A vehicle overspeed evidence chain credible storage method, device, equipment, medium and product

Abstract

This invention discloses a method, apparatus, device, medium, and product for credible evidence storage of vehicle overload control evidence chains. The method is applied to the edge nodes of vehicle overload control stations and includes: acquiring dynamic weighing data, environmental data, and location data of the target vehicle; determining vehicle characteristic factors of the target vehicle based on the dynamic weighing data, environmental data, and location data; determining a vehicle cheating flag of the target vehicle based on the vehicle characteristic factors; generating an evidence identifier for the target vehicle based on the vehicle location information, environmental information, and characteristic factors; constructing a block transaction based on the evidence identifier, characteristic factors, and cheating flag; invoking a smart contract to trigger a transaction consensus algorithm to achieve transaction consensus on the block transaction, and writing the block transaction into the edge chain block after the transaction consensus is passed, while simultaneously synchronizing it to the regional side chain and main chain through a cross-chain gateway, thereby realizing on-chain evidence storage of the target vehicle's overload control.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, and in particular to a method, apparatus, equipment, medium and product for credible evidence storage of vehicle overloading control evidence chains. Background Technology

[0002] With the development of the road freight industry, the phenomenon of overloaded vehicles evading supervision through cheating methods such as "jumping the scale" and "circumventing the scale" has become increasingly common. Traditional methods of controlling overloaded vehicles rely on manual identification and single-point weighing data, which suffers from slow response and broken chains of evidence. While existing blockchain evidence storage solutions can ensure data immutability, they struggle to accurately identify cheating behavior, and the correlation of on-chain evidence storage is weak, failing to meet the dynamic identification needs of complex cheating behaviors such as "jumping the scale" and "circumventing the scale." This results in low accuracy and efficiency in identifying cheating behavior during vehicle overload control enforcement. Summary of the Invention

[0003] This invention provides a reliable evidence storage method, device, equipment, medium, and product for vehicle overload control, to improve the relevance of evidence storage on the vehicle overload control chain, meet the dynamic identification needs of complex cheating behaviors such as "jumping the scale" and "circumventing the scale", and improve the accuracy and efficiency of cheating behavior identification in the process of vehicle overload control law enforcement.

[0004] According to one aspect of the present invention, a reliable evidence storage method for vehicle overload control is provided, applied to the edge nodes of vehicle overload control stations, the method comprising: Acquire dynamic weighing data, environmental data, and location data of the target vehicle when it arrives at the current vehicle weight control station; Based on the vehicle dynamic weighing data, the vehicle environmental data, and the vehicle location data, determine the vehicle characteristic factors of the target vehicle; Based on the vehicle characteristic factors of the target vehicle, determine the vehicle cheating flag of the target vehicle; Based on the vehicle location information, the vehicle environment information, and the vehicle characteristic factors, an evidence identifier for the target vehicle is generated; Based on the evidence identifier, the vehicle characteristic factor, and the vehicle cheating flag, construct a block transaction; The smart contract is invoked to trigger the transaction consensus algorithm to conduct transaction consensus on the block transaction. After the transaction consensus is passed, the block transaction is written into the edge chain block and simultaneously synchronized to the regional side chain and the main chain through the cross-chain gateway, so as to realize the on-chain storage of evidence of the target vehicle's overloading control.

[0005] According to another aspect of the present invention, a reliable evidence storage device for vehicle overload control is provided, configured at the edge node of a vehicle overload control station, the device comprising: The data acquisition module is used to acquire the target vehicle's dynamic weighing data, vehicle environmental data, and vehicle location data when the vehicle arrives at the current vehicle overload control station. The feature factor determination module is used to determine the vehicle feature factors of the target vehicle based on the vehicle dynamic weighing data, the vehicle environmental data, and the vehicle location data. The cheating flag determination module is used to determine the vehicle cheating flag of the target vehicle based on the vehicle characteristic factors of the target vehicle. The evidence identifier generation module is used to generate an evidence identifier for the target vehicle based on the vehicle location information, the vehicle environment information, and the vehicle characteristic factors. The block transaction construction module is used to construct block transactions based on the evidence identifier, the vehicle characteristic factor, and the vehicle cheating flag. The on-chain evidence storage module is used to call the smart contract to trigger the transaction consensus algorithm to conduct transaction consensus on the block transaction. After the transaction consensus is passed, the block transaction is written into the edge chain block and simultaneously synchronized to the regional side chain and the main chain through the cross-chain gateway, so as to realize the on-chain evidence storage of the target vehicle's overloading control.

[0006] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the reliable evidence storage method for vehicle overloading control as described in any embodiment of the present invention.

[0007] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the reliable evidence chain preservation method for vehicle overloading control according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the reliable evidence storage method for vehicle overloading control as described in any embodiment of the present invention.

[0009] The technical solution of this invention determines the vehicle characteristic factors of the target vehicle based on vehicle dynamic weighing data, vehicle environmental data, and vehicle location data. Based on the vehicle characteristic factors, it determines the vehicle cheating flag of the target vehicle. Based on the vehicle location information, vehicle environmental information, and vehicle characteristic factors, it generates the evidence identifier of the target vehicle. Based on the evidence identifier, vehicle characteristic factors, and vehicle cheating flag, it constructs a block transaction, calls a smart contract to trigger a transaction consensus algorithm to achieve transaction consensus on the block transaction, and writes the block transaction into the edge chain block after the transaction consensus is passed. This realizes the reliable evidence chain storage in the vehicle overload control scenario, improves the evidence storage correlation on the vehicle overload control chain, can meet the dynamic identification needs of complex cheating behaviors such as "jumping the scale" and "circumventing the scale", and improves the accuracy and efficiency of cheating behavior identification in the vehicle overload control law enforcement process.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are 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.

