A park access reservation management method, system and program product

Abstract

The application discloses a park access reservation management method, system and program product, and realizes whole-process speed-up through early reservation, information pre-verification and construction of convenient payment, so that the whole process is simplified from the traditional "on-site registration-manual verification-manual charging-on-site payment" to "reservation verification-quick payment-automatic release", and the time consumption of a single vehicle access can be shortened by more than 50%, so that the queuing and congestion phenomenon of vehicles in the peak period is effectively avoided, and the workload of on-site staff is greatly reduced, so that few people on duty or even no one on duty can be realized, and the park labor cost is significantly reduced, therefore, the application is very suitable for large-scale application and promotion.

Description

Technical Field

[0001] This invention belongs to the field of intelligent entry technology, specifically relating to a method, system, and program product for park entry reservation management. Background Technology

[0002] Currently, when vehicles enter the park, they are usually verified, registered, and paid for their entry fees offline. Park staff need to come out of their booths to check and confirm the types of goods in the vehicle, then manually select and charge the fees. Merchants need to pay in cash or scan a QR code to get a ticket before the driver can drive into the park. As such, the aforementioned offline entry management method has the following shortcomings: (1) The whole process is time-consuming, not only inefficient, but also prone to congestion during peak hours; (2) The traditional process requires a lot of manpower to be responsible for on-site registration, information verification, manual billing, and ticketing, resulting in high labor costs. Therefore, based on the aforementioned shortcomings, how to provide a park entry reservation management method with low labor costs and high efficiency has become an urgent problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, and program product for park entry reservation management, in order to solve the problems of long time consumption, low efficiency, and high labor costs existing in the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a method for managing park entry reservations is provided, including: The online reservation terminal obtains vehicle entry reservation information and sends it to the back-end management platform. The vehicle entry reservation information includes vehicle and personnel information, vehicle information, cargo information, cargo compliance certificate information, and associated account. The back-end management platform determines the vehicle entry fee based on the cargo information in the vehicle entry reservation information, and freezes an amount equivalent to the vehicle entry fee in the associated account as the target fee. The back-end management platform reviews and processes the vehicle entry reservation information, and after the review is approved, generates a valid reservation voucher and sends it to the online reservation terminal. The park entry verification terminal obtains the actual license plate image of the vehicle when it enters the park, and obtains the actual license plate of the vehicle entering the park based on the actual license plate image, so as to generate vehicle entry query information based on the actual license plate of the vehicle entering the park; The park entry verification terminal matches the valid reservation voucher corresponding to the vehicle entry query information from the back-end management platform based on the vehicle entry query information, and displays the matched valid reservation voucher visually so as to obtain the vehicle verification result after the visualization display. The park entry verification terminal sends the vehicle verification results to the back-end management platform, so that the back-end management platform can deduct the target fee from the associated account based on the vehicle verification results, generate an electronic entry certificate and send it to the online reservation terminal, and control the park gate system to open the gate.

[0005] Based on the aforementioned publicly available information, and addressing the excessive time consumption caused by the traditional process of "staff needing to leave the booth to check product categories and manually calculate fees," this invention accelerates the entire process through advance booking and information pre-verification. On one hand, merchants / drivers pre-enter vehicle entry reservation information, including goods and vehicle details, via an online booking terminal. Then, the back-end management platform automatically verifies the information and calculates the fee, eliminating the need for on-site manual verification and entry. On the other hand, after the vehicle arrives at the park, staff can retrieve the reservation information through a verification terminal for visual display. This allows for quick verification of the vehicle's actual information, ensuring consistency between the actual information and the reservation information. Simultaneously... This invention also establishes an online and convenient payment system. When making a reservation, the platform automatically freezes the corresponding amount of the entry fee in the associated account based on the goods information. Finally, after verification, payment and release are completed in the associated account. Thus, the entire process is simplified from the traditional "on-site registration - manual verification - manual billing - on-site payment" to "reservation verification - fast payment - automatic release," reducing the entry time for a single vehicle by more than 50%, effectively avoiding vehicle queues and congestion during peak hours. Furthermore, this invention significantly reduces the workload of on-site staff, enabling minimal or even no staffing, thereby significantly reducing the park's labor costs. Therefore, this invention is highly suitable for large-scale application and promotion.

[0006] In one possible design, cargo information includes: cargo type and total cargo weight; wherein, based on the cargo information in the vehicle entry reservation information, the vehicle entry fee is determined, including: Obtain a goods type and cost mapping table, wherein the goods type and cost mapping table stores the basic entry costs corresponding to different types of goods; Based on the type of goods, retrieve the basic entry fee that matches the type of goods from the goods type and fee mapping table; The vehicle entry fee is determined based on the total weight of the goods and the queried basic entry fee.

[0007] In one possible design, the actual license plate of the vehicle entering the parking lot is obtained based on the actual license plate image, including: Edge detection processing is performed on the actual license plate image to obtain a binarized edge image corresponding to the actual license plate image; The edge feature value of each pixel in the binarized edge image is calculated, and an edge feature matrix is ​​constructed using the edge feature values ​​of each pixel. The edge feature matrix is ​​subjected to feature sparsification processing to obtain a feature sparse matrix; Based on the feature sparse matrix, multiple potential license plate regions are identified from the binarized edge image; Edge enhancement processing is performed on multiple potential license plate region images to obtain multiple enhanced edge images. Noise edge points are removed from each enhanced edge image to obtain several clean edge images. Based on several clean edge images, at least one candidate license plate image is identified, and the real license plate image is identified from the at least one candidate license plate image. Image recognition is performed on the real license plate image to obtain the actual license plate of the vehicle entering the parking lot.

[0008] In one possible design, the edge feature matrix is ​​subjected to feature sparsification to obtain a feature sparse matrix, including: For any edge feature value in the edge feature matrix, obtain the row and column index of the any edge feature value; Based on row and column indices, the matching feature value corresponding to any edge feature value is determined from the edge feature matrix, where the row and column indices of any edge feature value are i and j, and the row and column indices of the matching feature value are i+2 and j, respectively. The ratio between any edge feature value and the matching feature value is calculated and used as the edge determination value; Based on any edge feature value and the edge determination value, the sparse feature value corresponding to any edge feature value is determined. After polling all edge feature values ​​in the edge feature matrix, the sparse feature value corresponding to all edge feature values ​​is used to construct the feature sparse matrix.

[0009] In one possible design, any element in the feature sparse matrix is ​​1, 2, or 0. Based on the feature sparse matrix, multiple potential license plate region images are determined from the binarized edge image, including: Elements with a value of 1 or 2 in the feature sparse matrix are used as candidate edge elements; For any candidate edge element in the feature sparse matrix, taking the candidate edge element as the center, the corresponding extended region of the candidate edge element is determined in the feature sparse matrix, wherein the size of the extended region of the candidate edge element corresponding to different values ​​is different. Set all elements in the extended region to 1, and after polling all candidate edge elements, transform the feature sparse matrix into an actual edge matrix, where the element value of any element in the actual edge matrix is ​​1 or 0. From the actual edge matrix, all elements with a value of 1 are selected, and the pixels corresponding to the selected elements are taken as edge pixels. From the binarized edge image, the connected regions formed by each edge pixel are extracted, and each connected region is used as the potential license plate region image.

[0010] In one possible design, edge enhancement processing is performed on multiple potential license plate region images to obtain multiple enhanced edge images, including: For any one of the multiple license plate potential region images, obtain the gradient image corresponding to that one license plate potential region image; Construct the enhanced neighborhood of the k-th pixel in the gradient image; Based on the enhanced neighborhood, the standard deviation of brightness and the mean gradient of the k-th pixel are calculated; The edge enhancement weight of the k-th pixel is calculated based on the brightness standard deviation and the edge feature value of the k-th pixel. Using the edge enhancement weights and the mean gradient, the edge enhancement value of the k-th pixel is calculated, and the gradient value of the k-th pixel is updated to the edge enhancement value. Increment k by 1 and reconstruct the enhanced neighborhood of the k-th pixel in the gradient image until k equals K, thus obtaining the enhanced gradient image corresponding to any potential license plate region image. Here, the initial value of k is 1, and K is the total number of pixels in the gradient image. Based on the enhanced gradient image, an enhanced edge image corresponding to any potential license plate region image is generated.

