CN115965919A - Green communication system, method and application based on data prediction and intelligent video detection - Google Patents
Green communication system, method and application based on data prediction and intelligent video detection Download PDFInfo
- Publication number
- CN115965919A CN115965919A CN202310000817.8A CN202310000817A CN115965919A CN 115965919 A CN115965919 A CN 115965919A CN 202310000817 A CN202310000817 A CN 202310000817A CN 115965919 A CN115965919 A CN 115965919A
- Authority
- CN
- China
- Prior art keywords
- data
- vehicle
- green
- intelligent
- algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention belongs to the technical field of image recognition, and discloses a green communication system, a green communication method and application based on data prediction and intelligent video detection. The method comprises the following steps: acquiring basic data and basic calculation data of a vehicle; the basic data of the vehicle comprises a vehicle type, an unloaded weight and a container volume; acquiring agricultural product information carried by a vehicle in a toll lane by using an AI camera, and identifying; and based on the acquired data and the recognized agricultural product information, combining with external environment influence factors, and carrying out intelligent calculation and comparison with AI according to a recursive algorithm to obtain a model conforming to vehicle passing. The invention realizes the quick checking of the green traffic vehicles and improves the traffic efficiency and the detection accuracy. The invention creates a green traffic vehicle passing verification model by combining the processes of theoretical calculation, actual test verification, existing data comparison and the like and an iterative model algorithm, thereby realizing intelligent green traffic control.
Description
Technical Field
The invention belongs to the technical field of image recognition, and particularly relates to a green communication system, a green communication method and application based on data prediction and intelligent video detection.
Background
Due to the passing fee reduction and exemption policy, the vehicles passing through the highway and green goods are inconsistent, and the phenomenon of false reporting and disguising exists, so that the workers in the ordinary toll lane need to check the vehicle goods. At present, manual inspection, camera inspection, X-ray inspection and large data record inspection are generally adopted, but the methods have defects.
The manual inspection is difficult to directly see what state the inside of the agricultural products is due to more stacked goods, and the turning takes long time to cause traffic congestion and possible damage to the agricultural products; in the camera inspection method, because the common camera cannot observe the interior of the goods from the outside, and the endoscopic camera needs to be inserted and inspected at multiple points, on one hand, the goods are difficult to insert under the tight condition, and on the other hand, the time consumption is long, and the agricultural products are damaged; the X-ray inspection can be used for photographing and scanning objects quickly, and the accuracy is high and the speed is high. But radiation in the field is harmful to the health of toll collectors and drivers, and X-ray inspection cannot penetrate through the totally-enclosed van, so that the effect is poor; the big data registration traceability mode is that vehicles and goods are pre-registered and checked to realize quick green-pass inspection, but the problem is that a nationwide unified registration platform cannot be established at the present stage, and the freight vehicle can pull fresh agricultural products to span multiple provinces. And the information that the freight car changes the carriage midway, adjusts goods and the like cannot be tracked, so that the defect exists.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) In the prior art, all technical means adopted for checking the green traffic vehicles are not perfect, and the method has the limitations of human, environment and the like, and is difficult to realize the quick checking of the green traffic vehicles.
(2) In the prior art, the quick checking system for the green traffic vehicles has the disadvantages of low checking speed and poor detection accuracy, so that the traffic efficiency is low.
Disclosure of Invention
In order to overcome the problems in the related art, the embodiments of the present disclosure provide a system, a method and an application for green channel based on data prediction and intelligent video detection.
The technical scheme is as follows: a green pass detection method based on a data prediction algorithm and intelligent video detection comprises the following steps:
s1, acquiring basic data and basic calculation data of a vehicle; the basic data of the vehicle comprise a vehicle type, an empty load weight and a container volume; the basic calculation data comprises vehicle types, license plates, the number of axles, weight, length, width and vehicle body characteristics;
s2, acquiring agricultural product information carried by a vehicle in the toll lane by using an AI camera (an independent camera or a handheld flat plate to acquire the agricultural product information), and identifying;
s3, based on the data obtained in the step S1 and the agricultural product information identified in the step S2, combining external environment influence factors, and carrying out intelligent calculation and comparison with AI according to a recursive algorithm to obtain a model conforming to vehicle passing;
and S4, comparing whether the vehicle after the vehicle is matched with the specified result, and displaying.