[0012] Figure 1 This is a flowchart of a reliable evidence storage method for vehicle overloading control provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of a reliable evidence preservation method for vehicle overloading control provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of a reliable evidence storage device for vehicle overloading control provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the reliable evidence chain storage method for vehicle overloading control according to embodiments of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Example 1 Figure 1 This is a flowchart of a reliable evidence storage method for vehicle overload control according to Embodiment 1 of the present invention. This embodiment is applicable to the on-chain storage of evidence results of cheating behavior identification during the enforcement of vehicle overload control in highway freight scenarios. This method can be executed by a reliable evidence storage device for vehicle overload control evidence chains. This reliable evidence storage device can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, this method can be applied to the edge nodes of vehicle overload control stations, and the specific steps include: S110. Obtain the target vehicle's dynamic weighing data, vehicle environment data, and vehicle location data when the vehicle is traveling to the current vehicle overload control station.

[0016] S120. Based on the vehicle dynamic weighing data, vehicle environmental data, and vehicle location data, determine the vehicle characteristic factors of the target vehicle.

[0017] S130. Determine the vehicle cheating flag of the target vehicle based on the vehicle characteristic factors of the target vehicle.

[0018] S140. Generate evidence identification for the target vehicle based on vehicle location information, vehicle environment information, and vehicle characteristic factors.

[0019] S150. Construct block transactions based on evidence identifiers, vehicle characteristic factors, and vehicle cheating flags.

[0020] S160. The smart contract is invoked to trigger the transaction consensus algorithm to conduct transaction consensus on the block transaction. After the transaction consensus is passed, the block transaction is written into the edge chain block and simultaneously synchronized to the regional side chain and the main chain through the cross-chain gateway, so as to realize the on-chain storage of evidence of the target vehicle's overloading control.

[0021] The target vehicle can be any vehicle subject to enforcement for overweight vehicle control during its journey in a highway freight scenario. A BeiDou positioning terminal device for vehicle location is installed on the top of the target vehicle. The BeiDou positioning terminal device can collect vehicle location data at a preset sampling frequency, such as 1Hz, which can specifically include vehicle latitude and longitude coordinates, instantaneous speed data, and corresponding timestamps. The vehicle location data must cover at least the vehicle's movement trajectory within the weighing area of ​​the overweight control station for 30 seconds before and after the weighing process.

[0022] The vehicle weight control station is used to detect the load or weight of target vehicles during transit. The station is equipped with a dynamic weighing system. This system can consist of a sensor array and an axle count reader. Quartz sensor arrays can be embedded in the weighing area road surface at 0.5m intervals, working in conjunction with the axle count reader. Once the target vehicle is fully on the scale, the system records the vehicle's dynamic weighing data, including total weight, number of axles, and weighing time, ensuring valid data is recorded only after the vehicle is fully on the scale.

[0023] The system includes symmetrically deployed environmental sensor arrays on both sides of the vehicle weighing area's road shoulders. These arrays collect environmental data about the target vehicle, such as road slope and wind speed. The sampling frequency can be, for example, 10Hz.

[0024] All the aforementioned devices can be connected to the edge gateway via industrial Ethernet, and time synchronization is achieved based on the BeiDou time signal, ensuring that the timestamp error of the data does not exceed a preset error threshold, such as 10ms. A unified spatiotemporal stamp is used for the same weighing event of the same vehicle. For example, for the same vehicle and the same weighing event, the weighing time is taken as the baseline, and the closest time point in the BeiDou trajectory is used as the matching point to extract its latitude and longitude data. Environmental data is handled similarly, retaining continuous and stable valid data segments.

[0025] For vehicle location data, the speed is greater than 120 km / h or the acceleration is greater than 5 m / s². 2 Abnormal jump points are removed, specifically by using median filtering to smooth continuous trajectories. Invalid measurement data in vehicle dynamic weighing data with fewer than 2 axles or a total vehicle weight of less than 1 ton are removed, retaining only valid data segments with a weighing duration of not less than 0.5 seconds. Extreme values ​​in vehicle environmental data with a road slope greater than 15° or a wind speed greater than 30 m / s are removed, as this data represents sensor fault indication data. The cleaned three-source datasets are associated with a unified spatiotemporal stamp and stored in the local time-series database of the edge node gateway.

[0026] Based on vehicle dynamic weighing data, vehicle environmental data, and vehicle location data, vehicle characteristic factors of the target vehicle are determined. These vehicle characteristic factors may include speed mutation coefficient, trajectory deviation index, and environmental impact factors, which can provide a quantitative basis for subsequent vehicle cheating behavior identification.

[0027] In one optional embodiment, vehicle characteristic factors of the target vehicle are determined based on vehicle dynamic weighing data, vehicle environmental data, and vehicle location data, including: S1201. Extract the first vehicle speed at the time of vehicle weighing and the second vehicle speed within a preset time range at the time of vehicle weighing from the vehicle location data. Determine the vehicle speed change coefficient based on the first vehicle speed and the second vehicle speed.

