Unmanned internet-of-things workshop cargo conveying robot scheduling method and system

Abstract

The invention relates to the technical field of transportation scheduling, and particularly discloses an unmanned Internet of Things workshop cargo transportation robot scheduling method and system, and the method comprises the steps: receiving demand point locations inputted by an administrator, calculating the virtual distance between the demand point locations, and creating a point location distribution map; obtaining task parameters of each demand point location based on a preset matrix to obtain a task parameter table, and calculating a delivery level of each demand point location based on the task parameter table; selecting a distribution path in a point location distribution diagram according to the distribution level, and feeding back the distribution path to an administrator; and correcting the distribution path based on the feedback information of the administrator, and generating a scheduling instruction according to the corrected distribution path. According to the method, the task parameter table is obtained by counting the task parameters of each demand point location based on the matrix, the task parameter table is subjected to self-identification, the dynamic weight of each index is determined, and then the path selection process is adjusted, so that the integrating degree of the path and the actual situation is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of transportation scheduling, and in particular to a method and system for scheduling a cargo transportation robot in an unmanned Internet of Things workshop. Background Art

[0002] With the development of automation and intelligent technology, unmanned workshops are gradually becoming popular. Unmanned workshops are also called "intelligent workshops" or "unmanned workshops". They refer to the use of automation, artificial intelligence and advanced production equipment to achieve full automation and intelligence of the production process in the workshop, so that human workers do not need to directly participate in the operation. Most of the existing technologies are intelligent extensions of the work at each fixed workstation. For the material transportation process, most of them are based on controlling the conveyor belt driven by the motor, which is suitable for batches of conventional materials. For some rare and unconventional materials, it is necessary to introduce cargo delivery robots for distribution; most of the existing robot cargo transportation paths use preset fixed paths, which are not highly consistent with the actual situation. When the on-site situation changes, it is difficult to adjust in time. How to provide a dynamic scheduling solution that is more consistent with the on-site situation is the technical problem that the technical solution of the present invention wants to solve. Summary of the invention

[0003] The purpose of the present invention is to provide an unmanned Internet of Things workshop cargo delivery robot scheduling method and system to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for dispatching a cargo delivery robot in an unmanned Internet of Things workshop, the method comprising:

[0006] Receive the demand points input by the administrator, calculate the virtual distance between the demand points, and create a point distribution map; the point distribution map includes nodes corresponding to the demand points one by one, there are directed lines between the nodes, and the length of the line is related to the virtual distance;

[0007] Obtaining task parameters at each demand point based on a preset matrix to obtain a task parameter table, and calculating the delivery level of each demand point based on the task parameter table;

[0008] Select a delivery path in the point distribution map according to the delivery level, and feed back the delivery path to the administrator;

[0009] The delivery path is modified based on the administrator's feedback information, and the dispatch instruction is generated according to the modified delivery path.

[0010] As a further solution of the present invention: the steps of receiving the demand points input by the administrator, calculating the virtual distances between the demand points, and creating a point distribution map include:

[0011] Obtain the BIM model of the IoT workshop and receive the required points selected by the administrator based on the BIM model;

[0012] Locate the moving object according to the monitoring system of the IoT workshop, and obtain the object type and movement trajectory of the moving object;

[0013] Pair all required points in pairs to obtain point groups, intercept the motion trajectory based on the point group, obtain the time span of the intercepted motion trajectory, and query the corresponding object type;

[0014] According to the object type, the standard speed is queried in the preset speed table, and the virtual distance is calculated by multiplying the standard speed by the time span;

[0015] Connect the required points in the BIM model, determine the module length based on the virtual distance, and create a directed line; the process of connecting the required points records the starting point and the end point, and the direction of the directed line is from the starting point to the end point.

[0016] As a further solution of the present invention: the step of obtaining the task parameters at each demand point based on a preset matrix to obtain a task parameter table, and calculating the delivery level of each demand point based on the task parameter table includes:

[0017] Receive the indicators input by the administrator, use the indicators as columns, and create a matrix;

[0018] Send the matrix to the interactive end at the demand point, receive the task parameters fed back by the interactive end, and obtain a task parameter table; the task parameters are the data under each indicator; each row in the task parameter table corresponds to a demand point;

[0019] Self-identify the task parameter table and determine the dynamic weight of each indicator;

[0020] Based on the dynamic weight, the data of each row is accumulated to obtain the accumulated value of each demand point, and the distribution level of the demand point is determined according to the accumulated value;

[0021] The dynamic weight is proportional to the standard deviation of all data of each indicator.