In one embodiment, in step S1, the weight, the corresponding volume and the theoretical density of the agricultural product are obtained according to the variety catalog of the green fresh agricultural product and the brand variety of the domestic truck, and a density-volume comparison value of the agricultural product transport vehicle is obtained through test detection, actually measured data of a toll station and expert evaluation;
basic data is obtained by sorting freight vehicle data of each truck manufacturer, and vehicle weight data is obtained through test detection, toll station actual measurement data and expert evaluation.
In one embodiment, in step S1, the basic data is learned through an intelligent algorithm, and the final basic calculation data is obtained through accumulation of data and algorithm.
The main idea of the invention is an error result, the density of the cargo weight, the theoretical value and the actual value of the weight must have errors, and a reasonable error value is obtained through accumulation of a large amount of data, so that the identification accuracy is improved.
In one embodiment, in step S2, the AI camera includes a vehicle type recognition camera for acquiring data of a license plate, a vehicle type, a manufacturer, and a total axle number of the vehicle, acquiring volume data of the pulled agricultural product cargo, and acquiring a weight of the pulled agricultural product cargo by using the weighing device.
In one embodiment, the method for acquiring the volume data of the pulled agricultural product cargoes comprises the following steps:
and identifying the volume of the pulled agricultural product cargo by adopting an intelligent algorithm and image identification according to the license plate, the model, the manufacturer and the total axle number data of the vehicle.
The method specifically comprises the following steps: the method comprises the steps of shooting a total photo of a freight warehouse by a fixed camera or a handheld camera, identifying the length, the width and the height of a truck body through AI (artificial intelligence), obtaining a gap by adopting a foam board and other packages, obtaining the number of axles of a truck through the AI camera, identifying the type of goods through an intelligent algorithm, and obtaining the basic cargo type of the truck, the length, the width and the height of the goods and the actual gap parameters for stacking the goods through the method.
In one embodiment, in step S3, based on the weight, model and volume data of the agricultural product cargo obtained in step S2 and the basic data of the vehicle obtained in step S1, the air temperature and air pressure environment correction parameters are combined, and according to a recursive algorithm and AI intelligent calculation, a result is obtained whether the vehicle meets the free regulation;
specifically, the essence of green traffic vehicles is that the characteristics obtained by the equipment are used for verifying whether the characteristics expressed by the vehicles and the measured parameters conform to the theoretical calculation result, so that whether the illegal fee evasion exists or not is rapidly verified, and the verification is a digital model verification;
the recursive algorithm is a process of decomposing data passed by the vehicle into vehicle body verification, weight verification, axle number verification, density verification and cargo type verification, comparing theoretical data with actual data in a weight form and judging within a certain allowable error range.
In step S4, according to the result that whether the green traffic vehicles meet the free regulation or not, displaying by using a display screen and carrying out voice broadcasting.
In one embodiment, the result that a green traffic vehicle does not meet the free regulations is retested using a weighted lane at the toll booth exit.
Another objective of the present invention is to provide a green channel system based on data prediction algorithm and intelligent video detection for implementing the green channel detection method based on data prediction algorithm and intelligent video detection, comprising:
the big data module is used for acquiring basic data and basic calculation data of the vehicle; the basic data of the vehicle comprise a vehicle type, an empty load weight and a container volume;
the vehicle type and volume identification module is used for acquiring the vehicle type of a vehicle in a toll lane and the volume information of the agricultural products loaded by pulling by using an AI camera and identifying;
the weighing module is used for weighing the total mass of the vehicle after the green traffic vehicle reaches the toll lane;
the comparison and calculation module is used for comparing the data obtained by the weighing module, the vehicle type and volume identification module with the weight ratio of the vehicle and the pulled agricultural products obtained in the big data module, and obtaining a result of whether the green traffic vehicle conforms to the free regulation or not by combining the air temperature and air pressure environment correction parameters;
the display and retest module is used for displaying whether the green traffic vehicle meets the free specified result on a field display screen and carrying out voice broadcast; and for retesting the weighing lane at the toll station exit;
and the intelligent algorithm module is used for combining actual measurement data of agricultural product loaded by the vehicle, existing green traffic vehicle traffic data and environmental influence factors according to basic parameters of the big data module and carrying out intelligent calculation according to a recursive algorithm and AI (intelligent algorithm) to obtain a model conforming to green traffic vehicle traffic.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the green pass detection method based on a data prediction algorithm and intelligent video detection.
