Intelligent mine car dispatching data management method and system based on Internet of Things

Abstract

The invention discloses an intelligent mine car scheduling data management method and system based on the Internet of Things, and relates to the technical field of mine car scheduling management, and the method comprises the following steps: monitoring the position information and use state of an intelligent mine car in real time through a sensor and a communication module, and obtaining the demand state of each mine spot; scheduling the intelligent mine car based on the position information, the use state and the demand state; optimizing a scheduling algorithm based on historical scheduling data; the method is used for solving the problems that according to an existing mine car dispatching management technology, the no-load rate of a mine car is high, dispatching efficiency is low, and the mineral yield of a mine is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of mine car dispatching management, and in particular to an intelligent mine car dispatching data management method and system based on the Internet of Things. Background Art

[0002] Mine car dispatching and management technology refers to a technical system that uses information technology, communication technology and automation technology to effectively dispatch, monitor and manage various mine cars in a mine. By collecting and analyzing data such as the location, status and operating conditions of the mine cars in real time, it can optimize the planning and scheduling of the mine car transportation process, improve transportation efficiency, reduce costs, enhance safety, and achieve optimal utilization of resources. This technology aims to realize the intelligent, automated and efficient internal logistics and transportation of mines, thereby improving overall production efficiency and competitiveness.

[0003] In a mine environment, traditional mine car dispatching and management technology has many problems, such as difficult positioning, low dispatching efficiency and waste of resources. The existing mine car dispatching and management technology usually dispatches mine cars only when the mine site needs them, which greatly increases the empty rate of mine cars and reduces the overall resource allocation efficiency. In addition, due to the different ore contents and personnel allocations at different mine sites in the mine, the loading efficiency of different mine sites is also different. If the mine car is dispatched and analyzed only when the mine site needs it, and then dispatched and controlled to go to the corresponding mine site, a lot of time will be wasted, resulting in a decrease in mineral production. The existing mine car dispatching and management technology also has the problem of a high empty rate of mine cars and low dispatching efficiency, which leads to a decrease in the mineral production of the mine. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to a certain extent. It monitors the location information and usage status of intelligent mine cars in real time through sensors and communication modules, obtains the demand status of each mining point, and marks it in the mine distribution map. Then, the intelligent mine cars are dispatched based on the location information, usage status and demand status, and the dispatching data is recorded to obtain historical dispatching data. Finally, the dispatching algorithm is optimized based on the historical dispatching data to solve the problem that the existing mine car dispatching management technology still has a high empty rate of mine cars and low dispatching efficiency, which leads to a reduction in the mineral output of the mine.

[0005] To achieve the above objectives, in a first aspect, the present application provides an intelligent mining car dispatching data management method based on the Internet of Things, comprising the following steps:

[0006] Through sensors and communication modules, the location information and usage status of the intelligent mining car are monitored in real time, and the demand status of each mining point is obtained; the mine distribution map is obtained and marked;

[0007] Dispatch intelligent mining vehicles based on location information, usage status, and demand status;

[0008] Optimize the scheduling algorithm based on historical scheduling data.

[0009] Furthermore, obtaining a mine channel distribution map and marking the mine channel distribution map includes the following sub-steps:

[0010] Mark the location of the 4G micro communication base station in the mine distribution map, and mark the location information of the mine car in the mine distribution map based on the relative position;

[0011] Green and red represent the idle state and loaded state of the smart mine car respectively. The mine track distribution map is a dynamic image, which will update the position of the smart mine car in real time. When the smart mine car is in the idle state, it will be rendered in green, and when the smart mine car is in the loaded state, it will be rendered in red.

[0012] 1 and 0 represent the need for a mine cart and the need for a mine cart at a mine point, respectively. When the demand status of a mine point is that a mine cart is needed, the mine point is marked with the number 1, and when the demand status of a mine point is that a mine cart is not needed, the mine point is marked with the number 0.

[0013] Furthermore, scheduling the intelligent mining vehicle based on the location information, usage status and demand status includes the following sub-steps:

[0014] The location information of the idle intelligent mine cart is marked as the mine cart location, the mine point where the demand state is that the mine cart is needed is marked as the demand point, and the length of the mine cart track between the mine cart location and the demand point is obtained and marked as the track distance;

[0015] The demand points are numbered and marked with symbol S n Indicates, where n is a positive integer and n is the serial number of S, the positions of the minecarts are numbered, and the symbol P m Represents, where m is a positive integer and m is the serial number of P;

[0016] S n With P m The orbital distance between them is marked as L(n,m), where (n,m) is the serial number of L;

[0017] Further analyze L(n,m) and dispatch the intelligent mining car according to the analysis results.

