Logistics intelligent early warning system based on Internet of Things

Abstract

The invention provides a logistics intelligent early warning system based on the Internet of Things, and relates to the technical field of logistics early warning, and the system comprises a vehicle state monitoring module which is used for obtaining the operation state information of a vehicle; the vehicle state scoring module is used for determining a vehicle running state score; the vehicle early warning module is used for determining whether to send out vehicle early warning or not; the carriage state monitoring module is used for acquiring temperature data and humidity data; the laser setting module is used for setting a laser combination; the laser starting module is used for starting the plurality of laser combinations and acquiring receiving results of the plurality of receivers; the cargo state scoring module is used for determining a cargo state score; the cargo early warning module is used for determining whether to send out cargo early warning or not; and the weather early warning module is used for acquiring weather forecast information and determining whether to send out weather early warning or not according to the weather forecast information and the operation state information. According to the invention, the comprehensiveness and effectiveness of logistics intelligent early warning can be improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics early warning technology, and in particular to a logistics intelligent early warning system based on the Internet of Things. Background Art

[0002] In the related art, CN112184115B discloses a logistics early warning method, a logistics recommendation method, a device and related equipment, the logistics early warning method includes: collecting real-time logistics track information; according to the collected real-time logistics track information, for each logistics node that is most recently updated in the real-time logistics track, calculating the first package quantity of the packages whose stay time at the logistics node exceeds the first time threshold and the second package quantity of the packages whose stay time at the logistics node exceeds the second time threshold; in response to the first package quantity being greater than the first quantity threshold, generating an alarm message, the alarm message indicating that there is a logistics node with a transportation failure in the logistics track where the logistics node is located; in response to the second package quantity being less than the second quantity threshold, generating a lost item prompt message, the lost item prompt message indicating that the packages whose stay time at the same logistics node exceeds the second time threshold are identified as lost items. The method and device provided by this scheme realize logistics early warning.

[0003] CN118411092B discloses a logistics intelligent early warning system based on the Internet of Things, including: a temperature data acquisition module, used for collecting temperature data and storing it in the cloud; a temperature data prediction module, used for reading the temperature data in the cloud, obtaining a temperature data sequence up to the current moment through a preprocessing unit, performing a denoising operation on the temperature data sequence, and sending the denoised temperature data sequence to a prediction optimization unit to evaluate the volatility of the temperature data sequence, adjusting the parameters of the #imgabs0# algorithm according to the volatility, using the adjusted #imgabs1# algorithm to predict the temperature data at the next moment, and feeding back the prediction result to the early warning unit to determine whether the prediction result exceeds a preset temperature range, so as to carry out timely temperature early warning. The scheme can accurately reflect the fluctuation of temperature data, improve the prediction accuracy of temperature data, can provide timely early warning, and ensure the quality of products.

[0004] Based on the above related technologies, timely temperature warnings can be made to ensure product quality. However, the related technologies do not take into account the impact of the status of the transport vehicles and the status of the transported goods on logistics, that is, it is impossible to provide intelligent warnings for the logistics process based on the status of the transport vehicles and the status of the transported goods.

[0005] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a logistics intelligent early warning system based on the Internet of Things, which can solve the technical problem that related technologies cannot provide intelligent early warning for the logistics process according to the status of the transport vehicles and the status of the transported goods.

[0007] According to a first aspect of the present invention, there is provided a logistics intelligent early warning system based on the Internet of Things, comprising:

[0008] The vehicle status monitoring module is used to obtain the vehicle's operating status information at multiple times during the monitoring cycle through sensors and monitoring equipment installed on the transport vehicle, wherein the operating status information includes: vehicle continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount and actual location information;

[0009] A vehicle status scoring module, used to determine a vehicle operating status score according to the operating status information;

[0010] A vehicle warning module, used to determine whether to issue a vehicle warning according to the vehicle operation status score;

[0011] The compartment state monitoring module is used to obtain temperature data and humidity data in the compartment through temperature sensors and humidity sensors installed in the compartment of the transport vehicle at multiple times in the monitoring cycle;

[0012] A laser setting module, used to set a laser combination at a plurality of preset positions in a carriage of a transport vehicle, wherein the laser combination comprises: a plurality of lasers and a plurality of receivers, the lasers being used to emit lasers, and the receivers being used to receive lasers emitted by corresponding lasers;

[0013] A laser start module is used to start multiple laser combinations at multiple times in a monitoring cycle and obtain reception results of multiple receivers;

[0014] A cargo status scoring module, used to determine a cargo status score according to the temperature data, the humidity data and the receiving result;

[0015] A cargo warning module, used to determine whether to issue a cargo warning according to the cargo status score;

[0016] The weather warning module is used to obtain weather forecast information and determine whether to issue a weather warning based on the weather forecast information and the operating status information.

