Wet sand transportation management method

Abstract

The invention discloses a wet sand transportation management method, relates to the technical field of wet sand transportation management, can perform moisture monitoring through a moisture detection device when wet sand leaves a factory, can effectively avoid the factory returning problem caused by excessive moisture, and is an intelligent path planning method based on path weight. The method can assist a wet sand transportation driver to find a better route, even an optimal route, instead of advancing according to the shortest route through conventional navigation, and finally, the wet sand transportation data of each time is stored, so that traceability data can be effectively formed.

Description

Technical Field

[0001] The invention relates to the technical field of wet sand transportation management, and in particular to a wet sand transportation management method. Background Art

[0002] In the prior art, there is no effective inspection and testing of wet sand leaving the warehouse, resulting in some wet sand being returned to the warehouse. This increases transportation costs and reduces operating efficiency. In the existing wet sand transportation process, navigation or the driver's self-planned route is often used for transportation, which has the problem of low transportation efficiency and is very unfriendly to drivers who have just joined the transportation. Self-planned roads can only be mechanically advanced according to navigation, resulting in low transportation efficiency. At the same time, the wet sand transportation process is not effectively managed. Summary of the invention

[0003] The purpose of the present invention is to provide a wet sand transportation management method to solve the problems of low transportation efficiency and ineffective management in the prior art.

[0004] The present invention is achieved through the following technical solutions:

[0005] A wet sand transportation management method, comprising:

[0006] When the wet sand leaves the factory, the moisture content of the wet sand is detected by a moisture detection device arranged on the target wet sand transport vehicle, and the wet sand leaving the factory state is determined according to the moisture content of the wet sand; the wet sand leaving the factory state includes that the wet sand can leave the factory or the wet sand cannot leave the factory;

[0007] When the wet sand factory state is that the wet sand cannot be shipped, a warning message prohibiting the departure is generated for the management personnel and the wet sand transport driver corresponding to the target wet sand transport vehicle; when the wet sand factory state is that the wet sand can be shipped, a message allowing the departure is generated for the management personnel and the wet sand transport driver corresponding to the target wet sand transport vehicle;

[0008] For a target wet sand transport vehicle whose wet sand delivery status is that wet sand can be delivered from the factory, at least one transport destination node is collected, and a transport path is planned for the target vehicle using a path weighted optimization algorithm according to the at least one transport destination node to obtain a planned driving path;

[0009] The planned driving path is transmitted to the wet sand transport driver, and in the process of the target vehicle transporting the wet sand, the positioning information with time stamp is collected by the GPS information collection device arranged on the wet sand transport vehicle, and the actual driving track of the target vehicle is formed according to the positioning information with time stamp;

[0010] When the target vehicle arrives at the last transportation destination node, the actual driving track, transportation plan and factory status of the target vehicle are encrypted to obtain encrypted data, and the encrypted data is uploaded to the cloud for storage to facilitate traceability by management personnel.

[0011] In a possible implementation, when the wet sand leaves the factory, the moisture content of the wet sand is detected by a moisture detection device disposed on a target wet sand transport vehicle, and the factory state of the wet sand is determined according to the moisture content of the wet sand, including:

[0012] The moisture content of the wet sand is detected by a moisture detection device disposed on the target wet sand transport vehicle;

[0013] It is determined whether the moisture content of the wet sand is higher than a preset threshold value. If so, the wet sand factory state is determined to be wet sand that cannot be shipped out of the factory. Otherwise, the wet sand factory state is determined to be wet sand that can be shipped out of the factory.

[0014] In a possible implementation, at least one transport destination node is collected, and according to the at least one transport destination node, a transport path is planned for the target vehicle using a path weighted optimization algorithm to obtain a planned driving path, including:

[0015] Collect at least one transport destination node;

[0016] When the number of transport destination nodes is 1, a third-party map is called to obtain the planned transport path of the target vehicle and obtain the planned driving path;

[0017] When the number of transport destination nodes is greater than 1, determine the position of each transport destination node, the path between any two transport destination nodes, and the length of the path between any two transport destination nodes, where the position of each transport destination node, the path between any two transport destination nodes, and the length of the path between any two transport destination nodes are all pre-stored data;

[0018] According to the position of each transportation destination node, the path between any two transportation destination nodes and the path length between any two transportation destination nodes, a path weighted optimization algorithm is used to plan the transportation path for the target vehicle to obtain a planned driving path.