[0011] In one possible design, the edge enhancement weight of the k-th pixel is calculated based on the brightness standard deviation and the edge feature value of the k-th pixel, including: Obtain several brightness ranges and edge feature ranges; From several brightness intervals, determine the brightness interval to which the brightness standard deviation belongs, as the first target interval; and from several edge feature intervals, determine the edge feature interval to which the edge feature value of the k-th pixel belongs, as the second target interval. A first edge enhancement factor is calculated using the first target interval and the brightness standard deviation, and a second edge enhancement factor is calculated using the second target interval and the edge feature value of the k-th pixel. The product of the first edge enhancement factor and the second edge enhancement factor is used as the edge enhancement weight of the k-th pixel.

[0012] In one possible design, determining the actual license plate image from at least one candidate license plate image includes: The first image centroid of the binarized edge image and the second image centroid of each candidate license plate image are determined. Feature extraction processing is performed on each candidate license plate image to obtain image features; The distances between the centroid of the first image and each centroid of the second image are calculated, and the distances are sorted in ascending order to obtain a distance sequence; The distance threshold is calculated based on the width and height of the binarized edge image and the second distance in the distance sequence; From all candidate license plate images, select those with a distance less than or equal to the distance threshold as images to be judged; By utilizing image features, interfering images are removed from each image to be judged in order to obtain at least one actual candidate license plate image; The actual license plate image is determined from each actual candidate license plate image based on the number of pixels in each actual candidate license plate image.

[0013] Secondly, a park entry reservation management system is provided, including: The online reservation terminal is used to obtain vehicle entry reservation information and send it to the back-end management platform. The vehicle entry reservation information includes vehicle and personnel information, vehicle information, cargo information, cargo compliance certificate information, and associated account. The back-end management platform is used to determine the vehicle entry fee based on the cargo information in the vehicle entry reservation information, and freeze an amount equivalent to the vehicle entry fee in the associated account as the target fee. The back-end management platform is used to review and process the vehicle entry reservation information, and after the review is approved, generate a valid reservation voucher and send it to the online reservation terminal. The park entry verification terminal is used to acquire the actual license plate image of the vehicle when it enters the park, and obtain the actual license plate of the vehicle entering the park based on the actual license plate image, so as to generate vehicle entry query information based on the actual license plate of the vehicle entering the park; The park entry verification terminal is used to match the valid reservation voucher corresponding to the vehicle entry query information from the back-end management platform based on the vehicle entry query information, and to visualize the matched valid reservation voucher so as to obtain the vehicle verification result after visualization. The park entry verification terminal is also used to send the vehicle verification results to the back-end management platform, so that the back-end management platform can deduct the target fee from the associated account based on the vehicle verification results, generate an electronic entry certificate and send it to the online reservation terminal, and control the park gate system to open the gate.

[0014] Thirdly, a park entry reservation management device is provided. Taking the device as an electronic device as an example, it includes a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the park entry reservation management method as described in the first aspect or any possible design in the first aspect.

[0015] Fourthly, a storage medium is provided, on which instructions are stored, which, when executed on a computer, perform the park entry reservation management method as described in the first aspect or any possible design of the first aspect.

[0016] Fifthly, a computer program product containing instructions is provided, which, when executed on a computer, causes the computer to perform the park entry reservation management method as described in the first aspect or any possible design of the first aspect.

[0017] Beneficial effects: (1) This invention speeds up the entire process by making advance reservations, pre-verifying information, and building a convenient payment system. As a result, the entire process is simplified from the traditional “on-site registration - manual verification - manual billing - on-site payment” to “reservation verification - fast payment - automatic release”. The time for a single vehicle to enter the site can be reduced by more than 50%, thereby effectively avoiding the phenomenon of vehicle queuing and congestion during peak hours. In addition, this invention greatly reduces the workload of on-site staff, enabling fewer or even no staff to be on duty, thus significantly reducing the labor costs of the park. Therefore, this invention is very suitable for large-scale application and promotion.

[0018] (2) The present invention adopts a two-dimensional automated billing mechanism of "category + load". The platform can preset the charging rules corresponding to different categories and different loads in advance, thus realizing billing standardization and reducing human error and disputes.

[0019] (3) The present invention requires the upload of goods compliance certificate information during the reservation stage. The manager can review it online in advance to prevent non-compliant goods from being reserved and entering the site. Therefore, the present invention strengthens information control and improves the compliance and security of entry.

[0020] (4) This invention supports flexible configuration of core rules according to the actual management needs of the park. For example, it can adjust the review requirements of compliance certificates and optimize the billing rule parameters according to the characteristics of different types of parks (such as logistics parks, bonded parks, and agricultural product trading parks). Therefore, it improves the flexibility of management and adapts to the diverse needs of parks. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of the park entry reservation management method provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the park entry reservation management system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0023] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0024] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0025] Example: See Figure 1As shown, the park entry reservation management method provided in this embodiment achieves a closed-loop management of entry business through a three-tier architecture of online reservation terminal + back-end management platform + park entry verification terminal. The online reservation terminal uses a mini-program as its core carrier, compatible with mobile web pages and other auxiliary channels, allowing merchants and drivers to easily complete vehicle entry reservation operations (i.e., upload vehicle entry reservation information) anytime, anywhere. The back-end management platform, as the system's "central hub," undertakes core functions such as data processing, rule configuration, and information verification. Specifically, upon receiving vehicle entry reservation information, it verifies the information, generates a valid reservation voucher, and calculates the vehicle entry fee, freezing an equivalent amount in the associated account. Finally, the park entry verification terminal is responsible for generating vehicle entry query information based on the actual license plate of the vehicle entering the park, and querying the corresponding valid reservation voucher based on the vehicle entry query information for visual display. This allows verification personnel to quickly... The system quickly verifies the actual vehicle information to determine its consistency with the reservation information. Finally, after verification, payment confirmation is made to ensure efficient vehicle entry. This method simplifies the entire process from the traditional "on-site registration - manual verification - manual billing - on-site payment" to "reservation verification - rapid payment - automatic release," reducing the entry time for a single vehicle by more than 50%. This effectively avoids vehicle queuing and congestion during peak hours. Furthermore, this method significantly reduces the workload of on-site staff, enabling minimal or even no staffing, thereby significantly reducing park labor costs. Therefore, this method is highly suitable for large-scale application and promotion. For example, this method can operate on, but is not limited to, online reservation terminals, back-end management platforms, and park entry verification terminals. It is understood that the aforementioned execution entities do not constitute a limitation on the embodiments of this application. Accordingly, the operation steps of this method can be, but are not limited to, the steps S1 to S6 below.

[0026] S1. The online reservation terminal obtains vehicle entry reservation information and sends it to the back-end management platform. The vehicle entry reservation information includes vehicle and personnel information, vehicle information, cargo information, cargo compliance certificate information, and associated account. In this embodiment, the core function of the online reservation terminal is to guide merchants or drivers to enter full-dimensional entry information in a standardized manner through a mini-program visual interface, so as to realize the information in advance before entry and reduce the pressure of on-site verification from the source. The collected vehicle entry reservation information covers six core dimensions: "merchant-driver-vehicle-cargo-certificate-payment account", which can build a full-chain information association system.

