Real-time monitoring method, system and equipment based on MES (Manufacturing Execution System) and storage medium thereof

Abstract

The invention discloses a real-time monitoring method, system and device based on an MES system and a storage medium thereof.The real-time monitoring method based on the MES system comprises the steps that data are obtained from the MES system; based on the classification standard, classifying the data acquired from the MES system to obtain a classification result; performing data monitoring analysis on the data under each classification result according to the classification result of the data to obtain a data monitoring analysis result; the data monitoring analysis of the data comprises the following steps: according to a time sequence of data acquisition, performing parameter change calculation on the data under the same classification result to obtain a parameter change calculation result; on the basis of the interval time value of data acquisition, according to the parameter change calculation result, parameter change rate calculation is carried out, and a parameter change rate calculation result is obtained; the parameter change and parameter change rate calculation result is fed back to the MES system, so that the MES system forms a fluctuation curve model through the parameter change and parameter change rate calculation result according to the time sequence.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring systems, and in particular to a real-time monitoring method, system, device and storage medium based on a MES system. Background Art

[0002] In the production field, both material control and production progress control are indicators that need to be monitored. After the front-end system receives an order and has to manufacture products in the manufacturing department, it faces a series of problems such as progress monitoring failure and production interruption caused by incomplete materials. For example, in the process of processing a circuit board, multiple links are involved, such as soldering, capacitor mounting, and wave soldering chips. The efficiency of production is determined by the progress of all process flows, so monitoring the process in the production field needs to be monitored from a holistic perspective.

[0003] In the current production process, the MES system is generally used to monitor the production progress. The MES system, full name Manufacturing Execution System, is a computerized system used to monitor, track and control the production process in real time. The MES system can integrate the production process, resources and data on the production line to improve production efficiency and quality and reduce production costs.

[0004] The existing MES system collects data from the production process, and after statistical analysis, pushes and displays real-time data and statistical results to all personnel. However, the current MES system can only collect and count production data, and has too few details for monitoring the production process, such as the fluctuation of parameters. All personnel still need to screen and analyze the uniformly displayed data to judge the tasks and status they are concerned about, and even need to calculate and analyze the corresponding data to obtain the corresponding results, making it difficult to formulate or adjust the factory's production in a timely manner. Summary of the invention

[0005] Therefore, in order to solve the above-mentioned shortcomings, in the first aspect, the present invention provides a real-time monitoring method based on the MES system, comprising:

[0006] Acquire data from the MES system, including product production data, energy data, and material inventory data acquired by the MES system and uploaded by the on-site collection unit;

[0007] Based on the classification criteria, classify the data obtained from the MES system to obtain the classification results;

[0008] According to the classification results of the data, data monitoring and analysis are performed on the data under each classification result to obtain data monitoring and analysis results;

[0009] The data monitoring and analysis includes:

[0010] According to the time series of data collection, the parameter change calculation is performed on the data under the same classification result to obtain the parameter change calculation result;

[0011] Based on the interval time value of data collection and according to the above parameter change calculation result, the parameter change rate calculation is performed to obtain the parameter change rate calculation result;

[0012] The parameter change calculation results and the parameter change rate calculation results are fed back to the MES system, so that the MES system forms a fluctuation curve model with the parameter change calculation results and the parameter change rate calculation results according to the time series.

[0013] Based on the above solution, further, the data also includes image data;

[0014] The data monitoring and analysis also includes

[0015] Based on the time series, the image data is segmented to obtain multiple segmented images;

[0016] Search the possible locations of the target features in the segmented image to obtain candidate regions, and coordinate the candidate regions;

[0017] Extract the target features in the candidate area to obtain the feature extraction results, and perform coordinate analysis and calculation of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain the coordinate analysis and calculation results of the target features;

[0018] Performing difference calculation on the target feature coordinate analysis calculation results in the segmented images in adjacent time series to obtain a first difference calculation result;

[0019] A plurality of first difference calculation results are obtained, and the plurality of first difference calculation results are arranged in chronological order to form a difference data set, and an entry-exit analysis and judgment is performed according to the difference data set to obtain an entry-exit analysis and judgment result.