[0019] In a possible implementation, a transportation path is planned for a target vehicle using a path weighted optimization algorithm according to the position of each transportation destination node, the path between any two transportation destination nodes, and the path length between any two transportation destination nodes, to obtain a planned driving path, including:

[0020] According to the location of each transport destination node, multiply the latitude by the longitude and round it to get the number corresponding to each transport destination node;

[0021] According to the number corresponding to the transport destination node, a path code is determined in a random coding manner, and multiple path codes are repeatedly obtained;

[0022] According to the path between any two transport destination nodes and the path length between any two transport destination nodes, the comprehensive score of each path code is determined by using the path weighting method, and the path code with the largest comprehensive score is taken as the optimal path code;

[0023] Determine whether the number of iterations reaches the maximum preset number. If so, output the optimal path code as the planned driving path. Otherwise, use the greedy algorithm to perform crossover or mutation operations on the path code and return to the step of obtaining the optimal path.

[0024] Among them, the path code always takes the factory site as the first element. After performing a crossover operation or a mutation operation, if the factory site is not the first element of the path code, the factory site is directly moved to the first element.

[0025] In a possible implementation, according to the path between any two transport destination nodes and the path length between any two transport destination nodes, a comprehensive score of each path code is determined by a path weighting method, including:

[0026] According to the path between any two transportation destination nodes, the weight parameter of each path is obtained; each path is initially assigned the same weight parameter, and after each target vehicle completes the task, the path weight between the two nodes in its actual driving trajectory and the planned driving path is updated;

[0027] Determine the path type corresponding to the path between any two transportation destination nodes, the path type includes expressways, provincial roads, national roads, county roads, township roads and unnamed roads, and is set with decreasing road condition scores;

[0028] The reciprocal of the path length between any two transport destination nodes is used as the path score;

[0029] The road condition score and the path score are added together by weighted summation to obtain the added value;

[0030] Multiply the added value by the weight parameter of the path to obtain the comprehensive score corresponding to the path between any two transportation destination nodes;

[0031] Determine a target path with the largest comprehensive score in at least one path between any two transport destination nodes, and use the target path as the preferred path between any two transport destination nodes;

[0032] In the order of path coding, the preferred paths between nodes are added up to obtain the comprehensive score of each path coding.

[0033] In a possible implementation, each path is initially assigned the same weight parameter, and after each target vehicle completes its task, the path weight between two nodes in its actual driving trajectory and the planned driving path is updated, including:

[0034] Each path is initially assigned the same weight parameter;

[0035] After each target vehicle completes the task, the weight is updated for the path between nodes in the actual driving trajectory:

[0036] ω t+1 =ω t +λ*ω t

[0037] For the paths in the planned driving path that are different from the actual driving trajectory, the update weight is:

[0038] ω t+1 =ω t -λ*ω t

[0039] Among them, ω t represents the weight before update, ω t+1 represents the updated weight, and λ represents the update coefficient.

[0040] In a possible implementation, a weighted sum method is adopted to add the road condition score and the path score, and the sum is obtained as Sum=αS1+(1-α)S2; wherein α represents a weighting coefficient, S1 represents a road condition score, and S2 represents a path score.

[0041] In a possible implementation, a greedy algorithm is used to perform a crossover operation or a mutation operation on the path encoding, including:

[0042] A1. Determine the first selection probability corresponding to the mutation operation, and determine the second selection probability corresponding to the crossover operation as (1-first selection probability) / 2 based on the first selection probability;

[0043] A2. Determine a target operation mode by using a roulette wheel method according to the first selection probability and the second selection probability, wherein the target operation mode is a crossover operation or a mutation operation;

[0044] A3. When the target operation mode is a crossover operation, the crossover operation is performed to obtain a crossover individual, and a greedy algorithm is used to retain the path code of the target operation to be performed and one individual in the crossover individual;

[0045] A4. When the target operation mode is a mutation operation, the mutation operation is performed to obtain a mutated individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one of the mutated individuals.