It is another object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the green channel detection method based on a data prediction algorithm and intelligent video detection.
By combining all the technical schemes, the invention has the advantages and positive effects that:
first, aiming at the technical problems existing in the prior art and the difficulty in solving the problems, the technical problems to be solved by the technical scheme of the present invention are closely combined with results, data and the like in the research and development process, and some creative technical effects are brought after the problems are solved. The specific description is as follows:
(1) The defects of traditional manual inspection, camera insertion inspection and X-ray inspection are avoided, and the detection speed is high.
(2) The mode of combining big data with AI algorithm is adopted, the technical level is high, and intelligent management can be realized by combining white lists and other modes.
The invention is innovated in model algorithm obtained by the position where the equipment is placed and big data, and accurate identification technology and algorithm of the image, and solves the problems that the adopted technical means are not perfect, the limit of man-made, environment and the like exists, and the quick inspection of the green traffic vehicle is difficult to realize in the prior art aiming at the inspection of the green traffic vehicle. The quick checking system for the green traffic vehicles based on the computer vision target detection, the vehicle type identification, the vehicle weighing, the model comparison algorithm and the big data comparison realizes the quick checking of the green traffic vehicles, and improves the vehicle passing efficiency and the detection accuracy.
Secondly, regarding the technical solution as a whole or from the perspective of products, the technical effects and advantages of the technical solution to be protected by the present invention are specifically described as follows:
the invention has fast detection speed: all data can be processed after the vehicle enters the toll lane, and the slow mode such as manual or camera puncture detection is avoided. If a manual or camera puncture mode is adopted, each vehicle generally needs 5-15 minutes of processing time, and if the mode is adopted, the green traffic vehicle can be generally finished within 15-60 seconds.
The invention has high accuracy:
the core factor of the green-channel detection is that green-channel vehicles transport goods which do not belong to the fresh agricultural product catalogue, and the goods are difficult to be consistent with the declared agricultural product quality ratio due to different volumes or qualities. If the Chinese cabbage is reported, the density after model checking calculation should be 177KG/M 3 And the vehicle with the density being more than or less than 20% of the density can be judged not to accord with the green preferential policy.
The invention has high intelligent degree: the transport vehicles entering the lane all process images through field intelligent equipment, corresponding vehicle type data are found through vehicle logos, axle numbers, overall dimensions and the like, cargos loaded in the carriage all process the images through the field intelligent equipment, height, length and width data of the cargos are obtained, rapid judgment is carried out, and manual intervention is not needed. The agricultural product data pulled by the truck can adopt an automatic image identification technology, and the name of the pulled agricultural product is accurately identified, so that manual intervention is avoided.
The application of the intelligent model and the big data of the invention is as follows: the big data are comprehensively input into all truck data on the market and corresponding vegetable data on the green traffic list, and through the processes of theoretical calculation, actual test verification, existing data comparison and the like, an iterative model algorithm is combined to create a green traffic vehicle traffic verification model, so that intelligent green traffic control is realized.
The invention provides a set of effective detection models: the data obtained by the manual inspection and the camera inspection which are commonly used in the green common inspection do not have a uniform standard, so the result is often disputed.
