Bulk material loading control system

Abstract

The invention relates to the field of logistics control, and discloses a bulk material loading control system, which comprises an internal management platform used for providing process control and management of intelligent bulk material loading order business by using the internal platform; the logistics service platform is used for providing registration, order input and management functions of clients, suppliers and logistics companies by using an internet platform; the offline management platform is used for monitoring and managing offline vehicles, goods and personnel; the data storage platform is used for storing system data; the remote maintenance platform is used for remotely maintaining and managing all related equipment of the system and actively pushing alarm information after detecting an equipment fault; and the report generation module is used for automatically acquiring various data types in the system, generating a report according to the data type required by each report type in a plurality of preset report types in the system, and intelligently generating a data analysis report of the report. Therefore, the financial service integrated bulk material loading control system is established.

Description

Technical Field

[0001] The present invention relates to the field of bulk logistics management, and in particular to a bulk material loading control system. Background Art

[0002] With the rapid development of intelligent management systems, combined with the existing management models and characteristics of enterprises, and taking full account of future development, it is now urgent to establish a modern enterprise mechanism with the help of advanced management information integration system management tools, enhance the core competitiveness of enterprises, and comprehensively control and plug the overall loopholes. At present, most of the work in the order placement and logistics transportation process of bulk logistics needs to be manually controlled and data entered, which often involves the collaboration of multiple departments. The personnel in each job position not only need to complete their own work, but also need to check with other departments. A large number of single copies need to be counted every day, which is a large workload and a high error rate, and has a great impact on the operation of the enterprise. The main problems of this technology are as follows: 1. Bulk logistics orders require multiple processes, including customer order placement, company receipt, company input, company arrangement, and customer docking. The process is cumbersome and prone to errors and omissions. 2. Manual verification and card issuance confirmation are required for vehicle entry and exit. Vehicles must queue for entry and exit, and the loading and unloading process requires manual confirmation before being entered into the system. 3. When there are problems with the management system or equipment, staff need to perform on-site maintenance. During the maintenance process, the original system cannot work, causing losses to the company; 4. Reports need to be manually prepared based on data, which increases the workload of corporate employees and the conclusions are not objective enough. Summary of the Invention

[0003] This summary is intended to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] The present invention proposes a bulk material loading control system to solve one or more of the technical problems mentioned in the above background technology section.

[0005] The present invention provides a bulk material loading control system, which is characterized by comprising: Internal management platform, used to provide process control and management of intelligent bulk material loading order business using the internal platform; A logistics service platform, which uses an internet platform to provide registration, order entry, and management functions for customers, suppliers, and logistics companies, and is connected to the internal management platform; An offline management platform, used for monitoring and managing offline vehicles, goods, and personnel, and connected to the internal management platform; A data storage platform, used for storing system data and connected to the internal management platform and the logistics service platform; A remote maintenance platform, used to remotely maintain and manage all relevant equipment in the system and proactively push alarm information upon detecting equipment failure. The remote maintenance platform is connected to the data storage platform, the internal management platform, the logistics service platform, and the offline management platform; The report generation module is used to automatically obtain multiple data types in the system, generate multiple reports according to the data type required by each report type in the multiple report types preset in the system, and intelligently generate data analysis reports for the reports.

[0006] Optionally, the offline management platform includes: The access control module is used to determine whether the license plate number of the vehicle at the designated location matches the preset license plate number based on the vehicle information entered by the logistics service platform, and to verify the driver's identity based on the driver information entered by the logistics service platform. If the license plate number matches the preset license plate number and the driver's identity is verified, the LED screen prompts the vehicle to enter the factory, drives the barrier bar to rise, and the radar controller automatically lowers the barrier bar after detecting that the vehicle has passed. Otherwise, a failure prompt is issued; The cargo inspection module is used to connect with the sampling device after the vehicle enters the factory, automatically collect samples after the driver's identity is verified, and bind the samples to the vehicle information; The weight detection module is used to detect the vehicle position through an infrared scale monitor. When the vehicle reaches the designated position, the weight of the vehicle before and after loading and unloading is automatically detected by the weight detection device, and the weight detection result is automatically generated according to the input weight adjustment information; The intelligent information entry module is used to automatically fill out the weighing slip based on the weight test results, and automatically capture the vehicle inspection process video through the video surveillance system and store it in the system as evidence; The intercom module is used to provide factory communication, monitoring and alarm functions using full-duplex two-way video intercom equipment.

[0007] Optionally, the intelligent information entry module includes: An information collection submodule is used to obtain vehicle information corresponding to an order from the order in the bulk material loading control system, obtain license plate information through a camera, and obtain the weight detection result through the weight detection module; The process verification submodule is used to use an infrared scale monitor to monitor whether the vehicle is fully loaded onto the scale. If the vehicle is not fully stopped, a prompt will be issued and data entry will be prevented. The order verification submodule is used for order matching verification, using the license plate information and the vehicle information corresponding to the order to verify whether the current vehicle is associated with a valid order. If there is no order, weighing is prohibited; The input submodule is used to automatically capture the front and rear images of the vehicle when weighing and enter them into the system, obtain the weight detection results, automatically adjust the weight detection results according to the water and impurity deduction rules, and simultaneously generate the corresponding documents and enter them into the system; The exception handling submodule is used to display specific error information on the LED screen and switch to manual verification mode when an abnormal situation occurs.

[0008] Optionally, the internal management platform includes: The business management module is used to input and manage business information and automatically settle accounts based on the progress of business completion; Logistics management module, used for vehicle registration and real-time tracking and scheduling of vehicle logistics information; System maintenance module, used to remotely maintain and manage all relevant equipment in the system, and proactively push alarm information when equipment fails; Access control management module, used for access control registration and management of business vehicle information and driver information, and setting queuing rules for business vehicle entry and exit; The hazardous chemicals management module is used to manage the documents and access of orders involving hazardous chemicals transportation, and to monitor the status of the entire logistics process; The quality inspection and testing module is used to automatically generate various statistical reports based on the entered quality inspection and testing orders, and to enter third-party quality inspection and testing orders for disputed quality inspection and testing orders.

[0009] Optionally, the logistics service platform includes: The customer registration module is used to determine whether the number and name entered by the customer are the same as the number and name corresponding to the customer in the system when the customer submits the registration record, and grant the user permission after the judgment is passed; The permission allocation module is used to determine the customer registration type and assign different usage permissions based on the registration type; The order entry module is used to provide customers with order information entry functions and automatically generate vehicle dispatch orders based on customer orders; The dispatch query module is used for customers to query vehicle dispatch information according to corresponding permissions; The information query module is used for customers to query vehicle weighing, logistics information and order status according to corresponding permissions.

