Belt weigher centralized monitoring system and method and related equipment

Abstract

The invention discloses a belt weigher centralized monitoring system, a belt weigher centralized monitoring method and related equipment, relates to the field of belt weigher supervision, and mainly aims to solve the problem that a more intelligent and convenient supervision method for a belt weigher is lacked in production at present. The system comprises a data acquisition layer which adopts a sensor array deployed in a distributed manner, and the sensors are deployed on a belt weigher; the 5G transmission layer is used for establishing a multi-link redundant transmission channel based on a dynamic spectrum allocation mechanism; the cloud platform processing layer comprises a space-time correlation analysis model; and the application service layer comprises a human-computer interaction interface. The method is used for the centralized monitoring process of the belt weigher.

Description

Technical Field

[0001] The present invention relates to the field of belt scale supervision, and in particular to a belt scale centralized monitoring system, method and related equipment. Background Art

[0002] In modern industrial production, belt scales serve as the core metering equipment in continuous material conveying systems. Accurate monitoring of their operating status directly impacts production efficiency and product quality. Traditional belt scale monitoring systems primarily utilize stand-alone sensors and PLC control architectures, which present significant technical limitations. The numerous and widely distributed locations of belt scales hinder preventative maintenance and management. Distributed belt scales lack a unified monitoring system, making it difficult to monitor and acquire data in a timely manner, identifying abnormalities and taking timely action. Furthermore, maintenance personnel must disperse to various locations for inspection and maintenance, increasing both the complexity and cost of the work. Currently, there is a lack of a more intelligent and convenient method for monitoring belt scales in production. Summary of the Invention

[0003] In view of the above problems, the present invention provides a belt scale centralized monitoring system, method and related equipment, the main purpose of which is to solve the problem of the lack of a more intelligent and convenient supervision method for belt scales in production.

[0004] To solve at least one of the above technical problems, in a first aspect, the present invention provides a belt scale centralized monitoring system, the system comprising:

[0005] The data acquisition layer uses a distributed sensor array, wherein the sensors are deployed on belt scales;

[0006] The 5G transport layer establishes multi-link redundant transmission channels based on a dynamic spectrum allocation mechanism;

[0007] Cloud platform processing layer, including spatiotemporal correlation analysis models;

[0008] Application service layer, including human-computer interaction interface.

[0009] Optionally, the data acquisition layer includes: a three-axis acceleration sensor, a strain gauge weighing sensor and an infrared temperature sensor,

[0010] The distributed sensor array adopts the IEC 61588 clock synchronization protocol.

[0011] Optionally, the 5G transport layer includes:

[0012] A priority queue management module based on network slicing and a lightweight protocol conversion module based on edge computing gateway.

[0013] Optionally, the cloud platform processing layer includes:

[0014] LSTM-based time series prediction model, CNN-based spatial pattern recognition module, and knowledge graph-based equipment fault inference engine.

[0015] Optionally, the application service layer includes:

[0016] Three-dimensional visual monitoring module, dynamic scheduling algorithm and intelligent report generation module.

[0017] In a second aspect, the present invention provides a belt scale centralized monitoring method for the above-mentioned belt scale centralized monitoring system, the method comprising:

[0018] Obtaining basic parameters of the belt scale, wherein the basic parameters include: load parameters, motion parameters and environmental parameters;

[0019] Performing data fusion processing on the basic parameters to determine health parameters of the belt scale;

[0020] A belt scale dispatch instruction is generated based on the health parameter.

[0021] Optionally, the above method further includes:

[0022] Monitoring material weight data and material flow data based on the basic parameters of the belt scale;

[0023] Determining a material blockage backlog parameter based on the material weight data and the material flow data;

[0024] A belt scale dispatch instruction is generated based on the material blockage backlog parameter.

[0025] In a third aspect, an embodiment of the present invention further provides a belt scale centralized monitoring device, comprising:

[0026] An acquisition unit, configured to acquire basic parameters of the belt scale, wherein the basic parameters include load parameters, motion parameters, and environmental parameters;

[0027] a determination unit, configured to perform data fusion processing on the basic parameters to determine health parameters of the belt scale;

[0028] A generating unit is used to generate a belt scale scheduling instruction based on the health parameter.

[0029] In order to achieve the above object, according to the third aspect of the present invention, a computer-readable storage medium is provided, which includes a stored program, wherein when the above program is executed by a processor, the steps of the above belt scale centralized monitoring method are implemented.

