Digital lean transportation and sales management system of coal mine enterprise

Abstract

The invention discloses a digital lean transportation and sales management system for a coal mine enterprise. The system comprises an operation management and control module, an intelligent regulation and control module and an intelligent weight house module. The system has the advantages that enterprises are helped to improve coal mine management efficiency through digital lean transportation and sales management, information such as coal production progress, stock conditions, positions and states of transport vehicles and the like can be monitored in real time, reasonable scheduling and optimal configuration of resources are realized, waiting time and resource waste are reduced, a unified data board is formed, and the management efficiency of the enterprises is improved. The coal market demand is predicted by using digital and machine learning technologies, sales and sales plans are guided, data support is provided for leader decision making, and enterprises are helped to manage mines well; according to the method, enterprises are helped to comprehensively manage and control all nodes in the whole transportation and sales process through digital lean transportation and sales management, safety risks in the coal sales and transportation process can be monitored and warned in real time, potential problems can be found and processed in time, leakage and leakage are effectively eradicated, and meanwhile the transportation efficiency is improved through digitization and unmanned operation.

Description

Technical field:

[0001] The present invention relates to the field of coal mine transportation and marketing management, and in particular to a digital lean transportation and marketing management system for coal mine enterprises. Background technology:

[0002] With the development of unmanned transportation and sales and single-pound bills for coal delivery, coal mines have improved transportation efficiency and reduced personnel input, but some previously neglected regulatory problems have emerged. Thanks to the development of existing digitalization, the Internet of Things, big data, and artificial intelligence, new developments have been brought about. Under the new situation of industrial digitalization, it is urgent to build a digital lean transportation and sales management platform.

[0003] After years of development, many coal mining companies have initially laid the foundation for industrial digitalization. In terms of system construction, some basic transportation and sales systems, unmanned weighing systems, intelligent loading systems and other systems have gradually taken shape. These systems can save labor and improve work efficiency while also reducing some human cheating behaviors.

[0004] However, there are still some problems listed below that need to be solved:

[0005] ① On-site management is cumbersome and requires high quality of personnel, otherwise chaos is likely to occur;

[0006] ② At the same time, the possibility of technical cheating by users interfering with the weighing scale and the gate through the equipment has increased, resulting in leakage in the coal mine, which has caused direct economic losses to the coal mine.

[0007] ③At the same time, because there are many internal systems currently in use, leaders need to obtain data from multiple systems when making decisions. Data acquisition is difficult, and multiple dimensions are difficult to count, and the accuracy needs to be considered. Summary of the invention:

[0008] In order to solve the above problems, the purpose of the present invention is to provide a digital lean transportation and marketing management system for coal mining enterprises.

[0009] The present invention is implemented by the following technical solutions:

[0010] A digital lean marketing management system for coal mining enterprises, comprising:

[0011] (1) Operation and control module: covers enterprise, finance, coal, equipment, and transportation and marketing management systems, and integrates with the group's unified planning and implementation or existing software systems to achieve multi-system data collection and centralized display, provide data support for leadership decision-making, and improve decision-making efficiency; and can also achieve online approval, customer management, contract management, and weighing control;

[0012] (2) Smart control module: By dividing the mining area into different areas, the vehicle routes in the mining area are planned according to the coal delivery order, and the gates and silos in the mine are designated. The vehicle positioning module identifies abnormal situations that exceed the scope of authority, monitors the operating status of all hardware equipment in the mining area, and transmits the acquired relevant data back to the monitoring center for comparison with normal data, so as to promptly warn of abnormal situations.

[0013] (3) Smart weighing room module: The hardware monitoring unit detects the hardware connection status of each weighing node in real time, and issues an alarm to prevent vehicles from passing when an abnormality occurs. The lifting bar alarm unit monitors abnormal lifting behavior and handles abnormal situations when the coal delivery order is invalidated and leaves the mine. The node monitoring unit verifies each node based on the coal delivery order, issues an error alarm in real time, and issues an alarm for vehicles that have exceeded the time limit when entering the mine. The data comparison unit regularly compares the weighing room data with the transportation and sales data in multiple dimensions, and issues an alarm for discrepancies in the data.

[0014] Furthermore, the operation control module includes:

[0015] Data support unit: collects system data from all links of transportation and marketing, and forms a data dashboard with the collected system data to provide data support for decision-making and improve decision-making efficiency;

[0016] Intelligent audit unit: According to the audit content of each node, the relevant information of the vehicle is audited according to the intelligent audit rules. If all audit rules cannot be met, it is considered abnormal, an early warning is issued and the corresponding audit content is rejected; if there is no abnormality, the corresponding audit content is passed and an audit record is generated;

[0017] Multi-dimensional data verification unit: Regularly compare the data of the transportation and sales management system with the corresponding data of the transportation and sales platform and the intelligent loading system. If there is any mismatch in data, an abnormal warning will be issued and the abnormal data will be recorded.

