A real-time collaborative supply chain monitoring system and method

Abstract

The present invention relates to the field of data processing technology, and in particular to a real-time collaborative supply chain supervision system and method. The method comprises the steps of: obtaining the road congestion degree of each group of transportation paths at each sampling moment according to the acceleration data of three different directions of each transport vehicle corresponding to each type of goods to be transported in each group of transportation paths at each sampling moment; obtaining the corrected bumpiness degree of each type of goods to be transported corresponding to each transport vehicle at each sampling moment in each group of transportation paths; obtaining the applicability of each group of transportation paths of each type of goods to be transported corresponding to each transport vehicle at each sampling moment according to the road congestion degree and the corrected bumpiness degree; obtaining the weighted path distance of each type of goods to be transported corresponding to each transport vehicle in each group of transportation paths at each sampling moment according to the applicability, and obtaining the optimal path of each type of goods to be transported corresponding to each transport vehicle at each sampling moment, and the present invention improves the transportation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a real-time collaborative supply chain monitoring system and method. Background Art

[0002] As the digital core of supply chain management, the real-time collaborative supply chain supervision system integrates various information tools such as enterprise resource planning (ERP), warehouse management system (WMS), and transportation management system (TMS) to ensure the timely flow of information in all links of the supply chain, covering all-round data from orders to logistics. The real-time collaborative supply chain supervision system promotes close collaboration and efficient decision-making across enterprises, optimizes production, inventory and logistics arrangements based on real-time data, reduces inventory pressure and waste, and promotes overall operational efficiency and profit. For cargo transportation, the system significantly improves the transparency and controllability of logistics, realizes the optimal planning and real-time tracking of transportation routes, reduces delays and costs, and improves the timeliness and customer satisfaction of cargo transportation.

[0003] The patent application document with the publication number CN117931795A currently discloses a method for checking table association relationships based on the Dijkstra algorithm, including reading data, randomly selecting two nodes, setting them as the starting point and the end point, the left arrow represents the left connection, the right arrow represents the right connection, and connecting the two nodes into a directed graph; designing a data table, which stores the cost from the starting point to each node, the parent node and the node processing status; using the Dijkstra shortest path algorithm to find the shortest path and update the data table at the same time; using the data table to check and determine whether an incorrect connection occurs.

[0004] The Dijkstra algorithm in the above method is used to update the cargo transportation route in real time according to the shortest path distance. It does not take into account that road congestion will cause an increase in transportation costs, and road bumps will cause an increase in cargo loss rate. Therefore, the transportation route obtained only by path distance is not the most reasonable transportation route. Summary of the invention

[0005] In order to solve the problem that traffic congestion will cause increased transportation costs, road bumps will lead to increased cargo loss rate, and therefore the shortest transportation path obtained only according to the Dijkstra algorithm is not the most reasonable transportation path, the present invention provides a real-time collaborative supply chain supervision system and method.

[0006] In a first aspect, the present invention provides a real-time collaborative supply chain supervision method, which adopts the following technical solution:

[0007] A real-time collaborative supply chain monitoring method, comprising the steps of:

[0008] Obtain the degree of road congestion of each transport route at each sampling time; obtain the degree of bumpiness of each type of transport vehicle corresponding to each type of goods to be transported on each set of transport routes at each sampling time; calculate the corrected degree of bumpiness:

[0009] , represents the corrected bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment on the e-th group of transport routes; represents the feasibility of the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time when the r-th group of transport routes is used except the e-th group of transport routes; as well as Indicates the degree of bumpiness of the transport vehicle corresponding to the jth type of goods to be transported at the i-th sampling moment when on the e-th group of transport paths and the degree of bumpiness of the transport vehicle corresponding to the r-th group of transport paths other than the e-th group of transport paths; R represents the number of transport paths corresponding to the jth type of goods to be transported except the e-th group of transport paths; obtains the suitability of each group of transport paths corresponding to each type of goods to be transported at each sampling moment, and the suitability is negatively correlated with the product of the corrected bumpiness and the road congestion degree;

[0010] The distance of each group of transport paths is weighted according to the suitability to obtain the weighted path distance of the transport vehicle corresponding to each type of goods to be transported in each group of transport paths at each sampling time; based on the weighted path distance, the optimal path of the transport vehicle corresponding to each type of goods to be transported at each sampling time is obtained.