[0020] Based on the above solution, further, the data monitoring and analysis also includes

[0021] Obtaining longitude and latitude at regular intervals, and mapping the longitude and latitude on the recorded electronic map to form coordinate points;

[0022] Based on the time series, multiple coordinate points are connected into lines to form a motion trajectory.

[0023] On the basis of the above scheme, further, after obtaining the factory entry and exit analysis and judgment result;

[0024] Obtain the time when the above-mentioned factory entry and exit analysis and judgment results are obtained, and based on the time when the above-mentioned factory entry and exit analysis and judgment results are obtained, perform historical time range calculation according to the preset historical time tracing standard to obtain the historical time range calculation result;

[0025] According to the above historical time range calculation result, a first motion trajectory corresponding to the time range calculation result is obtained;

[0026] Based on the time of obtaining the results of the factory entry and exit analysis and judgment, the future time range is calculated according to the preset future time verification standard to obtain the future time range calculation results;

[0027] Based on the above calculation result of the future time range, a second motion trajectory within the future time range is obtained in real time;

[0028] Factory entry and exit verification is performed according to the first motion trajectory and the second motion trajectory to obtain a factory entry and exit verification result.

[0029] Based on the above solution, further, the data monitoring and analysis also includes

[0030] Search for possible locations in the image data, obtain candidate areas, and coordinate the candidate areas;

[0031] Extract target features in the candidate area to obtain feature extraction results, and perform coordinate analysis of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain coordinate analysis results;

[0032] Performing difference calculation on the target feature coordinate analysis result to obtain a second difference calculation result;

[0033] An operation safety judgment is performed based on the second difference calculation result to obtain an operation safety judgment result.

[0034] Based on the above solution, further, the classification of the data obtained from the MES system includes

[0035] According to the data presentation form, the data obtained from the MES system is classified into primary categories to obtain the primary classification results;

[0036] Based on the above primary classification results, tracing back the collection units associated with the primary classified data to obtain tracing results;

[0037] The data is classified into secondary categories according to the tracing results to obtain the secondary classification results.

[0038] In a second aspect, the present invention provides a real-time monitoring system based on an MES system, which is used to implement the above-mentioned real-time monitoring method. The system includes

[0039] A data collection unit, which can obtain data from the MES system, including product production data, energy data, and material inventory data obtained by the MES system and uploaded by the on-site collection unit;

[0040] A data classification unit, which classifies the data obtained from the MES system based on the entered classification standard to obtain a classification result;

[0041] The monitoring and analysis unit performs data monitoring and analysis on the data under each classification result according to the classification result of the data to obtain the data monitoring and analysis result;

[0042] The monitoring and analysis unit includes

[0043] A parameter variable calculation module, wherein the parameter variable calculation module performs parameter change calculation on the data under the same classification result according to the time series of data collection to obtain the parameter change calculation result;

[0044] A change rate calculation module, wherein the change rate calculation module performs parameter change rate calculation based on the interval time value of data collection and the above parameter change calculation result to obtain the parameter change rate calculation result;

[0045] A feedback module feeds back the parameter change calculation result and the parameter change rate calculation result to the MES system, so that the MES system forms a fluctuation curve model with the parameter change calculation result and the parameter change rate calculation result according to the time series.

[0046] On the basis of the above solution, further, the monitoring and analysis unit also includes

[0047] An image segmentation module, wherein the image segmentation module performs segmentation processing on the image data based on a time series to obtain a plurality of segmented images;

[0048] A first image processing module, which searches for possible locations of target features in the segmented image to obtain candidate regions and performs coordinate processing on the candidate regions;

[0049] A feature extraction module, which can extract target features in the candidate area to obtain feature extraction results, and perform coordinate analysis and calculation of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain the coordinate analysis and calculation results of the target features;

[0050] A first calculation module, the first calculation module performs difference calculation on the target feature coordinate analysis calculation results in the segmented images in adjacent time series to obtain a first difference calculation result;

[0051] The factory entry and exit judgment module can obtain multiple first difference calculation results, arrange the multiple first difference calculation results in time sequence to form a difference data set, perform factory entry and exit analysis and judgment based on the difference data set, and obtain the factory entry and exit analysis and judgment result.

