Transportation risk judgment method and system based on big data

Abstract

The invention is suitable for the technical field of network freight supervision, and particularly relates to a transportation risk judgment method and system based on big data, and the method comprises the steps: determining the data and standards of transportation risk judgment through determining the target waybill data and a transportation risk judgment strategy, and enabling the data and standards to be used for subsequent risk analysis and evaluation; analyzing the target waybill data, and determining a waybill data state of the target waybill data; according to the transportation risk judgment strategy and the waybill data state of the target waybill data, determining a risk judgment execution strategy of a target waybill corresponding to the target waybill data, and adopting the most appropriate countermeasures for different conditions; and based on the risk judgment execution strategy of the target waybill corresponding to the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is judged, the risk judgment result of the target waybill corresponding to the target waybill data is obtained, various potential risks in the transportation process are systematically managed and reduced, and the supervision cost is reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of online freight supervision, and in particular, relates to a transportation risk determination method and system based on big data. Background Art

[0002] Online freight, also known as digital logistics or online freight, refers to a new logistics model that uses the Internet platform and technical means to connect cargo owners (shippers) and carriers (transportation service providers) to achieve information matching, scheduling optimization, tracking management and other services during the transportation of goods. This model greatly improves logistics efficiency, reduces costs, and provides participants with a more transparent and convenient service experience.

[0003] Traditional online waybill risk assessment mainly relies on manual inspections, which consumes a lot of human resources and is highly subjective. It lacks effective supervision methods, resulting in inadequate supervision of freight waybills, making it difficult to ensure the real-time and accuracy of the results, and increasing supervision costs and risks. Summary of the invention

[0004] The embodiments of the present application provide a transportation risk determination method and system based on big data, which can solve the problem of inadequate supervision, increased supervision costs and risks due to the lack of effective supervision measures in the process of risk determination of online waybills.

[0005] In a first aspect, an embodiment of the present application provides a method for determining transportation risk based on big data, comprising: Determine the target waybill data and the transportation risk determination strategy; wherein the transportation risk determination strategy is used to determine the risk situation of the target waybill corresponding to the target waybill data; Analyze the target waybill data to determine the waybill data status of the target waybill data; wherein the waybill data status is used to reflect the status of the target waybill data in the backend database; Determine a risk judgment execution strategy for a target waybill corresponding to the target waybill data according to the transport risk judgment strategy and the waybill data status of the target waybill data; Based on the risk judgment execution strategy of the target waybill corresponding to the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is judged to obtain the risk judgment result of the target waybill corresponding to the target waybill data.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The transportation risk determination method based on big data provided in the embodiment of the present application determines the target waybill data and the transportation risk determination strategy, and clarifies the data and standards for transportation risk determination for subsequent risk analysis and evaluation. The target waybill data is analyzed to determine the waybill data status of the target waybill data, so as to more accurately understand the actual situation of the target waybill data. According to the transportation risk determination strategy and the waybill data status of the target waybill data, the risk determination execution strategy of the target waybill corresponding to the target waybill data is determined to ensure that the most appropriate response measures are adopted for different situations. Based on the risk determination execution strategy of the target waybill corresponding to the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is determined, and the risk determination result of the target waybill corresponding to the target waybill data is obtained, so as to systematically manage and reduce various potential risks in the transportation process and reduce the supervision cost.

[0007] In a second aspect, an embodiment of the present application provides a transportation risk determination system based on big data, which is used to implement the method of the first aspect above, and the system includes: A confirmation unit, used to determine the target waybill data and the transportation risk determination strategy; wherein the transportation risk determination strategy is used to determine the risk situation of the target waybill corresponding to the target waybill data; A status unit, used to analyze the target waybill data and determine the waybill data status of the target waybill data; wherein the waybill data status is used to reflect the status of the target waybill data in the backend database; A strategy unit, configured to determine a risk judgment execution strategy for a target waybill corresponding to the target waybill data according to the transport risk judgment strategy and the waybill data status of the target waybill data; A determination unit is used to determine the transportation risk of the target waybill corresponding to the target waybill data based on the risk determination execution strategy of the target waybill corresponding to the target waybill data, and obtain the risk determination result of the target waybill corresponding to the target waybill data.

[0008] In the third aspect, an embodiment of the present application provides a transportation risk determination device based on big data, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the above aspects when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on a transportation risk determination device based on big data, the transportation risk determination device based on big data executes the method of any one of the above aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 It is a flow chart of a transportation risk determination method based on big data provided by an embodiment of the present application; Figure 2 It is a structural diagram of a transportation risk determination system based on big data provided by an embodiment of the present application; Figure 3 It is a structural diagram of a transportation risk determination device based on big data provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] Traditional online waybill risk assessment mainly relies on manual inspections, which consumes a lot of human resources and is highly subjective. It lacks effective supervision methods, resulting in inadequate supervision of freight waybills, making it difficult to ensure the real-time and accuracy of the results, and increasing supervision costs and risks.

