CN117196437A - Intelligent monitoring management system for logistics in intelligent park based on Internet of things - Google Patents
Intelligent monitoring management system for logistics in intelligent park based on Internet of things Download PDFInfo
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Abstract
The invention relates to the field of logistics monitoring management, in particular to an intelligent monitoring management system for logistics in an intelligent park based on the Internet of things, which can effectively identify illegal vehicles or drivers which do not meet regulations by acquiring and analyzing images of vehicles and drivers entering the park, is beneficial to improving the safety of the park, reduces the time and energy consumption of manual auditing and improves the management efficiency; by analyzing the recommendation coefficients of all the warehouses and further analyzing the optimal driving route of the vehicle, the warehouses and the vehicle can be reasonably scheduled and utilized, the optimal driving route can be rapidly determined, the logistics transportation efficiency is improved, and the transportation time is reduced; the goods loaded are scanned, the weight is detected and identified, the goods loading qualification degree of the vehicle is obtained, and then the vehicle with unqualified loading is subjected to early warning treatment, so that the manual error in the loading process is avoided, and the accuracy and the correctness of goods loading are ensured.
Description
Technical Field
The invention relates to the field of logistics monitoring management, in particular to an intelligent monitoring management system for logistics in an intelligent park based on the Internet of things.
Background
Along with the rapid development of global economy and the increasing demand for resources, the construction of the intelligent park becomes an important development goal of a plurality of countries and regions, various devices and systems are connected with each other by applying information technology and Internet of things technology, the industry is gathered and developed and different spaces occupied by urban life are organically combined to form a functional composite urban space area with association of community value, circle resource sharing and full time utilization of land, and the operation of logistics in the park is a key link of park operation, so that the intelligent park has important significance for improving transportation efficiency, reducing cost, optimizing resource allocation and the like.
However, the complexity and the large scale of the logistic service today make the conventional monitoring management mode unable to meet the requirements, and the following aspects are specifically shown: 1. traditional systems may rely on manual inspection of related documents and credentials of the vehicle, with subjective and erroneous judgment problems, and focusing on the vehicle may ignore driver inspection, failing to fully evaluate compliance of the vehicle and driver, with potential safety hazards.
2. In terms of logistics transportation route recommendation, conventional route recommendation is generally based on experience and rules, and logistics activities in a campus are often subject to various limitations and special requirements, and conventional route recommendation is difficult to consider these factors, and may not provide optimal route selection.
3. The conventional system cannot accurately identify the goods, which may cause manual errors in the loading process, resulting in inaccuracy and errors of the loading of the goods, and the conventional system may ignore the early warning mechanism, which means that even if a vehicle with unqualified loading is found, early warning measures cannot be timely taken, and the loading of the goods cannot be ensured to meet the specified standard.
Disclosure of Invention
In order to solve the technical problems, the invention is realized by the following technical scheme: intelligent garden commodity circulation intelligent monitoring management system based on thing networking includes: the transportation list generation matching module is used for generating a logistics transportation list and matching transportation vehicles corresponding to the logistics transportation list according to the volume of the goods in the logistics transportation list and the positions of the vehicles, wherein the logistics transportation list comprises all goods transported and the weight required to be transported by all goods.
And the image information acquisition module is used for acquiring images of vehicles and drivers entering the park through the camera, respectively recording the images as vehicle information images and driver figures, and extracting the appearance data of the license plates and the vehicles from the vehicle information images, wherein the appearance data of the vehicles comprise the length, the width and the height of the vehicles.
The coincidence degree analysis module is used for analyzing and obtaining the coincidence degree of the vehicle according to the vehicle information image, the license plate and the appearance data of the vehicle, combining the coincidence degree of the vehicle with the driver portrait to further analyze and obtain the coincidence degree between the vehicle and the driver, comparing the coincidence degree between the vehicle and the driver with a preset coincidence degree qualification value between the vehicle and the driver, and correspondingly processing the vehicle according to the obtained coincidence degree condition of the vehicle and the driver.
And the management database is used for storing the vehicle factory standard appearance data registered by each license plate, the driver figure set, the geographic position of each warehouse and the goods stored in each warehouse.
And the route analysis module is used for analyzing and obtaining the recommendation coefficient of each warehouse to be loaded according to the geographic position of each warehouse, the goods class stored in each warehouse and the logistics transportation list of the vehicle, and further analyzing and obtaining the optimal driving route of the vehicle.
The loading detection module is used for carrying out code scanning identification on the goods loaded, and detecting the weight of the loaded goods through a weight sensor arranged at the bottom of the carriage.
And the cargo loading analysis module is used for comparing the weight of the loaded cargoes with the weight of the transported cargoes in the logistics transportation list and analyzing to obtain the cargo loading qualification degree.
The early warning module is used for comparing the cargo loading qualification degree with a preset cargo loading qualification degree threshold value to obtain the loading qualification condition of the vehicle, and further carrying out early warning treatment on the unqualified vehicle.
