Cargo transportation information automatic auditing method, system, device and storage medium

Abstract

The application provides a cargo transportation information automatic auditing method, system, device and storage medium, and the method comprises the following steps: collecting cargo transportation information as to-be-published information; inputting cargo type information, weight information and volume information of the cargo in the to-be-published information into a trained neural network to obtain recommended vehicle model information adapted to the cargo transportation information; judging whether each parameter in a first preset carrying parameter combination of a vehicle corresponding to the demand vehicle model information meets the condition of being greater than or equal to a corresponding parameter in a second preset carrying parameter combination of a vehicle corresponding to the recommended vehicle model information, if yes, publishing the to-be-published information, and feeding back vehicle information meeting the matching according to the demand vehicle model information as a screening condition, if no, feeding back prompt information based on the parameter that does not meet the condition of the corresponding parameter. The application can check the relationship of the cargo transportation information, ensure the accuracy and reliability of the cargo transportation information publishing, and improve the efficiency of vehicle-cargo matching.

Description

Technical Field

[0001] This invention relates to the field of freight big data, and more specifically, to a method, system, equipment, and storage medium for automatic verification of freight transportation information. Background Technology

[0002] In the freight sector, shippers' transportation needs vary greatly across industries. Some shippers need to transport saplings, others require city-wide freight transport via highways to ensure timely delivery of fresh produce, some demand rapid loading and unloading with pallets, and still others require vehicles to be covered with tarpaulins to prevent leaks, and so on. How to standardize the information required by shippers quickly, accurately, and conveniently through a webpage has become a crucial issue, especially in the freight transportation field. Freight information, as an intangible transportation service rather than standardized goods, inherently possesses individual differences and uncertainties. Therefore, utilizing systematic methods to extract standardized freight information and using automatic prompts to complete and correct freight information improves the accuracy and completeness of freight listings. The industry generally provides standardized freight descriptions and classifications, including freight name, specifications, weight, volume, etc., allowing shippers to directly list their goods after posting, or for manual review before listing.

[0003] Because the requirements for cargo transportation information vary, platforms generally provide standard, generic templates to facilitate cargo owners in posting cargo information. This type of information typically only includes verification of the necessity of the cargo description, such as loading time, cargo name, type of cargo, required vehicle length, vehicle type, and corresponding weight and volume. Besides limiting the necessity of the information, there is no precise assessment of the accuracy of the information entered. For example, a driver might select a 4.2-meter flatbed truck, but the tonnage information clearly exceeds the 4.2-meter vehicle size limit.

[0004] Currently, most platforms only provide standardized information structures, generally only verifying essential content of cargo transportation information, failing to ensure the accuracy and reliability of cargo transportation information after its publication. Furthermore, in daily use of freight matching platforms, another problem arises: most shippers do not have clearly defined fixed loading and unloading locations. After the driver arrives at the target location for the first time, the temporarily found parking space may be significantly different from the loading and unloading location, requiring further communication and confirmation, thus impacting overall transportation efficiency. Moreover, because each vehicle has a different size (not all parking spaces are designed for freight vehicles of different sizes), existing technology cannot provide useful navigation guidance.

[0005] Therefore, the present invention provides a method, system, device and storage medium for automatic verification of cargo transportation information. Summary of the Invention

[0006] In view of the problems in the prior art, the purpose of this invention is to provide an automatic verification method, system, device and storage medium for cargo transportation information, which overcomes the difficulties of the prior art, can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching.

[0007] An embodiment of the present invention provides a method for automatic verification of cargo transportation information, comprising the following steps:

[0008] S110. Collect cargo transportation information as information to be published. The cargo transportation information includes at least cargo type information, cargo weight information, cargo volume information, and required vehicle type information.

[0009] S120. Input the cargo type information, cargo weight information, and cargo volume information from the information to be published into a trained neural network to obtain recommended vehicle model information that matches the cargo transportation information.

[0010] S130. Determine whether each parameter in the first preset carrying parameter combination of the vehicle corresponding to the required vehicle model information satisfies the condition that it is greater than or equal to the corresponding parameter of the second preset carrying parameter combination of the recommended vehicle model information. If yes, proceed to step S140; otherwise, proceed to step S150.

[0011] S140. Publish the information to be published, and match it in the vehicle database based on the required vehicle model information as a filtering condition, and return the information of vehicles that meet the match; and

[0012] S150. Feedback prompts are given based on the parameters that do not meet the conditions for the corresponding parameters.