[0010] Optionally, the data storage platform includes a data processing center, in which an internal server and an external server are arranged. The internal server is used to store the data of the internal management platform, and the external server is used to store the data of the logistics service platform. The data of the internal server and the external server are interconnected, and a firewall is arranged during the data connection between the internal server and the external server.

[0011] Optionally, a plurality of sub-servers are provided under the internal server, the sub-servers are connected to the internal server, and each of the sub-servers is independent of each other.

[0012] Optionally, the remote maintenance platform includes: A monitoring module for monitoring the working status of each part of the bulk material loading control system; An emergency processing module is used to activate a pre-set emergency processing plan for any part of the bulk material loading control system when it is detected that any part of the bulk material loading control system is working abnormally; An alarm module is used to notify the operation and maintenance personnel through system messages or intercom equipment when the bulk material loading control system operates abnormally; Maintenance module, used for remote configuration updates and fault repairs.

[0013] Optionally, the report generation module includes the following steps: Retrieve various types of data stored in the system through the system's internal interface; Classify the various types of data in the system into three categories: structured data, semi-structured data, and unstructured data according to their structured degree; Acquire the semi-structured data, and perform structured processing on the semi-structured data by using field screening; Acquire the unstructured data, and select an image recognition engine or a semantic recognition engine to perform structured processing on the unstructured data according to the specific data type of the unstructured data; Obtain all data after structured processing, delete duplicate data from all the data, detect missing data from all the data and complete them through rule inference, sort all the data according to timestamp information, and generate a database; Obtaining the data type required for each of the multiple report types preset in the system, matching the data type required for each report type in the database, and obtaining the corresponding data; generating a plurality of reports using the corresponding data of each report type, and generating an interactive chart corresponding to each report based on the plurality of reports; Analyze the data in each report based on its temporal and spatial order, locate problems through association rule mining, and make specific decisions based on the decision tree model; The issues and specific decisions for each report are determined as a data analysis report for that report and output.

[0014] Optionally, the system further includes a risk prediction module for using historical data to train an AI model to predict multi-dimensional risks in the bulk material loading system, and triggering a remote maintenance function or adjusting a scheduling strategy based on the risk prediction results, the steps of which include: Use internal sensors to obtain device data, use historical logistics information and orders to obtain vehicle and cargo information, and obtain software information based on system logs, and integrate the above information into multi-dimensional historical data; Performing data cleaning on the multi-dimensional historical data to remove duplicate values ​​and abnormal values ​​in the multi-dimensional historical data; Performing feature engineering on the cleaned multi-dimensional historical data, constructing features for each data type in the multi-dimensional historical data, normalizing numerical features, and constructing an underlying feature extraction database; Designing parallel output branches for different risk types, wherein the parallel output branches share the underlying feature extraction database; Randomly splitting the underlying feature extraction database into a training set, a validation set, and a test set, and designing a loss function to give a higher weight to minority types; Using a machine training model to construct a risk prediction model based on the training set and the loss function, using the validation set to verify the effectiveness of the risk prediction model, and using the test set to test the performance of the risk prediction model; Use the generated risk prediction model to predict the risk of current real-time data and determine the current risk index; When any real-time data risk index exceeds a preset threshold, the remote maintenance function is triggered or the scheduling strategy is adjusted for the real-time data source according to the real-time data source.

[0015] The present invention has the following beneficial effects: 1. By connecting the internal and external platforms of the enterprise, user orders can be directly connected to the internal management system. If there are any problems, they can be updated in real time, reducing communication costs; 2. Vehicle entry, exit and weighing can be completed fully automatically, with no or only a small number of employees required, reducing labor costs; 3. When there are problems with the management system or equipment, maintenance can be performed remotely. During the maintenance process, backup plans can be called up in real time, reducing the company's losses caused by system maintenance. 4. Generate multiple reports based on the multiple report types preset in the system, and intelligently generate data analysis reports for the reports, which is easy to access and reduces employee workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features, advantages, and aspects of the various embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the elements are not necessarily drawn to scale.

[0017] Figure 1 It is a structural schematic diagram of the present invention; Figure 2 It is a structural diagram of the internal management platform in the present invention; Figure 3 It is a structural diagram of the logistics service platform in the present invention. DETAILED DESCRIPTION

[0018] The present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0019] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other.

[0020] It should be noted that the concepts of "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] In one embodiment, Figure 1 As shown, a bulk material loading control system is characterized by comprising: Internal management platform 101, used to provide process control and management of intelligent bulk material loading order business using the internal platform; In some embodiments, the internal platform is an internal management platform of the enterprise, which can automatically and standardizedly manage the entire life cycle of bulk material loading orders through an independently developed logistics management platform, including order management, logistics management, access control management, hazardous chemicals management, cargo quality inspection, system maintenance and other functions; The logistics service platform 102 is used to provide registration, order entry and management functions for customers, suppliers and logistics companies using the Internet platform. The logistics service platform is connected to the internal management platform; In some embodiments, the service is mainly completed through an Internet platform, which can provide external customers, suppliers, logistics companies and other parties to register on the platform. After registration, permissions are allocated according to user type, so that customers, suppliers and logistics companies can complete various functions such as order entry, vehicle dispatch order generation, logistics information entry and tracking according to corresponding permissions; Offline management platform 103, used for monitoring and management of offline vehicles, goods and personnel, the offline management platform is connected to the internal management platform; In some embodiments, the offline management platform is mainly used for factory management, including queuing rule setting for access control equipment, access management, intelligent monitoring of the weighing process, and internal communication monitoring of the factory. Data storage platform 104, used for storing system data, connected to the internal management platform and the logistics service platform; In some embodiments, the storage of system data is mainly completed through the data center server set up within the enterprise. A remote maintenance platform 105 is used to remotely maintain and manage all relevant equipment in the system and proactively push alarm information upon detecting equipment failure. The remote maintenance platform is connected to the data storage platform, the internal management platform, the logistics service platform, and the offline management platform; In some embodiments, the remote maintenance platform centrally monitors the operating status of hardware equipment such as floor scales, access control machines, intercom terminals, self-service check-in machines, etc. scattered throughout the factory area, and collects sensor data in real time, such as floor scale pressure value and card reader online status. When the system detects an abnormality, it immediately pushes detailed fault information via SMS, corporate WeChat or internal alarm interface, including equipment location, fault code and possible impact range, and triggers emergency plans, such as switching to backup equipment or enabling manual processes; at the same time, operation and maintenance personnel can remotely log in to the equipment management interface to perform parameter adjustments, log downloads or firmware upgrades without on-site operations, ensuring rapid fault repair and business continuity: for example, when unattended weighing equipment fails, the system automatically switches to a nearby backup weighing room and dispatches the maintenance work order to the designated engineer, without the need for human intervention throughout the process.