[0018] Furthermore, L(n,m) is further analyzed, and scheduling the intelligent mining car according to the analysis results includes the following sub-steps:

[0019] Starting with n=1, obtain the minimum value in L(n,m), mark it as H(n,m), add n+1 and analyze again until the maximum value of n is reached and stop obtaining;

[0020] Get all H(n,m) obtained by analysis, and compare whether there is a repeated m. If so, output the allocation duplication signal; if not, output the allocation reasonable signal;

[0021] If the output distribution is a repeated signal, the minimum value among the m-repeated H(n,m) is retained, the remaining m-repeated H(n,m) is discarded and its value of n is extracted;

[0022] Eliminate the L(n,m) with the same m as the existing H(n,m), reanalyze the H(n,m) corresponding to the extracted n, and repeat the comparison of H(n,m) until the output distribution is reasonable.

[0023] Based on the final retained H(n,m), P m Assign to the corresponding S n Place.

[0024] Furthermore, optimizing the scheduling algorithm based on historical scheduling data includes the following sub-steps:

[0025] Record the dispatch data to obtain historical dispatch data;

[0026] Optimize the scheduling algorithm based on historical scheduling data.

[0027] Furthermore, recording the scheduling data to obtain historical scheduling data includes the following sub-steps:

[0028] For all the mining points in the mine, the mining points are numbered and represented by the symbol K i Represents, where i is a positive integer and i is the sequence number of K;

[0029] When K i When the demand state changes from needing a minecart to not needing a minecart, the timer starts counting until K is recorded. i When the demand status changes from no need for a minecart to need for a minecart, the timer stops counting and the recorded time is marked as the loading time.

[0030] For each K i The loading time of the first test quantity is recorded, and K i The loading time is marked as T(i,j), where j is a positive integer and 1≤j≤D, and D is the first test quantity;

[0031] The time it takes for the smart mine car to move from the mine car position to the demand point during each dispatch is named the moving time.i The moving time of the first test quantity is recorded;

[0032] The loading time and moving time are historical scheduling data.

[0033] Furthermore, optimizing the scheduling algorithm based on historical scheduling data includes the following sub-steps:

[0034] For any value of i, calculate the average value of T(i,j) and name the result as the average loading time, represented by the symbol R i express;

[0035] Calculate the average of the moving duration, and name the result as the moving average duration, represented by the symbol AT;

[0036] Calculate R i -AT, mark the calculation result as G i ;

[0037] When K i When the demand state changes from needing a minecart to not needing a minecart, the timer starts counting, and the recorded time is named the real-time loading duration, and the symbol F i express;

[0038] Real-time comparison F i With G i , when F i Equal to G i When K i The requirement status is changed to require a minecart and K i Dispatch the idle smart minecart.

[0039] In a second aspect, the present application provides an intelligent mine car dispatching data management system based on the Internet of Things, including a mine tunnel overall monitoring module, a mine car initial dispatching module and a mine car optimization dispatching module; the mine car initial dispatching module and the mine car optimization dispatching module are respectively connected to the data of the mine tunnel overall monitoring module;

[0040] The overall mine monitoring module is used to monitor the location information and usage status of the intelligent mine car in real time through sensors and communication modules, and obtain the demand status of each mine point;

[0041] The mine car initial scheduling module is used to schedule the intelligent mine car based on the location information, usage status and demand status;

[0042] The mine car optimization scheduling module is used to optimize the scheduling algorithm based on historical scheduling data.