[0017] According to a second aspect of the present invention, there is provided a logistics intelligent early warning method based on the Internet of Things, comprising:

[0018] At multiple moments in the monitoring cycle, the operating status information of the vehicle is obtained through sensors and monitoring equipment installed on the transport vehicle, wherein the operating status information includes: vehicle continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount and actual location information;

[0019] Determining a vehicle operating status score according to the operating status information;

[0020] Determining whether to issue a vehicle warning according to the vehicle operation status score;

[0021] At multiple moments in the monitoring cycle, the temperature data and humidity data in the compartment are obtained through the temperature sensor and humidity sensor arranged in the compartment of the transport vehicle;

[0022] A laser assembly is arranged at a plurality of preset positions in a carriage of a transport vehicle, wherein the laser assembly comprises: a plurality of lasers and a plurality of receivers, the lasers being used to emit lasers, and the receivers being used to receive lasers emitted by corresponding lasers;

[0023] At multiple moments in a monitoring cycle, multiple laser combinations are turned on, and reception results of multiple receivers are obtained;

[0024] Determine a cargo status score according to the temperature data, the humidity data and the receiving result;

[0025] Determining whether to issue a cargo warning according to the cargo status score;

[0026] Obtain weather forecast information, and determine whether to issue a weather warning based on the weather forecast information and the operating status information.

[0027] Technical effect: According to the present invention, the vehicle operation status, cargo status and weather conditions can be accurately analyzed, and according to the vehicle operation status, cargo status and weather conditions, it is determined whether to issue an early warning and determine the type of early warning, thereby improving the comprehensiveness and effectiveness of intelligent early warning in logistics. When determining the vehicle operation status score, the vehicle operation status score can be determined according to the vehicle speed change rate, the engine speed change rate, the operation status information and the actual distance information. During the calculation process, the operation status of the transmission system of the transport vehicle can be accurately analyzed according to the vehicle speed change rate and the engine speed change rate. Further, according to the operation status of the transmission system, the driver's fatigue driving status and the remaining fuel status, the operation status of the vehicle is comprehensively evaluated, thereby improving the comprehensiveness and accuracy of the vehicle operation status score. When determining the cargo status score, the cargo status score can be determined according to the cargo suitable environment vector, the vehicle environment vector and the receiving result. During the calculation process, the cargo status can be comprehensively evaluated according to the storage environment and collision damage of the cargo during transportation, thereby improving the comprehensiveness and accuracy of the cargo status score.

[0028] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work.

[0030] Figure 1 A block diagram of a logistics intelligent early warning system based on the Internet of Things according to an embodiment of the present invention is exemplarily shown;

[0031] Figure 2 The flowchart of the intelligent early warning method for logistics based on the Internet of Things according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0034] Figure 1 A block diagram of a logistics intelligent early warning system based on the Internet of Things according to an embodiment of the present invention is exemplarily shown, and the system includes:

[0035] The vehicle status monitoring module is used to obtain the vehicle's operating status information at multiple times during the monitoring cycle through sensors and monitoring equipment installed on the transport vehicle, wherein the operating status information includes: vehicle continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount and actual location information;

[0036] A vehicle status scoring module, used to determine a vehicle operating status score according to the operating status information;

[0037] A vehicle warning module, used to determine whether to issue a vehicle warning according to the vehicle operation status score;

[0038] The compartment state monitoring module is used to obtain temperature data and humidity data in the compartment through temperature sensors and humidity sensors installed in the compartment of the transport vehicle at multiple times in the monitoring cycle;

[0039] A laser setting module, used to set a laser combination at a plurality of preset positions in a carriage of a transport vehicle, wherein the laser combination comprises: a plurality of lasers and a plurality of receivers, the lasers being used to emit lasers, and the receivers being used to receive lasers emitted by corresponding lasers;

[0040] A laser start module is used to start multiple laser combinations at multiple times in a monitoring cycle and obtain reception results of multiple receivers;

[0041] A cargo status scoring module, used to determine a cargo status score according to the temperature data, the humidity data and the receiving result;

[0042] A cargo warning module, used to determine whether to issue a cargo warning according to the cargo status score;

[0043] The weather warning module is used to obtain weather forecast information and determine whether to issue a weather warning based on the weather forecast information and the operating status information.

