IoT-based remote monitoring system for single-shaft shredders

Abstract

This invention provides an IoT-based remote monitoring system for a single-shaft shredder, comprising a data acquisition module for collecting and acquiring operational status data of the single-shaft shredder; a data analysis module for performing anomaly monitoring and risk prediction analysis on the operational status data based on a cloud monitoring and analysis platform, and obtaining data analysis results; and a remote control module for performing multi-dimensional assessment and simulation testing based on the data analysis results, and remotely controlling the single-shaft shredder according to the assessment and testing results. This invention improves the quality of remote control of the single-shaft shredder by collecting and acquiring operational status data, performing anomaly monitoring and risk prediction analysis, and conducting multi-dimensional assessment and simulation testing, thus maximizing the working efficiency of the single-shaft shredder.

Description

Technical Field

[0001] This invention relates to the field of remote monitoring technology, and in particular to a remote monitoring system for a single-shaft shredder based on the Internet of Things. Background Technology

[0002] Single-shaft shredders are widely used crushing equipment in the industry. Various waste materials enter the shredding chamber through the feeding system. The chamber houses the moving cutter rollers and shredding blades. A hydraulic cylinder pushes the feed box to move the material near the blades. A geared motor drives the moving cutter rollers to rotate. The material is shredded into small pieces by the combined action of tearing, squeezing, and shearing by the shredding blades and discharged through the screen holes. Single-shaft shredders handle a wide variety of complex materials, and the working environment is correspondingly complex and open, requiring high safety standards. Currently, networked control and intelligent monitoring are lacking in the operation of single-shaft shredders, resulting in the need for a large amount of manpower to monitor the operation on-site. This not only affects work efficiency and incurs high labor costs, but also delays in the detection and handling of safety risks.

[0003] Therefore, a remote monitoring system for single-shaft shredders based on the Internet of Things is needed. Summary of the Invention

[0004] This invention provides a remote monitoring system for a single-shaft shredder based on the Internet of Things. By collecting and acquiring the working status data of the single-shaft shredder, and performing anomaly monitoring and risk prediction analysis, multi-dimensional judgment and simulation testing, this invention can improve the quality of remote control of the single-shaft shredder and facilitate the full utilization of the single-shaft shredder's working efficiency.

[0005] This invention provides a remote monitoring system for a single-shaft shredder based on the Internet of Things, comprising:

[0006] The data acquisition module is used to collect and acquire the operating status data of the single-shaft shredder;

[0007] The data analysis module is used to perform anomaly monitoring and analysis and risk prediction analysis on operational status data based on the cloud monitoring and analysis platform, and to obtain data analysis results.

[0008] The remote control module is used to conduct multi-dimensional analysis and simulation tests based on data analysis results, and to remotely control the single-shaft shredder according to the analysis and test results.

[0009] Furthermore, the data acquisition module includes a data acquisition setting unit and a data acquisition implementation unit;

[0010] The data acquisition and setting unit is used to set up several sensors at several target monitoring positions of the structure according to the structure and working process of the single-shaft shredder;

[0011] The data acquisition implementation unit is used to collect structural parameter data of the target monitoring location based on the sensors, and generate operational status data based on the structural parameter data.

[0012] Furthermore, the data analysis module includes a data transmission unit and a data analysis unit;

[0013] The data transmission unit is used to transmit operational status data to the cloud monitoring and analysis platform based on the constructed remote monitoring IoT.

[0014] The data analysis unit is used to match and compare the work operation status data with a preset standard data range database to obtain the corresponding standard data range; based on the work operation status stage corresponding to the standard data range, it performs anomaly monitoring analysis and risk prediction analysis on the work operation status data to obtain data analysis results.

[0015] Furthermore, the data analysis unit includes a standard data range setting subunit, a data matching and comparison subunit, and a data analysis subunit;

[0016] The standard data range interval setting sub-unit is used to analyze and obtain the working state stage corresponding to the historical working state data based on the historical working state data of the single-shaft shredder and according to the preset working stage state analysis model; several standard data range intervals are set according to the working state stage; the working state stages include the initial working stage, steady-state working stage, fatigue working stage and risk working stage;

[0017] The data matching and comparison subunit is used to match and compare the work operation status data with the preset standard data range interval database to obtain the standard data range interval corresponding to the work operation status data, and the work operation status stage corresponding to the standard data range interval.