[0020] To solve the above problems, the embodiments of the present application provide a transportation risk determination method and system based on big data. In this method, by determining the target waybill data and the transportation risk determination strategy, the data and standards for transportation risk determination are clarified for subsequent risk analysis and evaluation. The target waybill data is analyzed to determine the waybill data status of the target waybill data, so as to more accurately understand the actual situation of the target waybill data. According to the transportation risk determination strategy and the waybill data status of the target waybill data, the risk determination execution strategy of the target waybill corresponding to the target waybill data is determined to ensure that the most appropriate response measures are adopted for different situations. Based on the risk determination execution strategy of the target waybill corresponding to the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is determined, and the risk determination result of the target waybill corresponding to the target waybill data is obtained, so as to systematically manage and reduce various potential risks in the transportation process and reduce the supervision cost.

[0021] The transportation risk determination method based on big data provided in the embodiment of the present application can be applied to the transportation risk determination device based on big data. At this time, the transportation risk determination device based on big data is the executor of the transportation risk determination method based on big data provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the transportation risk determination device based on big data.

[0022] For example, transportation risk determination equipment based on big data may include, but is not limited to, desktop computers, smart screens, smart TVs, handheld devices with wireless communication functions, computing devices, computers, laptops, etc.

[0023] In order to better understand the transportation risk determination method based on big data provided in the embodiment of the present application, the specific implementation process of the transportation risk determination method based on big data provided in the embodiment of the present application is exemplarily introduced below.

[0024] Figure 1 A schematic flow chart of a transportation risk determination method based on big data provided by an embodiment of the present application is shown. The transportation risk determination method based on big data includes: S100, determining target waybill data and a transportation risk determination strategy; wherein the transportation risk determination strategy is used to determine the risk situation of the target waybill corresponding to the target waybill data.

[0025] It can be understood that the waybill data that needs to be risk-judged can be obtained from the preset database, and the waybill data that needs to be risk-judged is the target waybill data. The transportation risk judgment strategy is a multi-level framework that can judge whether the target waybill corresponding to the target waybill data has potential risks based on the characteristics of the waybill data and various judgment rules. The transportation risk judgment strategy can not only rely on a single data source or model, but also combine multiple related parameters for comprehensive analysis, including price, vehicle type, vehicle length, etc. By integrating these data, a more accurate risk assessment can be made according to different waybill situations.

[0026] In a possible implementation, S100, determining target waybill data and a transportation risk determination strategy includes: S110, obtaining transportation risk determination parameters of waybill data; wherein the transportation risk determination parameters are used to set a transportation risk determination strategy.

[0027] It can be understood that the transportation risk determination parameters of the waybill data can be manually added by entering the account and password in the user graphical interface to enter the background. The transportation risk determination parameters may include parameter types pre-set by various components, such as price parameters, vehicle type parameters, vehicle length parameters, mileage parameters, driving parameters, etc.

[0028] S120, binding the transport risk determination parameters to the preset risk components to obtain the basic strategy, risk control strategy model and interception strategy model of the waybill data.

[0029] It can be understood that the preset risk component is a data module or tool that has been pre-defined and configured, and is used to handle specific types of risks after receiving and binding specific parameters. The preset risk components have different functions and operating logic according to different business needs and risk types. The preset risk components are automated and can respond to changes in waybill data in real time. When the risk determination parameters of the waybill change, the calculation results of each component can be automatically updated, and the risk strategy can be adjusted according to the new data, so that the risk determination can respond quickly to various risks and avoid delays and errors that may be caused by manual intervention. The interception strategy model executes automated interception measures when the risk of the waybill data exceeds the preset threshold.

[0030] Exemplarily, the risk control strategy model may include a speed assessment component, a driving time scoring component, a trajectory assessment component, etc. The speed assessment component will perform a corresponding assessment on the speed of all trucks, and divide the speed of all vehicles into [low speed risk], [medium speed risk], and [high speed risk] according to the parameter settings. If the speed of the truck in the waybill is risky, the waybill will be intercepted and payment operations will not be allowed for the waybill. The driving time scoring component will perform a corresponding assessment on the driving time scoring components of all trucks, and divide the driving time scoring components of all vehicles into [time safety], [medium time risk], and [high driving time risk] according to the parameter settings. If the driving time of the truck in the waybill is risky, the waybill will be intercepted and payment operations will not be allowed for the waybill. The trajectory scoring component will perform a corresponding assessment on the driving trajectories of all trucks, and divide the driving trajectories of all vehicles into [low trajectory risk], [medium trajectory risk], and [high trajectory risk] according to the parameter settings. If the track of the truck in the waybill is risky, the waybill will be intercepted and payment operations will not be allowed for the waybill.

[0031] Optionally, the transport risk determination parameters include price parameters, vehicle type parameters and vehicle length parameters; the risk components include price components and vehicle type components; in step S120, the transport risk determination parameters are bound to the preset risk components to obtain the basic strategy of the waybill data, including: S121, binding the vehicle type parameter and the vehicle length parameter to the preset vehicle type component to obtain the first basic strategy of the waybill data.