[0043] Beneficial effects of the present invention: The present invention monitors the location information and usage status of the intelligent mine car in real time through sensors and communication modules, obtains the demand status of each mine point, and marks it in the mine distribution map, and then dispatches the intelligent mine car based on the location information, usage status and demand status. The advantage is that the nearest intelligent mine car can be matched to the mine point based on the location information, usage status and demand status, and the overall dispatching time is short, which improves the efficiency of mine car dispatching;

[0044] The present invention records the scheduling data to obtain the loading time and the moving time, and finally optimizes the scheduling algorithm based on the loading time and the moving time, and schedules the intelligent mine car in advance before the mine site needs the intelligent mine car. The advantage is that different mine sites have different loading efficiency for minerals. For mine sites with faster loading, if a scheduling method of issuing demands and then scheduling is adopted, frequent demands will lead to too much empty mine cars and long waiting times at the mine sites. By scheduling the mine sites in advance based on historical data, the time the mine sites wait for the intelligent mine cars can be effectively reduced, thereby improving the effectiveness and efficiency of mine car scheduling management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a functional block diagram of the system of the present invention;

[0046] Figure 2 is a schematic diagram of the track distance of the present invention;

[0047] Figure 3 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1, please refer to Figure 3 As shown, the present application provides an intelligent mining car scheduling data management method based on the Internet of Things, comprising the following steps:

[0050] Step S1, real-time monitoring of the location information and usage status of the intelligent mining vehicle through sensors and communication modules, and obtaining the demand status of each mining point; Step S1 includes the following sub-steps:

[0051] Step S101, presetting a first number of 4G micro communication base stations in the mine;

[0052] Step S102, a 4G communication module is installed on the intelligent mining car, and the 4G communication module is used to receive the communication signal sent by the 4G micro communication base station;

[0053] Step S103, obtaining the signal strength of the communication signal, marked as communication strength, and analyzing the signal strengths from different 4G micro communication base stations based on the signal strength ranging technology to obtain the relative position of the intelligent mining car relative to the 4G micro communication base station;

[0054] Step S104, a flap sensor is installed in the smart mine car, and the flap sensor is used to detect whether the status plate on the smart mine car is flipped. The smart mine car is initially in an idle state. If the smart mine car has been used, the worker flips the status plate. After the flap sensor detects that the status plate is flipped, the use state of the smart mine car is changed to a loaded state; if the worker empties the materials in the smart mine car and then flips the status plate, the flap sensor detects that the status plate is flipped, and the use state of the smart mine car is changed to an idle state;

[0055] Step S105, a demand button is set at the mine point in the mine. When the worker presses the demand button, the demand state of the mine point is set to require a mine car. After the mine car arrives at the mine point, it is automatically changed to not need a mine car. In actual applications, the 4G micro communication base station adopts the existing micro base station, and the relative position is measured by the existing signal strength ranging technology. Since the signal strength ranging technology is relatively mature and is not the main technical solution in this embodiment, the specific ranging process is omitted in this embodiment, and only the relative position is obtained; the status board consists of two sides, one side is a green reflective surface, and the other side is a red reflective surface. The worker can judge whether the smart mine car is in an idle state through the reflective surface displayed on the status board. The use status of the smart mine car can also be monitored by high-precision methods such as gravity monitoring, but monitoring the use status through a flip sensor can greatly reduce the monitoring cost in the smart mine car without reducing the monitoring accuracy; the demand button is only an adjective, representing a device for sending a demand. The demand button can exist in the form of a button or in the form of a communication instrument.

[0056] Step S106, obtaining a mine channel distribution map and marking the mine channel distribution map;

[0057] Step S106 includes the following sub-steps:

[0058] Step S1061, marking the location of the 4G micro communication base station in the mine tunnel distribution map, and marking the location information of the mine car in the mine tunnel distribution map based on the relative position;

[0059] Step S1062, green and red represent the idle state and loaded state of the smart mine car respectively. The mine track distribution map is a dynamic image, and the position of the smart mine car will be updated in real time. When the smart mine car is in the idle state, the smart mine car is rendered in green, and when the smart mine car is in the loaded state, the smart mine car is rendered in red;

[0060] Step S1063, 1 and 0 are used to represent the need for a mine cart and the need for a mine cart at the mine point respectively. When the demand status of the mine point is that a mine cart is needed, the mine point is marked with the number 1, and when the demand status of the mine point is that a mine cart is not needed, the mine point is marked with the number 0.

