Plateau railway new energy equipment comprehensive management and control system based on multi-mode perception

Abstract

The invention discloses a plateau railway new energy equipment comprehensive management and control system based on multi-modal perception, which is constructed based on a cloud side-end collaborative architecture and comprises an equipment perception layer, a cloud side-end collaborative architecture layer, a cloud side-end collaborative architecture layer, a cloud side-end collaborative architecture layer, a cloud side-end collaborative architecture layer, a cloud side-end collaborative architecture layer and a cloud side-end collaborative architecture layer, a mixed transmission mode is adopted, and a network transmission layer of LoRa wireless communication and 4G communication modes is configured; a unified data access standard is configured, and a data processing layer of a multi-dimension unified data system of multiple types of data is established; the platform application layer is used for carrying out full-process management and control on the plateau railway new energy equipment; the display layer is used for configuring a display large screen and a PC (Personal Computer) terminal for a user to check; according to the scheme, comprehensive digital and intelligent management of the plateau railway new energy equipment is realized, and the plateau railway construction efficiency and the green construction level are improved.

Description

Technical Field

[0001] This invention relates to the field of control technology for new energy equipment on plateau railways, and in particular to a comprehensive control system for new energy equipment on plateau railways based on multimodal perception. Background Technology

[0002] With the advancement of high-altitude railway construction, new energy construction equipment is gradually becoming a core force in high-altitude tunnel construction. The high-altitude environment is characterized by high altitude, low oxygen, low temperature, and large temperature differences, placing extremely high demands on the performance, reliability, and management of construction equipment. Traditional fuel-powered construction equipment suffers from reduced power, emissions pollution, and competition for oxygen with humans in high-altitude environments. While new energy equipment alleviates these problems to some extent, it still faces challenges such as insufficient range, charging difficulties, incomplete condition monitoring, and low data integration.

[0003] Currently, most high-altitude railway construction equipment management systems still rely on modifications based on traditional fuel-powered equipment management logic. These systems suffer from numerous shortcomings that are incompatible with technological advancements and actual needs, severely hindering improvements in construction efficiency and safety management in high-altitude environments. Specifically, existing systems exhibit the following technical deficiencies: First, the lack of unified standards and standardized interfaces for data access severely limits integration capabilities. New energy construction equipment from different manufacturers uses its own proprietary communication protocols and data formats. This heterogeneity prevents the direct integration and parsing of operational data and battery status information generated by various devices within a unified platform, creating serious "data silos." This not only necessitates customized development for different manufacturers for data integration, significantly increasing the complexity of system interoperability and the difficulty of future expansion, but also makes centralized monitoring and collaborative management of cross-brand and cross-model equipment groups extremely difficult.

[0004] Secondly, the system's monitoring dimensions are relatively limited, and its functional coverage has significant blind spots. Most existing management systems are still limited to monitoring the basic operating status of equipment, while key operating parameters, data on supporting energy facilities, and external condition parameters unique to the high-altitude construction environment are not systematically integrated and monitored. This makes it difficult to fully reflect the true state of the equipment-energy-environment system and to provide sufficient data support for comprehensive decision-making.

[0005] Multimodal perception technology mainly includes multimodal data alignment (ensuring spatiotemporal and semantic consistency of data from different sources), multimodal feature fusion (deeply integrating heterogeneous features using methods such as attention mechanisms and neural networks), and cross-modal understanding and reasoning (achieving information conversion and deep semantic understanding between modalities). In terms of condition monitoring and fault early warning, multimodal perception technology can achieve real-time health status monitoring of core systems of new energy equipment (such as power batteries, drive motors, and converters). Through the fusion analysis of multi-source data, the system can detect abnormal signs earlier and more accurately, enabling predictive maintenance, avoiding serious failures in harsh high-altitude environments, and greatly improving equipment availability and operational safety.

