Internet of Things platform system

Abstract

The invention relates to the field of big data, and discloses an Internet of Things platform system which comprises the steps of judging whether license plate data, wagon balance data and vehicle body data are matched with pre-stored license plate data, pre-stored wagon balance data and pre-stored vehicle body data or not; and checking whether the same newly acquired near field communication data is detected when the license plate data, the vehicle body data, the wagon balance data, the video monitoring data and the access control data are acquired, and if the same newly acquired near field communication data is not detected, sending an alarm signal and closing a door lock corresponding to the access control data. According to the scheme, the near field communication data is used as the user identity tag of the Internet of Things platform, and when the license plate of the user is not matched with the vehicle body data and the weighbridge data, the association between the near field communication data and the acquisition of the license plate data, the vehicle body data, the weighbridge data, the video monitoring data and the access control data is emphatically searched; the method is used as deeper safety behavior detection of the whole industrial park, so that safety guarantee is increased for the whole industrial park, and convenience is improved for users.

Description

Technical Field

[0001] This invention relates to the field of big data, and in particular to an Internet of Things (IoT) platform system for assisting industrial parks in authenticating identities. Background Technology

[0002] The core characteristic of the Internet of Things (IoT) is holistic sensing. Due to the nature of IoT, we no longer rely on single data variables. Single data variables lack a holistic perspective; relying solely on a single variable would either result in insufficient security or overly complex processes that degrade the user experience.

[0003] Near field communication technology was previously only used for signal transmission, such as traditional Bluetooth, WIFI, and Starlink technology. It was not used for identity authentication and identity detection. However, if near field communication technology is introduced into a traditional IoT platform system, it will inevitably increase the overall perception dimension of the IoT and improve the user experience.

[0004] At vehicle gates in industrial parks, the decision to raise the barrier often hinges solely on checking the license plate. However, due to traffic restrictions, some vehicles need to change their license plates four times a year to meet the requirement of obtaining only 12 entry permits to Beijing. This significantly increases the workload for both property management and residents. Alternatively, some residents may upgrade their vehicles while retaining their original license plates, making it difficult for security personnel to detect any irregularities. If these situations require repeated verification, data entry, and system updates, it will undoubtedly place a tremendous burden on property management.

[0005] Industrial parks often house multiple factories. While some parks don't conduct further security checks at the gates, simply charging fees, if a vehicle drives into the wrong factory area, it undoubtedly poses a safety hazard, and the property management staff will bear some responsibility for that.

[0006] Therefore, there is a need for an IoT platform system that can introduce near-field communication technology as a higher-dimensional security verification method in industrial parks to increase the security of industrial parks and the convenience of users. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide an Internet of Things (IoT) platform system that can increase the security of industrial parks and the convenience of users by introducing near-field communication (NFC) technology as a higher-dimensional security verification method.

[0008] In a first aspect, the present invention provides a data processing method for an Internet of Things (IoT) platform, comprising:

[0009] S100: Acquire license plate data, vehicle body data, weighbridge data, and near-field communication data;

[0010] S200: Determine whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data. If they match, proceed to S300. If they do not match, determine whether two near-field communication tags matching the vehicle body data, weighbridge data, and license plate data are newly acquired within the first unit time. If a matching near-field communication tag is acquired, proceed to S300 and update the vehicle body data, weighbridge data, and license plate data corresponding to the near-field communication tag. If no matching near-field communication tag is acquired, issue an alarm signal.

[0011] S300: Raises the barrier and acquires video surveillance data and access control data within the second unit of time.

[0012] S400: Based on the video surveillance data, output the parking location in the video surveillance data; determine whether the parking location matches the pre-stored location corresponding to the pre-stored license plate data; if they match, jump to S500; if they do not match, output an alarm signal.

[0013] S500: Based on the access control data, determine whether the number of unlocking attempts before opening the access control exceeds the first threshold. If it does, proceed to S600; otherwise, issue an alarm signal.

[0014] S600: Verify whether the same newly acquired near-field communication data is detected when acquiring license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data. If not detected, issue an alarm signal and close the door lock corresponding to the access control data.

[0015] Secondly, an Internet of Things (IoT) data platform system includes a server, a camera at the gate, a smart weighbridge, a smart door lock, a smart surveillance camera, and a near-field communication detection module. The camera at the gate, the smart weighbridge, the smart door lock, the smart surveillance camera, and the near-field communication detection module are all connected to the server. The server operates as follows:

[0016] S100: Acquire license plate data, vehicle body data, weighbridge data, and near-field communication data;

[0017] S200: Determine whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data. If they match, proceed to S300. If they do not match, determine whether two near-field communication tags matching the vehicle body data, weighbridge data, and license plate data are newly acquired within the first unit time. If a matching near-field communication tag is acquired, proceed to S300 and update the vehicle body data, weighbridge data, and license plate data corresponding to the near-field communication tag. If no matching near-field communication tag is acquired, issue an alarm signal.

[0018] S300: Raises the barrier and acquires video surveillance data and access control data within the second unit of time.

[0019] S400: Based on the video surveillance data, output the parking location in the video surveillance data; determine whether the parking location matches the pre-stored location corresponding to the pre-stored license plate data; if they match, jump to S500; if they do not match, output an alarm signal.

[0020] S500: Based on the access control data, determine whether the number of unlocking attempts before opening the access control exceeds the first threshold. If it does, proceed to S600; otherwise, issue an alarm signal.

[0021] S600: Verify whether the same newly acquired near-field communication data is detected when acquiring license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data. If not detected, issue an alarm signal and close the door lock corresponding to the access control data.

[0022] Thirdly, a computer program product.

[0023] The computer program product stores a computer program that is adapted to be loaded by a processor to execute the data processing method of the Internet of Things platform.

[0024] The IoT platform system of this invention differs from existing technologies in that it uses near-field communication (NFC) data as a user identification tag. When there is a mismatch between a user's license plate and vehicle body data, or weighbridge data, the system focuses on tracing the correlation between NFC data and the acquired license plate, vehicle body, weighbridge, video surveillance, and access control data to conduct more in-depth security behavior detection throughout the industrial park. In other words, when a vehicle shows significant changes compared to the past, or when an unfamiliar vehicle enters, NFC data is used to verify whether the user's behavior after entering the industrial park is the same as before, thereby further identifying whether the user or vehicle is suspicious, increasing security for the entire industrial park and improving user convenience.