[0061] Step S2, scheduling the intelligent mining car based on the location information, usage status and demand status; Step S2 includes the following sub-steps:

[0062] See also Figure 2 As shown, in step S201, the position information of the idle intelligent mine car is marked as the mine car position, the mine point where the demand state is that the mine car is needed is marked as the demand point, and the length of the mine car track between the mine car position and the demand point is obtained and marked as the track distance; in practical applications, since the tracks in the mine tunnel are usually planned, the length of each track is recorded, and the mine car position divides the track into two parts, the distance that the mine car needs to travel on this track can be obtained by the ratio of the two parts, and the track distance can be obtained by combining the length of other tracks, so the track distance in this embodiment can be directly obtained; Figure 2 The figure shows the track distance of a minecart relative to a demand point. Figure 2 The dotted line in is the orbital distance;

[0063] Step S202: number the demand points, using the symbol S n Indicates, where n is a positive integer and n is the serial number of S, the positions of the minecarts are numbered, and the symbol P m Represents, where m is a positive integer and m is the serial number of P;

[0064] Step S203: n With P m The orbital distance between them is marked as L(n,m), where (n,m) is the serial number of L;

[0065] Step S204, further analyzing L(n,m), and dispatching the intelligent mining car according to the analysis result;

[0066] Step S204 includes the following sub-steps:

[0067] Step S2041, starting with n=1, obtain the minimum value in L(n,m), mark it as H(n,m), add n+1 and analyze again, and stop obtaining after reaching the maximum value of n; in practical applications, assuming that there are currently 6 idle intelligent mining cars and 3 demand points, S1 to S3 are obtained by numbering, 1≤n≤3, P1 to P6, 1≤m≤6, thereby obtaining track distances L(1,1) to L(1,6), L(2,1) to L(2,6) and L(3,1) to L(3,6). In this embodiment, H(n,m) when n=1 to 6 is obtained as H(1,3), H(2,5), H(3,2), H(4,4), H(5,6) and H(6,3) respectively;

[0068] Step S2042, obtaining all H(n,m) obtained by analysis, comparing whether there is a repeated m therein, if so, outputting a duplicate allocation signal; if not, outputting a reasonable allocation signal;

[0069] Step S2043, if the output allocation repetition signal, retain the minimum value of the m-repeated H(n,m), remove the remaining m-repeated H(n,m) and extract the value of n;

[0070] Step S2044, remove the L(n,m) with the same m as the existing H(n,m), re-analyze the H(n,m) corresponding to the extracted n, and repeat the comparison of H(n,m) until a reasonable allocation signal is output;

[0071] Step S2045: Based on the final retained H(n,m), P m Assign to the corresponding S nIn practical applications, in the H(n,m) obtained by analysis, the m of H(1,3) and H(6,3) are both 3, that is, m is repeated, and the output distribution is repeated. Among them, H(1,3) is 136m, H(6,3) is 171m, H(1,3) is retained, H(6,3) is removed and the n of H(6,3) is extracted until n is 6. At this time, the remaining H(n,m) includes H(1,3), H(2,5), H(3,2), H(4,4) and H(5,6), and the m of the remaining H(n,m) includes 3, 5, 2, 4 and 6. L(n,m) with m=3, 5, 2, 4 and 6 in L(n,m) Eliminate; since n is extracted as 6, re-search the minimum value in the remaining L(6,m), and find that H(6,m) is H(6,1). At this time, H(n,m) includes H(1,3), H(2,5), H(3,2), H(4,4), H(5,6) and H(6,1). By comparison, it is found that there is no repeated m among them, and the output allocation is reasonable. Based on the finally retained H(n,m), P3 is assigned to the corresponding S1, P5 is assigned to the corresponding S2, P2 is assigned to the corresponding S3, P4 is assigned to the corresponding S4, P6 is assigned to the corresponding S5, and P1 is assigned to the corresponding S6.

[0072] Step S3, optimizing the scheduling algorithm based on historical scheduling data; Step S3 includes the following sub-steps:

[0073] Step S301, recording the scheduling data to obtain historical scheduling data;

[0074] Step S301 includes the following sub-steps:

[0075] Step S3011: number all the mining points in the mine, using the symbol K i Represents, where i is a positive integer and i is the sequence number of K;

[0076] Step S3012, when K i When the demand state changes from needing a minecart to not needing a minecart, the timer starts counting until K is recorded. i When the demand status changes from no need for a minecart to need for a minecart, the timer stops counting and the recorded time is marked as the loading time.