Third, as an inventive supplementary proof of the claims of the present invention, there are also presented several important aspects:
(1) The invention adopts a data model mode to process the green service, the required data sources are all the existing equipment of the truck lane, for example, the weight data comes from the existing weighbridge, the license plate data comes from the license plate recognizer of the lane, the vehicle type data comes from the AI camera of the lane, the goods image comes from the hand-held terminal of the green service bank worker at the toll station, so that no additional hardware investment is needed. The core of the technology is a data model and an AI algorithm, so the commercial value is higher. Each toll station in China has 1-4 green traffic lanes, so the demand is huge and the commercial value is high.
(2) The detection mode of the green channel at the present stage in China is mainly a manual inspection mode, the detection speed is low, the accuracy is low, and the truck congestion is easily caused. And for van trucks, the inspection is difficult to be performed by manual, X-ray and camera inspection modes. This technical scheme can realize the inspection of green logical vehicle goods fast, and the process is all through the mode record of data and photo, has avoided artificial error.
(3) The truck green inspection is always a problem that managers in toll stations are very headache, and the core of the invention is that the speed of manual inspection is slow, the accuracy rate is low, and artificial factors exist. However, the camera or the X-ray has various defects, and cannot satisfy various scenes. Therefore, the inspection of the green channel always seeks a fast, stable and efficient detection mode, and the scheme realizes the purpose through a data model and an AI algorithm.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart of a green channel detection method based on a data prediction algorithm and intelligent video detection according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a green channel detection method based on a data prediction algorithm and intelligent video detection according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a green channel system based on a data prediction algorithm and intelligent video detection according to an embodiment of the present invention;
in the figure: 1. a big data module; 2. a vehicle type and volume identification module; 3. a weighing module; 4. a comparison and calculation module; 5. a display and retest module; 6. and an intelligent algorithm module.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms than those specifically described herein, and it will be apparent to those skilled in the art that many more modifications are possible without departing from the spirit and scope of the invention.
1. Illustrative examples are illustrated:
example 1
As shown in fig. 1, the green channel detection method based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the present invention includes:
s101, acquiring basic data and basic calculation data of a vehicle; the basic data of the vehicle comprises a vehicle type, an unloaded weight and a container volume; the basic calculation data comprises vehicle types, license plates, the number of axles, weight, length, width and vehicle body characteristics;
s102, acquiring agricultural product information carried by a vehicle in a toll lane by using an AI camera, and identifying;
s103, based on the data obtained in the step S101 and the agricultural product information identified in the step S102, combining with external environment influence factors, and carrying out intelligent calculation and comparison with AI according to a recursive algorithm to obtain a model conforming to vehicle passing;
and S104, comparing whether the vehicle after the vehicle is matched with the specified result, and displaying.
Example 2
Based on the green traffic detection method based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the invention, in a preferred embodiment, in step S101, the weight, the corresponding volume and the theoretical density of agricultural products are obtained according to the variety catalog of green traffic fresh agricultural products and the brand types of domestic trucks, and the density-volume comparison value of the agricultural product transport vehicle is obtained through test detection, the actual measurement data of a toll station and expert evaluation;
basic data is obtained by sorting freight vehicle data of each truck manufacturer, and vehicle weight data is obtained through test detection, toll station actual measurement data and expert evaluation.
Example 3
Based on the green pass detection method based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the invention, in a preferred embodiment, in step S101, the basic data is learned through the intelligent algorithm, and the final basic calculation data is obtained through accumulating data and algorithm.
Example 4
Based on the green channel detection method based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the invention, in a preferred embodiment, in step S102, the AI camera comprises a vehicle type identification camera, and is used for acquiring data of a license plate, a vehicle type, a manufacturer and a total axle number of a vehicle, acquiring volume data of a pulled agricultural product cargo, and acquiring the weight of the pulled agricultural product cargo by using a weighing device.
In a preferred embodiment, the method for obtaining the volume data of the pulled agricultural product cargo comprises the following steps:
and identifying the volume of the pulled agricultural product cargo by adopting an intelligent algorithm and image identification according to the license plate, the model, the manufacturer and the total axle number data of the vehicle. The intelligent algorithm adopts a BP neural network algorithm or an intelligent interaction algorithm.
