Coal conveying monitoring method, device and equipment based on internet of things and storage medium

Abstract

The application discloses a coal conveying monitoring method and device based on an Internet of Things, equipment and a storage medium. The method comprises the following steps: collecting original conveying data generated in a coal conveying process through an Internet of Things sensor, and performing standardization processing on the original conveying data to obtain standard conveying data; dividing the standard conveying data into a plurality of data sets according to the types of the Internet of Things sensors corresponding to each data in the standard conveying data; sending the plurality of data sets to corresponding data processing nodes respectively, and obtaining data processing results returned by the data processing nodes; determining conveying evaluation indexes according to the data processing results returned by the data processing nodes; determining a coal conveying state according to the conveying evaluation indexes, and optimizing the coal conveying process according to the coal conveying state. The technical problem that the coal conveying process cannot be monitored as a whole and optimized according to monitoring data in the prior art is solved, and the accuracy of the coal conveying process monitoring and the coal conveying efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of coal conveying monitoring technology, and in particular to a coal conveying monitoring method, device, equipment and storage medium based on the Internet of Things. Background Technology

[0002] Coal still occupies an important position in my country's energy structure. In coal mining or coal-fired power generation, conveyor belts are often used as conveying devices due to their low cost and large conveying capacity. However, since conveyor belts need to operate continuously, problems with the conveyor belts during the coal conveying process can not only slow down the work progress but also potentially cause safety accidents. Therefore, regular inspection of conveyor belts is necessary. However, existing inspection methods mostly involve checking for cracks or belt misalignment, and stopping the machine for maintenance when problems are found. But sometimes, it may not be necessary to stop the machine for maintenance; simply adjusting the conveying parameters is sufficient. Each shutdown for maintenance not only reduces the efficiency of coal conveying but also makes it difficult to achieve overall monitoring of the coal conveying process. Therefore, how to monitor the coal conveying process as a whole and optimize it based on the monitoring data has become an urgent technical problem to be solved.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a coal conveying monitoring method, device, equipment, and storage medium based on the Internet of Things, aiming to solve the technical problem that existing technologies cannot monitor the coal conveying process as a whole and optimize the coal conveying process based on monitoring data.

[0005] To achieve the above objectives, the present invention provides a coal conveying monitoring method based on the Internet of Things, the method comprising the following steps:

[0006] Raw conveying data generated during the coal conveying process is collected by IoT sensors, and the raw conveying data is standardized to obtain standard conveying data.

[0007] The standard transmission data is divided into several data sets according to the IoT sensor type corresponding to each data point in the standard transmission data.

[0008] The data sets are sent to the corresponding data processing nodes, and the data processing results returned by each data processing node are obtained.

[0009] The delivery evaluation indicators are determined based on the data processing results returned by each data processing node.

[0010] The coal conveying status is determined based on the conveying evaluation indicators, and the coal conveying process is optimized based on the coal conveying status.

[0011] Optionally, the standardization process for the original transmission data to obtain standard transmission data includes:

[0012] Anomaly detection is performed on the raw transmission data to identify abnormal data in the raw transmission data;

[0013] The abnormal data in the original transmission data is replaced by a substitution method to obtain transmission data free of outliers;

[0014] Obtain the data type of each data in the outlier removal delivery data, and format the outlier removal delivery data according to the preset standard format corresponding to each data type to obtain standard delivery data.

[0015] Optionally, the step of dividing the standard transmission data into several data sets according to the IoT sensor type corresponding to each data point in the standard transmission data includes:

[0016] Obtain the IoT sensor type corresponding to the standard transmission data, and determine the amount of standard transmission data corresponding to the same type of IoT sensor based on the quantity of the IoT sensor type;

[0017] Obtain the node parameters of each data processing node, and divide the standard transmission data into several data sets according to the standard transmission data volume corresponding to the same type of IoT sensor and the node parameters.

[0018] Optionally, the step of obtaining the node parameters of each data processing node, and dividing the standard transmission data into several data sets according to the standard transmission data volume corresponding to the same type of IoT sensor and the node parameters, includes:

[0019] Obtain the node parameters of each data processing node, and determine the data processing type and maximum data processing volume of each data processing node based on the node parameters;

[0020] The target data processing node for the standard data transmission is determined based on the data processing type and the standard data transmission volume corresponding to the same type of IoT sensor.

[0021] The standard transmission data is divided into several data sets based on the target data processing node and the maximum data processing volume.

[0022] Optionally, determining the delivery evaluation index based on the data processing results returned by each data processing node includes:

[0023] The average data value of each type of IoT sensor is determined based on the data processing results returned by the data processing nodes corresponding to the same type of IoT sensor.