Preferably, the specific analysis process of the transportation list generation matching module is as follows: generating a logistics transportation list, reading the volume of goods in the logistics transportation list, screening vehicles with carriage capacity larger than the volume of goods in the logistics transportation list from a database, marking the vehicles as preferred vehicles, positioning the preferred vehicles and calculating the distance from the vehicles to the park, marking the preferred vehicles as the distance from the vehicles to the park, arranging the preferred vehicles from the park in a descending order, screening the preferred vehicles closest to the park as first selected vehicles, marking the vehicles as transportation vehicles corresponding to the logistics transportation list if the drivers of the vehicles agree to pick up, marking the vehicles as the license plates of the transportation vehicles corresponding to the logistics transportation list and sending the logistics transportation list to the drivers of the vehicles, continuing screening second selected vehicles according to a method for screening the first selected vehicles if the drivers of the vehicles disagree to pick up, sending transportation requests to the drivers of the second selected vehicles, and so on until the transportation requests are picked up.
The preferred specific analysis method for the vehicle conformity comprises the following steps: the method comprises the steps of firstly, extracting license plates of transport vehicles corresponding to a logistics transport list, acquiring images of vehicles entering a park through a camera, recording the acquired images as vehicle information images, identifying the license plates of the vehicles from the vehicle information images, comparing the license plates with the license plates of the transport vehicles corresponding to the logistics transport list, judging that the vehicles are the transport vehicles corresponding to the logistics transport list if the license plates are consistent, executing the second step, and judging that the vehicles are not the transport vehicles corresponding to the logistics transport list if the license plates are inconsistent, and not releasing.
The second step, extracting the length, width and height of the vehicle from the vehicle information image, respectively marked as l, w and h, simultaneously reading the delivery standard length, width and height of the vehicle registered by the license plate of the vehicle in the management database, comparing the delivery standard length, width and height with the length, width and height of the extracted vehicle, and obtaining the final product by the formulaObtaining vehicle compliance delta, l 0 、w 0 、h 0 The factory standard length, width, and height of the vehicle registered for the license plate are respectively expressed, and e is expressed as a natural constant.
Preferably, the specific analysis method of the coincidence degree between the vehicle and the driver comprises the following steps: firstly, acquiring a human image of a vehicle driver entering a park through a camera, recording the human image as a driver human image, matching the driver human image with a driver human image set in a management database, judging that the driver identity is abnormal if the driver human image is not present in the driver human image set, and not letting pass, if the driver human image is present in the driver human image set, determining driver identity information according to the matched driver human image, further obtaining the license plate of the responsible vehicle according to the driver identity information, reading the license plate extracted from the vehicle information image, comparing the license plate with the license plate of the responsible vehicle of the driver in the driver identity information, and determining the driver identity information according to a formulaObtaining compliance gamma between vehicle and driver, wherein R 1 R is the same as the license plate of the vehicle responsible for the driver in the driver identity information in the vehicle information image 2 The license plate in the vehicle information image is different from the license plate of the vehicle responsible for the driver in the driver identity information.
And secondly, reading the conformity between the vehicle and the driver, comparing the conformity between the vehicle and the driver with a preset conformity qualification value between the vehicle and the driver, judging the vehicle as a disqualified vehicle if the conformity between the vehicle and the driver of the vehicle is lower than the preset conformity qualification value between the vehicle and the driver, and not releasing the vehicle if the conformity between the vehicle and the driver of the vehicle is higher than or equal to the preset conformity qualification value between the vehicle and the driver, judging the vehicle as a qualified vehicle and permitting releasing the vehicle.
Preferably, the specific analysis method of the recommendation coefficient of each warehouse to be loaded comprises the following steps: the first step, a commodity circulation transportation list of the vehicle is read, the goods stored in each warehouse in the management database are called, each warehouse for storing corresponding goods is screened out according to the goods to be transported in the commodity circulation transportation list, and the goods are marked as the warehouses to be loaded.
Step two, positioning each warehouse to be loaded according to the geographic position of each warehouse in the management database, and respectively calculating the distance between each warehouse to be loaded and the park entrance, and marking as d i I represents the number of the i-th warehouse to be loaded, i=1, 2, i.e., k, and meanwhile, according to the goods stored in each warehouse, the weight required to be transported of the goods of each goods in the logistics transportation list is combined, the weight required to be transported of the goods of each goods in each warehouse to be loaded is obtained through comprehensive analysis, and is recorded as M in N represents the number of the n-th item, n=1, 2,..m.
Third, respectively reading the goods stored in each warehouse and the weight required to be transported for the goods of each goods in each warehouse to be loaded, and substituting the weights into a formulaObtaining the estimated loading time t of each warehouse to be loaded i Wherein t 'is expressed as a preset fixed time for carrying goods into each warehouse, and t' n ' reference conveyance time, η, expressed as unit weight of the nth article cargo 1 A correction factor representing a preset predicted loading time.