[0006] Furthermore, the level of intelligence in scheduling and operation and maintenance management is low, still heavily reliant on human experience. In the current system, decision-making processes such as energy replenishment scheduling, task allocation, and operation path planning for new energy equipment are mostly completed by dispatchers based on experience, lacking intelligent optimization and dynamic adjustment mechanisms based on multi-source data such as real-time electricity consumption, energy station load, and construction progress. Similarly, in terms of equipment maintenance, fault diagnosis and handling mainly rely on on-site personnel reporting and remote support from equipment manufacturers. The system itself lacks the ability to automatically identify, warn, and trace fault characteristics, and cannot achieve predictive maintenance.

[0007] Finally, existing systems fail to systematically integrate the unique environmental factors of high-altitude areas and lack quantitative assessment capabilities for environmental adaptability and economic efficiency. The low oxygen, low air pressure, and extreme temperature differences inherent in high-altitude environments significantly affect the capacity, lifespan, and charge / discharge performance of power batteries, and also exacerbate fatigue in mechanical components and stability issues in electrical systems. However, existing management systems have not constructed corresponding environmental adaptability evaluation models, making it impossible to predict and compensate for performance degradation of equipment under extreme conditions, nor to provide energy consumption and economic efficiency decision-making references for construction organization at different altitudes and in different seasons.

[0008] Therefore, there is an urgent need for a comprehensive management and control platform specifically designed for new energy equipment on plateau railways based on multimodal perception. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention provides a comprehensive management and control system for new energy equipment on plateau railways based on multimodal perception, in order to achieve comprehensive digital and intelligent management of new energy construction equipment and improve the efficiency and green construction level of plateau railway construction.

[0010] This invention is achieved using the following technical solution: A comprehensive management and control system for new energy equipment on plateau railways based on multimodal perception, constructed based on a cloud-edge-device collaborative architecture, specifically includes: Equipment sensing layer: Sensing devices are installed on new energy equipment to collect device and status data; Network transport layer: Employs a hybrid transmission method, configuring LoRa wireless communication and 4G communication; Data processing layer: Configure unified data access standards and establish a multi-dimensional unified data system for various types of data; Platform application layer: Configured with multiple functional modules to manage the entire process of new energy equipment for plateau railways; Presentation layer: Configures large screens and PCs for users to view.

[0011] Specifically, the cloud-edge-device collaborative architecture includes: a cloud platform deployed in the plateau railway data center, edge nodes deployed at the construction site for local data collection, protocol conversion and real-time control, and terminal sensing devices including battery management systems for new energy construction equipment, environmental sensors, positioning devices and energy station monitoring equipment.

[0012] Specifically, the equipment perception layer includes hardware devices and transmission interfaces responsible for data monitoring and control. The hardware devices are deployed at the construction site and configured to send services. The business scope includes engineering construction, environmental perception, equipment monitoring, vehicle positioning, and energy station monitoring. The collected data includes power battery codes, power battery SOC, equipment location, equipment operating status, and equipment cumulative working time data. The new energy construction equipment for which data needs to be collected includes: electric loaders, electric excavators, electric dump trucks, electric dust removal trolleys, electric rock drilling trolleys, electric wet spraying trolleys, and electric arch frame trolleys.

[0013] Specifically, the network transmission layer includes network transmission inside the tunnel and network transmission outside the tunnel, which respectively adopt LoRa wireless communication and 4G communication methods.

[0014] Specifically, the data processing layer defines multiple data interfaces including equipment status, battery parameters, energy station data, and environmental data, and a unified data access standard supports data access for new energy construction equipment from multiple manufacturers; the multi-dimensional unified data system specifically includes: equipment ledger data, battery status data, energy station data, environmental monitoring data, dispatch management data, and maintenance management data.

[0015] Specifically, the platform application layer also includes: real-time access to the operating status data of new energy equipment and the charging and swapping status data of integrated energy stations, providing real-time feedback on energy replenishment scheduling and maintenance status; and combining environmental perception and monitoring data within the tunnel to track and control the overall status of vehicles.