[0044] The IoT-based logistics intelligent early warning system according to the embodiments of the present invention can accurately analyze the vehicle operating status, cargo status and weather conditions, and determine whether to issue an early warning and determine the type of early warning based on the vehicle operating status, cargo status and weather conditions, thereby improving the comprehensiveness and effectiveness of the logistics intelligent early warning.

[0045] According to one embodiment of the present invention, in a vehicle status monitoring module, at multiple moments in a monitoring cycle, the vehicle's operating status information is acquired through sensors and monitoring equipment installed on the transport vehicle, wherein the operating status information includes: vehicle continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount and actual location information.

[0046] For example, the vehicle monitoring equipment installed on the transport vehicle can be used to obtain the vehicle's continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount, and actual location information.

[0047] According to an embodiment of the present invention, in the vehicle state scoring module, a vehicle operating state score is determined according to the operating state information.

[0048] According to one embodiment of the present invention, determining a vehicle operating status score according to the operating status information includes:

[0049] Fitting the vehicle speed and the time in the monitoring period to obtain a vehicle speed function of the vehicle speed in the monitoring period;

[0050] According to the vehicle speed function, obtaining a vehicle speed derivative function;

[0051] Determining the vehicle speed change rate at multiple moments in a monitoring period according to the vehicle speed derivative function;

[0052] Fitting the engine speed and the time in the monitoring period to obtain an engine speed function of the engine speed in the monitoring period;

[0053] According to the engine speed function, obtaining an engine speed derivative function;

[0054] Determining the engine speed change rate at multiple moments in a monitoring period according to the engine speed derivative function;

[0055] Obtain logistics destination location information;

[0056] Determine actual distance information according to the actual location information and the logistics destination location information;

[0057] A vehicle operating status score is determined according to the vehicle speed change rate, the engine speed change rate, the operating status information and the actual distance information.

[0058] For example, the vehicle speed and the moments in the monitoring period are fitted to obtain a vehicle speed function that is used to describe the law of vehicle speed changes over time in the monitoring period; the vehicle speed function is differentiated to obtain a vehicle speed derivative function; multiple moments in the monitoring period are substituted into the vehicle speed derivative function to determine the vehicle speed change rate at multiple moments in the monitoring period; the engine speed and the moments in the monitoring period are fitted to obtain an engine speed function that is used to describe the law of engine speed changes over time in the monitoring period; the engine speed function is differentiated to obtain an engine speed derivative function; multiple moments in the monitoring period are substituted into the engine speed derivative function to determine the engine speed change rate at multiple moments in the monitoring period; the logistics destination location information is obtained, and the actual distance information to the logistics destination is determined based on the actual location information of the vehicle, the logistics destination location information and the map; the vehicle's operating status is evaluated based on the vehicle speed change rate, the engine speed change rate, the operating status information and the actual distance information to determine the vehicle operating status score.

[0059] According to one embodiment of the present invention, the vehicle running state score is determined according to the vehicle speed change rate, the engine speed change rate, the running state information and the actual distance information, including: determining the vehicle running state score Vos at the i-th moment of the monitoring period according to formula (1): i ,

[0060]

[0061] Among them, α 1 , α 2 and α 3 is the preset weight, if is the conditional function, t i is the i-th moment of the monitoring period, is the vehicle speed change rate at the i-th moment of the monitoring period, is the engine speed change rate at the i-th moment of the monitoring period, RA 1 To preset the first ratio, RA 2 To preset the second ratio, RA 3 To preset the third ratio, UPT i is the continuous running time of the vehicle at the i-th moment of the monitoring cycle, UPT T To set the vehicle continuous operation time threshold, AD i is the actual distance information at the i-th moment of the monitoring period, OC i is the fuel consumption at the i-th moment of the monitoring period, RFQ i is the remaining fuel quantity at the i-th moment of the monitoring cycle, RFQ T is the preset remaining fuel amount threshold, i≥2, i is a positive integer, and and is the logical operator of "and".

