Intelligent cleaning logistics transportation monitoring management system

Abstract

The invention discloses an intelligent clean logistics transportation monitoring management system, and belongs to the technical field of logistics transportation management. An intelligent clean logistics transportation monitoring management system comprises a data acquisition layer, a data processing layer, a supervision platform layer and an application function layer. The data acquisition layer comprises an identity recognition module, a sensor module, an environment sensing module and a driving behavior module and is used for acquiring environment parameters, vehicle states and driving behavior data in a transportation process in real time; the data processing layer is used for cleaning, classifying and analyzing the collected data and then storing the data into a database; the supervision platform layer is used for calculating the score of the logistics transportation and the clean transportation according to the real-time state of the logistics transportation recorded by the database; the application function layer is used for providing path optimization and driving behavior optimization for follow-up vehicle driving according to the scores of logistics transportation and clean transportation.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics transportation management. More specifically, it relates to a smart clean logistics transportation monitoring and management system. Background Art

[0002] In current society, there is increasing emphasis on environmental protection, and the requirements for heavy industry emission reduction measures are constantly rising. Pollution problems in the logistics transportation process are becoming increasingly prominent. Vehicles fueled by diesel and gasoline emit a large amount of pollutants during operation, such as carbon monoxide, hydrocarbons, nitrogen oxides, particulate matter, etc. When vehicles transporting bulk goods such as coal, ore, and building materials are driving, the wheels rolling on the ground may also raise dust, affecting air quality. These pollutants can cause serious air pollution; logistics transportation vehicles, especially large trucks, tractors, etc., have large engine powers and generate relatively large noises during driving; these noises will interfere with residents, schools, hospitals, etc. along the road, affecting people's normal life, study, and work; Therefore, intelligent means are urgently needed in the logistics transportation process to improve environmental protection performance.

[0003] Existing logistics management systems mostly focus on efficiency improvement and lack comprehensive monitoring of environmental protection performance; taking a clean transportation vehicle control system for steel enterprises (CN202211393455.5) disclosed in Chinese invention patent literature as an example, this invention has basic functions such as collecting, displaying, allocating, switching, triggering control, recording, analyzing, and warning vehicle information, automatically determining the emission level to be executed according to the vehicle transportation purpose and the type of transported goods, and conducting vehicle exhaust supervision, automatically monitoring and recording the license plate number of the vehicle and the vehicle emission stage, meeting the standard requirements of clean transportation vehicles; however, this system fails to comprehensively consider multi-dimensional pollution factors such as dust and noise, resulting in incomplete environmental protection monitoring and making it difficult to achieve all-round environmental protection optimization in the transportation process.

[0004] Therefore, we need a smart clean logistics transportation monitoring and management system that can comprehensively monitor multi-dimensional pollution such as exhaust gas, dust, and noise. Summary of the Invention

[0005] The purpose of the present invention is to provide a smart clean logistics transportation monitoring and management system, which can collect data on multi-dimensional pollution factors, provide intelligent route planning and optimize driving behavior through data processing and monitoring management, and can quantitatively evaluate the clean transportation performance in real time according to the transportation process, achieving the effect of clean transportation.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A smart clean logistics transportation monitoring and management system of the present invention includes a data acquisition layer, a data processing layer, a supervision platform layer, and an application function layer.

[0008] The data acquisition layer includes an identity recognition module, a sensor module, an environment perception module, and a driving behavior module, which are used to collect environmental parameters, vehicle status, and driving behavior data during transportation in real time.

[0009] The data processing layer is used to clean, classify, and analyze the collected data and then store it in the database.

[0010] The supervision platform layer is used to calculate the score of clean transportation for this logistics transportation according to the real-time status of the logistics transportation recorded in the database.

[0011] The application function layer is used to provide route optimization and driving behavior optimization for subsequent vehicle driving according to the score of clean transportation of the logistics transportation.

[0012] As a further improvement of the present invention, the identity recognition module collects vehicle driver identity information, license plate number information, vehicle number information, and vehicle model information, which are used to accurately identify the vehicle and the driver to ensure error-free data traceability.

