Logistics transportation settlement method based on Internet of Things

Abstract

The invention discloses a logistics transportation settlement method based on the Internet of Things, and relates to the technical field of logistics transportation, and the method comprises the steps: collecting data in real time through an Internet of Things sensor network, carrying out the edge processing, improving the real-time performance of data processing, building a transportation scene model in combination with the transportation data, weather and road condition dynamic variables, and carrying out the calculation of the transportation scene model. The energy consumption, the delay risk and the predicted arrival time state are dynamically simulated, the change in the transportation process is accurately predicted, and the limitation of a static model is made up; besides, variables such as delay duration and energy consumption standard exceeding are brought into charge calculation through a dynamic charge adjustment model, so that the charge settlement accuracy is remarkably improved, the charge can accurately reflect the actual cost in the transportation process, and the problems of simpleness and unfairness of charge calculation in a traditional method are solved; meanwhile, encryption storage and transparent sharing of cost information are carried out by adopting a block chain technology, settlement is automatically executed in combination with an intelligent contract, the risk of manual intervention is eliminated, and trust among multiple subjects is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics and transportation, and in particular to a logistics and transportation settlement method based on the Internet of Things. Background Art

[0002] With the continuous development of Internet of Things technology, it has gradually been applied to logistics and transportation settlement scenarios. The settlement method based on the Internet of Things collects data during the transportation of goods through sensors, and then transmits it to the cloud platform through the network for processing, which improves the visualization of the transportation process and data transparency.

[0003] Current logistics and transportation settlement schemes mostly calculate transportation costs through the weight of the goods, transportation distance and the unit price agreed in the contract. This method is simple and easy to implement, and has high execution efficiency under relatively stable conditions. In order to cope with dynamic changes in actual scenarios, some schemes introduce GPS positioning, electronic scales and manual adjustments to record additional costs (such as delays, overloads, etc.) or emergencies during transportation. However, these methods mostly rely on manual intervention, data updates lag, and are inefficient. In addition, disputes are easily caused by data asymmetry in multi-party collaboration. Therefore, there is an urgent need for a logistics and transportation settlement method based on the Internet of Things to solve such problems. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] The present invention provides a logistics and transportation settlement method based on the Internet of Things, which solves the problems of the current logistics and transportation settlement scheme, such as insufficient accuracy of dynamic adjustment and settlement in the case of data transmission delay or sensor failure; low adaptability in multimodal transport and cold chain logistics scenarios; lack of a unified and transparent settlement mechanism, prominent data asymmetry between various entities, and easy to cause disputes.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The embodiment of the present invention provides a logistics transportation settlement method based on the Internet of Things, which includes:

[0008] Step S1, collect transportation data in real time through the IoT sensor network and perform edge preprocessing, the transportation data includes load, location, temperature and humidity, and vehicle status; calculate the accumulated transportation distance based on the transportation data, and detect abnormal conditions, including overload and abnormal parking;

[0009] Step S2, based on the transportation data, the accumulated transportation distance and the detected abnormal status, combined with the weather and road condition information, a transportation scenario model is constructed to simulate the transportation status of the current vehicle, including the current location, estimated arrival time, energy consumption estimation and potential delay risk;

[0010] Step S3, based on the simulation results of step S2, construct a cost model to dynamically adjust the transportation cost strategy, incorporate key variables into the cost model, and recalculate the actual transportation cost;

[0011] In step S4, blockchain technology is used to transmit the recalculated transportation cost information to multiple parties, including the shipper, the carrier, and the third-party regulatory agency; settlement is automatically executed through smart contracts, bills are generated in real time, and all transactions and adjustment records are stored.

[0012] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the step of collecting transportation data in real time through the Internet of Things sensor network and performing edge preprocessing is:

[0013] Transportation data is collected through IoT sensors installed on vehicles and cargo. The data includes load, location, temperature and humidity, and vehicle status, which is defined as: {W(t), P(t), T(t), S(t)}, where t is the time point, W(t) is the load of the cargo at time point t, measured by an electronic scale, P(t) is the location of the vehicle at time point t, provided by GPS, T(t) is the ambient temperature and humidity, collected by a temperature and humidity sensor, and S(t) is the status of the vehicle at time point t.