[0030] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned belt scale centralized monitoring method.

[0031] Through the above-mentioned technical solution, the present invention provides a centralized belt scale monitoring system, method, and related equipment. This system addresses the current lack of a more intelligent and convenient monitoring method for belt scales in production. The data acquisition layer of the present invention utilizes a distributed sensor array, wherein the sensors are deployed on the belt scale. The 5G transmission layer establishes multi-link redundant transmission channels based on a dynamic spectrum allocation mechanism. The cloud platform processing layer includes a spatiotemporal correlation analysis model. The application service layer includes a human-computer interaction interface. In this solution, a layered architecture design achieves decoupling and collaborative optimization of system functions. The data acquisition layer utilizes a distributed deployment and combines high-precision sensor technology to ensure the integrity and real-time nature of raw data acquisition. The 5G transmission layer effectively reduces cloud-side load. The cloud platform processing layer uses a spatiotemporal correlation model to perform multi-dimensional coupled analysis of the belt scale's instantaneous flow data and the physical properties of the material, significantly improving the accuracy of anomaly detection. The application service layer implements visual mapping of equipment status and predictive maintenance. Standardized interfaces are used at each layer to achieve seamless data flow, forming a closed-loop control chain from perception to decision-making, resolving the technical challenges of data silos and delayed response in traditional monitoring systems.

[0032] Correspondingly, the belt scale centralized monitoring device, equipment and computer-readable storage medium provided by the embodiments of the present invention also have the above-mentioned technical effects.

[0033] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0035] Figure 1 A schematic block diagram showing the composition of a belt scale centralized monitoring system provided by an embodiment of the present invention

[0036] Figure 2 A schematic diagram of a process for centralized monitoring of a belt scale provided by an embodiment of the present invention is shown;

[0037] Figure 3 A schematic block diagram of the composition of a belt scale centralized monitoring device provided by an embodiment of the present invention is shown;

[0038] Figure 4 A schematic block diagram of the composition of a belt scale centralized monitoring electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary 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 limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0040] In order to solve the problem of lack of a more intelligent and convenient supervision method for belt scales in production, the embodiment of the present invention provides a belt scale centralized monitoring device, such as Figure 1 As shown, the system includes:

[0041] The data acquisition layer uses a distributed sensor array, where the sensors are deployed on belt scales; the 5G transmission layer establishes a multi-link redundant transmission channel based on a dynamic spectrum allocation mechanism; the cloud platform processing layer includes a spatiotemporal correlation analysis model; and the application service layer includes a human-computer interaction interface.

[0042] For example, the data acquisition layer collects the load parameters, motion parameters and environmental parameters of the belt scale in real time through a distributed sensor array; the 5G transmission layer adopts a dynamic spectrum allocation mechanism to establish a multi-link redundant transmission channel to achieve data transmission with industrial-grade QoS guarantee; the cloud platform processing layer constructs a spatiotemporal correlation analysis model and combines the equipment health assessment algorithm to perform data fusion processing; the application service layer provides a visual monitoring interface and adaptive decision support system based on digital twins; two-way data interaction is achieved between each layer through a standardized API interface to form a closed-loop control architecture.

[0043] Specifically, 5G communication technology leverages 5G's high speed and low latency to enable real-time data transmission and remote monitoring. IoT technology connects belt scales to cloud platforms, enabling intelligent equipment management. Big data analytics deeply mines and analyzes collected data, identifying potential issues and providing early warnings. Cloud computing technology provides powerful data processing and storage capabilities, supporting the stable operation and expansion of the platform.

[0044] This application uses sensors installed on the belt scale to collect parameters such as weight, speed, and temperature in real time; the 5G transmission layer uses the 5G network to achieve fast and stable data transmission; the cloud platform processing layer processes, analyzes and stores the received data; and the application service layer provides users with real-time monitoring, historical query, data analysis, fault warning and other functions.

[0045] Based on the above scheme, this application realizes the decoupling and collaborative optimization of system functions through layered architecture design. The data acquisition layer adopts distributed deployment, combined with high-precision sensor technology to ensure the integrity and real-time nature of raw data collection; the 5G transmission layer uses dynamic spectrum management technology to achieve a 10Gbps transmission rate in the 28GHz millimeter wave band, and cooperates with edge computing nodes to complete data cleaning and compression, effectively reducing the cloud load; the cloud platform processing layer uses a spatiotemporal correlation model to conduct multi-dimensional coupling analysis of the instantaneous flow data of the belt scale and the physical properties of the material, significantly improving the accuracy of anomaly detection; the digital twin technology of the application service layer builds a virtual mirror system to realize visual mapping and predictive maintenance of equipment status. Each layer realizes seamless data flow through standardized interfaces, forming a full-chain closed-loop control from perception to decision-making, solving the technical problems of data islands and response lags in traditional monitoring systems.