[0052] In a third aspect, the present invention provides a monitoring device, the monitoring device comprising

[0053] Memory for storing computer programs;

[0054] The processor is used to execute the program to implement each step of the real-time monitoring method based on the MES system as described in any one of claims 1 to 7.

[0055] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the above-mentioned real-time monitoring method based on the MES system.

[0056] The present invention has the following advantages:

[0057] The present invention obtains parameter data, calculates parameter variables and parameter change rates according to the parameter data, and feeds them back to the MES system, so that the MES system forms a fluctuation curve model with the above parameter change calculation results and parameter change rate calculation results according to the time series. The monitoring personnel can monitor the parameter changes in each link of the production process in real time by observing the fluctuation curve model, and formulate corresponding strategies in time according to the fluctuation curve.

[0058] The present invention acquires images of the entry and exit collection points, decomposes, identifies and analyzes the collected images, and determines the entry and exit of the transportation equipment, thereby realizing automatic monitoring of the transportation equipment. At the same time, after judging the entry and exit status, the entry and exit status will be verified, thereby achieving accuracy of data judgment.

[0059] The present invention analyzes images of the construction work area and monitors in real time whether workers are wearing helmets, answering mobile phones and other dangerous behaviors, thereby improving the safety of operations.

[0060] The present invention can also monitor the movement trajectory of the transport equipment and the real-time position of the transport equipment in real time, thereby monitoring the transport operation of the transport equipment in real time and obtaining material position information in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of a real-time monitoring method;

[0062] Figure 2 yes Figure 1Schematic diagram of classification process in the real-time monitoring method shown;

[0063] Figure 3 yes Figure 1 The schematic diagram of the in-and-out monitoring process in the real-time monitoring method shown;

[0064] Figure 4 yes Figure 1 A schematic diagram of the parameter change monitoring process in the real-time monitoring method shown;

[0065] Figure 5 yes Figure 1 A schematic diagram of the parameter fluctuation analysis process in the real-time monitoring method shown;

[0066] Figure 6 yes Figure 1 Schematic diagram of the dangerous behavior analysis process in the real-time monitoring method shown. DETAILED DESCRIPTION

[0067] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0068] In this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises", or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0069] As described in the background technology, the existing MES system collects data during the production process, and after statistical analysis, pushes and displays real-time data and statistical results to all personnel. However, the current MES system can only collect and count production data, and has too few details for monitoring the production process, such as the fluctuation of parameters. All personnel still need to screen and analyze the uniformly displayed data to judge the tasks and status they are concerned about, and even need to calculate and analyze the corresponding data to obtain the corresponding results, making it difficult to formulate or adjust the factory's production in a timely manner.

[0070] Embodiment 1:

[0071] Therefore, in order to solve the above technical problems, the present invention provides a real-time monitoring method based on the MES system, such as Figure 1 As shown, the method includes

[0072] S100: Acquire data from the MES system, including product production data, energy data, and material inventory data acquired by the MES system and uploaded by the on-site collection unit;

[0073] S200: classify the data obtained from the MES system based on the classification standard to obtain the classification result;

[0074] S300: According to the classification results of the data, data monitoring and analysis are performed on the data under each classification result to obtain data monitoring and analysis results;

[0075] Among them, Figure 4 As shown, in step S300, data monitoring and analysis are performed on the data, including

[0076] S321: performing parameter change calculation on the data under the same classification result according to the time series of data collection to obtain the parameter change calculation result;

[0077] Specifically, based on the collection time of the parameter data, according to the order of data collection time, the parameter data of the current time node and the parameter data of the previous time node are difference calculated to obtain the difference calculation result, which is the change value of the parameter data based on the time relationship. The specific calculation method is:

[0078]

[0079] Among them, ΔA is the real-time parameter variable value, A is the parameter value, t1 is the current time node, and t0 is the previous time node adjacent to t1;

[0080] The real-time parameter variable value is obtained through the above calculation method.