[0018] Furthermore, the audit rules of the intelligent audit unit include:

[0019] (1) Data matching rules, including:

[0020] Vehicle information matching rules: The vehicle information obtained through the intelligent recognition system is compared with the vehicle information pre-stored in the system. If all the information is consistent, it is initially determined to be normal; if it is inconsistent, it is identified as abnormal;

[0021] Business process information matching rules: Match the corresponding standard operating procedures for each business process node, that is, a node requires the vehicle to complete a specific operation within a specific time. At the same time, the system will record the actual operation process of the vehicle; by comparing the standard operating procedures with the actual operation of the vehicle, if the vehicle fails to complete the corresponding operation within the specified time, it will be identified as an abnormality;

[0022] (2) Logical judgment rules, including:

[0023] Logical verification rules for associated data: The system presets the logical relationship between different businesses. If the actual vehicle operation process recorded in the system does not match the preset logical relationship, the system will identify it as an abnormality;

[0024] Historical data comparison and analysis rules: The system collects the vehicle's past historical operation data and calculates the average operation data by averaging. When a new operation occurs, the current operation data is compared with the average operation data. If the deviation is greater than the preset deviation value, it is identified as an abnormality.

[0025] (3) Permission control rules: Set corresponding permissions for each business node and operation. If the permissions do not match, it will be identified as an exception.

[0026] Furthermore, the intelligent control module includes:

[0027] Electronic fence unit: fences are used to divide the areas within the mine area, and the vehicle routes in the mine area are planned according to the coal delivery order, and the gates and silos in the mine are designated; by locating the vehicle, if the vehicle exceeds the authorized range, it will be identified as abnormal; at the same time, the movement trajectory is generated according to the vehicle's position in the mine, which is convenient for viewing abnormal alarm information;

[0028] Equipment monitoring unit: monitors the operating status of all hardware equipment in the mining area, transmits the relevant data of the hardware equipment back to the monitoring center, compares it with the normal data, and issues timely warnings for abnormal data.

[0029] Furthermore, the smart weighing room module includes:

[0030] Hardware monitoring unit: Detect the connection status of each weighing node hardware through sensors, controllers and other equipment. When the equipment is abnormal, it returns the abnormal problem and warns that the current node is temporarily inaccessible to vehicles;

[0031] The lifting bar warning unit monitors abnormal lifting behavior, such as taking photos of the vehicle and recording a 30-second video for uploading during manual lifting operations; when the vehicle has entered the mine but the coal pick-up order is invalid and needs to leave the mine, an invalid coal pick-up order is automatically generated. When leaving the mine, the overweight and overempty data are monitored for comparison. If there is a deviation, the bar will not be lifted and an alarm will be issued;

[0032] Node monitoring unit: Verify each node through coal delivery orders, report errors and issue warnings in real time, and issue timeout warnings if a vehicle does not leave the mine within a certain period of time after entering the mine;

[0033] Data comparison unit: Regularly conduct multi-dimensional comparisons of the data collected by the smart weighing room with the transportation and sales data, and issue warnings for discrepancies if any.

[0034] Advantages of the present invention:

[0035] Through digital lean transportation and marketing management, enterprises can improve the efficiency of coal mine management. They can monitor coal production progress, inventory status, location and status of transportation vehicles in real time, realize the rational dispatch and optimal allocation of resources, reduce waiting time and resource waste, form a unified data dashboard, and use digital and machine learning technologies to predict coal market demand, guide sales and sales plans, provide data support for leadership decision-making, and help enterprises manage mines well;

[0036] Through digital lean transportation and marketing management, we help enterprises calculate every account, realize information sharing and collaborative operation among suppliers, manufacturers, transporters and sellers, improve the collaborative efficiency of the entire supply chain, make accounts clear, and transportation data clear at a glance, greatly improve operating efficiency, reduce labor costs and operational errors, eliminate wrong accounts and missed accounts, and help enterprises calculate accounts well;

[0037] Through digital lean transportation and sales management, enterprises can comprehensively control all nodes in the entire transportation and sales process, conduct real-time monitoring and early warning of safety risks in the coal sales and transportation process, promptly discover and deal with potential problems, effectively prevent leakage, and at the same time improve transportation efficiency through digitalization and unmanned operations. Specific implementation method:

[0038] A digital lean marketing management system for coal mining enterprises, comprising:

[0039] (1) Operation and control module: covers enterprise, finance, coal, equipment, and transportation and marketing management systems, and integrates with the group's unified planning and implementation or existing software systems to achieve multi-system data collection and centralized display, provide data support for leadership decision-making, and improve decision-making efficiency; and can also achieve online approval, customer management, contract management, and weighing control;

[0040] (2) Smart control module: The control includes systems such as customers, contracts, vehicles, fence warnings, and equipment monitoring. It realizes the industrial digitalization and information management of equipment, transportation, circulation and other links in the entire transportation and marketing process of "machine, transportation, and communication", and organizes, analyzes and utilizes the data information collected by the hardware equipment. Real-time monitoring of equipment operation, coal sales, weighing and loading, vehicle transportation and other data in the entire transportation and marketing process, to achieve visual display, analysis and operation of the main digital control process of the enterprise. Realize online collaboration and data sharing between various positions, and improve the dispatching and control capabilities in business integration and smart transportation and marketing;

[0041] (3) Smart weighing room module: The management and control includes unattended weighing, intelligent loading, hardware monitoring and other systems. By monitoring the corresponding equipment for weighing and scanning the electronic ticket to verify the information, the vehicle weighing is confirmed and the abnormal status is determined. The historical data of the vehicle is collected and compared with the existing data to eliminate abnormal vehicle data. By digitizing and intelligentizing each stage of transportation, the participation of personnel in the entire process is reduced, and leakage is effectively reduced.