[0013] As a further improvement of the present invention, the sensor module includes an exhaust gas monitoring sensor, an OBD interface module, a load sensor, and an energy consumption monitoring module; the exhaust gas monitoring sensor is used to monitor NO x , PM, and CO exhaust gas data in real time; the OBD interface module is used to obtain the engine speed; the load sensor is used to detect the cargo weight; the energy consumption monitoring module is used to record the fuel consumption; by collecting and monitoring the exhaust gas emissions, engine speed, cargo weight, and fuel consumption data in real time during the logistics transportation process, the logistics transportation management system can more accurately evaluate the environmental performance of the transportation process.

[0014] As a further improvement of the present invention, the environment perception module includes a GPS positioning terminal and a dust perception terminal; the GPS positioning terminal is used to record the vehicle route, select the positions of several driving distances and obtain the number of residents within a range of 500 m in diameter centered on the vehicle at this position; the dust perception terminal is used to sense the positions of several driving distances and obtain the dust concentration within a range of 5 m in diameter centered on the vehicle at this position.

[0015] As a further improvement of the present invention, the driving behavior module includes an acceleration sensor and an idle time counting module; the acceleration sensor module is used to identify and record the hard acceleration time and hard braking time, where the hard acceleration is that the engine speed rises by more than 1500 RPM / s within 1 second, and the hard braking is that the engine speed drops by more than 900 RPM / s within 1 second; the idle time counting module is used to count the idle time of the vehicle, and the idle driving is that the engine speed remains at 500 - 800 RPM.

[0016] As a further improvement of the present invention, the data processing layer includes a multi-source data fusion module, an abnormal data cleaning engine, and a distributed storage architecture. The multi-source data fusion module is used to perform spatio-temporal alignment on environmental parameters, vehicle status, and driving behavior data during transportation; the abnormal data cleaning engine detects and eliminates invalid data, outliers, or error data through the isolation forest algorithm; the distributed storage architecture stores the cleaned data using a time-series database to ensure data integrity and consistency.

[0017] As a further improvement of the present invention, the scoring calculation formula for clean transportation in the supervision and evaluation layer is: Where W1 represents the actual effective freight volume; W max represents the maximum approved load of the vehicle; E1 represents the vehicle's benchmark fuel consumption; E x represents the actual fuel consumption of the vehicle; P represents the pollution parameter, and the larger the pollution parameter, the higher the pollution level; α, β, γ represent the module weight coefficients. The value range of α is 0.2 - 0.4, and it increases with the increase of the load influence degree. The value range of β is 0.3 - 0.5, and it increases with the increase of the fuel consumption influence degree. The value range of γ is 0.3 - 0.5, and it increases with the increase of the pollution parameter influence degree; k1, k2 represent the non-linear adjustment indexes. The range of k1 is 0.5 - 1.5, which is adjusted by the transportation scenario, and the range of k2 is 0.5 - 1.5, which is adjusted by cost and benefit.

[0018] As a further improvement of the present invention, the calculation formula for the pollution parameter in clean transportation is: Where A CO represents the real-time monitored value of the CO emission concentration; A xCO represents the industry standard value of the CO emission concentration; represents the real-time monitored value of the NO x emission concentration; represents the industry standard value of the NO x emission concentration; A PM represents the real-time monitored value of the PM emission concentration; A xPM represents the industry standard value of the PM emission concentration; B1 represents the dust concentration within a 5m diameter range centered on the vehicle; B x represents the reference value of the road dust load limit standard; C1 represents the number of residents within a 500m diameter range centered on the vehicle; C x represents the average number of residents within a 500m diameter range in this area; T1 represents the rapid acceleration time; T2 represents the rapid braking time; T3 represents the total idling time; T 总 represents the total driving time; ω1 represents the CO emission concentration weight coefficient, with a range of 0.1 - 0.2, and it increases with the increase of the CO influence degree on the pollution parameter; ω2 represents the NO x emission concentration weight coefficient, with a range of 0.4 - 0.6, and it increases with the increase of the NOx The influence degree on pollution parameters increases; ω3 represents the weight coefficient of PM emission concentration, with a range of 0.3 - 0.5, increasing with the influence degree of PM on pollution parameters; m represents the weight coefficient of dust concentration on clean transportation, with a range of 0.1 - 0.3; n represents the weight coefficient of resident aggregation degree on clean transportation, with a range of 0.3 - 0.5; μ1 represents the weight coefficient of sudden acceleration on clean transportation, with a range of 0.1 - 0.2; μ2 represents the weight coefficient of sudden braking on clean transportation, with a range of 0.1 - 0.3; μ3 represents the weight coefficient of idling on clean transportation, with a range of 0.2 - 0.4.