[0046] In one embodiment, the data acquisition layer includes: a three-axis acceleration sensor, a strain gauge weighing sensor, and an infrared temperature sensor.

[0047] The distributed sensor array adopts the IEC 61588 clock synchronization protocol.

[0048] Exemplarily, the data acquisition layer integrates a three-axis acceleration sensor, a strain gauge weighing sensor, and an infrared temperature sensor; the sensor array uses the IEC 61588 clock synchronization protocol to achieve microsecond-level time synchronization; and a MEMS vibration sensor is deployed for non-contact bearing condition monitoring.

[0049] Based on the above solution, the integrated design of a multimodal sensor array enables multi-dimensional perception of the belt scale's operating status. A triaxial accelerometer captures the time-frequency characteristics of belt vibration. Combined with the millivolt-level signal conditioning circuit of a strain-gauge load cell, this eliminates the interference of mechanical vibration on weighing accuracy. An infrared temperature sensor monitors temperature gradients in key areas of the scale in real time, employing Planck's blackbody radiation law for thermodynamic compensation. A MEMS vibration sensor detects weak vibration signals generated by early bearing wear through the piezoelectric effect and uses wavelet transforms to extract fault characteristic frequencies. The IEC61588 clock synchronization accuracy between sensors reaches ±1μs, ensuring the temporal and spatial consistency of multi-source data and providing a high-quality data foundation for subsequent analysis.

[0050] In one embodiment, the 5G transport layer includes:

[0051] A priority queue management module based on network slicing and a lightweight protocol conversion module based on edge computing gateway.

[0052] Based on the above solution, network slicing technology enables differentiated traffic carrying. By reconfiguring the user plane function (UPF) to create dedicated slice channels for real-time control signaling, the end-to-end latency of key instructions is guaranteed to be less than 5ms. The edge computing gateway deploys an MQTT-SN protocol conversion module to convert the OPC UA protocol into a lightweight protocol suitable for industrial sites, reducing communication overhead. This design effectively improves data transmission reliability in complex electromagnetic environments.

[0053] In one embodiment, the cloud platform processing layer includes:

[0054] LSTM-based time series prediction model, CNN-based spatial pattern recognition module, and knowledge graph-based equipment fault inference engine.

[0055] Based on the above solution, the integrated application of deep learning models enhances the intelligent level of anomaly detection. The LSTM neural network captures the long-term dependencies of time series data through a gating mechanism, enabling ultra-short-term predictions of belt scale load fluctuations and controlling the mean absolute percentage error. The CNN convolutional network extracts spatial features from infrared thermal imaging, identifying morphological features of localized overheated areas on the scale body and improving defect classification accuracy. The knowledge graph integrates equipment manuals, maintenance records, and industry knowledge to construct a fault causal inference model, significantly reducing the time required to locate the root cause of a fault. These three elements work together to form a multi-layered perception system, achieving a transition from data representation to mechanism understanding.

[0056] In one embodiment, the application service layer includes:

[0057] Three-dimensional visual monitoring module, dynamic scheduling algorithm and intelligent report generation module.

[0058] Exemplarily, the application service layer includes: a three-dimensional visualization monitoring module based on digital twins; a dynamic scheduling algorithm based on reinforcement learning; and an intelligent report generation module based on natural language processing.

[0059] Based on the above solution, digital twin technology achieves a virtual-real mapping of physical space. A three-dimensional geometric model of the belt scale is constructed using the Unity3D engine, overlaid with real-time operating data to form a digital twin, supporting dynamic multi-view display. A reinforcement learning algorithm, based on the Markov decision process, optimizes the load balancing strategy for multiple belt scales, improving overall system efficiency while ensuring metering accuracy. A BERT pre-trained model parses device logs to generate technical reports, accurately extracting fault codes and maintenance recommendations, and achieving more efficient report generation. This layer establishes an intelligent decision-making hub for human-machine collaboration, significantly improving the digitalization of operations and maintenance management.