[0024] The coal conveying evaluation value is determined by linear weighted summation based on the mean of the data.

[0025] The transportation evaluation index is determined based on the coal transportation evaluation value and the preset standard value.

[0026] Optionally, determining the coal conveying status based on the conveying evaluation index and optimizing the coal conveying process based on the coal conveying status includes:

[0027] When the conveying evaluation indicators do not meet the preset standards, it is determined that there is an abnormality in the coal conveying status;

[0028] When there is an abnormality in the coal conveying status, the current offset of the coal conveyor belt during the coal conveying process is collected by infrared sensors;

[0029] Determine whether the current offset is greater than a preset offset;

[0030] When the current offset is greater than the preset offset, the belt detection device is controlled to perform damage detection on the coal conveyor belt;

[0031] If no damage is detected in the coal conveyor belt, the operating speed of the coal conveyor belt is controlled to be rapidly reduced from the current operating speed to the target operating speed.

[0032] Optionally, after determining whether the current offset is greater than a preset offset, the method further includes:

[0033] When the current offset is less than or equal to the preset offset, the current coal weight of the coal conveying belt is collected by the weight sensor;

[0034] When the current coal weight exceeds a preset weight threshold, the humidity of the incoming coal is collected by a humidity sensor.

[0035] When the humidity of the incoming coal is greater than the preset humidity, the humidification amount and the coal inlet amount are controlled to be reduced so that the current coal conveying weight of the coal conveying belt is less than or equal to the preset weight threshold.

[0036] Furthermore, to achieve the above objectives, the present invention also proposes an Internet of Things-based coal conveying monitoring device, the device comprising:

[0037] The data acquisition module is used to collect raw conveying data generated during the coal conveying process through IoT sensors, and to standardize the raw conveying data to obtain standard conveying data.

[0038] The segmentation module is used to divide the standard transmitted data into several data sets according to the IoT sensor type corresponding to each data in the standard transmitted data;

[0039] The acquisition module is used to send the plurality of data sets to the corresponding data processing nodes respectively, and to acquire the data processing results returned by each data processing node;

[0040] The determination module is used to determine the delivery evaluation index based on the data processing results returned by each data processing node;

[0041] An optimization module is used to determine the coal conveying status based on the conveying evaluation indicators and to optimize the coal conveying process based on the coal conveying status.

[0042] Furthermore, to achieve the above objectives, the present invention also proposes an Internet of Things (IoT)-based coal conveying monitoring device, the device comprising: a memory, a processor, and an IoT-based coal conveying monitoring program stored in the memory and executable on the processor, the IoT-based coal conveying monitoring program being configured to implement the steps of the IoT-based coal conveying monitoring method described above.

[0043] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing an Internet of Things (IoT)-based coal conveying monitoring program, which, when executed by a processor, implements the steps of the IoT-based coal conveying monitoring method described above.

[0044] This invention collects raw transportation data generated during coal conveying using IoT sensors, and standardizes this raw data to obtain standard transportation data. The standard transportation data is then divided into several data sets based on the IoT sensor type corresponding to each data point. These data sets are then sent to corresponding data processing nodes, and the processing results returned by each node are obtained. Transportation evaluation indicators are determined based on the processing results returned by each node. The coal conveying status is determined based on these evaluation indicators, and the coal conveying process is optimized based on this status. Because this invention obtains standard transportation data by standardizing the raw transportation data collected by IoT sensors, distributes the standard transportation data (divided into several sets based on IoT sensor type) to corresponding data processing nodes, determines transportation evaluation indicators based on the processing results returned by the nodes, and optimizes the coal conveying process based on the determined coal conveying status, it solves the technical problem in existing technologies of being unable to monitor the entire coal conveying process and optimize it based on monitoring data. This improves the accuracy and efficiency of coal conveying process monitoring. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the structure of a coal conveying monitoring device based on the Internet of Things (IoT) in the hardware operating environment of the embodiment of the present invention.

[0046] Figure 2 This is a flowchart illustrating the first embodiment of the coal conveying monitoring method based on the Internet of Things of the present invention.

[0047] Figure 3 This is a flowchart illustrating the second embodiment of the coal conveying monitoring method based on the Internet of Things of the present invention.

[0048] Figure 4 This is a flowchart illustrating the third embodiment of the coal conveying monitoring method based on the Internet of Things of the present invention.

[0049] Figure 5 This is a structural block diagram of the first embodiment of the coal conveying monitoring device based on the Internet of Things of the present invention.

[0050] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0052] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of an IoT-based coal conveying monitoring device, which is part of the hardware operating environment of an embodiment of the present invention.