[0028] S1202. Obtain the center coordinates of the weighing area and the center coordinates of the weighing time from the vehicle dynamic weighing data, and determine the trajectory deviation index based on the center coordinates of the weighing area and the center coordinates of the weighing time.

[0029] S1203. Based on the slope and wind speed in the vehicle environmental data, determine the environmental impact factor. Based on the vehicle weighing weight in the vehicle dynamic weighing data, determine the vehicle correction weight based on the environmental impact factor.

[0030] S1204. Based on the corrected weight of the vehicle, determine the vehicle's excess weight according to the statutory limit for the corresponding vehicle model.

[0031] S1205. Generate vehicle characteristic factors including vehicle speed mutation coefficient, trajectory deviation index, vehicle correction weight, and vehicle over-limit value.

[0032] Specifically, extract the first speed of the target vehicle at the time of weighing from the vehicle's location. This involves extracting all second vehicle speeds within a preset time range around the weighing time. For example, the preset time range could be 5 seconds before and after the weighing time. The average of all second vehicle speeds within 5 seconds before and after the weighing time is then recorded as... Assuming a sampling frequency of 1Hz, the number of second vehicle speeds within 5 seconds before and after the weighing time is 5. Based on the first vehicle speed... Second average speed Determine the coefficient of change in vehicle speed : Where n represents the number of data points for the second vehicle speed within 5 seconds before and after the weighing time. Continuing with the previous example where the sampling frequency is 1Hz and the number of second vehicle speed data points is 5, then n is set to 5. Vehicle speed mutation coefficient. The term "jumping the scale" is used to identify a vehicle's "jumping the scale" behavior. Its physical meaning is the amount of deceleration a vehicle makes when approaching the weighing area. A negative value indicates deceleration; the larger the absolute value, the more severe the deceleration. When the weight is below a certain threshold, it indicates that the target vehicle is suspected of "weighing the vehicle" by rapidly decelerating to reduce its weight value. Specifically, weighing the vehicle involves rapidly decelerating so that the front or rear wheels briefly leave the weighing area, resulting in a lower weight value.

[0033] Obtain the center coordinates of the weighing area from the vehicle's dynamic weighing data. and the center coordinates of the weighing time The center coordinates of the weighing time can be the coordinates of the weighing time in the BeiDou trajectory. This is based on the center coordinates of the weighing area. and the center coordinates of the weighing time Determine the straight-line distance D between the target vehicle and the center of the weighing area at the time of weighing: The trajectory deviation index is determined based on the straight-line distance D and the radius R of the weighing area. : Among them, the trajectory deviation index Used to identify vehicles "circling the weighbridge". The larger the value, the farther the vehicle deviates from the center of the weighing area. When the value exceeds a certain threshold, it indicates that the vehicle is suspected of "weighing around" to evade detection by deviating from the weighing center. Specifically, weighing around refers to the vehicle's trajectory deviating from the center of the weighing area, with only some wheels on the scale to reduce the weight value.

[0034] Environmental impact factors are determined based on road slope S and wind speed F from vehicle environmental data. : Based on the vehicle weighing weight in the vehicle dynamic weighing data Based on the aforementioned environmental impact factors Determine the vehicle's calibration weight : According to the vehicle's calibrated weight Based on the legal limits for the vehicle type corresponding to the target vehicle Determine the vehicle's over-limit value : Among them, the legal limits for vehicle models The number of axles is obtained by referring to a table in the national standard GB1589.

[0035] Generate vehicle speed mutation coefficients Trajectory Deviation Index Vehicle calibration weight and vehicle over-limit values Vehicle characteristic factors Vehicle feature factors can be associated with timestamps and the target vehicle's license plate number, and edge nodes can be temporarily stored.

[0036] Based on the vehicle feature factors of the target vehicle, determine the vehicle cheating flag of the target vehicle. In an optional embodiment, determining the vehicle cheating flag of the target vehicle based on the vehicle feature factors includes: inputting the vehicle feature factors of the target vehicle into a pre-trained vehicle cheating probability prediction model to obtain the cheating probability value output by the model; and determining the vehicle cheating flag of the target vehicle based on the cheating probability value.

[0037] The vehicle cheating probability model can be obtained by training a pre-selected network model using historical vehicle feature factors and corresponding historical true cheating probability values ​​over historical time periods. The network model can be an LSTM (Long Short-Term Memory) model.

[0038] Deploying a vehicle cheating probability model at edge nodes enables accurate identification of vehicle cheating behaviors. The model can target core features of cheating behaviors such as "jumping the scale" and "circling the scale," using an attention mechanism to... and Give it higher weight, for example, The weight can be set to 0.35. The weight can be set to 0.35. and The weights are 0.2 and 0.1 respectively, to improve the model's sensitivity to cheating behavior.

[0039] During model training, 100,000 labeled samples can be used, including approximately 33,000 normal samples, 33,000 samples jumping over the scale, and 33,000 samples circling the scale. The loss function is binary cross-entropy, the optimizer is Adam, and the learning rate can be set to 0.001. The input layer uses vehicle feature factors from the input sample data. The LSTM layer consists of 128 units, and the time step can be set to 3, meaning 3 consecutive sampling points. The attention layer... The weight can be set to 0.35. The weight can be set to 0.35. and The weights are 0.2 and 0.1, respectively, allowing for fine-tuning of parameters during model learning. The Dropout layer is set to a ratio of 0.3 to suppress overfitting. The fully connected layer and the Sigmoid layer output the cheating probability P∈[0,1].