[0016] Specifically, the functional modules configured in the platform application layer include: Equipment monitoring module: Collects key parameter information of the battery system of construction equipment, monitors the operating status of green construction equipment in real time, and provides battery status alarm function; Energy station monitoring module: comprehensively monitors the real-time operation information and alarm information of energy storage batteries, and performs multi-faceted statistical analysis of energy storage information; Environmental monitoring module: Detects and manages information on toxic and harmful gases, methane concentration, temperature and humidity in the tunnel, and is also equipped with threshold alarm function; Dispatch and management module: Based on the minimum charging requirements for new energy equipment, it provides the optimal energy replenishment dispatch scheme for different types of construction equipment, such as charging and battery swapping. Maintenance and repair management module: Combines real-time monitoring data of vehicle operation status to present vehicle faults in three-dimensional visualization and highlight them with flashing prompts, and provides specific feedback on fault information of the vehicle power battery management system (BMS) control unit; Economic Analysis Module: Compares the costs of gasoline-powered vehicles and electric vehicles, providing economic decision support; Feature fusion module: performs deep fusion of text features and image features from the collected multi-source data.

[0017] Specifically, the feature fusion module is configured with a multimodal fusion algorithm, which includes: The Sigmoid function is used to normalize the modal features. For text modalities, the Sigmoid function is used to normalize the modal features in terms of dimension. Top Summing yields its dimension The dependence of features on image features, traversing the entire dimension. This yields the dependence of the text modality on the overall image modality; a larger dependence value indicates higher reliability of the text modality. The lower the value, the higher the confidence level of the image modality. The credibility calculation formula is expressed as: ; in, Represented as a metric function, where N is the modality dimension; Credibility and Linear normalization is performed, and the corresponding enhanced modal features are weighted and fused. The fused features F are then fed into a fully connected layer to perform fusion analysis on various modal data. The final fused modal features are represented as follows: ; in, For enhanced text modal features, This refers to the enhanced image modal features.

[0018] The beneficial effects of this invention are as follows: (1) This invention achieves unified access and integration of data from multiple manufacturers of new energy construction equipment, solving the problem of data silos and improving system scalability and maintenance efficiency. By constructing a standardized data access specification and communication protocol, this invention successfully solves the heterogeneity problem of data format and interface protocol at different manufacturers of new energy construction equipment. This not only greatly reduces the access development cost and cycle when adding new equipment models or brands, but also makes the system have good scalability, laying a solid foundation for the subsequent access of more types of intelligent equipment and sensing devices, while significantly reducing the complexity of system maintenance and manpower input.

[0019] (2) A comprehensive monitoring system has been constructed, covering multiple dimensions such as equipment status, energy station operation, and environmental parameters, achieving comprehensive perception of the plateau construction environment. This invention breaks through the limitations of traditional monitoring systems that only focus on the operating status of a single device, and constructs a multi-dimensional, three-dimensional monitoring network covering the real-time status of construction equipment, the operating status of energy facilities, and environmental parameters. Through the correlation analysis and fusion processing of multi-source data, the system can comprehensively and in real time perceive the overall status of the complex plateau construction environment, providing managers with an accurate and intuitive digital twin view, greatly improving the knowability and controllability of the overall situation.

[0020] (3) Through intelligent scheduling and maintenance management, the utilization rate and fault response efficiency of construction equipment are improved. This invention integrates a data-driven intelligent scheduling algorithm and a predictive maintenance model. In terms of scheduling, the system can automatically generate the optimal energy replenishment scheduling and task allocation scheme based on real-time power supply, urgency of work tasks, energy station load and path conditions, minimizing equipment idle waiting time, thereby significantly improving overall work efficiency and equipment utilization. In terms of maintenance, the system can identify potential fault characteristics in advance by continuously analyzing equipment operation data, realize early warning and accurate diagnosis of faults, and automatically generate maintenance suggestions or dispatch orders, changing "passive response" to "proactive maintenance", greatly shortening the average fault repair time, reducing the risk of unexpected downtime, and ensuring construction continuity and safety.