[0025] The following description, in conjunction with the accompanying drawings, further illustrates an Internet of Things (IoT) platform system of the present invention. Attached Figure Description

[0026] Figure 1 This is a flowchart of a method for an Internet of Things (IoT) platform system. Detailed Implementation

[0027] like Figure 1 As shown, the present invention provides a data processing method for an Internet of Things (IoT) platform, comprising:

[0028] S100: Acquire license plate data, vehicle body data, weighbridge data, and near-field communication data;

[0029] S200: Determine whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data. If they match, proceed to S300. If they do not match, determine whether two near-field communication tags matching the vehicle body data, weighbridge data, and license plate data are newly acquired within the first unit time. If a matching near-field communication tag is acquired, proceed to S300 and update the vehicle body data, weighbridge data, and license plate data corresponding to the near-field communication tag. If no matching near-field communication tag is acquired, issue an alarm signal.

[0030] S300: Raises the barrier and acquires video surveillance data and access control data within the second unit of time.

[0031] S400: Based on the video surveillance data, output the parking location in the video surveillance data; determine whether the parking location matches the pre-stored location corresponding to the pre-stored license plate data; if they match, jump to S500; if they do not match, output an alarm signal.

[0032] S500: Based on the access control data, determine whether the number of unlocking attempts before opening the access control exceeds the first threshold. If it does, proceed to S600; otherwise, issue an alarm signal.

[0033] S600: Verify whether the same newly acquired near-field communication data is detected when acquiring license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data. If not detected, issue an alarm signal and close the door lock corresponding to the access control data.

[0034] This invention uses near-field communication (NFC) data as a user identification tag for an IoT platform. When there is a mismatch between a user's license plate and vehicle body data, or weighbridge data, it focuses on tracing the correlation between NFC data and the data obtained from license plate, vehicle body, weighbridge, video surveillance, and access control systems. This allows for more in-depth security behavior detection throughout the industrial park. In other words, when a vehicle shows significant changes compared to the past, or when an unfamiliar vehicle enters, NFC data is used to verify whether the user's behavior after entering the industrial park is the same as before. This further helps to identify suspicious users or vehicles, increasing security for the entire industrial park and improving user convenience.

[0035] For example, before a vehicle can enter an industrial park, it typically needs to pass through a gate. While ordinary gates only have cameras, our new industrial park incorporates more IoT components, such as smart weighbridges, smart locks, smart surveillance cameras, and near-field communication (NFC) detection modules to sense vehicles. These IoT components are connected to a server, forming a dedicated IoT platform system for the industrial park. This system goes beyond simply matching a user's license plate with pre-stored plates to determine whether to raise the gate. It involves densely deploying multiple NFC sensing modules within the park. These modules can communicate with user terminals to identify their unique identifiers. Leveraging the multi-signal fusion mechanism of the IoT platform, the presence of NFC handshakes is combined with traditional security monitoring to create a user behavior-based security detection mechanism. This provides an extra layer of security for the industrial park.

[0036] In other words, when a vehicle enters the barrier gate, the barrier does not only lift when the license plate data matches the pre-stored license plate data, but also when the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data all match the license plate data, vehicle body data, and weighbridge data. Moreover, the matching of all these data does not require the introduction of near-field communication data as a matching basis, thereby increasing the speed of vehicle entry and avoiding queuing and congestion at the barrier gate.

[0037] However, if any pair of pre-stored license plate data, pre-stored weighbridge data, or pre-stored vehicle body data does not match with the license plate data, vehicle body data, or weighbridge data, it is considered a mismatch. In the event of a mismatch, we treat each pair of pre-stored near-field communication data, pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data as a whole. If, within the first unit of time prior to scanning the user's license plate data, the obtained near-field communication data output is used as the matching basis, and if this matching basis matches two of the license plate data, vehicle body data, or weighbridge data obtained within the first unit of time, then we proceed to step S300; otherwise, we issue an alarm signal.

[0038] It should be noted that near-field communication signals can be Wi-Fi signals, Bluetooth signals, NFC signals, NearLink signals, etc.

[0039] The method for obtaining near-field communication data or matching near-field communication tags described in this embodiment can be as follows: The near-field communication detection module can shake hands with the user terminal through the aforementioned near-field communication signals. For example, when a vehicle drives to the barrier gate, the near-field communication detection module of the industrial park and the user terminal shake hands through the WIFI protocol based on the SSID and password in the database. This indicates that the near-field communication detection module can shake hands with the user terminal through the aforementioned near-field communication signals. The unique identification code of this user terminal is the near-field communication tag.

[0040] It's important to clarify that the first unit of time refers to the time before the license plate recognition camera in the industrial park first recognizes the license plate data. In other words, the first unit of time can be understood as the time allotted for the near-field communication (NFC) detection module to perform a handshake and matching process. This first unit of time can range from 1 second to 1 minute, preferably 20 seconds. Alternatively, the first unit of time can also be the time between the moment the license plate recognition camera recognizes the license plate data and the moment it does, for example, 10 seconds before and 10 seconds after recognizing the license plate data. This first unit of time is the recognition time allocated to the NFC detection module.

[0041] In other words, we eliminated previously identified near-field communication tags in the first unit of time, thus ensuring the accuracy of the identification.

[0042] It needs to be explained that in S200, although new identifiable near-field communication (NFC) tags are introduced when vehicles are queuing or when vehicles pass by the gate, this may introduce some errors. This could cause the subsequent step of jumping to S300 after only matching two tags, causing some impact. However, the problem can be avoided by continuously verifying the detection of NFC data in the later S600 steps when NFC tags are used to verify license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data. In other words, continuous monitoring of NFC tags entering the industrial park can correct errors caused by queuing and passing identifiable NFC tags at the barrier (to further avoid this error, different matching and identification paths can be constructed based on the corresponding data overlap to minimize this erroneous barrier raising problem. That is, the more overlapping data, the more difficult the matching is, and the easier it is to directly trigger an alarm. Or, the more overlapping data, the more algorithms are introduced to assign 5 or 30 / x).

[0043] It should be explained that the near-field communication tag can be a unique identifier for the user terminal, or it can simply represent the near-field communication data acquired.