[0025] The report generation module 106 is used to automatically obtain multiple data types in the system, generate multiple reports according to the data types required by each of the multiple report types preset in the system, and intelligently generate data analysis reports for the reports.

[0026] In some embodiments, the report generation module works by retrieving various types of data stored in the system through the system's internal interface, classifying the various types of data according to their degree of structuring and using different processing measures to classify differently structured data, generating a database, using the corresponding data of the report type to generate multiple reports and interactive charts, analyzing the data in the report according to the time and space order of the data in the report, locating problems through association rule mining, and making specific decisions based on the decision tree model, generating and outputting a data analysis report for the report.

[0027] Working principle: The present invention mainly provides process control and management of intelligent bulk material loading order business within the enterprise through the internal management platform; provides registration, order entry and management functions for customers, suppliers and logistics companies through the logistics service platform; completes the monitoring and management of offline vehicles, goods and personnel through the offline management platform; uses the data storage platform to complete the storage of system data; through the remote maintenance platform, remotely maintains and manages all relevant equipment of the system, and actively pushes alarm information after detecting equipment failure; can generate multiple reports according to multiple report types preset in the system, and intelligently generate data analysis reports for the reports, completing the full life cycle automation and standardized management of bulk material loading orders.

[0028] Beneficial effects: Through the connection between the internal enterprise platform and the external platform, user orders can be directly connected to the internal management system, and problems can be updated in real time, reducing communication costs; offline vehicle entry and exit and weighing can be completed fully self-service, which improves the intelligence of the system; when there are problems with the management system or equipment, remote maintenance can be carried out, which improves the practicality and convenience of the invention; multiple reports are generated according to multiple report types preset in the system, and data analysis reports of the reports are intelligently generated for easy reference, reducing the burden on employees.

[0029] In one embodiment, the offline management platform includes: The access control module is used to determine whether the license plate number of the vehicle at the designated location matches the preset license plate number based on the vehicle information entered by the logistics service platform, and to verify the driver's identity based on the driver information entered by the logistics service platform. If the license plate number matches the preset license plate number and the driver's identity is verified, the LED screen prompts the vehicle to enter the factory, drives the barrier bar to rise, and the radar controller automatically lowers the barrier bar after detecting that the vehicle has passed. Otherwise, a failure prompt is issued; In some embodiments, the vehicle license plate determination is mainly completed by intelligent recognition of the vehicle license plate on the vehicle photo at the specified location, and the determination result can be obtained by comparing the identified license plate number with the preset license plate; the identity verification of the driver is mainly carried out through the personal information entered by the driver on the Internet platform, which can be divided into two ways: offline verification by swiping the ID card through the access control system or by generating a QR code after entering the verification information in the mobile APP, and scanning the QR code for verification. If the verification fails, the driver can enter the factory through manual verification and use the issued IC card; The cargo inspection module is used to connect with the sampling device after the vehicle enters the factory, automatically collect samples after the driver's identity is verified, and bind the samples to the vehicle information; In some embodiments, the above process means that after a vehicle enters the factory, the driver completes identity verification by swiping an ID card, IC card, or scanning a QR code. The system then automatically triggers the sampling machine to start the sampling process and synchronizes the sampling time, location, and operator information to the logistics management system through the Internet of Things technology. The sampling machine's built-in encoder generates a unique identification code for each sample, establishes a bidirectional association with information such as the license plate number, order number, and material type in the vehicle file, and writes it to the database in real time. The weight detection module is used to detect the vehicle position through an infrared scale monitor. When the vehicle reaches the designated position, the weight of the vehicle before and after loading and unloading is automatically detected by the weight detection device, and the weight detection result is automatically generated according to the input weight adjustment information; In some embodiments, the above process mainly uses an infrared scale monitor to scan the position of the vehicle on the weighing platform in real time. When the vehicle stops completely and the tires press the infrared detection area, the system determines that the vehicle has reached the designated position and triggers the scale meter to automatically read the weight data, that is, the tare weight or gross weight. Before loading and unloading, the system records the initial weight; after loading and unloading, the vehicle is weighed again, and the weight detection device collects data for the second time and calculates the net weight. If the operator enters the amount of impurities and water deduction through the handheld device, the system automatically adjusts the net weight and generates the final weight detection result, and pushes it to the settlement module simultaneously; for example, the tare weight of a vehicle is 20 tons when it is weighed for the first time, and the gross weight after loading is displayed as 50 tons. If 1 ton of impurities is entered, the system calculates the net weight as 29 tons and generates a weighing slip with a timestamp, license plate number and impurity deduction details. The entire process is monitored by infrared to ensure that the vehicle position is compliant, and the data is automatically associated with the order to prevent manual tampering. At the same time, the weighing image is captured and archived for future reference to ensure that the results are traceable; The intelligent information entry module is used to automatically fill out the weighing slip based on the weight test results, and automatically capture the vehicle inspection process video through the video surveillance system and store it in the system as evidence; The intelligent information entry module is used to automatically fill out the weighing slip based on the weight test results, and automatically capture the vehicle inspection process video through the video surveillance system and store it in the system as evidence; In some embodiments, the process uses a scale meter to obtain the vehicle's tare and gross weight data in real time. The system automatically calculates the net weight and fills in the weighing slip, which contains the license plate number, cargo type, weighing time, etc., and can be synchronously associated with order information. At the same time, the video surveillance system triggers a snapshot at the moment the vehicle is weighed, capturing the license plate, cargo loading status, and infrared monitoring scale detection status. High-definition images with time watermarks from front and rear angles are bound to the weighing slip data and stored in the database to form a complete electronic evidence chain. For example, when a vehicle is gross-weighed, the system detects that the vehicle number is inconsistent with the order, automatically freezes the process, and saves the captured image for the administrator to review. The intercom module is used to provide factory communication, monitoring and alarm functions using full-duplex two-way video intercom equipment.