Example 5
Based on the green channel detection method based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the invention, in a preferred embodiment, in step S103, based on the weight, model and volume data of the agricultural product and cargo which is pulled and carried and acquired in step S102, the basic data of the vehicle acquired in step S101, and the air temperature and air pressure environment correction parameters are combined, and according to the recursive algorithm and the AI intelligent calculation, whether the vehicle meets the free regulation result is obtained;
example 6
Based on the green traffic detection method based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the invention, in step S104, according to the result whether the green traffic vehicle meets the free regulation or not, the green traffic vehicle is displayed by using a display screen and is subjected to voice broadcast.
In a preferred embodiment, the result that the green traffic vehicle does not comply with the free regulations is retested using the weighted lane at the exit of the toll booth.
Example 7
As shown in fig. 2, the green channel detection method based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the present invention includes: image recognition of cargo type: the AI images identify parameters of vegetable type, interval, packaging, etc.
Vehicle identification: number plate, vehicle type, number of axles, etc.;
a weighing module: weight, car length, width, height.
Example 8
As shown in fig. 3, the green channel system based on the data prediction algorithm and the intelligent video detection provided by the embodiment of the present invention includes a big data module 1, a vehicle type and volume identification module 2, a weighing module 3, a comparison and calculation module 4, a display and retest module 5, and an intelligent algorithm module 6, and includes systems such as hardware and software.
The core of the method is that if the pulling load of the truck is inconsistent with the goods, the mass ratio (density) of the truck is different, and the fact that the green traffic vehicle is not in line with the reduced and exempted toll rule can be determined through big data accumulation and intelligent model calculation.
The big data module 1:
obtaining the weight, the corresponding volume and the theoretical density of agricultural products according to the national green-ventilation fresh agricultural product variety catalog and the domestic truck brand category, and obtaining a reasonable density-volume comparison value of the agricultural product transport vehicle through means of test detection, toll station actual measurement data, expert evaluation and the like; basic data such as basic vehicle models, no-load weights, container volumes and the like are obtained by arranging freight vehicle data of various truck manufacturers, and reasonable vehicle weight data are obtained by means of test detection, toll station actual measurement data, expert evaluation and the like. The data are learned through an intelligent algorithm, and final basic calculation data are obtained through data accumulation and the algorithm.
The vehicle type and volume identification module 2:
after a green traffic vehicle arrives at a toll lane, a vehicle type recognition camera obtains data such as a license plate, a vehicle type, a manufacturer and the total axle number of the vehicle, height, length and width information of goods are obtained according to side and upper cameras, and a toll collector inputs information such as names and weights of pulled agricultural products according to information provided by a vehicle green traffic document. The van is automatically identified by the AI through the on-site photographing of the toll collector, and the parameters of whether the agricultural product identification is correct or not and whether the goods are full are confirmed.
The vehicle type and volume identification needs intelligent algorithm and image identification, exact vehicle type information is obtained according to information such as vehicle labels and axle numbers, and data such as the volume of goods are intelligently identified according to the images.
The weighing module 3:
and after the green traffic vehicle reaches the toll lane, the weighing equipment obtains the total mass of the vehicle.
The comparison and calculation module 4:
according to the data obtained by the weighing module, the vehicle type and volume identification module, the mass ratio of the trucks and the cargos in the big data module is compared, and the result of whether the green traffic vehicles meet the free regulation or not is obtained according to the regulation that the green traffic cargos account for more than 80% of the total mass in the national green traffic policy by combining the environment correction parameters such as air temperature, air pressure and the like.
The display and retest module 5:
and if the green traffic vehicle meets the free specified result, displaying the result on a field display screen and carrying out voice broadcasting. If the driver of the truck makes an objection, the driver can go to the exit of the toll station to measure the repeated lanes.