[0013] Preferably, in step S120, the neural network is trained with a massive amount of historical completed waybills, which includes cargo type information, cargo weight information, cargo volume information, and vehicle type information of the vehicle executing the waybill.

[0014] Preferably, the vehicle model information includes vehicle length data, vehicle width data, length, width and height data of the cargo space, and maximum load data.

[0015] Preferably, step S130 includes the following steps:

[0016] S131. Compare each parameter in the first preset transport parameter combination of the vehicle corresponding to the required vehicle model information with the corresponding parameter in the second preset transport parameter combination of the recommended vehicle model information.

[0017] S132. Determine that each parameter in the first preset transport parameter combination is greater than or equal to the corresponding parameter in the second preset transport parameter combination. If yes, proceed to step S140; otherwise, proceed to step S150.

[0018] Preferably, the cargo transportation information also includes the coordinates of the points of interest (POIs) for loading and unloading cargo;

[0019] Step S140 includes:

[0020] S141. Based on the point of interest address coordinate information, search the historical vehicle information set of previous completed waybills in the historical completed waybill database;

[0021] S142. Obtain the intersection of parameters of the first preset transport parameter combination of the required vehicle model information and the corresponding parameters of the third preset transport parameter combination of the historical vehicle model information set.

[0022] S143. Based on the intersection as a filtering condition, perform matching in the vehicle database and return the vehicle information that meets the matching criteria.

[0023] Preferably, step S140 further includes:

[0024] S144. Collect the matching vehicle information to complete the loading and unloading location of the waybill based on the point of interest address coordinate information, and count the number of times the matching vehicle information is used at each loading and unloading location.

[0025] S145. Sort the loading and unloading positions by the number of times they are used, and obtain the top n positions with the most uses as alternative parking spaces;

[0026] S146. Send the candidate parking spaces corresponding to the address coordinates of the points of interest to the cargo owner or the vehicle owner who accepted the order.

[0027] Preferably, after step S150, the method further includes:

[0028] S160. After updating the cargo transportation information, return the cargo transportation information to step S110.

[0029] Embodiments of the present invention also provide an automatic cargo transportation information verification system for implementing the above-described automatic cargo transportation information verification method. The automatic cargo transportation information verification system includes:

[0030] The information collection module collects cargo transportation information as information to be published. The cargo transportation information includes at least cargo type information, cargo weight information, cargo volume information, and required vehicle type information.

[0031] The vehicle model adaptation module inputs the cargo type information, cargo weight information, and cargo volume information from the information to be published into a trained neural network to obtain recommended vehicle model information for the cargo transportation information.

[0032] The parameter comparison module determines whether each parameter in the first preset carrying parameter combination of the vehicle corresponding to the required vehicle model information satisfies the condition that it is greater than or equal to the corresponding parameter in the second preset carrying parameter combination of the recommended vehicle model information. If yes, the information publishing module is executed; otherwise, the feedback prompt module is executed.

[0033] The information publishing module publishes the information to be published, and uses the required vehicle model information as a filtering condition to match it in the vehicle database, and returns the information of vehicles that meet the match; and

[0034] The feedback and prompt module provides feedback and prompt information based on the parameters that do not meet the conditions for the corresponding parameters.

[0035] Embodiments of the present invention also provide an automatic cargo transportation information verification device, comprising:

[0036] processor;

[0037] A memory in which executable instructions of the processor are stored;

[0038] The processor is configured to execute the steps of the above-described automatic verification method for cargo transportation information by executing the executable instructions.

[0039] Embodiments of the present invention also provide a computer-readable storage medium for storing a program, which, when executed, implements the steps of the above-described automatic verification method for cargo transportation information.

[0040] The purpose of this invention is to provide a method, system, device and storage medium for automatic verification of cargo transportation information, which can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching. Attached Figure Description

[0041] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0042] Figure 1 This is a flowchart of the automatic verification method for cargo transportation information of the present invention.

[0043] Figure 2 This is a schematic diagram of the automatic verification system for cargo transportation information of the present invention.

[0044] Figure 3This is a schematic diagram of the structure of the automatic cargo transportation information verification device of the present invention.