[0011] The innovation of the present invention lies in obtaining the suitability of each group of transport paths according to the road congestion degree of each transport path and the corrected bumpiness degree of each group of transport paths, and finally using the suitability of each group of transport paths to correct the distance of each group of transport paths to obtain the weighted path distance of each group of transport paths, and finally selecting the transport route corresponding to the shortest weighted path distance as the optimal route through the Dijkstra algorithm, taking into account the impact of road congestion and road bumps on cargo transportation, so that the optimal path finally obtained is more accurate, and the cargo transportation efficiency is ensured under the premise of effectively ensuring the safety of cargo transportation.

[0012] Preferably, obtaining the traffic congestion level of each transportation path at each sampling time includes:

[0013] ;

[0014] In the formula, Indicates the traffic congestion degree of the e-th group of transportation routes at the i-th sampling time; Indicates the type and quantity of goods to be transported; as well as It represents the number of all transport vehicles corresponding to the j-th type of goods to be transported and the number of all sampling moments in the neighborhood sampling range time series of the i-th sampling moment; as well as It represents the horizontal acceleration data at the xth sampling time in the neighborhood sampling range time series of the ith sampling time for the nth transport vehicle of the jth type of goods to be transported on the eth group of transport paths, as well as the mean of the horizontal acceleration data at all sampling times; || represents the absolute value; norm() represents the linear normalization function.

[0015] The higher the traffic congestion level of the transport path, the less suitable the transport path is, which facilitates the subsequent acquisition of the suitability of the transport path according to the traffic congestion level.

[0016] Preferably, the acquisition of the degree of turbulence includes:

[0017] ;

[0018] In the formula, It represents the degree of bumping of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time on the e-th group of transport routes; as well as It represents the number of all transport vehicles corresponding to the j-th type of goods to be transported and the number of all sampling moments in the neighborhood sampling range time series of the i-th sampling moment; Indicates the type of acceleration data; as well as It indicates that the jth type of goods to be transported corresponds to the nth transport vehicle in the eth group of transport routes, and the neighborhood sampling range time series of the i-th sampling time is the x-th sampling time. The acceleration data in each direction and the acceleration data at all sampling times The mean of the acceleration data in each direction; || means taking the absolute value; norm() means the linear normalization function.

[0019] The higher the bumpiness of the transport route, the less suitable the transport route is, which facilitates the subsequent acquisition of the suitability of the transport route according to the bumpiness.

[0020] Preferably, obtaining the feasibility of the bumpiness degree includes:

[0021] ;

[0022] In the formula, represents the feasibility of the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time when the r-th group of transport routes is used except the e-th group of transport routes; as well as represents the cargo loss rate of the transport vehicle corresponding to the jth type of cargo to be transported on the eth group of transport routes and the cargo loss rate of the rth group of transport routes excluding the eth group of transport routes; as well as It represents the traffic congestion degree of the e-th group of transportation paths at the i-th sampling moment and the traffic congestion degree of the r-th group of transportation paths except the e-th group of transportation paths; norm() represents the linear normalization function.

[0023] The bumpiness of the transport path is corrected according to the feasibility of the bumpiness to obtain a corrected bumpiness to make it more accurate.

[0024] Preferably, obtaining the suitability of each group of transport paths corresponding to each type of transport vehicle for each type of goods to be transported at each sampling time includes:

[0025] ;

[0026] In the formula, Represents the suitability of the e-th group of transportation paths corresponding to the transport vehicle for the j-th type of goods to be transported at the i-th sampling time; Indicates the traffic congestion degree of the e-th group of transportation routes at the i-th sampling time; represents the corrected bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment on the e-th group of transport routes; exp() represents an exponential function with a natural constant as the base.

[0027] The greater the suitability of a transport path, the more the transport path can be used to transport goods.