[0060] This system leverages the high speed and low latency of 5G to enable real-time transmission and processing of belt scale data, ensuring the timeliness and accuracy of measurement data. A unified monitoring system enables centralized management of dispersed belt scales, facilitating monitoring and maintenance while reducing manual intervention and costs. The intelligent monitoring platform monitors the operating status of belt scales in real time, promptly identifying anomalies and automatically responding to them, improving the efficiency and timeliness of troubleshooting. Through data analysis and predictive models, the platform proactively identifies potential failures and conducts preventive maintenance, reducing equipment downtime and maintenance costs. 5G technology supports large-scale device connectivity and can handle the simultaneous monitoring needs of belt scales, solving the networking and management challenges of multi-location equipment. Automated and intelligent management reduces reliance on manual monitoring and maintenance, lowering labor costs and improving management efficiency.

[0061] The embodiment of the present invention also provides a belt scale centralized monitoring method, which is used in the above belt scale centralized monitoring system. Figure 2 As shown, the method includes:

[0062] S101. Obtain basic parameters of a belt scale, wherein the basic parameters include load parameters, motion parameters, and environmental parameters;

[0063] S102, performing data fusion processing on the basic parameters to determine health parameters of the belt scale;

[0064] S103: Generate a belt scale dispatch instruction based on the health parameter.

[0065] For example, this application proposes a health assessment method based on multi-source data fusion, which uses the Kalman filter algorithm to perform spatiotemporal registration of load parameters, motion parameters, and environmental parameters. Principal component analysis (PCA) dimensionality reduction technology extracts feature information and eliminates redundant data interference.

[0066] Health parameter generation utilizes fuzzy logic to map sensor data into a health score on a scale of 0-100. Score thresholds are set based on the statistical characteristics of the Weibull distribution. Material blockage monitoring is achieved through image processing technology. The Canny edge detection algorithm identifies the hopper contour, combined with the Hough transform to detect the material level curve, significantly improving the accuracy of blockage determination.

[0067] The dynamic scheduling algorithm, based on genetic algorithm optimization, generates the optimal load distribution plan through a roulette wheel selection mechanism, improving the collaborative efficiency of multiple belt scales. This method achieves a paradigm shift from experience-driven to data-driven operations and maintenance through the deep integration of data-driven and mechanism-based models.

[0068] This solution improves management efficiency, enables remote, real-time monitoring and intelligent scheduling of belt scales, and significantly reduces manual monitoring costs. It also improves data accuracy by ensuring the accuracy and reliability of belt scale measurement data through real-time data collection and analysis. It also enhances fault response capabilities, enabling timely detection and resolution of belt scale failures to avoid production interruptions and losses. It also optimizes resource allocation by dynamically adjusting the belt scale's operating status based on production needs, improving resource utilization efficiency.

[0069] In one embodiment, the method further includes:

[0070] Monitoring material weight data and material flow data based on the basic parameters of the belt scale;

[0071] Determining a material blockage backlog parameter based on the material weight data and the material flow data;

[0072] A belt scale dispatch instruction is generated based on the material blockage backlog parameter.

[0073] For example, the multimodal congestion monitoring solution proposed above improves detection reliability through the collaborative perception of microwave radar and visual sensors. The 24GHz microwave radar adopts the FMCW continuous wave system and realizes non-contact detection of material level through phase difference measurement, with a distance resolution of 5mm. The visual sensor is equipped with a global shutter CMOS image sensor with a frame rate of up to 120fps, and combines it with the ViBe background modeling algorithm to realize real-time monitoring of material flow status. Multimodal data fusion adopts DS evidence theory to weight the credibility of radar distance information and image texture features, and the conflict coefficient of the fusion result is reduced. The congestion judgment rule base is built based on the expert experience base and contains several characteristic patterns of typical congestion scenarios. This mechanism improves the accuracy of congestion identification and reduces the false alarm rate compared to the single sensor solution, effectively avoiding production capacity loss caused by congestion.

[0074] As you can understand, the belt scale monitoring platform described above can monitor and record material weight and flow in real time. Through data analysis and report generation, problems such as material blockages and backlogs can be promptly identified and resolved. Automatic data collection, transmission, and recording reduce the need for manual operations. This not only saves labor costs but also avoids problems caused by human error and operational errors. The belt scale monitoring platform accurately monitors material weight and flow, reducing material waste and loss. By analyzing belt speed, instantaneous flow, and alarm information, it provides a multi-dimensional assessment of belt scale operating status and promptly identifies equipment operating trends.