[0019] As a further improvement of the present invention, the application function layer includes an intelligent scheduling center, a dynamic route optimization module, and a driving behavior optimization module; the intelligent scheduling center is used to receive the feedback of the transportation result from the supervision platform layer and issue scheduling instructions to the dynamic route optimization module and the driving behavior optimization module; the dynamic route optimization module avoids dense areas based on the score of clean transportation, and the driving behavior optimization module optimizes the driving habits of drivers based on the score of clean transportation.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: A smart clean logistics transportation monitoring and management system involved in the present invention can collect environmental parameters, vehicle status, and driving behavior data during the transportation process in real time, calculate the clean transportation score, and provide route optimization and driving behavior optimization based on the score, comprehensively monitoring multi-dimensional pollution factors, thereby comprehensively improving the environmental protection effect of the logistics transportation system; through the combined application of a tail gas monitoring sensor, an OBD interface module, a load sensor, and an energy consumption monitoring module, comprehensive monitoring of tail gas emissions, engine speed, cargo weight, and fuel consumption during the logistics transportation process is realized. The real-time collection and monitoring of these data enable the logistics transportation management system to more accurately evaluate the environmental protection performance of the transportation process; by integrating the GPS positioning terminal and the dust sensing terminal, comprehensive monitoring of the vehicle's driving route and its surrounding environment can be achieved, thereby reducing pollution to the surrounding environment during the transportation process; through the collaborative work of the acceleration sensor and the idling timing module, comprehensive monitoring and optimization of driving behavior are realized, reducing fuel consumption and tail gas emissions caused by sudden acceleration and sudden braking, extending the service life of the vehicle while reducing maintenance costs; by setting reasonable weight coefficients and non-linear adjustment indices, the influence of each parameter on the clean transportation score can be flexibly adjusted. While being able to evaluate the cleanliness of the transportation process, it can also provide a basis for subsequent route optimization and driving behavior optimization, further enhancing the environmental protection performance of logistics transportation. Description of the Drawings

[0021] Figure 1 Schematic diagram of a smart clean logistics transportation monitoring and management system of the present invention;

[0022] Figure 2 This is a schematic diagram of the data processing layer of a smart clean logistics transportation monitoring and management system of the present invention. Specific implementation manners

[0023] Specific Example 1: Please refer to Figure 1 - Figure 2 A smart clean logistics transportation monitoring and management system, including a data acquisition layer, a data processing layer, a supervision platform layer, and an application function layer.

[0024] As Figure 2 shown, the data acquisition layer includes an identity recognition module, a sensor module, an environmental perception module, and a driving behavior module, which are used to collect environmental parameters, vehicle status, and driving behavior data during transportation in real time.

[0025] The data processing layer is used to clean, classify, and analyze the collected data and then store it in the database.

[0026] The supervision platform layer is used to calculate the score of clean transportation for this logistics transportation according to the real-time status of the logistics transportation recorded in the database.

[0027] The application function layer is used to provide route optimization and driving behavior optimization for subsequent vehicle driving according to the score of clean transportation for the logistics transportation.

[0028] Specifically, the identity recognition module collects vehicle driver identity information, license plate number information, vehicle number information, and vehicle model information, which are used to accurately identify the vehicle and the driver to ensure error-free data traceability.