[0028] Preferably, the step of obtaining the weighted path distance of each group of transportation paths corresponding to each type of goods to be transported at each sampling time includes:

[0029] ;

[0030] In the formula, Represents the weighted path distance of the transport vehicle corresponding to the j-th type of goods to be transported in the e-th group of transport paths at the i-th sampling time; Represents the suitability of the e-th group of transportation paths corresponding to the transport vehicle for the j-th type of goods to be transported at the i-th sampling time; Represents the distance of the transport path of group e.

[0031] Preferably, the step of obtaining the optimal path of the transport vehicle corresponding to each type of goods to be transported at each sampling time includes:

[0032] According to the weighted path distance of the transport vehicle corresponding to the j-th type of goods to be transported in each group of transport paths at the i-th sampling moment, the Dijkstra algorithm is used to obtain the optimal path of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment.

[0033] The impact of traffic congestion and road bumps on cargo transportation is taken into consideration, making the optimal route ultimately obtained more accurate, ensuring the efficiency of cargo transportation while effectively ensuring the safety of cargo transportation.

[0034] In a second aspect, the present invention provides a real-time collaborative supply chain monitoring system, which adopts the following technical solutions:

[0035] A real-time collaborative supply chain supervision system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned real-time collaborative supply chain supervision method is implemented.

[0036] By adopting the above technical solution, the above-mentioned real-time collaborative supply chain supervision method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0037] The present invention has the following technical effects: by analyzing the road congestion degree of each group of transport paths and the corrected bumpiness degree of each group of transport paths, the present invention is convenient for obtaining the suitability of each group of transport paths according to the road congestion degree of each group of transport paths and the corrected bumpiness degree of each group of transport paths, and finally using the suitability of each group of transport paths to correct the distance of each group of transport paths to obtain the weighted path distance of each group of transport paths, and finally selecting the transport route corresponding to the shortest weighted path distance as the optimal route through the Dijkstra algorithm, taking into account the influence of road congestion and road bumpiness on cargo transportation, so that the optimal path finally obtained is more accurate, and the cargo transportation efficiency is ensured on the premise of effectively ensuring the cargo transportation safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0039] Figure 1 It is a method flow chart of a real-time collaborative supply chain supervision method in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0042] The embodiment of the present invention discloses a real-time collaborative supply chain supervision method, referring to Figure 1 , comprising steps S1 to S3:

[0043] S1: Obtain acceleration data in three different directions at each sampling moment for each group of transport routes and transport vehicles corresponding to each type of goods to be transported and each transport vehicle corresponding to each type of goods to be transported in each group of transport routes.

[0044] In an embodiment of the present invention, a cargo transportation network model is obtained through a trusted in-transit supervision platform, and a plurality of groups of transportation routes and a plurality of transportation vehicles corresponding to each type of cargo to be transported are obtained according to the cargo transportation network model;

[0045] A three-axis acceleration sensor is installed on each transport vehicle corresponding to each type of goods to be transported. For any transport vehicle corresponding to any type of goods to be transported on any set of transport routes, every 10 minutes is a sampling moment, and the acceleration data in three different directions at each sampling moment are collected in turn to obtain the acceleration data in three different directions (horizontal acceleration data, left and right acceleration data, and vertical acceleration data) for each transport vehicle corresponding to each type of goods to be transported on each set of transport routes at each sampling moment.

[0046] It should be noted that the three-axis acceleration sensor can measure the acceleration of an object in three orthogonal directions at the same time and provide comprehensive motion state data. It collects acceleration data along the horizontal plane (front and back direction), acceleration data along the horizontal plane (left and right direction) and acceleration data along the vertical direction. The acceleration data along the horizontal plane (front and back direction) is recorded as horizontal acceleration data; the acceleration data along the horizontal plane (left and right direction) is recorded as left and right acceleration data; and the acceleration data along the vertical direction is recorded as vertical acceleration data.

[0047] S2: Based on the acceleration data in three different directions at each sampling moment of each transport vehicle corresponding to each type of goods to be transported in each group of transport routes; obtain the road congestion degree of each group of transport routes at each sampling moment and the bumpiness degree of each transport vehicle corresponding to each type of goods to be transported in each group of transport routes at each sampling moment; obtain the feasibility of the bumpiness degree of each transport vehicle corresponding to each type of goods to be transported in each group of transport routes except each group of transport routes at each sampling moment; based on the feasibility, obtain the corrected bumpiness degree of each transport vehicle corresponding to each type of goods to be transported in each group of transport routes at each sampling moment.