[0042] In this embodiment, the operation control module includes:

[0043] Data support unit: collects system data from all aspects of transportation and sales (such as customer fulfillment rate, transportation efficiency, production and sales balance, transportation efficiency, equipment efficiency, video analysis, etc.), and forms a data dashboard with the collected system data to provide data support for decision-making and improve decision-making efficiency;

[0044] Intelligent audit unit: According to the audit content of each node, the relevant information of the vehicle is audited according to the intelligent audit rules. If all audit rules cannot be met, it is considered abnormal, an early warning is issued and the corresponding audit content is rejected; if there is no abnormality, the corresponding audit content is passed and an audit record is generated to facilitate viewing and adjustment of the audit results; intelligent audit comparison is performed on basic audits such as manual lifting of the barrier, matching the corresponding vehicle information for lifting the barrier, automatic auditing of each node in the process, abnormal warning and automatic rejection, and an audit record is generated for those that pass, which is convenient for viewing and adjusting the audit results;

[0045] Multi-dimensional data verification unit: Regularly compare the data of the transportation and sales management system (including contracts, finance, transportation data, loading tonnage, weighing records, etc.) with the corresponding data of the transportation and sales platform and the intelligent loading system. If there is any mismatch in data, an abnormal warning will be issued (sound and light alarms will be issued on site and the warning information will be uploaded to the smart control center to remind the on-duty personnel), and the abnormal data will be recorded to ensure that there are no leaks in the transportation and sales processes.

[0046] In this embodiment, the audit rules of the intelligent audit unit include:

[0047] (1) Data matching rules, including:

[0048] Vehicle information matching rules: The vehicle information (including license plate number and vehicle model, etc.) obtained through the intelligent recognition system is compared with the vehicle information pre-stored in the system. If all the information is consistent, it is initially determined to be normal; if it is inconsistent, such as the license plate number does not exist in the authorized list, or the vehicle model does not match the registration, it is identified as abnormal;

[0049] Business process information matching rules: Match the corresponding standard operating procedures for each business process node, that is, a node requires the vehicle to complete a specific operation within a specific time, such as at the weighing node, the vehicle is required to complete weighing within X minutes after arrival. At the same time, the system will record the actual operation process of the vehicle (including time and behavior); by comparing the standard operating procedures with the actual operation of the vehicle, if the vehicle does not complete the corresponding operation within the specified time, such as the vehicle does not complete weighing within the specified time, such as repeated weighing, or the weighing interval is too short, etc., it will be identified as an abnormality;

[0050] (2) Logical judgment rules, including:

[0051] Logical verification rules for associated data: The system presets the logical relationship between different businesses. For example, if the actual operation process of the vehicle recorded in the system does not match the preset logical relationship, for example, the manual lifting of the barrier may be associated with the vehicle's delivery note and contract information. The system will check whether the lifting behavior is consistent with the delivery time, delivery location, and contractually agreed transportation route on the delivery note. If the delivery note has expired, or the vehicle's driving direction does not match the contractually agreed route, the system will identify it as an abnormality.

[0052] Historical data comparison and analysis rules: The system collects the vehicle's past historical operation data and calculates the average operation data by averaging it; when a new operation occurs, the current operation data is compared with the average operation data. If the deviation is greater than the preset deviation value, it is identified as an abnormality; for example, if the vehicle's weighing weight deviates too much from the average weight of the same vehicle and the same type of cargo in the past, or the frequency of lifting the bar is significantly higher than the average level, the system will determine it as an abnormality based on this deviation from historical behavior.

[0053] (3) Permission control rules: Set corresponding permissions for each business node and operation. For example, only people in specific positions or with specific permissions can initiate a manual lift operation. When the system is reviewing, it first checks whether the person initiating the operation has the corresponding permissions. If the permissions do not match, it is identified as an exception.

[0054] In this embodiment, the intelligent control module includes:

[0055] Electronic fence unit: fences are used to divide the areas within the mine area, and the vehicle routes in the mine area are planned according to the coal delivery order, and the gates and silos in the mine are designated; by locating the vehicle, if the vehicle exceeds the authorized range, it is identified as abnormal; at the same time, the movement trajectory is generated according to the vehicle's position in the mine, which is convenient for checking abnormal alarm information; specifically, the electronic fence unit determines whether the vehicle exceeds the authorized range in the following way:

[0056] (1) Regional division and authority setting

[0057] Geographic information digitization: Using geographic information technology (such as GIS, geographic information system), the boundaries of each area in the mining area are accurately digitally depicted and converted into electronic map data that can be recognized by computers. Each area is given a unique identifier so that the system can distinguish and manage it.

[0058] Permission association: Set corresponding regional permissions for different types of vehicles (such as coal trucks, engineering vehicles, visitor vehicles, etc.) or different business scenarios. For example, coal trucks may be allowed to move in specific areas such as coal loading and unloading areas and transportation trunk roads; while visitor vehicles may only be allowed to move in a specific range near the office area. These permission information will be associated with the vehicle's identity (such as license plate number, electronic tag, etc.) and stored in the system database.