Fourth, respectively reading the distance from each warehouse to be loaded to the park entrance, the weight required to be transported for each goods in each warehouse to be loaded and the expected loading time of each warehouse to be loaded, and substituting the distances and the expected loading time into a formulaObtaining transport recommendation coefficients of all warehouses to be loaded>Wherein a1, a2 and a3 are respectively expressed as weight factors of the required transportation of various goods in the warehouse to be loaded, the distance from the warehouse to be loaded to the park entrance and the estimated loading time of the warehouse to be loaded, and a1+a2+a3=1.
Preferably, the specific analysis method of the optimal driving route of the vehicle comprises the following steps: and reading the transportation recommendation coefficients of the warehouses to be loaded, arranging the transportation recommendation coefficients of the warehouses to be loaded in a sequence from large to small to obtain the ordered warehouses to be loaded, sequentially planning the running route of the transport vehicle corresponding to the logistics transportation list according to the geographical position of the ordered warehouses to be loaded, and marking the running route as the optimal running route of the vehicle.
Preferably, the specific analysis method of the cargo loading qualification degree comprises the following steps: the method comprises the steps that firstly, code scanning identification is conducted on goods loaded in a warehouse to be loaded where a vehicle is located, the goods are matched with the goods in a logistics transportation list, if the goods are certain goods in the logistics transportation list, loading operation is continuously conducted, and if the goods are certain goods in a non-logistics transportation list, alarming and reminding are conducted;
the second step, the weight of each goods to be transported in each warehouse to be loaded is read, and the sum is obtainedThe sum of the weight of the goods to be transported in each warehouse to be loaded is recorded asAnd the actual total weight of the warehouses to be loaded after the cargoes are loaded is detected by a weight sensor arranged at the bottom of the carriage of the vehicle and is recorded as +.>Comparing the total actual weight of the cargoes loaded in each warehouse to be loaded with the sum of the weight of the cargoes required to be transported in each warehouse to be loaded with the cargoes, and passing through the formulaObtaining the cargo loading qualification degree sigma of the vehicle, wherein k is expressed as the number of warehouses to be loaded, M 1 Total (S) Representing the sum of the weights required to transport the goods in the 1 st warehouse to be loaded, M' 1 Total (S) Indicating the actual total weight of the 1 st warehouse to be loaded with goods.
Preferably, the specific analysis method for carrying out early warning treatment on the unqualified vehicle comprises the following steps: comparing the cargo loading qualification degree of the vehicle with a preset cargo loading qualification degree range, if the cargo loading qualification degree of the vehicle exceeds the cargo loading qualification degree range, judging that the cargo of the vehicle in the warehouse to be loaded is unqualified, alarming and reminding the vehicle, and if the cargo loading qualification degree of the vehicle does not exceed the cargo loading qualification degree range, judging that the cargo of the vehicle in the warehouse to be loaded is qualified, and permitting continuous transportation.
Compared with the prior art, the invention has the following beneficial effects: 1. the system acquires images of vehicles and drivers entering the park through the camera, analyzes and acquires the vehicle coincidence degree, combines the driver portrait to further acquire the vehicle coincidence degree and the driver coincidence degree, carries out corresponding treatment on the vehicles according to the acquired coincidence degree conditions of the vehicles and the drivers, can effectively identify illegal vehicles or drivers which do not meet regulations through the image acquisition and analysis of the vehicles and the drivers entering the park, is beneficial to improving the safety of the park, prevents potential safety threats, simultaneously utilizes the image acquisition and analysis technology, can rapidly and accurately evaluate the coincidence degree of the vehicles and the drivers, and can carry out corresponding treatment according to the acquired result by park managers, thereby reducing the time and energy consumption of manual auditing and improving the management efficiency.
2. The system obtains the recommendation coefficient of each warehouse by analyzing the position data of each warehouse, the storage data of various cargoes in each warehouse and the transportation list of the vehicle, further analyzes and obtains the optimal running route of the vehicle, can automatically analyze the storage condition of the cargoes in each warehouse and the transportation list of the vehicle, reasonably schedules and utilizes the warehouse and the vehicle, quickly determines the optimal running route, reduces the transportation time and improves the logistics transportation efficiency.
3. The system can accurately identify the goods loaded by the goods, is favorable for avoiding manual errors in the loading process, ensures the accuracy and the correctness of the goods loading, can detect the weight of the goods loaded by the vehicle in real time by the weight sensor at the bottom, enables the monitoring system to accurately analyze the goods loading qualification degree of the vehicle, ensures the goods loading to meet the specified standard, and can immediately perform the early warning treatment when the monitoring system analyzes the unqualified loaded vehicle.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram illustrating a system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the intelligent monitoring and management system for logistics in an intelligent park based on the internet of things comprises an image information acquisition module, a coincidence degree analysis module, a management database, a route analysis module, a loading detection module, a loading analysis module and an early warning module.
The management database is connected with the transportation list generation matching module, the coincidence degree analysis module, the route analysis module, the shipment analysis module and the early warning module, the transportation list generation matching module is connected with the coincidence degree acquisition module and the shipment detection module, the shipment analysis module is connected with the shipment detection module and the early warning module, and the coincidence degree analysis module is connected with the image information acquisition module.
