Mining truck oil consumption management method and system based on big data

Abstract

The invention discloses a mining truck fuel consumption management method and system based on big data, belongs to the technical field of fuel consumption management, and can provide timely fuel consumption abnormity early warning. Comprising the following steps: responding to radio frequency signals sent by a first RFID transmitter and a second RFID transmitter which are arranged at a first preset position on a preset first path and received by a first RFID receiver on a first mining truck running on the first path; respectively acquiring and recording first remaining oil quantity information and second remaining oil quantity information of the first mining truck; calculating first oil consumption information of the first mining truck according to the first remaining oil quantity information and the second remaining oil quantity information; according to the first load information of the first mining truck, normal oil consumption information corresponding to the first load information is obtained through matching from a first database; when the difference value between the first oil consumption information and the normal oil consumption information is larger than or equal to a first preset value, oil consumption abnormal information is sent to preset terminal equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel consumption management, and in particular to a fuel consumption management method and system for mining trucks based on big data. Background Art

[0002] In the field of modern mining, mining trucks are one of the key equipment, and their fuel consumption management is of great significance to the operational efficiency and cost control of mining enterprises. However, the existing fuel consumption management methods mainly rely on manual recording of the refueling, oil delivery and fuel consumption values ​​of each shift of the truck, and performing simple statistical analysis. This traditional management method has the following problems:

[0003] First, the method of manually recording fuel consumption data is inefficient and wastes a lot of human resources. Mining operations are usually characterized by high intensity and high frequency. Truck fuel consumption data needs to be frequently recorded and updated. Manual operation is not only cumbersome but also prone to errors. Secondly, since the data recorded manually is relatively small, lacking in systematicness and comprehensiveness, it is impossible to fully and accurately reflect the fuel consumption of the truck and provide timely warning of abnormal fuel consumption.

[0004] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of the present application. Summary of the invention

[0005] The present application provides a fuel consumption management method and system for mining trucks based on big data, which can provide timely warning of abnormal fuel consumption.

[0006] To achieve the above objectives, the present application discloses the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a method for managing fuel consumption of a mining truck based on big data, comprising the following steps:

[0008] In response to a first RFID receiver on a first mining truck traveling on a preset first path receiving a radio frequency signal sent by a first RFID transmitter disposed at a first preset position on the first path, acquiring and recording first remaining fuel information of the first mining truck;

[0009] In response to the first RFID receiver receiving a radio frequency signal sent by a second RFID transmitter disposed at a second preset position on the first path, acquiring and recording second remaining fuel amount information of the first mining truck;

[0010] Calculating first fuel consumption information of the first mining truck from the first preset position to the second preset position according to the first remaining fuel amount information and the second remaining fuel amount information;

[0011] Obtaining first load information of a first mining truck;

[0012] According to the first load information, a first database storing load information of a plurality of vehicles that have traveled normally on the first path in the past and fuel consumption information from the first preset position to the second preset position associated with the load information is matched to obtain normal fuel consumption information corresponding to the first load information;

[0013] determining whether a difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value;

[0014] When the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to a first preset value, abnormal fuel consumption information is sent to a preset terminal device, where the abnormal fuel consumption information at least includes the license plate information of the first mining truck.

[0015] In an embodiment of the present application, when a mining truck is traveling on a preset path, the RFID receiver installed on the truck can receive the trigger of the radio frequency signal sent by the RFID transmitter at the first preset position and the second preset position set on the first path, respectively, and automatically obtain and record the remaining fuel information of the truck at these two positions, thereby effectively solving the problems of low efficiency, error-proneness and lack of systematicity in manually recording fuel consumption data, and improving the efficiency and accuracy of data collection.

[0016] By obtaining the load information of the truck and matching the normal fuel consumption information corresponding to the current load information (first load information) from a database storing a large amount of historical data, on the one hand, the normal fuel consumption information can be quickly obtained without spending a lot of time and resources on complex parameter measurement and calculation; on the other hand, compared with a single theoretical model, it can automatically adapt to different mining trucks and operating environments, and can more accurately reflect the fuel consumption situation in the real environment.