[0053] like Figure 1 As shown, the IoT-based coal conveying monitoring device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0054] Those skilled in the art will understand that Figure 1The structure shown does not constitute a limitation on IoT-based coal conveying monitoring equipment, which may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0055] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and an Internet of Things-based coal conveying monitoring program.

[0056] exist Figure 1 In the IoT-based coal conveying monitoring device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the IoT-based coal conveying monitoring device of the present invention can be set in the IoT-based coal conveying monitoring device, and the IoT-based coal conveying monitoring device calls the IoT-based coal conveying monitoring program stored in the memory 1005 through the processor 1001 and executes the IoT-based coal conveying monitoring method provided in the embodiment of the present invention.

[0057] This invention provides a coal conveying monitoring method based on the Internet of Things, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the coal conveying monitoring method based on the Internet of Things of the present invention.

[0058] In this embodiment, the IoT-based coal conveying monitoring method includes the following steps:

[0059] Step S10: Collect raw conveying data generated during the coal conveying process through IoT sensors, and standardize the raw conveying data to obtain standard conveying data.

[0060] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as an IoT-based coal conveying monitoring device. The following description uses an IoT-based coal conveying monitoring device as an example to illustrate this embodiment and the subsequent embodiments.

[0061] It is understandable that IoT sensors can include infrared sensors, humidity sensors, weight sensors, dust sensors, temperature sensors, etc. Among them, infrared sensors can collect the offset of the coal conveyor belt during the coal conveying process, humidity sensors can collect the humidity of the coal during the coal conveying process, weight sensors can collect the weight of the coal on the coal conveyor belt, dust sensors can collect the concentration of coal dust near the coal conveyor belt, and temperature sensors can collect the ambient temperature.

[0062] It should be understood that the raw conveying data is the data collected by IoT sensors during the coal conveying process without any processing. The raw conveying data includes data such as the offset of the coal conveyor belt, coal moisture, coal weight, dust concentration and ambient temperature; the standard conveying data is the coal conveying data without outliers and in a uniform format.

[0063] It is understood that the standard transmission data can be obtained by standardizing the original transmission data in the following ways: (1) deleting outliers in the original transmission data and unifying the data format to obtain standard transmission data; (2) replacing outliers in the original transmission data with the mode in the original data and unifying the data format to obtain standard transmission data; (3) deleting specific characters in the original transmission data, replacing outliers in the original transmission data and unifying the data format to obtain standard transmission data. This embodiment does not limit the specific method used.

[0064] In practical implementation, the IoT-based coal conveying monitoring equipment uses IoT sensors such as infrared sensors, humidity sensors, weight sensors, dust sensors, and temperature sensors to collect data such as coal conveyor belt offset, coal humidity, coal weight, dust concentration, and ambient temperature to form raw conveying data. Outliers in the raw conveying data are deleted and the data format is standardized to obtain standard conveying data.

[0065] Step S20: Divide the standard transmission data into several data sets according to the IoT sensor type corresponding to each data in the standard transmission data.

[0066] It is understandable that different types of IoT sensors collect different types of data. In order to improve data processing efficiency, the standard transmission data is divided into several data sets according to different types of IoT sensors. The standard transmission data in the same data set is obtained by standardizing the data collected by the same type of IoT sensors.

[0067] It should be understood that standard transmission data can be divided into several data sets in the following ways: (1) Add standard transmission data corresponding to the same type of IoT sensor to the same data set to obtain several data sets; (2) Add standard transmission data corresponding to the same type of IoT sensor to the same data set, and then divide the data sets corresponding to the same type of IoT sensor according to the data processing capability of the data processing node, i.e., the maximum data processing volume, so that the data volume of each data set is less than or equal to the maximum data processing volume of the data processing node, thereby dividing the standard transmission data into several data sets.

[0068] Step S30: Send the plurality of data sets to the corresponding data processing nodes respectively, and obtain the data processing results returned by each data processing node.

[0069] It is understandable that there can be multiple data processing nodes, each capable of processing data simultaneously. The parameters of each data processing node may differ, resulting in varying data processing capabilities. To improve data processing efficiency and maximize the utilization of data processing node resources, several data points can be combined and sent to the corresponding data processing node based on its processing capabilities. The data processing capability can be determined by the maximum data processing volume of each data processing node per unit time.

[0070] Step S40: Determine the delivery evaluation index based on the data processing results returned by each data processing node.

[0071] It is understandable that by combining the data processing results returned by each data processing node, the coal conveying evaluation index can be determined. The data processing results include the data mean, the maximum data deviation value, the minimum data deviation value, and the proportion of data with deviation values ​​greater than the preset threshold. The coal conveying evaluation index can be determined in the following ways: (1) The data mean is weighted and summed according to the weight of the data corresponding to different types of IoT sensors to obtain the conveying evaluation index; (2) The data mean corresponding to each type of sensor at different times is read from the data processing results, and the coal conveying evaluation index at different times is determined by the linear weighted sum method.