The transportation list generation matching module is used for generating a logistics transportation list and matching transportation vehicles corresponding to the logistics transportation list according to the volume of the goods in the logistics transportation list and the positions of the vehicles, wherein the logistics transportation list comprises all goods transported and the weight required to be transported by all goods.
The specific analysis process of the transportation list generation matching module is as follows: generating a logistics transportation list, reading the volume of goods in the logistics transportation list, screening vehicles with carriage capacity larger than the volume of goods in the logistics transportation list from a database, marking the vehicles as preferred vehicles, positioning the preferred vehicles and calculating the distance from the vehicles to the park, marking the preferred vehicles as the distance from the vehicles to the park, arranging the preferred vehicles from the park in a sequence from small to large, screening the preferred vehicles closest to the park, marking the preferred vehicles as first selected vehicles, sending a transportation request to the driver of the vehicles, if the driver of the vehicles agrees to pick up, taking the vehicles as transportation vehicles corresponding to the logistics transportation list, marking the vehicle license as the license plates of the transportation vehicles corresponding to the logistics transportation list, sending the logistics transportation list to the driver of the vehicles, if the driver of the vehicles does not agree to pick up, continuing to screen second selected vehicles according to a method for screening the first selected vehicles, sending the transportation request to the driver of the second selected vehicles, and so on until the transportation request is picked up; through screening the vehicle of suitable capacity, can ensure that the goods can be effectively transported, avoid the transportation difficulty that leads to because of the vehicle capacity is not enough, through location and calculation vehicle and the distance in garden, can select the vehicle that is closest from the garden, thereby reduce the travel distance and the transportation time of vehicle, improve transportation efficiency, through sending the transportation request to the driver of first selection vehicle, can initiate transportation arrangement fast, improve the communication efficiency with the driver, when meetting first selection vehicle and can't connect, through continuing screening the second selection vehicle, and send the transportation request, can continue to seek suitable transportation vehicle, finally ensure the going on of commodity circulation transportation.
And the image information acquisition module is used for acquiring images of vehicles and drivers entering the park through the camera, respectively recording the images as vehicle information images and driver figures, and extracting the appearance data of the license plates and the vehicles from the vehicle information images, wherein the appearance data of the vehicles comprise the length, the width and the height of the vehicles.
The coincidence degree analysis module is used for analyzing and obtaining the coincidence degree of the vehicle according to the vehicle information image, the license plate and the appearance data of the vehicle, combining the coincidence degree of the vehicle with the driver portrait to further analyze and obtain the coincidence degree between the vehicle and the driver, comparing the coincidence degree between the vehicle and the driver with a preset coincidence degree qualification value between the vehicle and the driver, and correspondingly processing the vehicle according to the obtained coincidence degree condition of the vehicle and the driver.
The specific analysis method of the vehicle conformity degree comprises the following steps: the method comprises the steps of firstly, extracting license plates of transport vehicles corresponding to a logistics transport list, acquiring images of vehicles entering a park through a camera, recording the acquired images as vehicle information images, identifying the license plates of the vehicles from the vehicle information images, comparing the license plates with the license plates of the transport vehicles corresponding to the logistics transport list, judging that the vehicles are the transport vehicles corresponding to the logistics transport list if the license plates are consistent, and executing the second step, judging that the vehicles are not the transport vehicles corresponding to the logistics transport list if the license plates are inconsistent, and not releasing; by identifying and comparing the license plates, the risk of illegal vehicles entering the park can be reduced, and the vehicles entering the park are ensured to be transport vehicles corresponding to the logistics transport list.
The second step, extracting the length, width and height of the vehicle from the vehicle information image, respectively marked as l, w and h, simultaneously reading the delivery standard length, width and height of the vehicle registered by the license plate of the vehicle in the management database, comparing the delivery standard length, width and height with the length, width and height of the extracted vehicle, and obtaining the final product by the formulaObtaining vehicle compliance delta, l 0 、w 0 、h 0 The length, width and height of the factory standard of the vehicle registered by the license plate are respectively expressed, and e is expressed as a natural constant; by comparing the vehicle sizes, whether the vehicle meets the standard requirement or not can be judged, the vehicle size entering the park is ensured to meet the requirement, and possible safety and traffic problems are avoided.
The specific analysis method of the coincidence degree between the vehicle and the driver comprises the following steps: firstly, acquiring a human image of a vehicle driver entering a park through a camera, recording the human image as a driver human image, matching the driver human image with a driver human image set in a management database, judging that the driver human image is abnormal if the driver human image is not present in the driver human image set, and not letting pass, if the driver human image is present in the driver human image set, determining driver identity information according to the matched driver human image, further obtaining the license plate of the vehicle responsible for the driver according to the driver identity information, comparing the driver human image with the license plate of the vehicle responsible for the driver in the driver identity information, and determining the driver identity information according to the matched driver human image, wherein the driver identity information is the license plate of the vehicle responsible for the driver in the driver identity information, and the driver identity information is recorded in the management database by a formula of the driver human imageObtaining a distance between the vehicle and the driverWherein R is 1 R is the same as the license plate of the vehicle responsible for the driver in the driver identity information in the vehicle information image 2 The license plate in the vehicle information image is different from the license plate of the vehicle responsible for the driver in the driver identity information; the license plate of the responsible vehicle can be obtained according to the identity information of the driver and is compared with the license plate extracted from the read vehicle information image, so that the relevance between the vehicle and the driver is ensured, and the illegal driving condition is avoided.