[0081] S322: Based on the interval time value of data collection and the above parameter change calculation result, the parameter change rate calculation is performed to obtain the parameter change rate calculation result;

[0082] Specifically, the above interval time value is the frequency of parameter collection, that is, how often the parameter data is collected, and then the parameter change rate is calculated in combination with the calculation result of the above real-time parameter variable value. The specific calculation method is as follows:

[0083]

[0084] Among them, V A is the parameter change rate; Δt is the parameter acquisition frequency;

[0085] The parameter change rate is obtained through the above calculation method.

[0086] S323: Feedback the parameter change calculation result and the parameter change rate calculation result to the MES system, so that the MES system forms a fluctuation curve model with the parameter change calculation result and the parameter change rate calculation result according to the time series.

[0087] Specifically, the MES system will establish a coordinate system based on time, parameter change value, and parameter change rate. After receiving the parameter change calculation results and parameter change rate calculation results fed back by the monitoring system, the MES system will map the above results into the coordinate system and connect the mapping points into lines according to the chronological order, thereby forming a parameter change curve and a parameter change rate curve.

[0088] Through the above curves, monitoring personnel can intuitively observe the growth of usage of various data such as output, energy, materials, etc. in the production process, as well as the corresponding relationship, thereby providing a basis for monitoring personnel to make decisions.

[0089] Embodiment 2:

[0090] At present, the registration of inbound and outbound storage is usually done manually, or the inbound and outbound registration of transportation equipment is done through a read-write module. This is inefficient and still requires human participation, resulting in large errors and prone to erroneous registration and missed registration.

[0091] Therefore, in order to solve the above technical problems, this embodiment proposes optimization based on embodiment 1.

[0092] In this embodiment, the data acquired by the monitoring system from the MES system also includes image data, which is a video image of the location where the transport equipment enters and leaves the factory;

[0093] like Figure 3 As shown, the data monitoring and analysis also includes

[0094] S311: Based on the time series, the image data is segmented to obtain a plurality of segmented images;

[0095] Specifically, through a preset time segmentation condition, for example, the time segmentation condition is t, and the unit is s, the video image will be captured after each interval of time t to obtain a static image.

[0096] S312: searching for possible locations of target features in the segmented image to obtain candidate regions, and performing coordinate processing on the candidate regions;

[0097] Specifically, searching for regions in the static image where target features may appear, thereby obtaining candidate regions where target features may appear, identifying scenes in the candidate regions to match scene coordinates in a database, thereby obtaining coordinate ranges of the candidate regions, and establishing a coordinate system of the candidate regions by obtaining the coordinate ranges of the candidate regions in multiple static images;

[0098] S313: extracting target features in the candidate area to obtain feature extraction results, and performing coordinate analysis and calculation of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain coordinate analysis and calculation results of the target features;

[0099] Specifically, by extracting the target features in the candidate area, and matching the extracted target features with the contrast features pre-entered in the database, if they match, the target features are analyzed and mapped to the coordinate points of the above-mentioned coordinate system, and the coordinate points of the target features in the single static image are obtained by calculation, and the matching includes the matching of the transport vehicle body and the matching of the transport vehicle code, number or logo;

[0100] S314: performing difference calculation on the target feature coordinate analysis calculation results in the segmented images in adjacent time series to obtain a first difference calculation result;

[0101] Specifically, the above target feature coordinate points are subjected to difference calculation according to the time sequence, so that the difference calculation result of the target feature coordinate points is calculated as follows:

[0102] [ΔX, ΔY] = [(X t -X0), Y t -Y0]

[0103] Among them, [ΔX, ΔY] is the target feature coordinate difference, [X t , Y t ] is the coordinate of the target feature in the static image captured at time t, and [X0, Y0] is the coordinate of the target feature in the static image captured at the previous adjacent time before time t.

[0104] Obtaining the difference coordinate value of the target feature by the above calculation method, obtaining a plurality of first difference calculation results, and arranging the plurality of first difference calculation results in time sequence to form a difference data set;

[0105] For example, the coordinate difference of the first time series t0-t1 is [ΔX1, ΔY1], the coordinate difference of the second time series t1-t2 is [ΔX2, ΔY2], ..., ... the coordinate difference of the nth time series t n-1 ~t n The coordinate difference is [ΔX n , ΔY n], the above coordinate differences are arranged according to the order of the time series to form a data set {[ΔX1, ΔY1], [ΔX1, ΔY2]…, …[ΔX n , ΔY n ]}, by analyzing the above data set, judging the in-and-out status of the transportation tool, and obtaining the in-and-out status of the transportation equipment;

[0106] An entry-exit analysis and judgment is performed based on the difference data set to obtain an entry-exit analysis and judgment result.