[0048] It should be noted that the traditional Dijkstra algorithm updates the cargo transportation route in real time according to the shortest path distance. It does not take into account that road congestion will cause the transportation cost to rise, and road bumps will lead to an increase in the cargo loss rate. Therefore, the transportation route obtained only by path distance is not the most reasonable transportation route.

[0049] Step S2 includes steps S20 and S21, which are specifically as follows:

[0050] S20: According to the acceleration data in three different directions at each sampling moment of each transport vehicle corresponding to each type of goods to be transported in each group of transport routes; the degree of road congestion of each group of transport routes at each sampling moment is obtained, and the degree of bumpiness of each type of goods to be transported corresponding to each transport vehicle at each sampling moment in each group of transport routes is obtained.

[0051] It should be noted that when the actual road conditions are congested during the transportation of goods, the transportation speed of the transport vehicle will be reduced, resulting in low transportation efficiency and increased time cost. It is known that the more congested the road conditions are, the more times the vehicle starts and brakes, so the horizontal acceleration data over a period of time is higher than that under normal vehicle driving conditions. Therefore, when each type of horizontal acceleration data for each type of goods to be transported corresponds to each type of transport vehicle in any group of transport routes over a period of time is larger than the average of all horizontal acceleration data within this period of time, it means that the road conditions of this group of transport routes are more congested.

[0052] In the embodiment of the present invention, a neighborhood parameter T is preset, and a time sequence consisting of T sampling moments before the i-th sampling moment is used as a neighborhood sampling range sequence of the i-th sampling moment;

[0053] Get the traffic congestion level of the e-th group of transportation routes at the i-th sampling time:

[0054] ;

[0055] In the formula, Indicates the traffic congestion degree of the e-th group of transportation routes at the i-th sampling time; Indicates the type and quantity of goods to be transported; represents the number of all transport vehicles corresponding to the j-th type of goods to be transported; Represents the number of all sampling moments in the neighborhood sampling range time series of the i-th sampling moment; It represents the horizontal acceleration data at the xth sampling time in the neighborhood sampling range time series of the jth type of goods to be transported corresponding to the nth transport vehicle on the eth group of transport routes at the ith sampling time; It represents the mean value of the horizontal acceleration data at all sampling times in the neighborhood sampling range time series of the i-th sampling time of the j-th type of goods to be transported corresponding to the n-th transport vehicle in the e-th group of transport paths; || represents the absolute value; norm() represents the linear normalization function.

[0056] It should be noted that dangerous chemicals, fragile goods, precision equipment and other goods should be transported without bumps as much as possible to reduce transportation risks. When the road is bumpy during transportation, the accelerations in three different directions collected on the vehicle will change. Therefore, when the acceleration data in each different direction of each transport vehicle corresponding to any type of goods to be transported on any set of transport routes within a period of time is greater than the mean value of the acceleration data in all different directions within the period of time, it means that the degree of bumpiness of the transport vehicle corresponding to the goods to be transported on the set of transport routes at the current sampling time is greater.

[0057] The specific formula for obtaining the degree of bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time on the e-th group of transport paths is:

[0058] ;

[0059] In the formula, It represents the degree of bumping of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time on the e-th group of transport routes; Indicates the number of all transport vehicles corresponding to the jth type of goods to be transported; Represents the number of all sampling moments in the neighborhood sampling range time series of the i-th sampling moment; Indicates the type of acceleration data; It indicates that the jth type of goods to be transported corresponds to the nth transport vehicle in the eth group of transport routes, and the neighborhood sampling range time series of the i-th sampling time is the x-th sampling time. Acceleration data in each direction; It represents the number of sampling times in the neighborhood sampling range of the i-th sampling time of the j-th type of goods to be transported corresponding to the n-th transport vehicle in the e-th group of transportation routes. The mean value of the acceleration data in each direction; It means taking the absolute value; norm() means the linear normalization function.