[0021] (4) It provides economic and adaptability analysis functions, providing data support for construction decisions. This invention uniquely integrates environmental adaptability evaluation and economic comprehensive analysis modules. The system can quantitatively analyze the impact of extreme environments such as low pressure, low temperature, and low oxygen on equipment endurance, energy consumption, and performance degradation, and establish predictive models. At the same time, by integrating full life-cycle data such as equipment energy consumption, maintenance costs, battery degradation, and carbon emissions, the system can conduct accurate comparisons of oil and electricity economics, calculate the return on investment and carbon emission reduction benefits, and provide scientific and intuitive data support and decision-making basis for construction units in equipment selection, resource allocation, cost control, and green construction level assessment.

[0022] (5) Adopting a cloud-edge-device collaborative architecture to adapt to complex network environments and ensure stable and reliable system operation. This invention adopts an advanced cloud-edge-device collaborative technology architecture, cleverly distributing computing power and storage resources among the cloud, edge nodes, and terminal devices. The cloud is responsible for massive data storage, complex model calculations, and macro-analysis; edge nodes are deployed near the construction site, responsible for real-time aggregation of local data, protocol conversion, intelligent inference, and real-time control, ensuring the continuity of critical business even in the event of intermittent network interruptions; terminal devices focus on data acquisition and execution. This architecture effectively overcomes challenges such as poor public network coverage, unstable bandwidth, and large transmission delays in plateau areas, ensuring the reliability of data acquisition, the efficiency of transmission, and the stability of system services, guaranteeing the continuous and reliable operation of the entire system in harsh environments. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0024] Figure 1 This is the overall architecture diagram of the integrated management and control system for new energy equipment on plateau railways based on multimodal perception, as described in this invention. Figure 2 This is a schematic diagram of the cloud-edge-device collaborative architecture in this embodiment; Figure 3 This is a status perception diagram of the new energy construction equipment in this embodiment; Figure 4 This is a schematic diagram of the network transmission of sensing devices for new energy construction equipment in this embodiment; Figure 5 This is a schematic diagram of the unified data protocol for new energy construction equipment in this embodiment; Figure 6 This is a functional architecture diagram of the integrated management and control system for new energy equipment on plateau railways based on multimodal perception in this embodiment. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0026] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0027] The following is in conjunction with the appendix Figures 1-6 The following describes some embodiments of the present invention in detail. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0028] This invention proposes a comprehensive management and control system for new energy equipment on plateau railways based on multimodal perception. It adopts a cloud-edge-device collaborative architecture. In a preferred embodiment, the management and control platform is divided into five layers from bottom to top: equipment perception layer, network transmission layer, data integration layer, platform application layer, and display layer, specifically: Cloud-edge-device collaborative architecture: such as Figure 2 As shown, the cloud platform is deployed in the data center, providing data storage, analysis, computing, and visualization services; edge nodes are deployed at the construction site, responsible for local data collection, protocol conversion, and real-time control; terminal sensing devices include battery management systems, environmental sensors, positioning devices, and energy station monitoring equipment for new energy construction equipment.

[0029] Equipment perception layer: This includes hardware devices and transmission interfaces responsible for data monitoring and control; necessary hardware devices and transmission services are deployed at construction sites, covering business scopes such as engineering construction, environmental perception, equipment monitoring, vehicle positioning, and energy station monitoring, to ensure real-time collection and transmission of on-site data.

[0030] Network transport layer: includes network transport inside the tunnel and network transport outside the tunnel.

[0031] Data processing layer: This includes building a unified access standard based on the status perception data of new energy construction equipment and establishing a unified data system that includes multiple dimensions such as equipment ledger, battery status, energy station operation, environmental monitoring, dispatch management, and maintenance management; Platform Application Layer: Business applications for the comprehensive management and control of new energy equipment are built based on the equipment perception layer, network transmission layer, and data integration layer, including functions such as equipment monitoring, energy station monitoring, environmental monitoring, dispatch management, maintenance management, and economic analysis. Presentation layer: Users access the system through devices such as large screens and PCs.