[0046] In a possible implementation, when the target operation mode is a crossover operation, a crossover operation is performed to obtain a crossover individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one individual in the crossover individual, including:

[0047] When the target operation mode is cross operation, the path code with the largest comprehensive score is determined according to the comprehensive score of each path code, and the extreme value individual is obtained;

[0048] Set counter t=1;

[0049] For the path code of the target operation to be executed, the factory location is selected as the tth element of the crossover individual;

[0050] Take out the elements on both sides of the same element as the t-th element from the extreme value individuals and the path codes of the target operation to be executed, and obtain the first candidate element, the second candidate element, the third candidate element and the fourth candidate element; at the same time, delete the elements in the path codes of the extreme value individuals and the target operation to be executed that are the same as the t-th element;

[0051] Determine the element with the largest comprehensive score with the t-th element from the first candidate element, the second candidate element, the third candidate element and the fourth candidate element, and use the determined element as the t+1-th element of the crossover individual;

[0052] Determine whether the counter t is equal to T-1. If so, output the crossover individual. Otherwise, increase the count value of the counter by one and return to the step of selecting the candidate element. Where T represents the total number of elements in the path encoding.

[0053] A greedy algorithm is used to retain the path encoding of the target operation to be executed and one of the individuals in the crossover individuals.

[0054] In a possible implementation, when the target operation mode is a mutation operation, the mutation operation is performed to obtain a mutation individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one of the mutation individuals, including:

[0055] When the target operation mode is a mutation operation, set the counter t=1, and select the factory location as the tth element of the mutation individual for the path code of the target operation to be executed;

[0056] Based on the tth element in the variant individual, determine the element with the smallest comprehensive score with the tth element in the path code of the target operation to be executed, and use the determined element as the t+1th element in the variant individual, and determine whether the counter t is equal to n-1. If so, output a half code with a length of n, otherwise increase the count value of the counter t by one and repeat this step;

[0057] Combine the n+1th to Tth elements in the half code of length n and the path code of the target operation to be executed to obtain a mutant individual;

[0058] Obtain the n+1th to Tth elements and semi-coded repeating elements in the variant individual;

[0059] Obtaining a difference element between the variant individual and the path code of the target operation to be executed, wherein the difference element represents an element that appears in the path code of the target operation to be executed but does not appear in the variant individual;

[0060] Use the difference element to replace the repeated element, and randomly select two elements from the n+1th to Tth elements in the mutant individual to exchange their positions to obtain the mutant individual;

[0061] A greedy algorithm is used to retain the path encoding of the target operation to be executed and one of the mutated individuals.

[0062] The wet sand transportation management method provided by the present invention can monitor the moisture content of the wet sand through a moisture detection device when the wet sand leaves the factory, which can effectively avoid the problem of returning to the factory due to excessive moisture content. In addition, the intelligent path planning method based on path weight can assist the wet sand transportation driver to find a better route, or even the optimal route, instead of conventional navigation following the shortest route. Finally, the wet sand transportation data of each time is saved, which can effectively form traceability data. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0064] Figure 1 The present invention provides a flow chart of a method for managing wet sand transportation. DETAILED DESCRIPTION

[0065] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0066] Example

[0067] like Figure 1 As shown, an embodiment of the present invention provides a wet sand transportation management method, comprising:

[0068] S101, when the wet sand is shipped out of the factory, the moisture content of the wet sand is detected by a moisture detection device installed on the target wet sand transport vehicle, and the shipping state of the wet sand is determined according to the moisture content of the wet sand. The shipping state of the wet sand includes that the wet sand can be shipped out of the factory or that the wet sand cannot be shipped out of the factory.

[0069] S102, when the wet sand factory state is that the wet sand cannot be shipped, a warning message of prohibiting the departure is generated to the management personnel and the wet sand transport driver corresponding to the target wet sand transport vehicle. When the wet sand factory state is that the wet sand can be shipped, a message of allowing the departure is generated to the management personnel and the wet sand transport driver corresponding to the target wet sand transport vehicle.

[0070] After starting the moisture detection device, when loading, if wet sand is encountered, the goods will be replaced immediately, and the inspection accuracy is high, which effectively reduces the unnecessary costs caused by returning to the warehouse due to the previous inability to detect, and improves the transportation conversion efficiency.

[0071] S103, for a target wet sand transport vehicle whose wet sand delivery status is wet sand ready to be delivered from the factory, collect at least one transport destination node, and plan a transport path for the target vehicle using a path weighted optimization algorithm according to the at least one transport destination node to obtain a planned driving path.