[0022] As a further solution of the present invention: the step of sending the matrix to the interactive terminal at the demand point, receiving the task parameters fed back by the interactive terminal, and obtaining the task parameter table includes:

[0023] Read the task parameter table of the previous moment, and determine the correlation array between each demand point and other demand points based on the task parameter table of the previous moment; each value in the correlation array corresponds to an indicator, and each value is used to determine the mapping relationship between the corresponding indicator value and the indicator value of the same indicator of other demand points;

[0024] Receive task parameters fed back by the interactive end and obtain a task parameter table;

[0025] Complete the task parameter table based on the relevant array of each demand point;

[0026] The value of the related array is: A[j] = αN[j] / E j ; In the formula, A[j] is the jth value in the relevant array, N[j] is the data under the jth indicator of the demand point, and E j is the mean of the data in the jth column of the task parameter table, and α is the preset correction coefficient.

[0027] As a further solution of the present invention: the step of selecting a delivery path in the point distribution map according to the delivery level and feeding back the delivery path to the administrator includes:

[0028] Receive the starting point selected by the administrator, connect each demand point in sequence based on the starting point, and randomly generate a preset number of alternative paths;

[0029] Calculate the path length of the alternative path based on the directed lines in the point distribution map;

[0030] Query the delivery level of each point in the alternative route in turn, obtain the level array, and determine the utility value based on the level array;

[0031] Selecting the best path from the candidate paths according to the path length and the utility value as the delivery path, and feeding back the delivery path to the administrator;

[0032] The selection criteria of the best path are: selecting the candidate path with the highest score as the best path; the calculation process of the score is:

[0033] In the formula, F is the score of the alternative path, β is the preset correction coefficient, K is the total number of demand points, Δ k is the difference level between the kth level in the level array and the kth level in the preset standard array, where the standard array is an array obtained by arranging all the levels in the level array in descending order; represents the utility value, and x is the path length of the alternative path.

[0034] As a further solution of the present invention: the step of correcting the delivery path based on the administrator's feedback information and generating a dispatch instruction according to the corrected delivery path includes:

[0035] Receive feedback from administrators and correct delivery routes;

[0036] Synchronously update the process of selecting the best path;

[0037] Generate dispatch instructions based on the revised delivery path.

[0038] The technical solution of the present invention also provides an unmanned Internet of Things workshop cargo delivery robot scheduling system, the system comprising:

[0039] A point statistics module is used to receive the required points input by the administrator, calculate the virtual distance between the required points, and create a point distribution map; the point distribution map includes nodes corresponding to the required points one by one, there are directed lines between the nodes, and the length of the line is related to the virtual distance;

[0040] A distribution level determination module, used to obtain task parameters at each demand point based on a preset matrix, obtain a task parameter table, and calculate the distribution level of each demand point based on the task parameter table;

[0041] A distribution path generation module, used for selecting a distribution path in the point distribution map according to the distribution level, and feeding back the distribution path to the administrator;

[0042] The delivery path correction module is used to correct the delivery path based on the administrator's feedback information and generate scheduling instructions according to the corrected delivery path.

[0043] As a further solution of the present invention: the point statistics module includes:

[0044] The point receiving unit is used to obtain the BIM model of the IoT workshop and receive the required points selected by the administrator based on the BIM model;

[0045] An object positioning unit, used to locate the moving object according to the monitoring system of the IoT workshop, and obtain the object type and movement trajectory of the moving object;

[0046] The point pairing unit is used to pair all required points in pairs to obtain a point group, intercept the motion trajectory based on the point group, obtain the time span of the intercepted motion trajectory, and query the corresponding object type;

[0047] A virtual distance calculation unit, used to query a standard speed in a preset speed table according to the object type, and calculate the virtual distance by multiplying the standard speed by the time span;

[0048] The point connection unit is used to connect the required points in the BIM model, determine the module length based on the virtual distance, and create a directed line; the process of connecting the required points records the starting point and the end point, and the direction of the directed line is from the starting point to the end point.

[0049] As a further solution of the present invention: the delivery level determination module includes:

[0050] A matrix creation unit is used to receive the indicators input by the administrator, and use the indicators as columns to create a matrix;

[0051] The task parameter acquisition unit is used to send the matrix to the interactive terminal at the demand point, receive the task parameters fed back by the interactive terminal, and obtain a task parameter table; the task parameters are data under each indicator; each row in the task parameter table corresponds to a demand point;

[0052] The weight generation unit is used to self-identify the task parameter table and determine the dynamic weight of each indicator;

[0053] The data accumulation application unit is used to accumulate each row of data based on the dynamic weight to obtain the accumulated value of each demand point, and determine the distribution level of the demand point according to the accumulated value;

[0054] The dynamic weight is proportional to the standard deviation of all data of each indicator.