[0040] The cheating probability P represents the likelihood that the target vehicle's behavior constitutes cheating. The calculation logic for P is derived by an LSTM model through multi-layer neural networks that automatically learn feature associations. The core principle is the quantification of the probability of cheating patterns such as sudden speed changes and overloading, and significant trajectory deviations without overloading. The vehicle's cheating flag is determined based on the cheating probability value. For example, if P ≥ 0.8, the vehicle is marked as "evidence of cheating"; if 0.5 ≤ P < 0.8, the vehicle is marked as "evidence to be verified"; and if P < 0.5, the vehicle is marked as "normal evidence".

[0041] To further improve the accuracy of vehicle cheating identification, static rules can be introduced, and the identification can be determined jointly by combining the rules and model output results. In one optional embodiment, determining the vehicle cheating mark of the target vehicle based on the cheating probability value includes: generating a rule determination result based on the vehicle feature factors of the target vehicle and a preset vehicle cheating determination rule; and determining the vehicle cheating mark of the target vehicle based on the cheating probability value and the rule determination result.

[0042] The rules for determining vehicle cheating can be pre-set by relevant technical personnel according to actual needs. Specifically, the rules for determining vehicle cheating can be: If and If so, there is suspicion of skipping the weighing scale; if and If this is not the case, there is a suspicion of weighing tampering. The threshold in the rules can be adaptively adjusted according to the scenario; for example, for steep slope sections, when... hour, The threshold can be adjusted from -2 to -2.5 to avoid misjudging normal deceleration caused by slope. For example, when the target vehicle is a large-item transport vehicle, The threshold can be adjusted from 0.75 to 0.9 to accommodate the characteristics of its vehicle width.

[0043] For example, if If the vehicle cheating detection rule is met, the vehicle cheating is marked as "evidence of cheating"; if If the vehicle cheating detection rules are not met, the vehicle cheating is marked as "Evidence pending verification"; if If the vehicle cheating detection rule is not met, the vehicle cheating will be marked as "normal evidence".

[0044] An evidence identifier for the target vehicle is generated based on vehicle location information, vehicle environment information, and vehicle characteristic factors. In one optional embodiment, generating the evidence identifier for the target vehicle based on vehicle location information, vehicle environment information, and vehicle characteristic factors includes: hashing the vehicle location information to generate a vehicle trajectory hash parameter; hashing the vehicle correction weight in the vehicle characteristic factors to generate a correction weight hash parameter; and hashing the slope and wind speed in the vehicle environment information to generate an environment hash parameter; and generating the evidence identifier for the target vehicle based on the vehicle trajectory hash parameter, the correction weight hash parameter, and the environment hash parameter.

[0045] The BeiDou trajectory JOSN (JavaScript Object Notation) string of vehicle location information is hashed using the SHA-256 (Secure Hash Algorithm 256-bit) algorithm to generate the vehicle trajectory hash parameter Hash_Pos. The vehicle correction weight in the vehicle feature factors is then calculated using the SHA-256 algorithm. Perform hash processing to generate the correction weight hash parameter Hash_Weight. Combine the road slope S and wind speed F from the vehicle environment information to generate a combined string, and then use the SHA-256 algorithm to hash the combined string to generate the environment hash parameter Hash_Env.

[0046] The vehicle trajectory hash parameter Hash_Pos, the correction weight hash parameter Hash_Weight, the environment hash parameter Hash_Env, and the unified spatiotemporal stamp string are concatenated to obtain the evidence ID (Identifier), also known as the evidence identifier: SHA-256(Hash_Pos||Hash_Weight||Hash_Env||TS). Here, "||" indicates string concatenation.

[0047] Among them, the evidence ID is unique, realizing the indivisible binding of multi-source data, and deeply associating Beidou trajectory location information, weighing information, environmental parameters and spatiotemporal information through hash algorithm, thereby improving the integrity and credibility of the evidence chain.

[0048] Edge nodes construct block transactions: {Evidence ID, Vehicle Feature Factor, Vehicle Fraud Flag, Timestamp, Signature}. The edge node invokes a smart contract to trigger a transaction consensus algorithm to reach a consensus on the block transaction. After the consensus is passed, the block transaction is written to the edge chain block. Simultaneously, it is synchronized to the regional sidechain and main chain via a cross-chain gateway to achieve on-chain evidence storage of the target vehicle's overloading control.

[0049] It should be noted that each overload control station deploys an edge node to store evidence IDs and vehicle characteristic factors. The system includes cheating flags and raw data hashes. It employs the RAFT (Reliable, Replicated, Redundant, and Fault-Tolerant) consensus algorithm. Block transactions are broadcast to the blockchain for storage when more than half or 3 / 5 of the edge nodes have signed and confirmed them. Each province or city can deploy a sidechain to synchronize the evidence IDs of its subordinate edge chains and store equipment calibration parameters, such as weighing sensor drift coefficients and BeiDou positioning deviations. The main chain can be a national-level consortium blockchain, storing the Merkle root hashes of the evidence IDs from each sidechain to maintain global data consistency and ensure the immutability and traceability of evidence.