The second step, the coincidence degree between the vehicle and the driver is read, the coincidence degree between the vehicle and the driver is compared with a preset coincidence degree qualification value between the vehicle and the driver, if the coincidence degree between the vehicle and the driver of the vehicle is lower than the preset coincidence degree qualification value between the vehicle and the driver, the vehicle is judged to be a disqualified vehicle and is not released, and if the coincidence degree between the vehicle and the driver of the vehicle is higher than or equal to the preset coincidence degree qualification value between the vehicle and the driver, the vehicle is judged to be a qualified vehicle and is permitted to be released; through verifying and identifying the association among driver figures, vehicles and drivers, the entry of unqualified vehicles and unauthorized personnel is reduced, so that the management and safety level of a park is improved.
And the management database is used for storing the vehicle factory standard appearance data registered by each license plate, the driver figure set, the geographic position of each warehouse and the goods stored in each warehouse.
And the route analysis module is used for analyzing and obtaining the recommendation coefficient of each warehouse to be loaded according to the geographic position of each warehouse, the goods class stored in each warehouse and the logistics transportation list of the vehicle, and further analyzing and obtaining the optimal driving route of the vehicle.
The specific analysis method of the recommendation coefficient of each warehouse to be loaded comprises the following steps: the first step, a commodity circulation transportation list of the vehicle is read, the goods stored in each warehouse in the management database are called, each warehouse for storing corresponding goods is screened out according to the goods to be transported in the commodity circulation transportation list, and the goods are marked as the warehouses to be loaded.
Second, according to the management dataThe geographical position of each warehouse in the warehouse locates each warehouse to be loaded, and the distance between each warehouse to be loaded and the park entrance is calculated and marked as d i I represents the number of the i-th warehouse to be loaded, i=1, 2, i.e., k, and meanwhile, according to the goods stored in each warehouse, the weight required to be transported of the goods of each goods in the logistics transportation list is combined, the weight required to be transported of the goods of each goods in each warehouse to be loaded is obtained through comprehensive analysis, and is recorded as M in N represents the number of the n-th item, n=1, 2,..m.
Third, respectively reading the goods stored in each warehouse and the weight required to be transported for the goods of each goods in each warehouse to be loaded, and substituting the weights into a formulaObtaining the estimated loading time t of each warehouse to be loaded i Wherein t 'is expressed as a preset fixed time for carrying goods into each warehouse, and t' n ' reference conveyance time, η, expressed as unit weight of the nth article cargo 1 A correction factor representing a preset predicted loading time.
Fourth, respectively reading the distance from each warehouse to be loaded to the park entrance, the weight required to be transported for each goods in each warehouse to be loaded and the expected loading time of each warehouse to be loaded, and substituting the distances and the expected loading time into a formulaObtaining transport recommendation coefficients of all warehouses to be loaded>A1, a2 and a3 are respectively expressed as weight factors of the required transportation of various goods in the warehouse to be loaded, the distance from the warehouse to be loaded to the park entrance and the estimated loading time of the warehouse to be loaded, and a1+a2+a3=1; by considering factors such as cargo information, geographic position, cargo weight and loading time of the warehouse, the quantitative method is adopted to evaluate and recommend the warehouse to be loaded, so that the efficiency and accuracy of transportation are improved, the transportation scheme is optimized, and the transportation cost is reduced.
The specific analysis method of the optimal running route of the vehicle comprises the following steps: the method comprises the steps of reading transport recommendation coefficients of all warehouses to be loaded, arranging the transport recommendation coefficients of all warehouses to be loaded in a sequence from large to small to obtain sequenced all warehouses to be loaded, sequentially planning a running route of a transport vehicle corresponding to a logistics transport list according to the geographic position of each sequenced warehouse to be loaded, and marking the running route as an optimal running route of the vehicle; by reading the transport recommendation coefficients of the warehouses to be loaded and arranging the warehouses in order from large to small, the first-to-high recommendation coefficient of the transport vehicles can be ensured, so that the idle running and waiting time of the transport vehicles can be reduced to the greatest extent by arranging the routes, and the transport efficiency is improved.
The loading detection module is used for carrying out code scanning identification on the goods loaded, and detecting the weight of the loaded goods through a weight sensor arranged at the bottom of the carriage.
And the cargo loading analysis module is used for comparing the weight of the loaded cargoes with the weight of the transported cargoes in the logistics transportation list and analyzing to obtain the cargo loading qualification degree.