[0045] Figure 4 This is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention. Detailed Implementation

[0046] The following specific examples illustrate the implementation methods of this application. Those skilled in the art can easily understand the other advantages and effects of this application from the content disclosed herein. This application can also be implemented or applied through other different specific embodiments, and various details in this application can be modified or changed according to different viewpoints and application systems without departing from the spirit of this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0047] The embodiments of this application will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can easily implement the application. This application may be embodied in many different forms and is not limited to the embodiments described herein.

[0048] In this application, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics represented in connection with that embodiment or example, which are included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics represented may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate different embodiments or examples represented in this application, as well as features of different embodiments or examples.

[0049] Furthermore, the terms "first" and "second" are used for illustrative purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the representation of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0050] For the purpose of clearly describing this application, devices that are not relevant to the description are omitted, and the same or similar components throughout the specification are given the same reference numerals.

[0051] Throughout this specification, when it is said that a device is "connected" to another device, this includes not only "direct connection" but also "indirect connection" by placing other components in between. Furthermore, when it is said that a device "comprises" a certain constituent element, unless otherwise stated otherwise, this does not exclude other constituent elements, but rather implies that other constituent elements may be included.

[0052] When we say that a device is "above" another device, this can mean that it is directly above the other device, or it can mean that other devices are present in between. Conversely, when we say that a device is "directly" "above" another device, there are no other devices present in between.

[0053] Although the terms first, second, etc., are used in some instances herein to refer to various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, first interface and second interface, etc., are used. Furthermore, as used herein, the singular forms “a,” “an,” and “the” are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms “comprising,” “including,” indicate the presence of features, steps, operations, elements, components, items, kinds, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms “or” and “and / or” as used herein are interpreted as inclusive, or mean any one or any combination thereof. Thus, “A, B, or C” or “A, B, and / or C” means “any one of: A; B; C; A and B; A and C; B and C; A, B, and C.” Exceptions to this definition will only occur if the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0054] The technical terms used herein are for reference only to specific embodiments and are not intended to limit the scope of this application. The singular form used herein includes the plural form unless the statement explicitly indicates otherwise. The word "comprising" as used in the specification means to specify a particular characteristic, region, integer, step, operation, element, and / or component, and does not exclude the presence or addition of other characteristics, regions, integers, steps, operations, elements, and / or components.

[0055] Although not explicitly defined, all terms, including technical and scientific terms used herein, shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. Terms defined in commonly used dictionaries shall be further interpreted as having a meaning consistent with the relevant technical literature and the content of this present application, and shall not be over-interpreted as having an ideal or overly formulaic meaning unless otherwise defined.

[0056] Figure 1This is a flowchart of the automatic verification method for cargo transportation information according to the present invention. Figure 1 As shown, the automatic verification method for cargo transportation information of the present invention includes:

[0057] S110. Collect cargo transportation information as information to be published. The cargo transportation information shall include at least cargo type information, cargo weight information, cargo volume information, and required vehicle type information.

[0058] S120. Input the cargo type information, cargo weight information, and cargo volume information from the information to be published into the trained neural network to obtain recommended vehicle model information that matches the cargo transportation information.

[0059] S130. Determine whether each parameter in the first preset carrying parameter combination of the vehicle corresponding to the demand vehicle model information satisfies the condition that it is greater than or equal to the corresponding parameter of the second preset carrying parameter combination of the recommended vehicle model information. If yes, proceed to step S140; otherwise, proceed to step S150.

[0060] S140. Publish the information to be released, and use the required vehicle model information as a filter to match it in the vehicle database, and return the information of vehicles that meet the match.

[0061] S150, Provide feedback prompts based on parameters that do not meet the conditions for the corresponding parameters.

[0062] S160. After updating the cargo transportation information, return the cargo transportation information to step S110.

[0063] In a preferred embodiment, in step S120, the neural network is trained on a massive amount of historical completed waybills. The historical completed waybills include information on the type of goods, the weight of the goods, the volume of the goods, and the vehicle type information of the vehicle executing the waybill, but are not limited thereto.

[0064] In a preferred embodiment, the vehicle model information includes vehicle length data, vehicle width data, length, width and height data of the cargo space, and maximum load data, but is not limited thereto.

[0065] In a preferred embodiment, step S130 includes the following steps:

[0066] S131. Compare each parameter in the first preset load parameter combination of the vehicle corresponding to the demand vehicle model information with the corresponding parameter in the second preset load parameter combination of the recommended vehicle model information.

[0067] S132. Determine that each parameter in the first preset vehicle parameter combination is greater than or equal to the corresponding parameter in the second preset vehicle parameter combination. If yes, proceed to step S140; otherwise, proceed to step S150, but this is not the only option.