[0072] Step S50: Determine the coal conveying status based on the conveying evaluation index, and optimize the coal conveying process based on the coal conveying status.

[0073] It should be understood that standard conveying evaluation indicators under normal operating conditions of the coal conveyor belt can be preset. The coal conveying status is determined based on the deviation rate between the conveying evaluation indicators and the standard conveying evaluation indicators. When the deviation rate between the conveying evaluation indicators and the standard evaluation indicators is greater than the preset deviation rate, the coal conveying status is judged to be abnormal. When the deviation rate is less than or equal to the preset deviation rate, the coal conveying status is judged to be normal.

[0074] Understandably, when the coal conveying status is abnormal, the type of abnormality can be determined based on the current coal conveying status, and the operating parameters of the coal conveyor belt can be adjusted accordingly to optimize the coal conveying process. For example, if the abnormality is determined to be an abnormal conveying weight, the coal feed rate can be reduced to below the standard weight. If the abnormality is determined to be a normal coal feed rate but abnormal humidity causing the coal conveying weight to exceed the standard, and the humidity of the coal feed is greater than the preset humidity, the humidification device can be reduced to lower the humidification rate to reduce the coal conveying weight to the standard humidity. If the abnormality is determined to be an excessive dust concentration in the air, the sprayer can be controlled to spray water mist into the air to reduce the dust concentration to the standard value.

[0075] Furthermore, due to interference from environmental factors and the inherent instability of IoT sensors, abnormal data may be present in the collected data, thereby interfering with the accuracy of coal conveying monitoring. To eliminate the interference of abnormal data on coal conveying monitoring, the standardization process of the original conveying data to obtain standard conveying data includes: performing outlier detection on the original conveying data to identify abnormal data in the original conveying data; replacing the abnormal data in the original conveying data using a substitution method to obtain outlier-free conveying data; obtaining the data type of each data in the outlier-free conveying data; and formatting the outlier-free conveying data according to the preset standard format corresponding to each data type to obtain standard conveying data.

[0076] It is understood that outlier detection includes null value detection, outlier detection, and mutation value detection, and the detected null values, outliers, and mutation values ​​are marked as abnormal data; the replacement method is a method of replacing abnormal data with preset data. The preset data can be the mode or average of normal data, or a pre-set standard value. This embodiment does not limit this.

[0077] It should be understood that there are differences in the data types collected by different types of sensors. In this embodiment, the data collected by IoT sensors of the same type are formatted to obtain standard transmission data. After formatting, the standard transmission data of different types of IoT sensors can be in the same or different formats, and the standard transmission data of the same type of IoT sensors are in the same format.

[0078] This embodiment collects raw transportation data generated during the coal conveying process using IoT sensors, and standardizes the raw transportation data to obtain standard transportation data. The standard transportation data is then divided into several data sets according to the IoT sensor type corresponding to each data point. These data sets are then sent to corresponding data processing nodes, and the processing results returned by each node are obtained. Transportation evaluation indicators are determined based on the processing results returned by each node. The coal conveying status is determined based on the transportation evaluation indicators, and the coal conveying process is optimized based on the coal conveying status. Because this embodiment obtains standard transportation data by standardizing the raw transportation data collected by IoT sensors, distributes the standard transportation data (divided into several sets according to the IoT sensor type) to corresponding data processing nodes, determines transportation evaluation indicators based on the processing results returned by the data processing nodes, and optimizes the coal conveying process based on the determined coal conveying status, it solves the technical problem in existing technologies that cannot monitor the entire coal conveying process and optimize it based on monitoring data. This improves the accuracy of coal conveying process monitoring and the efficiency of coal conveying.

[0079] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the coal conveying monitoring method based on the Internet of Things of the present invention.

[0080] Based on the first embodiment described above, in this embodiment, step S20 includes:

[0081] Step S201: Obtain the IoT sensor type corresponding to the standard transmission data, and determine the amount of standard transmission data corresponding to the same type of IoT sensor based on the number of IoT sensor types.

[0082] It is understandable that different types of IoT sensors will collect data that reflects different parameters of the coal conveyor belt. Therefore, it is necessary to classify the standard conveying data, group the standard data corresponding to the same type of IoT sensor into one category, and obtain the standard conveying data volume corresponding to the same type of IoT sensor. The standard conveying data volume is the quantity of standard conveying data.

[0083] Step S202: Obtain the node parameters of each data processing node, and divide the standard transmission data into several data sets according to the standard transmission data volume corresponding to the same type of IoT sensor and the node parameters.