The intelligent algorithm module 6:
the intelligent algorithm module is mainly used for obtaining a model which is most consistent with the passing of green traffic vehicles according to the recursive algorithm and AI intelligent calculation by combining actual measurement data of agricultural products loaded by vehicles, existing green traffic vehicle passing data, environmental influence factors and the like according to basic parameters of the big data module, so that the work of other modules is guided in a comparison manner.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
For the information interaction, execution process and other contents between the above devices/units, the specific functions and technical effects brought by the method embodiments of the present invention based on the same concept can be referred to the method embodiments, and are not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present invention. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
2. The application example is as follows:
application example 1
(1) The taro KR of the Dongfeng commercial vehicle is input into a large database, the self weight of the truck is 6.63 tons, the length of a cargo box is 6.8 meters, the width of the cargo box is 2.46 meters, and the loading height of the cargo box is 2.1 meters.
The theoretical unloaded mass of the truck is wideand 6.63 tons, and the transportation volume is 35.1288 cubic meters.
The actual average weight of such trucks was 6.97 tons, sampled according to field investigations.
According to expert opinion and model calculation, the weight of the truck is finally 6.83 tons.
According to the data in the big database, two green channel product parameters are selected as follows:
the Chinese cabbage accounts for 0.0105 ton/cubic meter.
Apple 0.54 ton/cubic meter.
The data is application data obtained by theoretical calculation, actual measurement and model algorithm adopted by experts.
(2) The green traffic lane passes a Dongfeng commercial vehicle Tianjin KR, the vegetable pulled is reported to be Chinese cabbage, the length of the goods calculated by the field camera is 6.71 multiplied by 2.42 multiplied by 2.3 meters, the volume is 54.046 cubic meters, and the goods are loaded in full cars; the total weight of the truck weighed on site was 7.46 tons.
(3) The cargo weight is 7.46-6.63 tons =0.83 tons, and the cargo density is 0.83 tons/54.046 cubic meter =0.0154 tons/cubic meter.
(4) The theoretical transport volume of the Chinese cabbage is 0.0105 tons/cubic meter, and the obtained data is 0.0154 tons/cubic meter and exceeds the specified 20% floating range. The vehicle is judged to be suspect of not meeting the green pass regulation.
(5) And after the secondary detection, agricultural products which do not accord with the green-passing catalogue are placed below the Chinese cabbage, and the green-passing free toll is not reduced because the agricultural products do not accord with the green-passing free toll regulation.
(6) The green lane passes through a Dongfeng commercial vehicle Tianjin KR again, the pulled vegetables are declared to be apples, the goods length calculated by the field camera is 6.71 multiplied by 2.42 multiplied by 2.3 meters, the volume is 54.046 cubic meters, and the goods are loaded in a full vehicle; the total weight of the truck weighed on site was 35.81 tons.
(7) The cargo weight was 35.81-6.63 tons =29.18 tons, and the cargo density was 29.18 tons/54.046 cubic meter =0.539 tons/cubic meter.
(8) The theoretical transport volume of the Chinese cabbage is 0.54 ton/cubic meter, and the obtained data of 0.539 ton/cubic meter does not exceed the specified 20% floating range. If the vehicle is judged to meet the green traffic regulation, the traffic fee is reduced.
Application example 2
An embodiment of the present invention further provides a computer device, where the computer device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor implementing the steps of any of the various method embodiments described above when executing the computer program.
Application example 3
Embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments may be implemented.
Application example 4
The embodiment of the present invention further provides an information data processing terminal, where the information data processing terminal is configured to provide a user input interface to implement the steps in the above method embodiments when implemented on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer, or a switch.
Application example 5
The embodiment of the present invention further provides a server, where the server is configured to provide a user input interface to implement the steps in the above method embodiments when implemented on an electronic device.
Application example 6
Embodiments of the present invention provide a computer program product, which, when running on an electronic device, enables the electronic device to implement the steps in the above method embodiments.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium and used for instructing related hardware to implement the steps of the embodiments of the method according to the embodiments of the present invention. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer memory, read-only memory (ROM), random Access Memory (RAM), electrical carrier signal, telecommunications signal, and software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc.
3. Evidence of the relevant effects of the examples:
the above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention should not be limited thereto, and any modifications, equivalents and improvements made by those skilled in the art within the technical scope of the present invention as disclosed in the present invention should be covered thereby.