[0032] It can be understood that the basic strategy for waybill data can be generated by binding the received transportation risk judgment parameters with preset risk components (such as price components and vehicle type components), such as price parameters, vehicle type parameters and vehicle length parameters. By rationally configuring and binding different risk components, a comprehensive risk assessment model with early warning and interception functions is formed, with the ultimate goal of achieving accurate risk judgment and effective risk control. After binding the vehicle type parameters and vehicle length parameters to the preset vehicle type components, a perfect vehicle type component judgment strategy is obtained, which is the first basic strategy for judging waybill data.

[0033] S122, binding the price parameter to a preset price component to obtain a second basic strategy for waybill data.

[0034] It can be understood that after receiving the price parameters, the price parameters can be bound to the preset price components. The core function of the price component is to evaluate the match between the transportation cost and the market standard to identify potential price risks. The price component analyzes the price based on the bound price parameters, which is the second basic strategy. If the price is too high or too low, the second basic strategy can take further risk control measures. If the price is within a reasonable range, it is confirmed to be risk-free.

[0035] S123: Determine the first basic policy and the second basic policy of the waybill data as the basic policies of the waybill data.

[0036] It can be understood that the two basic strategies (the first basic strategy and the second basic strategy) generated above can be integrated to form a basic strategy for forming waybill data. The basic strategy will make risk judgments based on vehicle type, vehicle length and price.

[0037] S130, determining the basic strategy of the waybill data, the risk control strategy model and the interception strategy model as the transportation risk determination strategy.

[0038] It can be understood that after determining the basic strategy, risk control strategy model and interception strategy model of the waybill data, the basic strategy, risk control strategy model and interception strategy model are used together as a comprehensive transportation risk determination strategy. The basic strategy evaluates the risk through simple parameter binding, while the risk control strategy model and interception strategy model conduct a deeper analysis and risk control of the waybill based on more complex rules and algorithms.

[0039] S200, analyzing the target waybill data to determine the waybill data status of the target waybill data; wherein the waybill data status is used to reflect the status of the target waybill data in the backend database.

[0040] It can be understood that the waybill data status refers to whether the waybill data has been generated in the back-end database during the processing, that is, the waybill data status reflects the status of the target waybill data in the back-end database, so the target waybill data can be divided into a first state and a second state. The first state is the state of the waybill data that has been created and saved on the user side, but has not yet been sent to the back-end or created in the database, that is, it has not been generated; the second state is the sign of the data creation on the user side, and the waybill data status that has been sent to the back-end or successfully created in the database.

[0041] S300, determining a risk judgment execution strategy for a target waybill corresponding to the target waybill data according to the transport risk judgment strategy and the waybill data status of the target waybill data.

[0042] It can be understood that by analyzing the status of the target waybill data and combining it with the transportation risk determination strategy, the risk determination execution strategy of the target waybill can be accurately determined. When the waybill data status of the target waybill data is different, different types of strategies are automatically selected for execution. The strategies executed under different waybill data status are the risk determination execution strategies corresponding to the waybill data status. In the initial stage of waybill generation, the basic risk strategy and interception strategy model are mainly relied on, while in the transportation process, the risk control strategy model is turned to improve the safety and efficiency of the transportation process, and the risks in the transportation process are minimized by real-time adjustment and application of appropriate risk assessment methods.

[0043] In a possible implementation, the waybill data state includes a first state and a second state; S300, according to the transportation risk determination strategy and the waybill data state of the target waybill data, determining a risk determination execution strategy of the target waybill corresponding to the target waybill data, including: S310, when the waybill data state of the target waybill data is the first state, determine the risk judgment execution strategy of the target waybill corresponding to the target waybill data as the basic strategy and the interception strategy model according to the transportation risk judgment strategy.

[0044] It can be understood that the first state is that the waybill data has been created and saved on the user side, but has not yet been sent to the backend or created in the database, that is, the ungenerated state. In this state, the target waybill data is automatically judged and identified for risk based on the basic strategy and interception strategy model.

[0045] S320, when the waybill data state of the target waybill data is the second state, determine, according to the transportation risk determination strategy, that the risk determination execution strategy of the target waybill corresponding to the target waybill data is a risk control strategy model.

[0046] It can be understood that when the waybill data status of the target waybill data is in the second status, that is, the mark of the user-side data creation, and the waybill data status that has been sent to the backend or successfully created in the database will be evaluated on the target waybill risk corresponding to the target waybill data according to the set risk control strategy model during and after the target waybill data.

[0047] S400, based on the risk judgment execution strategy of the target waybill corresponding to the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is judged to obtain the risk judgment result of the target waybill corresponding to the target waybill data.

[0048] It can be understood that based on the set risk judgment execution strategy and target waybill data, the transportation risk of the waybill can be evaluated and the final risk judgment result can be obtained. By applying models (such as basic strategies, risk control strategy models, interception strategy models, etc.) to analyze the waybill data in multiple dimensions, it is evaluated whether the target waybill corresponding to the target waybill data has risks.