[0029] Specifically, the identity recognition module can be implemented by using RFID technology or biometric technology. By embedding RFID tags in the vehicle and the driver's identity card, the relevant information can be read by using an RFID reader or the driver's biometric characteristics can be collected for identity verification; these technical means can be used alone or in combination to improve the reliability and security of the system.

[0030] Specifically, the sensor module includes an exhaust gas monitoring sensor, an OBD interface module, a load sensor, and an energy consumption monitoring module; the exhaust gas monitoring sensor is used to monitor NO x , PM, and CO exhaust gas data in real time; the OBD interface module is used to obtain the engine speed; the load sensor is used to detect the cargo weight; the energy consumption monitoring module is used to record the fuel consumption; by collecting and monitoring the exhaust gas emissions, engine speed, cargo weight, and fuel consumption data during the logistics transportation process in real time, the logistics transportation management system can more accurately evaluate the environmental protection performance of the transportation process.

[0031] Specifically, the exhaust gas monitoring sensor can be a laser sensor, which measures the gas concentration by using the scattering or absorption characteristics of a laser beam passing through a gas sample; the OBD interface module is signal-connected to the vehicle's electronic control unit through a standard OBD-II interface to obtain the parameter of the engine speed; the load sensor can be a strain gauge sensor to detect the cargo weight, and the strain gauge sensor determines the weight by measuring the resistance change on the strain gauge; the energy consumption monitoring module can be realized by installing a flow meter on the fuel pipeline or by reading the fuel consumption data in the vehicle ECU.

[0032] Specifically, the environmental perception module includes a GPS positioning end and a dust perception end; the GPS positioning end is used to record the vehicle route, select the positions of several driving distances and obtain the number of residents within a range of 500 m in diameter centered on the vehicle at this position, so as to avoid the impact of vehicle driving noise on populated areas; the dust perception end is used to sense the positions of several driving distances and obtain the dust concentration within a range of 5 m in diameter centered on the vehicle at this position, so as to avoid the impact of dust caused by transportation on the road.

[0033] Specifically, the GPS positioning end can obtain the accurate position of the vehicle in real time through satellite positioning technology, record the vehicle's driving route, and count the number of residents within a range of 500 meters in diameter centered on the vehicle at this position by calling the map API or other third-party data; the dust perception end can be installed on the vehicle chassis, and a laser dust meter can be used to monitor the dust concentration within 5 m around the vehicle in real time to ensure the accuracy and real-time of the data.

[0034] Specifically, the driving behavior module includes an acceleration sensor and an idle time counting module; the acceleration sensor module is used to identify and record the hard acceleration time and hard braking time. The hard acceleration means that the engine speed rises by more than 1500 RPM / s within 1 second, and the hard braking means that the engine speed drops by more than 900 RPM / s within 1 second; reducing the hard acceleration and hard braking duration is used to reduce fuel consumption and exhaust emissions; the idle time counting module is used to count the vehicle idle time, and the idle driving means that the engine speed remains at 500 - 800 RPM; reducing the idle time reduces unnecessary pollutant emissions, helps to achieve a cleaner transportation process, and can also extend the service life of the vehicle.

[0035] Specifically, the acceleration sensor monitors the change of the engine speed in real time and records the start and end times of hard acceleration and the start and end times of hard braking; the idle time counting module is connected to the vehicle's engine control unit (ECU), obtains the engine speed data in real time, and starts timing when the engine speed is within the range of 500 - 800 RPM; thus, it can more accurately identify and record the behaviors of hard acceleration, hard braking and idle driving.

[0036] Specifically, the data processing layer includes a multi-source data fusion module, an abnormal data cleaning engine, and a distributed storage architecture. The multi-source data fusion module is used to perform spatio-temporal alignment on environmental parameters, vehicle status, and driving behavior data during transportation. This process can be achieved by synchronizing data from different sources using GPS information and timestamps. The abnormal data cleaning engine detects and eliminates invalid data, outliers, or error data through the Isolation Forest algorithm, which is an unsupervised learning method based on tree structure and is suitable for detecting outliers in high-dimensional data. The distributed storage architecture uses a time series database to store the cleaned data. A time series database is a database specifically designed to process time series data and can efficiently store and retrieve time-based event data to ensure data integrity and consistency.