[0059] (2) Real-time vehicle positioning

[0060] Positioning technology application: Vehicles need to be equipped with positioning equipment. Common positioning technologies include GPS (Global Positioning System), Beidou Satellite Positioning System, etc. These devices receive satellite signals to obtain the vehicle's geographic location information (latitude and longitude coordinates) in real time, and transmit the positioning data to the intelligent control system through wireless communication networks (such as 4G, 5G, etc.).

[0061] Positioning data update frequency: In order to ensure that the vehicle's location can be monitored in a timely and accurate manner, the positioning device needs to update and upload the location data at a certain frequency (such as once per second or shorter time intervals). This ensures that the system obtains the latest location information of the vehicle and detects changes in the vehicle's movement trajectory in a timely manner.

[0062] (3) Position determination and warning triggering

[0063] Real-time comparison: After receiving the real-time positioning data of the vehicle, the intelligent control system will compare the current latitude and longitude coordinates of the vehicle with the pre-set electronic fence boundary data of each area in real time. The comparison process uses geospatial algorithms to determine whether the vehicle's position is within its authorized area.

[0064] Warning trigger mechanism: Once the system determines that the vehicle position exceeds the area corresponding to its authority, the warning process will be triggered immediately. On the one hand, by controlling the sound and light alarm equipment on site, obvious sound and light signals are emitted to attract the attention of on-site personnel; on the other hand, detailed warning information (including vehicle identity information, location information beyond the authority area, warning time, etc.) is uploaded to the smart control center to remind the on-duty personnel to pay attention to and deal with abnormal situations in a timely manner.

[0065] The vehicle positioning unit determines whether an abnormality occurs by:

[0066] (1) Determination of route and scope of authority

[0067] Coal pick-up order route matching: The system plans the standard route for vehicles in the mining area based on the pre-set information on the coal pick-up order, and clarifies the key nodes along the way, such as the designated gates and silo locations in the mine. During the vehicle's driving, real-time positioning data will be continuously fed back to the system. The system dynamically compares the vehicle's actual driving trajectory with the planned route on the coal pick-up order. If the vehicle deviates from the planned route, it may trigger an abnormal judgment. For example, the vehicle should have gone to a specific silo to load coal according to the route, but drove to other non-designated areas. At this time, the system can determine it as an abnormality.

[0068] Electronic fence permission restriction: In combination with the electronic fence function, different areas in the mine area are set with corresponding permissions. When a vehicle exceeds the area allowed by its permission, as mentioned in the electronic fence unit, the system will immediately identify it as an abnormal situation. For example, a coal transport vehicle is restricted to a specific coal loading and unloading and transportation area. If it enters a prohibited area such as an office area, the system will trigger an abnormal alarm.

[0069] (2) Analysis based on action trajectory

[0070] Trajectory continuity and rationality: The system continuously records the vehicle's position in the mine and generates a trajectory. Under normal circumstances, the vehicle's trajectory should be continuous and consistent with the road layout and business logic in the mine area. If there are interruptions in the trajectory, large jumps, or unreasonable detours, the system will determine that there may be an abnormality. For example, if a vehicle suddenly jumps from one end of the mine to the other in a short period of time without an actual road connection in between, this is obviously inconsistent with normal driving logic and should be considered an abnormality.

[0071] Comparison with historical trajectories: If the system saves the vehicle's past driving trajectory data, the current trajectory can be compared with the historical trajectory to determine anomalies. If the current trajectory is significantly different from the trajectory in similar business scenarios in the past, such as significant differences in driving speed, dwell time, and places passed, it may also be identified as an anomaly. For example, during the previous coal loading process, the stay time of a vehicle in the silo was relatively stable, but this time the stay time is too short or too long, which may trigger an abnormal judgment.

[0072] (3) Abnormal alarm information association

[0073] Integration of multi-source alarm information: The system integrates various abnormal alarm information generated during vehicle driving, such as area crossing alarm from electronic fence, route deviation alarm, abnormal movement trajectory alarm, etc. When these alarm information confirm each other, or multiple alarms appear in a short period of time, it can be more certain that the vehicle has an abnormal situation. For example, if the vehicle triggers both the route deviation alarm and the electronic fence crossing alarm at the same time, the possibility of the vehicle being abnormal is very high.

[0074] Real-time viewing and feedback: The digital two-dimensional view generated by the system allows staff to view vehicle activity routes and abnormal alarm information in real time. The on-duty personnel can quickly judge and confirm vehicle abnormalities based on intuitive graphical displays and detailed alarm prompts. If abnormal alarm information is found, the nature and severity of the abnormality can be further judged in combination with the vehicle's location and movement trajectory, and corresponding measures can be taken in a timely manner.