[0084] It should be understood that node parameters include node configuration, maximum data processing capacity, and optimal data processing capacity. Based on the type of IoT sensor, the standard transmitted data is first divided into sets equal to the number of IoT types. Then, based on the node parameters of the data processing nodes, the data sets are further divided into several data sets. Finally, the data sets are matched with the node parameters that process the data sets.

[0085] In a specific implementation, for example, there are 5 types of IoT sensors, with a total standard data transmission volume of 60,000. Let a, b, c, d, and e represent the 5 types of IoT sensors, with corresponding standard data volumes of a: 10,000, b: 15,000, c: 5,000, d: 23,000, and e: 10,000, respectively. First, the standard transmission data is divided into 5 data sets. If the maximum data processing volume of the data processing node corresponding to a is 5,000, b is 20,000, c is 10,000, d is 10,000, and e is 10,000, then the data sets are further divided. The data sets corresponding to b, c, and e are not divided. The data set corresponding to a is divided into two on average, and the data set corresponding to d is divided into three on average. Finally, the standard transmission data is divided into 8 data sets.

[0086] Furthermore, in order to maximize the utilization of resources of each data processing node while improving data processing efficiency, the step of obtaining the node parameters of each data processing node and dividing the standard transmission data into several data sets according to the standard transmission data volume corresponding to the same type of IoT sensor and the node parameters includes: obtaining the node parameters of each data processing node; determining the data processing type and maximum data processing volume of each data processing node according to the node parameters; determining the target data processing node of the standard transmission data according to the data processing type and the standard transmission data volume corresponding to the same type of IoT sensor; and dividing the standard transmission data into several data sets according to the target data processing node and the maximum data processing volume.

[0087] It is understandable that different data processing nodes may have different data processing types and maximum data processing volumes. In order to achieve the best data processing effect, the data type of the standard transmission data is matched with the data processing type of the data processing node. The matching node is the target data processing node for that type of standard transmission data. The standard transmission data is divided into several data sets according to the number of target data processing nodes corresponding to the same type of data and the maximum data processing volume of each data processing node.

[0088] Furthermore, in order to accurately reflect the coal conveying status, the step of determining the conveying evaluation index based on the data processing results returned by each data processing node includes: determining the average data value corresponding to each type of IoT sensor based on the data processing results returned by the data processing nodes corresponding to the same type of IoT sensor; determining the coal conveying evaluation value of the coal conveying process based on the average data value using a linear weighted sum method; and determining the conveying evaluation index based on the coal conveying evaluation value and a preset standard value.

[0089] Understandably, the average value of the data can be read from the data processing results, and then the average value of the data corresponding to the same type of IoT sensor can be calculated to obtain the evaluation value of the IoT sensor. The evaluation values ​​at different times within a preset historical period can be obtained, and the coal conveying evaluation value of the coal conveying process can be determined by the linear weighted sum method. When the difference between the coal conveying evaluation value and the preset standard value is greater than the preset threshold, the conveying evaluation index does not meet the preset standard. When the difference is less than or equal to the preset threshold, the conveying evaluation index meets the preset standard.

[0090] It should be understood that the coal conveying status can also be predicted based on the changing trend of the coal conveying evaluation value within a preset historical period. When the coal conveying evaluation value fluctuates within a preset range near the standard value, it can be predicted that the coal conveying status will not be abnormal in the future. When the coal conveying evaluation value exceeds the preset range, it can be predicted that the coal conveying status will be abnormal in the future, and the coal conveying process can be optimized in advance.

[0091] This embodiment obtains the IoT sensor type corresponding to the standard transmission data, and determines the standard transmission data volume corresponding to the same type of IoT sensor based on the quantity of the IoT sensor type; it obtains the node parameters of each data processing node, and divides the standard transmission data into several data sets based on the standard transmission data volume corresponding to the same type of IoT sensor and the node parameters. This can maximize the utilization of the resources of each data processing node while ensuring data processing efficiency.

[0092] refer to Figure 4 , Figure 4 This is a flowchart illustrating the third embodiment of the coal conveying monitoring method based on the Internet of Things of the present invention.

[0093] Based on the above embodiments, in this embodiment, step S50 includes:

[0094] Step S501: When the conveying evaluation index does not meet the preset standard, it is determined that there is an abnormality in the coal conveying status.

[0095] In practice, IoT-based coal conveying monitoring equipment determines that there is an abnormality in the coal conveying status when the conveying evaluation indicators do not meet the preset standards.

[0096] Step S502: When there is an abnormality in the coal conveying status, the current offset of the coal conveyor belt during the coal conveying process is collected by an infrared sensor.