[0044] The phrase "and update the vehicle body data, weighbridge data, and license plate data corresponding to the near-field communication tag" can be understood as follows: Although the acquired near-field communication data matched the pre-stored near-field communication data in previous steps, and two of the acquired license plate data, vehicle body data, and weighbridge data matched two of the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data corresponding to the pre-stored near-field communication data, one of these data must be mismatched. Therefore, we need to update the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data so that when the vehicle body data, weighbridge data, and license plate data are used again, it can directly jump to S300. The effect of the above steps is that since the user may change vehicles, change license plates, transport heavy objects, or be empty, the newly acquired vehicle body data, weighbridge data, and license plate data will change and may not match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data. If users are directly treated as dangerous individuals and alarm signals are triggered, congestion at the barrier gate is likely to occur. This congestion is clearly due to the lack of intelligence in traditional barrier gate systems. Instead of simply allowing license plate matching for the barrier to raise, we've introduced IoT-based data collection and processing—including license plate data, vehicle body data, weighbridge data, and near-field communication data—to add fault tolerance and automatic update mechanisms to the barrier gate. In other words, even if a user changes vehicles, license plates, or is transporting heavy loads or empty vehicles, we allow for a mismatch or abnormal change in one data point, but still allow the barrier to raise and allow entry into the park after meeting other conditions (S300). Then, we use S600 for verification to achieve better data correction.

[0045] If an alarm signal is output after step S300, the data updated in the step of "updating the vehicle body data, weighbridge data, and license plate data corresponding to the near-field communication tag" should be restored, i.e., no data update should be performed. This avoids the problem of potentially erroneous data updates that might occur earlier, through subsequent verification of near-field communication.

[0046] Among them, license plate data can be recognized by the camera at the barrier lifting point, and the license plate data can be "Beijing A12345"; vehicle body data can be recognized by the camera at the barrier lifting point, and the vehicle body data can be "Mercedes - Benz GLC black SUV"; weighbridge data can be recognized by the intelligent weighbridge, and the weighbridge data can be "two - axle, 2 tons"; near - field communication data can be recognized by the intelligent router or Bluetooth module or StarFlash module. Among them, the near - field communication data or near - field communication tag can be "MAC address (Media Access Control Address): 00:1A:2B:3C:4D:5E or 00 - 1A - 2B - 3C - 4D - 5E; DHCP options (DHCP Options): Option 6: DNS Servers = 8.8.8.8, 8.8.4.4; HTTP User - Agent field: Mozilla / 5.0 (Windows NT 10.0; Win64; x64) AppleWebKit / 537.36 (KHTML, like Gecko) Chrome / 114.0.0.0 Safari / 537.36".

[0047] Among them, video surveillance data can be recognized by multiple cameras in the park. The multiple cameras synthesize the video data into a large digital twin model of the park to facilitate overall observation and accurately find the parking position coordinates; or, the multiple cameras synthesize the video data into a complete video data. For example, the park is divided into 9 areas, that is, a nine - square grid. The cameras in each area generate a video from the above - mentioned video through a synthesis algorithm. This algorithm can be an existing technology or obtained by applying the video synthesis algorithm of the in - vehicle 360 reverse camera. Thus, the parking position can be obtained more accurately. Among them, when the user terminal drives into each nine - square grid area, it should shake hands with the near - field communication detection module corresponding to the camera to verify that the near - field communication data is detected by the near - field communication detection module within the second unit time when this license plate is driving in this area by the camera. The second unit time can be the same as the first unit time or 50% larger than the first unit time to reduce the influence of the speed of the passing vehicle on the error.

[0048] Among them, access control data can be recognized by the intelligent door locks in the park, and the server should be able to know the opening and closing of the intelligent door locks and the corresponding coordinate positions. For example, the access control data output by the intelligent door lock of the gate on the 3rd floor of Building 3 in the park is: The user with the near - field communication tag xxx opened the door by entering the password once at 13:00 on September 9, 2025.

[0049] The step "S400. Output the parking location from the video surveillance data; determine whether the parking location matches the pre-stored location corresponding to the pre-stored license plate data" can be understood as: determining whether the parking time of a vehicle at a certain location exceeds a second threshold based on the video surveillance data. If it exceeds the second threshold, the vehicle is determined to be in a parking state, and its parking location is considered a parking location. Specifically, parking times exceeding the second threshold on any road cannot be considered parking locations to avoid congestion. In other words, only fixed parking areas can be considered parking locations. The second threshold can be 10 seconds to 10 minutes, preferably 3 minutes, meaning a location where the vehicle remains stationary for 3 minutes is considered a parking location. Comparing this with pre-stored parking locations reveals whether the vehicle is parked in a frequently used or abnormal location.

[0050] Furthermore, smart parking locks can be installed within the park. A smart parking lock will only open and allow parking in a designated space when a vehicle authenticated by the lock approaches and sends an open signal. Conversely, when a vehicle leaves, the smart parking lock will automatically close, preventing any vehicle from parking in that space. The time from when the smart parking lock opens to when it closes is considered the vehicle's parking location.

[0051] It's important to note that video surveillance data can consistently use the same bounding box to mark a vehicle, allowing for trajectory tracking. Even a route map can be drawn to pinpoint parking locations. This helps us understand user driving habits by analyzing parking locations, enabling us to determine if a user frequently visits the area or if their behavior is unusual. For example, if a near-field communication tag that should regularly visit Building 3 parks in Building 1, it's highly likely due to a faulty near-field communication handshake or incorrect identification, allowing an inappropriate vehicle into the area. In such cases, we should issue an alarm signal for a precise investigation of the vehicle, ensuring the safety of the area.

[0052] The database stores pre-stored locations corresponding to the near-field communication tags, which could be one parking space or multiple parking spaces.

[0053] The statement "S500, based on access control data, determines whether the number of unlocking attempts before opening the access control exceeds the first threshold" can be understood as follows: Access control data can be directly output to the server via the smart lock. The first threshold can be 0-100 times, with a pre-selected value of 3 times. In other words, if a user enters the wrong password more than three times, or performs fingerprint recognition more than three times, or performs facial recognition more than three times, it can be determined that the number of unlocking attempts by the user exceeds the first threshold.

[0054] The phrase "S600 verifies whether newly acquired near-field communication data is detected when acquiring license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data" can be understood as follows: Within the first unit of time for acquiring license plate data, vehicle body data, weighbridge data, and access control data, it determines whether newly acquired near-field communication data is detected; within the second unit of time, it checks whether the near-field communication detection module at the corresponding camera area for acquiring video surveillance data detects the same newly acquired near-field communication data. It is crucial to emphasize that license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data are typically verified unidirectionally, which can provide an opportunity for hacking. Combining the acquisition of the aforementioned data with near-field communication data identification and handshake significantly increases data security, preventing hacking, password theft, cloned vehicles, and overloaded vehicles from posing security risks to the park. It also adds the advantages of automatic verification and automatic updates to the park's identity verification. In other words, it ensures user security while increasing user convenience.