[0068] In a preferred embodiment, the cargo transportation information also includes the coordinates of the points of interest (POIs) for loading and unloading cargo.

[0069] Step S140 includes:

[0070] S141. Based on the point of interest address coordinate information, search the historical completed waybill database for the set of vehicle model information that was previously completed based on the point of interest address coordinate information.

[0071] S142. Obtain the intersection of parameters of the first preset transport parameter combination of the required vehicle model information and the corresponding parameters of the third preset transport parameter combination of the historical vehicle model information set.

[0072] S143. Based on the intersection as a filtering condition, perform matching in the vehicle database and return the information of the matching vehicles, but not limited to this.

[0073] In a preferred embodiment, step S140 further includes:

[0074] S144. Collect vehicle information that meets the matching criteria to determine the loading and unloading locations of waybills based on point of interest address coordinates, and count the number of times each loading and unloading location is used by vehicle information that meets the matching criteria.

[0075] S145. Sort the loading and unloading positions by the number of times they are used, and obtain the top n positions with the most uses as alternative parking spaces.

[0076] S146. Send the alternative parking spaces with the corresponding point of interest address coordinates to the cargo owner or the vehicle owner who accepts the order, but not limited to this.

[0077] The automatic verification method for cargo transportation information of the present invention can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching.

[0078] Specific embodiments of the present invention include:

[0079] First, collect cargo transportation information as information to be published. Cargo transportation information includes at least cargo type information, cargo weight information, cargo volume information, required vehicle type information, and point of interest address coordinates for loading and unloading cargo.

[0080] The cargo type, weight, and volume information from the information to be published are input into a trained neural network to obtain recommended vehicle type information suitable for the cargo transportation information. The neural network is trained on a massive amount of historical completed waybills, which include cargo type, weight, volume, and vehicle type information. Vehicle type information includes vehicle length, width, cargo compartment dimensions (length, width, and height), and maximum load data, but is not limited to these.

[0081] Each parameter in the first preset load-carrying parameter combination corresponding to the required vehicle model information is compared item by item with the corresponding parameter in the second preset load-carrying parameter combination corresponding to the recommended vehicle model information. It is determined that each parameter in the first preset load-carrying parameter combination is greater than or equal to the corresponding parameter in the second preset load-carrying parameter combination. The load-carrying parameter combination includes, but is not limited to, vehicle length data, vehicle width data, length, width, and height data of the cargo compartment, and maximum load data.

[0082] If so, the system searches the historical completed waybill database for vehicle model information that was previously used for waybills based on the point of interest (POI) address coordinates. It then obtains the intersection of parameters from the first preset transport parameter combination for the required vehicle model with the corresponding parameters from the third preset transport parameter combination in the historical vehicle model information set. Using this intersection as a filtering criterion, the system matches vehicles in the vehicle database and returns information on vehicles that fully meet the matching criteria. Clearly, through filtering by transport parameter combinations, the obtained vehicle information fully meets the cargo transportation requirements. Next, the system collects the loading and unloading locations for waybills based on the POI address coordinates using the matched vehicle information and counts the number of times each loading and unloading location is used. The loading and unloading locations are sorted by usage frequency, and the top n locations with the most usage are selected as candidate parking spaces. These candidate parking spaces with the corresponding POI address coordinates are then sent to the cargo owner or the vehicle owner accepting the order. In this embodiment, suitable vehicle information is matched with cargo transportation information. The loading and unloading locations of the points of interest (points of interest) where the cargo is loaded and unloaded are located, which meet the requirements of the cargo transportation information, are used as the destination for navigation. This allows the truck to park at the loading and unloading location, ensuring that the vehicle owner can drive directly to the most suitable parking location for loading and unloading, thereby improving the overall transportation efficiency.

[0083] If not, a prompt message will be provided based on the parameter that does not meet the conditions for the corresponding parameter. After the cargo transportation information is updated again, the cargo transportation information will be returned to the cargo transportation information collection step.

[0084] The automatic verification method for cargo transportation information of the present invention can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching.