[0072] In the prior art, drivers often use third-party maps to plan routes. Although they can get to a better route most of the time, there are still problems with detours or slow navigation routes. For example, navigation generally follows the shortest route, but it can only drive slowly. Another road can drive quickly, thereby improving transportation efficiency. Or, it takes 5 kilometers to drive on the highway, but only 500 meters to go through the unnamed road. In this way, drivers who pass by frequently can avoid detours, while novice drivers or drivers from other places will have a lot of detours.

[0073] This embodiment constructs a weighted path optimization algorithm that can not only take into account road condition information, but also update path information in real time according to the driver's choice, thereby finding the best route.

[0074] S104, transmitting the planned driving route to the wet sand transport driver, and in the process of the target vehicle transporting the wet sand, collecting positioning information with time stamps through the GPS information collection device arranged on the wet sand transport vehicle, and forming the actual driving track of the target vehicle according to the positioning information with time stamps.

[0075] Collecting time-stamped positioning information through the GPS information collection device has two functions. One is that it can update the weight information of the path, thereby providing an effective reference for the wet sand transport drivers. The other is that it can form transportation data and store it in association with the transportation plan and the factory status of the wet sand, thereby achieving traceability and effective management of the transportation process.

[0076] S105. After the target vehicle arrives at the last transportation destination node, the actual driving track, transportation plan and factory status of the target vehicle are encrypted to obtain encrypted data, and the encrypted data is uploaded to the cloud for storage to facilitate tracing by management personnel.

[0077] The wet sand transportation management method provided by the present invention can monitor the moisture content of the wet sand through a moisture detection device when the wet sand leaves the factory, which can effectively avoid the problem of returning to the factory due to excessive moisture content. In addition, the intelligent path planning method based on path weight can assist the wet sand transportation driver to find a better route, or even the optimal route, instead of conventional navigation following the shortest route. Finally, the wet sand transportation data of each time is saved, which can effectively form traceability data.

[0078] In a possible implementation, when the wet sand leaves the factory, the moisture content of the wet sand is detected by a moisture detection device disposed on a target wet sand transport vehicle, and the factory state of the wet sand is determined according to the moisture content of the wet sand, including:

[0079] The moisture content of the wet sand is detected by a moisture detection device arranged on the target wet sand transport vehicle.

[0080] It is determined whether the moisture content of the wet sand is higher than a preset threshold value. If so, the wet sand factory state is determined to be wet sand that cannot be shipped out of the factory. Otherwise, the wet sand factory state is determined to be wet sand that can be shipped out of the factory.

[0081] Optionally, a staff member may manually detect the wet sand moisture data and upload the wet sand moisture data through human-computer interaction.

[0082] In a possible implementation, at least one transport destination node is collected, and according to the at least one transport destination node, a transport path is planned for the target vehicle using a path weighted optimization algorithm to obtain a planned driving path, including:

[0083] Collect at least one transportation destination node.

[0084] When the number of transport destination nodes is 1, a third-party map is called to obtain the planned transport path of the target vehicle and obtain the planned driving path;

[0085] Optionally, when the number of transport destination nodes is 1, in addition to calling a third-party map to obtain the planned transport path of the target vehicle, a comprehensive scoring method can also be used to determine the planned driving path between the factory location and the transport destination node.

[0086] When the number of transport destination nodes is greater than 1, determine the position of each transport destination node, the path between any two transport destination nodes, and the length of the path between any two transport destination nodes, where the position of each transport destination node, the path between any two transport destination nodes, and the length of the path between any two transport destination nodes are all pre-stored data;

[0087] According to the position of each transportation destination node, the path between any two transportation destination nodes and the path length between any two transportation destination nodes, a path weighted optimization algorithm is used to plan the transportation path for the target vehicle to obtain a planned driving path.

[0088] In a possible implementation, a transportation path is planned for a target vehicle using a path weighted optimization algorithm according to the position of each transportation destination node, the path between any two transportation destination nodes, and the path length between any two transportation destination nodes, to obtain a planned driving path, including:

[0089] According to the location of each transport destination node, the latitude is multiplied by the longitude and rounded to the integer to obtain the number corresponding to each transport destination node.

[0090] Optionally, a number may be directly assigned to each transport destination node to facilitate subsequent path coding, and the number of each transport destination node may be ensured to be unique.

[0091] According to the number corresponding to the transport destination node, the path code is determined in a random coding manner, and multiple path codes are repeatedly obtained.