[0018] The data analysis subunit is used to monitor and analyze anomalies in the operational status data during the initial and steady-state working phases; and to perform risk prediction analysis on the operational status data during the fatigue and risky working phases based on the risk prediction analysis model, thereby obtaining data analysis results.

[0019] Furthermore, the data analysis subunit includes an anomaly monitoring and analysis subunit, a risk prediction and analysis subunit, and a data analysis results summary subunit;

[0020] The anomaly monitoring and analysis unit is used to monitor the operating status data in the initial working stage and steady-state working stage according to the upper and lower limits of the preset normal fluctuation range of data. If the upper or lower limit is exceeded, the corresponding abnormal operating status data is located, and the first data analysis result is generated based on several abnormal operating status data.

[0021] The risk prediction and analysis unit is used to construct a risk prediction model using a neural network model, and to perform risk prediction and analysis on the work status data in the fatigue stage and the risk stage based on the risk prediction model. When the risk prediction value is greater than the preset risk prediction threshold, the corresponding risk work operation status data is located, and a second data analysis result is generated based on several risk work operation status data.

[0022] The data analysis results summary unit is used to summarize the first and second data analysis results to generate the final data analysis results.

[0023] Furthermore, the remote control module includes an analysis unit, a simulation testing unit, and a control implementation unit;

[0024] The analysis unit is used to establish a multi-dimensional analysis model of type, structural position, and cycle, to comprehensively analyze the first data analysis results, and obtain the impact value on the normal operation of the single-shaft shredder. If the impact value is greater than the preset impact value threshold, the first control command is generated.

[0025] The simulation test unit is used to simulate the operation of a single-shaft shredder based on a preset simulation test model and risk operation status data to obtain the number of simulated faults. If the number of simulated faults is greater than the preset fault number threshold, a second control command is generated.

[0026] The control implementation unit is used to remotely control the single-shaft shredder according to the first control command and the second control command.

[0027] Furthermore, the analysis unit also includes a multi-dimensional analysis model construction sub-unit; the multi-dimensional analysis model construction sub-unit includes a model parameter determination molecular unit and a model parameter setting molecular unit;

[0028] Model parameters determine molecular units, which are used to identify the types of abnormalities in the initial and steady-state working stages of a single-shaft shredder. Several types of abnormalities, abnormal structural locations, and abnormal cycles are traced back to obtain these types, structural locations, and cycles. Based on these types, structural locations, and cycles, the types, structural locations, and cycles are determined as parameters.

[0029] The model parameter setting molecular unit is used to set the specific content of the parameters; the specific content includes a single type or multiple types, a single structural position or a linked structural position, and intermittent bursts or continuous small-cycle occurrences within a period.

[0030] Furthermore, the control implementation unit includes a first processing subunit and a second processing subunit;

[0031] The first processing subunit is used to remotely control the single-shaft shredder according to the first control command and in accordance with the working guarantee strategy.

[0032] The second processing subunit is used to remotely control the single-shaft shredder according to the second control command and in accordance with the safety guarantee strategy.

[0033] Furthermore, it also includes a production process monitoring module, which is used to monitor the entire production process of several single-shaft shredders based on a cloud monitoring and analysis platform; the production process monitoring module includes a production link network setting unit and a production process monitoring unit;

[0034] The production process network setting unit is used to connect the production equipment or material and finished product conveying equipment involved in the material entry, product production and finished product exit stages to the cloud monitoring and analysis platform using Internet of Things technology, based on the production task requirements of several single-shaft shredders, and to set the monitoring parameters and monitoring parameter thresholds of the production equipment.

[0035] The production process monitoring unit is used to monitor the working status of production equipment or material and finished product conveying equipment based on monitoring parameters and monitoring parameter thresholds. If the monitoring parameters are greater than or less than the monitoring parameter thresholds, a preset processing strategy is adopted to deal with the production process.