[0060] S21: Obtain the feasibility of the bumpiness of each type of cargo to be transported corresponding to the transport vehicle at each sampling moment in each group of transport routes except each group of transport routes; based on the feasibility, obtain the corrected bumpiness of each type of cargo to be transported corresponding to the transport vehicle at each sampling moment in each group of transport routes.

[0061] It should be noted that if the number of corresponding transport vehicles for the j-th type of goods to be transported on the e-th group of transport paths at the ith sampling moment is small, the reliability of the obtained bumpiness degree of the transport vehicle corresponding to the j-th type of goods to be transported on the e-th group of transport paths at the ith sampling moment is low, so it needs to be corrected. If the road congestion degree of other transport paths is more similar to that of the e-th group of transport paths at the ith sampling moment, and the cargo loss rate of the transport vehicle corresponding to the j-th type of goods to be transported on the e-th group of transport paths is more similar to the cargo loss rate of other transport paths, it means that the bumpiness degree of the transport vehicle corresponding to the j-th type of goods to be transported on the r-th group of transport paths other than the e-th group of transport paths at the ith sampling moment can be corrected. Therefore, the feasibility of obtaining the bumpiness degree of the transport vehicle corresponding to the j-th type of goods to be transported on the r-th group of transport paths other than the e-th group of transport paths at the ith sampling moment is obtained.

[0062] In an embodiment of the present invention, the ratio of the weight of damaged goods generated by each transport vehicle corresponding to the jth type of goods to be transported in the e group of transport paths to the total weight of goods on each transport vehicle corresponding to the jth type of goods to be transported in the e group of transport paths is obtained, and is recorded as the damage rate of each transport vehicle corresponding to the jth type of goods to be transported in the e group of transport paths; the average of the damage rates of all transport vehicles corresponding to the jth type of goods to be transported in the e group of transport paths is recorded as the cargo loss rate of the transport vehicle corresponding to the jth type of goods to be transported in the e group of transport paths; similarly, the cargo loss rate of the transport vehicle corresponding to each type of goods to be transported in each group of transport paths is obtained.

[0063] Obtain the feasibility of the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time when the r-th group of transport routes is excluding the e-th group of transport routes:

[0064] ;

[0065] In the formula, represents the feasibility of the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time when the r-th group of transport routes is used except the e-th group of transport routes; It represents the cargo loss rate of the transport vehicle corresponding to the j-th type of cargo to be transported on the e-th group of transport routes; represents the cargo loss rate of the transport vehicle corresponding to the j-th type of cargo to be transported when it is on the r-th group of transport routes except the e-th group of transport routes; Indicates the traffic congestion degree of the e-th group of transportation routes at the i-th sampling time; represents the traffic congestion degree of the rth group of transportation routes except the eth group of transportation routes at the i-th sampling time; and The smaller the value is, the closer the road conditions and cargo loss rates of the two transport routes are. Therefore, the greater the feasibility of the bumpy degree of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment when the transport vehicle is on the r-th group of transport routes except the e-th group of transport routes. This means that the bumpy degree of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment when the transport vehicle is on the r-th group of transport routes except the e-th group of transport routes can be used to correct the bumpy degree of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment when the transport vehicle is on the e-th group of transport routes; norm() represents the normalization function.

[0066] It should be noted that, when the feasibility of the bumpy degree at the ith sampling moment when the transport vehicle corresponding to the j-th category of goods to be transported is in the r-th group of transport paths other than the e-th group of transport paths is greater, it means that the bumpy degree at the ith sampling moment when the transport vehicle corresponding to the j-th category of goods to be transported is in the r-th group of transport paths other than the e-th group of transport paths can be referred to, and the bumpy degree at the ith sampling moment when the transport vehicle corresponding to the j-th category of goods to be transported is in the e-th group of transport paths can be corrected.

[0067] In the embodiment of the present invention, the corrected bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment in the e-th group of transport paths is obtained:

[0068] ;

[0069] In the formula, represents the corrected bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment on the e-th group of transport routes; represents the feasibility of the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time when the r-th group of transport routes is used except the e-th group of transport routes; It represents the degree of bumping of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time on the e-th group of transport routes; represents the degree of bumping at the i-th sampling moment when the transport vehicle corresponding to the j-th type of goods to be transported is on the r-th group of transport routes except the e-th group of transport routes; R represents the number of transport routes corresponding to the j-th type of goods to be transported except the e-th group of transport routes; When the value of is 0, the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment on the r-th group of transport paths other than the e-th group of transport paths is not referred to.