[0017] The current fuel consumption information (first fuel consumption information) is compared with the normal fuel consumption information to determine whether their difference is greater than a preset threshold. If the difference is greater than or equal to the preset value, the system will immediately send fuel consumption abnormality information to the preset terminal device. In this way, timely fuel consumption abnormality warning can be provided, so that management personnel can promptly discover and handle fuel consumption abnormality problems, thereby helping to reduce fuel costs.

[0018] In some possible implementations of the first aspect, when the difference between the first fuel consumption information and the normal fuel consumption information is less than a first preset value, the first load information and the first fuel consumption information are associated and stored in the first database. In this way, fuel consumption data that meets the normal range can be stored in the database, and historical data can be continuously accumulated, which helps to build a richer and more detailed database and improve the accuracy of subsequent analysis and matching.

[0019] In some possible implementations of the first aspect, the step of matching and obtaining normal fuel consumption information corresponding to the first load information from a first database storing load information of a plurality of vehicles that have historically traveled normally on the first path and fuel consumption information from a first preset position to a second preset position associated with the load information according to the first load information includes:

[0020] Calculate the difference between the first load information and each load information stored in the first database, and select the load information whose difference is within a preset range;

[0021] The average value of the fuel consumption information corresponding to the load information whose difference is within the preset range is calculated to obtain the normal fuel consumption information. Among them, by selecting the load information whose difference is within the preset range, it can ensure that the data has similar conditions and improve the accuracy of matching the normal fuel consumption information. Using the average value of historical data as a reference improves the stability and reliability of the fuel consumption information, avoids the fluctuations and anomalies that may exist in a single data, and makes fuel consumption management more scientific.

[0022] In some possible implementations of the first aspect, the first load information of the first mining truck is obtained by receiving and processing data collected by a load sensor installed on the first mining truck, so that errors that may be caused by manual recording of load information can be avoided, thereby improving the accuracy and reliability of the data.

[0023] In some possible implementations of the first aspect, the method for managing fuel consumption of mining trucks based on big data further includes the following steps:

[0024] Obtaining speed information collected by a speed sensor on a first mining truck;

[0025] Analyze the change rate of the speed information in real time. When it is analyzed that the change rate of the speed information is greater than the preset change rate threshold for more than a preset number of times within a first time period, it is determined that the driver of the first mining truck has bad driving habits, and a first signal is generated; wherein the first time period is a time period from when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter to when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter;

[0026] In response to the first signal, controlling a camera installed in a cab of a first mining truck to capture facial image data of a driver;

[0027] Obtaining the driver's identity information from an identity information database according to the facial image data;

[0028] Generate a bad driving behavior report based on the driver's identity information, the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter received by the first RFID receiver, the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter received by the first RFID receiver, and the number of times the rate of change of speed information is greater than a preset rate of change threshold;

[0029] Send bad driving behavior reports to the preset terminal device. This is helpful for tracking and correcting bad driving behaviors, thereby reducing fuel consumption costs; on the other hand, it can improve the driver's sense of responsibility and encourage him to be more cautious during driving.

[0030] In some possible implementations of the first aspect, a plurality of RFID transmitters are arranged on the first path, and the first RFID transmitter and the second RFID transmitter refer to any two adjacent RFID transmitters among the plurality of RFID transmitters. In this way, the continuity and comprehensiveness of fuel consumption data collection can be enhanced, blind spots and omissions of data can be avoided, and abnormal fuel consumption can be detected more timely.