[0050] Furthermore, to ensure the accuracy of the predicted evidence ID of the target vehicle, the evidence ID of the target vehicle can be further reviewed and verified through cross-site collaborative verification and cross-domain mutual recognition.

[0051] In an optional embodiment, after generating the evidence identifier of the target vehicle based on the vehicle location information, vehicle environment information, and vehicle feature factors, the method further includes: if the vehicle cheating mark of the target vehicle is evidence of cheating or evidence to be verified, then sending a verification request to at least three adjacent overload control stations adjacent to its current overload control station, so that each adjacent overload control station can parse the verification request to obtain the vehicle feature factors and the license plate information of the target vehicle, and perform feature similarity matching based on the vehicle feature factors and the license plate information of the target vehicle to generate and return the similarity matching result; based on the similarity matching result returned by each adjacent overload control station, determining whether to update the vehicle mark of the target vehicle, and updating the evidence identifier of the target vehicle based on the determination result.

[0052] Specifically, when a target vehicle's cheating is marked as "cheating evidence" or "evidence pending verification," verification by adjacent vehicle control stations is automatically triggered, and cross-regional weight unification is achieved through the main chain. The current vehicle control station's edge node sends verification requests to at least three adjacent vehicle control stations. These verification requests must include at least the evidence ID, license plate number, and vehicle characteristic factors.

[0053] After receiving the verification request, the adjacent overload control station parses the request to obtain the vehicle speed mutation coefficient from the vehicle characteristic factors. and trajectory deviation index And the license plate number. Adjacent overload control stations query local databases or edge chains for historical records of the same license plate number within a time difference of no more than 30 minutes to determine the vehicle speed mutation coefficient in the corresponding vehicle characteristic factors. and trajectory deviation index .

[0054] Assume that the coefficient of change in vehicle speed in the vehicle characteristic factors obtained from adjacent overload control stations is denoted as... The trajectory offset index is denoted as The coefficient of change in vehicle speed obtained by querying the local database is denoted as... The trajectory offset index is denoted as Then calculate the vehicle characteristic factors. and The similarity between features is Sim. Specifically, it can be Pearson correlation coefficient, cosine similarity, or similarity based on Euclidean distance, etc. If the similarity is greater than a preset similarity threshold, such as 0.85, the similarity matching result is considered successful. If a vehicle's own vehicle control station receives similarity matching results from at least half of its neighboring vehicle control stations, and all results are successful, then the "evidence of cheating" is deemed valid; otherwise, it awaits manual review. The validity or invalidity of the verification conclusion is written into the edge chain and associated with the original evidence ID.

[0055] Furthermore, when a vehicle leaves the province where the original overload control station is located and enforcement needs to be carried out in another location, the evidence IDs of each overload control station the vehicle passed through can be obtained from the main chain, and the vehicle's corrected weight at each station can be extracted. It retrieves device calibration parameters, such as deviations, stored on the sidechains of each site. Calculate the compensated vehicle weight : Then, based on the legal limits for vehicle models on the main chain... Calculate the out-of-limit value Generate a main chain smart contract certificate: {Evidence ID list, uniform weight, on-chain signature, timestamp}. Cross-domain law enforcement agencies can accept the certificate by verifying the signature on the main chain.

[0056] When there is a law enforcement need, law enforcement officers can enter the license plate number or evidence ID on the web or app, and call the edge chain's query smart contract through the corresponding API (Application Programming Interface). The API return content may include evidence ID, feature factors, cheating flags, verification results, uniform weight, on-chain evidence storage time, and block height.

[0057] When verifying raw data, such as whether a BeiDou trajectory matches an on-chain hash, but without disclosing privacy-sensitive raw data, the prover (overload control station) generates a zero-knowledge proof locally: Proof = ZK-SNARK(raw data, Hash_Pos), proving the existence of a set of raw data whose SHA-256 is equal to the on-chain hash. The verifier (law enforcement officer) inputs the Proof and the on-chain hash, and the verification algorithm returns True or False, without needing to know the raw data. An arithmetic circuit can be built using the open-source library libsnark, with proof generation time no greater than 2 seconds and verification time no greater than 0.5 seconds. After the law enforcement officer makes a penalty decision, the penalty order number, fine amount, penalty basis (evidence ID), and the law enforcement officer's signature are packaged and written to the edge chain via a smart contract.

[0058] The technical solution of this invention determines the vehicle characteristic factors of the target vehicle based on vehicle dynamic weighing data, vehicle environmental data, and vehicle location data. Based on the vehicle characteristic factors, it determines the vehicle cheating flag of the target vehicle. Based on the vehicle location information, vehicle environmental information, and vehicle characteristic factors, it generates the evidence identifier of the target vehicle. Based on the evidence identifier, vehicle characteristic factors, and vehicle cheating flag, it constructs a block transaction, calls a smart contract to trigger a transaction consensus algorithm to achieve transaction consensus on the block transaction, and writes the block transaction into the edge chain block after the transaction consensus is passed. This realizes the reliable evidence chain storage in the vehicle overload control scenario, improves the evidence storage correlation on the vehicle overload control chain, can meet the dynamic identification needs of complex cheating behaviors such as "jumping the scale" and "circumventing the scale", and improves the accuracy and efficiency of cheating behavior identification in the vehicle overload control law enforcement process.