[0014] Perform edge preprocessing and smooth load data formula:

[0015]

[0016] Among them, W smooth (t) is the load data after smoothing at time point t, n is the moving average window size, i is the index within the time window,

[0017] Calculate the cumulative transportation distance using the following formula:

[0018]

[0019] Among them, D total is the cumulative transportation distance, P i ,P i+1 is the position coordinate of time points i and i+1, n is the total number of time points, d(P i ,P i+1 ) is the geographical distance between two points.

[0020] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, in the step of edge preprocessing, the geographical distance calculation formula between two points is:

[0021]

[0022] Among them, R is the radius of the earth, Δφ is the latitude difference between the two points, Δλ is the longitude difference between the two points, and φ1 and φ2 are the latitudes of the two points.

[0023] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the steps of calculating the accumulated transportation distance based on the transportation data and detecting the abnormal state are as follows:

[0024] Perform abnormal status detection, including:

[0025] Overload detection, the detection formula is:

[0026] If W(t)>W max , then Flag overload =1, otherwise, Flag overload =0,

[0027] Among them, Flag overload W is the mark for overload. max is the load threshold,

[0028] Abnormal parking detection, the detection formula is:

[0029] If v(t)≈0 and ΔP(t)≈0, then Flag stop =1, otherwise Flag stop =0,

[0030] Where v(t) is the speed of the vehicle at time point t, ΔP(t) is the current position change,

[0031] Step S1 collects transportation data in real time through IoT sensors, calculates the cumulative transportation distance through edge computing, performs abnormal state detection, and distinguishes abnormal states.

[0032] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the steps of constructing a transportation scenario model based on transportation data, cumulative transportation distance and detected abnormal status, combined with weather and road condition information, and simulating the transportation status of the current vehicle are as follows:

[0033] The input of the transportation scenario model is X(r), which includes:

[0034] X(t)={W(t),P(t),T(t),S(t),D total ,W e (t),R c (t)},

[0035] Among them, W e (t) is the weather information at time point t, R c(t) is the road condition level at time point t,

[0036] To estimate energy consumption, the formula for calculating transportation energy consumption is:

[0037] E(t)=α1W(t)+α2v(t)+α3R c (t),

[0038] Among them, E(t) is the current transportation energy consumption, α1, α2, α3 are the weight coefficients of the energy consumption model, corresponding to the influence of load, speed and road condition level respectively.

[0039] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the step of simulating the transportation status of the current vehicle also includes:

[0040] To estimate the arrival time, the formula for calculating the estimated arrival time is:

[0041]

[0042] Among them, T ETA is the estimated arrival time, T current is the current time, d remaining is the remaining distance, v average is the expected average speed,

[0043] Calculate the delay risk. The delay risk probability calculation formula is:

[0044] P delay =f(W e (t),R c (t),v(t)),

[0045] Among them, P delay is the delay risk probability, f(·) is the function of the impact of weather, road conditions and speed on delay;

[0046] Step S2 constructs a transportation scenario model based on the input data, simulates the vehicle's current location, estimated arrival time, energy consumption estimation, and delay risk, and predicts the transportation status to provide support for cost adjustment.

[0047] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the key variables include delay duration and excessive energy consumption.

[0048] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the steps of constructing a cost model based on the simulation result of step S2 to dynamically adjust the transportation cost strategy, incorporating key variables into the cost model, and recalculating the actual transportation cost are as follows:

[0049] Calculate the basic fee. The basic fee calculation formula is:

[0050] C base =C0+β1D total +β2W(t),

[0051] Among them, C base is the basic transportation cost, C0 is the fixed cost, β1 and β2 are the weight coefficients in the cost model, which respectively represent the cost ratio of the cumulative transportation distance and load.

[0052] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the step of recalculating the actual transportation cost also includes:

[0053] Dynamically adjust the fee, the fee adjustment formula is:

[0054] C adjusted =C base +γ1ΔT+γ2ΔE,

[0055] Among them, C adjusted is the dynamically adjusted cost, ΔT is the delay time, ΔE is the energy consumption exceeding the standard, γ1 and γ2 are the cost weights of delay and energy consumption, and the final transportation cost C final The calculation formula is: C final =C adjusted ;

[0056] Step S3 dynamically adjusts the cost model, incorporates the delay duration and energy consumption excess variables into the cost adjustment logic, and makes the actual cost consistent with the dynamic changes in the transportation process.