[0092] It is worth noting that no matter obtaining the path code or updating the path code, the factory location (i.e. the starting position of the wet sand) must be the first element of the path code. When it is not the first element, the number corresponding to the factory location will be directly moved to the first, that is, moved to the starting point of the path code.

[0093] According to the path between any two transport destination nodes and the path length between any two transport destination nodes, the comprehensive score of each path code is determined by using the path weighting method, and the path code with the largest comprehensive score is taken as the optimal path code;

[0094] Determine whether the number of iterations reaches the maximum preset number. If so, output the optimal path code as the planned driving path. Otherwise, use the greedy algorithm to perform crossover or mutation operations on the path code and return to the step of obtaining the optimal path.

[0095] Among them, the path code always takes the factory site as the first element. After performing a crossover operation or a mutation operation, if the factory site is not the first element of the path code, the factory site is directly moved to the first element.

[0096] The embodiment of the present invention is based on path weighting and is combined with a genetic algorithm for encoding and updating to find the optimal route. Compared with the prior art, the present invention can comprehensively select road condition information, discover new roads, or better roads, and effectively improve transportation efficiency.

[0097] In a possible implementation, according to the path between any two transport destination nodes and the path length between any two transport destination nodes, a comprehensive score of each path code is determined by a path weighting method, including:

[0098] According to the path between any two transportation destination nodes, the weight parameter of each path is obtained; each path is initially assigned the same weight parameter, and after each target vehicle completes the task, the path weight between the two nodes in its actual driving trajectory and the planned driving path is updated;

[0099] Determine the path type corresponding to the path between any two transportation destination nodes, the path type includes expressways, provincial roads, national roads, county roads, township roads and unnamed roads, and set with decreasing road condition scores. The road condition score is only used to characterize different roads, so no matter how much it is set, it will not affect the final planning result, and this embodiment does not limit it.

[0100] The reciprocal of the path length between any two transportation destination nodes is used as the path score. Under the same road conditions, the shorter the road length, the shorter the transportation time. Choosing a short road can effectively improve transportation efficiency. Therefore, this embodiment uses the reciprocal of the path length as the path score. The shorter the path, the higher the path score.

[0101] The road condition score and the path score are added together by weighted summation to obtain the added value;

[0102] Multiply the added value by the weight parameter of the path to obtain the comprehensive score corresponding to the path between any two transportation destination nodes;

[0103] Determine a target path with the largest comprehensive score in at least one path between any two transport destination nodes, and use the target path as the preferred path between any two transport destination nodes;

[0104] In the order of path coding, the preferred paths between nodes are added up to obtain the comprehensive score of each path coding.

[0105] In a possible implementation, each path is initially assigned the same weight parameter, and after each target vehicle completes its task, the path weight between two nodes in its actual driving trajectory and the planned driving path is updated, including:

[0106] Each path is initially assigned the same weight parameter;

[0107] After each target vehicle completes the task, the weight is updated for the path between nodes in the actual driving trajectory:

[0108] ω t+1 =ω t +λ*ω t

[0109] For the paths in the planned driving path that are different from the actual driving trajectory, the update weight is:

[0110] ω t+1 =ω t -λ*ω t

[0111] Among them, ω t represents the weight before update, ω t+1 represents the updated weight, and λ represents the update coefficient between (0,1).

[0112] It is worth noting that, assuming that the path taken by the wet sand transport driver is a new path, the new path will be recorded, and the path between the two nodes will be updated, and the initial weight parameter of the new path will be assigned. In addition to the entire transport path corresponding to the path code, other paths represent the path between the two nodes. Assuming that there is no direct path between the two nodes, an intermediate node can be determined to connect the two nodes.

[0113] Optionally, in addition to the above-mentioned path weight updating method, a nonlinear weight updating method may be used to update the path weight more quickly.

[0114] In a possible implementation, a weighted sum method is adopted to add the road condition score and the path score, and the sum is obtained as Sum=αS1+(1-α)S2; wherein α represents a weighting coefficient, S1 represents a road condition score, and S2 represents a path score.