[0075] Leveraging these technical solutions, a comprehensive intelligent monitoring system covering the entire "perception-transmission-decision-execution" chain has been constructed through innovative approaches such as a four-layer architecture, multimodal perception, 5G transmission enhancements, deep learning analysis, intelligent decision support, and multimodal congestion monitoring. Its core technical indicators are significantly improved over traditional solutions: fault response time is significantly shortened, equipment utilization is increased, and operational and maintenance costs are effectively reduced.

[0076] Furthermore, as a response to the above Figure 2 In order to realize the method shown in the figure, the embodiment of the present invention also provides a belt scale centralized monitoring device for the above Figure 2 This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not describe the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can implement all the contents of the aforementioned method embodiment. Figure 3 As shown, the device includes: a first acquisition unit 21, a determination unit 22, a second acquisition unit 23 and a generation unit 24, wherein

[0077] An acquisition unit 21 is configured to acquire basic parameters of the belt scale, wherein the basic parameters include load parameters, motion parameters, and environmental parameters;

[0078] A determination unit 22, configured to perform data fusion processing on the basic parameters to determine the health parameters of the belt scale;

[0079] The generating unit 23 is configured to generate a belt scale dispatching instruction based on the health parameter.

[0080] The processor contains a core, which retrieves the corresponding program unit from the memory. One or more cores can be configured, and by adjusting the core parameters, a centralized belt scale monitoring method can be implemented, addressing the current lack of a more intelligent and convenient monitoring method for belt scales in production.

[0081] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is executed by a processor, the centralized monitoring method for belt scales is implemented.

[0082] An embodiment of the present invention provides a processor, which is used to run a program, wherein the belt scale centralized monitoring method is executed when the program is run.

[0083] An embodiment of the present invention provides an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to call program instructions in the memory to execute the above-mentioned belt scale centralized monitoring method.

[0084] An embodiment of the present invention provides an electronic device 30, such as Figure 4 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned belt scale centralized monitoring method.

[0085] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0086] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program that initializes the steps of the above-mentioned belt scale centralized monitoring method.

[0087] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0088] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0089] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0092] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 2 This corresponds to the flow of memory control in the embodiment.

[0093] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0094] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0096] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0097] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0099] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A belt scale centralized monitoring system, characterized in that: include: The data acquisition layer uses a distributed sensor array, wherein the sensors are deployed on belt scales; The 5G transport layer establishes multi-link redundant transmission channels based on a dynamic spectrum allocation mechanism; Cloud platform processing layer, including spatiotemporal correlation analysis models; Application service layer, including human-computer interaction interface.

2. The system according to claim 1, wherein: The data acquisition layer includes: a three-axis acceleration sensor, a strain gauge weighing sensor and an infrared temperature sensor. The distributed sensor array adopts the IEC 61588 clock synchronization protocol.

3. The system according to claim 1, wherein: The 5G transport layer includes: A priority queue management module based on network slicing and a lightweight protocol conversion module based on edge computing gateway.

4. The system according to claim 1, wherein: The cloud platform processing layer includes: LSTM-based time series prediction model, CNN-based spatial pattern recognition module, and knowledge graph-based equipment fault inference engine.

5. The system according to claim 1, wherein: The application service layer includes: Three-dimensional visual monitoring module, dynamic scheduling algorithm and intelligent report generation module.

6. A belt scale centralized monitoring method, used in the belt scale centralized monitoring system according to any one of claims 1 to 5, characterized in that: Also includes: Obtaining basic parameters of the belt scale, wherein the basic parameters include: load parameters, motion parameters and environmental parameters; Performing data fusion processing on the basic parameters to determine health parameters of the belt scale; A belt scale dispatch instruction is generated based on the health parameter.

7. The method according to claim 6, characterized in that Also includes: Monitoring material weight data and material flow data based on the basic parameters of the belt scale; Determining a material blockage backlog parameter based on the material weight data and the material flow data; A belt scale dispatch instruction is generated based on the material blockage backlog parameter.

8. A belt scale centralized monitoring device, characterized in that: Also includes: An acquisition unit, configured to acquire basic parameters of the belt scale, wherein the basic parameters include load parameters, motion parameters, and environmental parameters; a determination unit, configured to perform data fusion processing on the basic parameters to determine health parameters of the belt scale; A generating unit is used to generate a belt scale scheduling instruction based on the health parameter.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the belt scale centralized monitoring method according to any one of claims 6 to 7 are implemented.

10. An electronic device, characterized in that: The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the belt scale centralized monitoring method according to any one of claims 6 to 7.

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