[0107] In addition, in order to grasp the transportation status of transportation equipment in real time, the above-mentioned data monitoring and analysis also includes:

[0108] Obtaining longitude and latitude at regular intervals, and mapping the longitude and latitude on the recorded electronic map to form coordinate points;

[0109] Based on the time series, multiple coordinate points are arranged to form a motion trajectory.

[0110] In order to verify the accuracy of the above factory entry and exit status, after obtaining the factory entry and exit analysis and judgment results;

[0111] Obtain the time when the above-mentioned factory entry and exit analysis and judgment results are obtained, and based on the time when the above-mentioned factory entry and exit analysis and judgment results are obtained, perform historical time range calculation according to the preset historical time tracing standard to obtain the historical time range calculation result;

[0112] For example, the historical time tracing standard is c, the unit is s, and the time when the above factory entry and exit analysis and judgment result is obtained is d, then d is used as the reference to trace back c seconds, so as to obtain the result of the time period range of dc to d;

[0113] According to the calculation result of the above historical time range, a first motion trajectory corresponding to the calculation result of the time range is obtained. Specifically, based on the above time period range, the historical motion trajectory of the transportation equipment within the time range is retrieved.

[0114] Similarly, based on the time when the results of the factory entry and exit analysis are obtained, the future time range calculation is performed according to the preset future time verification standard to obtain the future time range calculation results;

[0115] Exemplarily, the future time verification standard is e, and the unit is s. Then, d is used as a reference to push back e seconds, so as to obtain a time period range result of d to d+e.

[0116] Based on the above calculation result of the future time range, a second motion trajectory within the future time range is obtained in real time, specifically, a motion trajectory formed by real-time coordinate points within the time range of d to d+e is obtained in real time;

[0117] Factory entry and exit verification is performed according to the first motion trajectory and the second motion trajectory to obtain a factory entry and exit verification result.

[0118] If the verification is passed, the entry and exit judgment result is fed back to the MES system. If the verification is not passed, the entry and exit judgment result is associated with the abnormal identification and fed back to the MES system.

[0119] Embodiment 3:

[0120] In order to monitor the wearing of helmets and answering of mobile phones by operators, this embodiment proposes improvements based on the above embodiments, such as Figure 5 As shown, data monitoring and analysis are performed on the data, including

[0121] S331: Search for possible locations in the image data to obtain candidate regions, and coordinate the candidate regions. This process can refer to step S312 in real-time 2.

[0122] S332: extracting target features in the candidate area to obtain feature extraction results, and performing coordinate analysis on the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain coordinate analysis results;

[0123] S333: performing difference calculation on the target feature coordinate analysis result to obtain a second difference calculation result; performing operation safety judgment according to the second difference calculation result to obtain an operation safety judgment result.

[0124] Specifically, by extracting the target features and matching the extracted features, the type of the target features is obtained, and the feature types include the construction worker's head, handheld communicator, and helmet. After matching, the coordinate points between the head, handheld communicator, and helmet are analyzed, and the coordinate differences between the head and the handheld communicator, and between the head and the helmet are calculated. According to the coordinate difference results between the head and the handheld communicator, and between the head and the helmet, it is determined whether the operator is wearing a helmet and whether he answers the phone. The calculation method of the coordinate difference between the head and the handheld communicator, and between the head and the helmet can refer to the calculation method of the target feature coordinate difference in S314 in Example 2.

[0125] Embodiment 4:

[0126] In order to improve the data extraction efficiency, this embodiment proposes improvements based on the above embodiment, such as Figure 2 As shown, the classification of data obtained from the MES system includes

[0127] According to the data presentation form, the data obtained from the MES system is primarily classified to obtain a primary classification result; the data presentation form includes images and numerical values.