[0032] In this embodiment, the equipment perception layer acquires data such as power battery code, power battery SOC, equipment location, equipment operating status, and equipment cumulative working time by mounting state perception devices on new energy equipment. The new energy construction equipment for which data needs to be collected includes: electric loaders, electric excavators, electric dump trucks, electric dust removal trolleys, electric rock drilling trolleys, electric wet spraying trolleys, and electric arch frame trolleys.

[0033] The network transmission layer uses LoRa wireless communication technology inside the tunnel to achieve long-distance data transmission, and uses 4G communication based on operator base stations outside the tunnel to achieve connection with the remote integrated management and control platform.

[0034] The data processing layer defines multiple data interfaces, including equipment status, battery parameters, energy station data, and environmental data. A unified data access standard supports data access from multiple manufacturers' new energy construction equipment. A multi-dimensional unified data system is established, specifically including: Equipment ledger data: equipment number, model, manufacturer, entry and exit time, etc.; Battery status data: SOC, SOH, voltage, current, temperature, etc.; Energy station data includes: charging volume, battery swapping frequency, energy storage status, and photovoltaic power generation data. Environmental monitoring data: temperature and humidity, concentration of harmful gases, oxygen content, etc. Dispatch management data: dispatch instructions, execution status, vehicle location, etc.; Maintenance management data: fault codes, repair records, maintenance plans, etc.

[0035] The platform application layer specifically includes the following functional modules: Equipment monitoring module: Real-time monitoring of equipment battery status, working status, location information, etc.; Energy station monitoring module: Monitors the operating status of charging and battery swapping stations, the status of energy storage batteries, photovoltaic power generation data, etc. Environmental monitoring module: Monitors environmental parameters such as toxic and harmful gases, temperature, and humidity inside the tunnel; Dispatch Management Module: Intelligently generates dispatch schemes based on equipment power and energy station status, and supports closed-loop command management; Maintenance management module: Supports fault alarms, maintenance record management, maintenance plan management, etc. Economic Analysis Module: Compares the costs of gasoline-powered vehicles and electric vehicles, providing economic decision support; Equipment monitoring module: Collects key parameter information of the battery system of construction equipment, monitors the operating status of green construction equipment in real time, and provides battery status alarm function; Energy station monitoring module: comprehensively monitors the real-time operation information and alarm information of energy storage batteries, and performs multi-faceted statistical analysis of energy storage information; Environmental monitoring module: Detects and manages information on toxic and harmful gases, methane concentration, temperature and humidity in the tunnel, and is also equipped with threshold alarm function; Dispatch and management module: Based on the minimum charging requirements for new energy equipment, it provides the optimal energy replenishment dispatch scheme for different types of construction equipment, such as charging and battery swapping. Maintenance and repair management module: Combines real-time monitoring data of vehicle operation status to present vehicle faults in three-dimensional visualization and highlight them with flashing prompts, and provides specific feedback on fault information of the vehicle power battery management system (BMS) control unit; Economic Analysis Module: Compares the costs of gasoline-powered vehicles and electric vehicles, providing economic decision support; Feature fusion module: performs deep fusion of text features and image features from the collected multi-source data.

[0036] In this embodiment, due to the different representational differences between text features and image features, directly performing adaptive linear fusion on features from different modalities often leads to the model over-reliance on a particular modality, lacking attention to the contribution of each modality. This problem can be specifically expressed in the formula as: if The larger the value, the more characteristic the query features. More dependent on key features This indicates that the reliability of the query features is low. Meanwhile, the attention coefficient... The softmax operation leads to complementary features. The images are compressed, making it impossible to fully capture complementary information from the modalities. To address this issue, this invention proposes a credibility-based attention fusion module method for deep fusion of image and text modalities in the feature fusion module. The specific formula is as follows: ; in, Represented as a metric function, where N is the modality dimension; in the fusion module, this invention uses the Sigmoid function to normalize the modality features. The Sigmoid function can normalize a single response value. For text modalities, in terms of dimension... Top Summing can yield its dimension. The dependence of features on image features when traversing the entire dimension. This allows us to determine the dependence of the text modality on the overall image modality. A higher dependence value indicates greater reliability of the text modality. The lower the value, the better. Similarly, the confidence level of the image modality can be obtained. .