[0027] The online reservation terminal can obtain the aforementioned vehicle entry reservation information in response to human-computer interaction. Optionally, the aforementioned vehicle and personnel information may include, but is not limited to, driver information, such as the driver's name and contact number. In this way, the collection of this information can ensure that the responsible person can be accurately contacted throughout the vehicle entry process, thereby enabling rapid response to emergencies. Similarly, the vehicle information may include, but is not limited to, the license plate number and vehicle model of the reserved vehicle. This information serves as the core identifier for vehicle entry identity verification and can provide key data support for subsequent rapid identification and efficient entry. At the same time, the cargo information may include, but is not limited to, the type of cargo and the total weight of the cargo. This is the basis for the park's classification and control of entering cargo and also the core reference for subsequent billing standard matching and amount freeze calculation. The cargo compliance certificate information includes customs declarations, quarantine certificates, and other certificates (which can be flexibly configured according to the park's management requirements). The aforementioned certificates are used to verify the compliance of the cargo entering the park, which can prevent non-compliant cargo from entering the park and strengthen the park's security and compliance control capabilities.

[0028] Furthermore, the associated account can be, but is not limited to, the merchant's payment account, which is linked to membership card information. As the core carrier for freezing funds and settling payments, it can provide account protection for subsequent billing freezes and fast payments. At the same time, as mentioned above, the vehicle entry reservation information also includes merchant information. Therefore, the merchant information can include, but is not limited to, the merchant's name and contact number, which is used to clarify the ownership of the goods entering the site, making it easier for the park management to accurately trace responsibility and efficiently connect with business.

[0029] In addition, in this embodiment, after obtaining the vehicle entry reservation information, the online reservation terminal will also perform information verification. It can perform real-time verification of license plate number format, contact phone number validity, and voucher file format, thereby effectively reducing invalid information input and improving data accuracy and subsequent business processing efficiency. Specifically, for license plate verification, regular expressions can be used for matching verification (such as checking whether the province abbreviation, issuing authority code, and serial number combination conform to the standard). For contact phone number validity verification, number length verification can be performed. For voucher file format verification, file extension checks (such as PDF, JPG, word, doc, etc.), MIME type verification (by reading the file binary header information to determine the real file type and prevent forged extensions), file size verification, and file content parsing and keyword matching verification can be performed.

[0030] Thus, after obtaining the vehicle entry reservation information, it can be transmitted to the back-end management platform for information review and data processing, as shown in steps S2 and S3 below.

[0031] S2. The backend management platform determines the vehicle entry fee based on the cargo information in the vehicle entry reservation information and freezes an amount equivalent to the vehicle entry fee in the associated account as the target fee. In specific implementation, for example, but not limited to, first obtaining a cargo type and fee mapping table (which stores the basic entry fees corresponding to different types of cargo); then, querying the basic entry fee matching the cargo type from the cargo type and fee mapping table according to the cargo type; finally, determining the vehicle entry fee based on the total weight of the cargo and the queried basic entry fee. Entry fees; optionally, the back-end management platform pre-stores the increase ratios corresponding to different load capacities, such as a 10% increase on the basic entry fee for a load capacity of (0, 10] tons, and a 20% increase on the basic entry fee for a load capacities of (10, 20] tons, etc. Thus, based on the increase ratios corresponding to different load capacities and combined with the queried basic entry fees, the vehicle entry fee can be calculated. Of course, differentiated calculation rules can be set according to the management costs and risk levels of different categories of goods entering the site (such as general cargo, fragile goods, dangerous goods, etc.) and the maximum load capacity standard of the vehicle. This embodiment is not limited to the aforementioned examples.

[0032] Thus, after calculating the vehicle entry fee, the same amount can be frozen in the aforementioned associated account to be used for subsequent entry fee deductions. During this period, the funds cannot be used arbitrarily to ensure traceability and effective control of the fees. Optionally, if the remaining amount in the associated account is less than the vehicle entry fee, all amounts in the associated account will be frozen. If the remaining amount in the associated account is greater than or equal to the vehicle entry fee, only the amount equivalent to the vehicle entry fee will be frozen. At the same time, the online reservation terminal (i.e., the user terminal) will be notified after the freezing.

[0033] Therefore, after the entry fee is frozen, information verification can be carried out, as shown in step S3 below.

[0034] S3. The backend management platform reviews and processes the vehicle entry reservation information, and after approval, generates a valid reservation voucher and sends it to the online reservation terminal. In this embodiment, for example, but not limited to, reviewing the compliance certificate information of the goods and the type of goods in the goods information in the vehicle entry reservation information; wherein, but not limited to, visually displaying the aforementioned compliance certificate information of the goods and the type of goods, so that park management personnel can view the uploaded customs declaration, quarantine certificate and other documents and view the type of goods online; optionally, if the documents are complete and qualified and the type of goods is compliant, the review is deemed to be approved, and a valid reservation voucher can be generated directly based on the vehicle entry reservation information; of course, for non-compliant situations such as missing documents, false information and / or non-compliant goods categories, the voucher will be directly rejected and the specific reasons will be fed back; at the same time, prompt information can also be generated to guide the user to supplement, modify and resubmit.

[0035] Furthermore, the aforementioned valid reservation voucher may include, but is not limited to, the vehicle entry reservation information, the amount frozen status, the reservation number, and the reservation mobile phone number. This facilitates vehicle verification based on the reservation information on the valid reservation voucher upon entry.

[0036] Furthermore, the back-end management platform also supports aggregating all appointment records in a list format, clearly presenting key information such as appointment time, merchant name, license plate number, goods category, voucher status, and amount freeze status. It also supports multi-dimensional searching and filtering by time, license plate number, merchant name, etc., which can improve the efficiency of information retrieval.

[0037] After the vehicle entry reservation information is reviewed and the entry fee is calculated, and the generated valid reservation voucher is sent to the user's terminal, the valid reservation voucher can be stored so that it can be retrieved when the vehicle enters the site, thereby realizing vehicle entry verification. The vehicle entry verification process is as shown in steps S4 and S5 below.

[0038] S4. The park entry verification terminal acquires the actual license plate image of the vehicle entering the park, and obtains the actual license plate number based on the actual license plate image, thereby generating vehicle entry query information based on the actual license plate number. In this embodiment, the barrier gate system is connected to the park entry verification terminal. Therefore, when a vehicle enters the park, the barrier gate system can capture the actual license plate image of the vehicle and transmit it to the park entry verification terminal. At this time, the park entry verification terminal can perform license plate recognition on the actual license plate image to obtain the actual license plate number of the vehicle entering the park. Of course, verification personnel can also use the park entry verification terminal to directly capture the license plate number of the entering vehicle to obtain the actual license plate image. In this way, the tedious step of manually inputting the license plate number can be eliminated.

[0039] Optionally, the detailed process of license plate recognition described below may be, but is not limited to, as shown in steps S41 to S47 below.

[0040] S41, perform edge detection processing on the actual license plate image to obtain a binarized edge image corresponding to the actual license plate image; in specific implementation, for example, but not limited to, perform grayscale processing on the actual license plate image to obtain a grayscale image; then, use a gradient operator (such as the Sobel operator) to determine the gradient image of the grayscale image; then perform binarization processing on the gradient image, so that a binarized edge image is obtained after binarization processing.

[0041] Thus, after obtaining the binarized edge image, the edge feature value of each pixel can be calculated so that the potential area of ​​the license plate can be segmented based on the edge feature value. The process is shown in step S42 below.

[0042] S42. Calculate the edge feature value of each pixel in the binarized edge image, and construct an edge feature matrix using the edge feature values ​​of each pixel. In a specific application, taking any pixel in the binarized edge image as an example, the calculation process of the edge feature value is explained as follows: For any pixel in the binarized edge image, take the pixel as the center and determine the neighborhood region of the pixel according to a preset size (such as 4×4, 8×8, or 16×16, etc.); then, count the number of pixels with a pixel value of 1 in the neighborhood region; finally, the ratio between the number of pixels with a pixel value of 1 and the total number of pixels in the neighborhood region can be used as the edge feature value of the pixel.