[0036] Furthermore, it also includes a blade wear monitoring and evaluation module, which is used to acquire real-time images of the shredding blade assembly of the single-shaft shredder based on the camera component, and to compare and match them with preset standard images to evaluate the wear degree, and to issue a replacement reminder based on the evaluation results; the blade wear monitoring and evaluation module includes a blade wear image acquisition unit and a blade replacement reminder unit;

[0037] The blade wear image acquisition unit is used to acquire real-time images of the shredding blade assembly according to a preset cycle based on the camera component installed on the single-shaft shredder, and upload the real-time images to the cloud monitoring and analysis platform through the logistics network;

[0038] The blade replacement reminder unit compares the real-time image with a preset standard blade image. If the similarity is less than a preset similarity threshold, it obtains the usage cycle and type of the shredding blade group. If the usage cycle is greater than a preset usage cycle threshold and the shredding blade group is a fixed blade on a single-shaft shredder, it assesses the shredding blade group as having a high risk of wear and issues a blade replacement reminder.

[0039] Compared with the prior art, the present invention has the following advantages and beneficial effects: by collecting and acquiring the working status data of the single-shaft shredder, and conducting anomaly monitoring and risk prediction analysis, multi-dimensional judgment and simulation testing, the quality of remote control of the single-shaft shredder can be improved, which is conducive to the performance of the single-shaft shredder.

[0040] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0041] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a schematic diagram of the structure of the IoT-based remote monitoring system for a single-shaft shredder according to the present invention.

[0044] Figure 2 This is a schematic diagram of the data acquisition module structure of the IoT-based remote monitoring system for a single-shaft shredder according to the present invention.

[0045] Figure 3 This is a schematic diagram of the data analysis module structure of the IoT-based remote monitoring system for a single-shaft shredder according to the present invention. Detailed Implementation

[0046] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0047] This invention provides a remote monitoring system for a single-shaft shredder based on the Internet of Things, such as... Figure 1 As shown, it includes:

[0048] The data acquisition module is used to collect and acquire the operating status data of the single-shaft shredder;

[0049] The data analysis module is used to perform anomaly monitoring and analysis and risk prediction analysis on operational status data based on the cloud monitoring and analysis platform, and to obtain data analysis results.

[0050] The remote control module is used to conduct multi-dimensional analysis and simulation tests based on data analysis results, and to remotely control the single-shaft shredder according to the analysis and test results.

[0051] The working principle of the above technical solution is as follows: the data acquisition module is used to collect and acquire the working status data of the single-shaft shredder;

[0052] The data analysis module is used to perform anomaly monitoring and analysis and risk prediction analysis on operational status data based on the cloud monitoring and analysis platform, and to obtain data analysis results.

[0053] The remote control module is used to conduct multi-dimensional analysis and simulation tests based on data analysis results, and to remotely control the single-shaft shredder according to the analysis and test results.

[0054] The beneficial effects of the above technical solution are as follows: By adopting the solution provided in this embodiment, by collecting and acquiring the working status data of the single-shaft shredder, and conducting abnormal monitoring analysis and risk prediction analysis, multi-dimensional judgment and simulation testing, the quality of remote control of the single-shaft shredder can be improved, which is conducive to the performance of the single-shaft shredder.

[0055] In one embodiment, such as Figure 2 As shown, the data acquisition module includes a data acquisition setting unit and a data acquisition implementation unit;

[0056] The data acquisition and setting unit is used to set up several sensors at several target monitoring positions of the structure according to the structure and working process of the single-shaft shredder;

[0057] The data acquisition implementation unit is used to collect structural parameter data of the target monitoring location based on the sensors, and generate operational status data based on the structural parameter data.

[0058] The working principle of the above technical solution is as follows: the data acquisition module includes a data acquisition setting unit and a data acquisition implementation unit;

[0059] The data acquisition and setting unit is used to set up several sensors at several target monitoring positions of the structure according to the structure and working process of the single-shaft shredder;

[0060] The data acquisition implementation unit is used to collect structural parameter data of the target monitoring location based on the sensors, and generate operational status data based on the structural parameter data.

[0061] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, structural parameter data can be obtained through sensors, and working status data can be generated, which can ensure that comprehensive and accurate working status data is obtained.