[0030] Working principle: The offline management platform mainly realizes unmanned control of the entire process of bulk material loading through access control, inspection, weighing, information entry and intercom functions. The access control module verifies the license plate and driver's identity and then links the gate to release the vehicle. The radar automatically drops the barrier after detecting the vehicle passing, and the LED screen warns in case of abnormality. The cargo inspection module starts the sampling machine after identity verification and automatically binds the sample code with the vehicle order information. The weight detection module uses an infrared monitor to locate the vehicle, automatically records the tare weight / gross weight, and generates the net weight result based on the manual deduction rules. The intelligent information entry module generates an electronic weighing slip in real time and simultaneously captures the weighing video. The intercom module supports two-way audio and video communication to realize remote monitoring, fault alarm and emergency command functions.

[0031] Beneficial effects: Through automatic verification and release by access control, precise binding of samples by sampling machines, anti-cheating weighing by infrared monitoring scales, and linkage between electronic scale slips and video evidence, unmanned operation of the entire process is achieved, which greatly reduces human intervention and errors. Combined with full-duplex video intercom and radar detection, a closed-loop control is formed to effectively eliminate the risks of vehicle fraud, cargo substitution and weight tampering; automated sampling, intelligent deduction calculation and encrypted evidence storage functions ensure that data is authentic and traceable. At the same time, remote alarm and emergency response mechanisms ensure rapid handling of emergencies, significantly improving the efficiency, safety and compliance of bulk material loading.

[0032] In one embodiment, the intelligent information entry module includes: An information collection submodule is used to obtain vehicle information corresponding to an order from the order in the bulk material loading control system, obtain license plate information through a camera, and obtain the weight detection result through the weight detection module; The process verification submodule is used to use an infrared monitoring scale to monitor whether the vehicle is fully loaded onto the scale. If the vehicle is not fully stopped, a prompt will be issued and data entry will be prevented. In some embodiments, the function of this module is mainly to determine the vehicle position through infrared detection. If the vehicle is parked at the designated location, it is determined that the vehicle has been weighed. If the vehicle is in motion or not parked at the designated location, it is determined that the vehicle has not been weighed. The reminder of not being weighed is to issue a reminder to the vehicle that has not been weighed through an LED screen or broadcast. When it is detected that the vehicle has not been weighed, the vehicle data collection is automatically terminated. The order verification submodule is used for order matching verification, using the license plate information and the vehicle information corresponding to the order to verify whether the current vehicle is associated with a valid order. If there is no order, weighing is prohibited; In some embodiments, the order matching and verification process is implemented through real-time linkage between the automatic license plate recognition system and the logistics platform order database. When a vehicle enters the factory and completes identity verification, the system automatically retrieves the valid order associated with the license plate to verify the order status, cargo type, and loading location matching. For example, when a vehicle with the license plate "Jin A12345" is weighed, the system retrieves and finds that it is only associated with one coke sales order, and allows it to be weighed. If no valid order is found or the vehicle number does not match the vehicle registered in the order, the system immediately locks the weighing process, and the LED screen displays "No valid order, weighing prohibited", and simultaneously pushes an alarm message to the dispatch center and records the abnormal operation log. The input submodule is used to automatically capture the front and rear images of the vehicle when weighing and enter them into the system, obtain the weight detection results, automatically adjust the weight detection results according to the water and impurity deduction rules, and simultaneously generate the corresponding documents and enter them into the system; In some embodiments, after the infrared monitoring scale determines that the vehicle has been fully weighed, the process triggers the camera to automatically capture high-definition images of the front and rear of the vehicle and the loading status of the cargo, and stores them in the database after superimposing the timestamp and scale number information. At the same time, the system reads the gross weight data transmitted by the scale meter, automatically calculates the net weight based on the amount of impurities deducted or the preset water deduction ratio entered by the handheld device, and generates an electronic weighing slip with adjustment details. The slip is linked to the order number, vehicle file and captured image in real time, and is pushed to the financial settlement system to generate a reconciliation voucher. The exception handling submodule is used to display specific error information on the LED screen and switch to manual verification mode when an abnormal situation occurs.

[0033] In some embodiments, the exception handling mechanism is to dynamically display specific error codes and prompts through the LED screen when license plate recognition failure, weight data out of tolerance or sampling abnormality is detected: for example, "Error E201: The vehicle number does not match the order, please manually review", and simultaneously suspend the automated process and start the manual verification mode. For example, when the vehicle is weighed, the infrared detection shows that it is not fully weighed. The LED screen prompts "The vehicle has not stopped steadily, please weigh again" and flashes a red light. At the same time, the current vehicle information, captured images and abnormal data are pushed to the duty room terminal. The staff will check the documents on site, manually enter corrections or authorize release. After manual confirmation, the system will resume the automated process and record the abnormal event log to ensure that the problem can be traced and does not affect the normal operation of other vehicles.

[0034] Beneficial effects: Corresponding information is obtained from the bulk material loading control system, ensuring the accuracy and real-time nature of the information and improving work efficiency; an infrared weighing monitor is used to monitor whether the vehicle is fully weighed, and a prompt is issued when it is not completely stopped, and data entry is prevented, ensuring the accuracy of the input information and reducing errors; order matching verification, using the license plate information and the vehicle information corresponding to the order to verify whether the current vehicle is associated with a valid order, and weighing is prohibited if there is no order, improving the security and detection efficiency of the system; the front and rear images of the vehicle are automatically captured and entered into the system during weighing, and the weight detection results are obtained and automatically adjusted according to the water and impurity deduction rules, and the corresponding documents are generated and entered into the system simultaneously, improving the intelligence of the system; when there is an abnormal situation, the specific error information is displayed on the LED screen and the mode is switched to manual verification. When problems arise, manual verification is performed to ensure the normal operation and operation of the factory.