[0097] Understandably, when the coal conveyor belt is operating normally, the distance between the coal conveyor belt and the edge of the conveyor wheel should be greater than a preset distance, which can be set according to the specific scenario; an infrared sensor can be set at the edge of the conveyor wheel to collect the distance between the coal conveyor belt and the edge of the conveyor wheel, and the difference between this distance and the distance between the coal conveyor belt and the edge of the conveyor wheel when the coal conveyor belt stops is the current offset.

[0098] Step S503: Determine whether the current offset is greater than the preset offset.

[0099] In its specific implementation, the IoT-based coal conveying monitoring method determines the current offset based on the current distance collected by the infrared sensor and judges whether the current offset is greater than the preset offset; the preset offset is the maximum allowable offset of the coal conveyor belt.

[0100] Step S504: When the current offset is greater than the preset offset, control the belt detection device to perform damage detection on the coal conveyor belt.

[0101] It should be understood that most deviations of coal conveyor belts are caused by cracks or breaks in the belts. Therefore, when the current deviation exceeds the preset deviation, the IoT-based coal conveying monitoring equipment controls the belt detection device to detect damage to the coal conveyor belt.

[0102] Step S505: If no damage is detected in the coal conveyor belt, control the operating speed of the coal conveyor belt to be reduced from the current operating speed to the target operating speed.

[0103] It is understandable that the deviation of the coal conveyor belt may also be caused by excessive operating speed. In order to ensure the safety of the coal conveying process, if no damage is detected in the coal conveyor belt, the operating speed of the coal conveyor belt should be reduced to the target operating speed. If the coal conveyor belt returns to normal after running at the target operating speed for a period of time, the operating speed of the coal conveyor belt can be gradually increased.

[0104] Furthermore, in order to ensure the normal operation of the coal conveyor belt, after step S503, the method further includes: when the current offset is less than or equal to a preset offset, collecting the current coal conveying weight of the coal conveyor belt through a weight sensor; when the current coal conveying weight is greater than a preset weight threshold, collecting the coal inlet humidity through a humidity sensor; when the coal inlet humidity is greater than a preset humidity, controlling the reduction of humidification and coal inlet to make the current coal conveying weight of the coal conveyor belt less than or equal to the preset weight threshold.

[0105] Understandably, when the offset is less than or equal to the preset offset, it can be determined that the coal conveyor belt has not deviated. At this time, the current coal weight of the coal conveyor belt is collected by the weight sensor. During the coal conveying process, in order to suppress dust, the coal is usually humidified. If the coal moisture content is too high, it will not only cause the weight to exceed the standard, but also waste resources.

[0106] It should be understood that when the humidity of the incoming coal is greater than the preset humidity, it indicates that the humidity of the coal is too high. At this time, the coal conveying monitoring device based on the Internet of Things controls the humidification device to reduce the humidification amount and controls the reduction of the coal inlet, so that the coal conveying weight of the conveyor belt is reduced to the standard value.

[0107] In this embodiment, when the conveying evaluation indicators do not meet the preset standards, an abnormality in the coal conveying state is determined. When an abnormality is detected, the current offset of the coal conveyor belt during the conveying process is collected using an infrared sensor. It is then determined whether the current offset is greater than a preset offset. If the current offset is greater than the preset offset, a belt detection device is controlled to perform damage detection on the coal conveyor belt. If no damage is detected on the coal conveyor belt, the operating speed of the coal conveyor belt is controlled to decrease rapidly from the current operating speed to the target operating speed. This allows for optimization of the coal conveying process based on the conveying state, thereby ensuring normal and stable operation of the coal conveying process, improving conveying efficiency while reducing the probability of safety accidents.

[0108] Furthermore, this embodiment of the invention also proposes a storage medium storing an Internet of Things (IoT)-based coal conveying monitoring program. When the IoT-based coal conveying monitoring program is executed by a processor, it implements the steps of the IoT-based coal conveying monitoring method described above.

[0109] Reference Figure 5 , Figure 5 This is a structural block diagram of the first embodiment of the coal conveying monitoring device based on the Internet of Things of the present invention.

[0110] like Figure 5 As shown, the coal conveying monitoring device based on the Internet of Things proposed in this embodiment of the invention includes: a data acquisition module 10, a division module 20, an acquisition module 30, a determination module 40, and an optimization module 50.

[0111] The data acquisition module 10 is used to collect raw conveying data generated during the coal conveying process through IoT sensors, and to standardize the raw conveying data to obtain standard conveying data.