[0062] In one embodiment, such as Figure 3 As shown, the data analysis module includes a data transmission unit and a data analysis unit;

[0063] The data transmission unit is used to transmit operational status data to the cloud monitoring and analysis platform based on the constructed remote monitoring IoT.

[0064] The data analysis unit is used to match and compare the work operation status data with a preset standard data range database to obtain the corresponding standard data range; based on the work operation status stage corresponding to the standard data range, it performs anomaly monitoring analysis and risk prediction analysis on the work operation status data to obtain data analysis results.

[0065] The working principle of the above technical solution is as follows: the data analysis module includes a data transmission unit and a data analysis unit;

[0066] The data transmission unit is used to transmit operational status data to the cloud monitoring and analysis platform based on the constructed remote monitoring IoT.

[0067] The data analysis unit is used to match and compare the work operation status data with a preset standard data range database to obtain the corresponding standard data range; based on the work operation status stage corresponding to the standard data range, it performs anomaly monitoring analysis and risk prediction analysis on the work operation status data to obtain data analysis results.

[0068] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, through receiving data, performing anomaly monitoring and analysis and risk prediction analysis, it is possible to ensure that data analysis results are obtained at different working operation stages.

[0069] In one embodiment, the data analysis unit includes a standard data range setting subunit, a data matching and comparison subunit, and a data analysis subunit;

[0070] The standard data range interval setting sub-unit is used to analyze and obtain the working state stage corresponding to the historical working state data based on the historical working state data of the single-shaft shredder and according to the preset working stage state analysis model; several standard data range intervals are set according to the working state stage; the working state stages include the initial working stage, steady-state working stage, fatigue working stage and risk working stage;

[0071] The data matching and comparison subunit is used to match and compare the work operation status data with the preset standard data range interval database to obtain the standard data range interval corresponding to the work operation status data, and the work operation status stage corresponding to the standard data range interval.

[0072] The data analysis subunit is used to monitor and analyze anomalies in the operational status data during the initial and steady-state working phases; and to perform risk prediction analysis on the operational status data during the fatigue and risky working phases based on the risk prediction analysis model, thereby obtaining data analysis results.

[0073] The working principle of the above technical solution is as follows: the data analysis unit includes a standard data range setting subunit, a data matching and comparison subunit, and a data analysis subunit;

[0074] The standard data range interval setting sub-unit is used to analyze and obtain the working state stage corresponding to the historical working state data based on the historical working state data of the single-shaft shredder and according to the preset working stage state analysis model; several standard data range intervals are set according to the working state stage; the working state stages include the initial working stage, steady-state working stage, fatigue working stage and risk working stage;

[0075] The data matching and comparison subunit is used to match and compare the work operation status data with the preset standard data range interval database to obtain the standard data range interval corresponding to the work operation status data, and the work operation status stage corresponding to the standard data range interval.

[0076] The data analysis subunit is used to monitor and analyze anomalies in the operational status data during the initial and steady-state working phases; and to perform risk prediction analysis on the operational status data during the fatigue and risky working phases based on the risk prediction analysis model, thereby obtaining data analysis results.

[0077] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, different data analysis methods can be performed by distinguishing different working stages, thus ensuring the pertinence of data analysis.

[0078] In one embodiment, the data analysis subunit includes an anomaly monitoring and analysis subunit, a risk prediction and analysis subunit, and a data analysis result summarization subunit.

[0079] The anomaly monitoring and analysis unit is used to monitor the operating status data in the initial working stage and steady-state working stage according to the upper and lower limits of the preset normal fluctuation range of data. If the upper or lower limit is exceeded, the corresponding abnormal operating status data is located, and the first data analysis result is generated based on several abnormal operating status data.

[0080] The risk prediction and analysis unit is used to construct a risk prediction model using a neural network model, and to perform risk prediction and analysis on the work status data in the fatigue stage and the risk stage based on the risk prediction model. When the risk prediction value is greater than the preset risk prediction threshold, the corresponding risk work operation status data is located, and a second data analysis result is generated based on several risk work operation status data.