[0062] According to one embodiment of the present invention, is the ratio of the engine speed change rate to the vehicle speed change rate at the i-th moment of the monitoring period. When the engine speed increases steadily with the increase of vehicle speed, it indicates that the vehicle is in good condition. When the engine speed fluctuates abnormally or increases too fast compared to the increase of vehicle speed, it may indicate that there is a problem with the engine or transmission system of the transport vehicle. In formula (1), the inner condition function The value of includes the following two cases, when satisfying When the condition is met, it means that the ratio of the engine speed change rate to the vehicle speed change rate at the i-th moment of the monitoring period is within the interval of the preset first ratio and the preset second ratio, indicating that the engine or transmission system is working normally, and the value of the inner condition function is 1. When the condition is met, it means that there is a problem with the working condition of the engine or transmission system, and the value of the inner condition function is 0. The ratio is calculated based on the number of the first i moments of the monitoring cycle. The larger the ratio, the better the working condition of the engine or transmission system in the first i moments of the monitoring cycle, and the smaller the possibility of failure. The conditional function The value of includes the following two cases, when it satisfies When the condition is met, it means that the engine or transmission system is in good overall working condition in the first i moments of the monitoring period, and the value of the condition function is 0. When the condition is met, it means that the engine or transmission system may fail within the first i moments of the monitoring period, and the value of the condition function is 1.

[0063] According to one embodiment of the present invention, in formula (1), the conditional function if{UPT i ≥UPT T , 1, 0} includes the following two cases, when UPT is satisfied i ≥UPT T When the condition of UPT is met, the continuous running time of the vehicle at the i-th moment of the monitoring period is greater than or equal to the set vehicle continuous running time threshold, indicating that the driver has been driving for a long time, there is fatigue driving, and the driving risk is relatively high. The value of the condition function is 1. i ≥UPT T When the condition is met, it means that the driver is not fatigued, the driving risk is low, and the value of the conditional function is 0.

[0064] According to one embodiment of the present invention, AD i ×OC i is the product of the actual distance information at the i-th moment of the monitoring period and the fuel consumption at the i-th moment of the monitoring period, which represents the predicted fuel consumption required to reach the destination. In formula (1), the conditional function if{(ADi ×OC i ≥RFQ i )and(RFQ i ≤RFQ T ), 1, 0} includes the following two cases, when (AD i ×OC i ≥RFQ i )and(RFQ i ≤RFQ T ), the fuel consumption required to reach the destination is predicted to be greater than or equal to the remaining fuel at the i-th moment of the monitoring period, and the remaining fuel at the i-th moment of the monitoring period is less than or equal to the preset remaining fuel threshold, indicating that there may be a shortage of fuel, reminding the driver to refill the fuel in time. The value of the conditional function is 1. If (AD i ×OC i ≥RFQ i )and(RFQ i ≤RFQ T ), it means that there is no fuel shortage and the value of the conditional function is 0.

[0065] In this way, the vehicle operating status score can be determined based on the vehicle speed change rate, engine speed change rate, operating status information and actual distance information. During the calculation process, the operating condition of the transport vehicle's transmission system can be accurately analyzed based on the vehicle speed change rate and the engine speed change rate. Furthermore, the vehicle's operating status can be comprehensively evaluated based on the operating condition of the transmission system, the driver's fatigue driving condition and the remaining fuel condition, thereby improving the comprehensiveness and accuracy of the vehicle's operating status score.

[0066] According to one embodiment of the present invention, in the vehicle warning module, it is determined whether to issue a vehicle warning according to the vehicle operating status score.

[0067] According to one embodiment of the present invention, determining whether to issue a vehicle warning according to the vehicle operation status score includes:

[0068] If the vehicle running status score is α 1 , determine to issue a vehicle fault warning;

[0069] If the vehicle running status score is α 2 , determine to issue a driver fatigue warning;

[0070] If the vehicle running status score is α 3 , determine to issue a fuel shortage warning;

[0071] If the vehicle running status score is α 1 +α 2, determine to issue vehicle failure warning and driver fatigue warning;

[0072] If the vehicle running status score is α 1 +α 3 , determine to issue vehicle failure warning and fuel shortage warning;

[0073] If the vehicle running status score is α 1 +α 2 +α 3 , determine the issuance of vehicle failure warning, driver fatigue warning and fuel shortage warning;

[0074] If the vehicle running status score is α 2 +α 3 , determine to issue driver fatigue warning and fuel shortage warning.