[0031] In a second aspect, the embodiment of the present application provides a mining truck fuel consumption management system based on big data, including:

[0032] A first acquisition module is used to acquire and record first remaining fuel information of the first mining truck in response to a first RFID receiver on the first mining truck traveling on the preset first path receiving a radio frequency signal sent by a first RFID transmitter arranged at a first preset position on the first path;

[0033] A second acquisition module, configured to acquire and record second remaining fuel quantity information of the first mining truck in response to the first RFID receiver receiving a radio frequency signal sent by a second RFID transmitter disposed at a second preset position on the first path;

[0034] A first calculation module is used to calculate first fuel consumption information of the first mining truck from the first preset position to the second preset position according to the first remaining fuel information and the second remaining fuel information;

[0035] A third acquisition module is used to acquire first load information of a first mining truck;

[0036] A first matching module is used to match the normal fuel consumption information corresponding to the first load information from a first database storing load information of a plurality of vehicles that have historically traveled normally on the first path and fuel consumption information from a first preset position to a second preset position associated with the load information, according to the first load information;

[0037] A first judging module, used for judging whether a difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value;

[0038] The first sending module is used to send abnormal fuel consumption information to a preset terminal device when the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to a first preset value, and the abnormal fuel consumption information at least includes the license plate information of the first mining truck.

[0039] In some possible implementations of the second aspect, the big data-based mining truck fuel consumption management system also includes a first adding module, which is used to associate the first load information and the first fuel consumption information and store them in the first database when the difference between the first fuel consumption information and the normal fuel consumption information is less than a first preset value.

[0040] In some possible implementations of the second aspect, the first matching module is specifically used to: calculate the difference between the first load information and each load information stored in the first database, and filter out the load information whose difference is within a preset range; calculate the average value of the fuel consumption information corresponding to the load information whose difference is within the preset range, and obtain normal fuel consumption information.

[0041] In some possible implementations of the second aspect, the third acquisition module obtains the first load information of the first mining truck by receiving and processing data collected by a load sensor installed on the first mining truck.

[0042] In some possible implementations of the second aspect, the big data-based mining truck fuel consumption management system also includes a driving behavior recognition module for obtaining speed information collected by a speed sensor on the first mining truck; analyzing the rate of change of the speed information in real time, and when the analysis shows that the rate of change of the speed information is greater than a preset rate of change threshold for a number of times greater than a preset number of times within a first time period, it is determined that the driver of the first mining truck has bad driving habits, and a first signal is generated; wherein the first time period is a time period from when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter to when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter; in response to the first signal, controlling the camera installed in the cab of the first mining truck to capture the driver's facial image data; obtaining the driver's identity information from the identity information database based on the facial image data; generating a bad behavior driving report based on the driver's identity information, the timestamp information corresponding to when the first RFID receiver receives the radio frequency signal sent by the first RFID transmitter, the timestamp information corresponding to when the first RFID receiver receives the radio frequency signal sent by the second RFID transmitter, and the number of times the rate of change of the speed information is greater than a preset rate of change threshold; and sending the bad behavior driving report to a preset terminal device.

[0043] In some possible implementations of the second aspect, a plurality of RFID transmitters are arranged on the first path, and the first RFID transmitter and the second RFID transmitter refer to any two adjacent RFID transmitters among the plurality of RFID transmitters.

[0044] In a third aspect, an embodiment of the present application provides an electronic device, comprising one or more processors; a storage device on which one or more programs are stored; when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any technical solution of the first aspect.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any technical solution of the first aspect is implemented.

[0046] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in any technical solution of the first aspect.

[0047] Among them, the technical effects brought about by any design method in the second to fifth aspects can refer to the technical effects brought about by different design methods in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0049] Figure 1 A flowchart of a method for managing fuel consumption of mining trucks based on big data provided in some embodiments of the present application;

[0050] Figure 2 A schematic diagram of a big data-based fuel consumption management system for mining trucks provided in some embodiments of the present application;

[0051] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present application. DETAILED DESCRIPTION

[0052] Specific embodiments of the present invention will now be mentioned in detail. Although the present invention is described in conjunction with these specific embodiments, it should be appreciated that it is not intended to limit the present invention to these specific embodiments. On the contrary, these embodiments are intended to cover substitutions, changes or equivalent embodiments that may be included in the spirit and scope of the invention defined by the claims. In the following description, a large number of specific details are set forth in order to provide a comprehensive understanding of the present invention. The present invention may be implemented without some or all of these specific details.