[0055] See Figure 1 In some embodiments, the step of "S200, determining whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data" includes:

[0056] S201. When acquiring the license plate data in the third unit of time before acquiring the license plate data, the number of times and steps of the alarm signal in steps S200~S600 are generated, and the severity weight S and time decay coefficient V are output.

[0057] S202. Based on the severity weight S and the time decay coefficient V, calculate the comprehensive risk value R of the previous process according to the following formula: R = Σ(S i ×V i ×C i ), where Si is the severity weight of the i-th step of the alarm signal (e.g., S200=0.8, S400=0.3, S500=0.5, S600=1.0), and V i The time decay coefficient for the i-th step of issuing the alarm signal (where V) i =V i-1 ×0.9t, where t is the number of days since the i-th step of the last alarm signal was issued, i.e., the closer the alarm date is to today, the smaller the attenuation and the greater the impact. (V0 is 0.9 for S200, 0.7 for S400, 0.8 for S500, and 1.0 for S600). C i This represents the cumulative number of times the i-th step, which issues an alarm signal, occurs within the third unit of time.

[0058] S203. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data; output the weighbridge matching range based on the weighbridge constant. If the vehicle body data is for a passenger car, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum passenger weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight; if the vehicle body data is for a truck, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum permissible cargo weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight.

[0059] S204. Determine whether the license plate data, weighbridge data, and vehicle body data all match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data, wherein the pre-stored weighbridge data is the weighbridge matching range.

[0060] In this invention, different vehicles are allowed to have two of their license plate data, weighbridge data, and vehicle body data matched. Obviously, license plate and vehicle body data change less frequently, while weighbridge data changes more frequently, meaning the weighbridge data varies significantly depending on whether the vehicle is fully loaded or empty. To prevent unauthorized personnel or vehicles carrying different goods from potentially causing safety hazards in the industrial park—for example, a vehicle bringing unauthorized personnel to damage the park, or a truck carrying different weights of hazardous materials—the industrial park can use weighbridge data linked to the IoT system, combined with previous abnormal behavior, to generate corresponding alerts. Based on past alarm steps, alarm frequency, and proximity to today's alarm, the system can output the allowable weighbridge matching range, allowing the park to accurately determine whether to adjust or widen the weighbridge matching range. This ensures that the monitoring of unauthorized vehicles in the industrial park based on the IoT platform system is both efficient and safe.

[0061] Specifically, "S201. During the third unit of time before acquiring the license plate data, the number of times and steps of alarm signals in steps S200~S600 occur, and the severity weight S and time decay coefficient V are output" can be understood as follows: the third unit of time can be 1 day, 1 week, 1 month, or 1 year, preferably 1 month. That is, within one month before the vehicle reaches the barrier and we acquire the license plate data, we search for the number of alarms and steps in steps S200~S600 during several previous barrier entry attempts corresponding to this license plate data, and output a severity weight S and a time decay coefficient V based on this. The output severity weight S and time decay coefficient V can be described as follows: The database should pre-store the following data:

[0062] Alarm Procedure reason Severity weight Time decay coefficient illustrate S200 No cicada communication data obtained 0.8 0.9 The identity of the associated vehicle is questionable, and the severity is high. S400 Parking space mismatch 0.3 0.7 Driving habits or purpose are questionable, severity moderate. S500 Too many unlocking errors 0.5 0.8 Access control security issues can arise due to recognition accuracy, with a severity level of medium to high. S600 Near-field communication data missing 1.0 0.95 End-to-end identity fragmentation, the most severe

[0063] Among them, “S202, based on the severity weight S and the time decay coefficient V, calculate the comprehensive risk value R of the previous process according to the following formula: R=Σ(S i ×V i ×C i ), where Si is the severity weight of the i-th step of the alarm signal (e.g., S). S200 =0.8, S S400 =0.3, S S500 =0.5, S S600 =1.0), Vi is the time decay coefficient of the i-th step in which the alarm signal is issued (where, V = 1.0). i =V0 T Where T is the number of days since the i-th step of the last alarm signal was issued, i.e., the closer the alarm date is to today, the smaller the attenuation and the greater the impact. Specifically, V0 is 0.9 for S200, 0.7 for S400, 0.8 for S500, and 1.0 for S600; V S200 =0.9 T V S400 =0.7 T V S500 =0.8 T V S600 =0.95 T ), C i "The cumulative number of times the i-th step of issuing an alarm signal occurs within the third unit of time" can be understood as follows: Suppose there is a Mercedes-Benz GLC sedan (anything not a truck is a sedan, i.e., sedans and SUVs are sedans, and all trucks, vans, and construction vehicles are considered trucks), which triggered an S200 alarm step 6 days ago. Then, S S200 =0.8; V S200 =0.9 T =0.9 6 =0.53; C S200 =1. Therefore, R = 0.8 × 0.53 × 1 + 0 + 0 = 0.425.

[0064] The statement "S203, output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle data; output the weighbridge matching range based on the weighbridge constant, where, if the vehicle data is a passenger car, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum passenger weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible total mass; if the vehicle data is a truck, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum permissible cargo weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible total mass" can be understood as follows: the vehicle's curb weight represents the vehicle's weight when it can drive normally and is fully fueled and filled with water; 45kg can be understood as the minimum weight of a normal driver, and includes miscellaneous items inside the vehicle, which can be understood as the minimum weight of the vehicle when passing over the smart weighbridge. While the minimum weight inside the vehicle might be less than 45kg in extreme situations like an empty tank and a petite female driver, this is negligible given that the display units of ordinary smart weighbridges are in units of 0.1 tons or 0.01 tons. In other words, these extreme cases can be corrected by continuously updating the alarm count. The maximum passenger weight of a passenger car and the maximum permissible cargo weight of a truck are based on the maximum values ​​of the people and goods they primarily carry, respectively. These are configured as the initial upper limit for a half-full load. This means that under normal circumstances, when there is only one driver in the vehicle, or when it is about half-loaded, the smart weighbridge data will not show a mismatch with the weighbridge's matching range. Mismatches may only occur in more extreme situations where the vehicle is close to full passenger or cargo load, possibly due to carrying too many people or too much cargo. Excessive people or cargo could indicate the transport of unusual goods or personnel, and such behavior should be thoroughly investigated to ensure the safety of the industrial park.