[0037] Specifically, the calculation formula for pollution parameters in clean transportation is as follows: Where A CO represents the real-time monitoring value of the CO emission concentration; A xCO represents the industry standard value of the CO emission concentration; represents the real-time monitoring value of the NO x emission concentration; represents the industry standard value of the NO x emission concentration; A PM represents the real-time monitoring value of the PM emission concentration; A xPM represents the industry standard value of the PM emission concentration; B1 represents the dust concentration within a 5m radius centered on the vehicle; B x represents the reference value of the road dust load limit; C1 represents the number of residents within a 500m radius centered on the vehicle; C x represents the average number of residents within a 500m diameter area of this location; T1 represents the hard acceleration time; T2 represents the hard braking time; T3 represents the total idling time; T 总 represents the total driving time; ω1 represents the CO emission concentration weight coefficient, ranging from 0.1 - 0.2, increasing with the influence degree of CO on pollution parameters; ω2 represents the NO x emission concentration weight coefficient, ranging from 0.4 - 0.6, with the increase of NO xThe influence degree of pollution parameters increases; ω3 represents the weight coefficient of PM emission concentration, ranging from 0.3 to 0.5, increasing with the influence degree of PM on pollution parameters; m represents the weight coefficient of dust concentration on clean transportation, ranging from 0.1 to 0.3; n represents the weight coefficient of resident aggregation degree on clean transportation, ranging from 0.3 to 0.5; μ1 represents the weight coefficient of rapid acceleration on clean transportation, ranging from 0.1 to 0.2; μ2 represents the weight coefficient of rapid braking on clean transportation, ranging from 0.1 to 0.3; μ3 represents the weight coefficient of idling on clean transportation, ranging from 0.2 to 0.4; for example, A measured by the exhaust gas monitoring sensor CO = 5 g / kWh, A PH = 0.008 g / kWh, and according to the technical requirements of heavy-duty diesel vehicles, A xCO = 6 g / kWh, B1 = 1.8 g / m 3 , and according to the urban road design specification, the reference value of the road dust load limit standard is B x = 1.5 g / m 3 ; C1 = 120 people, and C is taken as x = 80 people according to the average regional distribution of the local population; the engine speed is obtained through the OBD interface module to get T1 = 0.05 h, T2 = 0.2 h, T3 = 0.1 h, while T 总 = 2 h; take ω1 = 0.18, ω2 = 0.42, ω3 = 0.4; m = 0.3, indicating that the dust concentration has a greater impact on clean transportation, n = 0.4, indicating that the resident aggregation degree has a moderate impact on clean transportation; μ1 = 0.13, μ2 = 0.24, μ3 = 0.35, indicating that the rapid acceleration of this type of transport vehicle has a small impact on clean transportation, and the rapid braking and idling have a small impact on clean transportation; then the calculation formula for pollution parameters in clean transportation is:

[0038] Specifically, the scoring calculation formula for clean transportation in the supervision and evaluation layer is: where W1 represents the actual effective cargo volume; W max represents the maximum rated load of the vehicle; E1 represents the vehicle's benchmark fuel consumption; E xRepresents the actual fuel consumption of the vehicle; P represents the pollution parameter, and the larger the pollution parameter, the higher the pollution level; α, β, and γ represent the module weight coefficients. The value range of α is 0.2 - 0.4 and increases with the increase of the load influence degree. The value range of β is 0.3 - 0.5 and increases with the increase of the fuel consumption influence degree. The value range of γ is 0.3 - 0.5 and increases with the increase of the pollution parameter influence degree; k1 and k2 represent the non-linear adjustment indices. The range of k1 is 0.5 - 1.5 and is adjusted by the transportation scenario. The range of k2 is 0.5 - 1.5 and is adjusted by the cost and benefit; for example, W1 = 8 tons, W max = 12 tons. If more empty loads of goods are allowed, take k1 = 0.8; E1 = 0.2 L / km, E x = 0.22 L / km. Affected by the cost and benefit, if we want to stimulate fuel savings during the driving process, take k2 = 1.2; take α = 0.25, β = 0.44, γ = 0.31, P = 1.84; then the scoring calculation formula for clean transportation is:

[0039] Specifically, the application function layer includes an intelligent scheduling center, a dynamic route optimization module, and a driving behavior optimization module; the intelligent scheduling center is used to receive the feedback of the transportation results from the supervision platform layer, adopt machine learning algorithms to predict possible traffic conditions and environmental changes, and issue the optimal adjustment and scheduling plan to the dynamic route optimization module and the driving behavior optimization module in advance through artificial intelligence algorithms and big data analysis technologies; the dynamic route optimization module dynamically adjusts the driving route of the vehicle based on the score of clean transportation and in combination with real-time geographic information systems, traffic flow models, and environmental monitoring data to avoid traffic congestion and high-pollution areas. The driving behavior optimization module monitors the operation behavior of the driver in real time through in-vehicle terminal devices, provides personalized driving suggestions and training to the driver based on the score of clean transportation and the driver's behavior data, and reminds the driver to improve driving habits by means of voice prompts or vibration feedback, etc., reducing rapid acceleration, rapid braking, and idle time, and further enhancing the driver's environmental awareness and driving skills.

[0040] The user data and other data involved in this application are all obtained with full consent and authorization, and the collection, use, and processing of relevant information all comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0041] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent cleaning logistics transportation monitoring and management system, characterized in that: It includes a data acquisition layer, a data processing layer, a supervision platform layer, and an application function layer; The data acquisition layer includes an identity recognition module, a sensor module, an environmental perception module, and a driving behavior module, which are used to collect environmental parameters, vehicle status, and driving behavior data during transportation in real time; The data processing layer is used to clean, classify, and analyze the collected data and then store it in a database; The supervision platform layer is used to calculate the score of clean transportation for this logistics transportation according to the real-time status of logistics transportation recorded in the database; The application function layer is used to provide route optimization and driving behavior optimization for subsequent vehicle driving according to the score of clean transportation of logistics transportation.

2. The intelligent cleaning logistics transportation monitoring and management system according to claim 1, wherein: The identity recognition module collects vehicle driver identity information, license plate number information, vehicle number information, and vehicle model information, which are used to accurately identify the vehicle and the driver.

3. The intelligent cleaning logistics transportation monitoring and management system according to claim 1, wherein: The sensor module includes an exhaust gas monitoring sensor, an OBD interface module, a load sensor, and an energy consumption monitoring module; the exhaust gas monitoring sensor is used to monitor NO in real time x , PM, and CO exhaust gas data; the OBD interface module is used to obtain the engine speed; the load sensor is used to detect the cargo weight; the energy consumption monitoring module is used to record the fuel consumption.

4. The intelligent cleaning logistics transportation monitoring and management system according to claim 1, characterized in that: The environmental perception module includes a GPS positioning terminal and a dust perception terminal; the GPS positioning terminal is used to record the vehicle route, select the positions of several driving distances and obtain the number of residents within a range of 500m in diameter centered on the vehicle at this position; the dust perception terminal is used to sense the positions of several driving distances and obtain the dust concentration within a range of 5m in diameter centered on the vehicle at this position.

5. The intelligent cleaning logistics transportation monitoring and management system according to claim 1, characterized in that: The driving behavior module includes an acceleration sensor and an idle timing module; the acceleration sensor module is used to identify and record the hard acceleration time and the hard braking time. The hard acceleration means that the engine speed rises by more than 1500 RPM / s within 1 second, and the hard braking means that the engine speed drops by more than 900 RPM / s within 1 second; the idle timing module is used to count the idle duration of the vehicle. The idle driving means that the engine speed remains at 500 - 800 RPM.