[0115] In a possible implementation, a greedy algorithm is used to perform a crossover operation or a mutation operation on the path encoding, including:

[0116] A1. Determine the first selection probability corresponding to the mutation operation, and determine the second selection probability corresponding to the crossover operation as (1-first selection probability) / 2 based on the first selection probability;

[0117] A2. Determine a target operation mode by using a roulette wheel method according to the first selection probability and the second selection probability, wherein the target operation mode is a crossover operation or a mutation operation;

[0118] A3. When the target operation mode is a crossover operation, the crossover operation is performed to obtain a crossover individual, and a greedy algorithm is used to retain the path code of the target operation to be performed and one individual in the crossover individual;

[0119] A4. When the target operation mode is a mutation operation, the mutation operation is performed to obtain a mutated individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one of the mutated individuals.

[0120] In a possible implementation, when the target operation mode is a crossover operation, a crossover operation is performed to obtain a crossover individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one individual in the crossover individual, including:

[0121] When the target operation mode is cross operation, the path code with the largest comprehensive score is determined according to the comprehensive score of each path code to obtain the extreme value individual.

[0122] Optionally, this embodiment mainly adopts the global extreme value as the extreme value individual, and the historical optimal value of each path encoding can also be used as the extreme value individual.

[0123] Set counter t=1;

[0124] For the path code of the target operation to be executed, the factory location is selected as the tth element of the crossover individual;

[0125] Take out the elements on both sides of the same element as the t-th element from the extreme value individuals and the path codes of the target operation to be executed, and obtain the first candidate element, the second candidate element, the third candidate element and the fourth candidate element; at the same time, delete the elements in the path codes of the extreme value individuals and the target operation to be executed that are the same as the t-th element;

[0126] Determine the element with the largest comprehensive score with the t-th element from the first candidate element, the second candidate element, the third candidate element and the fourth candidate element, and use the determined element as the t+1-th element of the crossover individual;

[0127] Determine whether the counter t is equal to T-1. If so, output the crossover individual. Otherwise, increase the count value of the counter by one and return to the step of selecting the candidate element. Where T represents the total number of elements in the path encoding.

[0128] A greedy algorithm is used to retain the path encoding of the target operation to be executed and one of the individuals in the crossover individuals.

[0129] Through the above cross-update, the path encoding can be guided to move toward the optimal solution, thereby achieving optimal path planning.

[0130] In a possible implementation, when the target operation mode is a mutation operation, the mutation operation is performed to obtain a mutation individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one of the mutation individuals, including:

[0131] When the target operation mode is a mutation operation, set the counter t=1, and select the factory location as the tth element of the mutation individual for the path code of the target operation to be executed;

[0132] Based on the tth element in the variant individual, determine the element with the smallest comprehensive score with the tth element in the path code of the target operation to be executed, and use the determined element as the t+1th element in the variant individual, and determine whether the counter t is equal to n-1. If so, output a half code with a length of n, otherwise increase the count value of the counter t by one and repeat this step;

[0133] Combine the n+1th to Tth elements in the half code of length n and the path code of the target operation to be executed to obtain a mutant individual;

[0134] Obtain the n+1th to Tth elements and semi-coded repeating elements in the variant individual;

[0135] Obtaining a difference element between the variant individual and the path code of the target operation to be executed, wherein the difference element represents an element that appears in the path code of the target operation to be executed but does not appear in the variant individual;

[0136] Use the difference element to replace the repeated element, and randomly select two elements from the n+1th to Tth elements in the mutant individual to exchange their positions to obtain the mutant individual;

[0137] A greedy algorithm is used to retain the path encoding of the target operation to be executed and one of the mutated individuals.

[0138] As the iteration proceeds, the populations become more and more similar, and the ability to escape from the local optimal solution becomes weaker and weaker, eventually leading to stagnation of the algorithm search. In order to avoid falling into the local optimum, this embodiment adopts the mutation operation method for updating, while retaining its optimization characteristics, it can expand the search range, increase the diversity of the population, and be more conducive to finding the global optimal position.