[0077] Step S3013, for each K i The loading time of the first test quantity is recorded, and K i The loading time is marked as T(i,j), where j is a positive integer and 1≤j≤D, and D is the first test quantity;

[0078] Step S3014: the time taken for the intelligent mining car to move from the mining car position to the demand point during each scheduling is named as the moving time. i The moving time of the first test quantity is recorded;

[0079] Step S3015, the loading time and the moving time are the historical scheduling data; in actual application, there are 13 mining points in the mine in this embodiment, so the numbering is K1 to K 13 , 1≤i≤13, the loading time actually represents the time required for the mine to fill the smart mine car from empty to full; the first test quantity is set to obtain sufficient basic data to support subsequent analysis, which has no practical significance, so the user can set it according to the needs, usually greater than or equal to 50, in this embodiment, the first test quantity is set to 100; the moving time corresponds to T(i,j), after the moving time, the smart mine car arrives at the demand point, and then the workers load the minerals into the smart mine car, after T(i,j), the smart mine car is fully loaded, so the moving time is the time it takes for the mine car to move to the demand point before each T(i,j).

[0080] Step S302, optimizing the scheduling algorithm based on historical scheduling data;

[0081] Step S302 includes the following sub-steps:

[0082] Step S3021, for any value of i, calculate the average value of T(i,j), and name the calculated result as the average loading time, represented by the symbol R i express;

[0083] Step S3022, calculating the average of the moving durations, and naming the calculation result as the moving average duration, represented by the symbol AT;

[0084] Step S3023, calculate R i -AT, mark the calculation result as G i ;

[0085] Step S3024, when K i When the demand state changes from needing a minecart to not needing a minecart, the timer starts counting, and the recorded time is named the real-time loading duration, and the symbol F i express;

[0086] Step S3025, real-time comparison F i With G i , when F i Equal to G i When K i The requirement status is changed to require a minecart and K iDispatch the idle smart mine car; in practical applications, taking i=1 as an example, calculate the average value of T(1,j), that is, the average value from T(1,1) to T(1,100). Due to the large amount of data, this embodiment will not be specifically displayed, and only the final calculation result is displayed. The calculation results show that R1 is 32.56min, and the average value of the moving time is calculated to obtain the moving average time AT of 4.24min. For R1, it is calculated that R1-AT=G1=28.32min. The real-time monitoring shows that the real-time loading time F1 is 24.08min. By comparison, it is found that F1 is equal to G1. At this time, it means that the previous smart mine car is about to be loaded. At this time, it is necessary to dispatch the next smart mine car to the mine point K1 to reduce the waiting time and improve the dispatching efficiency. The demand state of K1 is changed to the need for a mine car and the idle smart mine car is dispatched for K1 in advance.

[0087] Example 2, please refer to Figure 1 As shown, the present application provides an intelligent mine car scheduling data management system based on the Internet of Things, including a mine track overall monitoring module, a mine car initial scheduling module and a mine car optimization scheduling module; the mine car initial scheduling module and the mine car optimization scheduling module are respectively connected to the mine track overall monitoring module data;

[0088] The overall mine monitoring module is used to monitor the location information and usage status of the intelligent mine car in real time through sensors and communication modules, and obtain the demand status of each mine point;

[0089] The initial scheduling module of the mine car is used to schedule the intelligent mine car based on the location information, usage status and demand status;

[0090] The mine car optimization scheduling module is used to optimize the scheduling algorithm based on historical scheduling data.

[0091] In the embodiments provided in the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent mine car dispatching data management method based on the Internet of Things, characterized in that: The steps include: Through sensors and communication modules, the location information and usage status of the intelligent mining car are monitored in real time, and the demand status of each mining point is obtained; the mine distribution map is obtained and marked; Dispatch intelligent mining vehicles based on location information, usage status, and demand status; Optimize the scheduling algorithm based on historical scheduling data.

2. The method for managing intelligent mine car dispatching data based on the Internet of Things according to claim 1 is characterized in that: Obtaining a mine channel distribution map and marking the mine channel distribution map includes the following sub-steps: Mark the location of the 4G micro communication base station in the mine distribution map, and mark the location information of the mine car in the mine distribution map based on the relative position; Green and red represent the idle state and loaded state of the smart mine car respectively. The mine track distribution map is a dynamic image, which will update the position of the smart mine car in real time. When the smart mine car is in the idle state, it will be rendered in green, and when the smart mine car is in the loaded state, it will be rendered in red. 1 and 0 represent the need for a mine cart and the need for a mine cart at a mine point, respectively. When the demand status of a mine point is that a mine cart is needed, the mine point is marked with the number 1, and when the demand status of a mine point is that a mine cart is not needed, the mine point is marked with the number 0.