[0035] In one embodiment, Figure 2 As shown, the internal management platform includes: The business management module 1011 is used to input and manage business information and automatically settle accounts based on the progress of business completion; In some embodiments, this module is represented by the centralized entry and management of business data such as orders, vehicle dispatch orders, and quality inspection results through the logistics platform. The system tracks the completion of weighing and the progress of testing in real time. For example, the moisture and ash content of coke meet the standards. When the loading, weighing, and quality inspection links are completed, the settlement rule engine is automatically triggered to calculate the payable amount according to the unit price and miscellaneous deduction formula agreed in the contract, generate a settlement statement and push it to the financial system; for example, the net weight of a coke order is 100 tons after weighing, and the test shows that the moisture content exceeds the standard by 2%. The system automatically deducts 5 tons and settles it at 95 tons. At the same time, an electronic settlement statement with deduction details is generated, which is synchronized to the ERP to generate accounting vouchers after review, reducing manual verification and repeated entry, ensuring the integrity of the data chain and efficient and accurate settlement; Logistics management module 1012, used to register vehicles and conduct real-time tracking and scheduling management of vehicle logistics information; In some embodiments, this module registers vehicle information, such as license plate number, rated load, driver's ID, and carrier, through the logistics platform. An electronic file is created upon initial entry to the factory, and subsequent data is automatically retrieved based on the license plate. GPS positioning and factory electronic fencing are integrated to track vehicle locations in real time and dynamically schedule loading priorities. For example, if a vehicle is delayed due to a breakdown, the system automatically reassigns its originally scheduled loading slot to another vehicle and notifies the driver of the new queue number via the app. Simultaneously, the logistics dashboard displays vehicle density in each area in real time, allowing dispatchers to manually adjust loading point assignments. Data is synchronized with the ERP system to ensure consistency across transportation plans, weighing orders, and settlement documents, reducing idle runs and waiting time.

[0036] System maintenance module 1013 is used to remotely maintain and manage all relevant equipment in the system and proactively push alarm information when equipment fails; In some embodiments, this module uses IoT technology to monitor the operating status of equipment such as weighbridges, access control machines, and sampling machines in real time. When an equipment anomaly is detected, such as a data interruption in the weighbridge meter, an access control card reader being offline, or an overload on the server CPU, a hierarchical alarm mechanism is automatically triggered: Level 1 faults are sent to the operation and maintenance team via SMS and WeChat for Business simultaneously, while Level 2 faults are displayed in a pop-up window on the dispatch center's large screen. A work order is generated with the equipment number, fault code, and historical maintenance records, and remote operation and maintenance is supported. Access control management module 1014, used for access control registration and management of business vehicle information and driver information, and setting queuing rules for business vehicle entry and exit; In some embodiments, this module mainly performs access control registration and management of business vehicles and drivers according to preset queuing rules; Hazardous chemicals management module 1015, used to manage documents and access for orders involving hazardous chemicals transportation, and to monitor the status of the entire logistics process; The quality inspection and testing module 1016 is used to automatically generate various statistical reports based on the entered quality inspection and testing reports, and to enter third-party quality inspection and testing reports for disputed quality inspection and testing reports.

[0037] In some embodiments, this module automatically generates daily / monthly statistical reports by customer, variety, and batch by connecting to automated testing equipment or manually entered quality inspection data, and supports custom filtering. When suppliers or customers have doubts about the test results, the system opens a third-party test order entry interface, supports uploading PDF reports or scanned copies with authoritative agency signatures and unique codes, automatically parses key indicators and compares them with the original results to generate a difference analysis report; for example, a batch of coal has a dispute over calorific value. After the supplier uploads a third-party test report, the system automatically marks the original test order as "disputed status", freezes the relevant settlement process, and regenerates the settlement order according to the final approved value after the administrator reviews and confirms it, while retaining two copies of the report and the audit log.

[0038] Working Principle: In this platform, the business management module can input and manage business information and automatically settle accounts; the logistics management module handles vehicle registration and real-time tracking; the system maintenance module is used for remote maintenance and fault alarms; the access control management module handles the entry and exit management of vehicles and drivers; the hazardous chemicals management module specifically handles the documents and monitoring of hazardous chemicals transportation; and the quality inspection and testing module generates reports and handles disputes.

[0039] Beneficial Effects: Automated settlement, real-time logistics tracking, remote equipment maintenance, and intelligent access control verification significantly improve efficiency and safety. Full-process monitoring and mandatory document verification for hazardous chemical transportation reduce compliance risks. The quality inspection module supports third-party report uploads and dispute resolution, ensuring data transparency and reliability. Data interoperability across modules creates a closed-loop management system, reduces human intervention, and ensures efficient, secure, and compliant bulk material logistics operations.

[0040] In one embodiment, Figure 3 As shown, the logistics service platform includes: The customer registration module 1021 is used to determine whether the number and name entered by the customer are the same as the number and name corresponding to the customer in the system when the customer submits the registration record, and grant the user rights if the judgment is passed; In some embodiments, when a supplier or logistics company submits a registration application, the system automatically verifies whether the "customer number" and "customer name" filled in by the supplier are completely consistent with the system's preset files. If the verification passes, it is automatically marked as "authenticated" and the logistics platform permissions are opened; if any field of the number or name does not match, the system will trigger an interception and prompt "Customer information does not match, please contact the administrator for verification", and the abnormal application will be transferred to the manual review queue. The business department will check the contract file for a second time and then manually authorize or reject it, preventing unauthorized third parties from impersonating identities to obtain data access rights, ensuring the confidentiality of the company's sensitive information and business data; The permission allocation module 1022 is used to determine the customer registration type and allocate different usage permissions according to the registration type; In some embodiments, different permissions are assigned based on the different identities of logistics companies, suppliers, and customers. Each customer can only query orders related to the company, ensuring information security. The order entry module 1023 is used to provide customers with order information entry functions and automatically generate vehicle dispatch orders based on customer orders; In some embodiments, customers are allowed to enter sales / purchase orders through the logistics platform PC or APP. The system automatically verifies whether the customer number and name are consistent with the preset file, and then synchronizes the order to the ERP to generate a contract. For example, after a customer enters a sales order for 500 tons of coke, the system automatically generates a dispatch order based on the preset dispatch rules, including the vehicle number, driver's ID card, pre-loaded quantity and pick-up QR code, and pushes it to the carrier's APP. If a private vehicle is dispatched, the auditor must confirm the carrier's qualifications online. After arriving at the factory, the driver signs in by himself with a QR code or ID card. Only after the system verifies that the driver has passed the verification can he enter the queue loading process, ensuring that the transportation task strictly corresponds to the order, reducing manual scheduling errors and the risk of fraudulent collection. The dispatch query module 1024 is used for customers to query vehicle dispatch information according to corresponding permissions; The information query module 1025 is used for customers to query vehicle weighing, logistics information and order status according to corresponding permissions.

[0041] In some embodiments, customers can use a dedicated account, such as a customer number + password / verification code, to restrict their access to vehicle weighing records associated with their orders. The weighing records include the weighing order number, net weight, miscellaneous deduction details, real-time logistics status, such as vehicle GPS location, factory queue number, and order progress, such as "vehicle dispatched, loading in progress, weighing completed, settlement pending review"; Working Principle: It integrates customer registration, authority allocation, order entry, dispatch and information query functions to realize online management of the entire process from customer qualification verification to order generation and logistics tracking; customers complete registration applications and order submissions through a unified entrance, and the system automatically verifies information and generates vehicle dispatch orders. At the same time, logistics data query permissions are opened according to authority levels to ensure a closed business loop.