[0081] The data analysis results summary unit is used to summarize the first and second data analysis results to generate the final data analysis results.

[0082] The working principle of the above technical solution is as follows: the data analysis subunit includes an anomaly monitoring and analysis subunit, a risk prediction and analysis subunit, and a data analysis result summarization subunit;

[0083] The anomaly monitoring and analysis unit is used to monitor the operating status data in the initial working stage and steady-state working stage according to the upper and lower limits of the preset normal fluctuation range of data. If the upper or lower limit is exceeded, the corresponding abnormal operating status data is located, and the first data analysis result is generated based on several abnormal operating status data.

[0084] The risk prediction and analysis unit is used to construct a risk prediction model using a neural network model, and to perform risk prediction and analysis on the work status data in the fatigue stage and the risk stage based on the risk prediction model. When the risk prediction value is greater than the preset risk prediction threshold, the corresponding risk work operation status data is located, and a second data analysis result is generated based on several risk work operation status data.

[0085] The data analysis results summary unit is used to summarize the first and second data analysis results to generate the final data analysis results.

[0086] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, through anomaly monitoring and analysis and risk prediction analysis, it is possible to ensure the acquisition of abnormal working status data and risky working status data, providing targeted reference for subsequent remote control.

[0087] In one embodiment, the remote control module includes an analysis unit, a simulation test unit, and a control implementation unit;

[0088] The analysis unit is used to establish a multi-dimensional analysis model of type, structural position, and cycle, to comprehensively analyze the first data analysis results, and obtain the impact value on the normal operation of the single-shaft shredder. If the impact value is greater than the preset impact value threshold, the first control command is generated.

[0089] The simulation test unit is used to simulate the operation of a single-shaft shredder based on a preset simulation test model and risk operation status data to obtain the number of simulated faults. If the number of simulated faults is greater than the preset fault number threshold, a second control command is generated.

[0090] The control implementation unit is used to remotely control the single-shaft shredder according to the first control command and the second control command.

[0091] The working principle of the above technical solution is as follows: the remote control module includes an analysis unit, a simulation test unit, and a control implementation unit;

[0092] The analysis unit is used to establish a multi-dimensional analysis model of type, structural position, and cycle, to comprehensively analyze the first data analysis results, and obtain the impact value on the normal operation of the single-shaft shredder. If the impact value is greater than the preset impact value threshold, the first control command is generated.

[0093] The simulation test unit is used to simulate the operation of a single-shaft shredder based on a preset simulation test model and risk operation status data to obtain the number of simulated faults. If the number of simulated faults is greater than the preset fault number threshold, a second control command is generated.

[0094] The control implementation unit is used to remotely control the single-shaft shredder according to the first control command and the second control command.

[0095] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, through the analysis unit and simulation test, it can be ensured that different control commands and control methods are generated, so as to better perform remote control.

[0096] In one embodiment, the analysis unit further includes a multi-dimensional analysis model construction subunit; the multi-dimensional analysis model construction subunit includes a model parameter determination molecular unit and a model parameter setting molecular unit;

[0097] Model parameters determine molecular units, which are used to identify the types of abnormalities in the initial and steady-state working stages of a single-shaft shredder. Several types of abnormalities, abnormal structural locations, and abnormal cycles are traced back to obtain these types, structural locations, and cycles. Based on these types, structural locations, and cycles, the types, structural locations, and cycles are determined as parameters.

[0098] The model parameter setting molecular unit is used to set the specific content of the parameters; the specific content includes a single type or multiple types, a single structural position or a linked structural position, and intermittent bursts or continuous small-cycle occurrences within a period.

[0099] The working principle of the above technical solution is as follows: the judgment unit also includes a multi-dimensional judgment model construction sub-unit; the multi-dimensional judgment model construction sub-unit includes a model parameter determination molecular unit and a model parameter setting molecular unit;

[0100] Model parameters determine molecular units, which are used to identify the types of abnormalities in the initial and steady-state working stages of a single-shaft shredder. Several types of abnormalities, abnormal structural locations, and abnormal cycles are traced back to obtain these types, structural locations, and cycles. Based on these types, structural locations, and cycles, the types, structural locations, and cycles are determined as parameters.