[0055] As a further solution of the present invention: the delivery path generation module includes:

[0056] A path random generation unit is used to receive the starting point selected by the administrator, connect each demand point in sequence based on the starting point, and randomly generate a preset number of alternative paths;

[0057] A length calculation unit, used for calculating the path length of the alternative path according to the directed lines in the point distribution map;

[0058] A utility value calculation unit is used to query the delivery level of each point in the alternative path in turn, obtain a level array, and determine the utility value according to the level array;

[0059] A selection execution unit is used to select the best path from the candidate paths according to the path length and the utility value as the delivery path, and feed back the delivery path to the administrator;

[0060] The selection criteria of the best path are: selecting the candidate path with the highest score as the best path; the calculation process of the score is:

[0061] In the formula, F is the score of the alternative path, β is the preset correction coefficient, K is the total number of demand points, Δ k is the difference level between the kth level in the level array and the kth level in the preset standard array, where the standard array is an array obtained by arranging all the levels in the level array in descending order; represents the utility value, and x is the path length of the alternative path.

[0062] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention obtains a task parameter table based on matrix statistics of task parameters at each demand point, performs self-identification on the task parameter table, determines the dynamic weight of each indicator, and then adjusts the path selection process, thereby greatly improving the fit between the path and the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0064] Figure 1 This is a flowchart of the method for scheduling cargo delivery robots in unmanned IoT workshops.

[0065] Figure 2 This is the first sub-process flowchart of the method for scheduling cargo delivery robots in unmanned IoT workshops.

[0066] Figure 3 This is the second sub-process flowchart of the method for scheduling cargo delivery robots in unmanned IoT workshops.

[0067] Figure 4 This is the third sub-process flowchart of the unmanned IoT workshop cargo delivery robot scheduling method.

[0068] Figure 5 This is the fourth sub-process flowchart of the method for scheduling cargo delivery robots in unmanned IoT workshops.

[0069] Figure 6 This is a structural block diagram of the robot scheduling system for cargo delivery in an unmanned IoT workshop. DETAILED DESCRIPTION

[0070] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] Figure 1 The flowchart of the method for dispatching a cargo delivery robot in an unmanned Internet of Things workshop is as follows. In an embodiment of the present invention, a method for dispatching a cargo delivery robot in an unmanned Internet of Things workshop is provided. The method includes:

[0072] Step S100: receiving the demand points input by the administrator, calculating the virtual distance between the demand points, and creating a point distribution map; the point distribution map includes nodes corresponding to the demand points one by one, there are directed lines between the nodes, and the length of the line is related to the virtual distance;

[0073] The demand points are locations where goods need to be delivered, which are input by the administrator. In actual applications, these locations are fixed, that is, the work stations where various tasks are completed. After receiving the demand points entered by the administrator, the distance between the demand points is calculated. Since there may be height differences between the demand points, this requires the use of lifting equipment, and there is waiting time for lifting. Therefore, if only the spatial travel distance is used as the distance between the demand points, it is very likely to be inconsistent with the actual situation. That is, when delivering between demand points that are close to each other, it may take more time. In order to avoid this phenomenon, this application introduces a virtual distance. The longer the delivery time between two demand points, the longer the virtual distance.

[0074] It should be noted that the virtual distance between demand points is a directed distance. For example, the virtual distance from point A to point B is different from the virtual distance from point B to point A. Since the positions of the demand points are fixed, the demand points in the created point distribution map are also fixed, and the distance between them is also fixed. Based on this, the present application introduces directed connecting lines to represent the virtual distance. That is, the obtained point distribution map is a three-dimensional map including the demand points. There are direction lines between the demand points, and the length of the direction lines is used to characterize the virtual distance.

[0075] Step S200: acquiring task parameters at each demand point based on a preset matrix, obtaining a task parameter table, and calculating the delivery level of each demand point based on the task parameter table;

[0076] Each demand point will upload task parameters at regular intervals. Task parameters are data under various preset indicators, uploaded by the staff at the demand point. In order to make the data of each demand point more clearly represented, this application uses a matrix to count the task parameters at each demand point. Each column in the matrix corresponds to an indicator, and each row in the matrix corresponds to a demand point. All data corresponding to all demand points in the same time period are inserted into the same matrix to obtain the matrix corresponding to the time period, which is called the task parameter table. By identifying the task parameter table, the importance of each row of data can be determined, which is represented by the parameter of the distribution level.