[0043] Thus, based on the aforementioned method, after calculating the edge feature value of each pixel in the binarized edge image, an edge feature matrix can be constructed based on this. The size of the edge feature matrix is ​​the same as that of the binarized edge image, and the element in the i-th row and j-th column represents the edge feature value of the pixel in the i-th row and j-th column of the binarized edge image.

[0044] After constructing the edge feature matrix, feature sparsification can be performed to transform the edge feature matrix into a sparse feature matrix containing only 0, 1, and 2, so that non-edge regions and edge regions can be distinguished based on this. The feature sparsification process is shown in step S43 below.

[0045] S43. Perform feature sparsification processing on the edge feature matrix to obtain a feature sparse matrix. In this embodiment, for any edge feature value in the edge feature matrix, for example, but not limited to, first obtain the row and column index of the any edge feature value; then, based on the row and column index, determine the matching feature value corresponding to the any edge feature value from the edge feature matrix, wherein the row and column index of the any edge feature value are i and j respectively, and the row and column index of the matching feature value are i+2 and j respectively; finally, the sparse feature value corresponding to the any edge feature value can be determined according to the any edge feature value and the edge determination value; thus, after polling all edge feature values ​​in the edge feature matrix, the aforementioned feature sparse matrix can be constructed using the sparse feature values ​​corresponding to all edge feature values.

[0046] Optionally, the process for determining the aforementioned sparse eigenvalues ​​may, but is not limited to, the following: If any edge feature value and the edge determination value meet the first determination condition, then the sparse feature value is determined to be 2, wherein the first determination condition is that any edge feature value belongs to (a1, a2) ∪ [a3, a4), and the edge determination value is greater than the first feature threshold; in this embodiment, a1, a2, a3, a4 are constants, and can be set to 0.1, 0.15, 0.2 and 0.32 respectively; at the same time, for example, the first feature threshold can be set to 5.

[0047] Similarly, if any edge feature value and edge determination value meet the second determination condition, the sparse feature value is determined to be 1. The second determination condition is that any edge feature value belongs to [a2, a3), the edge determination value is greater than the second feature threshold, and the first feature threshold is greater than the second feature threshold. In this embodiment, the second feature threshold is 3.

[0048] Finally, if any edge feature value and edge determination value do not meet the first determination condition and the second determination condition, then the sparse feature value is determined to be 0.

[0049] In this embodiment, a sparse feature value of 1 or 2 indicates that the corresponding pixel is a potential region of interest point, i.e., an edge point; conversely, a value of 0 indicates that the corresponding pixel is a non-region of interest point.

[0050] Furthermore, the principle for determining the aforementioned sparse eigenvalues ​​is as follows: License plates consist of multiple characters, which form numerous vertical edges with the background. Compared to a smooth car body or a cluttered background, the proportion of edge points is neither too high (avoiding excessive texturing) nor too low (avoiding flatness). Therefore, setting an effective threshold for the proportion of edge points can filter out areas that clearly do not conform to the characteristics of a license plate. At the same time, license plates are usually installed above the bumper or in specific positions on the car body, and the area directly below them (such as the bumper or body panel) is usually relatively smooth with sparse edges. Therefore, the density at the lower edge of the license plate will be much higher than that in the area below it, which will create a local abrupt change. Based on this, the ratio of the proportion of edge points in the vertical direction (i.e., the ratio of the proportion of edge points in row i, column j to the proportion of edge points in row i+2, column j) is used to capture this abrupt change. A ratio much greater than 1 (5 or 3) indicates that the current point is located at the lower boundary of an "edge-dense area," which is a typical position for a license plate.

[0051] Therefore, the first layer of edge point ratio range filtering is used to check whether the pixel corresponding to any edge feature value falls within the preset "license plate effective density range". This step uses extremely low computational cost to filter out most of the areas in the image with excessively high edge density (such as grid) or excessively low edge density (such as smooth car body). The second layer of local contrast filtering is to further calculate the ratio of the edge point ratio of the interest point with the two units directly below it (i.e., moving down two pixels) for the interest point with qualified density. This step ensures that the interest point not only has a suitable density, but is also located on the boundary between a dense area (license plate) and a non-dense area (car body). This is a strong indication of the lower edge of the license plate.

[0052] Meanwhile, this embodiment uses two levels of confidence for classification, that is, the pixel points corresponding to the edge feature values ​​that meet the first preset condition are high confidence points, while those that meet the second preset condition are low confidence points.

[0053] Therefore, after constructing the feature sparse matrix based on the aforementioned method, the potential region of the license plate can be extracted based on it, as shown in step S44 below.

[0054] S44. Based on the feature sparse matrix, determine multiple potential license plate region images from the binarized edge image; in this embodiment, for example, but not limited to, the following steps S44a to S44e can be used to determine the aforementioned multiple potential license plate region images.

[0055] S44a. Elements with values ​​of 1 or 2 in the sparse feature matrix are selected as candidate edge elements. In specific implementation, as explained above, the pixels corresponding to elements with values ​​of 1 and 2 are potential license plate points, while the pixels corresponding to elements with values ​​of 0 are non-interest points. Therefore, elements with values ​​of 1 and 2 can be selected as candidate edge elements. Then, for each candidate edge element, region expansion is performed, as shown in step S44b below.

[0056] S44b. For any candidate edge element in the feature sparse matrix, taking that candidate edge element as the center, determine the corresponding extended region in the feature sparse matrix. The size of the extended region for candidate edge elements corresponding to different values ​​is different. In specific implementation, to ensure the license plate area is completely covered while excluding irrelevant areas as much as possible, it is necessary to expand the region of each candidate edge element to generate a larger candidate region for subsequent precise positioning. For example, when any of the aforementioned candidate edge elements is 1, the size of its corresponding extended region is 4×5. When any candidate edge element is 2, the size of its corresponding extended region is 3×5. Thus, for candidate edge elements with a value of 1, the extended region is larger, which can ensure that the license plate area is completely covered with a high probability (i.e., a larger extended range is needed to capture the license plate area that may be offset). The reason why the extended range of candidate edge elements with a value of 2 is smaller is that since it is already a strong signal point with high confidence, a compact window centered on it is sufficient to cover the core area of ​​the license plate. If an excessively large extension is introduced, unnecessary background areas may be introduced, increasing the subsequent computational burden.

[0057] After obtaining the extended region of any candidate edge element, all elements in the region can be set to 1 to indicate that the pixels corresponding to each element in the region are pixels of the potential license plate region (i.e. edge pixels); the element value update process is as shown in step S44c below.

[0058] S44c. Set all elements in the extended region to 1, and after polling all candidate edge elements, transform the feature sparse matrix into an actual edge matrix, where the value of any element in the actual edge matrix is ​​1 or 0. In this embodiment, when two candidate edge elements are adjacent, their corresponding extended regions overlap. After expansion, the matrix element values ​​of the overlapping region remain 1 (because they are set to 1 multiple times, the result remains unchanged). In this way, multiple isolated candidate edge elements are actually connected into a larger connected candidate region, thereby avoiding the problem of license plate being fragmented.

[0059] Through the aforementioned operations, the feature sparse matrix can be transformed into an actual edge matrix with element values ​​of 1 or 0. Then, based on the pixels corresponding to the elements with a value of 1 in the actual edge matrix, multiple potential license plate regions can be determined, as shown in steps S44d and S44e below. S44d. From the actual edge matrix, all elements with a value of 1 are selected, and the pixels corresponding to the selected elements are taken as edge pixels. In this embodiment, the pixels corresponding to the elements with a value of 1 (i.e., edge pixels) represent the pixels in the potential area of ​​the license plate. Therefore, the multiple connected regions formed by each edge pixel can be used as the potential area image of the license plate. The process is shown in step S44e below.