[0075] Equipment monitoring unit: monitors the operating status of all hardware equipment in the mining area, transmits the relevant data of the hardware equipment back to the monitoring center, compares it with normal data, and promptly warns of abnormal data to ensure normal operation of the equipment. At the same time, it conducts anti-cheating monitoring on important equipment such as gates, weighbridges, loading, infrared shooting, etc. If cheating occurs, the operation will not be executed and an early warning will be issued. Specifically, in the equipment monitoring unit, the method for judging whether cheating occurs is as follows:

[0076] (1) Data logic analysis

[0077] Data mutation detection: Under normal circumstances, the data collected by the equipment should change smoothly within a reasonable range. For example, the weighing data of the scale should be consistent with the weight change of the goods carried, and there should be no sudden jumps or irregular fluctuations. If the scale data increases or decreases far beyond the normal range in a short period of time, the system can determine that there may be cheating.

[0078] Data association verification: There is usually a certain logical relationship between the data of different devices. For example, the loading data of the loading equipment should match the weighing data of the scale, and the opening record of the gate should correspond to the vehicle entry and exit records and related operation procedures. If the loading equipment shows that a certain amount of coal is loaded, but the weighing data of the scale is far lower than expected, or there is no corresponding vehicle entry and exit record when the gate is opened, there may be suspicion of cheating.

[0079] (2) Behavioral pattern analysis

[0080] Abnormal operation frequency: All kinds of equipment have their normal operation frequency. For example, under normal circumstances, the gate opens and closes according to the rhythm of vehicles entering and exiting. If the gate opens and closes frequently in a short period of time and there is no reasonable record of vehicles entering and exiting, it may be that someone is trying to cheat by abnormally operating the gate.

[0081] Abnormal equipment operation time: The operation time of some equipment also has a regular pattern. For example, loading equipment usually takes a certain time to complete a loading operation. If the loading time is too short, far less than the normal operation time, there may be illegal fast loading to achieve cheating purposes.

[0082] (3) Hardware status monitoring

[0083] Device connection abnormality: By monitoring the connection status of hardware devices, if it is found that the device suddenly goes offline and comes back online in a short time without reasonable maintenance or fault records, it may be that someone is trying to interfere with data collection by disconnecting the device, thereby cheating. For example, if an infrared radio device frequently goes offline abnormally, it may be that someone is trying to disrupt its normal monitoring function.

[0084] Abnormal sensor data: Various sensors in the equipment are used to collect key data, such as weighing sensors of floor scales and limit sensors of gates. If the data fed back by the sensor does not match the actual operating status of the equipment, or the sensor repeatedly displays erroneous data, it may be that the sensor has been interfered with or tampered with, thereby implementing cheating.

[0085] (4) Preset rules and model judgment

[0086] Set threshold rules: Set reasonable thresholds for each data according to the normal operating parameters and business logic of the equipment. For example, the upper and lower limits of weighing data of the scale, the loading capacity range of the loading equipment, etc. When the data collected by the equipment exceeds these preset thresholds, the system automatically triggers a cheating warning.

[0087] Establishing a behavior model: Use machine learning or data analysis technology to establish a normal behavior model based on the historical operation data of the device. When the real-time operation data of the device deviates too much from the normal behavior pattern predicted by the model, the system determines that cheating may occur. For example, by analyzing the opening and closing modes of the gate in different time periods and different business scenarios, a behavior model is established. Once the gate operation mode does not match the model, a cheating warning is issued.

[0088] In this embodiment, the smart weighing room module includes:

[0089] Hardware monitoring unit: Detect the connection status of each weighing node hardware (including mine entry and exit gates, empty / overweight gates, empty / overweight scales, empty / overweight weighing reset sensors, empty / overweight cameras, empty / overweight infrared transmitters) through sensors, controllers and other equipment. When the equipment is abnormal, it returns the abnormal problem and warns that the current node is temporarily inaccessible to vehicles. Specifically, in the hardware monitoring unit, the method for judging whether the equipment is abnormal is as follows:

[0090] (1) Connection status detection

[0091] Communication signal judgment: Sensors, controllers and devices communicate with each other via wired or wireless means. For wired connections, the line can be monitored for disconnection or short circuit; for wireless connections, the signal strength is checked to see if the handshake communication is normal. If an effective communication connection cannot be established within the set time or the communication signal strength is continuously lower than the normal threshold (such as wireless signal strength lower than -80dBm), the device connection is judged to be abnormal. For example, if an air camera cannot transmit image data to the monitoring system, and an inspection finds that its network connection indicator is off, and a network line disconnection is detected, it can be judged that the camera connection is abnormal.

[0092] Heartbeat detection mechanism: The device sends heartbeat packets to the monitoring system regularly, indicating that it is in normal working condition. If the monitoring system still does not receive the heartbeat signal of the device after exceeding the preset heartbeat interval (e.g. the normal heartbeat interval is 1 minute, if the heartbeat packet is not received for more than 3 minutes), it is considered that the device may be abnormal. For example, if the weighing zeroing sensor of an overweight scale does not send a heartbeat packet within the specified time, the system will preliminarily determine that the sensor is abnormal.

[0093] (2) Equipment operating parameter monitoring

[0094] Reasonableness of sensor data: Various sensors are responsible for collecting key data, such as the weighing sensor of the scale, which collects weight data. These data should be within a reasonable range. If the weighing data is significantly beyond or below the normal range (such as an ordinary coal truck weighing 0 tons or far exceeding the maximum load of the vehicle and cargo), it may indicate that the weighing sensor is abnormal. At the same time, the change of sensor data should be consistent with the actual situation. For example, when the vehicle is slowly weighed, the weighing data should rise steadily. If the data jumps or changes suddenly, it can also be judged that there is a problem with the sensor.