[0085] This solution automatically improves and alerts users regarding cargo transportation information. This invention enhances the efficiency of cargo owners' cargo information posting efficiency by using intelligent recommendation and association methods to improve posting accuracy. It allows drivers to clearly understand the characteristics of the cargo they need to transport when browsing freight transportation information, reducing the double losses caused by incomplete cargo information leading to unclaimed cargo and drivers being unable to haul goods. This invention also contributes to improved human efficiency by scoring the behavioral characteristics of freight transportation information using threshold settings, reducing the human input required for information improvement and increasing efficiency. When constructing cargo information postings, the solution intelligently matches the required vehicle length and model for the selected cargo category. Different cargo categories require different vehicle information; by collecting and analyzing industry characteristics, different categories can correspond to different vehicle lengths and models, improving both posting efficiency and accuracy.

[0086] The system intelligently correlates vehicle length with cargo weight and volume. It identifies the volume and weight of cargo that different vehicle types can transport. This method collects vehicle length and model data, restricting the length, width, and height of cargo that each individual vehicle type can transport. When a user enters incorrect information, the system promptly provides prompts and guides the user to correct it.

[0087] When a user enters the loading / unloading address, the system calculates and returns the estimated transportation time in real time based on navigation information. By parsing the POI coordinates of the loading / unloading address, the system accurately identifies whether there are restricted areas. Furthermore, by combining the characteristics of the historical POI addresses of the cargo source address, the system prompts the user to confirm the restricted area handling plan, reducing driver confusion and improving the transparency of cargo source information.

[0088] By collecting users' historical behavior data, including other characteristics of the goods information posted by users online, a user profile is built. When users fill in the remarks of the goods information, the remarks of the most frequently posted goods information in history are given as supplementary information for the goods information.

[0089] The system performs a diagnostic check on the completeness of product information, verifies the information in real time, and scores each issue. If the total score exceeds a certain threshold, the product can be automatically listed without further review.

[0090] For product listings that do not meet the requirements for exemption from review, after posting, AI will be used to query the seller and verify the information. Once the seller has verified the information, the data will be automatically synchronized to the product listing, and a new query will be conducted before the product is automatically reviewed and listed.

[0091] The key point of this invention lies in the judgment logic of the scoring dimensions for cargo information, including information completeness scoring, accuracy scoring, question type scoring, and prediction of user historical behavior. Regarding the scoring of cargo information dimensions, real-time big data is needed for model training to collect historical behavior data and predict current user actions. Relying on extensive machine algorithm training, as data accumulates and improves, it allows for more accurate predictions of question diagnosis and recommendations. Simultaneously, it analyzes the differences in behavior between completed and incomplete cargo information, adjusts different model parameters, and, combined with modifications and adjustments made by cargo owners to cargo information, adjusts thresholds and review strategies in real time. Furthermore, regarding AI-assisted outbound calls to cargo owners, the uncertainty of cargo owner-machine interaction requires extensive model training of machine algorithms to understand the cargo owner's true intentions.

[0092] This invention helps improve the certainty for cargo owners in finding transportation information. After cargo owners independently publish their transportation information, intelligent recommendations update cargo data, reducing the opacity of transportation information and improving the completion rate of transportation. For transportation companies, with comprehensive cargo information, carriers can more clearly select cargo information they can handle, increasing the certainty of transportation and greatly reducing the possibility of cancellations due to information opacity. Regarding efficiency, through reasonable system rule settings, cargo that meets certain thresholds can be listed directly without manual review; only cargo that does not meet the thresholds requires manual review. This greatly improves manpower efficiency and reduces the problem of information not being exposed due to insufficient manpower for review.

[0093] Figure 2 This is a schematic diagram of the automatic verification system for cargo transportation information of the present invention. Figure 2 As shown, embodiments of the present invention also provide an automatic cargo transportation information verification system for implementing the above-described automatic cargo transportation information verification method. The automatic cargo transportation information verification system 5 includes:

[0094] The information collection module 51 collects cargo transportation information as information to be published. The cargo transportation information includes at least cargo type information, cargo weight information, cargo volume information, and required vehicle type information.

[0095] The vehicle model adaptation module 52 inputs the cargo type information, cargo weight information, and cargo volume information from the information to be published into a trained neural network to obtain recommended vehicle model information for cargo transportation information adaptation.

[0096] The parameter comparison module 53 determines whether each parameter in the first preset carrying parameter combination of the vehicle corresponding to the demand vehicle model information satisfies the condition that it is greater than or equal to the corresponding parameter in the second preset carrying parameter combination of the recommended vehicle model information. If yes, the information publishing module 54 is executed; otherwise, the feedback prompt module 55 is executed.