[0037] The final fusion modal features are described as follows: ; In the formula, For enhanced text modal features, For the enhanced image modal features, the above formula can be expressed as follows: first, adjust the confidence level... and Linear normalization is performed, followed by weighted fusion of the enhanced modal features. Higher modal confidence results in higher feature fusion coefficients. The fused features F are then fed into a fully connected layer for fusion analysis of various modal data.

[0038] The integrated management and control system proposed in this solution transforms from a "general data processing architecture" to a "specific scenario problem-solving architecture." It proposes a five-layer architecture optimized for the "high-altitude tunnel new energy equipment management and control" scenario. The core objective is to resolve three special contradictions: unreliable communication, heterogeneous equipment, and energy efficiency management. It focuses on the semantic alignment and business integration of multi-dimensional data in "equipment-energy-environment-economy," aiming to support high-end intelligent applications such as scheduling, evaluation, and analysis, and elevate the system from the "data management" level to the "business intelligence" level. Furthermore, based on the objective condition that tunnels lack public networks, it proposes a "LoRa+4G" hybrid network communication solution. Edge nodes undertake protocol conversion, real-time control, and local autonomy during network interruptions, forming a robust control mode of "normal collaboration and autonomous operation during downtime" with the cloud.

[0039] In one specific embodiment, such as Figure 1 As shown, the control platform (i.e., the integrated control system for new energy equipment on plateau railways based on multimodal perception proposed in this invention) is divided into an equipment perception layer, a network transmission layer, a data integration layer, a platform application layer, and a display layer, wherein: Device sensing layer, such as Figure 3 As shown, integrated status sensing devices are installed and deployed on new energy construction equipment (dump trucks, excavators, loaders, wet spraying trolleys, arch frame trolleys, dust removal trolleys, rock drilling trolleys, etc.) to collect construction equipment data. The integrated status sensing devices connect to the communication interface of the new energy construction equipment and collect data via the CAN protocol. Data collection types include battery number, remaining battery power, equipment location, cumulative working time, and operating status. The transmission intervals for different data types must meet requirements. The integrated status sensing devices installed on the new energy construction equipment measure and collect key vehicle operating parameters in real time. This data is processed and analyzed by the integrated status sensing devices to form vehicle operating status information. The information sensing types to be acquired by the integrated status sensing devices cover equipment status information and equipment location information. Information sensing types include battery number, remaining battery power, equipment location, cumulative working time, and operating status. Each information sensing type includes at least one or more information contents. Different collection intervals are set for different information contents, as shown in Table 1 below. Table 1. Data Collection Interval and Information Content The implementation of stable data transmission in the multimodal sensing layer is as follows: Figure 4As shown, this design adapts to the complex communication environment of high-altitude regions. LoRa communication modules are deployed at specific locations within the tunnel, enabling wireless transmission of data from the construction equipment via integrated status sensing devices. Simultaneously, to connect with a remote integrated management platform, a 4G communication method based on carrier base stations was chosen to meet the long-distance data transmission requirements outside the tunnel.

[0040] To address the issue of inconsistent data formats and protocols among various manufacturers of new energy construction equipment, the data processing layer constructs a unified access standard based on the status perception data of new energy construction equipment. This standardizes various protocol data, including industrial and controller protocols such as CANOpen, ModBusTCP, EtherCAT, Profinet, and J1939, and network protocols such as HTTP, MQTT, TCP / IP, and RTSP. Figure 5 As shown. Simultaneously, the format and content of the construction work logs, real-time status data, and electrical real-time data for each type of equipment were standardized.

