lot with an outstanding fee of 1.15 yuan. It tries to enter again after 7 days. Module coordination: Gate recognition system: The key monitoring unit prioritizes the license plate recognition to 99.9%, completing the license plate recognition within 0.5 seconds.

[0103] The cloud management system's credit management module's collection and enforcement unit detected historical arrears, calculated a late payment fee of 2 yuan, and deducted a total of 1.15 + 2 = 3.15 yuan.

[0104] Vehicle owner terminal: Forces display of the overdue payment interface. After successful payment, activates in-park guidance and marks the license plate as "key monitoring" (subsequent entry recognition rate is improved to 99.9%).

[0105] Please refer to the details. Figure 2 ,include:

[0106] Vehicle owner terminal system: used to realize dual-segment navigation interaction and payment operations, including:

[0107] A dual-segment navigation interaction module is used to switch between the off-site navigation segment (S1) and the on-site fine guidance segment (S2);

[0108] The payment interaction module is used for real-time deduction, credit limit management, and overdue payment collection.

[0109] Gate recognition system: used for vehicle entry identification and billing triggering, including:

[0110] The license plate recognition processing module is equipped with a 5-megapixel CMOS sensor for recognizing license plates and calculating confidence levels.

[0111] The entry trigger module generates a billing trigger event based on the ground loop signal and geofence positioning.

[0112] Cloud-based management system: used for dual-segment navigation control, dynamic billing, and credit management, including:

[0113] The dual-stage navigation control module includes an off-site path planning unit and an on-site fine-guided engine unit, which generate paths S1 and S2 respectively;

[0114] The dynamic billing engine module includes a parameter acquisition unit and a rate calculation unit, which are used to perform multi-factor dynamic pricing.

[0115] The credit management module includes a credit limit freezing unit and a debt collection execution unit, which are used to manage credit limits and recover overdue fees.

[0116] In this embodiment: 1. The dual-segment navigation interaction module and the gate recognition collaborative vehicle owner terminal: When the user requests navigation to parking lot A, the dual-segment navigation interaction module calls the off-site route planning unit to generate route S1 (free);

[0117] Gate recognition system: When a vehicle passes over the ground loop, the license plate recognition processing module outputs a confidence level of 96%, the entry trigger module confirms that the GPS is within the fence, and sends a billing trigger event to the cloud;

[0118] Cloud Management System: The dynamic billing engine calculates the cost and deducts the payment. After successful deduction, the S2 route is pushed to the vehicle owner's terminal through the in-field fine guidance engine unit of the dual-segment navigation control module.

[0119] Please refer to the details. Figures 1-2According to claim 5, the system is characterized in that the rate calculation unit of the dynamic billing engine module supports the following formula:

[0120] Fee=Base×(1+α)×(1+β)×(1+γ), where:

[0121] Base price (0.3 yuan);

[0122] α is the vehicle type coefficient, β is the time period coefficient, and γ is the parking space shortage rate mapping value.

[0123] In this embodiment: 1. Billing calculation for trucks entering the parking lot during peak hours:

[0124] Parameter acquisition process: Vehicle model coefficient α = 0.8: The gate recognition module matches the vehicle model to "truck" through the license plate database;

[0125] Time period coefficient β = 0.5: Cloud timestamp matching peak time period table (17:00-19:00);

[0126] Parking space shortage rate γ = 0.42: The parameter acquisition unit synchronizes the remaining parking space data in real time from the parking management system (total parking spaces 100, 30 remaining, shortage rate 70%, γ = 0.6 × 70% = 0.42);

[0127] Formula calculation: Fee = 0.3 × (1 + 0.8) × (1 + 0.5) × (1 + 0.42) = 1.15 yuan.

[0128] 2. Settlement of fares for cars during off-peak hours:

[0129] Module coordination: Gate recognition: Confirms vehicle type as car (α=0), time period as off-peak (β=0);

[0130] Parking space data: Gap rate 30% (γ=0.18);

[0131] Billing execution: The rate calculation unit outputs 0.3×1×1×1.18=0.354 yuan, and the payment interaction module deducts the amount in real time, taking 4 seconds.

[0132] Please refer to the details. Figures 1-3 The recovery execution unit of the credit management module includes:

[0133] The late payment fee calculation unit is used to charge a late payment fee of 2 yuan per instance for overdue fees.

[0134] The key monitoring unit is used to prioritize the identification of license plates with outstanding fees to 99.9%.