[0059] Example 2 Figure 2 This is a schematic flowchart illustrating a reliable evidence chain preservation method for vehicle overloading control provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment provides a preferred example.

[0060] like Figure 2 As shown, the method includes the following steps: S21. Obtain the target vehicle's dynamic weighing data, vehicle environment data, and vehicle location data when the target vehicle arrives at the current vehicle overload control station.

[0061] S22A. Determine the vehicle speed change coefficient based on the first vehicle speed at the time of vehicle weighing and the second vehicle speed within a preset time range at the time of vehicle weighing.

[0062] S22B. Determine the trajectory offset index based on the center coordinates of the weighing area and the center coordinates of the weighing position at the time of weighing.

[0063] S22C1. Determine the environmental impact factor based on the slope and wind speed in the vehicle environmental data, and determine the vehicle correction weight based on the vehicle weighing weight and the environmental impact factor.

[0064] S22C2. Based on the corrected weight of the vehicle, determine the vehicle's excess weight according to the statutory limit for the corresponding vehicle model.

[0065] S23. Generate vehicle characteristic factors including vehicle speed mutation coefficient, trajectory deviation index, vehicle correction weight, and vehicle over-limit value.

[0066] S24. Input the vehicle characteristic factors of the target vehicle into the vehicle cheating probability prediction model, obtain the cheating probability value output by the model, and determine the vehicle cheating mark of the target vehicle in combination with the vehicle cheating judgment rules.

[0067] S25. Generate evidence identifiers for the target vehicle based on vehicle location information, vehicle environment information, and vehicle characteristic factors.

[0068] S26. Construct block transactions based on evidence identifiers, vehicle characteristic factors, and vehicle cheating markers.

[0069] S27. Call the smart contract to trigger the transaction consensus algorithm to conduct transaction consensus on the block transaction, and after the transaction consensus is passed, write the block transaction into the edge chain block, and simultaneously synchronize it to the regional side chain and the main chain through the cross-chain gateway, so as to realize the on-chain storage of evidence of the target vehicle's overloading control.

[0070] The technical solution of this invention pre-trains a vehicle cheating probability prediction model, incorporating vehicle speed mutation coefficient, trajectory deviation index, and environmental influence factors into the identification dimensions. Combined with the model and a dynamic threshold rule engine, it achieves multi-dimensional judgment of cheating behaviors such as "jumping the scale" and "circumventing the scale." Compared to traditional identification methods that rely solely on weighing values, this solution effectively correlates vehicle dynamic trajectory characteristics with environmental parameters, significantly reducing missed or false detections caused by misjudgments based on a single indicator, thus qualitatively improving the ability to distinguish cheating behaviors.

[0071] Through a spatiotemporal correlation evidence storage mechanism, a unique evidence ID containing vehicle location trajectory hash, weighing value, environmental parameters, and spatiotemporal stamp is generated using blockchain smart contracts. This achieves chain-like binding of multi-source data, ensuring the immutability and traceability of data throughout the entire process of collection, transmission, and storage. Simultaneously, the cross-station collaborative verification protocol, through feature similarity calculation and consensus adjudication mechanisms, solves the problems of non-standardized errors in equipment from different overload control stations and difficulties in mutual recognition of evidence. This expands the evidence chain from a single data point to a complete spatiotemporally correlated chain, significantly enhancing the persuasiveness of evidence in administrative review.

[0072] Deploying the identification algorithm on edge nodes enables localized real-time marking of cheating behavior, avoiding transmission delays caused by traditional cloud deployments and ensuring that suspect vehicles can be intercepted in a timely manner. Furthermore, the layered evidence storage architecture and dynamic sharding consensus mechanism of the main chain and side chains reduce storage load while improving the efficiency of cross-regional data interaction. This allows the identification model parameters and judgment standards of different vehicle control stations to achieve coordinated unification through feature-sharing contracts, simplifying the cross-domain evidence verification and mutual recognition process and significantly improving overall law enforcement efficiency.

[0073] Example 3 Figure 3 This is a schematic diagram of a reliable evidence storage device for vehicle overload control provided in Embodiment 3 of the present invention. The reliable evidence storage device for vehicle overload control provided in this embodiment of the present invention is applicable to the on-chain storage of evidence of cheating behavior during the enforcement of vehicle overload control in highway freight scenarios. This reliable evidence storage device for vehicle overload control can be implemented in hardware and / or software, such as... Figure 3 As shown, the device includes: a data acquisition module 301, a feature factor determination module 302, a cheating mark determination module 303, an evidence identifier generation module 304, a block transaction construction module 305, and an on-chain evidence storage module 306. Among them, Data acquisition module 301 is used to acquire the target vehicle's dynamic weighing data, vehicle environment data, and vehicle location data when the vehicle arrives at the current vehicle overload control station. The feature factor determination module 302 is used to determine the vehicle feature factors of the target vehicle based on the vehicle dynamic weighing data, the vehicle environmental data and the vehicle location data. The cheating flag determination module 303 is used to determine the vehicle cheating flag of the target vehicle based on the vehicle characteristic factors of the target vehicle. The evidence identifier generation module 304 is used to generate an evidence identifier for the target vehicle based on the vehicle location information, the vehicle environment information, and the vehicle characteristic factors. Block transaction construction module 305 is used to construct block transactions based on the evidence identifier, the vehicle feature factor, and the vehicle cheating flag; The on-chain evidence storage module 306 is used to call the smart contract to trigger the transaction consensus algorithm to conduct transaction consensus on the block transaction, and after the transaction consensus is passed, write the block transaction into the edge chain block, and simultaneously synchronize it to the regional side chain and the main chain through the cross-chain gateway, so as to realize the on-chain evidence storage of the overloading control evidence of the target vehicle.