[0060] S44e. Extract the connected regions formed by each edge pixel from the binarized edge image, and use each connected region as a potential license plate region image.

[0061] After extracting multiple potential license plate region images from the binarized edge image through the aforementioned steps S44a to S44e, edge enhancement and noise edge removal can be performed to reduce the impact of light and noise on subsequent license plate precise localization; wherein, the aforementioned edge enhancement and noise edge removal process is as shown in step S45 below.

[0062] S45. Perform edge enhancement processing on multiple potential license plate area images to obtain multiple enhanced edge images, and perform noise edge point removal processing on each enhanced edge image to obtain several clean edge images.

[0063] In specific implementation, taking any potential license plate area image as an example, the edge enhancement process can be illustrated. For example, but not limited to, the following steps S45a to S45g can be used.

[0064] S45a. For any one of the multiple license plate potential region images, obtain the gradient image corresponding to the any one license plate potential region image; in this embodiment, during the process of obtaining the binarized edge map in the aforementioned step S41, the gradient image of the entire actual license plate image is obtained. Therefore, the gradient image corresponding to the any one license plate potential region image can be segmented from the gradient image of the entire actual license plate image; then, edge enhancement can be performed based on this, as shown in the following steps S45b to S45g.

[0065] S45b. Construct the enhanced neighborhood of the k-th pixel in the gradient image; in this embodiment, for example, the size of the enhanced neighborhood is the same as the size of the neighborhood region used to calculate the edge feature value, and it is constructed with the k-th pixel as the center; thus, after obtaining the enhanced neighborhood of the k-th pixel, the brightness standard deviation and gradient mean can be calculated, as shown in step S45c below.

[0066] S45c. Based on the enhanced neighborhood, calculate the brightness standard deviation and gradient mean of the k-th pixel. In this embodiment, the original image region corresponding to the enhanced neighborhood can be extracted from the actual license plate image. Then, the original image region is converted to Lab space to obtain a Lab image. Next, the brightness standard deviation of the Lab image is calculated based on the brightness components of all pixels in the Lab image. Finally, the brightness standard deviation of the Lab image is used as the brightness standard deviation of the k-th pixel. Similarly, the gradient mean of all pixels in the enhanced neighborhood is used as the gradient mean of the k-th pixel.

[0067] Thus, after calculating the standard deviation of brightness and the mean of gradient, the edge enhancement weight can be calculated, as shown in step S45d below.

[0068] S45d. Based on the brightness standard deviation and the edge feature value of the kth pixel, calculate the edge enhancement weight of the kth pixel. In specific implementation, for example, but not limited to, first obtain several brightness intervals and edge feature intervals; then, determine the brightness interval to which the brightness standard deviation belongs from the several brightness intervals as the first target interval, and determine the edge feature interval to which the edge feature value of the kth pixel belongs from the several edge feature intervals as the second target interval; next, use the first target interval and the brightness standard deviation to calculate the first edge enhancement factor, and use the second target interval and the edge feature value of the kth pixel to calculate the second edge enhancement factor; finally, the product between the first edge enhancement factor and the second edge enhancement factor can be used as the edge enhancement weight of the kth pixel.

[0069] Furthermore, for example, some brightness intervals can be, but are not limited to, [0,20), [20,60); and some edge feature intervals can be, [0,0.15), [0.15,0.35). Therefore, there are four combinations of the interval to which the brightness standard deviation and edge feature value of the k-th pixel belong. For example, the brightness standard deviation of the k-th pixel belongs to [0,20), and its edge feature value belongs to [0,0.15); or the brightness standard deviation of the k-th pixel belongs to [0,20), and its edge feature value belongs to [0.15,0.35); or the brightness standard deviation of the k-th pixel belongs to [20,60), and its edge feature value belongs to [0,0.15); or the brightness standard deviation of the k-th pixel belongs to [20,60), and its edge feature value belongs to [0.15,0.35].

[0070] Thus, after determining the brightness standard deviation and the interval to which the edge feature value of the k-th pixel belongs, the edge enhancement factor can be calculated based on this. Taking the first edge enhancement factor as an example, its corresponding calculation formula is: ; In the formula, Indicates the first edge enhancement factor. Let represent the right and left endpoints of the first target interval (e.g., assuming the first target interval is [20, 60), then the right endpoint is 60 and the left endpoint is 20). Indicates the standard deviation of brightness. Represents a constant. To enhance the control coefficient; in this embodiment, for example... Possible values ​​include, but are not limited to, those that can be: ,3,2, and for example The value is either 20 or 0.15; where it is 20 when calculating the first edge enhancement factor and 0.15 when calculating the second edge enhancement factor.

[0071] In addition, in this embodiment, if the brightness standard deviation of the k-th pixel does not belong to any of the aforementioned brightness intervals, and / or the edge feature value does not belong to any of the aforementioned edge feature intervals, then the edge enhancement weight of the k-th pixel is set to 0.

[0072] Thus, after calculating the edge enhancement weight of the kth pixel, the edge enhancement value of the kth pixel can be calculated by combining the aforementioned gradient mean, as shown in step S45e below.

[0073] S45e. Using the edge enhancement weights and the mean gradient, calculate the edge enhancement value of the k-th pixel, and update the gradient value of the k-th pixel to the edge enhancement value. In specific implementation, for example, but not limited to, first calculate the gradient difference between the gradient value of the k-th pixel and the mean gradient, and then use the product of the gradient difference and the edge enhancement weights as an intermediate parameter. Finally, add the gradient value of the k-th pixel to the intermediate parameter to obtain the edge enhancement value of the k-th pixel. After obtaining the edge enhancement value, gradient update can be performed, and the edge enhancement of the k-th pixel can be completed. Then, the edge enhancement of the remaining pixels is performed in the same way until all pixels in any potential license plate region image have been traversed, and the enhanced gradient image can be obtained. The process is shown in step S45f below.

[0074] S45f. Increment k by 1 and reconstruct the enhanced neighborhood of the k-th pixel in the gradient image until k equals K, thus obtaining the enhanced gradient image corresponding to any potential license plate region image, where the initial value of k is 1 and K is the total number of pixels in the gradient image; In this embodiment, after obtaining the enhanced gradient image corresponding to any potential license plate region image, an enhanced edge image can be generated based on it, as shown in step S45g below.

[0075] S45g. Based on the enhanced gradient image, generate an enhanced edge image corresponding to any potential license plate region image; in this embodiment, after edge enhancement, the gradient image corresponding to any potential license plate region image changes, which is essentially a change in grayscale, that is, each pixel value represents the modulated gradient intensity (edge ​​intensity), and its value range is continuous or has multiple grayscale levels; therefore, by binarizing the enhanced gradient image, some useless edges can be re-binarized to 0, thereby achieving the purpose of edge enhancement.

[0076] After edge enhancement of each potential license plate region image is completed through the aforementioned steps S45a to S45g, noise edge points can be removed. The process of removing noise edge points will be illustrated using any enhanced edge image as an example: First, obtain all connected regions in any enhanced edge image; then, calculate the length of each connected region; next, remove connected regions whose length is less than the minimum length or greater than the maximum length from the enhanced edge image to obtain the processed edge image; then, for any edge point (pixel with a value of 1) in the processed edge image, place a horizontal window and a vertical window centered on that edge point (the horizontal window is 11×3 in size, and the vertical window is 3×11 in size, with an aspect ratio similar to a license plate to capture the arrangement features of the license plate characters); finally, count the total number of edge points in the horizontal window and the total number of edge points in the vertical window, and determine whether the total number of edge points in the horizontal window or the vertical window exceeds a preset threshold. If so, retain that edge point; otherwise, discard it as a redundant point. In this way, after traversing all edge points in the processed edge image in the aforementioned manner, the corresponding clean edge image can be obtained.