[0095] Controller command feedback: The controller is responsible for sending operation commands to the device and receiving feedback from the device. If the controller sends a gate opening command but does not receive a feedback signal indicating that the gate has been opened within a specified time (such as 5 seconds), or the received feedback signal does not match the command (such as sending a gate opening command but receiving a gate closing feedback), it can be determined that the gate or related control circuit is abnormal.

[0096] (3) Equipment status indicator and self-test information

[0097] Indicator status: Many hardware devices are equipped with status indicators, and different colors or flashing modes represent different working states. For example, when working normally, the indicator light of an over-empty / over-loaded infrared radiation device is always green. If the indicator light turns to flashing red, according to the device manual, it may mean that the infrared radiation is blocked or the device is faulty. At this time, the device can be judged to be abnormal.

[0098] Self-test information acquisition: Some devices have a self-test function, which can perform self-tests regularly or when receiving instructions, and return a self-test report. If the self-test report shows that there are problems such as hardware failure and component damage, such as the camera self-test prompting an image sensor failure, it can be determined that the device is abnormal.

[0099] (4) Comparison between historical data and models

[0100] Historical data comparison: The system stores the historical operation data of the equipment, including various parameters and performance indicators during normal operation. The data collected by the current equipment is compared with the historical data. If there is a large deviation, such as a sudden and significant drop in the image quality of the weighing camera, which is significantly different from the previous clear image data, it may mean that the camera is faulty.

[0101] Behavior pattern analysis: Analyze the operating behavior pattern of the equipment, such as the time interval and frequency of the gate opening and closing. If the frequency of gate opening and closing suddenly increases abnormally, and there is no corresponding vehicle pass record matching, it may indicate that the gate control equipment is faulty or abnormally disturbed.

[0102] The lifting bar warning unit monitors abnormal lifting behavior, such as taking photos of the vehicle and recording a 30-second video for uploading during manual lifting operations; when the vehicle has entered the mine but the coal pick-up order is invalid and needs to leave the mine, an invalid coal pick-up order is automatically generated. When leaving the mine, the overweight and overempty data are monitored for comparison. If there is a deviation, the bar will not be lifted and an alarm will be issued;

[0103] Node monitoring unit: Verify each node (including entry, empty, loading, overweight, exit, etc.) through coal delivery orders, report errors in real time (error reasons include: code scanning failure, node verification failure, wrong node, unstable weight, unrecognizable license plate, etc.) and issue warnings. If a vehicle does not exit the mine within a certain period of time after entering the mine, a timeout warning will be issued;

[0104] Data comparison unit: regularly compare the data collected by the smart scale room (including the number of times the gate is lifted, the number of mine cars entering and leaving the mine recorded by the camera, the number of weighing vehicles on the scale, the weight of the weighing on the scale, etc.) with the transportation and sales data in multiple dimensions. If there is a difference in the data, an early warning will be issued for the difference data, so as to facilitate the inspection of whether there is leakage of abnormal data. Specifically, in the data comparison unit, the basis for judging abnormalities is:

[0105] (1) Data consistency comparison

[0106] The number of times the barrier is lifted and the actual number of vehicles passing through: Under normal circumstances, the number of times the barrier is lifted should be basically consistent with the number of vehicles entering and leaving the mine recorded by the camera. Because each time a vehicle legally enters and exits the mine, the barrier should be lifted once. If the number of times the barrier is lifted is significantly more than the number of vehicles recorded by the camera, there may be a malfunction of the barrier or someone maliciously operating the barrier; conversely, if the number of times the barrier is lifted is less than the number of vehicles recorded by the camera, there may be a situation where a vehicle passes through illegally (such as following the vehicle in front without triggering the barrier to lift). For example, the number of times the barrier is lifted is 100 times during a certain period of time, but the number of vehicles entering and leaving the mine recorded by the camera is only 80, with a difference of 20 times, which exceeds the reasonable error range and can be judged as abnormal.

[0107] The number of vehicles weighed by the scale and the number of vehicles entering and leaving the mine recorded by the camera: The number of vehicles weighed by the scale should roughly match the number of vehicles entering and leaving the mine recorded by the camera, because each vehicle entering and leaving the mine usually needs to be weighed on the scale. If the number of vehicles weighed by the scale is significantly different from the number of vehicles recorded by the camera, for example, the scale records 120 vehicles weighed, while the camera records 100 vehicles entering and leaving the mine, with a difference of 20 vehicles, this indicates that there may be abnormal situations such as vehicles entering and leaving the mine without being weighed on the scale, or the weighing data recorded on the scale is incorrect.

[0108] (2) Comparison of data logical relevance

[0109] Weighing weight on the scale and vehicle carrying capacity and cargo type: The weighing weight on the scale should be within the vehicle's carrying capacity and the normal weight of the cargo being transported. Different types of vehicles have their own specific maximum carrying weights, and the cargo being transported, such as coal, also has a rough weight range depending on its type and loading method. If the weight on the scale far exceeds the vehicle's carrying capacity, or is too different from the normal weight of the cargo being transported by the vehicle, such as a coal truck with a rated load of 30 tons, but the scale shows 50 tons, this is obviously illogical and can be considered abnormal.