[0074] The technical solution of this invention determines the vehicle characteristic factors of the target vehicle based on vehicle dynamic weighing data, vehicle environmental data, and vehicle location data. Based on the vehicle characteristic factors, it determines the vehicle cheating flag of the target vehicle. Based on the vehicle location information, vehicle environmental information, and vehicle characteristic factors, it generates the evidence identifier of the target vehicle. Based on the evidence identifier, vehicle characteristic factors, and vehicle cheating flag, it constructs a block transaction, calls a smart contract to trigger a transaction consensus algorithm to achieve transaction consensus on the block transaction, and writes the block transaction into the edge chain block after the transaction consensus is passed. This realizes the reliable evidence chain storage in the vehicle overload control scenario, improves the evidence storage correlation on the vehicle overload control chain, can meet the dynamic identification needs of complex cheating behaviors such as "jumping the scale" and "circumventing the scale", and improves the accuracy and efficiency of cheating behavior identification in the vehicle overload control law enforcement process.

[0075] Optionally, the feature factor determination module 302 is specifically used for: Extract the first vehicle speed at the time of vehicle weighing from the vehicle location data, and the second vehicle speed within a preset time range at the time of vehicle weighing; and determine the vehicle speed mutation coefficient based on the first vehicle speed and the second vehicle speed. The center coordinates of the weighing area and the center coordinates of the weighing time are obtained from the vehicle dynamic weighing data. Based on the center coordinates of the weighing area and the center coordinates of the weighing time, the trajectory deviation index is determined. Based on the slope and wind speed in the vehicle environmental data, determine the environmental impact factor; based on the vehicle weighing weight in the vehicle dynamic weighing data, determine the vehicle correction weight based on the environmental impact factor. Based on the corrected weight of the vehicle, and the statutory limit for the vehicle type corresponding to the target vehicle, the vehicle excess value is determined; Generate vehicle characteristic factors including the vehicle speed mutation coefficient, trajectory deviation index, vehicle correction weight, and vehicle over-limit value.

[0076] Optionally, the cheating flag determination module 303 includes: The cheating probability value determination unit is used to input the vehicle feature factors of the target vehicle into the pre-trained vehicle cheating probability prediction model to obtain the cheating probability value output by the model. A cheating flag determination unit is used to determine the vehicle cheating flag of the target vehicle based on the cheating probability value.

[0077] Optional, a cheating flag determination unit, specifically used for: Based on the vehicle characteristic factors of the target vehicle, and according to the preset vehicle cheating judgment rules, a rule judgment result is generated. Based on the cheating probability value and the rule determination result, the vehicle cheating flag of the target vehicle is determined.

[0078] Optionally, the device further includes: The identification verification module is used to, after generating the evidence identifier of the target vehicle based on the vehicle location information, the vehicle environment information, and the vehicle feature factors, if the vehicle cheating mark of the target vehicle is cheating evidence or evidence to be verified, send a verification request to at least three adjacent overload control stations adjacent to its current overload control station, so that each of the adjacent overload control stations can parse the verification request to obtain the vehicle feature factors and the license plate information of the target vehicle, and perform feature similarity matching based on the vehicle feature factors and the license plate information of the target vehicle, generate and return the similarity matching result; The vehicle marking judgment module is used to determine whether to update the vehicle marking of the target vehicle based on the similarity matching results fed back by each of the adjacent overload control stations, and to update the evidence identifier of the target vehicle according to the judgment result.

[0079] Optionally, the evidence identification generation module 304 is specifically used for: The vehicle location information is hashed to generate vehicle trajectory hash parameters; and, The vehicle correction weight in the vehicle characteristic factors is hashed to generate a correction weight hash parameter; and, The slope and wind speed in the vehicle environment information are hashed to generate environment hash parameters; An evidence identifier for the target vehicle is generated based on the vehicle trajectory hash parameter, the corrected weight hash parameter, and the environment hash parameter.

[0080] The vehicle overload control evidence chain credible evidence storage device provided in the embodiments of the present invention can execute the vehicle overload control evidence chain credible evidence storage method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0081] Example 4 Figure 4A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0082] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0083] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the trusted evidence storage method for vehicle overloading control.

[0085] In some embodiments, the method for credible evidence storage of vehicle overload control evidence chain can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the method for credible evidence storage of vehicle overload control evidence chain described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to execute the method for credible evidence storage of vehicle overload control evidence chain by any other suitable means (e.g., by means of firmware).