[0053] When used in conjunction with "including," "methods comprising," or similar language in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0054] Application Overview:

[0055] In the field of modern mining, mining trucks are one of the key equipment, and their fuel consumption management is of great significance to the operational efficiency and cost control of mining enterprises. However, the existing fuel consumption management methods mainly rely on manual recording of the refueling, oil delivery and fuel consumption values ​​of each shift of the truck, and performing simple statistical analysis. This traditional management method has the following problems:

[0056] First, the method of manually recording fuel consumption data is inefficient and wastes a lot of human resources. Mining operations are usually characterized by high intensity and high frequency. Truck fuel consumption data needs to be frequently recorded and updated. Manual operation is not only cumbersome but also prone to errors. Secondly, since the data recorded manually is relatively small, lacking in systematicness and comprehensiveness, it is impossible to fully and accurately reflect the fuel consumption of the truck and provide timely warning of abnormal fuel consumption.

[0057] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows: a method for managing fuel consumption of mining trucks based on big data is provided, comprising the following steps: in response to a first RFID receiver on a first mining truck traveling on a preset first path receiving a radio frequency signal sent by a first RFID transmitter set at a first preset position on the first path, obtaining and recording first remaining fuel information of the first mining truck; in response to the first RFID receiver receiving a radio frequency signal sent by a second RFID transmitter set at a second preset position on the first path, obtaining and recording second remaining fuel information of the first mining truck; calculating a first remaining fuel information according to the first remaining fuel information and the second remaining fuel information. The invention relates to a method for obtaining first fuel consumption information of a mining truck from a first preset position to a second preset position; obtaining first load information of the first mining truck; matching normal fuel consumption information corresponding to the first load information from a first database storing load information of multiple vehicles that have historically traveled normally on a first path and fuel consumption information from the first preset position to the second preset position associated with the load information; determining whether the difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value; when the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to the first preset value, sending abnormal fuel consumption information to a preset terminal device, the abnormal fuel consumption information including at least the license plate information of the first mining truck.

[0058] In this way, when the mining truck is traveling on a preset path, the RFID receiver installed on the truck can receive the trigger of the radio frequency signal sent by the RFID transmitter at the first preset position and the second preset position set on the first path, respectively, and automatically obtain and record the remaining fuel information of the truck at these two positions, effectively solving the problems of low efficiency, error-proneness and lack of systematicity in manual recording of fuel consumption data, and improving the efficiency and accuracy of data collection.

[0059] By obtaining the load information of the truck and matching the normal fuel consumption information corresponding to the current load information (first load information) from a database storing a large amount of historical data, on the one hand, the normal fuel consumption information can be quickly obtained without spending a lot of time and resources on complex parameter measurement and calculation; on the other hand, compared with a single theoretical model, it can automatically adapt to different mining trucks and operating environments, and can more accurately reflect the fuel consumption situation in the real environment.

[0060] The current fuel consumption information (first fuel consumption information) is compared with the normal fuel consumption information to determine whether their difference is greater than a preset threshold. If the difference is greater than or equal to the preset value, the system will immediately send fuel consumption abnormality information to the preset terminal device. In this way, timely fuel consumption abnormality warning can be provided, so that management personnel can promptly discover and handle fuel consumption abnormality problems, thereby helping to reduce fuel costs.

[0061] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the accompanying drawings. Figure 1 The embodiment of the present application provides a method for managing fuel consumption of mining trucks based on big data, comprising the following steps:

[0062] S101: In response to a first RFID receiver on a first mining truck traveling on a preset first path receiving a radio frequency signal sent by a first RFID transmitter set at a first preset position on the first path, first remaining fuel information of the first mining truck is acquired and recorded.

[0063] It is understandable that the first RFID receiver is disposed on the first mining truck.