[0070] S3: According to the degree of road congestion and the corrected degree of bumpiness, the suitability of each group of transportation paths for each type of goods to be transported corresponding to the transport vehicle at each sampling moment is obtained. According to the suitability of each group of transportation paths for each type of goods to be transported corresponding to the transport vehicle at each sampling moment, the weighted path distance of each group of transportation paths for each type of goods to be transported corresponding to the transport vehicle at each sampling moment is obtained; based on the weighted path distance, the optimal path for each type of goods to be transported corresponding to the transport vehicle at each sampling moment is obtained.

[0071] Step S3 includes steps S30 and S31, which are specifically as follows:

[0072] S30: According to the degree of road congestion and the corrected bumpiness, the suitability of each group of transport paths corresponding to each type of transported goods at each sampling moment is obtained.

[0073] It should be noted that, when the degree of traffic congestion and bumpiness of the transportation route is greater, it means that the applicability of the current transportation route is lower.

[0074] In the embodiment of the present invention, the suitability of the e-th group of transport paths corresponding to the transport vehicle for the j-th type of goods to be transported at the i-th sampling time is obtained:

[0075] ;

[0076] In the formula, Represents the suitability of the e-th group of transportation paths corresponding to the transport vehicle for the j-th type of goods to be transported at the i-th sampling time; Indicates the traffic congestion degree of the e-th group of transportation routes at the i-th sampling time; represents the corrected bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment on the e-th group of transport routes; exp() represents an exponential function with a natural constant as the base; The larger the product of is, the less suitable the e-th group of transportation paths for the j-th type of goods to be transported corresponding to the transport vehicle at the i-th sampling time is.

[0077] S31: According to the suitability of each group of transportation paths of the transportation vehicles corresponding to each type of goods to be transported at each sampling moment, obtain the weighted path distance of each group of transportation paths of the transportation vehicles corresponding to each type of goods to be transported at each sampling moment; based on the weighted path distance, obtain the optimal path of the transportation vehicles corresponding to each type of goods to be transported at each sampling moment.

[0078] It should be noted that when using the Dijkstra algorithm to update the cargo transportation situation in real time according to the shortest path distance, the losses caused by road conditions and bumps are not taken into account, resulting in the selected path being inappropriate. When the suitability weight of the transportation path is higher, the priority of selecting the transportation path is higher. At this time, the transportation path needs to be reduced to facilitate the subsequent selection of the transportation path according to the Dijkstra algorithm.

[0079] Get the weighted path distance of the transport vehicle corresponding to the j-th type of goods to be transported in the e-th group of transport paths at the i-th sampling time:

[0080] ;

[0081] In the formula, Represents the weighted path distance of the transport vehicle corresponding to the j-th type of goods to be transported in the e-th group of transport paths at the i-th sampling time; Represents the suitability of the e-th group of transportation paths corresponding to the transport vehicle for the j-th type of goods to be transported at the i-th sampling time; Represents the distance of the transport path of group e.

[0082] According to the weighted path distance of the transport vehicle corresponding to the j-th type of goods to be transported in each group of transport paths at the i-th sampling moment, the Dijkstra algorithm is used to obtain the optimal path of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment.

[0083] An embodiment of the present invention further discloses a real-time collaborative supply chain supervision system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a real-time collaborative supply chain supervision method according to the present invention is implemented.