[0065] In other words, within the weighbridge matching range, we determine the lower limit by adding 45kg to the curb weight, and determine the upper limit by dividing the maximum passenger weight by half or the maximum permissible cargo weight by half. Then, we output a weighbridge constant based on the number of alarms to adjust the upper limit, thus ensuring both the safety and convenience of the inspection. It should be noted that this solution typically does not directly adjust the lower limit unless the weighbridge data is below the lower limit more than three times, in which case the detected weighbridge data will be updated to the lower limit. This is because the lower limit is too extreme and rarely occurs.

[0066] The section "S203, output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data; output the weighbridge matching range based on the weighbridge constant, where, if the vehicle body data is a passenger car, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum passenger weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight; if the vehicle body data is a truck, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum permissible cargo weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight," can be understood as follows: the vehicle body data could be "Mercedes-Benz GLC Black SUV," which we usually classify as either a passenger car or a truck. "Mercedes-Benz GLC Black SUV" is clearly a passenger car. Since the curb weight of the Mercedes-Benz GLC is 2080kg and the maximum passenger weight is 680kg. Therefore, we can calculate that under normal circumstances, i.e., when the weighbridge constant is 0, the weighbridge matching range is [2125kg, 2420kg]. If we adjust the weighbridge matching range by outputting the weighbridge constant based on the comprehensive risk value R generated from the alarm count at each step of the IoT system, we can output the weighbridge constant for passenger cars according to the comprehensive risk value R as shown in Table 1 below:

[0067] Overall Risk Value R Weighbridge constant [0，0.5] 30% of maximum passenger weight （0.5，1.5] 0 （1.5，2.5] -10% of maximum passenger capacity （2.5，3.5] -20% of maximum passenger capacity （3.5，+∞） -30% of maximum passenger capacity

[0068] The truck outputs the weighbridge constant based on the comprehensive risk value R, as shown in Table 2 below:

[0069] Overall Risk Value R Weighbridge constant [0，0.5] 30% of maximum permissible cargo weight （0.5，1.5] 0 （1.5，2.5] -10% of the maximum permissible cargo weight （2.5，3.5] -20% of the maximum permissible cargo weight （3.5，+∞） -Maximum permissible cargo weight × 30%

[0070] If the weighbridge data exceeds the maximum permissible total mass, an alarm signal will be issued directly.

[0071] In other words, this invention adjusts the weighbridge constant by using the comprehensive risk values ​​located in different intervals, thereby adjusting the range of the weighbridge matching interval. As a result, the number of alarms, alarm steps, and alarm times for vehicles before the third unit of time are summed up to have different degrees of impact on whether the current weighbridge data matches the pre-stored weighbridge data. This allows the invention to continuously learn and update its corresponding weighbridge data matching mechanism, making stricter matching judgments for vehicles that frequently alarm, alarm in more serious steps, or have recently alarmed; while other vehicles are relatively lenient, thus ensuring the safety and convenience of the industrial park.

[0072] In step 204, license plate data matching can be achieved using a conventional gate-raising system's license plate recognition and matching system. Vehicle body data matching can be achieved using a conventional video recognition system, identifying each vehicle as "brand, model, color, and vehicle type," such as "Mercedes-Benz GLC Black SUV." This involves converting the image into text to match different vehicle types using the text, achieving a relatively singular vehicle body recognition category.

[0073] See Figure 1 In some embodiments, step S203, outputting the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data, includes:

[0074] S2031. Define the number of pre-stored vehicle body data that matches the acquired vehicle body data as the first quantity;

[0075] S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, determine whether the number of license plate data that issued alarm data in the third unit time is greater than the number of license plates that did not issue alarm data. If it is greater, increase the score of all license plate data that issued alarm data in the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. If it is not greater, increase the score of all license plate data that did not issue alarm data in the third unit time by 5.

[0076] Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031.

[0077] S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

[0078] This invention employs a unique allocation mechanism, grouping vehicles into sets of three time units, which can be a month, a week, a day, or an hour. We'll use a day as an example. Every day, many vehicles pass through the barrier gate. We first categorize them using vehicle body data. If there's only one vehicle body data set, the identification accuracy is high, and mismatches in the data are unlikely to affect subsequent alarms probabilistically. However, if there are multiple sets of this vehicle body data within the park, the risk of outputting alarm signals increases in both S200 and S400 systems. Even two vehicles with identical vehicle body data can experience errors due to the long handshake distance in near-field communication. Therefore, vehicles with identical vehicle body data should be given more attention. The third threshold is 1. If there are more than one such set, it indicates that the vehicle body data is not unique, potentially leading to the aforementioned errors and problems. Therefore, we can group each third unit of time into a group. Within this group, for the same vehicle body data, we can categorize the multiple license plate data that issued alarm signals or did not issue alarm signals within this third unit of time into two categories. If more alarm signals are issued, the score of each license plate data that issued an alarm signal will be increased by 5 times the first number / the number of license plate data that issued alarm signals. If more alarm signals are not issued, the score of each license plate data that did not issue an alarm signal will be increased by 5.

[0079] For example, suppose there are six vehicles (a, b, c, d, e, f) in the park, all with the same vehicle data: a Mercedes-Benz GLC Black SUV. If the third unit of time is, say, one day, some vehicles might consistently have a comprehensive risk value (R) below 0.5, thus using a relatively lenient weighbridge data range as the final weighbridge matching interval, making it relatively easy to match weighbridge data. However, if each day is considered a third unit of time, and the data from each day is accumulated—for example, on day 1, the scores of a, b, and c increase by 5; on day 2, the total score of b, c, d, and e increases by 30 / 4 = 7.5, and so on—vehicles reaching a score of 30 first will have their weighbridge constant reduced by 10% of the maximum permissible load weight. The original value of the weighbridge constant is obtained by referring to Tables 1 and 2, and the resulting weighbridge constant is reduced by 10% of the maximum permissible load weight. Based on multiple historical data segments, this invention selects potential safety hazards that were not detected due to mismatches between weighbridge data and pre-stored weighbridge data. Specifically, by adjusting the weighbridge constant, it affects the likelihood of matching the weighbridge data with pre-stored weighbridge data, thereby increasing the probability of triggering alarm data. This periodically increases the likelihood of the vehicle's license plate data triggering an alarm signal, thus enhancing the security of the industrial park.