[0064] Exemplarily, in some embodiments, the execution subject (e.g., computer equipment) of the mining truck fuel consumption management method based on big data can be connected to the oil level sensor in the mining truck tank by wired connection or wireless connection, obtain the height of the oil level in the tank, and calculate the remaining fuel information according to the height of the oil level in the tank. Of course, the present application is not limited to this. In other embodiments, the execution subject of the mining truck fuel consumption management method based on big data can be connected to the mining truck's on-board system by wired connection or wireless connection to obtain the remaining fuel information.

[0065] S102: in response to the first RFID receiver receiving a radio frequency signal sent by a second RFID transmitter disposed at a second preset position on the first path, acquiring and recording second remaining fuel amount information of the first mining truck;

[0066] S103: Calculating first fuel consumption information of the first mining truck from the first preset position to the second preset position according to the first remaining fuel information and the second remaining fuel information;

[0067] The calculation formula is as follows: first fuel consumption information = first remaining fuel information - second remaining fuel information.

[0068] S104: Acquire first load information of a first mining truck;

[0069] Specifically, in some embodiments, the execution subject may obtain the first load information of the first mining truck by receiving and processing data collected by a load sensor installed on the first mining truck. In this way, errors that may be caused by manual recording of load information can be avoided, thereby improving the accuracy and reliability of the data.

[0070] Exemplarily, the load sensor may be a pressure sensor mounted on a tire or an axle, and the load of the vehicle is estimated by measuring the pressure change. Of course, the present application is not limited thereto. In other embodiments, the load sensor may also be a bridge sensor, mounted on the suspension system or axle of the vehicle, and the applied weight is calculated by detecting material deformation.

[0071] S105: According to the first load information, obtain the normal fuel consumption information corresponding to the first load information from a first database storing load information of a plurality of vehicles that have traveled normally on the first path in the past and fuel consumption information from the first preset position to the second preset position associated with the load information;

[0072] Specifically, in some embodiments, the execution subject may obtain the normal fuel consumption information corresponding to the first load information by matching the first load information from a first database storing load information of a plurality of vehicles that have historically traveled normally on the first path and fuel consumption information from the first preset position to the second preset position associated with the load information through the following steps:

[0073] The first step is to calculate the difference between the first load information and each load information stored in the first database, and filter out the load information whose difference is within a preset range;

[0074] The second step is to calculate the average value of the fuel consumption information corresponding to the load information whose difference is within the preset range to obtain the normal fuel consumption information. Among them, by selecting the load information whose difference is within the preset range, it can ensure that the data has similar conditions and improve the accuracy of matching the normal fuel consumption information. Using the average value of historical data as a reference improves the stability and reliability of fuel consumption information, avoids possible fluctuations and anomalies in single data, and makes fuel consumption management more scientific.

[0075] S106: Determine whether the difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value;

[0076] S107: When the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to a first preset value, abnormal fuel consumption information is sent to a preset terminal device, where the abnormal fuel consumption information at least includes the license plate information of the first mining truck.

[0077] Preferably, in some embodiments, when the difference between the first fuel consumption information and the normal fuel consumption information is less than a first preset value, the first load information and the first fuel consumption information are associated and stored in the first database. In this way, fuel consumption data that meets the normal range can be stored in the database, and historical data can be continuously accumulated, which is helpful to build a richer and more detailed database and improve the accuracy of subsequent analysis and matching.

[0078] Preferably, in some embodiments, a plurality of RFID transmitters are arranged on the first path, and the first RFID transmitter and the second RFID transmitter refer to any two adjacent RFID transmitters among the plurality of RFID transmitters. In this way, the continuity and comprehensiveness of fuel consumption data collection can be enhanced, blind spots and omissions of data can be avoided, and abnormal fuel consumption can be detected more timely.