[0077] Step S300: selecting a delivery path in the point distribution map according to the delivery level, and feeding back the delivery path to the administrator;

[0078] After the distribution level is generated, multiple distribution paths are randomly generated in the point distribution map. The best path is selected from the multiple distribution paths based on the distribution level. The selection rule is that the distribution path with higher distribution level is easier to be selected. If the path score is used to represent the quality of each distribution path, the higher the distribution level, the higher the path score. After the distribution path is selected, the distribution path will be fed back to the administrator.

[0079] Step S400: modifying the delivery path based on the administrator's feedback information, and generating a dispatch instruction according to the modified delivery path;

[0080] Step S300 is the optimal delivery path automatically generated by the execution subject of this method. There will definitely be some differences between it and the actual situation. Therefore, the execution subject of this method sends it to the administrator and receives the adjustment information fed back by the administrator. The adjustment information is the path adjustment process actively input by the administrator and has a higher priority. The delivery path is adjusted based on the adjustment information to obtain the most accurate path, and the scheduling instructions are generated based on the corrected path.

[0081] It is worth mentioning that the performance of the execution body of this method is very high, the distribution path obtained is accurate enough, and most of the feedback information from the administrator is confirmation information, and no adjustments will be made; in addition, since the visualization process of the point distribution map is very easy, the distribution path can be adjusted by touch screen, which is very intuitive and convenient.

[0082] Figure 2 This is a first sub-process flowchart of the method for scheduling a cargo delivery robot in an unmanned IoT workshop. The steps of receiving the required points input by the administrator, calculating the virtual distances between the required points, and creating a point distribution map include:

[0083] Step S101: Obtain a BIM model of the IoT workshop, and receive demand points selected by an administrator based on the BIM model;

[0084] Step S102: locating the moving object according to the monitoring system of the IoT workshop, and obtaining the object type and movement trajectory of the moving object;

[0085] Step S103: Pair all required points in pairs to obtain point groups, intercept the motion trajectory based on the point group, obtain the time span of the intercepted motion trajectory, and query the corresponding object type;

[0086] Step S104: querying the standard speed in a preset speed table according to the object type, and calculating the virtual distance by multiplying the standard speed by the time span;

[0087] Step S105: Connect the required points in the BIM model, determine the module length based on the virtual distance, and create a directed line; the process of connecting the required points records the starting point and the end point, and the direction of the directed line is from the starting point to the end point.

[0088] The above content provides a specific solution for creating a point distribution map, obtaining the BIM model of the Internet of Things workshop, which includes a workshop, a warehouse and a campus. The BIM model is a three-dimensional model, which displays the BIM model and receives the required points selected by the administrator; the moving object is located according to the monitoring system of the Internet of Things workshop, and the object type and motion trajectory of the moving object are obtained. The motion trajectory is used to characterize the moving object from which position to which position, with the focus on the starting point and the end point.

[0089] Then, for any two demand points, obtain the moving object and its movement trajectory between the two demand points, determine the movement speed according to the object type, and multiply it by the movement time to get the virtual distance; in actual applications, the moving object and its movement trajectory between the two demand points are not unique, but the distance calculated by the movement speed and movement time is similar. The average of the virtual distances corresponding to multiple moving objects is calculated, which can be used as the final virtual distance.

[0090] Finally, the required points are connected in the BIM model, the module length is determined based on the virtual distance, and a directed line is created; the process of connecting the required points records the starting point and the end point, and the direction of the directed line is from the starting point to the end point.

[0091] The principle of the above content is to count the movement between any two demand points, and calculate the virtual distance based on the theoretical average speed and actual time of the moving object. The more moving objects are counted, the more accurate the calculated virtual distance is.

[0092] Figure 3 The second sub-flow diagram of the method for dispatching cargo delivery robots in unmanned IoT workshops is as follows: the task parameters at each demand point are obtained based on a preset matrix to obtain a task parameter table; and the steps of calculating the delivery level of each demand point based on the task parameter table include:

[0093] Step S201: receiving the indicators input by the administrator, taking the indicators as columns, and creating a matrix;

[0094] Step S202: Send the matrix to the interactive end at the demand point, receive the task parameters fed back by the interactive end, and obtain a task parameter table; the task parameters are data under each indicator; each row in the task parameter table corresponds to a demand point;

[0095] Step S203: self-identify the task parameter table and determine the dynamic weight of each indicator;

[0096] Step S204: accumulating each row of data based on the dynamic weight to obtain the accumulated value of each demand point, and determining the distribution level of the demand point according to the accumulated value.