[0042] Beneficial effects: Automated permission control and order processing reduce manual intervention and operational error rates; real-time logistics status transparency enhances customer trust; customer self-service query functions reduce employee pressure and shorten response time; identity verification and data isolation mechanisms ensure the security of corporate information and customer privacy.

[0043] In one embodiment, the data storage platform includes a data processing center, in which an internal server and an external server are arranged. The internal server is used to store the data of the internal management platform, and the external server is used to store the data of the logistics service platform. The data of the internal server and the external server are interconnected, and a firewall is arranged during the data connection between the internal server and the external server.

[0044] In one embodiment, the internal server is provided with a plurality of sub-servers, the sub-servers are connected to the internal server, and each of the sub-servers is independent of each other; In one embodiment, the remote maintenance platform includes: A monitoring module for monitoring the working status of each part of the bulk material loading control system; In some embodiments, the monitoring module monitors offline devices primarily through built-in sensors and operating parameters fed back by the offline devices. It monitors online systems primarily through data feedback and operating status, and can also monitor by manually uploading fault information: An emergency processing module is used to activate a pre-set emergency processing plan for any part of the bulk material loading control system when it is detected that any part of the bulk material loading control system is working abnormally; In some embodiments, when a system anomaly is detected, such as a failure of the access control card reader, a data interruption on the weighbridge, or an offline sampling machine, the system automatically matches the preset plan library to execute countermeasures. For example, if the core server is down, the system switches to the local database of the industrial computer in the weighing room to continue weighing, and after the network is restored, the data is incrementally synchronized to the main server; if the infrared monitoring scale fails, the system turns off the position verification function but forces video capture and marks it as "manual review required"; when the card reader fails, the system switches to a nearby backup device or activates the desktop card reader at the gate to temporarily take over; An alarm module is used to notify the operation and maintenance personnel through system messages or intercom equipment when the bulk material loading control system operates abnormally; In some embodiments, this module mainly triggers multi-level alarm push, for example: core fault SMS notification to the operation and maintenance supervisor, common abnormality dispatch center pop-up prompt, and automatically generates a maintenance work order with fault code, equipment location and historical records, and assigns it to the responsible team for time-limited processing. Maintenance module, used for remote configuration updates and fault repairs.

[0045] In some embodiments, the remote configuration update and fault repair are mainly performed by the operation and maintenance personnel receiving updates or fault prompts, remotely updating the system configuration through the internal LAN, and repairing software faults. If it is an offline equipment failure, it is remotely pushed to the responsible team and a processing request is issued.

[0046] Working principle: By real-time monitoring of the status of each component of the loading control system, the system automatically triggers the emergency plan when an abnormality occurs, simultaneously pushes alarm information to operation and maintenance personnel, and supports remote configuration updates and fault repairs to ensure the continuous and stable operation of the system.

[0047] Beneficial effects: Real-time monitoring and automatic emergency response mechanisms significantly reduce downtime; multi-level alarm strategies improve response speed; remote maintenance reduces manual inspection costs; and emergency plan libraries and closed-loop work order management ensure fault traceability and standardized processing, guaranteeing the efficiency and safety of bulk material loading operations.

[0048] In one embodiment, the report generation module comprises the following steps: Retrieve various types of data stored in the system through the system's internal interface; In some embodiments, the system internal interface is a data interface for calling the system internal data. The various types of data stored in the system include sales data, inventory data, financial data, etc. Classify the various types of data in the system into three categories: structured data, semi-structured data, and unstructured data according to their structured degree; In some embodiments, the structured data mainly refers to order tables, inventory tables, customer information tables, etc. in the database, which have clear field definitions and relational models; semi-structured data refers to log files, configuration files, etc., which have no fixed format but contain a certain structure; unstructured data refers to unstructured data such as surveillance videos and photos; Acquire the semi-structured data, and perform structured processing on the semi-structured data by using field screening; Acquire the unstructured data, and select an image recognition engine or a semantic recognition engine to perform structured processing on the unstructured data according to the specific data type of the unstructured data; Obtain all data after structured processing, delete duplicate data from all the data, detect missing data from all the data and complete them through rule inference, sort all the data according to timestamp information, and generate a database; In some embodiments, the data standards of multiple systems are not unified, and all data need to be normalized first. The method of filling missing data is mainly through rule inference or machine learning prediction. The timestamp information is the timestamp information when the data file is generated. Duplicate data refers to data that overlaps after collecting multiple data. Obtaining the data type required for each of the multiple report types preset in the system, matching the data type required for each report type in the database, and obtaining the corresponding data; In some embodiments, the preset multiple report types may include basic statistical reports, trend analysis reports, comparative analysis reports, etc., and the required data types include sales data, inventory data, order volume, order source, and other types of data. Matching the data types required for each report type in the database is represented by searching the database for the data required to generate the report within the report statistics time period; generating a plurality of reports using the corresponding data of each report type, and generating an interactive chart corresponding to each report based on the plurality of reports; In some embodiments, this process mainly generates a standardized data set to obtain a corresponding report through data extraction, conversion and loading based on the pre-set report required data and report generation rules, and visualizes the table to generate an interactive chart; Analyze the data in each report based on its temporal and spatial order, locate problems through association rule mining, and make specific decisions based on the decision tree model; In some embodiments, data analysis includes statistical means, medians, and distributions to locate specific problems, and specific decisions based on decision tree models are expressed as action recommendations in combination with optimization algorithms; The issues and specific decisions for each report are determined as a data analysis report for that report and output.

[0049] How it works: Data from multiple sources within the system is categorized by level of structure and then processed separately: semi-structured data fields are filtered and converted to structured data, and unstructured data is parsed and standardized. Duplicates are removed, missing data is filled, and a database is sorted by time. Data is extracted based on pre-set report types and interactive charts are generated. Spatiotemporal data is analyzed using association rules and decision tree models to identify issues and generate decision recommendations, ultimately integrating them into a structured analysis report.

[0050] Beneficial effects: Supports automatic integration of multi-source heterogeneous data, reducing manual cleaning costs; intelligent algorithms improve problem location accuracy and avoid empiricism bias; interactive charts intuitively present data insights, lowering the threshold for interpretation; fully automated report generation shortens analysis cycles to minutes; flexibly adapts to business needs, with customizable report templates and decision-making rules.