[0075] For example, if the vehicle operating status score is α 1 , it means that the vehicle's transmission system or engine is abnormal, and a vehicle fault warning is issued; if the vehicle's operating status score is α 2 , it means that the driver may be driving fatigued after a long time, and a driver fatigue warning is issued; if the vehicle operation status score is α 3 , it indicates that there may be a shortage of fuel, reminding the driver to refuel in time and issue a fuel shortage warning; if the vehicle operation status score is α 1 +α 2 , it means that the vehicle's transmission system or engine is abnormal, and the driver may be driving fatigued, so it is determined to issue a vehicle fault warning and a driver fatigue warning; if the vehicle operation status score is α 1 +α 3 , indicating that the vehicle's transmission system or engine is abnormal, and the vehicle may be short of fuel, and it is determined to issue a vehicle fault warning and a fuel shortage warning; if the vehicle's operating status score is α 2 +α 3 , it means that the driver may be driving fatigued and the vehicle may be running low on fuel, so a driver fatigue warning and a low fuel warning are issued; if the vehicle running status score is α 1 +α 2 +α 3 , it means that there is an abnormality in the vehicle's transmission system or engine, the driver may be driving fatigued, and the vehicle may be low on fuel, so it is determined to issue a vehicle fault warning, a driver fatigue warning, and a low fuel warning.

[0076] According to one embodiment of the present invention, in the compartment state monitoring module, at multiple moments in the monitoring cycle, temperature data and humidity data in the compartment are acquired through temperature sensors and humidity sensors disposed in the compartment of the transport vehicle.

[0077] For example, the temperature data and humidity data in the vehicle cabin during the monitoring period are collected by installing a temperature sensor and a humidity sensor inside the vehicle.

[0078] According to one embodiment of the present invention, in a laser setting module, a laser combination is set at multiple preset positions in a transport vehicle compartment, wherein the laser combination includes: multiple lasers and multiple receivers, the lasers are used to emit lasers, and the receivers are used to receive lasers emitted by corresponding lasers.

[0079] For example, the lasers and receivers in the laser combination are evenly arranged on the side walls of the transport vehicle compartment, and the lasers and receivers correspond one to one. The lasers can emit lasers with a specific light intensity to distinguish them from natural light, and the receivers are set to receive lasers with a preset frequency.

[0080] According to an embodiment of the present invention, in the laser starting module, multiple laser combinations are started at multiple moments in the monitoring cycle, and reception results of multiple receivers are obtained.

[0081] For example, the laser combination is turned on to emit laser light. When the goods are between the laser and the corresponding receiver, the receiver cannot receive the laser light. The receiving results of the receiver at multiple moments in the monitoring cycle are obtained.

[0082] According to an embodiment of the present invention, in the cargo status scoring module, a cargo status score is determined according to the temperature data, the humidity data and the receiving result.

[0083] According to one embodiment of the present invention, determining a cargo status score according to the temperature data, the humidity data and the receiving result includes:

[0084] Get the type of cargo transported by the transport vehicle;

[0085] Determine the appropriate temperature and humidity for the cargo according to the type of cargo;

[0086] Determining a cargo suitable environment vector according to the cargo suitable temperature and the cargo suitable humidity;

[0087] determining a vehicle environment vector according to the temperature data and the humidity data;

[0088] A cargo state score is determined according to the cargo suitable environment vector, the vehicle environment vector and the receiving result.

[0089] For example, determine the types of goods transported by the transport vehicle (for example, refrigerated goods, fresh goods, and perishable goods); determine the suitable temperature and humidity for storage of the goods based on the types of goods; determine the suitable environment vector for the goods based on the suitable temperature and humidity; determine the vehicle environment vector based on the temperature and humidity data in the carriage; evaluate the storage and transportation status of the goods based on the suitable environment vector, the vehicle environment vector, and the receiving results, and determine the cargo status score.

[0090] According to an embodiment of the present invention, determining the cargo status score according to the cargo suitable environment vector, the vehicle environment vector and the receiving result includes: determining the monitoring environment score according to formula (2):

[0091] The cargo status score Gs at the i-th moment of the period i ,

[0092]

[0093] Among them, β 1 and β 2 is the preset weight, T i is the temperature data at the i-th moment of the monitoring period, H i is the humidity data at the i-th moment of the monitoring period, is the vehicle environment vector at the i-th moment of the monitoring period, for The transposed vector, T T H is the suitable temperature for the cargo, T To ensure the appropriate humidity for the goods, is the cargo suitable environment vector, AC k，i is the reception result of the kth receiver at the ith moment of the monitoring period, AC k，o is the reception result of the kth receiver at the beginning of the monitoring period, K is the number of receivers, k≤K, and both k and K are positive integers.