[0041] The platform application layer of the integrated management and control system for new energy equipment on plateau railways based on multimodal perception is as follows: Figure 6 As shown, by real-time access to the operational status data of new energy equipment such as excavators and loaders, and the charging and swapping status data of integrated energy stations, the system provides construction units with real-time feedback services on energy replenishment scheduling and maintenance status. Simultaneously, by combining environmental perception and monitoring data within the tunnel, the system tracks and controls the overall status of vehicles. Comprehensive management and control are implemented from aspects such as simulation scenario display, equipment ledger management, construction equipment monitoring, energy station monitoring, energy replenishment scheduling management, maintenance management, environmental monitoring, technical management, battery status assessment, engineering adaptability evaluation, and economic cost analysis of oil and electricity.

[0042] The equipment monitoring module collects key battery system parameters such as battery SOC, battery SOH, total battery system current, and total battery system voltage to reflect the real-time operating status of green construction equipment and provide battery status alarm functions. The energy station monitoring module comprehensively monitors the real-time operation and alarm information of the energy storage battery, and performs multi-faceted statistics and analysis on the energy storage; it displays key information such as the current dischargeable amount, chargeable amount, maximum discharge power, current discharge power, dischargeable time, total charge amount today, and total discharge amount today in real time, and realizes a unified display of information such as the number of charging and swapping batteries, the status of backup batteries, and the status of charging piles; The scheduling and management module combines the construction party's minimum charging requirements for new energy equipment and provides the optimal energy replenishment scheduling scheme for different types of construction equipment, such as charging and battery swapping. The environmental monitoring module provides unified detection and management of information such as toxic and harmful gases, methane concentration, temperature, and humidity within the tunnel, and offers threshold alarm functions to ensure the safety of construction personnel inside the tunnel. Simultaneously, it monitors the carbon emissions of new energy equipment such as excavation and loading / transportation equipment. Through comprehensive observation, combined with numerical simulation and statistical analysis, it obtains information on the intensity of gas emissions within the tunnel, environmental concentrations, and the status and trends of carbon sources and sinks in the ecosystem, constructing a carbon emission database for analysis and management of carbon emission indicators. The maintenance management module combines real-time monitoring data of vehicle operation status to present vehicle faults in a 3D visualization. The fault location is visually displayed on the vehicle's 3D model and highlighted with a flashing indicator. Simultaneously, it provides specific feedback on fault information from the vehicle's Battery Management System (BMS) control unit, including fault location, fault type, and fault code, forming a fault diagnosis to assist maintenance personnel in troubleshooting. Furthermore, completed repairs generate a fault repair record sheet, allowing for review of past faults and repair records.

[0043] For the foregoing embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0044] The above embodiments describe the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Modifications and variations made by those skilled in the art without departing from the spirit and scope of the invention should be within the protection scope of the appended claims.

Claims

1. A comprehensive management and control system for new energy equipment on plateau railways based on multimodal perception, characterized in that, Built on a cloud-edge-device collaborative architecture, specifically including: Equipment sensing layer: Sensing devices are installed on new energy equipment to collect device and status data; Network transport layer: Employs a hybrid transmission method, configuring LoRa wireless communication and 4G communication; Data processing layer: Configure unified data access standards and establish a multi-dimensional unified data system for various types of data; Platform application layer: Configured with multiple functional modules to manage the entire process of new energy equipment for plateau railways; Presentation layer: Configures large screens and PCs for users to view.

2. The integrated management and control system for new energy equipment on plateau railways based on multimodal perception as described in claim 1, characterized in that, The cloud-edge-device collaborative architecture specifically includes: a cloud platform deployed in the plateau railway data center, edge nodes deployed at the construction site for local data collection, protocol conversion and real-time control, and terminal sensing devices including battery management systems for new energy construction equipment, environmental sensors, positioning devices and energy station monitoring equipment.