[0077] After obtaining several clean edge images, fine segmentation of the license plate can be performed, as shown in step S46 below.

[0078] S46. Based on several clean edge images, at least one candidate license plate image is determined, and the real license plate image is determined from the at least one candidate license plate image. In specific implementation, morphological operations are performed on each clean edge image to extract the connected components in each clean edge image, and then each connected component is used as a candidate license plate image. In this way, after obtaining the candidate license plate image, vehicle localization can be performed, and the process is as shown in the following steps S46a to S46g.

[0079] S46a. Determine the first image centroid of the binarized edge image and the second image centroid of each candidate license plate image; in specific implementation, the first image centroid is the average coordinate of all foreground pixels in the binarized edge image (i.e., the average coordinate of pixels with a pixel value of 1); similarly, the calculation process of the second image centroid is also the same, and will not be repeated here.

[0080] After obtaining the centroid of the first image and the centroid of the second image of each candidate license plate image, feature extraction can be performed, as shown in step S46b below.

[0081] S46b. Perform feature extraction processing on each candidate license plate image to obtain image features; in this embodiment, for any candidate license plate image, the minimum bounding rectangle of the candidate license plate image can be determined, but is not limited to; then, the aspect ratio of the minimum bounding rectangle, the number of non-zero pixels, and the ratio between the number of non-zero pixels and the area of ​​the minimum bounding rectangle (hereinafter referred to as pixel density) are used as image features.

[0082] After extracting the image features of each candidate license plate image, the license plate can be extracted by combining the distance between the centroids mentioned above, as shown in steps S46c to S46g below.

[0083] S46c. Calculate the distance between the first image centroid and each of the second image centroids, and sort the distances in ascending order to obtain a distance sequence; In this embodiment, the calculation process of the distance between the first image centroid and any second image centroid is as follows: sum the absolute value of the difference between the horizontal coordinate of the first image centroid and the horizontal coordinate of any second image centroid, and the absolute value of the difference between the vertical coordinate of the first image centroid and the vertical coordinate of any second image centroid, and the distance between the first image centroid and any second image centroid can be obtained.

[0084] Thus, after calculating the distance between the centroid of the first image and each centroid of the second image, the distances can be sorted in ascending order. Then, the distance threshold can be calculated based on the distance sequence, as shown in step S46d below.

[0085] S46d. Calculate the distance threshold based on the width and height of the binarized edge image and the second distance in the distance sequence; in this embodiment, the distance threshold = min(d2, M / 4 + N / 4), where M and N are the width and height of the binarized edge image; after calculating the distance threshold, preliminary screening of license plates can be performed, as shown in step S46e below.

[0086] S46e. From all candidate license plate images, select candidate license plate images whose distance is less than or equal to the distance threshold as images to be judged; after completing the initial screening of candidate license plate images and obtaining multiple images to be judged, a secondary screening can be performed by combining the aforementioned image features, as shown in step S46f below.

[0087] S46f. Using image features, remove interfering images from each image to be judged to obtain at least one actual candidate license plate image; in specific applications, images to be judged with an aspect ratio between (2,5) and a pixel density greater than 0.65 are considered as actual candidate license plate images, while those that do not meet the aforementioned conditions are considered as interfering images.

[0088] Thus, after obtaining at least one actual candidate license plate image, the final real license plate image can be determined by combining the number of non-zero pixels mentioned above, as shown in step S46g below.

[0089] S46g. Based on the number of pixels in each actual candidate license plate image, determine the real license plate image from each actual candidate license plate image; in this embodiment, after the aforementioned screening, the remaining actual candidate license plate images may have areas such as car logos, license plates, and heat dissipation mesh, so the number of pixels contained in the aforementioned areas is significantly different. Based on this, the license plate can be extracted based on the number of non-zero pixels, that is, the aforementioned number of pixels is the number of non-zero pixels in the actual candidate license plate image. Therefore, sort the number of pixels in each actual candidate license plate image in descending order, and then select the first and second number of pixels in the sorted order as the first target value and the second target value; next, calculate the ratio between the first target value and the second target value. If the ratio is greater than a preset value (such as 1.5), then the actual candidate license plate image corresponding to the second target value is taken as the real license plate image; otherwise, the actual candidate license plate image corresponding to the first target value is taken as the real license plate image.

[0090] Thus, after determining the real license plate image through the aforementioned steps S46a to S46g, license plate recognition can be performed, as shown in step S47 below.

[0091] S47. Perform image recognition on the real license plate image to obtain the actual license plate of the vehicle entering the venue; in this embodiment, the real license plate image is input into the license plate recognition model to obtain the actual license plate of the vehicle entering the venue; wherein, the license plate recognition model may be, but is not limited to, a trained YOLOv5 model.

[0092] Therefore, after identifying the actual license plate of the vehicle entering the venue through the aforementioned steps S41 to S47, entry query information can be generated based on this. For example, but not limited to, obtaining the reservation number and mobile phone number first, and then using the reservation number, mobile phone number and / or the actual license plate of the vehicle entering the venue to form the vehicle entry query information.

[0093] Furthermore, as explained above, the valid reservation voucher will be sent to the online reservation terminal. Therefore, when a vehicle enters the venue, it can display the valid reservation voucher. In this way, the park's verification personnel can obtain the reservation number and mobile phone number through the displayed valid reservation voucher. Finally, they can use any one of the three—the reservation number, the mobile phone number, and the actual license plate of the vehicle entering the venue—to query the reservation information. The process is shown in step S5 below.

[0094] S5. The park entry verification terminal matches the valid reservation voucher corresponding to the vehicle entry query information from the back-end management platform and displays the matched valid reservation voucher visually to obtain the vehicle verification result. After retrieving the corresponding valid reservation voucher from the back-end management platform based on the vehicle entry query information and displaying it visually, the park verification personnel can verify whether the actual license plate number, vehicle type, cargo type, and other key information are consistent with the queried reservation information. At the same time, they can check the compliance voucher review status and the amount freeze status. When the aforementioned key information is consistent, the compliance voucher review status is passed, and the amount freeze status is successfully frozen, the verification result of the vehicle that has passed the verification is entered into the park entry verification terminal. In this way, the park entry verification terminal can obtain the vehicle verification result in response to human-computer interaction. Finally, the back-end management platform can control vehicle entry based on this, as shown in step S6 below.

[0095] S6. The park entry verification terminal sends the vehicle verification result to the back-end management platform, so that the back-end management platform can deduct the target fee from the associated account based on the vehicle verification result, generate an electronic entry voucher and send it to the online reservation terminal, and control the park gate system to open the gate; In this embodiment, when the vehicle verification result is that the verification is passed, the target fee can be deducted from the associated account, and at the same time, a corresponding electronic entry voucher (such as an electronic release slip) will be generated, and the park gate system can be linked to automatically open and release, realizing a seamless connection of "payment-release".

[0096] Meanwhile, if the amount frozen in the aforementioned linked account is less than the vehicle entry fee, a message indicating insufficient funds will be output to prompt the user to pay the difference and complete the entry payment. Furthermore, all verification and payment records are recorded in real time to form a complete entry ledger, ensuring full traceability.

[0097] Furthermore, the back-end management platform can automatically calculate key operational metrics such as daily / monthly / yearly total reservations, successful entry volume, rejection volume, percentage of various goods entering the park, average entry time, and payment completion rate. It also presents data trends intuitively through various chart formats such as bar charts, pie charts, and line charts, helping managers quickly grasp core information such as vehicle entry traffic and cargo structure. Of course, it also supports exporting statistical data to common formats such as Excel, facilitating subsequent report preparation and business analysis, and providing data support for managers to optimize entry management rules and rationally allocate human resources.