[0128] Based on the above primary classification results, tracing back the collection units associated with the primary classified data to obtain tracing results;

[0129] The data is classified into secondary categories according to the tracing results to obtain the secondary classification results.

[0130] Embodiment 5:

[0131] In this embodiment, the present invention provides a real-time monitoring system based on the MES system based on the above embodiment, which is used to implement the above real-time monitoring method. The system includes

[0132] A data collection unit, which can obtain data from the MES system, including product production data, energy data, and material inventory data obtained by the MES system and uploaded by the on-site collection unit;

[0133] A data classification unit, which classifies the data obtained from the MES system based on the entered classification standard to obtain a classification result;

[0134] The monitoring and analysis unit performs data monitoring and analysis on the data under each classification result according to the classification result of the data to obtain the data monitoring and analysis result;

[0135] The monitoring and analysis unit includes

[0136] A parameter variable calculation module, wherein the parameter variable calculation module performs parameter change calculation on the data under the same classification result according to the time series of data collection to obtain the parameter change calculation result;

[0137] A change rate calculation module, wherein the change rate calculation module performs parameter change rate calculation based on the interval time value of data collection and the above parameter change calculation result to obtain the parameter change rate calculation result;

[0138] A feedback module feeds back the parameter change calculation result and the parameter change rate calculation result to the MES system, so that the MES system forms a fluctuation curve model with the parameter change calculation result and the parameter change rate calculation result according to the time series.

[0139] The monitoring and analysis unit also includes

[0140] An image segmentation module, wherein the image segmentation module performs segmentation processing on the image data based on a time series to obtain a plurality of segmented images;

[0141] A first image processing module, which searches for possible locations of target features in the segmented image to obtain candidate regions and performs coordinate processing on the candidate regions;

[0142] A first feature extraction module, which can extract target features in the candidate area to obtain feature extraction results, and perform coordinate analysis and calculation of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain coordinate analysis and calculation results of the target features;

[0143] A first calculation module, the first calculation module performs difference calculation on the target feature coordinate analysis calculation results in the segmented images in adjacent time series to obtain a first difference calculation result;

[0144] The factory entry and exit judgment module can obtain multiple first difference calculation results, arrange the multiple first difference calculation results in time sequence to form a difference data set, perform factory entry and exit analysis and judgment based on the difference data set, and obtain the factory entry and exit analysis and judgment result.

[0145] The monitoring and analysis unit also includes

[0146] A coordinate generation module is used to obtain longitude and latitude at regular intervals and map the longitude and latitude on the recorded electronic map to form coordinate points;

[0147] The trajectory generation module connects multiple coordinate points into lines based on the time series to form a motion trajectory.

[0148] The monitoring and analysis unit also includes

[0149] The tracing time determination module, after obtaining the factory entry and exit analysis and judgment result, obtains the time when the factory entry and exit analysis and judgment result is obtained, and takes the factory entry and exit analysis and judgment result obtaining time as a benchmark, performs historical time range calculation according to the preset historical time tracing standard, and obtains the historical time range calculation result;

[0150] A first trajectory acquisition module, which acquires a first motion trajectory corresponding to the calculation result of the time range according to the calculation result of the historical time range;

[0151] The retrospective time determination module takes the time when the factory entry and exit analysis and judgment results are obtained as the benchmark, calculates the future time range according to the preset future time verification standard, and obtains the future time range calculation result;

[0152] A second trajectory acquisition module, based on the above future time range calculation result, acquires the second motion trajectory within the future time range in real time;

[0153] The verification module performs factory entry and exit verification according to the first motion trajectory and the second motion trajectory to obtain a factory entry and exit verification result.

[0154] The monitoring and analysis unit also includes

[0155] The second image processing module searches for possible locations in the image data to obtain candidate regions and performs coordinate processing on the candidate regions;

[0156] The second feature extraction module extracts the target features in the candidate area to obtain a feature extraction result, and performs coordinate analysis of the target features based on the feature extraction result and the coordinate processing result of the candidate area to obtain a coordinate analysis result;

[0157] A second calculation module performs difference calculation on the target feature coordinate analysis result to obtain a second difference calculation result;

[0158] The operation safety judgment module performs operation safety judgment according to the second difference calculation result to obtain an operation safety judgment result.