[0057] As a preferred solution of the logistics transportation settlement method based on the Internet of Things described in the present invention, the step of using blockchain technology to transmit the recalculated transportation cost information to multiple parties is as follows:

[0058] The recalculated cost information is encrypted and stored on the blockchain and shared with shippers, carriers and regulators.

[0059] Smart contracts automatically execute the settlement process, including fund transfer and bill generation, and all transaction information is stored in the blockchain.

[0060] Step S4 uses blockchain technology to achieve transparent sharing of fee information and automatically completes settlement through smart contracts to improve efficiency and trust.

[0061] The beneficial effects of the present invention are as follows: the present invention collects data in real time through the Internet of Things sensor network and performs edge processing, thereby improving the real-time performance of data processing; secondly, a transportation scenario model is constructed by combining transportation data, weather and road condition dynamic variables, dynamically simulating energy consumption, delay risk and estimated arrival time status, accurately predicting changes in the transportation process, and making up for the limitations of static models; in addition, through a dynamic cost adjustment model, variables such as delay duration and excessive energy consumption are included in the cost calculation, which significantly improves the accuracy of cost settlement, enables the costs to accurately reflect the actual costs in the transportation process, and solves the problems of single cost calculation and unfairness in traditional methods; at the same time, blockchain technology is used for encrypted storage and transparent sharing of cost information, combined with smart contracts to automatically execute settlement, eliminating the risk of manual intervention and enhancing trust among multiple subjects. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 The figure is a flow chart of the flow transportation settlement method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] Example 1, reference Figure 1 This embodiment provides a logistics transportation settlement method based on the Internet of Things, comprising the following steps:

[0068] Step S1, collect transportation data in real time through the IoT sensor network and perform edge preprocessing, the transportation data includes load, location, temperature and humidity, and vehicle status; calculate the accumulated transportation distance based on the transportation data, and detect abnormal conditions, including overload and abnormal parking;

[0069] The steps of collecting transportation data in real time through the IoT sensor network and performing edge preprocessing are as follows:

[0070] Transportation data is collected through IoT sensors installed on vehicles and cargo. The data includes load, location, temperature and humidity, and vehicle status, which is defined as: {W(t), P(t), T(t), S(t)}, where t is the time point, W(t) is the load of the cargo at time point t, measured by an electronic scale, P(t) is the location of the vehicle at time point t, provided by GPS, T(t) is the ambient temperature and humidity, collected by a temperature and humidity sensor, and S(t) is the status of the vehicle at time point t.

[0071] Perform edge preprocessing and smooth load data formula:

[0072]

[0073] Among them, W smooth (t) is the load data after smoothing at time point t, n is the moving average window size, i is the index within the time window,

[0074] Calculate the cumulative transportation distance using the following formula:

[0075]

[0076] Among them, D total is the cumulative transportation distance, P i ,P i+1 is the position coordinate of time points i and i+1, n is the total number of time points, d(P i ,P i+1 ) is the geographical distance between two points,

[0077] In the edge preprocessing step, the geographic distance between two points is calculated as:

[0078]

[0079] Where R is the radius of the earth, Δφ is the latitude difference between the two points, Δλ is the longitude difference between the two points, and φ1 and φ2 are the latitudes of the two points;

[0080] The steps of calculating the accumulated transportation distance based on the transportation data and detecting abnormal conditions are as follows:

[0081] Perform abnormal status detection, including:

[0082] Overload detection, the detection formula is:

[0083] If W(t)>W max , then Flag overload =1, otherwise, Flag overload =0,

[0084] Among them, Flag overload W is the mark for overload. max is the load threshold,

[0085] Abnormal parking detection, the detection formula is:

[0086] If v(t)≈0 and ΔP(t)≈0, then Flag stop =1, otherwise Flag stop =0,

[0087] Where v(t) is the speed of the vehicle at time point t, ΔP(t) is the current position change,

[0088] Step S1 collects transportation data in real time through IoT sensors, calculates the cumulative transportation distance through edge computing, performs abnormal state detection, and distinguishes abnormal states;

[0089] Specifically, by collecting transportation data in real time through the IoT sensor network and combining it with edge computing for pre-processing, the cumulative transportation distance can be dynamically calculated and abnormal conditions can be detected during transportation, significantly shortening the delay time for information collection and processing, and realizing real-time monitoring and rapid response to transportation status, solving the problem of inaccurate settlement caused by data lag in traditional settlement methods.