[0097] The information publishing module 54 publishes the information to be published, and matches it with the vehicle database based on the required vehicle model information as a filter, then returns the information of the vehicles that match the criteria.

[0098] The feedback prompt module 55 provides feedback prompts based on parameters that do not meet the conditions for the corresponding parameters.

[0099] The information update module 56 will return the cargo transportation information to the information collection module 51 after updating the cargo transportation information.

[0100] In a preferred embodiment, the vehicle model adaptation module 52 is configured to train the neural network through a massive amount of historical completed waybills, including cargo type information, cargo weight information, cargo volume information, and vehicle model information of the vehicle executing the waybill, but not limited thereto.

[0101] In a preferred embodiment, the vehicle model information includes vehicle length data, vehicle width data, length, width and height data of the cargo space, and maximum load data, but is not limited thereto.

[0102] In a preferred embodiment, the parameter comparison module 53 is configured to compare each parameter in the first preset transport parameter combination corresponding to the vehicle with the corresponding parameter in the second preset transport parameter combination corresponding to the recommended vehicle with the vehicle. If each parameter in the first preset transport parameter combination is greater than or equal to the corresponding parameter in the second preset transport parameter combination, the information publishing module 54 is executed; otherwise, the feedback prompt module 55 is executed, but this is not a limitation.

[0103] In a preferred embodiment, the cargo transportation information also includes the coordinates of points of interest (POIs) for loading and unloading cargo. The information publishing module 54 is configured to search the historical completed waybill database for a set of historical vehicle model information that was previously completed based on the POI coordinates. It obtains the intersection of parameters from a first preset combination of transport parameters for the required vehicle model information with the corresponding parameters from a third preset combination of transport parameters in the historical vehicle model information set. Using this intersection as a filtering condition, it performs matching in the vehicle database and returns information on vehicles that meet the matching criteria, but this is not a limitation.

[0104] In a preferred embodiment, the information publishing module 54 is further configured to collect vehicle information that matches the loading and unloading locations of waybills based on point-of-interest (POI) address coordinates, and count the number of times each loading and unloading location is used by the matching vehicle information. The number of times each loading and unloading location is used is sorted, and the top n locations with the most uses are selected as candidate parking spaces. The candidate parking spaces corresponding to the POI address coordinates are sent to the cargo owner or the vehicle owner who accepted the order, but this is not a limitation.

[0105] The automatic cargo transportation information verification system of the present invention can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching.

[0106] This invention also provides an automatic cargo transportation information verification device, including a processor and a memory storing executable instructions for the processor. The processor is configured to execute steps of an automatic cargo transportation information verification method by executing the executable instructions.

[0107] As shown above, the automatic cargo transportation information verification device of the present invention in this embodiment can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching.

[0108] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0109] Figure 3 This is a structural schematic diagram of the automatic cargo transportation information verification device of the present invention. See below for reference. Figure 3 To describe an electronic device 600 according to this embodiment of the present invention. Figure 3 The electronic device 600 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0110] like Figure 3 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0111] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0112] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0113] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0114] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0115] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0116] This invention also provides a computer-readable storage medium for storing a program, which, when executed, implements the steps of an automatic verification method for cargo transportation information. In some possible implementations, various aspects of this invention can also be implemented as a program product comprising program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the above-described method section of this specification according to various exemplary embodiments of the invention.

[0117] As shown above, the cargo transportation information automatic verification system of the present invention in this embodiment can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching.

[0118] Figure 4 This is a schematic diagram of the structure of the computer-readable storage medium of the present invention. (Reference) Figure 4 As shown, a program product 800 for implementing the above-described method according to an embodiment of the present invention is described. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0119] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0120] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0121] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0122] In summary, the purpose of this invention is to provide a method, system, device, and storage medium for automatic verification of cargo transportation information, which can verify the relationship of cargo transportation information, ensure the accuracy and reliability of cargo transportation information release, and improve the efficiency of vehicle-cargo matching.