[0094] According to one embodiment of the present invention, It is the cosine similarity between the vehicle environment vector at the i-th moment of the monitoring period and the cargo suitable environment vector. The larger the cosine similarity, the smaller the difference between the temperature data and humidity data at the i-th moment of the monitoring period and the cargo suitable temperature and cargo suitable humidity, the better the storage environment of the cargo, and the higher the cargo status score.

[0095] According to one embodiment of the present invention, when the receiving result is 1, it indicates that the receiver does not receive the laser and there is cargo blocking the laser at the receiver. When the receiving result is 0, it indicates that the receiver receives the laser and there is no cargo blocking the laser at the receiver. k，i -AC k，o | is the absolute value of the difference between the reception result of the kth receiver at the i-th moment of the monitoring period and the reception result of the kth receiver at the start time of the monitoring period. When the absolute value is 1, it means that the goods are moved at the kth receiver at the i-th moment of the monitoring period. In order to find the average value according to the number of receivers, the smaller the ratio is, the less likely the cargo in the carriage is to move at the i-th moment of the monitoring period, the smaller the possibility of collision damage to the cargo, and the higher the cargo status score.

[0096] In this way, the cargo status score can be determined based on the cargo suitable environment vector, vehicle environment vector and receiving results. During the calculation process, the cargo status can be comprehensively evaluated based on the storage environment conditions and collision damage of the cargo during transportation, thereby improving the comprehensiveness and accuracy of the cargo status score.

[0097] According to an embodiment of the present invention, in the cargo warning module, it is determined whether to issue a cargo warning according to the cargo status score.

[0098] For example, if the cargo status score is less than the set cargo status score threshold, it means that the cargo has rolled over in the cargo box, or the storage environment of the cargo is not good. A cargo warning is determined to remind the driver to check the cargo to ensure the quality of logistics.

[0099] According to an embodiment of the present invention, in the weather warning module, weather forecast information is obtained, and based on the weather forecast information and the operating status information, it is determined whether to issue a weather warning.

[0100] According to an embodiment of the present invention, obtaining weather forecast information, and determining whether to issue a weather warning according to the weather forecast information and the operating status information, includes:

[0101] Obtain the transportation route of the transport vehicle;

[0102] Determine the transit cities according to the transportation route;

[0103] Determine an estimated arrival time based on the actual location information, the cities passed through, and the vehicle speed;

[0104] Obtaining real-time weather forecast information for the city being passed through at the estimated arrival time;

[0105] Determining a severe weather identification result according to the real-time weather forecast information;

[0106] Determine whether to issue a weather warning based on the severe weather identification result.

[0107] For example, the transport route of the transport vehicle is determined based on the real-time position and destination position of the transport vehicle, and the en route cities are determined based on the transport route (for example, city A is required to reach the destination); the estimated arrival time at each en route city is determined based on the actual position information, en route cities and vehicle speed (for example, arriving at city A at 15:00); real-time weather forecast information of the en route cities at the estimated arrival time is obtained, for example, the weather forecast for city A at 15:00 is obtained; based on the real-time weather forecast information, it is determined whether city A will experience severe weather at 15:00, such as heavy rain, hail and strong winds; if severe weather occurs, the severe weather recognition result is 1, and if no severe weather occurs, the severe weather recognition result is 0; if the severe weather recognition result is 1, it is determined to issue a weather warning to remind drivers that there is severe weather in city A and to pay attention to driving safety.

[0108] According to the intelligent early warning system for logistics based on the Internet of Things according to the embodiment of the present invention, the vehicle operation status, cargo status and weather conditions can be accurately analyzed, and according to the vehicle operation status, cargo status and weather conditions, it is determined whether to issue an early warning and the type of early warning can be determined, thereby improving the comprehensiveness and effectiveness of the intelligent early warning for logistics. When determining the vehicle operation status score, the vehicle operation status score can be determined according to the vehicle speed change rate, the engine speed change rate, the operation status information and the actual distance information. During the calculation process, the operation status of the transmission system of the transport vehicle can be accurately analyzed according to the vehicle speed change rate and the engine speed change rate. Further, according to the operation status of the transmission system, the driver's fatigue driving status and the remaining fuel status, the operation status of the vehicle is comprehensively evaluated, thereby improving the comprehensiveness and accuracy of the vehicle operation status score. When determining the cargo status score, the cargo status score can be determined according to the cargo suitable environment vector, the vehicle environment vector and the receiving result. During the calculation process, the cargo status can be comprehensively evaluated according to the storage environment and collision damage of the cargo during transportation, thereby improving the comprehensiveness and accuracy of the cargo status score.