[0090] Step S2, based on the transportation data, the accumulated transportation distance and the detected abnormal status, combined with the weather and road condition information, a transportation scenario model is constructed to simulate the transportation status of the current vehicle, including the current location, estimated arrival time, energy consumption estimation and potential delay risk;

[0091] Based on the transportation data, the accumulated transportation distance and the detected abnormal status, combined with the weather and road condition information, the transportation scenario model is constructed to simulate the transportation status of the current vehicle.

[0092] The input of the transportation scenario model is X(t), which includes:

[0093] X(t)={W(t),P(t),T(t),S(t),D total ,W e (t),R c (t)},

[0094] Among them, W e(t) is the weather information at time point t, R c (t) is the road condition level at time point t,

[0095] To estimate energy consumption, the formula for calculating transportation energy consumption is:

[0096] E(t)=α1W(t)+α2v(t)+α3R c (t),

[0097] Among them, E(t) is the current transportation energy consumption, α1, α2, α3 are the weight coefficients of the energy consumption model, corresponding to the influence of load, speed and road condition level respectively.

[0098] The steps of simulating the current transportation status of the vehicle also include:

[0099] To estimate the arrival time, the formula for calculating the estimated arrival time is:

[0100]

[0101] Among them, T ETA is the estimated arrival time, T current is the current time, d remaining is the remaining distance, v average is the expected average speed,

[0102] Calculate the delay risk. The delay risk probability calculation formula is:

[0103] P delay =f(W e (t),R c (t),v(t)),

[0104] Among them, P delay is the delay risk probability, f(·) is the function of the impact of weather, road conditions and speed on delay;

[0105] Step S2 builds a transportation scenario model based on the input data, simulates the current location of the vehicle, estimated arrival time, energy consumption estimation and delay risk, and predicts the transportation status to provide support for cost adjustment;

[0106] Specifically, in dynamic scenario modeling, the transportation scenario model is constructed by combining transportation data, accumulated distance, abnormal status, and external weather and road condition information; the energy consumption estimation, delay risk, and arrival time are simulated to adapt to the changing conditions in complex transportation scenarios, making logistics settlement closer to the actual transportation process.

[0107] Step S3, based on the simulation results of step S2, construct a cost model to dynamically adjust the transportation cost strategy, incorporate key variables into the cost model, and recalculate the actual transportation cost;

[0108] Key variables include delay duration and energy consumption exceeding the limit,

[0109] Based on the simulation results of step S2, a cost model is constructed to dynamically adjust the transportation cost strategy, key variables are incorporated into the cost model, and the steps of recalculating the actual transportation cost are as follows:

[0110] Calculate the basic fee. The basic fee calculation formula is:

[0111] C base =C0+β1D total +β2W(t),

[0112] Among them, C base is the basic transportation cost, C0 is the fixed cost, β1 and β2 are the weight coefficients in the cost model, which respectively represent the cost ratio of the cumulative transportation distance and load.

[0113] The steps to recalculate actual shipping costs also include:

[0114] Dynamically adjust the fee, the fee adjustment formula is:

[0115] C adjusted =C base +γ1ΔT+γ2ΔE,

[0116] Among them, C adjusted is the dynamically adjusted cost, ΔT is the delay time, ΔE is the excess energy consumption, γ1 and γ2 are the cost weights of delay and energy consumption, and the final transportation cost C final The calculation formula is: C final =C adjusted ;

[0117] Step S3 dynamically adjusts the cost model, incorporates the delay duration and energy consumption excess variables into the cost adjustment logic, and makes the actual cost consistent with the dynamic changes in the transportation process;

[0118] Specifically, based on the results of dynamic scenario modeling, a dynamic cost adjustment model is introduced, and key variables such as delay duration and excessive energy consumption are included in the cost calculation logic. The final cost not only reflects the basic transportation cost, but also takes into account the dynamic changes in the transportation process to make the settlement result fairer. Compared with the static cost model, it effectively solves the problem of unquantifiable dynamic factors and improves the accuracy and rationality of the settlement.