[0049] In a possible implementation, S400 determines the transportation risk of the target waybill corresponding to the target waybill data based on the risk judgment execution strategy of the target waybill corresponding to the target waybill data, and obtains the risk judgment result of the target waybill corresponding to the target waybill data, including: S410, based on the basic strategy and interception strategy model of the target waybill data, determine the transportation risk of the target waybill corresponding to the target waybill data, and obtain a risk determination result of the target waybill corresponding to the target waybill data.

[0050] It can be understood that the target waybill data can be matched with the basic strategy and interception strategy model, and the risk of the target waybill data can be determined. If the waybill data meets the preset security standards, the target waybill data is allowed to enter the backend database for creation and distribution; if it does not meet the standards, the waybill is intercepted through further interception strategy model, so that the corresponding target waybill cannot be created in the backend database, and the transportation order cannot be distributed.

[0051] Optionally, S410, based on the basic strategy and interception strategy model of the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is determined to obtain a risk determination result of the target waybill corresponding to the target waybill data, including: S411, performing calculations based on the target waybill data to obtain a vehicle model mileage-price curve corresponding to the target waybill data; wherein the vehicle model mileage-price curve is used to reflect the relationship between the transportation cost of the target waybill data and the mileage.

[0052] It can be understood that the vehicle type mileage-price curve of the target waybill can be obtained by calculating the mileage, price, and vehicle type information of the target waybill data. The vehicle type mileage-price curve can reflect the transportation cost of different vehicle types in the target waybill data as the mileage changes, and then evaluate the authenticity risk of the target waybill data.

[0053] S412, determining curve parameter information of the vehicle model mileage-price curve based on the vehicle model mileage-price curve corresponding to the target waybill data.

[0054] It can be understood that the key parameters of the curve can be extracted based on the generated vehicle model mileage-price curve, and these parameters will be used to determine whether the target waybill meets the preset risk criteria. Curve parameter information refers to the mathematical parameters that describe the vehicle model mileage-price curve, such as slope, intercept, etc. Curve parameter information helps to evaluate the relationship between different vehicle models and transportation mileage.

[0055] S413, based on the basic strategy and interception strategy model of the target waybill data, when the curve parameter information of the vehicle model mileage-price curve conforms to the basic strategy and the interception strategy model, obtain the first risk judgment result of the target waybill corresponding to the target waybill data.

[0056] It can be understood that the curve parameters of the vehicle model mileage-price curve of the target waybill are compared with the preset basic strategy to check whether it meets the specified risk standards. If the curve parameters meet the predetermined basic strategy, the data of the waybill is considered to meet the normal transportation requirements and is judged as "compliant with the rules". It is a real order, that is, the first risk judgment result is obtained, indicating that the target waybill corresponding to the target waybill data does not have authenticity risk, and the target waybill data is allowed to be uploaded to the back-end database and distributed.

[0057] Optionally, S410, based on the basic strategy and interception strategy model of the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is determined to obtain a risk determination result of the target waybill corresponding to the target waybill data, including: S414, when the curve parameter information of the vehicle model mileage-price curve does not conform to the basic strategy and interception strategy model, the target waybill data is intercepted and the curve parameter information is threshold detected to obtain a second risk determination result of the target waybill corresponding to the target waybill data.

[0058] It can be understood that if the curve parameter meets the "non-compliance" judgment of the basic strategy and interception strategy model, the interception mechanism will be enabled, and the target waybill data will be judged as "non-compliant with the rules", that is, the second risk judgment result is obtained. The purpose of the interception mechanism is to ensure that the waybill data that does not comply with the rules will not continue to circulate or execute. At this time, the target waybill data will be suspended and the target waybill data will not be allowed to be uploaded to the backend database.

[0059] In a possible implementation, S400 determines the transportation risk of the target waybill corresponding to the target waybill data based on the risk judgment execution strategy of the target waybill corresponding to the target waybill data, and obtains the risk judgment result of the target waybill corresponding to the target waybill data, including: S420, based on the risk control strategy model of the target waybill data, determine the transportation risk of the target waybill corresponding to the target waybill data, and obtain a risk determination result of the target waybill corresponding to the target waybill data.

[0060] It can be understood that for the target waybill data that has been uploaded to the back-end database, the transportation risk of the target waybill data can be judged according to the set risk control strategy model during and after the target waybill data to obtain the risk judgment result of the target waybill corresponding to the target waybill data.

[0061] Optionally, S420, based on the risk control strategy model of the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is determined to obtain a risk determination result of the target waybill corresponding to the target waybill data, including: S421, determining the travel data information of the target waybill data according to the target waybill data.

[0062] It is understood that relevant driving data information can be extracted from the target waybill. Driving data information refers to specific information about truck transportation that is directly related to the transportation process of the target waybill, and may include the vehicle's driving route, speed, driving time, driver, and other information. Driving data information can come from multiple data sources, which may include: GPS device: provides real-time location, vehicle speed, driving trajectory and other information.

[0063] On-board sensors: measure the real-time status of the vehicle, including fuel consumption, engine speed, braking status, etc.