[0101] The model parameter setting molecular unit is used to set the specific content of the parameters; the specific content includes a single type or multiple types, a single structural position or a linked structural position, and intermittent bursts or continuous small-cycle occurrences within a period.

[0102] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, the quality of the multi-dimensional judgment model construction can be guaranteed by determining and setting the model parameters in the multi-dimensional judgment model.

[0103] In one embodiment, the control implementation unit includes a first processing subunit and a second processing subunit;

[0104] The first processing subunit is used to remotely control the single-shaft shredder according to the first control command and in accordance with the working guarantee strategy.

[0105] The second processing subunit is used to remotely control the single-shaft shredder according to the second control command and in accordance with the safety guarantee strategy.

[0106] The working principle of the above technical solution is as follows: the control implementation unit includes a first processing subunit and a second processing subunit;

[0107] The first processing subunit is used to remotely control the single-shaft shredder according to the first control command and in accordance with the working guarantee strategy.

[0108] The second processing subunit is used to remotely control the single-shaft shredder according to the second control command and in accordance with the safety guarantee strategy.

[0109] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, and by taking corresponding strategies according to control commands for remote control, the pertinence and effectiveness of remote control can be guaranteed.

[0110] In one embodiment, it also includes a production process monitoring module, which is used to monitor the entire production process of several single-shaft shredders based on a cloud monitoring and analysis platform; the production process monitoring module includes a production link network setting unit and a production process monitoring unit.

[0111] The production process network setting unit is used to connect the production equipment or material and finished product conveying equipment involved in the material entry, product production and finished product exit stages to the cloud monitoring and analysis platform using Internet of Things technology, based on the production task requirements of several single-shaft shredders, and to set the monitoring parameters and monitoring parameter thresholds of the production equipment.

[0112] The production process monitoring unit is used to monitor the working status of production equipment or material and finished product conveying equipment based on monitoring parameters and monitoring parameter thresholds. If the monitoring parameters are greater than or less than the monitoring parameter thresholds, a preset processing strategy is adopted to deal with the production process.

[0113] The working principle of the above technical solution is as follows: it also includes a production process monitoring module, which is used to monitor the entire production process of several single-shaft shredders based on a cloud monitoring and analysis platform; the production process monitoring module includes a production link network setting unit and a production process monitoring unit.

[0114] The production process network setting unit is used to connect the production equipment or material and finished product conveying equipment involved in the material entry, product production and finished product exit stages to the cloud monitoring and analysis platform using Internet of Things technology, based on the production task requirements of several single-shaft shredders, and to set the monitoring parameters and monitoring parameter thresholds of the production equipment.

[0115] The production process monitoring unit is used to monitor the working status of production equipment or material and finished product conveying equipment based on monitoring parameters and monitoring parameter thresholds. If the monitoring parameters are greater than or less than the monitoring parameter thresholds, a preset processing strategy is adopted to deal with the production process.

[0116] The beneficial effects of the above technical solution are as follows: by adopting the solution provided in this embodiment, the entire production process of several single-shaft shredders can be monitored in an overall manner through a cloud monitoring and analysis platform, thereby improving the intelligent control level of the entire production process of the single-shaft shredder.

[0117] In one embodiment, the device further includes a blade wear monitoring and evaluation module, which is used to acquire real-time images of the shredding blade assembly of the single-shaft shredder based on a camera component, and to compare and match them with preset standard images to evaluate the wear degree, and to issue a replacement reminder based on the evaluation results; the blade wear monitoring and evaluation module includes a blade wear image acquisition unit and a blade replacement reminder unit;

[0118] The blade wear image acquisition unit is used to acquire real-time images of the shredding blade assembly according to a preset cycle based on the camera component installed on the single-shaft shredder, and upload the real-time images to the cloud monitoring and analysis platform through the logistics network;

[0119] The blade replacement reminder unit compares the real-time image with a preset standard blade image. If the similarity is less than a preset similarity threshold, it obtains the usage cycle and type of the shredding blade group. If the usage cycle is greater than a preset usage cycle threshold and the shredding blade group is a fixed blade on a single-shaft shredder, it assesses the shredding blade group as having a high risk of wear and issues a blade replacement reminder.