[0084] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0085] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0086] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0087] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A real-time collaborative supply chain monitoring method, characterized in that: Includes steps: Obtain the traffic congestion level of each transportation route at each sampling time, including: ; In the formula, Indicates the traffic congestion degree of the e-th group of transportation routes at the i-th sampling time; Indicates the type and quantity of goods to be transported; as well as It represents the number of all transport vehicles corresponding to the j-th type of goods to be transported and the number of all sampling moments in the neighborhood sampling range time series of the i-th sampling moment; as well as It represents the horizontal acceleration data at the xth sampling time in the neighborhood sampling range time series of the i-th sampling time for the j-th type of goods to be transported corresponding to the n-th transport vehicle in the e-th group of transport routes, as well as the mean of the horizontal acceleration data at all sampling times; || represents taking the absolute value; norm() represents the linear normalization function; the degree of bumpiness of the transport vehicle corresponding to each type of goods to be transported at each sampling time in each group of transport routes is obtained, including: ; In the formula, It represents the degree of bumping of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time on the e-th group of transport routes; Indicates the type of acceleration data; as well as It indicates that the jth type of goods to be transported corresponds to the nth transport vehicle in the eth group of transport routes, and the neighborhood sampling range time series of the i-th sampling time is the x-th sampling time. The acceleration data in each direction and the acceleration data at all sampling times The average value of the acceleration data in each direction; calculate the corrected turbulence degree: , represents the corrected bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment on the e-th group of transport routes; represents the feasibility of the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time when the r-th group of transport routes is used except the e-th group of transport routes; represents the degree of bumpiness at the i-th sampling moment when the transport vehicle corresponding to the j-th type of goods to be transported is on the r-th group of transport paths except the e-th group of transport paths; R represents the number of transport paths corresponding to the transport vehicle of the j-th type of goods to be transported except the e-th group of transport paths; obtains the suitability of each group of transport paths corresponding to each type of goods to be transported at each sampling moment, and the suitability is negatively correlated with the product of the corrected bumpiness and the road congestion degree; The distance of each group of transport paths is weighted according to the suitability to obtain the weighted path distance of the transport vehicle corresponding to each type of goods to be transported in each group of transport paths at each sampling time; based on the weighted path distance, the optimal path of the transport vehicle corresponding to each type of goods to be transported at each sampling time is obtained.

2. A real-time collaborative supply chain monitoring method according to claim 1, characterized in that: The acquisition of the feasibility of the turbulence degree includes: ； In the formula, represents the feasibility of the bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling time when the r-th group of transport routes is used except the e-th group of transport routes; as well as represents the cargo loss rate of the transport vehicle corresponding to the jth type of cargo to be transported on the eth group of transport routes and the cargo loss rate of the rth group of transport routes excluding the eth group of transport routes; as well as It represents the traffic congestion degree of the e-th group of transportation paths at the i-th sampling moment and the traffic congestion degree of the r-th group of transportation paths except the e-th group of transportation paths; norm() represents the linear normalization function.

3. A real-time collaborative supply chain monitoring method according to claim 1, characterized in that: The obtaining of the suitability of each group of transport paths corresponding to each type of transport vehicle for each type of goods to be transported at each sampling time includes: ； In the formula, Represents the suitability of the e-th group of transportation paths corresponding to the transport vehicle for the j-th type of goods to be transported at the i-th sampling time; Indicates the traffic congestion degree of the e-th group of transportation routes at the i-th sampling time; represents the corrected bumpiness of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment on the e-th group of transport routes; exp() represents an exponential function with a natural constant as the base.

4. A real-time collaborative supply chain monitoring method according to claim 1, characterized in that: The step of obtaining the weighted path distance of each group of transportation paths corresponding to each type of goods to be transported at each sampling time includes: ； In the formula, Represents the weighted path distance of the transport vehicle corresponding to the j-th type of goods to be transported in the e-th group of transport paths at the i-th sampling time; Represents the suitability of the e-th group of transportation paths for the j-th type of goods to be transported corresponding to the transport vehicle at the i-th sampling time; Represents the distance of the transport path of group e.

5. A real-time collaborative supply chain monitoring method according to claim 1, characterized in that: The obtaining of the optimal path of the transport vehicle corresponding to each type of goods to be transported at each sampling time includes: According to the weighted path distance of the transport vehicle corresponding to the j-th type of goods to be transported in each group of transport paths at the i-th sampling moment, the Dijkstra algorithm is used to obtain the optimal path of the transport vehicle corresponding to the j-th type of goods to be transported at the i-th sampling moment.

6. A real-time collaborative supply chain monitoring system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a real-time collaborative supply chain supervision method according to any one of claims 1-5 is implemented.

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