Claims (10)
1. A green channel detection method based on a data prediction algorithm and intelligent video detection is characterized by comprising the following steps:
s1, obtaining basic data and basic calculation data of a vehicle, wherein the basic data of the vehicle comprises data of a vehicle type, a no-load weight and a container volume; the basic calculation data comprises vehicle type, license plate, number of axles, weight, length, width and vehicle body characteristic data;
s2, acquiring agricultural product information pulled by a vehicle in a toll lane by using an AI camera, and identifying;
s3, based on the data obtained in the step S1 and the agricultural product information identified in the step S2, combining with external environment influence factors, and according to recursive algorithm and AI intelligent calculation and comparison, obtaining a model conforming to vehicle passing;
and S4, checking whether the compared vehicle meets the specified result, and displaying.
2. The green traffic detection method based on the data prediction algorithm and the intelligent video detection is characterized in that in the step S1, the weight, the corresponding volume and the theoretical density of agricultural products are obtained according to a variety catalog of green traffic fresh agricultural products and brand types of domestic trucks, and a density-volume comparison value of a transport vehicle for the agricultural products is obtained through test detection, actually measured data of a toll station and expert evaluation data;
basic data is obtained by sorting freight vehicle data of each truck manufacturer, and vehicle weight data is obtained through test detection, toll station actual measurement data and expert evaluation.
3. The green channel detection method based on the data prediction algorithm and the intelligent video detection as claimed in claim 2, wherein in step S1, the basic data of the vehicle is learned through the intelligent algorithm, and the final basic calculation data is obtained through the accumulated data and algorithm.
4. The green channel detection method based on the data prediction algorithm and the intelligent video detection as claimed in claim 1, wherein in step S2, the AI camera comprises a vehicle type recognition camera for obtaining data of a license plate, a vehicle type, a manufacturer and a total axle number of the vehicle, obtaining volume data of the pulled agricultural product cargo, and obtaining the weight of the pulled agricultural product cargo by using a weighing device.
5. The green channel detection method based on the data prediction algorithm and the intelligent video detection as claimed in claim 4, wherein the method for acquiring the volume data of the pulled agricultural product cargoes comprises the following steps:
according to the data of the license plate, the type, the manufacturer and the total axle number of the vehicle, the volume of the pulled agricultural product cargos is identified by adopting an intelligent algorithm and image identification.
6. The green channel detection method based on the data prediction algorithm and the intelligent video detection as claimed in claim 1, wherein in step S3, based on the weight, model and volume data of the agricultural goods pulled and loaded obtained in step S2, the basic data of the vehicle obtained in step S1, the air temperature and air pressure environment correction parameters are combined, and according to the recursive algorithm and the AI intelligent calculation, the result of whether the vehicle meets the free regulation is obtained.
7. The green traffic detection method based on the data prediction algorithm and the intelligent video detection, according to claim 1, is characterized in that in step S4, according to the result of whether the green traffic vehicle meets the free regulation, the green traffic vehicle is displayed by using a display screen and is broadcasted by voice.
8. A green channel system based on data prediction algorithm and intelligent video detection for implementing the green channel detection method based on data prediction algorithm and intelligent video detection as claimed in any one of claims 1-7, wherein the green channel system based on data prediction algorithm and intelligent video detection comprises:
the big data module (1) is used for acquiring basic data and basic calculation data of the vehicle; the basic data of the vehicle comprises a vehicle type, an unloaded weight and a container volume;
the vehicle type and volume identification module (2) acquires the vehicle type in the toll lane and the volume information of the agricultural products loaded by the AI camera and identifies the vehicle type and the volume information;
the weighing module (3) is used for weighing the total mass of the green traffic vehicle after the green traffic vehicle arrives at the toll lane;
the comparison and calculation module (4) is used for comparing the data obtained by the weighing module (3) and the vehicle type and volume identification module (2) with the weight ratio of the vehicle and the pulled agricultural products obtained in the big data module (1), and obtaining a result whether the green traffic vehicle conforms to the free regulation or not by combining the air temperature and air pressure environment correction parameters;
the display and retest module (5) is used for displaying whether the green traffic vehicle meets the free specified result on a field display screen and carrying out voice broadcast; and for retesting the weighing lane at the toll station exit;
and the intelligent algorithm module (6) is used for combining the actual measurement data of agricultural product loaded by the vehicle, the existing green traffic vehicle traffic data and environmental influence factors according to the basic parameters of the big data module (1) and carrying out intelligent calculation according to a recursive algorithm and AI to obtain a model which accords with the traffic of the green traffic vehicle.