[0064] Transportation Management System (TMS): records basic information of transportation tasks, vehicle scheduling information, etc.

[0065] Third-party data sources: such as traffic flow information and real-time road conditions provided by traffic monitoring systems.

[0066] S422, analyzing the driving data information based on the risk control strategy model of the target waybill data, and determining the first driving risk result, the second driving risk result, and the third driving risk result of the target waybill corresponding to the target waybill data.

[0067] It can be understood that the risk control strategy model will perform threshold detection on the driving speed and driving time information of the target waybill data, that is, check whether these data meet the predetermined risk control requirements according to the corresponding component parameters in the set risk control strategy model. The risk control strategy model will detect the trajectory of the vehicle to determine whether the trajectory of the freight vehicle meets the requirements set in the risk control strategy model.

[0068] Exemplarily, the speed assessment component will evaluate the speed of all trucks accordingly, and divide the speed of all vehicles into [safe speed], [low speed risk], [medium speed risk], and [high speed risk] according to the parameter settings, that is, the first driving risk result is obtained. If the speed of a truck in a waybill is risky, the waybill will be intercepted and payment operations will not be allowed for the waybill. The driving time scoring component will evaluate the driving time scoring components of all trucks accordingly, and divide the driving time scoring components of all vehicles into [safe time], [medium time risk], and [high driving time risk] according to the parameter settings, that is, the second driving risk result is obtained.

[0069] Exemplarily, S422, analyzing the driving data information based on the risk control strategy model of the target waybill data, and determining a first driving risk result, a second driving risk result, and a third driving risk result of the target waybill corresponding to the target waybill data, including: S4221, obtaining the third-party driving data corresponding to the target waybill data.

[0070] It is understandable that in addition to the driving data information of the driver's APP trajectory and GPS trajectory in the internal target waybill data, third-party traffic data can also be obtained through API or data interface. Obtain external driving data related to the target waybill from a third-party system or database. Third-party driving data may include information such as vehicle speed, route, traffic conditions, etc. provided by a third party, which is used to compare and verify with the driving data of the target waybill. For example, GPS data or road condition information provided by a third party can help determine whether there is abnormal driving in the target waybill.

[0071] S4222, based on the driving data information of the target waybill data and the third-party driving data corresponding to the target waybill data, determine the driving speed information and driving time information of the target waybill data.

[0072] It can be understood that after obtaining the target waybill data and its corresponding third-party driving data, the target waybill data and its corresponding third-party driving data can be combined to further analyze and extract the driving speed information and driving time information of the target waybill. The speed of the target waybill at each moment during its driving process can be extracted based on the GPS data in the target waybill data and the speed data provided by a third party. Third-party driving data can provide supplementary information, especially when the GPS data is incomplete or there are errors, the third-party speed information can be used for comparison and verification. The starting and end point information of the target waybill data and the real-time road condition information provided by a third party can be combined to determine the actual time consumed during the transportation process. The traffic conditions or estimated time provided by the third-party data can help confirm the difference between the actual transportation time and the expected time.

[0073] S4223, based on the risk control strategy model of the target waybill data, threshold detection is performed on the driving speed information and driving time information of the target waybill data respectively, to obtain a first driving risk result and a second driving risk result of the target waybill corresponding to the target waybill data.

[0074] It can be understood that driving speed information and driving time information are key risk determination indicators. Based on driving speed information and driving time information, the risk control strategy model will perform threshold detection on each data point through preset risk components to evaluate whether the target waybill data has potential risks. The risk control model will set a safety threshold for vehicle speed, such as the upper limit of speeding. If the speed of the target waybill exceeds this threshold, it will be judged as [high speed risk]. The risk control model will determine whether the transportation time is reasonable. If the transportation time is too long, it may indicate delays or abnormal vehicle operation, which is judged as [high driving time risk]; if the transportation time is too short, it may indicate that the vehicle is at risk of fast driving, or illegal operations have been performed during transportation, which is judged as [high driving time risk].

[0075] S4224, based on the driving data information of the target waybill data and the third-party driving data corresponding to the target waybill data, determine the third driving risk result of the target waybill corresponding to the target waybill data.

[0076] It can be understood that through comprehensive analysis of the driving data information of the target waybill data and the third-party driving data, the third-party driving risk result of the target waybill corresponding to the target waybill data can be evaluated and determined, and through cross-validation of multi-dimensional data, the risk situations that may occur during the transportation process can be quantified.

[0077] Exemplarily, S4224, based on the driving data information of the target waybill data and the third-party driving data corresponding to the target waybill data, determining the third driving risk result of the target waybill corresponding to the target waybill data includes: S42241, based on the driving data information of the target waybill data, determine the first longitude and latitude trajectory and the second longitude and latitude trajectory of the target waybill data; wherein the first longitude and latitude trajectory is used to reflect the relationship between the longitude and latitude coordinates of the freight vehicle in the target waybill data and the time, and the second longitude and latitude trajectory is used to reflect the relationship between the longitude and latitude coordinates of the freight vehicle in the third-party driving data and the time.