[0109] Figure 2 A flow chart of a logistics intelligent early warning method based on the Internet of Things according to an embodiment of the present invention is exemplarily shown, and the method includes:

[0110] Step S101, at multiple moments in the monitoring cycle, the operating status information of the vehicle is obtained through sensors and monitoring equipment installed on the transport vehicle, wherein the operating status information includes: vehicle continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount and actual location information;

[0111] Step S102, determining a vehicle operating status score according to the operating status information;

[0112] Step S103, determining whether to issue a vehicle warning according to the vehicle operation status score;

[0113] Step S104, at multiple moments in the monitoring cycle, temperature data and humidity data in the compartment are obtained through a temperature sensor and a humidity sensor disposed in the compartment of the transport vehicle;

[0114] Step S105, setting a laser assembly at a plurality of preset positions in the carriage of the transport vehicle, wherein the laser assembly comprises: a plurality of lasers and a plurality of receivers, the lasers are used to emit lasers, and the receivers are used to receive lasers emitted by corresponding lasers;

[0115] Step S106, at multiple moments in the monitoring cycle, multiple laser combinations are turned on, and reception results of multiple receivers are obtained;

[0116] Step S107, determining a cargo status score according to the temperature data, the humidity data and the receiving result;

[0117] Step S108, determining whether to issue a cargo warning according to the cargo status score;

[0118] Step S109, obtaining weather forecast information, and determining whether to issue a weather warning based on the weather forecast information and the operating status information.

[0119] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0120] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

Claims

1. A logistics intelligent early warning system based on the Internet of Things, characterized in that: include: The vehicle status monitoring module is used to obtain the vehicle's operating status information at multiple times during the monitoring cycle through sensors and monitoring equipment installed on the transport vehicle, wherein the operating status information includes: vehicle continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount and actual location information; A vehicle status scoring module, used to determine a vehicle operating status score according to the operating status information; A vehicle warning module, used to determine whether to issue a vehicle warning according to the vehicle operation status score; The compartment state monitoring module is used to obtain temperature data and humidity data in the compartment through temperature sensors and humidity sensors installed in the compartment of the transport vehicle at multiple times in the monitoring cycle; A laser setting module, used to set a laser combination at a plurality of preset positions in a carriage of a transport vehicle, wherein the laser combination comprises: a plurality of lasers and a plurality of receivers, the lasers being used to emit lasers, and the receivers being used to receive lasers emitted by corresponding lasers; A laser start module is used to start multiple laser combinations at multiple times in a monitoring cycle and obtain reception results of multiple receivers; A cargo status scoring module, used to determine a cargo status score according to the temperature data, the humidity data and the receiving result; A cargo warning module, used to determine whether to issue a cargo warning according to the cargo status score; The weather warning module is used to obtain weather forecast information and determine whether to issue a weather warning based on the weather forecast information and the operating status information.

2. According to the IoT-based logistics intelligent early warning system of claim 1, it is characterized in that: Determining a vehicle operating status score according to the operating status information includes: Fitting the vehicle speed and the time in the monitoring period to obtain a vehicle speed function of the vehicle speed in the monitoring period; According to the vehicle speed function, obtaining a vehicle speed derivative function; Determining the vehicle speed change rate at multiple moments in a monitoring period according to the vehicle speed derivative function; Fitting the engine speed and the time in the monitoring period to obtain an engine speed function of the engine speed in the monitoring period; According to the engine speed function, obtaining an engine speed derivative function; Determining the engine speed change rate at multiple moments in a monitoring period according to the engine speed derivative function; Obtain logistics destination location information; Determine actual distance information according to the actual location information and the logistics destination location information; A vehicle operating status score is determined according to the vehicle speed change rate, the engine speed change rate, the operating status information and the actual distance information.

3. The intelligent early warning system for logistics based on the Internet of Things according to claim 2 is characterized in that: Determining a vehicle operation status score according to the vehicle speed change rate, the engine speed change rate, the operation status information, and the actual distance information includes: According to the formula Determine the vehicle operation status score at the i-th moment of the monitoring cycle Among them, α1, α2 and α3 are preset weights, if is the conditional function, t i is the i-th moment of the monitoring period, V'1(t i ) is the vehicle speed change rate at the i-th moment of the monitoring period, R'1(t i ) is the engine speed change rate at the i-th moment of the monitoring period, RA1 is the preset first ratio, RA2 is the preset second ratio, RA3 is the preset third ratio, UPT i is the continuous running time of the vehicle at the i-th moment of the monitoring cycle, UPT T To set the vehicle continuous operation time threshold, AD i is the actual distance information at the i-th moment of the monitoring period, OC i is the fuel consumption at the i-th moment of the monitoring period, RFQ i is the remaining fuel quantity at the i-th moment of the monitoring cycle, RFQ T is the preset remaining fuel threshold, i≥2, i is a positive integer, and and is the logical operator of "and".