[0123] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for automatically verifying cargo transportation information, characterized in that, Includes the following steps: S110. Collect cargo transportation information as information to be published. The cargo transportation information includes at least cargo type information, cargo weight information, cargo volume information, required vehicle type information, and point of interest address coordinates for loading and unloading cargo. S120. Input the cargo type information, cargo weight information, and cargo volume information from the information to be published into a trained neural network to obtain recommended vehicle model information that matches the cargo transportation information. S130. Determine whether each parameter in the first preset carrying parameter combination of the vehicle corresponding to the required vehicle model information satisfies the condition that it is greater than or equal to the corresponding parameter of the second preset carrying parameter combination of the recommended vehicle model information. If yes, proceed to step S140; otherwise, proceed to step S150. S140. Publish the information to be published, and match it in the vehicle database based on the required vehicle model information as a filtering condition, and provide feedback on the vehicle information that meets the match, including: searching the historical completed waybill database for a set of historical vehicle model information that was previously completed based on the point of interest address coordinate information; obtaining the intersection of parameters of the first preset transport parameter combination of the required vehicle model information and the corresponding parameters of the third preset transport parameter combination of the historical vehicle model information set; using the intersection as a filtering condition to match it in the vehicle database, and providing feedback on the vehicle information that meets the match; and S150. Feedback prompts are given based on the parameters that do not meet the conditions for the corresponding parameters.

2. The automatic verification method for cargo transportation information as described in claim 1, characterized in that, In step S120, the neural network is trained on a massive amount of historical completed waybills, which includes cargo type information, cargo weight information, cargo volume information, and vehicle type information of the vehicle executing the waybill.

3. The automatic verification method for cargo transportation information as described in claim 2, characterized in that, The vehicle information includes vehicle length data, vehicle width data, length, width and height data of the cargo space, and maximum load data.

4. The automatic verification method for cargo transportation information as described in claim 1, characterized in that, Step S130 includes the following steps: S131. Compare each parameter in the first preset transport parameter combination of the vehicle corresponding to the required vehicle model information with the corresponding parameter in the second preset transport parameter combination of the recommended vehicle model information. S132. Determine that each parameter in the first preset transport parameter combination is greater than or equal to the corresponding parameter in the second preset transport parameter combination. If yes, proceed to step S140; otherwise, proceed to step S150.

5. The automatic verification method for cargo transportation information as described in claim 1, characterized in that, Step S140 further includes: S144. Collect the matching vehicle information to complete the loading and unloading location of the waybill based on the point of interest address coordinate information, and count the number of times the matching vehicle information is used at each loading and unloading location. S145. Sort the loading and unloading positions by the number of times they are used, and obtain the top n positions with the most uses as alternative parking spaces; S146. Send the candidate parking spaces corresponding to the address coordinates of the points of interest to the cargo owner or the vehicle owner who accepted the order.

6. The automatic verification method for cargo transportation information as described in claim 1, characterized in that, Following step S150, the method further includes: S160. After updating the cargo transportation information, return the cargo transportation information to step S110.

7. An automatic cargo transportation information verification system, used to implement the automatic cargo transportation information verification method of claim 1, characterized in that, include: The information collection module collects cargo transportation information as information to be published. The cargo transportation information includes at least cargo type information, cargo weight information, cargo volume information, required vehicle type information, and point of interest address coordinates for loading and unloading cargo. The vehicle model adaptation module inputs the cargo type information, cargo weight information, and cargo volume information from the information to be published into a trained neural network to obtain recommended vehicle model information for the cargo transportation information. The parameter comparison module determines whether each parameter in the first preset carrying parameter combination of the vehicle corresponding to the required vehicle model information satisfies the condition that it is greater than or equal to the corresponding parameter in the second preset carrying parameter combination of the recommended vehicle model information. If yes, the information publishing module is executed; otherwise, the feedback prompt module is executed. The information publishing module publishes the information to be published and, based on the required vehicle model information as a filtering condition, matches it in the vehicle database and returns the vehicle information that meets the match. This includes: searching the historical completed waybill database for a set of vehicle model information that was previously completed based on the point of interest address coordinates; obtaining the intersection of parameters of the first preset transport parameter combination of the required vehicle model information and the corresponding parameters of the third preset transport parameter combination of the historical vehicle model information set; using the intersection as a filtering condition to match it in the vehicle database and returning the vehicle information that meets the match; and... The feedback and prompt module provides feedback and prompt information based on the parameters that do not meet the conditions for the corresponding parameters.

8. An automatic verification device for cargo transportation information, characterized in that, include: processor; A memory in which executable instructions of the processor are stored; The processor is configured to execute the steps of the automatic verification method for cargo transportation information according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium for storing a program, characterized in that, When the program is executed by the processor, it implements the steps of the automatic verification method for cargo transportation information as described in any one of claims 1 to 6.

Images