[0078] It can be understood that the driving information of the target waybill data is obtained through the vehicle real-time positioning device (such as the GPS system), which contains the correspondence between the timestamp and the longitude and latitude coordinates. Based on the driving data information of the target waybill data, a first longitude and latitude trajectory can be generated, that is, the trajectory of the actual position of the freight vehicle changing over time during the entire transportation process. The third-party driving data provides a reference trajectory independent of the target vehicle, which can come from the traffic management platform or other data sharing sources, and is used to construct a second longitude and latitude trajectory, reflecting the environment related to the target vehicle's transportation path or the location information of the reference vehicle. By parsing the two sets of data, the longitude and latitude coordinates of each time point can be extracted separately, so that the data points of the two trajectories have the same or close timestamps to ensure the accuracy of subsequent analysis.

[0079] S42242, calculate the coordinate difference of each corresponding data point in the first longitude and latitude track and the second longitude and latitude track to obtain the coordinate difference curve and the start and end point offsets of the target waybill data; wherein the start and end point offsets are used to reflect the actual situation of the freight start and end positions of the target waybill data.

[0080] It can be understood that by analyzing the difference between the first longitude and latitude track and the second longitude and latitude track, the deviation of the vehicle's driving trajectory can be quantified. The formula for calculating the coordinate difference is as follows: Δx i =x i(1) −x i(2) ,Δy i =y i(1) −y i(2) Among them, (x i(1) ,y i(1) ) is the i-th point of the first longitude and latitude trajectory, (x i(2) ,y i(2) ) is the i-th point of the second longitude and latitude track. By calculating the difference between each pair of data points, a set of coordinate difference data (Δx i ,Δy i ). All the difference points can be plotted as a curve that changes over time, namely the coordinate difference curve, which intuitively shows the deviation during the entire driving process. The start-end point offset is to calculate the difference in the starting point coordinates and the end point coordinates between the first longitude and latitude track and the second longitude and latitude track, which is used to measure whether there is a deviation between the freight starting point track and the freight end point track in the overall transportation track process.

[0081] S42243, screening the coordinate difference curve of the target waybill data to determine the trajectory deviation rate of the target waybill data.

[0082] It can be understood that the trajectory deviation rate is used to measure the overall deviation between the vehicle's driving path and the expected path. To calculate the trajectory deviation rate, it is first necessary to filter out points in the coordinate difference curve that are greater than a set threshold. The filtering conditions can be: , that is, the point whose offset distance is greater than the preset distance. The number of all offset points can be counted, recorded as the offset point capacity N, and the total mileage L of the target trajectory can be calculated to determine the trajectory deviation rate, trajectory deviation rate = N / L. The trajectory deviation rate reflects the frequency and magnitude of the freight vehicle's deviation from the established path.

[0083] S42244, based on the trajectory deviation rate and the start and end point offsets of the target waybill data, determine the third driving risk result of the target waybill corresponding to the target waybill data.

[0084] It can be understood that after obtaining the trajectory deviation rate and start-end point offsets of the target waybill data, the trajectory deviation rate and start-end point offsets can be judged by a threshold value through a preset driving trajectory component to obtain a risk assessment of the driving trajectory of the target waybill data, that is, the third driving risk result of the target waybill corresponding to the target waybill data is determined.

[0085] S423, obtaining a risk determination result of the target waybill corresponding to the target waybill data according to the first driving risk result, the second driving risk result, and the third driving risk result of the target waybill corresponding to the target waybill data.

[0086] It can be understood that the various driving risk results obtained will eventually output the total risk determination result of the waybill. The risk determination result is derived based on the comprehensive results of each individual risk result. For example, if the first driving risk result shows [safe speed], and the second driving risk result shows [high risk of driving time], then the final risk determination result is "high risk". If there is a risk in any result, there is a risk. Further interception measures will be taken for the target waybill data, ultimately achieving systematic management and reducing various potential risks in the transportation process and reducing regulatory costs.

[0087] Corresponding to the transportation risk determination method based on big data in the above embodiment, the embodiment of the present application also provides a transportation risk determination system based on big data, and each unit of the system can implement each step of the transportation risk determination method based on big data. Figure 2 A structural block diagram of a transportation risk determination system based on big data provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0088] Reference Figure 2, the transportation risk determination system based on big data includes: A confirmation unit, used to determine the target waybill data and the transportation risk determination strategy; wherein the transportation risk determination strategy is used to determine the risk situation of the target waybill corresponding to the target waybill data; A status unit, used to analyze the target waybill data and determine the waybill data status of the target waybill data; wherein the waybill data status is used to reflect the status of the target waybill data in the backend database; A strategy unit, configured to determine a risk judgment execution strategy for a target waybill corresponding to the target waybill data according to the transport risk judgment strategy and the waybill data status of the target waybill data; A determination unit is used to determine the transportation risk of the target waybill corresponding to the target waybill data based on the risk determination execution strategy of the target waybill corresponding to the target waybill data, and obtain the risk determination result of the target waybill corresponding to the target waybill data.