The specific analysis method of the cargo loading qualification degree comprises the following steps: the method comprises the steps that firstly, code scanning identification is conducted on goods loaded in a warehouse to be loaded where a vehicle is located, the goods are matched with the goods in a logistics transportation list, if the goods are certain goods in the logistics transportation list, loading operation is continuously conducted, and if the goods are certain goods in a non-logistics transportation list, alarming and reminding are conducted; the code scanning identifies the goods and matches the goods in the logistics transportation list, so that the loaded goods are consistent with the list, and the risk of loading wrong goods is reduced.
The second step, the weight of the goods in each warehouse to be loaded is read, and the sum of the weight of the goods in each warehouse to be loaded is obtained by summing up the weight of the goods in each warehouse to be loaded, and the sum is recorded asAnd detected by a weight sensor arranged at the bottom of the carriage of the vehicleThe actual total weight of each warehouse to be loaded with goods is measured and recorded as +.>Comparing the total actual weight of the cargoes loaded in each warehouse to be loaded with the sum of the weight of the cargoes required to be transported in each warehouse to be loaded with the cargoes, and passing through the formulaObtaining the cargo loading qualification degree sigma of the vehicle, wherein k is expressed as the number of warehouses to be loaded, M 1 Total (S) Representing the sum of the weights required to transport the goods in the 1 st warehouse to be loaded, M' 1 Total (S) Indicating the actual total weight of the 1 st warehouse to be loaded with goods. By analyzing the cargo loading qualification degree, the accuracy and the efficiency of cargo loading are improved, and errors and risks are reduced to the greatest extent.
The early warning module is used for comparing the cargo loading qualification degree with a preset cargo loading qualification degree threshold value to obtain the loading qualification condition of the vehicle, and further carrying out early warning treatment on the unqualified vehicle.
The specific analysis method for carrying out early warning treatment on the unqualified vehicle comprises the following steps: comparing the cargo loading qualification degree of the vehicle with a preset cargo loading qualification degree range, if the cargo loading qualification degree of the vehicle exceeds the cargo loading qualification degree range, judging that the cargo of the vehicle in the warehouse to be loaded is unqualified, alarming and reminding the vehicle, and if the cargo loading qualification degree of the vehicle does not exceed the cargo loading qualification degree range, judging that the cargo of the vehicle in the warehouse to be loaded is qualified, and permitting continuous transportation; by comparing the cargo loading qualification of the vehicles in the warehouses to be loaded, the unqualified vehicles can be detected in time, and the loading of cargoes which do not accord with the standard can be avoided, so that the cargo loading quality is improved, and the damage, accidents or disputes of cargoes caused by the unqualified loading are reduced.
The system can effectively identify illegal vehicles or drivers which do not accord with regulations by acquiring and analyzing images of the vehicles and drivers entering the park, thereby being beneficial to improving the safety of the park, reducing the time and energy consumption of manual auditing and improving the management efficiency; by analyzing the recommendation coefficients of all the warehouses and further analyzing the optimal driving route of the vehicle, the warehouses and the vehicle can be reasonably scheduled and utilized, the optimal driving route can be rapidly determined, the logistics transportation efficiency is improved, and the transportation time is reduced; the goods loaded are scanned, the weight is detected and identified, the goods loading qualification degree of the vehicle is obtained, and then the vehicle with unqualified loading is subjected to early warning treatment, so that the manual error in the loading process is avoided, and the accuracy and the correctness of goods loading are ensured.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (8)
1. Intelligent garden commodity circulation intelligent monitoring management system based on thing networking, its characterized in that includes:
the transportation list generation matching module is used for generating a logistics transportation list and matching transportation vehicles corresponding to the logistics transportation list according to the volume of the goods in the logistics transportation list and the positions of the vehicles, wherein the logistics transportation list comprises all goods transported and the weight required to be transported by all goods;
the image information acquisition module is used for acquiring images of vehicles and drivers entering the park through the camera, respectively marking the images as vehicle information images and driver figures, and extracting the appearance data of the license plates and the vehicles from the vehicle information images, wherein the appearance data of the vehicles comprise the length, the width and the height of the vehicles;
the coincidence degree analysis module is used for analyzing and obtaining the coincidence degree of the vehicle according to the vehicle information image, the license plate and the appearance data of the vehicle, combining the coincidence degree of the vehicle with the driver portrait to further analyze and obtain the coincidence degree between the vehicle and the driver, comparing the coincidence degree between the vehicle and the driver with a preset coincidence degree qualification value between the vehicle and the driver, and correspondingly processing the vehicle according to the obtained coincidence degree condition of the vehicle and the driver;
the management database is used for storing the vehicle factory standard appearance data registered by each license plate, the driver figure set, the geographic position of each warehouse and the goods stored in each warehouse;
the route analysis module is used for analyzing and obtaining the recommendation coefficient of each warehouse to be loaded according to the geographic position of each warehouse, the goods class stored in each warehouse and the logistics transportation list of the vehicle, and further analyzing and obtaining the optimal driving route of the vehicle;
the loading detection module is used for carrying out code scanning identification on the goods loaded, and detecting the weight of the loaded goods through a weight sensor arranged at the bottom of the carriage;
the cargo loading analysis module is used for comparing the weight of the loaded cargoes with the weight of the transported cargoes in the logistics transportation list and analyzing to obtain the cargo loading qualification;
the early warning module is used for comparing the cargo loading qualification degree with a preset cargo loading qualification degree threshold value to obtain the loading qualification condition of the vehicle, and further carrying out early warning treatment on the unqualified vehicle.