[0139] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for managing wet sand transportation, characterized in that: include: When the wet sand leaves the factory, the moisture content of the wet sand is detected by a moisture detection device arranged on the target wet sand transport vehicle, and the wet sand leaving the factory state is determined according to the moisture content of the wet sand; the wet sand leaving the factory state includes that the wet sand can leave the factory or the wet sand cannot leave the factory; When the wet sand factory state is that the wet sand cannot be shipped, a warning message prohibiting the departure is generated for the management personnel and the wet sand transport driver corresponding to the target wet sand transport vehicle; when the wet sand factory state is that the wet sand can be shipped, a message allowing the departure is generated for the management personnel and the wet sand transport driver corresponding to the target wet sand transport vehicle; For a target wet sand transport vehicle whose wet sand delivery status is that wet sand can be delivered from the factory, at least one transport destination node is collected, and a transport path is planned for the target vehicle using a path weighted optimization algorithm according to the at least one transport destination node to obtain a planned driving path; The planned driving path is transmitted to the wet sand transport driver, and in the process of the target vehicle transporting the wet sand, the positioning information with time stamp is collected by the GPS information collection device arranged on the wet sand transport vehicle, and the actual driving track of the target vehicle is formed according to the positioning information with time stamp; When the target vehicle arrives at the last transportation destination node, the actual driving track, transportation plan and factory status of the target vehicle are encrypted to obtain encrypted data, and the encrypted data is uploaded to the cloud for storage to facilitate traceability by management personnel.

2. The wet sand transportation management method according to claim 1, characterized in that: When the wet sand leaves the factory, the moisture content of the wet sand is detected by a moisture detection device installed on the target wet sand transport vehicle, and the factory status of the wet sand is determined according to the moisture content of the wet sand, including: The moisture content of the wet sand is detected by a moisture detection device disposed on the target wet sand transport vehicle; It is determined whether the moisture content of the wet sand is higher than a preset threshold value. If so, the wet sand factory state is determined to be wet sand that cannot be shipped out of the factory. Otherwise, the wet sand factory state is determined to be wet sand that can be shipped out of the factory.

3. The wet sand transportation management method according to claim 1, characterized in that: Collecting at least one transportation destination node, and planning a transportation path for the target vehicle using a path weighted optimization algorithm according to the at least one transportation destination node to obtain a planned driving path, including: Collect at least one transport destination node; When the number of transport destination nodes is 1, a third-party map is called to obtain the planned transport path of the target vehicle and obtain the planned driving path; When the number of transport destination nodes is greater than 1, determine the position of each transport destination node, the path between any two transport destination nodes, and the length of the path between any two transport destination nodes, where the position of each transport destination node, the path between any two transport destination nodes, and the length of the path between any two transport destination nodes are all pre-stored data; According to the position of each transportation destination node, the path between any two transportation destination nodes and the path length between any two transportation destination nodes, a path weighted optimization algorithm is used to plan the transportation path for the target vehicle to obtain a planned driving path.

4. The wet sand transportation management method according to claim 3, characterized in that: According to the location of each transport destination node, the path between any two transport destination nodes, and the path length between any two transport destination nodes, a path weighted optimization algorithm is used to plan a transport path for the target vehicle, and a planned driving path is obtained, including: According to the location of each transport destination node, multiply the latitude by the longitude and round it to get the number corresponding to each transport destination node; According to the number corresponding to the transport destination node, a path code is determined in a random coding manner, and multiple path codes are repeatedly obtained; According to the path between any two transport destination nodes and the path length between any two transport destination nodes, the comprehensive score of each path code is determined by using the path weighting method, and the path code with the largest comprehensive score is taken as the optimal path code; Determine whether the number of iterations reaches the maximum preset number. If so, output the optimal path code as the planned driving path. Otherwise, use the greedy algorithm to perform crossover or mutation operations on the path code and return to the step of obtaining the optimal path. Among them, the path code always takes the factory site as the first element. After performing a crossover operation or a mutation operation, if the factory site is not the first element of the path code, the factory site is directly moved to the first element.

5. The wet sand transportation management method according to claim 4, characterized in that: According to the path between any two transport destination nodes and the path length between any two transport destination nodes, the comprehensive score of each path code is determined by path weighting, including: According to the path between any two transportation destination nodes, the weight parameter of each path is obtained; each path is initially assigned the same weight parameter, and after each target vehicle completes the task, the path weight between the two nodes in its actual driving trajectory and the planned driving path is updated; Determine the path type corresponding to the path between any two transportation destination nodes, the path type includes expressways, provincial roads, national roads, county roads, township roads and unnamed roads, and is set with decreasing road condition scores; The reciprocal of the path length between any two transport destination nodes is used as the path score; The road condition score and the path score are added together by weighted summation to obtain the added value; Multiply the added value by the weight parameter of the path to obtain the comprehensive score corresponding to the path between any two transportation destination nodes; Determine a target path with the largest comprehensive score in at least one path between any two transport destination nodes, and use the target path as the preferred path between any two transport destination nodes; In the order of path coding, the preferred paths between nodes are added up to obtain the comprehensive score of each path coding.