[0051] In one embodiment, the system further includes a risk prediction module for using historical data to train an AI model to predict multi-dimensional risks in the bulk material loading system, and triggering remote maintenance functions or adjusting scheduling strategies based on risk prediction results, including the following steps: Use internal sensors to obtain device data, use historical logistics information and orders to obtain vehicle and cargo information, and obtain software information based on system logs, and integrate the above information into multi-dimensional historical data; Performing data cleaning on the multi-dimensional historical data to remove duplicate values ​​and abnormal values ​​in the multi-dimensional historical data; Performing feature engineering on the cleaned multi-dimensional historical data, constructing features for each data type in the multi-dimensional historical data, normalizing numerical features, and constructing an underlying feature extraction database; In some embodiments, when performing feature engineering on cleaned multi-dimensional historical data, features are first constructed based on data type differentiation: for numerical data, derived features are generated through time window statistics and normalized to eliminate dimensional differences; for time series data, periodic features and time intervals are extracted; for text data, semantic vectors are extracted through models, and clustering is used to generate high-level features such as "device anomaly description classification." All features are standardized and stored in the underlying feature library, and a feature lineage tracing mechanism is established to support dynamic updates and version control. Designing parallel output branches for different risk types, wherein the parallel output branches share the underlying feature extraction database; In some embodiments, when building a multi-task risk prediction model, independent parallel output branches are designed for different risk types such as equipment failure, transportation delay, and safety incidents. Each branch shares a unified feature representation provided by the underlying feature extraction database. The shared underlying features reduce the number of model parameters through a weight sharing mechanism, avoiding overfitting of a single task. At the same time, the multi-task correlation is used to improve the feature generalization ability, ensuring that the work can be completed with a smaller amount of data. Randomly splitting the underlying feature extraction database into a training set, a validation set, and a test set, and designing a loss function to give a higher weight to minority types; In some embodiments, when constructing a risk prediction model, the underlying feature extraction database is first randomly split into a training set, a validation set, and a test set in a ratio of 7:1.5:1.5. A time-sensitive splitting strategy is adopted for time series data to ensure that the time windows of the validation set and the test set are later than those of the training set to avoid data leakage. A weighted cross-entropy loss function is designed to address the class imbalance problem, and a dynamic weight mechanism is assigned to minority class samples. By adjusting parameters, the weight contribution of easy-to-classify samples is reduced, forcing the model to focus on difficult samples. During training, the validation set is used to evaluate the weight parameters after each round of training, and the learning rate and batch size are dynamically adjusted to ensure the timeliness and reliability of risk warnings. Using a machine training model to construct a risk prediction model based on the training set and the loss function, using the validation set to verify the effectiveness of the risk prediction model, and using the test set to test the performance of the risk prediction model; Use the generated risk prediction model to predict the risk of current real-time data and determine the current risk index; In some embodiments, the risk prediction model receives real-time sensor data from the loading system, such as weighbridge pressure, vehicle GPS coordinates, equipment temperature, business data, and environmental data. After feature engineering, the data is input into a multi-task model, and each branch outputs equipment failure probability, transportation delay level, and safety risk score in parallel. When any real-time data risk index exceeds a preset threshold, the remote maintenance function is triggered or the scheduling strategy is adjusted for the real-time data source according to the real-time data source.

[0052] In some embodiments, when the real-time risk index exceeds the threshold, the risk response mechanism automatically traces the abnormal data source: if the scale sensor fails, the remote maintenance module is triggered to restart the equipment or switch to the backup sensor, and a maintenance work order is generated and distributed to the responsible person; if the transportation is delayed due to weather, the vehicle scheduling strategy is dynamically adjusted, and the new route and loading priority are pushed to the driver through the APP; if the safety risk exceeds the limit, the loading valve is immediately remotely locked and the emergency intercom is activated to notify security intervention; all handling instructions are recorded in real time in the operation and maintenance log, and the system automatically evaluates the effect after execution, such as the fault repair time, delay relief rate, etc., and feeds back to the model optimization closed loop.

[0053] Working Principle: The risk prediction module integrates data from equipment sensors, logistics orders, and system logs, generates a structured feature library through cleaning and feature engineering, designs a multi-task parallel model for risks such as equipment failure and transportation delays, addresses category imbalance issues through a weighted loss function, and deploys after training and verification of the split data set. The model outputs a risk index based on real-time data input, which automatically triggers remote maintenance or dynamic scheduling when the threshold is exceeded.

[0054] Beneficial effects: Multi-source data fusion and feature engineering improve the comprehensiveness of risk identification; multi-task models balance efficiency and accuracy, and shared features reduce redundant calculations; dynamic loss weights and verification mechanisms enhance early warning capabilities for rare risks; real-time response strategies minimize business interruption duration; closed-loop feedback continuously optimizes the model to adapt to complex working conditions; full-process automation reduces human error, ensures the safety and efficiency of bulk material loading, and meets the high real-time and high reliability industry requirements.

[0055] The above descriptions are merely some preferred embodiments of the present invention and illustrate the underlying technical principles. Those skilled in the art should understand that the scope of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A bulk material loading control system, characterized in that: include: Internal management platform, used to provide process control and management of intelligent bulk material loading order business using the internal platform; A logistics service platform, which uses an internet platform to provide registration, order entry, and management functions for customers, suppliers, and logistics companies, and is connected to the internal management platform; An offline management platform, used for monitoring and managing offline vehicles, goods, and personnel, and connected to the internal management platform; A data storage platform, used for storing system data and connected to the internal management platform and the logistics service platform; A remote maintenance platform for remotely maintaining and managing all relevant equipment in the system and proactively sending alarm messages upon detecting equipment failures. The remote maintenance platform is connected to the data storage platform, the internal management platform, the logistics service platform, and the offline management platform; The report generation module is used to automatically obtain multiple data types in the system, generate multiple reports according to the data type required by each report type in the multiple report types preset in the system, and intelligently generate data analysis reports for the reports.