[0097] The administrator inputs some indicators in advance, including resource demand, resource surplus, resource quantity level and usage level, etc., creates the same number of columns as the total number of indicators, obtains a matrix, sends the matrix to the interactive end at the demand point, receives the task parameters fed back by the interactive end (controlled by the staff), and obtains a task parameter table; self-identification of the task parameter table can determine the dynamic weight of each indicator. After the dynamic weight is determined, the dynamic weight is multiplied by the data under the corresponding indicator and then accumulated to obtain the cumulative value of each row of data, indicating the importance of each demand point. The threshold reached by the cumulative value can determine the distribution level of the demand point; among them, one threshold corresponds to one distribution level.

[0098] The dynamic weight is proportional to the standard deviation of all data of each indicator.

[0099] As a preferred embodiment of the technical solution of the present invention, the step of sending the matrix to the interactive terminal at the demand point, receiving the task parameters fed back by the interactive terminal, and obtaining the task parameter table includes:

[0100] Read the task parameter table of the previous moment, and determine the correlation array between each demand point and other demand points based on the task parameter table of the previous moment; each value in the correlation array corresponds to an indicator, and each value is used to determine the mapping relationship between the corresponding indicator value and the indicator value of the same indicator of other demand points;

[0101] Receive task parameters fed back by the interactive end and obtain a task parameter table;

[0102] Complete the task parameter table based on the relevant array of each demand point;

[0103] The value of the related array is: A[j] = αN[j] / E j ; In the formula, A[j] is the jth value in the relevant array, N[j] is the data under the jth indicator of the demand point, and E j is the mean of the data in the jth column of the task parameter table, and α is the preset correction coefficient.

[0104] In an example of the technical solution of the present invention, a data completion solution is also provided. In actual applications, the task parameters fed back by the interactive end may only have data for some indicators, but not all indicators. In this case, it is necessary to complete the indicator data that has not been fed back.

[0105] The filling plan is to read the task parameter table of the previous moment, and for any demand point, obtain the correlation between the corresponding row data and other row data, which is represented by a related array. The dimension of the related array is the same as the number of columns. When the task parameters fed back by the interactive end are received, the blank data can be filled according to the related array and the existing data.

[0106] It is worth mentioning that the task parameter table at the previous moment has been completed based on the task parameter table at an even earlier moment.

[0107] Furthermore, regarding the meaning and application of the correlation array, each value in the correlation array is the ratio between the corresponding data under the indicator and the mean of other data, and then combined with a preset coefficient to get the corrected ratio. For empty data, the corrected ratio can be multiplied by the mean of the data under the current indicator.

[0108] Figure 4 The third sub-flow diagram of the method for dispatching a cargo delivery robot in an unmanned IoT workshop is shown in FIG. 1 , wherein the step of selecting a delivery path in the point distribution map according to the delivery level and feeding back the delivery path to the administrator includes:

[0109] Step S301: receiving a starting point selected by an administrator, connecting each demand point in sequence based on the starting point, and randomly generating a preset number of alternative paths;

[0110] Step S302: Calculating the path length of the candidate path according to the directed lines in the point distribution map;

[0111] Step S303: query the delivery level of each point in the candidate path in turn, obtain a level array, and determine the utility value according to the level array;

[0112] Step S304: selecting the best path from the candidate paths according to the path length and the utility value as the delivery path, and feeding back the delivery path to the administrator;

[0113] The selection criteria of the best path are: selecting the candidate path with the highest score as the best path; the calculation process of the score is:

[0114] In the formula, F is the score of the alternative path, β is the preset correction coefficient, K is the total number of demand points, Δ k is the difference level between the kth level in the level array and the kth level in the preset standard array, where the standard array is an array obtained by arranging all the levels in the level array in descending order; represents the utility value, and x is the path length of the alternative path.

[0115] The path generation process is not complicated. The administrator first selects a starting point. The application scenario of this application is the transportation of materials by robots. The starting point is generally the temporary stop of the robot. By connecting each demand point in sequence from the starting point, multiple alternative paths can be obtained. In the process of connecting each demand point in sequence, the selection process of the next demand point is completely random (only selected from the nearest few demand points, that is, the next demand point is randomly selected within the preset distance range). Therefore, multiple alternative paths can be obtained.