[0135] In this embodiment: 1. Triggering logic of the late payment fee calculation unit:

[0136] Condition: Overdue payment period > 7 days (calculated from the entry timestamp);

[0137] Re-entry triggers a recall;

[0138] Late payment fee = 2 yuan per instance (regardless of the amount owed).

[0139] Example: A user owed 0.35 yuan on January 1st and re-entered the venue on January 9th. The system automatically deducted 0.35 + 2 = 2.35 yuan. The late payment fee calculation unit recorded that the late payment fee was generated on January 8th (7 days overdue).

[0140] 2. Increased priority for identifying key monitoring units

[0141] Technical implementation: The cloud management system stores license plates with outstanding fees in a "high-priority recognition queue";

[0142] The AI ​​chip in the gate recognition module uses a dual neural network model to verify the license plates of the queue (normally only a single model is used);

[0143] The identification time was reduced from the usual 1.2 seconds to 0.5 seconds, and the confidence level was increased from 95% to 99.9%.

[0144] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time calculation of parking navigation service fees in a two-stage manner, characterized in that, include: Dual-segment path segmentation steps: Divide the parking navigation path into an external navigation segment (S1) and an internal fine-guided segment (S2), where S1 is the navigation path before the parking lot entrance and S2 is the precise navigation path inside the parking lot; Entry gate trigger billing steps: When a vehicle passes through the parking lot gate, the S2 segment service fee is triggered to be charged in real time through dual verification of license plate recognition and geofence positioning; Multi-factor dynamic pricing steps: Based on vehicle type coefficient, time period coefficient and parking space shortage rate, the dynamic service fee is calculated using the formula Fee=Base×(1+α)×(1+β)×(1+γ), where α is the vehicle type coefficient, β is the time period coefficient, and γ is the parking space shortage rate mapping value.

2. The method according to claim 1, characterized in that, The specific conditions for the gate entry trigger billing step include: License plate recognition confidence level > 95%; The vehicle's GPS location is within the electronic fence area of ​​the parking lot; The gate's inductive loop signal triggers an entry event.

3. The method according to claim 1, characterized in that, In the multi-factor dynamic pricing step: The vehicle type coefficient α is set as follows: α = 0 for cars and α = 0.8 for trucks; The time period coefficient β is set as follows: β = 0.5 during peak hours and β = 0 during off-peak hours; The parking space shortage rate γ is calculated as follows: γ = 0.6 × (real-time parking space shortage rate / 100%), where the parking space shortage rate ranges from 0 to 100%.

4. The method according to claim 1, characterized in that, It also includes credit recovery steps: When the account balance is insufficient, the credit limit of the corresponding license plate will be automatically frozen (default 20 yuan); If the fee is not paid within 7 days, the outstanding amount and late payment fee will be automatically deducted upon the next entry, and the license plate will be added to the key monitoring list.

5. A two-stage parking navigation service fee real-time billing system, characterized in that, include: Vehicle owner terminal system: used to realize dual-segment navigation interaction and payment operations, including: A dual-segment navigation interaction module is used to switch between the off-site navigation segment (S1) and the on-site fine guidance segment (S2); The payment interaction module is used for real-time deduction, credit limit management, and overdue payment collection. Gate recognition system: used for vehicle entry identification and billing triggering, including: The license plate recognition processing module is equipped with a 5-megapixel CMOS sensor for recognizing license plates and calculating confidence levels. The entry trigger module generates a billing trigger event based on the ground loop signal and geofence positioning. Cloud-based management system: used for dual-segment navigation control, dynamic billing, and credit management, including: The dual-stage navigation control module includes an off-site path planning unit and an on-site fine-guided engine unit, which generate paths S1 and S2 respectively; The dynamic billing engine module includes a parameter acquisition unit and a rate calculation unit, which are used to perform multi-factor dynamic pricing. The credit management module includes a credit limit freezing unit and a debt collection execution unit, which are used to manage credit limits and recover overdue fees.

6. The dual-segment parking navigation service fee real-time calculation system and method according to claim 5, characterized in that: The system according to claim 5, characterized in that the rate calculation unit of the dynamic billing engine module supports the following formula: Fee=Base×(1+α)×(1+β)×(1+γ), where: Base price (0.3 yuan); α is the vehicle type coefficient, β is the time period coefficient, and γ is the parking space shortage rate mapping value.

7. The system according to claim 5, characterized in that, The recovery execution unit of the credit management module includes: The late payment fee calculation unit is used to charge a late payment fee of 2 yuan per instance for overdue fees. The key monitoring unit is used to prioritize the identification of license plates with outstanding fees to 99.9%.

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