[0080] Furthermore, after adjusting the weighbridge constant once, all scores are reset to zero, and the new scores are recalculated to see if they exceed 30.

[0081] See Figure 1 As a variation of this embodiment, step S203, outputting the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data, may also include:

[0082] S2031. Define the number of pre-stored vehicle body data that matches the acquired vehicle body data as the first quantity;

[0083] S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, increase the score of the license plate data that issued alarm data within the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. Increase the score of the license plate data that did not issue alarm data within the third unit time by 5.

[0084] Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031.

[0085] S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

[0086] This scheme differs from the previous one in that it scores both license plate data that triggered alarm signals and those that did not within each third unit of time. Specifically, the score for license plate data that triggered alarm signals in this third unit of time is increased by 5 times the first number / the number of license plate data that triggered alarm signals; the score for license plate data that did not trigger alarm signals is increased by 5. This further increases the number of license plate data points that may indicate potentially dangerous behavior, i.e., those exceeding 30 points. We apply a more stringent weighbridge constant to these vehicles to increase the risk of mismatch between the weighbridge data and pre-stored weighbridge data, thereby increasing the likelihood of triggering alarms and enhancing the safety of the industrial park.

[0087] For example, suppose there are six vehicles in the park, a, b, c, d, e, and f, all with the same vehicle data: a Mercedes-Benz GLC Black SUV. If the third unit of time is, say, one day, some vehicles might consistently have a comprehensive risk value (R) below 0.5, thus using a relatively lenient weighbridge data range as the final weighbridge matching interval, making it relatively easy to match weighbridge data. However, if each day is considered a third unit of time, and the data from each day is accumulated—for example, on day 1, the scores of a and f increase by 5, the total score of b, c, d, and e increases by 30 / 4 = 7.5, and so on—vehicles reaching a score of 30 first will have their weighbridge constant reduced by 10% of the maximum permissible load weight. The original value of the weighbridge constant is obtained by referring to Tables 1 and 2, and the resulting weighbridge constant is reduced by 10% of the maximum permissible load weight.

[0088] This invention utilizes the unique advantages of industrial parks through an IoT system platform. By fusing multiple data in steps S300-S600, the resulting historical data influences the current weighbridge data matching mechanism, ensuring both safety and convenience for the industrial park.

[0089] See Figure 1 In some embodiments, the near-field communication signal is a WIFI signal, Bluetooth signal, NFC signal, or star flash.

[0090] See Figure 1 An Internet of Things (IoT) data platform system includes a server, a camera at the gate lifting point, a smart weighbridge, a smart door lock, a smart surveillance camera, and a near-field communication detection module. The camera at the gate lifting point, the smart weighbridge, the smart door lock, the smart surveillance camera, and the near-field communication detection module are all connected to the server. The server operates as follows:

[0091] S100: Acquire license plate data, vehicle body data, weighbridge data, and near-field communication data;

[0092] S200: Determine whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data. If they match, proceed to S300. If they do not match, determine whether two near-field communication tags matching the vehicle body data, weighbridge data, and license plate data are newly acquired within the first unit time. If a matching near-field communication tag is acquired, proceed to S300 and update the vehicle body data, weighbridge data, and license plate data corresponding to the near-field communication tag. If no matching near-field communication tag is acquired, issue an alarm signal.

[0093] S300: Raises the barrier and acquires video surveillance data and access control data within the second unit of time.

[0094] S400: Based on the video surveillance data, output the parking location in the video surveillance data; determine whether the parking location matches the pre-stored location corresponding to the pre-stored license plate data; if they match, jump to S500; if they do not match, output an alarm signal.

[0095] S500: Based on the access control data, determine whether the number of unlocking attempts before opening the access control exceeds the first threshold. If it does, proceed to S600; otherwise, issue an alarm signal.

[0096] S600: Verify whether the same newly acquired near-field communication data is detected when acquiring license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data. If not detected, issue an alarm signal and close the door lock corresponding to the access control data.

[0097] This invention uses near-field communication (NFC) data as a user identification tag for an IoT platform. When there is a mismatch between a user's license plate and vehicle body data, or weighbridge data, it focuses on tracing the correlation between NFC data and the data obtained from license plate, vehicle body, weighbridge, video surveillance, and access control systems. This allows for more in-depth security behavior detection throughout the industrial park. In other words, when a vehicle shows significant changes compared to the past, or when an unfamiliar vehicle enters, NFC data is used to verify whether the user's behavior after entering the industrial park is the same as before. This further helps to identify suspicious users or vehicles, increasing security for the entire industrial park and improving user convenience.

[0098] See Figure 1 In some embodiments, the step of "S200, determining whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data" includes:

[0099] S201. When acquiring the license plate data in the third unit of time before acquiring the license plate data, the number of times and steps of the alarm signal in steps S200~S600 are generated, and the severity weight S and time decay coefficient V are output.

[0100] S202. Based on the severity weight S and the time decay coefficient V, calculate the comprehensive risk value R of the previous process according to the following formula: R = Σ(S i ×V i ×C i ), where Si is the severity weight of the i-th step of the alarm signal (e.g., S200=0.8, S400=0.3, S500=0.5, S600=1.0), and Vi is the time decay coefficient of the i-th step of the alarm signal (where V i =V i-1×0.9t, where t is the number of days since the i-th step of the last alarm signal was issued, i.e., the closer the alarm date is to today, the smaller the attenuation and the greater the impact. (V0 is 0.9 for S200, 0.7 for S400, 0.8 for S500, and 1.0 for S600). C i This represents the cumulative number of times the i-th step, which issues an alarm signal, occurs within the third unit of time.

[0101] S203. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data; output the weighbridge matching range based on the weighbridge constant. If the vehicle body data is for a passenger car, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum passenger weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight; if the vehicle body data is for a truck, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum permissible cargo weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight.

[0102] S204. Determine whether the license plate data, weighbridge data, and vehicle body data all match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data, wherein the pre-stored weighbridge data is the weighbridge matching range.