[0116] By selecting from multiple alternative routes, the best route can be obtained as the final delivery route, which can then be fed back to the administrator.

[0117] Regarding the process of selecting the best path, the smaller the path length, the higher the score. If the level order conforms to the standard order (descending order, the higher the level, the higher the front), the higher the score. It is worth mentioning that the influence of the path length is small. Therefore, an exponential function is compounded on the path length to reduce its influence. Of course, other functions can also be introduced.

[0118] Figure 5 The fourth sub-flow diagram of the method for dispatching a cargo delivery robot in an unmanned IoT workshop is shown in the figure. The steps of correcting the delivery path based on the feedback information of the administrator and generating a dispatch instruction according to the corrected delivery path include:

[0119] Step S401: receiving feedback information sent by the administrator and correcting the delivery route;

[0120] Step S402: Synchronously update the selection process of the best path;

[0121] Step S403: Generate a dispatch instruction according to the corrected delivery path.

[0122] In an example of the technical solution of the present invention, after the final delivery path is fed back to the administrator, the feedback information sent by the administrator is received, the delivery path is corrected, and a scheduling instruction is generated according to the corrected delivery path. At the same time, the selection process of the best path is synchronously updated to make the automatic selection process of step S300 more accurate and minimize the administrator's active adjustment links, that is, step S400. This is actually a negative feedback optimization process.

[0123] Figure 6 The structure diagram of the unmanned Internet of Things workshop cargo delivery robot scheduling system is shown in the figure. In the embodiment of the present invention, an unmanned Internet of Things workshop cargo delivery robot scheduling system is provided. The system 10 includes:

[0124] The point statistics module 11 is used to receive the required points input by the administrator, calculate the virtual distance between the required points, and create a point distribution map; the point distribution map includes nodes corresponding to the required points one by one, there are directed lines between the nodes, and the length of the line is related to the virtual distance;

[0125] A delivery level determination module 12 is used to obtain task parameters at each demand point based on a preset matrix, obtain a task parameter table, and calculate the delivery level of each demand point based on the task parameter table;

[0126] A delivery path generation module 13 is used to select a delivery path in the point distribution map according to the delivery level, and feed back the delivery path to the administrator;

[0127] The delivery path correction module 14 is used to correct the delivery path based on the feedback information from the administrator and generate a dispatch instruction according to the corrected delivery path.

[0128] Furthermore, the point statistics module 11 includes:

[0129] The point receiving unit is used to obtain the BIM model of the IoT workshop and receive the required points selected by the administrator based on the BIM model;

[0130] An object positioning unit, used to locate the moving object according to the monitoring system of the IoT workshop, and obtain the object type and movement trajectory of the moving object;

[0131] The point pairing unit is used to pair all required points in pairs to obtain a point group, intercept the motion trajectory based on the point group, obtain the time span of the intercepted motion trajectory, and query the corresponding object type;

[0132] A virtual distance calculation unit, used to query a standard speed in a preset speed table according to the object type, and calculate the virtual distance by multiplying the standard speed by the time span;

[0133] The point connection unit is used to connect the required points in the BIM model, determine the module length based on the virtual distance, and create a directed line; the process of connecting the required points records the starting point and the end point, and the direction of the directed line is from the starting point to the end point.

[0134] Specifically, the delivery level determination module 12 includes:

[0135] A matrix creation unit is used to receive the indicators input by the administrator, and use the indicators as columns to create a matrix;

[0136] The task parameter acquisition unit is used to send the matrix to the interactive terminal at the demand point, receive the task parameters fed back by the interactive terminal, and obtain a task parameter table; the task parameters are data under each indicator; each row in the task parameter table corresponds to a demand point;

[0137] The weight generation unit is used to self-identify the task parameter table and determine the dynamic weight of each indicator;

[0138] The data accumulation application unit is used to accumulate each row of data based on the dynamic weight to obtain the accumulated value of each demand point, and determine the distribution level of the demand point according to the accumulated value;

[0139] The dynamic weight is proportional to the standard deviation of all data of each indicator.