[0112] The segmentation module 20 is used to divide the standard transmission data into several data sets according to the IoT sensor type corresponding to each data in the standard transmission data;

[0113] The acquisition module 30 is used to send the plurality of data sets to the corresponding data processing nodes respectively, and to acquire the data processing results returned by each data processing node;

[0114] The determination module 40 is used to determine the delivery evaluation index based on the data processing results returned by each data processing node;

[0115] The optimization module 50 is used to determine the coal conveying status based on the conveying evaluation index and to optimize the coal conveying process based on the coal conveying status.

[0116] This embodiment collects raw transportation data generated during the coal conveying process using IoT sensors, and standardizes the raw transportation data to obtain standard transportation data. The standard transportation data is then divided into several data sets according to the IoT sensor type corresponding to each data point. These data sets are then sent to corresponding data processing nodes, and the processing results returned by each node are obtained. Transportation evaluation indicators are determined based on the processing results returned by each node. The coal conveying status is determined based on the transportation evaluation indicators, and the coal conveying process is optimized based on the coal conveying status. Because this embodiment obtains standard transportation data by standardizing the raw transportation data collected by IoT sensors, distributes the standard transportation data (divided into several sets according to the IoT sensor type) to corresponding data processing nodes, determines transportation evaluation indicators based on the processing results returned by the data processing nodes, and optimizes the coal conveying process based on the determined coal conveying status, it solves the technical problem in existing technologies that cannot monitor the entire coal conveying process and optimize it based on monitoring data. This improves the accuracy of coal conveying process monitoring and the efficiency of coal conveying.

[0117] Based on the first embodiment of the IoT-based coal conveying monitoring device of the present invention, a second embodiment of the IoT-based coal conveying monitoring device of the present invention is proposed.

[0118] In this embodiment, the data acquisition module 10 is further configured to perform outlier detection on the original transmission data to identify abnormal data in the original transmission data; replace the abnormal data in the original transmission data with a substitution method to obtain outlier-free transmission data; obtain the data type of each data in the outlier-free transmission data; and format the outlier-free transmission data according to the preset standard format corresponding to each data type to obtain standard transmission data.

[0119] The partitioning module 20 is further configured to obtain the IoT sensor type corresponding to the standard transmission data, and determine the amount of standard transmission data corresponding to the same type of IoT sensor based on the number of IoT sensor types; obtain the node parameters of each data processing node, and divide the standard transmission data into several data sets based on the amount of standard transmission data corresponding to the same type of IoT sensor and the node parameters.

[0120] The partitioning module 20 is further configured to acquire node parameters of each data processing node, determine the data processing type and maximum data processing volume of each data processing node based on the node parameters, determine the target data processing node for the standard data transmission based on the data processing type and the standard transmission data volume corresponding to the same type of IoT sensor, and partition the standard transmission data into several data sets based on the target data processing node and the maximum data processing volume.

[0121] The division module 20 is further configured to determine the average data value corresponding to each type of IoT sensor based on the data processing results returned by the data processing nodes corresponding to the same type of IoT sensors; determine the coal conveying evaluation value of the coal conveying process by means of linear weighted sum based on the average data value; and determine the conveying evaluation index based on the coal conveying evaluation value and the preset standard value.

[0122] The optimization module 50 is further configured to determine that there is an abnormality in the coal conveying state when the conveying evaluation index does not meet the preset standard; when there is an abnormality in the coal conveying state, the current offset of the coal conveying belt during the coal conveying process is collected by an infrared sensor; it is determined whether the current offset is greater than a preset offset; when the current offset is greater than the preset offset, the belt detection device is controlled to perform damage detection on the coal conveying belt; when no damage is detected on the coal conveying belt, the operating speed of the coal conveying belt is controlled to be reduced from the current operating speed to the target operating speed.

[0123] The optimization module 50 is further configured to: collect the current coal conveying weight of the coal conveying belt through a weight sensor when the current offset is less than or equal to a preset offset; collect the coal feeding humidity through a humidity sensor when the current coal conveying weight is greater than a preset weight threshold; and control the reduction of humidification and coal feeding amount when the coal feeding humidity is greater than a preset humidity, so that the current coal conveying weight of the coal conveying belt is less than or equal to the preset weight threshold.

[0124] Other embodiments or specific implementations of the coal conveying monitoring device based on the Internet of Things of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0125] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0126] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0128] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A coal conveying monitoring method based on Internet of Things, characterized in that, The method comprises: Collecting original conveying data generated in the coal conveying process through an Internet of Things sensor, and performing standardization processing on the original conveying data to obtain standard conveying data; Dividing the standard conveying data into a plurality of data sets according to the types of the Internet of Things sensors corresponding to each data in the standard conveying data; Sending the plurality of data sets to corresponding data processing nodes respectively, and obtaining data processing results returned by each data processing node; Determining a conveying evaluation index according to the data processing results returned by each data processing node; When the conveying evaluation index does not meet a preset standard, determining that the coal conveying state is abnormal; When the coal conveying state is abnormal, collecting a current offset of a coal conveying belt in the coal conveying process through an infrared sensor; Determining whether the current offset is greater than a preset offset; When the current offset is greater than the preset offset, controlling a belt detection device to perform damage detection on the coal conveying belt; When no damage is detected on the coal conveying belt, controlling the running speed of the coal conveying belt to decrease from a current running speed to a target running speed.