[0103] In this invention, different vehicles are allowed to have two of their license plate data, weighbridge data, and vehicle body data matched. Obviously, license plate and vehicle body data change less frequently, while weighbridge data changes more frequently, meaning the weighbridge data varies significantly depending on whether the vehicle is fully loaded or empty. To prevent unauthorized personnel or vehicles carrying different goods from potentially causing safety hazards in the industrial park—for example, a vehicle bringing unauthorized personnel to damage the park, or a truck carrying different weights of hazardous materials—the industrial park can use weighbridge data linked to the IoT system, combined with previous abnormal behavior, to generate corresponding alerts. Based on past alarm steps, alarm frequency, and proximity to today's alarm, the system can output the allowable weighbridge matching range, allowing the park to accurately determine whether to adjust or widen the weighbridge matching range. This ensures that the monitoring of unauthorized vehicles in the industrial park based on the IoT platform system is both efficient and safe.

[0104] See Figure 1 In some embodiments, step S203, outputting the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data, includes:

[0105] S2031. The number of pre-stored vehicle body data that matches the acquired vehicle body data is defined as the first quantity;

[0106] S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, determine whether the number of license plate data that issued alarm data in the third unit time is greater than the number of license plates that did not issue alarm data. If it is greater, increase the score of all license plate data that issued alarm data in the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. If it is not greater, increase the score of all license plate data that did not issue alarm data in the third unit time by 5.

[0107] Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031.

[0108] S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

[0109] This invention employs a unique allocation mechanism, grouping vehicles into sets of three time units, which can be a month, a week, a day, or an hour. We'll use a day as an example. Every day, many vehicles pass through the barrier gate. We first categorize them using vehicle body data. If there's only one vehicle body data set, the identification accuracy is high, and mismatches in the data are unlikely to affect subsequent alarms probabilistically. However, if there are multiple sets of this vehicle body data within the park, the risk of outputting alarm signals increases in both S200 and S400 systems. Even two vehicles with identical vehicle body data can experience errors due to the long handshake distance in near-field communication. Therefore, vehicles with identical vehicle body data should be given more attention. The third threshold is 1. If there are more than one such set, it indicates that the vehicle body data is not unique, potentially leading to the aforementioned errors and problems. Therefore, we can group each third unit of time into a group. Within this group, for the same vehicle body data, we can categorize the multiple license plate data that issued alarm signals or did not issue alarm signals within this third unit of time into two categories. If more alarm signals are issued, the score of each license plate data that issued an alarm signal will be increased by 5 times the first number / the number of license plate data that issued alarm signals. If more alarm signals are not issued, the score of each license plate data that did not issue an alarm signal will be increased by 5.

[0110] For example, suppose there are six vehicles (a, b, c, d, e, f) in the park, all with the same vehicle data: a Mercedes-Benz GLC Black SUV. If the third unit of time is, say, one day, some vehicles might consistently have a comprehensive risk value (R) below 0.5, thus using a relatively lenient weighbridge data range as the final weighbridge matching interval, making it relatively easy to match weighbridge data. However, if each day is considered a third unit of time, and the data from each day is accumulated—for example, on day 1, the scores of a, b, and c increase by 5; on day 2, the total score of b, c, d, and e increases by 30 / 4 = 7.5, and so on—vehicles reaching a score of 30 first will have their weighbridge constant reduced by 10% of the maximum permissible load weight. The original value of the weighbridge constant is obtained by referring to Tables 1 and 2, and the resulting weighbridge constant is reduced by 10% of the maximum permissible load weight. Based on multiple historical data segments, this invention selects potential safety hazards that were not detected due to mismatches between weighbridge data and pre-stored weighbridge data. Specifically, by adjusting the weighbridge constant, it affects the likelihood of matching the weighbridge data with pre-stored weighbridge data, thereby increasing the probability of triggering alarm data. This periodically increases the likelihood of the vehicle's license plate data triggering an alarm signal, thus enhancing the security of the industrial park.

[0111] See Figure 1 As a variation of this embodiment, step S203, outputting the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data, may also include:

[0112] S2031. The number of pre-stored vehicle body data that matches the acquired vehicle body data is defined as the first quantity;

[0113] S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, increase the score of the license plate data that issued alarm data within the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. Increase the score of the license plate data that did not issue alarm data within the third unit time by 5.

[0114] Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031.

[0115] S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

[0116] This scheme differs from the previous one in that it scores both license plate data that triggered alarm signals and those that did not within each third unit of time. Specifically, the score for license plate data that triggered alarm signals in this third unit of time is increased by 5 times the first number / the number of license plate data that triggered alarm signals; the score for license plate data that did not trigger alarm signals is increased by 5. This further increases the number of license plate data points that may indicate potentially dangerous behavior, i.e., those exceeding 30 points. We apply a more stringent weighbridge constant to these vehicles to increase the risk of mismatch between the weighbridge data and pre-stored weighbridge data, thereby increasing the likelihood of triggering alarms and enhancing the safety of the industrial park.

[0117] See Figure 1 A computer program product, characterized in that,

[0118] The computer program product stores a computer program that is adapted to be loaded by a processor to execute a data processing method for an Internet of Things (IoT) platform.

[0119] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A data processing method for an Internet of Things (IoT) platform, characterized in that: include S100: Acquire license plate data, vehicle body data, weighbridge data, and near-field communication data; S200: Determine whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data. If they match, proceed to S300. If they do not match, determine whether two near-field communication tags matching the vehicle body data, weighbridge data, and license plate data are newly acquired within the first unit of time. If matching near-field communication tags are acquired, proceed to S300. If no matching near-field communication tags are acquired, issue an alarm signal. S300: Raises the barrier and acquires video surveillance data and access control data within the second unit of time. S400: Based on the video surveillance data, output the parking location in the video surveillance data; determine whether the parking location matches the pre-stored location corresponding to the pre-stored license plate data; if they match, jump to S500; if they do not match, output an alarm signal. S500: Based on the access control data, determine whether the number of unlocking attempts before opening the access control exceeds the first threshold. If it does, proceed to S600; otherwise, issue an alarm signal. S600: Verify whether the same newly acquired near-field communication data is detected when acquiring license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data. If not detected, issue an alarm signal.