[0140] Furthermore, the delivery path generation module 13 includes:

[0141] A path random generation unit is used to receive the starting point selected by the administrator, connect each demand point in sequence based on the starting point, and randomly generate a preset number of alternative paths;

[0142] A length calculation unit, used for calculating the path length of the alternative path according to the directed lines in the point distribution map;

[0143] A utility value calculation unit is used to query the delivery level of each point in the alternative path in turn, obtain a level array, and determine the utility value according to the level array;

[0144] A selection execution unit is used to select the best path from the candidate paths according to the path length and the utility value as the delivery path, and feed back the delivery path to the administrator;

[0145] The selection criteria of the best path are: selecting the candidate path with the highest score as the best path; the calculation process of the score is:

[0146] In the formula, F is the score of the alternative path, β is the preset correction coefficient, K is the total number of demand points, Δ k is the difference level between the kth level in the level array and the kth level in the preset standard array, where the standard array is an array obtained by arranging all the levels in the level array in descending order; represents the utility value, and x is the path length of the alternative path.

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for dispatching a cargo delivery robot in an unmanned Internet of Things workshop, characterized in that: The method comprises: Receive the demand points input by the administrator, calculate the virtual distance between the demand points, and create a point distribution map; the point distribution map includes nodes corresponding to the demand points one by one, there are directed lines between the nodes, and the length of the line is related to the virtual distance; Obtaining task parameters at each demand point based on a preset matrix, obtaining a task parameter table, and calculating the delivery level of each demand point based on the task parameter table; Select a delivery path in the point distribution map according to the delivery level, and feed back the delivery path to the administrator; The delivery path is modified based on the administrator's feedback information, and the dispatch instruction is generated according to the modified delivery path.

2. The unmanned IoT workshop cargo delivery robot scheduling method according to claim 1 is characterized in that: The steps of receiving the demand points input by the administrator, calculating the virtual distances between the demand points, and creating a point distribution map include: Obtain the BIM model of the IoT workshop and receive the required points selected by the administrator based on the BIM model; Locate the moving object according to the monitoring system of the IoT workshop, and obtain the object type and movement trajectory of the moving object; Pair all required points in pairs to obtain point groups, intercept the motion trajectory based on the point group, obtain the time span of the intercepted motion trajectory, and query the corresponding object type; According to the object type, the standard speed is queried in the preset speed table, and the virtual distance is calculated by multiplying the standard speed by the time span; Connect the required points in the BIM model, determine the module length based on the virtual distance, and create a directed line; the process of connecting the required points records the starting point and the end point, and the direction of the directed line is from the starting point to the end point.

3. The unmanned IoT workshop cargo delivery robot scheduling method according to claim 1 is characterized in that: The step of obtaining the task parameters at each demand point based on a preset matrix to obtain a task parameter table, and calculating the delivery level of each demand point based on the task parameter table includes: Receive the indicators input by the administrator, use the indicators as columns, and create a matrix; Send the matrix to the interactive end at the demand point, receive the task parameters fed back by the interactive end, and obtain a task parameter table; the task parameters are the data under each indicator; each row in the task parameter table corresponds to a demand point; Self-identify the task parameter table and determine the dynamic weight of each indicator; Based on the dynamic weight, the data of each row is accumulated to obtain the accumulated value of each demand point, and the distribution level of the demand point is determined according to the accumulated value; The dynamic weight is proportional to the standard deviation of all data of each indicator.

4. The unmanned IoT workshop cargo delivery robot scheduling method according to claim 3 is characterized in that: The step of sending the matrix to the interactive terminal at the demand point, receiving the task parameters fed back by the interactive terminal, and obtaining the task parameter table includes: Read the task parameter table of the previous moment, and determine the correlation array between each demand point and other demand points based on the task parameter table of the previous moment; each value in the correlation array corresponds to an indicator, and each value is used to determine the mapping relationship between the corresponding indicator value and the indicator value of the same indicator of other demand points; Receive task parameters fed back by the interactive end and obtain a task parameter table; Complete the task parameter table based on the relevant array of each demand point; The value of the related array is: A[j] = αN[j] / E j ; In the formula, A[j] is the jth value in the relevant array, N[j] is the data under the jth indicator of the demand point, and E j is the mean of the data in the jth column of the task parameter table, and α is the preset correction coefficient.