2. The intelligent monitoring and management system for logistics in an intelligent park based on the internet of things of claim 1, wherein: the specific analysis process of the transportation list generation matching module is as follows: generating a logistics transportation list, reading the volume of goods in the logistics transportation list, screening vehicles with carriage capacity larger than the volume of goods in the logistics transportation list from a database, marking the vehicles as preferred vehicles, positioning the preferred vehicles and calculating the distance from the vehicles to the park, marking the preferred vehicles as the distance from the vehicles to the park, arranging the preferred vehicles from the park in a descending order, screening the preferred vehicles closest to the park as first selected vehicles, marking the vehicles as transportation vehicles corresponding to the logistics transportation list if the drivers of the vehicles agree to pick up, marking the vehicles as the license plates of the transportation vehicles corresponding to the logistics transportation list and sending the logistics transportation list to the drivers of the vehicles, continuing screening second selected vehicles according to a method for screening the first selected vehicles if the drivers of the vehicles disagree to pick up, sending transportation requests to the drivers of the second selected vehicles, and so on until the transportation requests are picked up.
3. The intelligent monitoring and management system for logistics in an intelligent park based on the internet of things of claim 1, wherein: the specific analysis method of the vehicle conformity degree comprises the following steps:
the method comprises the steps of firstly, extracting license plates of transport vehicles corresponding to a logistics transport list, acquiring images of vehicles entering a park through a camera, recording the acquired images as vehicle information images, identifying the license plates of the vehicles from the vehicle information images, comparing the license plates with the license plates of the transport vehicles corresponding to the logistics transport list, judging that the vehicles are the transport vehicles corresponding to the logistics transport list if the license plates are consistent, and executing the second step, judging that the vehicles are not the transport vehicles corresponding to the logistics transport list if the license plates are inconsistent, and not releasing;
the second step, extracting the length, width and height of the vehicle from the vehicle information image, respectively marked as l, w and h, simultaneously reading the delivery standard length, width and height of the vehicle registered by the license plate of the vehicle in the management database, comparing the delivery standard length, width and height with the length, width and height of the extracted vehicle, and obtaining the final product by the formulaObtaining vehicle compliance delta, l 0 、w 0 、h 0 The factory standard length, width, and height of the vehicle registered for the license plate are respectively expressed, and e is expressed as a natural constant.
4. The intelligent monitoring and management system for logistics in an intelligent park based on the internet of things of claim 2, wherein: the specific analysis method of the coincidence degree between the vehicle and the driver comprises the following steps:
firstly, acquiring a human image of a vehicle driver entering a park through a camera, recording the human image as a driver human image, matching the driver human image with a driver human image set in a management database, judging that the driver identity is abnormal if the driver human image is not present in the driver human image set, and not letting pass, if the driver human image is present in the driver human image set, determining driver identity information according to the matched driver human image, further obtaining the license plate of the responsible vehicle according to the driver identity information, reading the license plate extracted from the vehicle information image, comparing the license plate with the license plate of the responsible vehicle of the driver in the driver identity information, and determining the driver identity information according to a formulaObtaining compliance gamma between vehicle and driver, wherein R 1 R is the same as the license plate of the vehicle responsible for the driver in the driver identity information in the vehicle information image 2 The license plate in the vehicle information image is different from the license plate of the vehicle responsible for the driver in the driver identity information;
and secondly, reading the conformity between the vehicle and the driver, comparing the conformity between the vehicle and the driver with a preset conformity qualification value between the vehicle and the driver, judging the vehicle as a disqualified vehicle if the conformity between the vehicle and the driver of the vehicle is lower than the preset conformity qualification value between the vehicle and the driver, and not releasing the vehicle if the conformity between the vehicle and the driver of the vehicle is higher than or equal to the preset conformity qualification value between the vehicle and the driver, judging the vehicle as a qualified vehicle and permitting releasing the vehicle.