[0119] Step S4, using blockchain technology, transmits the recalculated transportation cost information to multiple parties, including the shipper, the carrier, and the third-party regulator; automatically executes settlement through smart contracts, generates bills in real time, and stores all transaction and adjustment records;

[0120] The steps to transmit the recalculated shipping cost information to multiple parties using blockchain technology are:

[0121] The recalculated cost information is encrypted and stored on the blockchain and shared with shippers, carriers and regulators.

[0122] Smart contracts automatically execute the settlement process, including fund transfer and bill generation, and all transaction information is stored in the blockchain.

[0123] Step S4 uses blockchain technology to achieve transparent sharing of fee information and automatically completes settlement through smart contracts to improve efficiency and trust;

[0124] Specifically, through blockchain technology, transportation cost information is transparently stored and shared, and multiple parties including shippers, carriers and regulators can access and verify relevant data; smart contracts further automate the cost settlement process, without human intervention from fund transfer to bill generation, thereby improving the credibility of the data.

[0125] In summary, the present invention collects data in real time through the Internet of Things sensor network and performs edge processing to improve the real-time performance of data processing. Secondly, it builds a transportation scenario model in combination with transportation data, weather and road condition dynamic variables, dynamically simulates energy consumption, delay risk and estimated arrival time status, accurately predicts changes in the transportation process, and makes up for the limitations of static models. In addition, through the dynamic cost adjustment model, variables such as delay duration and excessive energy consumption are included in the cost calculation, which significantly improves the accuracy of cost settlement, so that the cost can accurately reflect the actual cost in the transportation process, and solves the problem of single cost calculation and unfairness in traditional methods. At the same time, blockchain technology is used for encrypted storage and transparent sharing of cost information, combined with smart contracts to automatically execute settlement, eliminating the risk of manual intervention and enhancing trust among multiple subjects.

[0126] In addition, the solution provided by the present invention can automate the entire process in operation, from data collection to cost calculation and settlement, which can be completed automatically, greatly reducing the time cost of manual reconciliation and data verification, and has good scalability. It supports access to more data sources or upgrades settlement logic, adapts to the ever-changing needs of the logistics industry, and provides effective technical support for the modernization of the industry.

[0127] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A logistics transportation settlement method based on the Internet of Things, characterized by: include, Step S1, collect transportation data in real time through the IoT sensor network and perform edge preprocessing. The transportation data includes load, location, temperature and humidity, and vehicle status; Calculate the accumulated transport distance based on transport data and detect abnormal conditions, including overload and abnormal parking; Step S2, based on the transportation data, the accumulated transportation distance and the detected abnormal status, combined with the weather and road condition information, a transportation scenario model is constructed to simulate the transportation status of the current vehicle, including the current location, estimated arrival time, energy consumption estimation and potential delay risk; Step S3, based on the simulation results of step S2, construct a cost model to dynamically adjust the transportation cost strategy, incorporate key variables into the cost model, and recalculate the actual transportation cost; Step S4, using blockchain technology to transmit the recalculated transportation cost information to multiple parties, including the shipper, the carrier and the third-party regulatory agency; Settlements are automatically executed through smart contracts, bills are generated in real time, and all transactions and adjustment records are stored.

2. A logistics transportation settlement method based on the Internet of Things as claimed in claim 1, characterized in that: The steps of collecting transportation data in real time through the IoT sensor network and performing edge preprocessing are: Transportation data is collected through IoT sensors installed on vehicles and cargo. The data includes load, location, temperature and humidity, and vehicle status, which are defined as: {W(t), P(t), T(t), S(t)}, where t is the time point, W(t) is the load of the cargo at time point t, measured by an electronic scale, P(t) is the location of the vehicle at time point t, provided by GPS, T(t) is the ambient temperature and humidity, collected by a temperature and humidity sensor, and S(t) is the status of the vehicle at time point t. Perform edge preprocessing and smooth load data formula: Among them, W smooth (t) is the load data after smoothing at time point t, n is the moving average window size, i is the index within the time window, Calculate the cumulative transportation distance using the following formula: Among them, D total is the cumulative transportation distance, P i , P i+1 is the position coordinate of time points i and i+1, n is the total number of time points, d(P i , P i+1 ) is the geographical distance between two points.