3. The integrated management and control system for new energy equipment on plateau railways based on multimodal perception as described in claim 1, characterized in that, The equipment perception layer includes hardware devices and transmission interfaces responsible for data monitoring and control. The hardware devices are deployed at the construction site and configured to send services. The business scope includes engineering construction, environmental perception, equipment monitoring, vehicle positioning, and energy station monitoring. The collected data includes power battery codes, power battery SOC, equipment location, equipment operating status, and equipment cumulative working time data. The new energy construction equipment for which data needs to be collected includes: electric loaders, electric excavators, electric dump trucks, electric dust removal trolleys, electric rock drilling trolleys, electric wet spraying trolleys, and electric arch frame trolleys.

4. The integrated management and control system for new energy equipment on plateau railways based on multimodal perception as described in claim 1, characterized in that, The network transmission layer specifically includes network transmission inside the tunnel and network transmission outside the tunnel, which respectively adopt LoRa wireless communication and 4G communication methods.

5. The integrated management and control system for new energy equipment on plateau railways based on multimodal perception as described in claim 1, characterized in that, The data processing layer defines multiple data interfaces, including equipment status, battery parameters, energy station data, and environmental data. A unified data access standard supports data access for new energy construction equipment from multiple manufacturers. The multi-dimensional unified data system specifically includes: equipment ledger data, battery status data, energy station data, environmental monitoring data, dispatch management data, and maintenance management data.

6. The integrated management and control system for new energy equipment on plateau railways based on multimodal perception as described in claim 1, characterized in that, The platform application layer also includes: real-time access to the operating status data of new energy equipment and the charging and swapping status data of integrated energy stations, providing real-time feedback on energy replenishment scheduling and maintenance status; and combining environmental perception and monitoring data within the tunnel to track and control the overall status of vehicles.

7. A comprehensive management and control system for new energy equipment on plateau railways based on multimodal perception as described in claim 6, characterized in that, The functional modules configured in the platform application layer specifically include: Equipment monitoring module: Collects key parameter information of the battery system of construction equipment, monitors the operating status of green construction equipment in real time, and provides battery status alarm function; Energy station monitoring module: comprehensively monitors the real-time operation information and alarm information of energy storage batteries, and performs multi-faceted statistical analysis of energy storage information; Environmental monitoring module: Detects and manages information on toxic and harmful gases, methane concentration, temperature and humidity in the tunnel, and is also equipped with threshold alarm function; Dispatch and management module: Based on the minimum charging requirements for new energy equipment, it provides the optimal energy replenishment dispatch scheme for different types of construction equipment, such as charging and battery swapping. Maintenance and repair management module: Combines real-time monitoring data of vehicle operation status to present vehicle faults in three-dimensional visualization and highlight them with flashing prompts, and provides specific feedback on fault information of the vehicle power battery management system (BMS) control unit; Economic Analysis Module: Compares the costs of gasoline-powered vehicles and electric vehicles, providing economic decision support; Feature fusion module: performs deep fusion of text features and image features from the collected multi-source data.

8. The integrated management and control system for new energy equipment on plateau railways based on multimodal perception as described in claim 7, characterized in that, The feature fusion module is configured with a multimodal fusion algorithm, specifically including: The Sigmoid function is used to normalize the modal features. For text modalities, the Sigmoid function is used to normalize the modal features in terms of dimension. Top Summing yields its dimension The dependence of features on image features, traversing the entire dimension. This yields the dependence of the text modality on the overall image modality; a larger dependence value indicates higher reliability of the text modality. The lower the value, the higher the confidence level of the image modality. The credibility calculation formula is expressed as: ； in, Represented as a metric function, where N is the modality dimension; Credibility and Linear normalization is performed, and the corresponding enhanced modal features are weighted and fused. The fused features F are then fed into a fully connected layer to perform fusion analysis on various modal data. The final fused modal features are represented as follows: ； in, For enhanced text modal features, This refers to the enhanced image modal features.

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