[0110] The logical relationship between the number of times the barrier is lifted and the number of vehicles weighed on the scale: theoretically, each time the barrier is lifted, the vehicle should be weighed on the scale (special circumstances require clear records and explanations). If the ratio between the number of times the barrier is lifted and the number of vehicles weighed on the scale is seriously deviated from the normal range, for example, the barrier is lifted 100 times, but the number of vehicles weighed on the scale is only 60 times, and there is no reasonable explanation for the special circumstances, there may be an abnormal situation where some vehicles pass through the barrier without being weighed on the scale.

[0111] (3) Data trends and historical data comparison

[0112] Data trend analysis: observe the changing trends of various data. For example, over a period of time, the number of gate lifts, the number of weighing vehicles, and the weight of weighing should show a relatively stable trend, which matches the production and operation rhythm of the mining area. If a certain data suddenly fluctuates greatly and does not match the overall trend, such as when the production and operation are stable, the weighing weight of the weighing scale suddenly drops by 50%, and there is no reasonable reason (such as equipment maintenance, production adjustment, etc.), it can be regarded as abnormal.

[0113] Comparison with historical data for the same period: Compare the data of the current statistical period with the historical data for the same period. If the current data such as the number of gate lifts, the number of mine cars entering and leaving as recorded by the camera, the number of weighing vehicles, and the weight of weighing vehicles compared with the same period in history are different from the normal fluctuation range, for example, the number of weighing vehicles has decreased by 30% compared with the same period in history, but the production plan of the mining area has not been adjusted, then this difference may indicate an abnormal situation, such as problems in the transportation link or errors in data recording.

[0114] In this embodiment, in the bar lifting warning unit, the method for determining whether the bar lifting is abnormal includes:

[0115] (1) Manual lever operation judgment

[0116] Operation permission verification: The system pre-sets the personnel, roles or operation procedures with the permission to manually lift the barrier. When a manual barrier lifting operation is detected, first check whether the identity of the person performing the operation or the operation method meets the preset permissions. For example, only authorized security personnel can perform manual barrier lifting operations at a specific control terminal by entering the correct password or using an authorized access card; if the operation comes from an unauthorized person, or the manual barrier lifting is triggered through informal means (such as directly violating the regulations on the barrier hardware), it can be determined as abnormal behavior; Judgment of the rationality of the operation scenario: Manual barrier lifting operations should occur in specific reasonable scenarios; for example, manual barrier lifting is usually enabled when the vehicle cannot lift the barrier through the normal automatic process due to special circumstances (such as system failure, emergency rescue, etc.). If a manual barrier lifting operation is performed in a scenario where the normal automatic barrier lifting process can be run and there is no special situation to report, it can be considered an abnormality. In addition, if the manual barrier lifting operation occurs during non-working hours and there is no relevant special situation description, it should also be determined as abnormal behavior;

[0117] (2) Determination of lifting rod after the coal delivery order is invalidated

[0118] Matching the coal pick-up order status with the vehicle status: The system monitors the vehicle's entry status and the status of the coal pick-up order in real time. When the vehicle has entered the mine and the coal pick-up order is marked as invalid, if the vehicle attempts to leave the mine, the system will focus on the lifting of the pole. Under normal circumstances, after the coal pick-up order is invalidated, the vehicle should follow a specific process to leave the mine, such as additional approval. If the lifting of the pole is triggered without completing these necessary processes, it can be judged as abnormal;

[0119] Comparison and analysis of weighing data: When the vehicle leaves the mine, the system will automatically obtain the overweight and empty data for comparison. Under normal circumstances, the overweight data minus the empty data when the vehicle leaves the mine should be roughly consistent with the actual load of the vehicle after loading coal, and within a reasonable error range (such as an error of no more than ±5%). If the comparison result shows that the deviation between the two exceeds the preset reasonable range, it means that the vehicle may be overloaded, underloaded or in other abnormal conditions. At this time, it is determined that the lifting bar is abnormal, the bar should not be lifted, and an early warning is issued.

[0120] (3) Combined with other related information

[0121] Vehicle driving trajectory and operation logic: Combine the vehicle's driving trajectory information in the mining area to determine whether the lifting operation is reasonable. For example, if the vehicle does not drive along the prescribed route, but triggers the lifting operation at a certain gate, this is inconsistent with the normal business logic, and the lifting of the gate can be determined to be abnormal; in addition, if the vehicle stays in the mining area abnormally, such as far exceeding the time required for normal coal loading, weighing and other processes, and then suddenly triggers the lifting of the gate to leave the mine, the abnormality of the lifting operation should also be suspected; linkage with the status of surrounding equipment: linkage judgment with the status of other equipment around the gate (such as infrared beamforming devices, cameras, etc.) (such as infrared beamforming devices detect that an object is abnormally close to the gate, and the lifting operation occurs at this time, and the camera image shows no signs of normal vehicle passage, then the lifting operation is likely to be abnormal).