2. The method of claim 1, wherein, The standardization processing on the original conveying data to obtain standard conveying data comprises: Performing outlier detection on the original conveying data to determine abnormal data in the original conveying data; Replacing the abnormal data in the original conveying data through a replacement method to obtain outlier-free conveying data; Obtaining the data types of each data in the outlier-free conveying data, and performing format processing on the outlier-free conveying data according to a preset standard format corresponding to each data type to obtain standard conveying data.

3. The method of claim 1, wherein, The dividing of the standard conveying data into a plurality of data sets according to the types of the Internet of Things sensors corresponding to each data in the standard conveying data comprises: Obtaining the types of the Internet of Things sensors corresponding to the standard conveying data, and determining the amount of standard conveying data corresponding to the same type of Internet of Things sensor according to the number of the types of the Internet of Things sensors; Obtaining node parameters of each data processing node, and dividing the standard conveying data into a plurality of data sets according to the amount of standard conveying data corresponding to the same type of Internet of Things sensor and the node parameters.

4. The method of claim 3, wherein, The obtaining of the node parameters of each data processing node and the dividing of the standard conveying data into a plurality of data sets according to the amount of standard conveying data corresponding to the same type of Internet of Things sensor and the node parameters comprises: Obtaining node parameters of each data processing node, and determining the data processing type and the maximum data processing amount of each data processing node according to the node parameters; Determining a target data processing node of the standard conveying data according to the data processing type and the amount of standard conveying data corresponding to the same type of Internet of Things sensor; Dividing the standard conveying data into a plurality of data sets according to the target data processing node and the maximum data processing amount.

5. The method of claim 1, wherein, The determination of a conveying evaluation index according to the data processing results returned by each data processing node comprises: Determining the data mean value of each type of Internet of Things sensor according to the data processing results returned by the data processing nodes corresponding to the same type of Internet of Things sensor; Determine the coal conveying evaluation value of the coal conveying process by linear weighting and method according to the data mean value; Determine the conveying evaluation index according to the coal conveying evaluation value and the preset standard value.

6. The method of claim 1, wherein, After judging whether the current offset is greater than the preset offset, the method further comprises: When the current offset is less than or equal to the preset offset, collect the current coal conveying weight of the coal conveying belt by the weight sensor; When the current coal conveying weight is greater than the preset weight threshold, collect the coal inlet humidity by the humidity sensor; When the coal inlet humidity is greater than the preset humidity, control the reduction of the humidification amount and the coal inlet amount, so that the current coal conveying weight of the coal conveying belt is less than or equal to the preset weight threshold.

7. A coal conveying monitoring device based on Internet of Things, characterized in that, The device comprises: A data acquisition module for collecting original conveying data generated in the coal conveying process by Internet of Things sensors and performing standardization processing on the original conveying data to obtain standard conveying data; A division module for dividing the standard conveying data into a plurality of data sets according to the types of Internet of Things sensors corresponding to each data in the standard conveying data; An acquisition module for sending the plurality of data sets to corresponding data processing nodes respectively and acquiring data processing results returned by each data processing node; A determination module for determining a conveying evaluation index according to the data processing results returned by each data processing node; An optimization module for determining that the coal conveying state is abnormal when the conveying evaluation index does not meet the preset standard, collecting the current offset of the coal conveying belt in the coal conveying process by the infrared sensor when the coal conveying state is abnormal, judging whether the current offset is greater than the preset offset, controlling the belt detection device to perform damage detection on the coal conveying belt when the current offset is greater than the preset offset, and controlling the running speed of the coal conveying belt to decrease from the current running speed to the target running speed when no damage of the coal conveying belt is detected.

8. A coal conveying monitoring device based on Internet of Things, characterized in that, The device comprises a memory, a processor, and an Internet of Things-based coal conveying monitoring program stored on the memory and executable on the processor, and the Internet of Things-based coal conveying monitoring program is configured to implement the steps of the Internet of Things-based coal conveying monitoring method according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium stores an Internet of Things-based coal conveying monitoring program, and the Internet of Things-based coal conveying monitoring program implements the steps of the Internet of Things-based coal conveying monitoring method according to any one of claims 1 to 6 when executed by the processor.

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