3. A logistics transportation settlement method based on the Internet of Things as claimed in claim 2, characterized in that: In the step of edge preprocessing, the geographic distance between two points is calculated as follows: Among them, R is the radius of the earth, Δφ is the latitude difference between the two points, Δλ is the longitude difference between the two points, and φ1 and φ2 are the latitudes of the two points.

4. A logistics transportation settlement method based on the Internet of Things as claimed in claim 3, characterized in that: The steps of calculating the accumulated transportation distance based on the transportation data and detecting abnormal conditions are as follows: Perform abnormal status detection, including: Overload detection, the detection formula is: If W(t)>W max , then Flag overload =1, otherwise, Flag overload =0, Among them, Flag overload W is the mark for overload. max is the load threshold, Abnormal parking detection, the detection formula is: If v(t)≈0 and ΔP(t)≈0, then Flag stop =1, otherwise Flag stop =0, Among them, v(t) is the speed of the vehicle at time point t, and ΔP(t) is the current position change.

5. A logistics transportation settlement method based on the Internet of Things as claimed in claim 4, characterized in that: The steps of constructing a transportation scenario model based on transportation data, cumulative transportation distance and detected abnormal conditions, combined with weather and road condition information, and simulating the transportation status of the current vehicle are as follows: The input of the transportation scenario model is X(t), which includes: X(t)={W(t),P(t),T(t),S(t),D total ,W e (t),R c (t)}, Among them, W e (t) is the weather information at time point t, Rc(t) is the road condition level at time point t, To estimate energy consumption, the formula for calculating transportation energy consumption is: E(t)=α1W(t)+α2v(t)+α3R c (t), Among them, E(t) is the current transportation energy consumption, α1, α2, and α3 are the weight coefficients of the energy consumption model, corresponding to the influence of load, speed, and road condition level, respectively.

6. A logistics transportation settlement method based on the Internet of Things as claimed in claim 5, characterized in that: The step of simulating the current transportation state of the vehicle also includes: To estimate the arrival time, the formula for calculating the estimated arrival time is: Among them, T ETA is the estimated arrival time, T current is the current time, d remaining is the remaining distance, v average is the expected average speed, Calculate the delay risk. The delay risk probability calculation formula is: P delay =f(W e (t),R c (t),v(t)), Among them, P delay is the delay risk probability, and f(·) is the function of the impact of weather, road conditions and speed on delay.

7. A logistics transportation settlement method based on the Internet of Things as claimed in claim 6, characterized in that: The key variables include delay duration and excess energy consumption.

8. A logistics transportation settlement method based on the Internet of Things as claimed in claim 7, characterized in that: The steps of constructing a cost model based on the simulation result of step S2 to dynamically adjust the transportation cost strategy, incorporating key variables into the cost model, and recalculating the actual transportation cost are as follows: Calculate the basic fee. The basic fee calculation formula is: C base =C0+β1D total +β2W(t), Among them, C base is the basic transportation cost, C0 is the fixed cost, β1 and β2 are the weight coefficients in the cost model, which respectively represent the cost proportion of the cumulative transportation distance and load.

9. A logistics transportation settlement method based on the Internet of Things as claimed in claim 8, characterized in that: The step of recalculating the actual transportation cost also includes: Dynamically adjust the fee, the fee adjustment formula is: C adjusted =C base +γ1ΔT+γ2ΔE, Among them, C adjusted is the dynamically adjusted cost, ΔT is the delay time, ΔE is the energy consumption exceeding the standard, γ1 and γ2 are the cost weights of delay and energy consumption, and the final transportation cost C final The calculation formula is: C final =C adjusted .

10. A logistics transportation settlement method based on the Internet of Things as claimed in claim 9, characterized in that: The steps of using blockchain technology to transmit the recalculated transportation cost information to multiple parties are: The recalculated cost information is encrypted and stored on the blockchain and shared with shippers, carriers and regulators. Smart contracts automatically execute the settlement process, including fund transfer and bill generation, and all transaction information is stored in the blockchain.

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