[0089] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0090] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0091] The present application also provides a transportation risk determination device based on big data. Figure 3 This is a schematic diagram of the structure of a transportation risk determination device based on big data provided by an embodiment of the present application. Figure 3 As shown, the transportation risk determination device 6 based on big data of this embodiment includes: at least one processor 60 ( Figure 3 Only one is shown), at least one memory 61 ( Figure 3Only one is shown) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the transportation risk determination device 6 based on big data implements the steps in any of the above-mentioned transportation risk determination method embodiments based on big data, or implements the functions of each unit in the above-mentioned system embodiments.

[0092] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the transportation risk determination device 6 based on big data.

[0093] The transportation risk determination device based on big data may include but is not limited to desktop computers, smart screens, smart TVs, handheld devices with wireless communication functions, computing devices, computers, laptop computers, etc. The transportation risk determination device based on big data may include a processor 60 and a memory 61. Those skilled in the art will understand that Figure 3 It is only an example of the transportation risk determination device 6 based on big data, and does not constitute a limitation of the transportation risk determination device 6 based on big data. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, buses, etc.

[0094] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0095] In some embodiments, the memory 61 may be an internal storage unit of the transportation risk determination device 6 based on big data, such as a hard disk or memory of the transportation risk determination device 6 based on big data. In other embodiments, the memory 61 may also be an external storage device of the transportation risk determination device 6 based on big data, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the transportation risk determination device 6 based on big data. Further, the memory 61 may also include both the internal storage unit of the transportation risk determination device 6 based on big data and an external storage device. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0096] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0097] An embodiment of the present application provides a computer program product. When the computer program product is run on a transportation risk determination device based on big data, the transportation risk determination device based on big data implements the steps in any of the above-mentioned method embodiments.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the transportation risk determination device based on big data, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0099] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0100] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In the embodiments provided in the present application, it should be understood that the disclosed transportation risk determination system based on big data / transportation risk determination device and method based on big data can be implemented in other ways. For example, the above-described transportation risk determination system based on big data / transportation risk determination device based on big data embodiments are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0102] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A transportation risk determination method based on big data, characterized in that: include: Determine the target waybill data and the transportation risk determination strategy; wherein the transportation risk determination strategy is used to determine the risk situation of the target waybill corresponding to the target waybill data; Analyze the target waybill data to determine the waybill data status of the target waybill data; wherein the waybill data status is used to reflect the status of the target waybill data in the backend database; Determine a risk judgment execution strategy for a target waybill corresponding to the target waybill data according to the transport risk judgment strategy and the waybill data status of the target waybill data; Based on the risk judgment execution strategy of the target waybill corresponding to the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is judged to obtain the risk judgment result of the target waybill corresponding to the target waybill data; Wherein, the determination of target waybill data and transportation risk determination strategy includes: Acquire the transport risk determination parameters of the waybill data; wherein the transport risk determination parameters are used to set the transport risk determination strategy; Binding the transport risk determination parameters to the preset risk components to obtain the basic strategy, risk control strategy model and interception strategy model of the waybill data; The basic strategy of waybill data, the risk control strategy model and the interception strategy model are determined as a transportation risk determination strategy.

2. The transportation risk determination method based on big data according to claim 1, characterized in that: The transportation risk determination parameters include price parameters, vehicle type parameters and vehicle length parameters; the risk components include price components and vehicle type components; The transport risk determination parameters are bound to the preset risk components to obtain the basic strategy for waybill data, including: Binding the vehicle type parameter and the vehicle length parameter to a preset vehicle type component to obtain a first basic strategy for waybill data; Binding the price parameter to a preset price component to obtain a second basic strategy for waybill data; The first basic policy and the second basic policy of the waybill data are determined as basic policies of the waybill data.

3. The transportation risk determination method based on big data as claimed in claim 2 is characterized in that: The waybill data status includes a first status and a second status; Determining a risk judgment execution strategy for a target waybill corresponding to the target waybill data according to the transport risk judgment strategy and the waybill data status of the target waybill data includes: When the waybill data state of the target waybill data is the first state, determining, according to the transportation risk determination strategy, the risk determination execution strategy of the target waybill corresponding to the target waybill data as the basic strategy and the interception strategy model; When the waybill data state of the target waybill data is the second state, determining, according to the transportation risk determination strategy, that the risk determination execution strategy of the target waybill corresponding to the target waybill data is the risk control strategy model; And / or, the risk judgment execution strategy based on the target waybill corresponding to the target waybill data determines the transportation risk of the target waybill corresponding to the target waybill data to obtain the risk judgment result of the target waybill corresponding to the target waybill data, including: Based on the basic strategy of the target waybill data and the interception strategy model, the transportation risk of the target waybill corresponding to the target waybill data is determined to obtain a risk determination result of the target waybill corresponding to the target waybill data; And / or, the risk judgment execution strategy based on the target waybill corresponding to the target waybill data determines the transportation risk of the target waybill corresponding to the target waybill data to obtain the risk judgment result of the target waybill corresponding to the target waybill data, including: Based on the risk control strategy model of the target waybill data, the transportation risk of the target waybill corresponding to the target waybill data is determined to obtain a risk determination result of the target waybill corresponding to the target waybill data.