6. The method for managing the transportation of wet sand according to claim 5, characterized in that: Each path is initially assigned the same weight parameter, and after each target vehicle completes its task, its actual driving trajectory and the path weight between two nodes in the planned driving path are updated, including: Each path is initially assigned the same weight parameter; After each target vehicle completes the task, the weight is updated for the path between nodes in the actual driving trajectory: oh t+1 =ω t +l*w t For the paths in the planned driving path that are different from the actual driving trajectory, the update weight is: oh t+1 =ω t -l*o t Among them, ω t represents the weight before update, ω t+1 represents the updated weight, and λ represents the update coefficient.

7. The method for managing the transportation of wet sand according to claim 5, characterized in that: The road condition score and the path score are added by weighted summation, and the sum is Sum=αS1+(1-α)S2; wherein α represents the weighting coefficient, S1 represents the road condition score, and S2 represents the path score.

8. The method for managing the transportation of wet sand according to claim 5, characterized in that: A greedy algorithm is used to perform crossover or mutation operations on the path encoding, including: A1. Determine the first selection probability corresponding to the mutation operation, and determine the second selection probability corresponding to the crossover operation as (1-first selection probability) / 2 based on the first selection probability; A2. Determine a target operation mode by using a roulette wheel method according to the first selection probability and the second selection probability, wherein the target operation mode is a crossover operation or a mutation operation; A3. When the target operation mode is a crossover operation, the crossover operation is performed to obtain a crossover individual, and a greedy algorithm is used to retain the path code of the target operation to be performed and one individual in the crossover individual; A4. When the target operation mode is a mutation operation, the mutation operation is performed to obtain a mutated individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one of the mutated individuals.

9. The method for managing the transportation of wet sand according to claim 8, characterized in that: When the target operation mode is a crossover operation, the crossover operation is performed to obtain a crossover individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one individual in the crossover individual, including: When the target operation mode is cross operation, the path code with the largest comprehensive score is determined according to the comprehensive score of each path code, and the extreme value individual is obtained; Set counter t=1; For the path code of the target operation to be executed, the factory location is selected as the tth element of the crossover individual; Take out the elements on both sides of the same element as the t-th element from the extreme value individuals and the path codes of the target operation to be executed, and obtain the first candidate element, the second candidate element, the third candidate element and the fourth candidate element; at the same time, delete the elements in the path codes of the extreme value individuals and the target operation to be executed that are the same as the t-th element; Determine the element with the largest comprehensive score with the t-th element from the first candidate element, the second candidate element, the third candidate element and the fourth candidate element, and use the determined element as the t+1-th element of the crossover individual; Determine whether the counter t is equal to T-1. If so, output the crossover individual. Otherwise, increase the count value of the counter by one and return to the step of selecting the candidate element. Where T represents the total number of elements in the path encoding. A greedy algorithm is used to retain the path encoding of the target operation to be executed and one of the individuals in the crossover individuals.

10. The method for managing the transportation of wet sand according to claim 8, characterized in that: When the target operation mode is a mutation operation, the mutation operation is performed to obtain a mutated individual, and a greedy algorithm is used to retain the path encoding of the target operation to be performed and one of the mutated individuals, including: When the target operation mode is a mutation operation, set the counter t=1, and select the factory location as the tth element of the mutation individual for the path code of the target operation to be executed; Based on the tth element in the variant individual, determine the element with the smallest comprehensive score with the tth element in the path code of the target operation to be executed, and use the determined element as the t+1th element in the variant individual, and determine whether the counter t is equal to n-1. If so, output a half code with a length of n, otherwise increase the count value of the counter t by one and repeat this step; Combine the n+1th to Tth elements in the half code of length n and the path code of the target operation to be executed to obtain a mutant individual; Obtain the n+1th to Tth elements and semi-coded repeating elements in the variant individual; Obtaining a difference element between the variant individual and the path code of the target operation to be executed, wherein the difference element represents an element that appears in the path code of the target operation to be executed but does not appear in the variant individual; Use the difference element to replace the repeated element, and randomly select two elements from the n+1th to Tth elements in the mutant individual to exchange their positions to obtain the mutant individual; A greedy algorithm is used to retain the path encoding of the target operation to be executed and one of the mutated individuals.

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