[0120] The working principle of the above technical solution is as follows: it also includes a blade wear monitoring and evaluation module, which is used to acquire real-time images of the shredding blade group of the single-axis shredder based on the camera component, and compare and match them with preset standard images to evaluate the wear degree, and provide a replacement reminder based on the evaluation results; the blade wear monitoring and evaluation module includes a blade wear image acquisition unit and a blade replacement reminder unit;

[0121] The blade wear image acquisition unit is used to acquire real-time images of the shredding blade assembly according to a preset cycle based on the camera component installed on the single-shaft shredder, and upload the real-time images to the cloud monitoring and analysis platform through the logistics network;

[0122] The blade replacement reminder unit compares the real-time image with a preset standard blade image. If the similarity is less than a preset similarity threshold, it obtains the usage cycle and type of the shredding blade group. If the usage cycle is greater than a preset usage cycle threshold and the shredding blade group is a fixed blade on a single-shaft shredder, it assesses the shredding blade group as having a high risk of wear and issues a blade replacement reminder.

[0123] The beneficial effects of the above technical solution are as follows: by using the solution provided in this embodiment, the wear blades can be detected in time by judging the real-time image of the shredding blade group of the single-shaft shredder, and timely replacement reminders can be given, which is beneficial to the efficiency and quality of equipment maintenance of the single-shaft shredder.

[0124] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A remote monitoring system for a single-shaft shredder based on the Internet of Things, characterized in that, include: The data acquisition module is used to collect and acquire the operating status data of the single-shaft shredder; The data analysis module is used to perform anomaly monitoring and analysis and risk prediction analysis on operational status data based on the cloud monitoring and analysis platform, and to obtain data analysis results. The remote control module is used to conduct multi-dimensional analysis and simulation tests based on data analysis results, and to remotely control the single-shaft shredder according to the analysis and test results; The data analysis module includes a data transmission unit and a data analysis unit; The data transmission unit is used to transmit operational status data to the cloud monitoring and analysis platform based on the constructed remote monitoring IoT. The data analysis unit is used to match and compare the work operation status data with a preset standard data range database to obtain the corresponding standard data range. Based on the operational status stages corresponding to the standard data range, anomaly monitoring and risk prediction analysis are performed on the operational status data to obtain data analysis results. The data analysis unit includes a standard data range setting subunit, a data matching and comparison subunit, and a data analysis subunit; The standard data range interval setting sub-unit is used to analyze and obtain the working state stage corresponding to the historical working state data based on the historical working state data of the single-shaft shredder and according to the preset working stage state analysis model; and to divide and set several standard data range intervals according to the working state stage. The operational phases include the initial working phase, the steady-state working phase, the fatigue working phase, and the risky working phase. The data matching and comparison subunit is used to match and compare the work operation status data with the preset standard data range interval database to obtain the standard data range interval corresponding to the work operation status data, and the work operation status stage corresponding to the standard data range interval. The data analysis subunit is used to monitor and analyze anomalies in the operational status data during the initial and steady-state working phases; and to perform risk prediction analysis on the operational status data during the fatigue and risky working phases based on the risk prediction analysis model, thereby obtaining data analysis results. The data analysis subunit includes an anomaly monitoring and analysis subunit, a risk prediction and analysis subunit, and a data analysis results summary subunit; The anomaly monitoring and analysis unit is used to monitor the operating status data in the initial working stage and steady-state working stage according to the upper and lower limits of the preset normal fluctuation range of data. If the upper or lower limit is exceeded, the corresponding abnormal operating status data is located, and the first data analysis result is generated based on several abnormal operating status data. The risk prediction and analysis unit is used to construct a risk prediction model using a neural network model, and to perform risk prediction and analysis on the work status data in the fatigue stage and the risk stage based on the risk prediction model. When the risk prediction value is greater than the preset risk prediction threshold, the corresponding risk work operation status data is located, and a second data analysis result is generated based on several risk work operation status data. The data analysis results summary unit is used to summarize the first and second data analysis results to generate the data analysis results. The remote control module includes an analysis unit, a simulation testing unit, and a control implementation unit; The analysis unit is used to establish a multi-dimensional analysis model of type, structural position, and cycle, to comprehensively analyze the first data analysis results, and obtain the impact value on the normal operation of the single-shaft shredder. If the impact value is greater than the preset impact value threshold, the first control command is generated. The simulation test unit is used to simulate the operation of a single-shaft shredder based on a preset simulation test model and risk operation status data to obtain the number of simulated faults. If the number of simulated faults is greater than the preset fault number threshold, a second control command is generated. The control implementation unit is used to remotely control the single-shaft shredder according to the first control command and the second control command.