2. The data processing method for an Internet of Things platform according to claim 1, characterized in that: The step "S200, determining whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data" includes: S201. When acquiring the license plate data in the third unit of time before acquiring the license plate data, the number of times and steps of the alarm signal in steps S200~S600 are generated, and the severity weight S and time decay coefficient V are output. S202. Based on the severity weight S and the time decay coefficient V, calculate the comprehensive risk value R of the previous process according to the following formula: R = Σ(S i ×V i ×C i ), where S i V represents the severity weight of the i-th step of the issued alarm signal. i C is the time decay coefficient for the i-th step in issuing the alarm signal. i This represents the cumulative number of times the i-th step, which issues an alarm signal, occurs within the third unit of time. S203. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data; output the weighbridge matching range based on the weighbridge constant. If the vehicle body data is for a passenger car, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum passenger weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight; if the vehicle body data is for a truck, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum permissible cargo weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight. S204. Determine whether the license plate data, weighbridge data, and vehicle body data all match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data, wherein the pre-stored weighbridge data is the weighbridge matching range.

3. The data processing method for an Internet of Things platform according to claim 2, characterized in that: S203, based on the comprehensive risk value R and vehicle body data, outputs the corresponding weighbridge constant, including: S2031. Define the number of pre-stored vehicle body data that matches the acquired vehicle body data as the first quantity; S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, determine whether the number of license plate data that issued alarm data in the third unit time is greater than the number of license plates that did not issue alarm data. If it is greater, increase the score of all license plate data that issued alarm data in the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. If it is not greater, increase the score of all license plate data that did not issue alarm data in the third unit time by 5. Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031. S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

4. The data processing method for an Internet of Things platform according to claim 2, characterized in that: S203, based on the comprehensive risk value R and vehicle body data, outputs the corresponding weighbridge constant, including: S2031. Define the number of pre-stored vehicle body data that matches the acquired vehicle body data as the first quantity; S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, increase the score of the license plate data that issued alarm data within the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. Increase the score of the license plate data that did not issue alarm data within the third unit time by 5. Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031. S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

5. The data processing method for an Internet of Things platform according to claim 4, characterized in that: Near-field communication signals include Wi-Fi, Bluetooth, NFC, or starlight.

6. An Internet of Things (IoT) data platform system, characterized in that: The system includes a server, a camera at the boom lift, a smart weighbridge, a smart door lock, a smart surveillance camera, and a near-field communication detection module. The camera at the boom lift, the smart weighbridge, the smart door lock, the smart surveillance camera, and the near-field communication detection module are all connected to the server. The server operates as follows: S100: Acquire license plate data, vehicle body data, weighbridge data, and near-field communication data; S200: Determine whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data. If they match, proceed to S300. If they do not match, determine whether two near-field communication tags matching the vehicle body data, weighbridge data, and license plate data are newly acquired within the first unit time. If matching near-field communication tags are acquired, proceed to S300. If no matching near-field communication tags are acquired, issue an alarm signal. S300: Raises the barrier and acquires video surveillance data and access control data within the second unit of time. S400: Based on the video surveillance data, output the parking location in the video surveillance data; determine whether the parking location matches the pre-stored location corresponding to the pre-stored license plate data; if they match, jump to S500; if they do not match, output an alarm signal. S500: Based on the access control data, determine whether the number of unlocking attempts before opening the access control exceeds the first threshold. If it does, proceed to S600; otherwise, issue an alarm signal. S600: Verify whether the same newly acquired near-field communication data is detected when acquiring license plate data, vehicle body data, weighbridge data, video surveillance data, and access control data. If not detected, issue an alarm signal.

7. The Internet of Things (IoT) data platform system according to claim 6, characterized in that: The step "S200, determining whether the license plate data, weighbridge data, and vehicle body data match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data" includes: S201. When acquiring the license plate data in the third unit of time before acquiring the license plate data, the number of times and steps of the alarm signal in steps S200~S600 are generated, and the severity weight S and time decay coefficient V are output. S202. Based on the severity weight S and the time decay coefficient V, calculate the comprehensive risk value R of the previous process according to the following formula: R = Σ(S i ×V i ×C i ), where S i V represents the severity weight of the i-th step of the issued alarm signal. i C is the time decay coefficient for the i-th step in issuing the alarm signal. i This represents the cumulative number of times the i-th step, which issues an alarm signal, occurs within the third unit of time. S203. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data; output the weighbridge matching range based on the weighbridge constant. If the vehicle body data is for a passenger car, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum passenger weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight; if the vehicle body data is for a truck, the weighbridge matching range is: [curb weight + 45kg, curb weight + maximum permissible cargo weight / 2 + weighbridge constant], where (curb weight + maximum passenger weight / 2 + weighbridge constant) is less than the maximum permissible gross weight. S204. Determine whether the license plate data, weighbridge data, and vehicle body data all match the pre-stored license plate data, pre-stored weighbridge data, and pre-stored vehicle body data, wherein the pre-stored weighbridge data is the weighbridge matching range.

8. The Internet of Things (IoT) data platform system according to claim 7, characterized in that: S203, based on the comprehensive risk value R and vehicle body data, outputs the corresponding weighbridge constant, including: S2031. Define the number of pre-stored vehicle body data that matches the acquired vehicle body data as the first quantity; S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, determine whether the number of license plate data that issued alarm data in the third unit time is greater than the number of license plates that did not issue alarm data. If it is greater, increase the score of all license plate data that issued alarm data in the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. If it is not greater, increase the score of all license plate data that did not issue alarm data in the third unit time by 5. Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031. S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

9. The Internet of Things (IoT) data platform system according to claim 8, characterized in that: S203, based on the comprehensive risk value R and vehicle body data, outputs the corresponding weighbridge constant, including: S2031. Define the number of pre-stored vehicle body data that matches the acquired vehicle body data as the first quantity; S2032. Determine whether the first quantity exceeds the third threshold. If it does not exceed the threshold, proceed to S2033. If it does exceed the threshold, increase the score of the license plate data that issued alarm data within the third unit time by 5 times the first quantity / the number of license plate data that issued alarm data. Increase the score of the license plate data that did not issue alarm data within the third unit time by 5. Until the score of one or more of the pre-stored vehicle data matched with the vehicle data exceeds 5 times the first quantity, the weighbridge constant is reduced by 10% of the maximum allowable cargo weight, wherein the corresponding weighbridge constant is output based on the comprehensive risk value R and the vehicle data; and after clearing the score of the pre-stored vehicle data matched with the acquired vehicle data to zero, the process jumps to S2031. S2033. Output the corresponding weighbridge constant based on the comprehensive risk value R and vehicle body data.

10. A computer program product, characterized in that, The computer program product stores a computer program adapted for loading by a processor to execute a data processing method for an Internet of Things platform as described in any one of claims 1 to 5.

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