9. A computer arrangement, characterized in that the computer arrangement comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the green pass detection method based on the data prediction algorithm and intelligent video detection of any one of claims 1-7.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the method for green channel detection based on data prediction algorithm and intelligent video detection as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310000817.8A CN115965919A (en) | 2023-01-03 | 2023-01-03 | Green communication system, method and application based on data prediction and intelligent video detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310000817.8A CN115965919A (en) | 2023-01-03 | 2023-01-03 | Green communication system, method and application based on data prediction and intelligent video detection |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115965919A true CN115965919A (en) | 2023-04-14 |
Family
ID=87354681
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310000817.8A Pending CN115965919A (en) | 2023-01-03 | 2023-01-03 | Green communication system, method and application based on data prediction and intelligent video detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115965919A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116863205A (en) * | 2023-06-15 | 2023-10-10 | 深圳市软筑信息技术有限公司 | Container empty detection method and system for customs |
-
2023
- 2023-01-03 CN CN202310000817.8A patent/CN115965919A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116863205A (en) * | 2023-06-15 | 2023-10-10 | 深圳市软筑信息技术有限公司 | Container empty detection method and system for customs |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110057292B (en) | Method and device for determining carriage loading rate | |
| CN115965919A (en) | Green communication system, method and application based on data prediction and intelligent video detection | |
| CN113515985B (en) | Self-service weighing system, weighing detection method, weighing detection equipment and storage medium | |
| CN110633638A (en) | Garbage collection and transportation supervision method and device, storage medium and intelligent equipment | |
| CN110954546A (en) | Container image acquisition and inspection system for non-fixed scene | |
| CN108458878A (en) | The detection method and device of green channel vehicle | |
| CN114022537B (en) | Method for analyzing loading rate and unbalanced loading rate of vehicle in dynamic weighing area | |
| CN115860645B (en) | Logistics storage management method and system based on big data | |
| CN116167596A (en) | Distribution path analysis method and system based on big data | |
| CN117196437A (en) | Intelligent monitoring management system for logistics in intelligent park based on Internet of things | |
| CN110807493A (en) | Optimization method and equipment of vehicle classification model | |
| CN112699738B (en) | Expressway green channel vehicle rapid detection method based on unmanned aerial vehicle platform | |
| CN111784031A (en) | Logistics vehicle classification prediction method, device, equipment and storage medium | |
| CN115641728A (en) | ETC (electronic toll collection) inspection method, system, electronic equipment and medium | |
| CN114581002A (en) | Industrial production raw material intelligent management system based on cloud computing and artificial intelligence | |
| CN108460542B (en) | Grain depot warehouse-in and warehouse-out management method and device and computer readable storage medium | |
| CN106384099A (en) | Analysis method and system of vehicle model decision-making mechanism | |
| EP4198463A1 (en) | Method and system for measuring weight of cargo of an articulated vehicle | |
| KR102623236B1 (en) | Container terminal gate automation container damage detection system based on ai and method performing thereof | |
| CN115171060A (en) | Vehicle type determination method and device, storage medium and electronic device | |
| CN114742685A (en) | Green channel cargo detection method based on equivalent density and big data analysis | |
| CN117973971A (en) | Aviation logistics information management method and device based on block chain | |
| CN115346118A (en) | Identification and positioning method for vehicles in scrap steel discharge yard | |
| CN116772987A (en) | Method, device, system and storage medium for detecting wagon balance | |
| CN116844152A (en) | Method for realizing container storage yard container placement accuracy detection based on OCR technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |