Temperature intelligent monitoring method and system based on temperature measuring gun

Abstract

The present invention provides a temperature intelligent monitoring method and system based on a temperature measuring gun, which relates to the field of temperature monitoring technology. A temperature measuring gun array is configured for multiple temperature monitoring nodes of multiple monitoring components, and the temperature measuring gun array is controlled to perform temperature monitoring on multiple temperature monitoring nodes with a temperature measuring interval threshold as a constraint, thereby obtaining multiple temperature monitoring sequences and combining them with pattern information to identify equipment operation faults, obtain equipment fault information, and then determine the fault operation and maintenance node through fault tracing. The technical problem that the data processing process of the prior art using data analysis to identify production line equipment faults is complex and cannot accurately describe the degree of production line equipment faults is solved. The technical effect of reducing the complexity of data analysis and processing in the production line equipment fault identification process and quantifying the severity of production line equipment faults while improving the response speed of production line equipment fault identification is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature monitoring, and in particular to a temperature intelligent monitoring method and system based on a temperature measuring gun. Background Art

[0002] Currently, although data analysis is used to process equipment failure data for fault identification of production line equipment, the data processing process is complex and it is difficult to accurately describe the extent of equipment failure.

[0003] For example, suppose a manufacturing plant uses sensors to collect equipment data, including temperature, pressure, etc., and then applies machine learning algorithms to identify abnormal patterns. However, even if the algorithm detects an anomaly, it is difficult to determine the severity of the anomaly. This uncertainty makes it challenging to take maintenance measures in a timely manner, which indirectly affects the production efficiency of the production line and the life of production equipment.

[0004] In summary, the prior art has technical problems in that the data processing process for identifying production line equipment faults using data analysis is complex and cannot accurately describe the degree of production line equipment faults. Summary of the invention

[0005] The present application provides a temperature intelligent monitoring method and system based on a temperature measuring gun, which is used to solve the technical problems in the prior art that the data processing process for identifying production line equipment faults using data analysis is complex and cannot accurately describe the degree of production line equipment faults.

[0006] In view of the above problems, the present application provides a temperature intelligent monitoring method and system based on a temperature measuring gun.

[0007] The first aspect of the present application provides a temperature intelligent monitoring method based on a temperature measuring gun, the method comprising: interactively obtaining multiple target monitoring components of a target monitoring device, wherein the multiple target monitoring components constitute the target monitoring device; performing apparent temperature change analysis on the multiple target monitoring components to obtain multiple target temperature monitoring nodes; configuring multiple temperature measuring guns for the multiple target temperature monitoring nodes, the multiple temperature measuring guns constituting a temperature measuring gun array; presetting a temperature measurement interval threshold, and after the target monitoring device starts running, controlling the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes with the temperature measurement interval threshold as a constraint to obtain multiple temperature monitoring sequences; interactively obtaining target mode information of the target monitoring device, and identifying equipment operation faults based on the target mode information and the multiple temperature monitoring sequences to obtain equipment fault information; tracing the fault based on the equipment fault information to obtain fault operation and maintenance node information.

[0008] The second aspect of the present application provides a temperature intelligent monitoring system based on a temperature measuring gun, the system comprising: a monitoring component determination unit, used to interactively obtain multiple target monitoring components of a target monitoring device, wherein the multiple target monitoring components constitute the target monitoring device; a monitoring node determination unit, used to perform apparent temperature change analysis on the multiple target monitoring components to obtain multiple target temperature monitoring nodes; a monitoring array construction unit, used to configure multiple temperature measuring guns for the multiple target temperature monitoring nodes, the multiple temperature measuring guns constitute a temperature measuring gun array; a monitoring sequence acquisition unit, used to preset a temperature measurement interval threshold, after the target monitoring device starts running, control the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes with the temperature measurement interval threshold as a constraint, and obtain multiple temperature monitoring sequences; an operation fault identification unit, used to interactively obtain target mode information of the target monitoring device, and perform equipment operation fault identification according to the target mode information and the multiple temperature monitoring sequences to obtain equipment fault information; a fault tracing execution unit, used to perform fault tracing according to the equipment fault information to obtain fault operation and maintenance node information.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The method provided by the embodiment of the present application obtains multiple target monitoring components of a target monitoring device through interaction, wherein the multiple target monitoring components constitute the target monitoring device; performs apparent temperature change analysis on the multiple target monitoring components to obtain multiple target temperature monitoring nodes; configures multiple temperature measuring guns for the multiple target temperature monitoring nodes, and the multiple temperature measuring guns constitute a temperature measuring gun array; presets a temperature measuring interval threshold, and after the target monitoring device starts running, controls the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes with the temperature measuring interval threshold as a constraint to obtain multiple temperature monitoring sequences; interactively obtains the target mode information of the target monitoring device, and performs equipment operation fault identification based on the target mode information and the multiple temperature monitoring sequences to obtain equipment fault information; performs fault tracing based on the equipment fault information to obtain fault operation and maintenance node information. The technical effect of improving the response speed of production line equipment fault identification while reducing the complexity of data analysis and processing in the production line equipment fault identification process and quantifying the severity of production line equipment faults is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of the process flow of the intelligent temperature monitoring method based on a temperature measuring gun provided in this application;

[0012] Figure 2 A schematic diagram of the process of obtaining a target temperature monitoring node in the temperature intelligent monitoring method based on a temperature measuring gun provided in this application;

[0013] Figure 3 This is a schematic diagram of the structure of the temperature intelligent monitoring system based on the temperature measuring gun provided in this application.

[0014] Explanation of the accompanying drawings: monitoring component determination unit 1, monitoring node determination unit 2, monitoring array construction unit 3, monitoring sequence acquisition unit 4, operation fault identification unit 5, fault tracing execution unit 6. DETAILED DESCRIPTION

[0015] The present application provides a temperature intelligent monitoring method and system based on a temperature measuring gun, which is used to solve the technical problem that the data processing process of applying data analysis to identify production line equipment faults is complex and cannot accurately describe the degree of production line equipment faults. The technical effect of reducing the complexity of data analysis and processing in the production line equipment fault identification process while improving the response speed of production line equipment fault identification and quantifying the severity of production line equipment faults is achieved.

[0016] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the present application provides a temperature intelligent monitoring method based on a temperature measuring gun, comprising:

[0019] A100: Interactively obtain multiple target monitoring components of a target monitoring device, wherein the multiple target monitoring components constitute the target monitoring device.

[0020] Specifically, in this embodiment, the target monitoring device is an unspecified production device among multiple production devices having upstream and downstream relationships on the production line, the target monitoring component is a constituent component of the target monitoring device, and the multiple target monitoring components constitute the target monitoring device.

[0021] At the same time, it should be understood that multiple target monitoring components are partially or completely exposed to the structural surface of the target monitoring equipment, and multiple target monitoring components undergo temperature changes during the operation of the target monitoring equipment. Therefore, this embodiment can determine whether the target monitoring equipment has a fault based on the temperature conditions of the multiple target monitoring components.

[0022] A200: Analyze the apparent temperature changes of the multiple target monitoring components to obtain multiple target temperature monitoring nodes.

[0023] In one embodiment, Figure 2 As shown, the apparent temperature change analysis is performed on the multiple target monitoring components to obtain multiple target temperature monitoring nodes. The method step A200 provided in the present application also includes:

[0024] A210: Divide the target monitoring device into apparent regions of interest based on the multiple target monitoring components to obtain multiple component regions of interest.

[0025] A220: Interactively obtain the monitoring range of the temperature measuring gun and generate a grid spacing based on the monitoring range.

[0026] A230: Divide the first component region of interest into a first network of interest using the grid spacing, wherein the first network of interest includes K grids of interest, and the first component region of interest is any one of the multiple component regions of interest.

[0027] A240: Perform temperature change fluctuation analysis on the K grids of interest, and extract a first target temperature monitoring node from the K grids of interest based on the analysis results.

[0028] A250: Similarly, analyze the apparent temperature changes of the multiple target monitoring components to obtain the multiple target temperature monitoring nodes.

[0029] In one embodiment, temperature change fluctuation analysis is performed on the K grids of interest, and a first target temperature monitoring node is extracted from the K grids of interest based on the analysis result. Step A240 of the method provided by the present application further includes:

[0030] A241: Preset a standard operating mode, and when using the standard operating mode to control the production operation of the target monitoring equipment, schedule the infrared thermal imager to collect temperature of the area of ​​interest of the first component with the temperature measurement interval threshold as a constraint to obtain multiple temperature distribution images.

[0031] A242: Split the multiple temperature distribution images according to the first network of interest to obtain K groups of temperature change sequences.

[0032] A243: Perform temperature fluctuation analysis on the K groups of temperature change sequences, and extract the first target temperature monitoring node from the K grids of interest based on the analysis results.

[0033] In one embodiment, temperature fluctuation analysis is performed on the K groups of temperature change sequences, and the first target temperature monitoring node is extracted from the K grids of interest based on the analysis results. Step A243 of the method provided by the present application further includes:

[0034] A243-1: Perform image conversion on the first group of temperature change sequences to obtain a first temperature change curve, wherein the first group of temperature change sequences is any one of the K groups of temperature change sequences.

[0035] A243-2: Preset a set of temperature fluctuation related indicators, wherein the set of temperature fluctuation related indicators includes temperature maximum, temperature minimum, extreme value horizontal distance, fluctuation frequency and amplitude mean.

[0036] A243-3: Use the temperature fluctuation associated indicator set to extract data from the first temperature change curve to obtain the first temperature maximum, the first temperature minimum, the first extreme value horizontal distance, the first fluctuation frequency and the first amplitude mean.

[0037] A243-4: Pre-build a temperature fluctuation evaluation model, and synchronize the first temperature maximum, the first temperature minimum, the first extreme horizontal distance, the first fluctuation frequency and the first amplitude mean to the temperature fluctuation evaluation model to obtain a first temperature fluctuation coefficient.

[0038] A243-5: Similarly, temperature fluctuation analysis is performed on the K groups of temperature change sequences to obtain K temperature fluctuation coefficients, extreme value extraction is performed on the K temperature fluctuation coefficients, and the first target temperature monitoring node is extracted from the K grids of interest based on the extreme value extraction results.

[0039] In one embodiment, a temperature fluctuation evaluation model is pre-constructed, and the first temperature maximum value, the first temperature minimum value, the first extreme value horizontal distance, the first fluctuation frequency and the first amplitude mean are synchronized to the temperature fluctuation evaluation model to obtain a first temperature fluctuation coefficient. Step A243-4 of the method provided in the present application also includes:

[0040] A243-41: Pre-construct a temperature fluctuation evaluation function, the temperature fluctuation evaluation function is as follows:

[0041]

[0042] Among them, F is the temperature fluctuation coefficient, A is the amplitude mean, D is the temperature maximum, f is the fluctuation frequency, w1 is the amplitude weight assignment, w2 is the fluctuation frequency weight assignment, α, β, γ are adjustment coefficients.

[0043] A243-42: Construct a computing layer of the temperature fluctuation evaluation model based on the temperature fluctuation evaluation function, wherein the temperature fluctuation evaluation model also includes an input layer, and the input layer is cascaded with the computing layer.

[0044] A243-43: Substitute the first temperature maximum, the first temperature minimum, the first extreme horizontal distance, the first fluctuation frequency and the first amplitude mean into the calculation layer of the temperature fluctuation evaluation model, perform calculation in the calculation layer using the temperature fluctuation evaluation function, and obtain the first temperature fluctuation coefficient.

[0045] Specifically, it should be understood that, based on step A100, the multiple target monitoring components are partially or completely exposed to the structural surface of the target monitoring device. Based on this, this embodiment divides the target monitoring device into apparent regions of interest based on the multiple target monitoring components to obtain multiple component regions of interest. The multiple component regions of interest are regions on the multiple target monitoring components that can be visually observed from the structural surface of the target monitoring device.

[0046] It should be understood that the multiple component interest regions can visually observe the multiple target monitoring components, and accordingly, the multiple component interest regions can perform remote direct temperature acquisition through temperature monitoring equipment.

[0047] It should be understood that a temperature measuring gun usually measures the temperature of a small area or point within a specific distance, so this embodiment interactively obtains a monitoring range that characterizes the size of the area where the temperature measuring gun can measure the temperature, and generates a grid spacing based on the monitoring range.

[0048] The grid spacing is used to divide the first component region of interest into a first network of interest, wherein the first network of interest includes K grids of interest, the first component region of interest is any one of the multiple component regions of interest, and the first component region of interest is the temperature long-distance monitoring region of interest of the first target monitoring component among the multiple target monitoring components.

[0049] A standard operating mode is preset, and when the standard operating mode is used to control the production operation of the target monitoring equipment, the infrared thermal imager is scheduled to collect temperature of the area of ​​interest of the first component with the temperature measurement interval threshold as a constraint to obtain multiple temperature distribution images, wherein each temperature distribution image is the instantaneous temperature distribution of the area of ​​interest of the first component.

[0050] The multiple temperature distribution images are split according to the first network of interest to obtain K groups of temperature change sequences, each group of temperature change sequences is temperature data of a network of interest at K intervals with the same temperature measurement interval threshold.

[0051] A temperature fluctuation analysis is performed on the K groups of temperature change sequences, and based on the analysis results, the first target temperature monitoring node is extracted from the K grids of interest, where the first target temperature monitoring node is the optimal position for monitoring the temperature changes of the first target monitoring component during equipment operation.

[0052] The specific method of performing temperature fluctuation analysis on the K groups of temperature change sequences and extracting the first target temperature monitoring node from the K grids of interest based on the analysis results is as follows:

[0053] A temperature image coordinate is constructed using temperature as the Y-axis and time as the X-axis, and the temperature image coordinate is used to perform image conversion on a first group of temperature change sequences to obtain a first temperature change curve, wherein the first group of temperature change sequences is any one of the K groups of temperature change sequences.

[0054] A preset temperature fluctuation associated indicator set, wherein the temperature fluctuation associated indicator set includes a temperature maximum, a temperature minimum, an extreme value horizontal distance, a fluctuation frequency and an amplitude mean, the extreme value horizontal distance is the X-axis spacing between the temperature maximum and the temperature minimum in the temperature change curve, the fluctuation frequency is the number of peaks or troughs, and the amplitude mean is the average of multiple peak-to-valley values ​​of multiple groups of peaks and troughs in the temperature change curve.

[0055] The temperature fluctuation associated index set is used to perform data extraction and numerical calculation on the first temperature change curve to obtain a first temperature maximum value, a first temperature minimum value, a first extreme value horizontal distance, a first fluctuation frequency and a first amplitude mean value.

[0056] A temperature fluctuation evaluation function is pre-constructed, and the temperature fluctuation evaluation function is as follows:

[0057]

[0058] Among them, F is the temperature fluctuation coefficient, A is the amplitude mean, D is the temperature maximum, f is the fluctuation frequency, w1 is the amplitude weight assignment, w2 is the fluctuation frequency weight assignment, α, β, γ are adjustment coefficients.

[0059] A calculation layer of the temperature fluctuation evaluation model is constructed based on the temperature fluctuation evaluation function, wherein the temperature fluctuation evaluation model further includes an input layer, and the input layer is cascaded with the calculation layer.

[0060] The first temperature maximum, the first temperature minimum, the first extreme value horizontal distance, the first fluctuation frequency and the first amplitude mean are substituted into the calculation layer of the temperature fluctuation evaluation model, and calculation is performed in the calculation layer using the temperature fluctuation evaluation function to obtain the first temperature fluctuation coefficient.

[0061] By analogy, the temperature fluctuation analysis is performed on the K groups of temperature change sequences to obtain K temperature fluctuation coefficients, and the extreme values ​​(maximum values) of the K temperature fluctuation coefficients are extracted, and the first target temperature monitoring node is extracted from the K grids of interest based on the extreme value extraction results. It should be understood that during the operation of the target monitoring device, the temperature change of the first target temperature monitoring node is the most obvious, and it is suitable for monitoring the temperature change of the first target monitoring component. By analogy, the apparent temperature change analysis is performed on the multiple target monitoring components to obtain the multiple target temperature monitoring nodes.

[0062] This embodiment analyzes and determines the component temperature measurement position based on the apparent temperature changes of the components during the operation of the equipment, thereby achieving the technical effect of narrowing the temperature data collection range, improving the temperature data collection accuracy, and reducing the data processing complexity for target monitoring equipment fault judgment based on temperature analysis.

[0063] A300: Multiple temperature measuring guns are configured for the multiple target temperature monitoring nodes, and the multiple temperature measuring guns constitute a temperature measuring gun array.

[0064] A400: Preset a temperature measurement interval threshold. After the target monitoring device starts running, control the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes with the temperature measurement interval threshold as a constraint to obtain multiple temperature monitoring sequences.

[0065] Specifically, in this embodiment, a plurality of temperature measuring guns are configured for the plurality of target temperature monitoring nodes, and the plurality of temperature measuring guns constitute a temperature measuring gun array.

[0066] A temperature measurement interval threshold is preset. After the target monitoring device starts running, the temperature measurement interval threshold is used as a constraint to control the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes, so as to obtain multiple temperature monitoring sequences, each temperature monitoring sequence containing multiple temperature data of the same target temperature monitoring node at multiple different time points.

[0067] A500: Interactively obtain the target mode information of the target monitoring device, and identify equipment operation faults based on the target mode information and the multiple temperature monitoring sequences to obtain equipment fault information.

[0068] In one embodiment, target mode information of the target monitoring device is interactively obtained, and device operation fault identification is performed according to the target mode information and the multiple temperature monitoring sequences to obtain device fault information. Before that, step A500 of the method provided by the present application also includes:

[0069] A511: Interactively obtain multiple preset operating modes of the target monitoring device.

[0070] A512: Using the model information of the target monitoring device as a constraint, match and obtain the conceptual monitoring device.

[0071] A513: Configure a conceptual temperature measuring gun array for the conceptual monitoring device according to the multiple target temperature monitoring nodes.

[0072] A514: Use the first preset operating mode to start the operation of the concept monitoring device, and use the temperature measurement interval threshold as a constraint to control the concept temperature measuring gun array to perform temperature monitoring on the concept monitoring device, and obtain multiple first sample temperature monitoring sequences, wherein the first preset operating mode is any one of the multiple preset operating modes.

[0073] A515: And so on, obtain multiple groups of sample temperature monitoring sequences of the multiple preset operating modes.

[0074] A516: Use the multiple groups of sample temperature monitoring sequences to construct multiple operating mode detection branches, and set the multiple operating mode detection branches in parallel using the multiple preset operating mode identifiers to complete the construction of the operating fault identification model.

[0075] In one embodiment, target mode information of the target monitoring device is interactively obtained, and device operation fault identification is performed according to the target mode information and the multiple temperature monitoring sequences to obtain device fault information. Step A500 of the method provided by the present application also includes:

[0076] A521: Use the target mode information to traverse the running fault identification model to activate the target mode detection branch, wherein the target mode detection branch stores multiple target sample temperature monitoring sequences.

[0077] A522: Extract a first temperature monitoring sequence from the multiple temperature monitoring sequences, wherein the first temperature monitoring sequence is mapped to a first target monitoring component among the multiple target monitoring components.

[0078] A523: Use the first target monitoring component to map and traverse the first target sample temperature monitoring sequence of the multiple target sample temperature monitoring sequences of the target pattern detection branch to perform data alignment to obtain a comparison starting point and a comparison end point.

[0079] A524: Use the alignment starting point and the alignment end point to optimize the multiple target sample temperature monitoring sequences to obtain multiple aligned sample temperature monitoring sequences.

[0080] A525: Perform deviation calculation on the multiple aligned sample temperature monitoring sequences and the multiple temperature monitoring sequences to obtain multiple fault deviation coefficients.

[0081] A526: Interactively obtain multiple fault weight parameters of the multiple target monitoring components, and use the multiple fault weight parameters to perform weighted calculation on the multiple fault deviation coefficients to obtain the equipment fault information.

[0082] Specifically, in this embodiment, the control system of the target monitoring device is interacted to directly obtain the multiple preset operation modes of the target monitoring device that can perform production operation.

[0083] The model information of the target monitoring device is used as a constraint to match and obtain a conceptual monitoring device, which is an ideal fault-free device. A conceptual temperature measuring gun array is configured for the conceptual monitoring device according to the multiple target temperature monitoring nodes so as to use the conceptual temperature measuring gun array to perform temperature monitoring of the conceptual monitoring device during the simulated production operation.

[0084] The first preset operation mode is any one of the plurality of preset operation modes, the first preset operation mode is adopted to start the concept monitoring device, and the concept temperature measuring gun array is controlled to perform temperature monitoring on the concept monitoring device with the temperature measurement interval threshold as a constraint, so as to obtain a plurality of first sample temperature monitoring sequences. Similarly, a plurality of groups of sample temperature monitoring sequences of the plurality of preset operation modes are obtained.

[0085] The plurality of groups of sample temperature monitoring sequences are used to construct a plurality of operation mode detection branches, and the plurality of operation mode detection branches are arranged in parallel using the plurality of preset operation mode identifiers to complete the construction of the operation fault identification model.

[0086] Since the multiple operating mode detection branches are data processing branches rather than data analysis branches, the construction process of the multiple operating mode detection branches is integrated and explained in the subsequent data processing process part of the multiple operating mode detection branches.

[0087] The target mode information is used to traverse the operating fault identification model to activate the target mode detection branch. It should be understood that the preset operating mode identifier of the target mode detection branch is consistent with the operating mode of the target mode information, and the target mode detection branch stores the multiple target sample temperature monitoring sequences consisting of multiple sample temperature monitoring sequences corresponding to the preset operating mode.

[0088] A first temperature monitoring sequence is extracted from the plurality of temperature monitoring sequences, wherein the first temperature monitoring sequence is mapped to a first target monitoring component among the plurality of target monitoring components, and data alignment is performed using the first target monitoring component to map the first target sample temperature monitoring sequence among the plurality of target sample temperature monitoring sequences traversing the target pattern detection branch.

[0089] The specific data processing process of data alignment in the target mode detection branch is as follows:

[0090] The target pattern detection branch is pre-configured with a sliding window, and it should be understood that the data sequence length of the first target sample temperature monitoring sequence is much longer than that of the first temperature monitoring sequence.

[0091] The first temperature monitoring sequence is used as the size of the sliding window. Starting from the beginning of the first target sample temperature monitoring sequence, the sliding window is gradually moved. Each time the window is moved, a subsequence of the first target sample temperature monitoring sequence corresponding to the sliding window is taken out and compared with the first temperature monitoring sequence to obtain an overlap index of each moving window, where the overlap index is the Euclidean distance between the subsequence and the first temperature monitoring data.

[0092] After using a sliding window to traverse and compare multiple Euclidean distances of multiple subsequences of the first temperature monitoring sequence and the first target sample temperature monitoring sequence, the subsequence with the smallest Euclidean distance is used as the comparison starting point at the beginning of the first target sample temperature monitoring sequence and the end point as the comparison end point.

[0093] Local subsequence interception is performed from the multiple target sample temperature monitoring sequences using the time interval corresponding to the comparison start point and the comparison end point of the first target sample temperature monitoring sequence to obtain multiple aligned sample temperature monitoring sequences.

[0094] Deviation calculation is performed on the plurality of aligned sample temperature monitoring sequences and the plurality of temperature monitoring sequences based on Euclidean distance and absolute value to obtain a plurality of fault deviation coefficients of a plurality of target monitoring components.

[0095] Interactively obtain multiple fault weight parameters of the multiple target monitoring components, and use the multiple fault weight parameters to perform weighted calculation on the multiple fault deviation coefficients to obtain the equipment fault information, where the equipment fault information is a quantified value of the deviation of the actual operating state from the standard operating state during the operation of the target monitoring equipment under the target mode information.

[0096] This embodiment achieves the technical effect of quickly determining whether there is an operating fault in the monitoring device based on single-dimensional data monitoring, reducing the difficulty of determining an operating fault in the monitoring device, and quantifying the degree of fault in the monitoring device.

[0097] A600: Perform fault tracing based on the equipment fault information to obtain fault operation and maintenance node information.

[0098] Specifically, it should be understood that there is a cascade effect between the devices on the production line where the target monitoring device is located. When one device fails, it will cause one or more other devices to fail as well, and the failure of these other one or more devices is false.

[0099] Based on this, this embodiment interactively obtains a cascading failure device that has a cascading effect with the target monitoring device. If the cascading failure device is in a faulty state, the fault operation and maintenance node information is the cascading failure device. If the cascading failure device is not in a faulty state, the fault operation and maintenance node information is the target monitoring device, and the fault intensity identifier of the fault operation and maintenance node information is the device fault information.

[0100] This embodiment quickly quantifies whether production line equipment is faulty and the severity of the fault based on intelligent temperature monitoring, achieving the technical effect of improving the response speed of production line equipment fault identification while reducing the complexity of data processing in the production line equipment fault identification process.

[0101] Embodiment 2

[0102] Based on the same inventive concept as the temperature intelligent monitoring method based on the temperature measuring gun in the above-mentioned embodiment, Figure 3 As shown, the present application provides a temperature intelligent monitoring system based on a temperature measuring gun, wherein the system comprises:

[0103] The monitoring component determination unit 1 is used to interactively obtain multiple target monitoring components of a target monitoring device, wherein the multiple target monitoring components constitute the target monitoring device.

[0104] The monitoring node determination unit 2 is used to analyze the apparent temperature changes of the multiple target monitoring components to obtain multiple target temperature monitoring nodes.

[0105] The monitoring array construction unit 3 is used to configure a plurality of temperature measuring guns for the plurality of target temperature monitoring nodes, and the plurality of temperature measuring guns constitute a temperature measuring gun array.

[0106] The monitoring sequence acquisition unit 4 is used to preset a temperature measurement interval threshold. After the target monitoring device starts running, the temperature measurement interval threshold is used as a constraint to control the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes to obtain multiple temperature monitoring sequences.

[0107] The operation fault identification unit 5 is used to interactively obtain the target mode information of the target monitoring device, and perform equipment operation fault identification according to the target mode information and the multiple temperature monitoring sequences to obtain equipment fault information.

[0108] The fault tracing execution unit 6 is used to perform fault tracing according to the device fault information and obtain the fault operation and maintenance node information.

[0109] In one embodiment, the monitoring node determination unit 2 further includes:

[0110] The target monitoring device is divided into apparent regions of interest based on the constraints of the multiple target monitoring components to obtain multiple component regions of interest; the monitoring range of the temperature gun is interactively obtained, and a grid spacing is generated according to the monitoring range; the first component region of interest is divided into a first network of interest using the grid spacing, wherein the first network of interest includes K grids of interest, and the first component region of interest is any one of the multiple component regions of interest; temperature change fluctuation analysis is performed on the K grids of interest, and a first target temperature monitoring node is extracted from the K grids of interest based on the analysis results; and by analogy, the multiple target monitoring components are analyzed for apparent temperature changes to obtain the multiple target temperature monitoring nodes.

[0111] In one embodiment, the monitoring node determination unit 2 further includes:

[0112] A standard operating mode is preset, and when the standard operating mode is used to control the production operation of the target monitoring equipment, an infrared thermal imager is scheduled to collect temperature of the area of ​​interest of the first component with the temperature measurement interval threshold as a constraint to obtain a plurality of temperature distribution images; the plurality of temperature distribution images are split according to the first network of interest to obtain K groups of temperature change sequences; temperature fluctuation analysis is performed on the K groups of temperature change sequences, and the first target temperature monitoring node is extracted from the K grids of interest based on the analysis results.

[0113] In one embodiment, the monitoring node determination unit 2 further includes:

[0114] Perform image conversion on the first group of temperature change sequences to obtain a first temperature change curve, wherein the first group of temperature change sequences is any one of the K groups of temperature change sequences; preset a temperature fluctuation associated indicator set, wherein the temperature fluctuation associated indicator set includes a temperature maximum, a temperature minimum, an extreme horizontal distance, a fluctuation frequency and an amplitude mean; use the temperature fluctuation associated indicator set to perform data extraction on the first temperature change curve to obtain a first temperature maximum, a first temperature minimum, a first extreme horizontal distance, a first fluctuation frequency and a first amplitude mean; pre-construct a temperature fluctuation evaluation model, and synchronize the first temperature maximum, the first temperature minimum, the first extreme horizontal distance, the first fluctuation frequency and the first amplitude mean to the temperature fluctuation evaluation model to obtain a first temperature fluctuation coefficient; and by analogy, perform temperature fluctuation analysis on the K groups of temperature change sequences to obtain K temperature fluctuation coefficients, perform extreme value extraction on the K temperature fluctuation coefficients, and extract the first target temperature monitoring node from the K grids of interest based on the extreme value extraction result.

[0115] In one embodiment, the monitoring node determination unit 2 further includes:

[0116] A temperature fluctuation evaluation function is pre-constructed, and the temperature fluctuation evaluation function is as follows:

[0117]

[0118] Among them, F is the temperature fluctuation coefficient, A is the amplitude mean, D is the temperature maximum, f is the fluctuation frequency, w1 is the amplitude weight assignment, w2 is the fluctuation frequency weight assignment, α, β, γ are adjustment coefficients; based on the temperature fluctuation evaluation function, a calculation layer of the temperature fluctuation evaluation model is constructed, wherein the temperature fluctuation evaluation model also includes an input layer, and the input layer is cascaded with the calculation layer; the first temperature maximum, the first temperature minimum, the first extreme horizontal distance, the first fluctuation frequency and the first amplitude mean are substituted into the calculation layer of the temperature fluctuation evaluation model, and the temperature fluctuation evaluation function is used to perform calculation in the calculation layer to obtain the first temperature fluctuation coefficient.

[0119] In one embodiment, the operation fault identification unit 5 further includes:

[0120] Interactively obtain multiple preset operating modes of the target monitoring device; use the model information of the target monitoring device as a constraint to match and obtain a conceptual monitoring device; configure a conceptual temperature measuring gun array for the conceptual monitoring device according to the multiple target temperature monitoring nodes; adopt a first preset operating mode to start running the conceptual monitoring device, and use the temperature measurement interval threshold as a constraint to control the conceptual temperature measuring gun array to perform temperature monitoring on the conceptual monitoring device, and obtain multiple first sample temperature monitoring sequences, wherein the first preset operating mode is any one of the multiple preset operating modes; and so on, obtain multiple groups of sample temperature monitoring sequences of the multiple preset operating modes; use the multiple groups of sample temperature monitoring sequences to construct multiple operating mode detection branches, and set the multiple operating mode detection branches in parallel after using the multiple preset operating mode identifiers to complete the construction of the operating fault identification model.

[0121] In one embodiment, the operation fault identification unit 5 further includes:

[0122] The target pattern information is used to activate the target pattern detection branch in the running fault identification model traversal, wherein the target pattern detection branch stores multiple target sample temperature monitoring sequences; a first temperature monitoring sequence is extracted from the multiple temperature monitoring sequences, wherein the first temperature monitoring sequence is mapped to the first target monitoring component among the multiple target monitoring components; the first target monitoring component is used to map the first target sample temperature monitoring sequence among the multiple target sample temperature monitoring sequences traversing the target pattern detection branch to perform data alignment to obtain a comparison starting point and a comparison end point; the multiple target sample temperature monitoring sequences are optimized using the comparison starting point and the comparison end point to obtain multiple aligned sample temperature monitoring sequences; deviation calculation is performed on the multiple aligned sample temperature monitoring sequences and the multiple temperature monitoring sequences to obtain multiple fault deviation coefficients; multiple fault weight parameters of the multiple target monitoring components are interactively obtained, and the multiple fault weight parameters are used to perform weighted calculation on the multiple fault deviation coefficients to obtain the equipment fault information.

[0123] Any of the methods or steps described above may be stored as computer instructions or programs in various types of computer memories, and the computer instructions or programs may be recognized by various types of computer processors to implement any of the methods or steps described above.

[0124] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principles of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. The temperature intelligent monitoring method based on the temperature measuring gun is characterized by: The method comprises: interactively obtaining a plurality of target monitoring components of a target monitoring device, wherein the plurality of target monitoring components constitute the target monitoring device; Performing apparent temperature change analysis on the multiple target monitoring components to obtain multiple target temperature monitoring nodes; A plurality of temperature measuring guns are configured for the plurality of target temperature monitoring nodes, and the plurality of temperature measuring guns constitute a temperature measuring gun array; A temperature measurement interval threshold is preset, and after the target monitoring device starts running, the temperature measurement interval threshold is used as a constraint to control the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes, so as to obtain multiple temperature monitoring sequences; interactively obtaining target mode information of the target monitoring device, and performing device operation fault identification according to the target mode information and the multiple temperature monitoring sequences to obtain device fault information; The fault source is traced based on the equipment fault information to obtain the fault operation and maintenance node information.

2. The method according to claim 1, characterized in that Performing apparent temperature change analysis on the multiple target monitoring components to obtain multiple target temperature monitoring nodes includes: Dividing the target monitoring device into apparent regions of interest based on the multiple target monitoring components as constraints to obtain multiple component regions of interest; Interactively obtain the monitoring range of the temperature measuring gun, and generate a grid spacing according to the monitoring range; Dividing the first component region of interest into a first network of interest using the grid spacing, wherein the first network of interest includes K grids of interest, and the first component region of interest is any one of the multiple component regions of interest; Performing temperature change fluctuation analysis on the K grids of interest, and extracting a first target temperature monitoring node from the K grids of interest based on the analysis result; By analogy, the apparent temperature change analysis is performed on the multiple target monitoring components to obtain the multiple target temperature monitoring nodes.

3. The method according to claim 2, characterized in that Performing temperature change fluctuation analysis on the K grids of interest, and extracting a first target temperature monitoring node from the K grids of interest based on the analysis result, comprising: Preset a standard operation mode, and when the standard operation mode is used to control the production operation of the target monitoring equipment, schedule an infrared thermal imager to collect temperature of the area of ​​interest of the first component with the temperature measurement interval threshold as a constraint to obtain multiple temperature distribution images; Splitting the plurality of temperature distribution images according to the first network of interest to obtain K groups of temperature change sequences; A temperature fluctuation analysis is performed on the K groups of temperature change sequences, and the first target temperature monitoring node is extracted from the K grids of interest based on the analysis result.