[0159] The data classification unit also includes

[0160] The primary classification module performs primary classification on the data obtained from the MES system according to the data presentation form and obtains the primary classification results;

[0161] The associated tracing module, based on the above primary classification results, traces the collection units associated with the primary classified data to obtain a tracing result;

[0162] The secondary classification module performs secondary classification on the data according to the tracing results to obtain the secondary classification results.

[0163] like Figure 6 As shown, a sensor unit 100 is provided on the production equipment. The sensor unit is composed of various types of data detection sensors and is used to collect data in the production process, including output data, energy usage data, material usage data, etc. A positioning module 200 is provided on the transportation equipment. The positioning module 200 is associated with the number and identification of the transportation equipment itself. An image acquisition unit 300 is provided at the place where the transportation equipment must pass through when entering and leaving the factory and in the personnel working area. Images are collected by the image acquisition unit. The data collected by the above-mentioned units or modules can be uploaded to the database through the communication unit for storage or recorded and counted in the MES system. The MES system can also retrieve historical data stored in the database. The monitoring system is connected to the MES system and can transmit data with the MES system.

[0164] Embodiment 5:

[0165] This embodiment is based on Embodiments 1 to 3, and provides a monitoring device, wherein the monitoring device includes a memory for storing a computer program;

[0166] The processor is used to execute the program to implement the various steps of the real-time monitoring method based on the MES system recorded in Examples 1 to 3.

[0167] It should be noted that, in the above embodiments, the description of each embodiment has its own focus, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0168] It should be understood by those skilled in the art that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-readable program codes.

[0169] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement the above-mentioned real-time monitoring method based on the MES system.

[0173] If the module / unit integrated in the multi-task model training device / terminal device of the newly added task is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0174] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0178] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present application. The above embodiments are only used to illustrate the technical solution of the present application, not to limit it; although the present application is described in detail with reference to the above embodiments, ordinary technicians in this field should understand that: it is still possible to modify the technical solutions recorded in the above embodiments, or to replace some of the technical features therein by equivalent; and these modifications or replacements do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

[0179] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A real-time monitoring method based on MES system, characterized in that: include Acquire data from the MES system, including product production data, energy data, and material inventory data acquired by the MES system and uploaded by the on-site collection unit; Based on the classification criteria, classify the data obtained from the MES system to obtain the classification results; According to the classification results of the data, data monitoring and analysis are performed on the data under each classification result to obtain data monitoring and analysis results; The data monitoring and analysis includes: According to the time series of data collection, the parameter change calculation is performed on the data under the same classification result to obtain the parameter change calculation result; Based on the interval time value of data collection and according to the above parameter change calculation result, the parameter change rate calculation is performed to obtain the parameter change rate calculation result; The parameter change calculation results and the parameter change rate calculation results are fed back to the MES system, so that the MES system forms a fluctuation curve model with the parameter change calculation results and the parameter change rate calculation results according to the time series.

2. A real-time monitoring method based on MES system according to claim 1, characterized in that: The data also includes image data; The data monitoring and analysis also includes Based on the time series, the image data is segmented to obtain multiple segmented images; Search the possible locations of the target features in the segmented image to obtain candidate regions, and coordinate the candidate regions; Extract the target features in the candidate area to obtain the feature extraction results, and perform coordinate analysis and calculation of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain the coordinate analysis and calculation results of the target features; Performing difference calculation on the target feature coordinate analysis calculation results in the segmented images in adjacent time series to obtain a first difference calculation result; A plurality of first difference calculation results are obtained, and the plurality of first difference calculation results are arranged in chronological order to form a difference data set, and an entry-exit analysis and judgment is performed according to the difference data set to obtain an entry-exit analysis and judgment result.

3. A real-time monitoring method based on MES system according to claim 2, characterized in that: The data monitoring and analysis also includes Obtaining longitude and latitude at regular intervals, and mapping the longitude and latitude on the recorded electronic map to form coordinate points; Based on the time series, multiple coordinate points are connected into lines to form a motion trajectory.

4. A real-time monitoring method based on MES system according to claim 3, characterized in that: After obtaining the factory entry and exit analysis and judgment results; Obtain the time when the above-mentioned factory entry and exit analysis and judgment results are obtained, and based on the time when the above-mentioned factory entry and exit analysis and judgment results are obtained, perform historical time range calculation according to the preset historical time tracing standard to obtain the historical time range calculation result; According to the above historical time range calculation result, a first motion trajectory corresponding to the time range calculation result is obtained; Based on the time of obtaining the results of the factory entry and exit analysis and judgment, the future time range is calculated according to the preset future time verification standard to obtain the future time range calculation results; Based on the above calculation result of the future time range, a second motion trajectory within the future time range is obtained in real time; Factory entry and exit verification is performed according to the first motion trajectory and the second motion trajectory to obtain a factory entry and exit verification result.

5. The real-time monitoring method based on the MES system according to claim 2 is characterized in that: The data monitoring and analysis also includes Search for possible locations in the image data, obtain candidate areas, and coordinate the candidate areas; Extract target features in the candidate area to obtain feature extraction results, and perform coordinate analysis of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain coordinate analysis results; Performing difference calculation on the target feature coordinate analysis result to obtain a second difference calculation result; An operation safety judgment is performed based on the second difference calculation result to obtain an operation safety judgment result.

6. The real-time monitoring method based on the MES system according to claim 1 is characterized in that: The classification of data obtained from the MES system includes According to the data presentation form, the data obtained from the MES system is classified into primary categories to obtain the primary classification results; Based on the above primary classification results, tracing back the collection units associated with the primary classified data to obtain tracing results; The data is classified into secondary categories according to the tracing results to obtain the secondary classification results.

7. A real-time monitoring system based on an MES system, used to implement the real-time monitoring method described in any one of claims 1 to 6 above, characterized in that: The system comprises A data collection unit, which can obtain data from the MES system, including product production data, energy data, and material inventory data obtained by the MES system and uploaded by the on-site collection unit; A data classification unit, which classifies the data obtained from the MES system based on the entered classification standard to obtain a classification result; The monitoring and analysis unit performs data monitoring and analysis on the data under each classification result according to the classification result of the data to obtain the data monitoring and analysis result; The monitoring and analysis unit includes A parameter variable calculation module, wherein the parameter variable calculation module performs parameter change calculation on the data under the same classification result according to the time series of data collection to obtain the parameter change calculation result; A change rate calculation module, wherein the change rate calculation module performs parameter change rate calculation based on the interval time value of data collection and the above parameter change calculation result to obtain the parameter change rate calculation result; A feedback module feeds back the parameter change calculation result and the parameter change rate calculation result to the MES system, so that the MES system forms a fluctuation curve model with the parameter change calculation result and the parameter change rate calculation result according to the time series.

8. A real-time monitoring system based on MES system according to claim 7, characterized in that: The monitoring and analysis unit also includes An image segmentation module, wherein the image segmentation module performs segmentation processing on the image data based on a time series to obtain a plurality of segmented images; A first image processing module, which searches for possible locations of target features in the segmented image to obtain candidate regions and performs coordinate processing on the candidate regions; A feature extraction module, which can extract target features in the candidate area to obtain feature extraction results, and perform coordinate analysis and calculation of the target features based on the feature extraction results and the coordinate processing results of the candidate area to obtain the coordinate analysis and calculation results of the target features; A first calculation module, the first calculation module performs difference calculation on the target feature coordinate analysis calculation results in the segmented images in adjacent time series to obtain a first difference calculation result; The factory entry and exit judgment module can obtain multiple first difference calculation results, arrange the multiple first difference calculation results in time sequence to form a difference data set, perform factory entry and exit analysis and judgment based on the difference data set, and obtain the factory entry and exit analysis and judgment result.

9. A real-time monitoring device based on the MES system according to claim 8, characterized in that: The monitoring device includes Memory for storing computer programs; The processor is used to execute the program to implement each step of the real-time monitoring method based on the MES system as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, each step of the real-time monitoring method based on the MES system as described in any one of claims 1 to 6 is implemented.

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