[0079] Preferably, in some embodiments, the mining truck fuel consumption management method based on big data further includes the following steps:

[0080] The first step is to obtain speed information collected by a speed sensor on the first mining truck;

[0081] The second step is to analyze the rate of change of the speed information in real time. When it is analyzed that the number of times the rate of change of the speed information is greater than a preset rate of change threshold value within the first time period is greater than a preset number of times, it is determined that the driver of the first mining truck has bad driving habits, and a first signal is generated; wherein the first time period is a time period from when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter to when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter;

[0082] The third step is to control a camera installed in the cab of the first mining truck to capture facial image data of the driver in response to the first signal;

[0083] The fourth step is to obtain the driver's identity information from the identity information database according to the facial image data;

[0084] Step 5: Generate a bad driving behavior report based on the driver's identity information, the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter received by the first RFID receiver, the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter received by the first RFID receiver, and the number of times the rate of change of the speed information is greater than a preset rate of change threshold;

[0085] The sixth step is to send the bad driving behavior report to the preset terminal device. This is helpful to track and correct bad driving behavior, thereby reducing fuel consumption costs; on the other hand, it can improve the driver's sense of responsibility and encourage him to be more cautious during driving.

[0086] See also Figure 2 Based on the same inventive concept as the method for managing fuel consumption of a mining truck based on big data in the aforementioned embodiment, the embodiment of the present application provides a fuel consumption management system for a mining truck based on big data, including:

[0087] A first acquisition module 201 is used to acquire and record first remaining fuel information of the first mining truck in response to a first RFID receiver on the first mining truck traveling on a preset first path receiving a radio frequency signal sent by a first RFID transmitter arranged at a first preset position on the first path;

[0088] A second acquisition module 202 is used to acquire and record second remaining fuel information of the first mining truck in response to the first RFID receiver receiving a radio frequency signal sent by a second RFID transmitter disposed at a second preset position on the first path;

[0089] The first calculation module 203 is used to calculate the first fuel consumption information of the first mining truck from the first preset position to the second preset position according to the first remaining fuel information and the second remaining fuel information;

[0090] A third acquisition module 204 is used to acquire first load information of a first mining truck;

[0091] The first matching module 205 is used to match the normal fuel consumption information corresponding to the first load information from a first database storing load information of a plurality of vehicles that have historically traveled normally on the first path and fuel consumption information from the first preset position to the second preset position associated with the load information, according to the first load information;

[0092] A first determination module 206, configured to determine whether a difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value;

[0093] The first sending module 207 is used to send abnormal fuel consumption information to a preset terminal device when the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to a first preset value. The abnormal fuel consumption information at least includes the license plate information of the first mining truck.

[0094] In some embodiments, the big data-based mining truck fuel consumption management system also includes a first adding module 208, which is used to associate the first load information and the first fuel consumption information and store them in the first database when the difference between the first fuel consumption information and the normal fuel consumption information is less than a first preset value.

[0095] In some embodiments, the first matching module 205 is specifically used to: calculate the difference between the first load information and each load information stored in the first database, and filter out the load information whose difference is within a preset range; calculate the average value of the fuel consumption information corresponding to the load information whose difference is within the preset range, and obtain normal fuel consumption information.

[0096] In some embodiments, the third acquisition module 204 obtains the first load information of the first mining truck by receiving and processing data collected by a load sensor installed on the first mining truck.

[0097] In some embodiments, the big data-based mining truck fuel consumption management system also includes a driving behavior recognition module 209, which is used to obtain speed information collected by a speed sensor on the first mining truck; analyze the change rate of the speed information in real time, and when it is analyzed that the change rate of the speed information is greater than the preset change rate threshold for more than the preset number of times within the first time period, it is determined that the driver of the first mining truck has bad driving habits, and a first signal is generated; wherein the first time period is a time period from when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter to when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter; in response to the first signal, control the camera installed in the cockpit of the first mining truck to capture the driver's facial image data; obtain the driver's identity information from the identity information database according to the facial image data; generate a bad behavior driving report according to the driver's identity information, the timestamp information corresponding to when the first RFID receiver receives the radio frequency signal sent by the first RFID transmitter, the timestamp information corresponding to when the first RFID receiver receives the radio frequency signal sent by the second RFID transmitter, and the number of times the change rate of the speed information is greater than the preset change rate threshold; and send the bad behavior driving report to a preset terminal device.