4. The method for determining transportation risk based on big data according to claim 3, characterized in that: The basic strategy based on the target waybill data and the interception strategy model, determining the transportation risk of the target waybill corresponding to the target waybill data, and obtaining the risk determination result of the target waybill corresponding to the target waybill data, includes: Calculate according to the target waybill data to obtain a vehicle type mileage-price curve corresponding to the target waybill data; wherein the vehicle type mileage-price curve is used to reflect the relationship between the transportation cost of the target waybill data and the mileage; Determining curve parameter information of the vehicle type mileage-price curve based on the vehicle type mileage-price curve corresponding to the target waybill data; Based on the basic strategy of the target waybill data and the interception strategy model, when the curve parameter information of the vehicle model mileage-price curve conforms to the basic strategy and the interception strategy model, a first risk judgment result of the target waybill corresponding to the target waybill data is obtained.

5. The method for determining transportation risk based on big data according to claim 4, characterized in that: The basic strategy based on the target waybill data and the interception strategy model, determining the transportation risk of the target waybill corresponding to the target waybill data, and obtaining the risk determination result of the target waybill corresponding to the target waybill data, includes: When the curve parameter information of the vehicle model mileage-price curve does not conform to the basic strategy and the interception strategy model, the target waybill data is intercepted and the curve parameter information is threshold detected to obtain a second risk judgment result of the target waybill corresponding to the target waybill data.

6. The method for determining transportation risk based on big data according to claim 3, characterized in that: The risk control strategy model based on the target waybill data determines the transportation risk of the target waybill corresponding to the target waybill data to obtain a risk determination result of the target waybill corresponding to the target waybill data, including: Determining the travel data information of the target waybill data according to the target waybill data; Analyze the driving data information based on the risk control strategy model of the target waybill data to determine a first driving risk result, a second driving risk result, and a third driving risk result of the target waybill corresponding to the target waybill data; A risk determination result of a target waybill corresponding to the target waybill data is obtained according to the first driving risk result, the second driving risk result, and the third driving risk result of the target waybill data.

7. The method for determining transportation risk based on big data according to claim 6, characterized in that: The risk control strategy model based on the target waybill data analyzes the driving data information to determine a first driving risk result, a second driving risk result, and a third driving risk result of the target waybill corresponding to the target waybill data, including: Obtaining third-party driving data corresponding to the target waybill data; Determine the driving speed information and driving time information of the target waybill data based on the driving data information of the target waybill data and the third-party driving data corresponding to the target waybill data; Based on the risk control strategy model of the target waybill data, threshold detection is performed on the driving speed information and the driving time information of the target waybill data to obtain a first driving risk result and a second driving risk result of the target waybill corresponding to the target waybill data; Based on the driving data information of the target waybill data and the third-party driving data corresponding to the target waybill data, a third driving risk result of the target waybill corresponding to the target waybill data is determined.

8. The method for determining transportation risk based on big data according to claim 7, characterized in that: The determining, based on the driving data information of the target waybill data and the third-party driving data corresponding to the target waybill data, a third driving risk result of the target waybill corresponding to the target waybill data comprises: Based on the driving data information of the target waybill data, determine a first longitude and latitude track and a second longitude and latitude track of the target waybill data; wherein the first longitude and latitude track is used to reflect the relationship between the longitude and latitude coordinates of the freight vehicle in the target waybill data and the time, and the second longitude and latitude track is used to reflect the relationship between the longitude and latitude coordinates of the freight vehicle in the third-party driving data and the time; Calculate the coordinate difference of each corresponding data point in the first longitude and latitude track and the second longitude and latitude track to obtain the coordinate difference curve and the start and end point offset of the target waybill data; wherein the start and end point offset is used to reflect the actual situation of the freight start and end positions in the target waybill data; Screening the coordinate difference curve of the target waybill data to determine the trajectory deviation rate of the target waybill data; Based on the trajectory deviation rate and the start-end point offset of the target waybill data, a third driving risk result of the target waybill corresponding to the target waybill data is determined.

9. A transportation risk determination system based on big data, characterized in that: For implementing the method according to any one of claims 1 to 8, the system comprises: A confirmation unit, used to determine the target waybill data and the transportation risk determination strategy; wherein the transportation risk determination strategy is used to determine the risk situation of the target waybill corresponding to the target waybill data; A status unit, used to analyze the target waybill data and determine the waybill data status of the target waybill data; wherein the waybill data status is used to reflect the status of the target waybill data in the backend database; A strategy unit, configured to determine a risk judgment execution strategy for a target waybill corresponding to the target waybill data according to the transport risk judgment strategy and the waybill data status of the target waybill data; A determination unit is used to determine the transportation risk of the target waybill corresponding to the target waybill data based on the risk determination execution strategy of the target waybill corresponding to the target waybill data, and obtain the risk determination result of the target waybill corresponding to the target waybill data.

10. A transportation risk determination device based on big data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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