2. A bulk material loading control system according to claim 1, characterized in that: The offline management platform includes: The access control module is used to determine whether the license plate number of the vehicle at the designated location matches the preset license plate number based on the vehicle information entered by the logistics service platform, and to verify the driver's identity based on the driver information entered by the logistics service platform. If the license plate number matches the preset license plate number and the driver's identity is verified, the LED screen prompts the vehicle to enter the factory, drives the barrier bar to rise, and the radar controller automatically lowers the barrier bar after detecting that the vehicle has passed. Otherwise, a failure prompt is issued; The cargo inspection module is used to connect with the sampling device after the vehicle enters the factory, automatically collect samples after the driver's identity is verified, and bind the samples to the vehicle information; The weight detection module is used to detect the vehicle position through an infrared scale monitor. When the vehicle reaches the designated position, the weight of the vehicle before and after loading and unloading is automatically detected by the weight detection device, and the weight detection result is automatically generated according to the input weight adjustment information; The intelligent information entry module is used to automatically fill out the weighing slip based on the weight test results, and automatically capture the vehicle inspection process video through the video surveillance system and store it in the system as evidence; The intercom module is used to provide factory communication, monitoring and alarm functions using full-duplex two-way video intercom equipment.

3. A bulk material loading control system according to claim 2, characterized in that: The intelligent information entry module includes: An information collection submodule is used to obtain vehicle information corresponding to an order from the order in the bulk material loading control system, obtain license plate information through a camera, and obtain the weight detection result through the weight detection module; The process verification submodule is used to use an infrared monitoring scale to monitor whether the vehicle is fully loaded onto the scale. If the vehicle is not fully stopped, a prompt will be issued and data entry will be prevented. The order verification submodule is used for order matching verification, using the license plate information and the vehicle information corresponding to the order to verify whether the current vehicle is associated with a valid order. If there is no order, weighing is prohibited; The input submodule is used to automatically capture the front and rear images of the vehicle when weighing and enter them into the system, obtain the weight detection results, automatically adjust the weight detection results according to the water and impurity deduction rules, and simultaneously generate the corresponding documents and enter them into the system; The exception handling submodule is used to display specific error information on the LED screen and switch to manual verification mode when an abnormal situation occurs.

4. A bulk material loading control system according to claim 1, characterized in that: The internal management platform includes: The business management module is used to input and manage business information and automatically settle accounts based on the progress of business completion; Logistics management module, used for vehicle registration and real-time tracking and scheduling of vehicle logistics information; System maintenance module, used to remotely maintain and manage all relevant equipment in the system, and proactively push alarm information when equipment fails; Access control management module, used for access control registration and management of business vehicle information and driver information, and setting queuing rules for business vehicle entry and exit; The hazardous chemicals management module is used to manage the documents and access of orders involving hazardous chemicals transportation, and to monitor the status of the entire logistics process; The quality inspection and testing module is used to automatically generate various statistical reports based on the entered quality inspection and testing orders, and to enter third-party quality inspection and testing orders for disputed quality inspection and testing orders.

5. A bulk material loading control system according to claim 1, characterized in that: The logistics service platform includes: The customer registration module is used to determine whether the number and name entered by the customer are the same as the number and name corresponding to the customer in the system when the customer submits the registration record, and grant the user permission after the judgment is passed; The permission allocation module is used to determine the customer registration type and assign different usage permissions based on the registration type; The order entry module is used to provide customers with order information entry functions and automatically generate vehicle dispatch orders based on customer orders; The dispatch query module is used for customers to query vehicle dispatch information according to corresponding permissions; The information query module is used for customers to query vehicle weighing, logistics information and order status according to corresponding permissions.

6. A bulk material loading control system according to claim 1, characterized in that: The data storage platform includes a data processing center, in which an internal server and an external server are arranged. The internal server is used to store the data of the internal management platform, and the external server is used to store the data of the logistics service platform. The data of the internal server and the external server are interconnected, and a firewall is arranged during the data communication between the internal server and the external server.

7. A bulk material loading control system according to claim 6, characterized in that: A plurality of sub-servers are arranged under the internal server, and the sub-servers are connected to the internal server, and each of the sub-servers is independent of each other.

8. The bulk material loading control system according to claim 1, characterized in that: The remote maintenance platform includes: A monitoring module for monitoring the working status of each part of the bulk material loading control system; An emergency processing module is used to activate a pre-set emergency processing plan for any part of the bulk material loading control system when it is detected that any part of the bulk material loading control system is working abnormally; An alarm module is used to notify the operation and maintenance personnel through system messages or intercom equipment when the bulk material loading control system operates abnormally; Maintenance module, used for remote configuration updates and fault repairs.

9. The bulk material loading control system according to claim 1, characterized in that: The report generation module comprises the following steps: Retrieve various types of data stored in the system through the system's internal interface; Classify the various types of data in the system into three categories: structured data, semi-structured data, and unstructured data according to their structured degree; Acquire the semi-structured data, and perform structured processing on the semi-structured data by using field screening; Acquire the unstructured data, and select an image recognition engine or a semantic recognition engine to perform structured processing on the unstructured data according to the specific data type of the unstructured data; Obtain all data after structured processing, delete duplicate data from all the data, detect missing data from all the data and complete them through rule inference, sort all the data according to timestamp information, and generate a database; Obtaining the data type required for each of the multiple report types preset in the system, matching the data type required for each report type in the database, and obtaining the corresponding data; generating a plurality of reports using the corresponding data of each report type, and generating an interactive chart corresponding to each report based on the plurality of reports; Analyze the data in each report based on its temporal and spatial order, locate problems through association rule mining, and make specific decisions based on the decision tree model; The issues and specific decisions for each report are determined as a data analysis report for that report and output.

10. The bulk material loading control system according to claim 1, characterized in that: The system also includes a risk prediction module for using historical data to train an AI model to predict multi-dimensional risks in the bulk material loading system, and triggering remote maintenance functions or adjusting scheduling strategies based on risk prediction results. The steps include: Use internal sensors to obtain device data, use historical logistics information and orders to obtain vehicle and cargo information, and obtain software information based on system logs, and integrate the above information into multi-dimensional historical data; Performing data cleaning on the multi-dimensional historical data to remove duplicate values ​​and abnormal values ​​in the multi-dimensional historical data; Performing feature engineering on the cleaned multi-dimensional historical data, constructing features for each data type in the multi-dimensional historical data, normalizing numerical features, and constructing an underlying feature extraction database; Designing parallel output branches for different risk types, wherein the parallel output branches share the underlying feature extraction database; Randomly splitting the underlying feature extraction database into a training set, a validation set, and a test set, and designing a loss function to give a higher weight to minority types; Using a machine training model to construct a risk prediction model based on the training set and the loss function, using the validation set to verify the effectiveness of the risk prediction model, and using the test set to test the performance of the risk prediction model; Use the generated risk prediction model to predict the risk of current real-time data and determine the current risk index; When any real-time data risk index exceeds a preset threshold, the remote maintenance function is triggered or the scheduling strategy is adjusted for the real-time data source according to the real-time data source.

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