[0086] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0090] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0091] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0092] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0093] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for reliable evidence preservation of vehicle overloading control evidence chain, characterized in that, Edge nodes applied to vehicle overload control stations include: Acquire dynamic weighing data, environmental data, and location data of the target vehicle when it arrives at the current vehicle weight control station; Based on the vehicle dynamic weighing data, the vehicle environmental data, and the vehicle location data, determine the vehicle characteristic factors of the target vehicle; Based on the vehicle characteristic factors of the target vehicle, determine the vehicle cheating flag of the target vehicle; Based on the vehicle location information, the vehicle environment information, and the vehicle characteristic factors, an evidence identifier for the target vehicle is generated; Based on the evidence identifier, the vehicle characteristic factor, and the vehicle cheating flag, construct a block transaction; The smart contract is invoked to trigger the transaction consensus algorithm to conduct transaction consensus on the block transaction. After the transaction consensus is passed, the block transaction is written into the edge chain block and simultaneously synchronized to the regional side chain and the main chain through the cross-chain gateway, so as to realize the on-chain storage of evidence of the target vehicle's overloading control.

2. The method according to claim 1, characterized in that, The step of determining the vehicle characteristic factors of the target vehicle based on the vehicle dynamic weighing data, the vehicle environmental data, and the vehicle location data includes: Extract the first vehicle speed at the time of vehicle weighing from the vehicle location data, and the second vehicle speed within a preset time range at the time of vehicle weighing; and determine the vehicle speed mutation coefficient based on the first vehicle speed and the second vehicle speed. The center coordinates of the weighing area and the center coordinates of the weighing time are obtained from the vehicle dynamic weighing data. Based on the center coordinates of the weighing area and the center coordinates of the weighing time, the trajectory deviation index is determined. Based on the slope and wind speed in the vehicle environmental data, determine the environmental impact factor; based on the vehicle weighing weight in the vehicle dynamic weighing data, determine the vehicle correction weight based on the environmental impact factor. Based on the corrected weight of the vehicle, and the statutory limit for the vehicle type corresponding to the target vehicle, the vehicle excess value is determined; Generate vehicle characteristic factors including the vehicle speed mutation coefficient, trajectory deviation index, vehicle correction weight, and vehicle over-limit value.

3. The method according to claim 1, characterized in that, The step of determining the vehicle cheating flag of the target vehicle based on the vehicle characteristic factors of the target vehicle includes: The vehicle feature factors of the target vehicle are input into the pre-trained vehicle cheating probability prediction model to obtain the cheating probability value output by the model. Based on the cheating probability value, the vehicle cheating flag of the target vehicle is determined.

4. The method according to claim 3, characterized in that, The step of determining the vehicle cheating flag of the target vehicle based on the cheating probability value includes: Based on the vehicle characteristic factors of the target vehicle, and according to the preset vehicle cheating judgment rules, a rule judgment result is generated. Based on the cheating probability value and the rule determination result, the vehicle cheating flag of the target vehicle is determined.

5. The method according to claim 1, characterized in that, After generating the evidence identifier of the target vehicle based on the vehicle location information, the vehicle environment information, and the vehicle characteristic factors, the method further includes: If the vehicle cheating mark of the target vehicle is cheating evidence or evidence to be verified, a verification request is sent to at least three adjacent overload control stations adjacent to its current overload control station, so that each of the adjacent overload control stations can parse the verification request to obtain the vehicle feature factors and the license plate information of the target vehicle, and perform feature similarity matching based on the vehicle feature factors and the license plate information of the target vehicle to generate and return the similarity matching result. Based on the similarity matching results reported by each of the adjacent overload control stations, it is determined whether to update the vehicle marker of the target vehicle, and the evidence identifier of the target vehicle is updated according to the determination result.

6. The method according to claim 1, characterized in that, The step of generating an evidence identifier for the target vehicle based on the vehicle location information, the vehicle environment information, and the vehicle characteristic factors includes: The vehicle location information is hashed to generate vehicle trajectory hash parameters; and, The vehicle correction weight in the vehicle characteristic factors is hashed to generate a correction weight hash parameter; and, The slope and wind speed in the vehicle environment information are hashed to generate environment hash parameters; An evidence identifier for the target vehicle is generated based on the vehicle trajectory hash parameter, the corrected weight hash parameter, and the environment hash parameter.

7. A reliable evidence storage device for vehicle overloading control, characterized in that, The edge nodes configured at vehicle overload control stations include: The data acquisition module is used to acquire the target vehicle's dynamic weighing data, vehicle environmental data, and vehicle location data when the vehicle arrives at the current vehicle overload control station. The feature factor determination module is used to determine the vehicle feature factors of the target vehicle based on the vehicle dynamic weighing data, the vehicle environmental data, and the vehicle location data. The cheating flag determination module is used to determine the vehicle cheating flag of the target vehicle based on the vehicle characteristic factors of the target vehicle. The evidence identifier generation module is used to generate an evidence identifier for the target vehicle based on the vehicle location information, the vehicle environment information, and the vehicle characteristic factors. The block transaction construction module is used to construct block transactions based on the evidence identifier, the vehicle characteristic factor, and the vehicle cheating flag. The on-chain evidence storage module is used to call the smart contract to trigger the transaction consensus algorithm to conduct transaction consensus on the block transaction. After the transaction consensus is passed, the block transaction is written into the edge chain block and simultaneously synchronized to the regional side chain and the main chain through the cross-chain gateway, so as to realize the on-chain evidence storage of the target vehicle's overloading control.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the reliable evidence storage method for vehicle overloading control as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the reliable evidence storage method for vehicle overloading control evidence chain as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the reliable evidence storage method for vehicle overloading control evidence chain according to any one of claims 1-6.

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