[0122] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital lean marketing management system for coal mining enterprises, characterized in that: include: (1) Operation and control module: covers enterprise, finance, coal, equipment, and transportation and marketing management systems, and integrates with the group's unified planning and implementation or existing software systems to achieve multi-system data collection and centralized display, providing data support for leadership decision-making and improving decision-making efficiency; It can also realize online approval, customer management, contract management, and weighing control; (2) Smart control module: By dividing the mining area into different areas, the vehicle routes in the mining area are planned according to the coal delivery order, and the gates and silos in the mine are designated. The vehicle positioning module identifies abnormal situations that exceed the scope of authority, monitors the operating status of all hardware equipment in the mining area, and transmits the acquired relevant data back to the monitoring center for comparison with normal data, so as to promptly warn of abnormal situations. (3) Smart weighing room module: The hardware monitoring unit detects the hardware connection status of each weighing node in real time, and issues an alarm to prevent vehicles from passing when an abnormality occurs. The lifting bar alarm unit monitors abnormal lifting behavior and handles abnormal situations when the coal delivery order is invalidated and leaves the mine. The node monitoring unit verifies each node based on the coal delivery order, issues an error alarm in real time, and issues an alarm for vehicles that have exceeded the time limit when entering the mine. The data comparison unit regularly compares the weighing room data with the transportation and sales data in multiple dimensions, and issues an alarm for discrepancies in the data.

2. A digital lean marketing management system for coal mining enterprises according to claim 1, characterized in that: The operation control module includes: Data support unit: collects system data from all links of transportation and marketing, and forms a data dashboard with the collected system data to provide data support for decision-making and improve decision-making efficiency; Intelligent audit unit: According to the audit content of each node, the relevant information of the vehicle is audited according to the intelligent audit rules. If all audit rules cannot be met, it is considered abnormal, an early warning is issued and the corresponding audit content is rejected; if there is no abnormality, the corresponding audit content is passed and an audit record is generated; Multi-dimensional data verification unit: Regularly compare the data of the transportation and sales management system with the corresponding data of the transportation and sales platform and the intelligent loading system. If there is any mismatch in data, an abnormal warning will be issued and the abnormal data will be recorded.

3. The digital lean marketing management system for coal mining enterprises according to claim 2 is characterized in that: The audit rules of the intelligent audit unit include: (1) Data matching rules, including: Vehicle information matching rules: The vehicle information obtained through the intelligent recognition system is compared with the vehicle information pre-stored in the system. If all the information is consistent, it is initially determined to be normal; if it is inconsistent, it is identified as abnormal; Business process information matching rules: Match the corresponding standard operating procedures for each business process node, that is, a node requires the vehicle to complete a specific operation within a specific time. At the same time, the system will record the actual operation process of the vehicle; by comparing the standard operating procedures with the actual operation of the vehicle, if the vehicle fails to complete the corresponding operation within the specified time, it will be identified as an abnormality; (2) Logical judgment rules, including: Logical verification rules for associated data: The system presets the logical relationship between different businesses. If the actual vehicle operation process recorded in the system does not match the preset logical relationship, the system will identify it as an abnormality; Historical data comparison and analysis rules: The system collects the vehicle's past historical operation data and calculates the average operation data by averaging. When a new operation occurs, the current operation data is compared with the average operation data. If the deviation is greater than the preset deviation value, it is identified as an abnormality. (3) Permission control rules: Set corresponding permissions for each business node and operation. If the permissions do not match, it will be identified as an exception.

4. The digital lean marketing management system for coal mining enterprises according to claim 1 is characterized in that: The intelligent control module includes: Electronic fence unit: fences are used to divide the areas within the mine area, and the vehicle routes in the mine area are planned according to the coal delivery order, and the gates and silos in the mine are designated; by locating the vehicle, if the vehicle exceeds the authorized range, it will be identified as abnormal; at the same time, the movement trajectory is generated according to the vehicle's position in the mine, which is convenient for viewing abnormal alarm information; Equipment monitoring unit: monitors the operating status of all hardware equipment in the mining area, transmits the relevant data of the hardware equipment back to the monitoring center, compares it with the normal data, and issues timely warnings for abnormal data.

5. The digital lean marketing management system for coal mining enterprises according to claim 1 is characterized in that: The smart weighing room module includes: Hardware monitoring unit: Detect the connection status of each weighing node hardware through sensors, controllers and other equipment. When the equipment is abnormal, it returns the abnormal problem and warns that the current node is temporarily inaccessible to vehicles; The lifting bar warning unit monitors abnormal lifting behavior, such as taking photos of the vehicle and recording a 30-second video for uploading during manual lifting operations; when the vehicle has entered the mine but the coal pick-up order is invalid and needs to leave the mine, an invalid coal pick-up order is automatically generated. When leaving the mine, the overweight and overempty data are monitored for comparison. If there is a deviation, the bar will not be lifted and an alarm will be issued; Node monitoring unit: Verify each node through coal delivery orders, report errors and issue warnings in real time, and issue timeout warnings if a vehicle does not leave the mine within a certain period of time after entering the mine; Data comparison unit: Regularly conduct multi-dimensional comparisons of the data collected by the smart weighing room with the transportation and sales data, and issue warnings for discrepancies if any.