[0098] In some embodiments, a plurality of RFID transmitters are disposed on the first path, and the first RFID transmitter and the second RFID transmitter refer to any two adjacent RFID transmitters among the plurality of RFID transmitters.

[0099] It is understandable that the modules recorded in the mining truck fuel consumption management system based on big data are similar to the reference Figure 1 The steps in the described method for managing fuel consumption of mining trucks based on big data correspond to each other. Therefore, the operations, features and beneficial effects described above for the method are also applicable to the fuel consumption management system for mining trucks based on big data and the modules contained therein, and will not be repeated here.

[0100] See also Figure 3 Based on the inventive concept of a mining truck fuel consumption management method based on big data in the aforementioned embodiment, an embodiment of the present application provides an electronic device. The electronic device may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device includes a processing device 301 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a ROM 302 (read-only memory) or a program loaded from a storage device 308 to a RAM 303 (random access memory). In RAM 303, various programs and data required for the operation of the electronic device are also stored. The processing device 301, ROM 302, and RAM 303 are connected to each other via a bus 304. The input / output interface (i.e., an I / O interface 305) is also connected to the bus 304.

[0101] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data.

[0102] In particular, according to some embodiments of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present application are executed.

[0103] It should be noted that the computer-readable medium recorded in some embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0104] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an adhoc peer-to-peer network), as well as any currently known or future developed network.

[0105] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to a first RFID receiver on a first mining truck traveling on a preset first path receiving a radio frequency signal sent from a first RFID transmitter disposed at a first preset position on the first path, acquires and records first remaining fuel information of the first mining truck; in response to the first RFID receiver receiving a radio frequency signal sent from a second RFID transmitter disposed at a second preset position on the first path, acquires and records second remaining fuel information of the first mining truck; calculates the remaining fuel information of the first mining truck based on the first remaining fuel information and the second remaining fuel information. The invention discloses a first fuel consumption information of a truck from a first preset position to a second preset position; obtains first load information of the first mining truck; matches normal fuel consumption information corresponding to the first load information from a first database storing load information of multiple vehicles that have historically traveled normally on a first path and fuel consumption information from the first preset position to the second preset position associated with the load information according to the first load information; determines whether the difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value; when the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to the first preset value, sends abnormal fuel consumption information to a preset terminal device, and the abnormal fuel consumption information includes at least the license plate information of the first mining truck.

[0106] The computer program code for performing the operation of some embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0107] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0108] The modules described in some embodiments of the present application may be implemented by software or hardware. The modules described may also be set in a processor: for example, they may be described as: a first acquisition module, a second acquisition module, a first calculation module, a third acquisition module, a first matching module, a first judgment module, a first sending module, a first adding module, and a driving behavior recognition module. The names of these modules do not, in some cases, constitute limitations on the modules themselves. For example, the first acquisition module may also be described as a "first remaining fuel acquisition module."

[0109] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0110] Some embodiments of the present application also provide a computer program product, including a computer program, which implements any of the above-mentioned big data-based mining truck fuel consumption management methods when executed by a processor.

[0111] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for managing fuel consumption of mining trucks based on big data, characterized in that: The following steps are involved: In response to a first RFID receiver on a first mining truck traveling on a preset first path receiving a radio frequency signal sent by a first RFID transmitter disposed at a first preset position on the first path, acquiring and recording first remaining fuel amount information of the first mining truck; In response to the first RFID receiver receiving a radio frequency signal sent by a second RFID transmitter disposed at a second preset position on the first path, acquiring and recording second remaining fuel amount information of the first mining truck; Calculate first fuel consumption information of the first mining truck from a first preset position to a second preset position according to the first remaining fuel information and the second remaining fuel information; Acquire first load information of the first mining truck; According to the first load information, a first database storing load information of a plurality of vehicles that have traveled normally on the first path in the past and fuel consumption information from the first preset position to the second preset position associated with the load information is matched to obtain normal fuel consumption information corresponding to the first load information; determining whether a difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value; When the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to a first preset value, abnormal fuel consumption information is sent to a preset terminal device, and the abnormal fuel consumption information at least includes the license plate information of the first mining truck.