4. The intelligent early warning system for logistics based on the Internet of Things according to claim 1 is characterized in that: Determining whether to issue a vehicle warning according to the vehicle operation status score includes: If the vehicle operation status score is α1, it is determined to issue a vehicle fault warning; If the vehicle operation status score is α2, it is determined to issue a driver fatigue warning; If the vehicle operation status score is α3, it is determined to issue a fuel shortage warning; If the vehicle operation status score is α1+α2, it is determined to issue a vehicle failure warning and a driver fatigue warning; If the vehicle operation status score is α1+α3, it is determined to issue a vehicle failure warning and a fuel shortage warning; If the vehicle operation status score is α1+α2+α3, it is determined to issue a vehicle failure warning, a driver fatigue warning, and a fuel shortage warning; If the vehicle operation status score is α2+α3, it is determined to issue a driver fatigue warning and a fuel shortage warning.

5. The intelligent early warning system for logistics based on the Internet of Things according to claim 1 is characterized in that: Determining a cargo status score according to the temperature data, the humidity data and the receiving result, including: Get the type of cargo transported by the transport vehicle; Determine the appropriate temperature and humidity for the cargo according to the type of cargo; Determining a cargo suitable environment vector according to the cargo suitable temperature and the cargo suitable humidity; determining a vehicle environment vector according to the temperature data and the humidity data; A cargo state score is determined according to the cargo suitable environment vector, the vehicle environment vector and the receiving result.

6. The intelligent early warning system for logistics based on the Internet of Things according to claim 5 is characterized in that: Determining a cargo state score according to the cargo suitable environment vector, the vehicle environment vector and the receiving result includes: According to the formula Determine the cargo status score Gs at the i-th moment of the monitoring period i , where β1 and β2 are preset weights, T i is the temperature data at the i-th moment of the monitoring period, H i is the humidity data at the i-th moment of the monitoring period, is the vehicle environment vector at the i-th moment of the monitoring period, for The transposed vector, T T H is the suitable temperature for the cargo, T To ensure the appropriate humidity for the goods, is the cargo suitable environment vector, AC k，i is the reception result of the kth receiver at the ith moment of the monitoring period, AC k，o is the reception result of the kth receiver at the beginning of the monitoring period, K is the number of receivers, k≤K, and both k and K are positive integers.

7. The intelligent early warning system for logistics based on the Internet of Things according to claim 1 is characterized in that: Acquiring weather forecast information, and determining whether to issue a weather warning based on the weather forecast information and the operating status information, including: Obtain the transportation route of the transport vehicle; Determine the transit cities according to the transportation route; Determine an estimated arrival time based on the actual location information, the cities passed through, and the vehicle speed; Obtaining real-time weather forecast information for the city being passed through at the estimated arrival time; Determining a severe weather identification result according to the real-time weather forecast information; Determine whether to issue a weather warning based on the severe weather identification result.

8. A logistics intelligent early warning method based on the Internet of Things, characterized in that: include: At multiple moments in the monitoring cycle, the operating status information of the vehicle is obtained through sensors and monitoring equipment installed on the transport vehicle, wherein the operating status information includes: vehicle continuous running time, vehicle speed, engine speed, fuel consumption, remaining fuel amount and actual location information; Determining a vehicle operating status score according to the operating status information; Determining whether to issue a vehicle warning according to the vehicle operation status score; At multiple moments in the monitoring cycle, the temperature data and humidity data in the compartment are obtained through the temperature sensor and humidity sensor arranged in the compartment of the transport vehicle; A laser assembly is arranged at a plurality of preset positions in a carriage of a transport vehicle, wherein the laser assembly comprises: a plurality of lasers and a plurality of receivers, the lasers being used to emit lasers, and the receivers being used to receive lasers emitted by corresponding lasers; At multiple moments in a monitoring cycle, multiple laser combinations are turned on, and reception results of multiple receivers are obtained; Determine a cargo status score according to the temperature data, the humidity data and the receiving result; Determining whether to issue a cargo warning according to the cargo status score; Obtain weather forecast information, and determine whether to issue a weather warning based on the weather forecast information and the operating status information.

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