[0098] In addition, the platform can, but is not limited to, encrypt and store sensitive information such as merchant information, voucher information, and payment data to prevent data leakage, and can also back up data through a data backup mechanism to ensure data security and no loss. At the same time, in specific applications, differentiated permissions can be configured for different positions of personnel in the park management (e.g., auditors only have information audit permissions, financial personnel can view payment data, and administrators have full permissions), thereby effectively avoiding the risk of unauthorized operations. It will also automatically retain the operation trajectory of all users (including merchants, drivers, and managers), including appointment submission, information modification, audit operations, payment operations, etc. In this way, a complete operation log can be formed, providing a reliable basis for subsequent problem tracing.

[0099] Therefore, through the park entry reservation management method described in detail in steps S1 to S6, the present invention has the following beneficial effects: (1) Significantly shorten entry time and alleviate peak congestion; In response to the problem of excessive time consumption caused by "staff leaving the booth to check the category and manually calculate the fee" in the traditional process, the platform speeds up the entire process through advance reservation and information pre-verification; On the one hand, merchants / drivers enter core data such as the category of goods and vehicle information in advance through the mini program, and the system automatically completes the information verification and calculation of the fee amount, without the need for on-site manual verification and manual entry; On the other hand, after the vehicle arrives at the park, staff only need to retrieve the reservation information through the verification terminal, quickly verify the consistency between the actual information and the reservation information, and complete the payment and release after the verification is passed; The whole process is simplified from the traditional "on-site registration - manual verification - manual calculation - on-site payment" to "reservation verification - fast payment - automatic release", which can shorten the entry time of a single vehicle by more than 50%, thereby effectively avoiding the phenomenon of vehicle queuing and congestion during peak periods.

[0100] (2) Optimize the payment experience and eliminate the need for merchants to travel to the site. In response to the cumbersome problem of the traditional process that "merchants need to pay in cash or scan the code to pay and get a ticket", this invention has built an online and convenient payment system. In this way, merchants do not need to come to the park in person. They only need to link their accounts during the reservation stage, and the system can automatically freeze the corresponding amount according to the type of goods and the load. When the vehicle enters the park, the system directly links the frozen amount to complete the deduction payment. If the amount is insufficient, the difference can be paid online. There is no need to get a ticket or cash transaction in the whole process, realizing "one-click payment and seamless passage". Based on this, this model completely eliminates the cost of merchants traveling to the site. It is especially suitable for merchants who are far away from the park or have busy business, and significantly improves the convenience of payment and merchant satisfaction.

[0101] (3) Standardized billing has been achieved, reducing human error and disputes. Traditional manual billing is prone to errors due to subjective judgment and memory bias of staff, which can lead to disputes between merchants and the park. This invention adopts a dual-dimensional automated billing mechanism of "category + load". The park management can preset the charging rules corresponding to different categories and loads in advance. The system automatically matches the rules according to the reservation information, calculates the fee and freezes the amount, without human intervention. This standardized billing model not only ensures the accuracy and consistency of the billing results and avoids human error, but also makes the billing basis traceable and verifiable, reducing disputes caused by billing issues and enhancing the credibility of park management.

[0102] (4) Strengthen information control to improve compliance and security of goods entering the site. In the traditional process, the verification of goods categories relies on manual on-site inspection, which is prone to omissions and errors, making it difficult to ensure the compliance of goods entering the site. This invention requires the uploading of compliance documents such as customs declarations and quarantine certificates during the reservation stage, and the management can review them online in advance to prevent non-compliant goods from being reserved and entering the site. At the same time, the reservation information clearly specifies the core data such as goods category and vehicle information, and on-site verification only needs to check for consistency, which further strengthens the dual control of goods and vehicles. In addition, the platform retains all reservation information, review records and payment data throughout the entire process to form a complete traceability ledger, which facilitates subsequent problem verification and improves the security and compliance of the park's entry management.

[0103] (5) Reduce park management costs and improve operational efficiency; Traditional processes require a large amount of manpower to handle on-site registration, information verification, manual billing, and ticketing, which are labor-intensive and inefficient; This invention replaces offline registration with online reservations, manual calculation with automatic billing, and comprehensive manual verification with rapid verification, which greatly reduces the workload of on-site staff and can achieve less or even no staff on duty, significantly reducing the park's manpower management costs; At the same time, this invention can automatically summarize and statistically analyze the data of the entire process, including reservations, entry, and payment, and generate standardized reports without manual processing, providing accurate data support for management decisions and further improving the overall operational efficiency of the park.

[0104] (6) Enhance management flexibility and adapt to diverse park needs; This invention supports flexible configuration of core rules according to the actual management needs of the park. For example, it can adjust the review requirements of compliance certificates and optimize the billing rule parameters according to the characteristics of different types of parks (such as logistics parks, bonded parks, and agricultural product trading parks). At the same time, the mini-program, as the core reservation channel, is compatible with auxiliary channels such as mobile web pages, adapts to the usage habits of different merchants and drivers, avoids the rigidity of the traditional process of "one-size-fits-all", and can accurately match the diverse park entry management needs, thereby improving the flexibility and adaptability of management.

[0105] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the park entry reservation management method described in the first aspect of the embodiment, including: The online reservation terminal is used to obtain vehicle entry reservation information and send it to the back-end management platform. The vehicle entry reservation information includes vehicle and personnel information, vehicle information, cargo information, cargo compliance certificate information, and associated account.

[0106] The back-end management platform is used to determine the vehicle entry fee based on the cargo information in the vehicle entry reservation information, and freeze an amount equivalent to the vehicle entry fee in the associated account as the target fee.

[0107] The back-end management platform is used to review and process the vehicle entry reservation information, and after the review is approved, generate a valid reservation voucher and send it to the online reservation terminal.

[0108] The park entry verification terminal is used to acquire the actual license plate image of a vehicle when it enters the park, and to obtain the actual license plate number of the vehicle entering the park based on the actual license plate image, so as to generate vehicle entry query information based on the actual license plate number of the vehicle entering the park.

[0109] The park entry verification terminal is used to match the valid reservation voucher corresponding to the vehicle entry query information from the back-end management platform, and to visualize the matched valid reservation voucher so as to obtain the vehicle verification result after visualization.

[0110] The park entry verification terminal is also used to send the vehicle verification results to the back-end management platform, so that the back-end management platform can deduct the target fee from the associated account based on the vehicle verification results, generate an electronic entry certificate and send it to the online reservation terminal, and control the park gate system to open the gate.

[0111] The working process, working details and technical effects of the system provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0112] like Figure 3 As shown, the third aspect of this embodiment provides a park entry reservation management device. Taking the device as an electronic device as an example, it includes: a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the park entry reservation management method as described in the first aspect of the embodiment.

[0113] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0114] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0115] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0116] The fourth aspect of this embodiment provides a storage medium that stores instructions containing the park entry reservation management method described in the first aspect of the embodiment. That is, the storage medium stores instructions, and when the instructions are run on a computer, the park entry reservation management method described in the first aspect of the embodiment is executed.

[0117] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0118] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0119] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the park entry reservation management method as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0120] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for managing park entry reservations, characterized in that, include: The online reservation terminal obtains vehicle entry reservation information and sends it to the back-end management platform. The vehicle entry reservation information includes vehicle and personnel information, vehicle information, cargo information, cargo compliance certificate information, and associated account. The back-end management platform determines the vehicle entry fee based on the cargo information in the vehicle entry reservation information, and freezes an amount equivalent to the vehicle entry fee in the associated account as the target fee. The back-end management platform reviews and processes the vehicle entry reservation information, and after the review is approved, generates a valid reservation voucher and sends it to the online reservation terminal. The park entry verification terminal obtains the actual license plate image of the vehicle when it enters the park, and obtains the actual license plate of the vehicle entering the park based on the actual license plate image, so as to generate vehicle entry query information based on the actual license plate of the vehicle entering the park; The park entry verification terminal matches the valid reservation voucher corresponding to the vehicle entry query information from the back-end management platform based on the vehicle entry query information, and displays the matched valid reservation voucher visually so as to obtain the vehicle verification result after the visualization display. The park entry verification terminal sends the vehicle verification results to the back-end management platform, so that the back-end management platform can deduct the target fee from the associated account based on the vehicle verification results, generate an electronic entry certificate and send it to the online reservation terminal, and control the park gate system to open the gate.