6. The intelligent cleaning logistics transportation monitoring and management system according to claim 1, characterized in that: The data processing layer includes a multi-source data fusion module, an abnormal data cleaning engine, and a distributed storage architecture. The multi-source data fusion module is used to perform spatio-temporal alignment on environmental parameters, vehicle status, and driving behavior data during transportation; the abnormal data cleaning engine detects and eliminates invalid data, outliers, or incorrect data through the isolation forest algorithm; the distributed storage architecture uses a time-series database to store the cleaned data, which is used to ensure data integrity and consistency.

7. The intelligent cleaning logistics transportation monitoring and management system according to claim 1, characterized in that: The scoring calculation formula for clean transportation in the supervision and evaluation layer is as follows: Where W1 represents the actual effective freight volume; W max represents the maximum approved load of the vehicle; E1 represents the baseline fuel consumption of the vehicle; E x represents the actual fuel consumption of the vehicle; P represents the pollution parameter, and the larger the pollution parameter, the higher the pollution level; α, β, γ represent the module weight coefficients, the value range of α is 0.2 - 0.4, which increases with the influence degree of load, the value range of β is 0.3 - 0.5, which increases with the influence degree of fuel consumption, the value range of γ is 0.3 - 0.5, which increases with the influence degree of pollution parameter; k1, k2 represent the non-linear adjustment indexes, the range of k1 is 0.5 - 1.5, which is adjusted by the transportation scenario, and the range of k2 is 0.5 - 1.5, which is adjusted by cost and benefit.

8. The intelligent cleaning logistics transportation monitoring and management system according to claim 7, wherein: The calculation formula for pollution parameters in clean transportation is as follows: Where A CO represents the real-time monitoring value of the emission concentration of CO; A xCO represents the industry standard value of the emission concentration of CO; represents the real-time monitoring value of the emission concentration of NO x ; represents the industry standard value of the emission concentration of NO x ; A PM represents the real-time monitoring value of the emission concentration of PM; A xPM represents the industry standard value of the emission concentration of PM; B1 represents the dust concentration within a range of 5 m in diameter centered on the vehicle; B x represents the reference value of the road dust load limit; C1 represents the number of residents within a range of 500 m in diameter centered on the vehicle; C x represents the average number of residents within a range of 500 m in diameter in this area; T1 represents the hard acceleration time; T2 represents the hard braking time; T3 represents the total idle time; T 总 represents the total driving time; ω1 represents the CO emission concentration weight coefficient, ranging from 0.1 - 0.2, increasing with the increasing influence degree of CO on pollution parameters; ω2 represents NO x emission concentration weight coefficient, ranging from 0.4 - 0.6, increasing with the increasing influence degree of NO x on pollution parameters; ω3 represents the PM emission concentration weight coefficient, ranging from 0.3 - 0.5, increasing with the increasing influence degree of PM on pollution parameters; m represents the weight coefficient of dust concentration on clean transportation, ranging from 0.1 - 0.3; n represents the weight coefficient of the impact of resident aggregation on clean transportation, and the range is 0.3 - 0.5; μ1 represents the weight coefficient of the impact of hard acceleration on clean transportation, and the range is 0.1 - 0.2; μ2 represents the weight coefficient of the impact of hard braking on clean transportation, and the range is 0.1 - 0.3; μ3 represents the weight coefficient of the impact of idle driving on clean transportation, and the range is 0.2 - 0.

4.

9. The intelligent cleaning logistics transportation monitoring and management system according to claim 1, characterized in that: The application function layer includes an intelligent scheduling center, a dynamic route optimization module, and a driving behavior optimization module; the intelligent scheduling center is used to receive the feedback of the transportation result from the supervision platform layer and issue scheduling instructions to the dynamic route optimization module and the driving behavior optimization module; the dynamic route optimization module avoids dense areas based on the score of clean transportation, and the driving behavior optimization module optimizes the driving habits of drivers based on the score of clean transportation.

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