4. The method according to claim 3, characterized in that Performing temperature fluctuation analysis on the K groups of temperature change sequences, and extracting the first target temperature monitoring node from the K grids of interest based on the analysis result, comprising: Performing image conversion on a first group of temperature change sequences to obtain a first temperature change curve, wherein the first group of temperature change sequences is any one of the K groups of temperature change sequences; Preset a temperature fluctuation related indicator set, wherein the temperature fluctuation related indicator set includes a temperature maximum value, a temperature minimum value, a temperature extreme value horizontal distance, a fluctuation frequency and an amplitude mean value; The temperature fluctuation associated indicator set is used to extract data from the first temperature change curve to obtain a first temperature maximum value, a first temperature minimum value, a first extreme value horizontal distance, a first fluctuation frequency and a first amplitude mean value; Pre-constructing a temperature fluctuation evaluation model, and synchronizing the first temperature maximum value, the first temperature minimum value, the first extreme value horizontal distance, the first fluctuation frequency and the first amplitude mean to the temperature fluctuation evaluation model to obtain a first temperature fluctuation coefficient; By analogy, temperature fluctuation analysis is performed on the K groups of temperature change sequences to obtain K temperature fluctuation coefficients, extreme value extraction is performed on the K temperature fluctuation coefficients, and the first target temperature monitoring node is extracted from the K grids of interest based on the extreme value extraction results.

5. The method according to claim 1, characterized in that Interactively obtaining target mode information of the target monitoring device, and performing device operation fault identification according to the target mode information and the multiple temperature monitoring sequences to obtain device fault information, before that, the method further includes: Interactively obtaining a plurality of preset operating modes of the target monitoring device; Taking the model information of the target monitoring device as a constraint, matching and obtaining a conceptual monitoring device; According to the plurality of target temperature monitoring nodes, the conceptual monitoring device is configured with a conceptual temperature measuring gun array; The concept monitoring device is started and operated in a first preset operation mode, and the concept temperature measuring gun array is controlled to perform temperature monitoring on the concept monitoring device with the temperature measurement interval threshold as a constraint, to obtain a plurality of first sample temperature monitoring sequences, wherein the first preset operation mode is any one of the plurality of preset operation modes; By analogy, multiple groups of sample temperature monitoring sequences of the multiple preset operating modes are obtained; The plurality of groups of sample temperature monitoring sequences are used to construct a plurality of operation mode detection branches, and the plurality of operation mode detection branches are arranged in parallel using the plurality of preset operation mode identifiers to complete the construction of the operation fault identification model.

6. The method according to claim 5, characterized in that Interactively obtaining target mode information of the target monitoring device, and performing device operation fault identification according to the target mode information and the multiple temperature monitoring sequences to obtain device fault information, including: Using the target mode information to traverse and activate a target mode detection branch in the running fault identification model, wherein the target mode detection branch stores a plurality of target sample temperature monitoring sequences; Extracting a first temperature monitoring sequence from the plurality of temperature monitoring sequences, wherein the first temperature monitoring sequence is mapped to a first target monitoring component among the plurality of target monitoring components; Using the first target monitoring component to map and traverse the first target sample temperature monitoring sequence of the plurality of target sample temperature monitoring sequences of the target pattern detection branch to perform data alignment, and obtain a comparison starting point and a comparison end point; Optimizing the plurality of target sample temperature monitoring sequences by using the comparison starting point and the comparison end point to obtain a plurality of aligned sample temperature monitoring sequences; performing deviation calculation on the plurality of aligned sample temperature monitoring sequences and the plurality of temperature monitoring sequences to obtain a plurality of fault deviation coefficients; A plurality of fault weight parameters of the plurality of target monitoring components are interactively obtained, and the plurality of fault weight parameters are used to perform weighted calculation on the plurality of fault deviation coefficients to obtain the equipment fault information.

7. The temperature intelligent monitoring system based on the temperature measuring gun is characterized by: The steps for implementing the method according to any one of claims 1 to 6 include: A monitoring component determination unit, configured to interactively obtain a plurality of target monitoring components of a target monitoring device, wherein the plurality of target monitoring components constitute the target monitoring device; A monitoring node determination unit, configured to analyze the apparent temperature changes of the plurality of target monitoring components to obtain a plurality of target temperature monitoring nodes; A monitoring array construction unit, configured to configure a plurality of temperature measuring guns for the plurality of target temperature monitoring nodes, wherein the plurality of temperature measuring guns constitute a temperature measuring gun array; A monitoring sequence obtaining unit, configured to preset a temperature measurement interval threshold, and after the target monitoring device starts running, control the temperature measuring gun array to perform temperature monitoring on the multiple target temperature monitoring nodes with the temperature measurement interval threshold as a constraint to obtain multiple temperature monitoring sequences; An operation fault identification unit, used for interactively obtaining target mode information of the target monitoring device, and performing equipment operation fault identification according to the target mode information and the multiple temperature monitoring sequences to obtain equipment fault information; The fault tracing execution unit is used to perform fault tracing according to the device fault information and obtain the fault operation and maintenance node information.

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