3. The method for managing intelligent mine car dispatching data based on the Internet of Things according to claim 2 is characterized in that: Scheduling intelligent mining vehicles based on location information, usage status, and demand status includes the following sub-steps: The location information of the idle intelligent mine cart is marked as the mine cart location, the mine point where the demand state is that the mine cart is needed is marked as the demand point, and the length of the mine cart track between the mine cart location and the demand point is obtained and marked as the track distance; The demand points are numbered and marked with symbol S n Indicates, where n is a positive integer and n is the serial number of S, the positions of the minecarts are numbered, and the symbol P m Represents, where m is a positive integer and m is the serial number of P; S n With P m The orbital distance between them is marked as L(n,m), where (n,m) is the serial number of L; Further analyze L(n,m) and dispatch the intelligent mining car according to the analysis results.

4. The method for managing intelligent mine car dispatching data based on the Internet of Things according to claim 3 is characterized in that: Further analysis of L(n,m) and scheduling of intelligent mining vehicles based on the analysis results include the following sub-steps: Starting with n=1, obtain the minimum value in L(n,m), mark it as H(n,m), add n+1 and analyze again until the maximum value of n is reached and stop obtaining; Get all H(n,m) obtained by analysis, and compare whether there is a repeated m. If so, output the allocation duplication signal; if not, output the allocation reasonable signal; If the output distribution is a repeated signal, the minimum value among the m-repeated H(n,m) is retained, the remaining m-repeated H(n,m) is discarded and its value of n is extracted; Eliminate the L(n,m) with the same m as the existing H(n,m), reanalyze the H(n,m) corresponding to the extracted n, and repeat the comparison of H(n,m) until the output distribution is reasonable. Based on the final retained H(n,m), P m Assign to the corresponding S n Place.

5. The method for managing intelligent mine car dispatching data based on the Internet of Things according to claim 4 is characterized in that: Optimizing the scheduling algorithm based on historical scheduling data includes the following sub-steps: Record the dispatch data to obtain historical dispatch data; Optimize the scheduling algorithm based on historical scheduling data.

6. The method for managing intelligent mine car dispatching data based on the Internet of Things according to claim 5 is characterized in that: Recording the dispatch data to obtain historical dispatch data includes the following sub-steps: For all the mining points in the mine, the mining points are numbered and represented by the symbol K i Represents, where i is a positive integer and i is the sequence number of K; When K i When the demand state changes from needing a minecart to not needing a minecart, the timer starts counting until K is recorded. i When the demand status changes from no need for a minecart to need for a minecart, the timer stops counting and the recorded time is marked as the loading time. For each K i The loading time of the first test quantity is recorded, and K i The loading time is marked as T(i,j), where j is a positive integer and 1≤j≤D, and D is the first test quantity; The time it takes for the smart mine car to move from the mine car position to the demand point during each dispatch is named the moving time. i The moving time of the first test quantity is recorded; The loading time and moving time are historical scheduling data.

7. The method for managing intelligent mine car dispatching data based on the Internet of Things according to claim 6 is characterized in that: Optimizing the scheduling algorithm based on historical scheduling data includes the following sub-steps: For any value of i, calculate the average value of T(i,j) and name the result as the average loading time, represented by the symbol R i express; Calculate the average of the moving duration, and name the result as the moving average duration, represented by the symbol AT; Calculate R i -AT, mark the calculation result as G i ; When K i When the demand state changes from needing a minecart to not needing a minecart, the timer starts counting, and the recorded time is named the real-time loading duration, and the symbol F i express; Real-time comparison F i With G i , when F i Equal to G i When K i The requirement status is changed to require a minecart and K i Dispatch the idle smart minecart.

8. An intelligent mine car dispatching data management system based on the Internet of Things, used to implement the intelligent mine car dispatching data management method based on the Internet of Things according to any one of claims 1 to 7, characterized in that: It includes a mine tunnel overall monitoring module, a mine car initial scheduling module and a mine car optimization scheduling module; the mine car initial scheduling module and the mine car optimization scheduling module are respectively connected with the mine tunnel overall monitoring module data; The overall mine monitoring module is used to monitor the location information and usage status of the intelligent mine car in real time through sensors and communication modules, and obtain the demand status of each mine point; The mine car initial scheduling module is used to schedule the intelligent mine car based on the location information, usage status and demand status; The mine car optimization scheduling module is used to optimize the scheduling algorithm based on historical scheduling data.

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