5. The intelligent monitoring and management system for logistics in an intelligent park based on the internet of things of claim 1, wherein: the specific analysis method of the recommendation coefficient of each warehouse to be loaded comprises the following steps:
firstly, reading a logistics transportation list of a vehicle, calling the goods stored in each warehouse in a management database, screening each warehouse for storing corresponding goods according to the goods to be transported in the logistics transportation list, and marking the warehouses as warehouses to be loaded;
step two, positioning each warehouse to be loaded according to the geographic position of each warehouse in the management database, and respectively calculating the distance between each warehouse to be loaded and the park entrance, and marking as d i I represents the number of the i-th warehouse to be loaded, i=1, 2, i.e., k, and meanwhile, according to the goods stored in each warehouse, the weight required to be transported of the goods of each goods in the logistics transportation list is combined, the weight required to be transported of the goods of each goods in each warehouse to be loaded is obtained through comprehensive analysis, and is recorded as M in N represents the number of the n-th item, n=1, 2, m;
third, respectively reading the goods stored in each warehouse and the weight required to be transported for the goods of each goods in each warehouse to be loaded, and substituting the weights into a formulaObtaining the estimated loading time t of each warehouse to be loaded i Wherein t' is expressed as a preset fixed time for entering each warehouse to carry goods, and t n Reference conveyance time, η, expressed as unit weight of the nth article 1 A correction coefficient representing a preset estimated loading time;
fourth, respectively reading the distance from each warehouse to be loaded to the park entrance, the weight required to be transported for each goods in each warehouse to be loaded and the expected loading time of each warehouse to be loaded, and substituting the distances and the expected loading time into a formulaObtaining transport recommendation coefficients of all warehouses to be loaded>Wherein a1, a2 and a3 are respectively expressed as weight factors of the required transportation of various goods in the warehouse to be loaded, the distance from the warehouse to be loaded to the park entrance and the estimated loading time of the warehouse to be loaded, and a1+a2+a3=1.
6. The intelligent monitoring and management system for logistics in an intelligent park based on the internet of things of claim 5, wherein: the specific analysis method of the optimal running route of the vehicle comprises the following steps: and reading the transportation recommendation coefficients of the warehouses to be loaded, arranging the transportation recommendation coefficients of the warehouses to be loaded in a sequence from large to small to obtain the ordered warehouses to be loaded, sequentially planning the running route of the transport vehicle corresponding to the logistics transportation list according to the geographical position of the ordered warehouses to be loaded, and marking the running route as the optimal running route of the vehicle.
7. The intelligent monitoring and management system for logistics in an intelligent park based on the internet of things of claim 1, wherein: the specific analysis method of the cargo loading qualification degree comprises the following steps:
the method comprises the steps that firstly, code scanning identification is conducted on goods loaded in a warehouse to be loaded where a vehicle is located, the goods are matched with the goods in a logistics transportation list, if the goods are certain goods in the logistics transportation list, loading operation is continuously conducted, and if the goods are certain goods in a non-logistics transportation list, alarming and reminding are conducted;
the second step, the weight of the goods in each warehouse to be loaded is read, and the sum of the weight of the goods in each warehouse to be loaded is obtained by summing up the weight of the goods in each warehouse to be loaded, and the sum is recorded asAnd the actual total weight of the warehouses to be loaded after the cargoes are loaded is detected by a weight sensor arranged at the bottom of the carriage of the vehicle and is recorded as +.>Comparing the total actual weight of the cargoes loaded in each warehouse to be loaded with the sum of the weight of the cargoes required to be transported in each warehouse to be loaded with the cargoes, and passing through the formulaObtaining the cargo loading qualification degree sigma of the vehicle, wherein k is expressed as the number of warehouses to be loaded, M 1 Total (S) Representing the sum of the weights required to transport the goods in the 1 st warehouse to be loaded, M' 1 Total (S) Indicating the actual total weight of the 1 st warehouse to be loaded with goods.
8. The intelligent monitoring and management system for logistics in an intelligent park based on the internet of things of claim 7, wherein: the specific analysis method for carrying out early warning treatment on the unqualified vehicle comprises the following steps: comparing the cargo loading qualification degree of the vehicle with a preset cargo loading qualification degree range, if the cargo loading qualification degree of the vehicle exceeds the cargo loading qualification degree range, judging that the cargo of the vehicle in the warehouse to be loaded is unqualified, alarming and reminding the vehicle, and if the cargo loading qualification degree of the vehicle does not exceed the cargo loading qualification degree range, judging that the cargo of the vehicle in the warehouse to be loaded is qualified, and permitting continuous transportation.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117933672A (en) * | 2024-03-22 | 2024-04-26 | 长春理工大学 | Park logistics vehicle dispatching management system based on artificial intelligence |
| CN118014444A (en) * | 2024-04-09 | 2024-05-10 | 如皋市联政中小企业服务有限公司 | Intelligent park operation data analysis processing system based on Internet of things |
| CN118014444B (en) * | 2024-04-09 | 2024-06-11 | 如皋市联政中小企业服务有限公司 | Intelligent park operation data analysis processing system based on Internet of things |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117933672A (en) * | 2024-03-22 | 2024-04-26 | 长春理工大学 | Park logistics vehicle dispatching management system based on artificial intelligence |
| CN117933672B (en) * | 2024-03-22 | 2024-05-24 | 长春理工大学 | Park logistics vehicle dispatching management system based on artificial intelligence |
| CN118014444A (en) * | 2024-04-09 | 2024-05-10 | 如皋市联政中小企业服务有限公司 | Intelligent park operation data analysis processing system based on Internet of things |
| CN118014444B (en) * | 2024-04-09 | 2024-06-11 | 如皋市联政中小企业服务有限公司 | Intelligent park operation data analysis processing system based on Internet of things |
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