5. The unmanned IoT workshop cargo delivery robot scheduling method according to claim 1 is characterized in that: The step of selecting a delivery path in the point distribution map according to the delivery level and feeding back the delivery path to the administrator includes: Receive the starting point selected by the administrator, connect each demand point in sequence based on the starting point, and randomly generate a preset number of alternative paths; Calculate the path length of the alternative path based on the directed lines in the point distribution map; Query the delivery level of each point in the alternative route in turn, obtain the level array, and determine the utility value based on the level array; Selecting the best path from the candidate paths according to the path length and the utility value as the delivery path, and feeding back the delivery path to the administrator; The selection criteria of the best path are: selecting the candidate path with the highest score as the best path; the calculation process of the score is: In the formula, F is the score of the alternative path, β is the preset correction coefficient, K is the total number of demand points, Δ k is the difference level between the kth level in the level array and the kth level in the preset standard array, where the standard array is an array obtained by arranging all the levels in the level array in descending order; represents the utility value, and x is the path length of the alternative path.

6. The unmanned IoT workshop cargo delivery robot scheduling method according to claim 1 is characterized in that: The step of correcting the delivery path based on the administrator's feedback information and generating a dispatch instruction according to the corrected delivery path includes: Receive feedback from administrators and correct delivery routes; Synchronously update the process of selecting the best path; Generate dispatch instructions based on the revised delivery path.

7. An unmanned IoT workshop cargo delivery robot dispatching system, characterized in that: The system comprises: A point statistics module is used to receive the required points input by the administrator, calculate the virtual distance between the required points, and create a point distribution map; the point distribution map includes nodes corresponding to the required points one by one, there are directed lines between the nodes, and the length of the line is related to the virtual distance; A distribution level determination module, used to obtain task parameters at each demand point based on a preset matrix, obtain a task parameter table, and calculate the distribution level of each demand point based on the task parameter table; A distribution path generation module, used for selecting a distribution path in the point distribution map according to the distribution level, and feeding back the distribution path to the administrator; The delivery path correction module is used to correct the delivery path based on the administrator's feedback information and generate scheduling instructions according to the corrected delivery path.

8. The unmanned IoT workshop cargo delivery robot dispatching system according to claim 7 is characterized in that: The point statistics module includes: The point receiving unit is used to obtain the BIM model of the IoT workshop and receive the required points selected by the administrator based on the BIM model; An object positioning unit, used to locate the moving object according to the monitoring system of the IoT workshop, and obtain the object type and movement trajectory of the moving object; The point pairing unit is used to pair all required points in pairs to obtain a point group, intercept the motion trajectory based on the point group, obtain the time span of the intercepted motion trajectory, and query the corresponding object type; A virtual distance calculation unit, used to query a standard speed in a preset speed table according to the object type, and calculate the virtual distance by multiplying the standard speed by the time span; The point connection unit is used to connect the required points in the BIM model, determine the module length based on the virtual distance, and create a directed line; the process of connecting the required points records the starting point and the end point, and the direction of the directed line is from the starting point to the end point.

9. The unmanned IoT workshop cargo delivery robot dispatching system according to claim 7 is characterized in that: The delivery level determination module includes: A matrix creation unit is used to receive the indicators input by the administrator, and use the indicators as columns to create a matrix; The task parameter acquisition unit is used to send the matrix to the interactive terminal at the demand point, receive the task parameters fed back by the interactive terminal, and obtain a task parameter table; the task parameters are data under each indicator; each row in the task parameter table corresponds to a demand point; The weight generation unit is used to self-identify the task parameter table and determine the dynamic weight of each indicator; The data accumulation application unit is used to accumulate each row of data based on the dynamic weight to obtain the accumulated value of each demand point, and determine the distribution level of the demand point according to the accumulated value; The dynamic weight is proportional to the standard deviation of all data of each indicator.

10. The unmanned IoT workshop cargo delivery robot dispatching system according to claim 7, characterized in that: The delivery path generation module includes: A path random generation unit is used to receive the starting point selected by the administrator, connect each demand point in sequence based on the starting point, and randomly generate a preset number of alternative paths; A length calculation unit, used for calculating the path length of the alternative path according to the directed lines in the point distribution map; A utility value calculation unit is used to query the delivery level of each point in the alternative path in turn, obtain a level array, and determine the utility value according to the level array; A selection execution unit is used to select the best path from the candidate paths according to the path length and the utility value as the delivery path, and feed back the delivery path to the administrator; The selection criteria of the best path are: selecting the candidate path with the highest score as the best path; the calculation process of the score is: In the formula, F is the score of the alternative path, β is the preset correction coefficient, K is the total number of demand points, Δ k is the difference level between the kth level in the level array and the kth level in the preset standard array, where the standard array is an array obtained by arranging all the levels in the level array in descending order; represents the utility value, and x is the path length of the alternative path.

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