2. The IoT-based remote monitoring system for a single-shaft shredder according to claim 1, characterized in that, The data acquisition module includes a data acquisition setting unit and a data acquisition implementation unit; The data acquisition and setting unit is used to set up several sensors at several target monitoring positions of the structure according to the structure and working process of the single-shaft shredder; The data acquisition implementation unit is used to collect structural parameter data of the target monitoring location based on the sensors, and generate operational status data based on the structural parameter data.

3. The IoT-based remote monitoring system for a single-shaft shredder according to claim 1, characterized in that, The analysis unit also includes a multi-dimensional analysis model construction sub-unit; the multi-dimensional analysis model construction sub-unit includes a model parameter determination molecular unit and a model parameter setting molecular unit; Model parameters determine molecular units, which are used to identify the types of abnormalities in the initial and steady-state working stages of a single-shaft shredder. Several types of abnormalities, abnormal structural locations, and abnormal cycles are traced back to obtain these types, structural locations, and cycles. Based on these types, structural locations, and cycles, the types, structural locations, and cycles are determined as parameters. The model parameter setting molecular unit is used to set the specific content of the parameters; the specific content includes a single type or multiple types, a single structural position or a linked structural position, and intermittent bursts or continuous small-cycle occurrences within a period.

4. The IoT-based remote monitoring system for a single-shaft shredder according to claim 1, characterized in that, The control implementation unit includes a first processing subunit and a second processing subunit; The first processing subunit is used to remotely control the single-shaft shredder according to the first control command and the working guarantee strategy. The second processing subunit is used to remotely control the single-shaft shredder according to the second control command and in accordance with the safety guarantee strategy.

5. The IoT-based remote monitoring system for a single-shaft shredder according to claim 1, characterized in that, It also includes a production process monitoring module, which is used to monitor the entire production process of several single-shaft shredders based on a cloud monitoring and analysis platform; the production process monitoring module includes a production link network setting unit and a production process monitoring unit. The production process network setting unit is used to connect the production equipment or material and finished product conveying equipment involved in the material entry, product production and finished product exit stages to the cloud monitoring and analysis platform using Internet of Things technology, based on the production task requirements of several single-shaft shredders, and to set the monitoring parameters and monitoring parameter thresholds of the production equipment. The production process monitoring unit is used to monitor the working status of production equipment or material and finished product conveying equipment based on monitoring parameters and monitoring parameter thresholds. If the monitoring parameters are greater than or less than the monitoring parameter thresholds, a preset processing strategy is adopted to deal with the production process.

6. The IoT-based remote monitoring system for a single-shaft shredder according to claim 1, characterized in that, It also includes a blade wear monitoring and assessment module, which uses a camera component to acquire real-time images of the shredding blade assembly of the single-shaft shredder, compares and matches them with preset standard images to assess the wear, and provides replacement reminders based on the assessment results; The tool wear monitoring and evaluation module includes a tool wear image acquisition unit and a tool replacement reminder unit; The blade wear image acquisition unit is used to acquire real-time images of the shredding blade assembly according to a preset cycle based on the camera component installed on the single-shaft shredder, and upload the real-time images to the cloud monitoring and analysis platform through the logistics network; The blade replacement reminder unit compares the real-time image with a preset standard blade image. If the similarity is less than a preset similarity threshold, it obtains the usage cycle and type of the shredding blade group. If the usage cycle is greater than a preset usage cycle threshold and the shredding blade group is a fixed blade on a single-shaft shredder, it assesses the shredding blade group as having a high risk of wear and issues a blade replacement reminder.

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