2. The method for managing fuel consumption of mining trucks based on big data according to claim 1 is characterized in that: When the difference between the first fuel consumption information and the normal fuel consumption information is less than a first preset value, the first load information and the first fuel consumption information are associated and stored in the first database.

3. The method for managing fuel consumption of mining trucks based on big data according to claim 1, characterized in that: The step of matching and obtaining the normal fuel consumption information corresponding to the first load information from a first database storing load information of a plurality of vehicles that have historically traveled normally on a first path and fuel consumption information from a first preset position to a second preset position associated with the load information according to the first load information comprises: Calculating the difference between the first load information and each load information stored in the first database, and filtering out the load information whose difference is within a preset range; The average value of the fuel consumption information corresponding to the load information whose difference is within a preset range is calculated to obtain the normal fuel consumption information.

4. The method for managing fuel consumption of mining trucks based on big data according to claim 1 is characterized in that: The first load information of the first mining truck is obtained by receiving and processing data collected by a load sensor installed on the first mining truck.

5. The method for managing fuel consumption of mining trucks based on big data according to claim 1 is characterized in that: The following steps are also included: Acquiring speed information collected by a speed sensor on the first mining truck; Analyze the change rate of the speed information in real time. When it is analyzed that within a first time period, the change rate of the speed information is greater than a preset change rate threshold for more than a preset number of times, it is determined that the driver of the first mining truck has bad driving habits, and a first signal is generated; wherein the first time period is a time period from when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter to when the first RFID receiver receives the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter; In response to the first signal, controlling a camera installed in a cab of the first mining truck to capture facial image data of a driver; Obtaining the driver's identity information from an identity information database according to the facial image data; Generate a bad driving behavior report based on the driver's identity information, the timestamp information corresponding to the radio frequency signal sent by the first RFID transmitter received by the first RFID receiver, the timestamp information corresponding to the radio frequency signal sent by the second RFID transmitter received by the first RFID receiver, and the number of times the rate of change of speed information is greater than a preset rate of change threshold; The bad driving behavior report is sent to the preset terminal device.

6. The method for managing fuel consumption of mining trucks based on big data according to claim 1, characterized in that: A plurality of RFID transmitters are arranged on the first path, and the first RFID transmitter and the second RFID transmitter refer to any two adjacent RFID transmitters among the plurality of RFID transmitters.

7. A mining truck fuel consumption management system based on big data, characterized in that: include: A first acquisition module, configured to acquire and record first remaining fuel amount information of the first mining truck in response to a first RFID receiver on the first mining truck traveling on the first path receiving a radio frequency signal sent by a first RFID transmitter arranged at a first preset position on the preset first path; a second acquisition module, configured to acquire and record second remaining fuel quantity information of the first mining truck in response to the first RFID receiver receiving a radio frequency signal sent by a second RFID transmitter disposed at a second preset position on the first path; A first calculation module, configured to calculate first fuel consumption information of a first mining truck from a first preset position to a second preset position according to the first remaining fuel information and the second remaining fuel information; A third acquisition module, used to acquire first load information of the first mining truck; a first matching module, configured to match, according to the first load information, a first database storing load information of a plurality of vehicles that have historically traveled normally on a first path and fuel consumption information from the first preset position to the second preset position associated with the load information, to obtain normal fuel consumption information corresponding to the first load information; A first judging module, configured to judge whether a difference between the first fuel consumption information and the normal fuel consumption information is greater than a first preset value; The first sending module is used to send abnormal fuel consumption information to a preset terminal device when the difference between the first fuel consumption information and the normal fuel consumption information is greater than or equal to a first preset value, and the abnormal fuel consumption information at least includes the license plate information of the first mining truck.

8. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processing device, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processing device, the method according to any one of claims 1 to 6 is implemented.

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