2. The method according to claim 1, characterized in that, Cargo information includes: cargo type and total weight; among which, based on the cargo information in the vehicle entry reservation information, the vehicle entry fee is determined, including: Obtain a goods type and cost mapping table, wherein the goods type and cost mapping table stores the basic entry costs corresponding to different types of goods; Based on the type of goods, retrieve the basic entry fee that matches the type of goods from the goods type and fee mapping table; The vehicle entry fee is determined based on the total weight of the goods and the queried basic entry fee.

3. The method according to claim 1, characterized in that, Based on the actual license plate image, the actual license plates of the vehicles entering the venue are obtained, including: Edge detection processing is performed on the actual license plate image to obtain a binarized edge image corresponding to the actual license plate image; The edge feature value of each pixel in the binarized edge image is calculated, and an edge feature matrix is ​​constructed using the edge feature values ​​of each pixel. The edge feature matrix is ​​subjected to feature sparsification processing to obtain a feature sparse matrix; Based on the feature sparse matrix, multiple potential license plate regions are identified from the binarized edge image; Edge enhancement processing is performed on multiple potential license plate region images to obtain multiple enhanced edge images. Noise edge points are removed from each enhanced edge image to obtain several clean edge images. Based on several clean edge images, at least one candidate license plate image is identified, and the real license plate image is identified from the at least one candidate license plate image. Image recognition is performed on the real license plate image to obtain the actual license plate of the vehicle entering the parking lot.

4. The method according to claim 3, characterized in that, The edge feature matrix is ​​subjected to feature sparsification to obtain a feature sparse matrix, including: For any edge feature value in the edge feature matrix, obtain the row and column index of the any edge feature value; Based on row and column indices, the matching feature value corresponding to any edge feature value is determined from the edge feature matrix, where the row and column indices of any edge feature value are i and j, and the row and column indices of the matching feature value are i+2 and j, respectively. The ratio between any edge feature value and the matching feature value is calculated and used as the edge determination value; Based on any edge feature value and the edge determination value, the sparse feature value corresponding to any edge feature value is determined. After polling all edge feature values ​​in the edge feature matrix, the sparse feature value corresponding to all edge feature values ​​is used to construct the feature sparse matrix.

5. The method according to claim 3, characterized in that, Any element in the feature sparse matrix is ​​1, 2, or 0. Based on the feature sparse matrix, multiple potential license plate region images are determined from the binarized edge image, including: Elements with a value of 1 or 2 in the feature sparse matrix are used as candidate edge elements; For any candidate edge element in the feature sparse matrix, taking the candidate edge element as the center, the corresponding extended region of the candidate edge element is determined in the feature sparse matrix, wherein the size of the extended region of the candidate edge element corresponding to different values ​​is different. Set all elements in the extended region to 1, and after polling all candidate edge elements, transform the feature sparse matrix into an actual edge matrix, where the element value of any element in the actual edge matrix is ​​1 or 0. From the actual edge matrix, all elements with a value of 1 are selected, and the pixels corresponding to the selected elements are taken as edge pixels. From the binarized edge image, the connected regions formed by each edge pixel are extracted, and each connected region is used as the potential license plate region image.

6. The method according to claim 3, characterized in that, Edge enhancement processing was performed on multiple potential license plate region images to obtain multiple enhanced edge images, including: For any one of the multiple license plate potential region images, obtain the gradient image corresponding to that one license plate potential region image; Construct the enhanced neighborhood of the k-th pixel in the gradient image; Based on the enhanced neighborhood, the standard deviation of brightness and the mean gradient of the k-th pixel are calculated; The edge enhancement weight of the k-th pixel is calculated based on the brightness standard deviation and the edge feature value of the k-th pixel. Using the edge enhancement weights and the mean gradient, the edge enhancement value of the k-th pixel is calculated, and the gradient value of the k-th pixel is updated to the edge enhancement value. Increment k by 1 and reconstruct the enhanced neighborhood of the k-th pixel in the gradient image until k equals K, thus obtaining the enhanced gradient image corresponding to any potential license plate region image. Here, the initial value of k is 1, and K is the total number of pixels in the gradient image. Based on the enhanced gradient image, an enhanced edge image corresponding to any potential license plate region image is generated.

7. The method according to claim 6, characterized in that, Based on the brightness standard deviation and the edge feature value of the k-th pixel, the edge enhancement weight of the k-th pixel is calculated, including: Obtain several brightness ranges and edge feature ranges; From several brightness intervals, determine the brightness interval to which the brightness standard deviation belongs, as the first target interval; and from several edge feature intervals, determine the edge feature interval to which the edge feature value of the k-th pixel belongs, as the second target interval. A first edge enhancement factor is calculated using the first target interval and the brightness standard deviation, and a second edge enhancement factor is calculated using the second target interval and the edge feature value of the k-th pixel. The product of the first edge enhancement factor and the second edge enhancement factor is used as the edge enhancement weight of the k-th pixel.

8. The method according to claim 3, characterized in that, Determining the real license plate image from at least one candidate license plate image includes: The first image centroid of the binarized edge image and the second image centroid of each candidate license plate image are determined. Feature extraction processing is performed on each candidate license plate image to obtain image features; The distances between the centroid of the first image and each centroid of the second image are calculated, and the distances are sorted in ascending order to obtain a distance sequence; The distance threshold is calculated based on the width and height of the binarized edge image and the second distance in the distance sequence; From all candidate license plate images, select those with a distance less than or equal to the distance threshold as images to be judged; By utilizing image features, interfering images are removed from each image to be judged in order to obtain at least one actual candidate license plate image; The actual license plate image is determined from each actual candidate license plate image based on the number of pixels in each actual candidate license plate image.

9. A park entry reservation management system, characterized in that, include: The online reservation terminal is used to obtain vehicle entry reservation information and send it to the back-end management platform. The vehicle entry reservation information includes vehicle and personnel information, vehicle information, cargo information, cargo compliance certificate information, and associated account. The back-end management platform is used to determine the vehicle entry fee based on the cargo information in the vehicle entry reservation information, and freeze an amount equivalent to the vehicle entry fee in the associated account as the target fee. The back-end management platform is used to review and process the vehicle entry reservation information, and after the review is approved, generate a valid reservation voucher and send it to the online reservation terminal. The park entry verification terminal is used to acquire the actual license plate image of the vehicle when it enters the park, and obtain the actual license plate of the vehicle entering the park based on the actual license plate image, so as to generate vehicle entry query information based on the actual license plate of the vehicle entering the park; The park entry verification terminal is used to match the valid reservation voucher corresponding to the vehicle entry query information from the back-end management platform based on the vehicle entry query information, and to visualize the matched valid reservation voucher so as to obtain the vehicle verification result after visualization. The park entry verification terminal is also used to send the vehicle verification results to the back-end management platform, so that the back-end management platform can deduct the target fee from the associated account based on the vehicle verification results, generate an electronic entry certificate and send it to the online reservation terminal, and control the park gate system to open the gate.

10. A computer program product containing instructions, characterized in that, When the instruction is executed on the computer, it causes the computer to perform the park entry reservation management method as described in any one of claims 1 to 8.

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