A big data processing and analysis method, system, electronic device, and storage medium

Abstract

A big data processing and analysis method, system, electronic device, and storage medium are disclosed, relating to the field of data processing technology. The method includes: acquiring the layout locations and process flow sequences of multiple production devices; constructing a spatial correlation matrix based on the layout locations and a process correlation matrix based on the process flow sequences; determining multiple associated equipment groups based on the spatial and process correlation matrices, and acquiring vibration data, temperature data, and current data of each production device in each associated equipment group; determining the equipment status value of each associated equipment group by combining the vibration, temperature, and current data of each production device; and generating a warning message for the corresponding associated equipment group when the equipment status value of any associated equipment group exceeds the standard status value range. Implementing the technical solution provided in this application improves the accuracy of big data processing and analysis.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a big data processing and analysis method, system, electronic device, and storage medium. Background Technology

[0002] With the development of the industrial sector, the digitalization and networking of production equipment are constantly improving, and the massive amounts of data generated during equipment operation provide an important data foundation for intelligent manufacturing. By conducting in-depth analysis and mining of this industrial big data, abnormal equipment operation can be detected in a timely manner, equipment failures can be prevented, and thus production can be guaranteed.

[0003] Currently, existing big data processing and analysis methods in the industrial sector mainly rely on collecting data from different production equipment and analyzing the data separately to identify and issue early warnings about abnormal equipment. However, in practical applications, due to the mutual influence between equipment during the production process, the abnormal state of one piece of equipment often propagates and affects other equipment. Using existing methods that only process and analyze data from individual devices often overlooks the inter-device impact, resulting in low accuracy in big data processing and analysis. Summary of the Invention

[0004] This application provides a big data processing and analysis method, system, electronic device, and storage medium, which can improve the accuracy of big data processing and analysis.

[0005] Firstly, this application provides a big data processing and analysis method, including:

[0006] Obtain the layout location and process flow sequence of multiple production equipment;

[0007] Based on the layout positions described, a spatial correlation matrix is ​​constructed, and based on the process flow sequence described, a process correlation matrix is ​​constructed.

[0008] Based on the spatial correlation matrix and the process correlation matrix, multiple associated equipment groups are determined, and vibration data, temperature data and current data of each production equipment in each associated equipment group are obtained.

[0009] For each of the associated equipment groups, the equipment status value of the associated equipment group is determined by combining the vibration data, temperature data and current data of each of the production equipment.

[0010] When the device status value of any associated device group exceeds the standard status value range, a warning message for the corresponding associated device group is generated.

[0011] A second aspect of this application provides a big data processing and analysis system, the system comprising:

[0012] The data acquisition module is used to acquire the layout location and process flow sequence of multiple production equipment;

[0013] The association matrix construction module is used to construct a spatial association matrix according to each of the layout positions, and to construct a process association matrix according to each of the process flow sequences;

[0014] The equipment status value determination module is used to determine multiple associated equipment groups based on the spatial correlation matrix and the process correlation matrix, and to obtain vibration data, temperature data and current data of each production equipment in each associated equipment group; for each associated equipment group, the module determines the equipment status value of the associated equipment group by combining the vibration data, temperature data and current data of each production equipment.

[0015] The early warning module is used to generate early warning information for the corresponding associated device group when the device status value of any associated device group exceeds the standard status value range.

[0016] A third aspect of this application provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, the program being loaded and executed by the processor to implement a big data processing and analysis method.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement a big data processing and analysis method.

[0018] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0019] By employing the aforementioned technical solution, the layout locations and process flow sequences of multiple production equipment are obtained, and spatial and process correlation matrices are constructed, enabling a comprehensive analysis of the physical and technological relationships between equipment. Based on these matrices, the solution can accurately identify multiple related equipment groups and acquire vibration, temperature, and current data of each production equipment within these groups in real time. Through comprehensive analysis of multi-dimensional data such as vibration, temperature, and current, the solution effectively calculates the equipment status value for each related equipment group. When the equipment status value of a certain related equipment group exceeds the preset standard status value range, the system can promptly generate corresponding early warning information. This method considers the mutual influence and dependencies between equipment, avoiding the inaccurate early warning problems caused by isolated analysis of individual equipment. Furthermore, through the fusion analysis of multi-dimensional data, it can accurately detect anomalies and provide timely early warnings, thereby improving the accuracy of big data processing and analysis. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a big data processing and analysis method provided in an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of the structure of a big data processing and analysis system provided in an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0023] Explanation of reference numerals in the attached drawings: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0025] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0026] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0027] This application provides a big data processing and analysis method. In one embodiment, please refer to... Figure 1 , Figure 1This is a flowchart illustrating the big data processing and analysis method provided in this application embodiment. This method can be implemented using a computer program, which can be integrated into an application or run as a standalone tool application. The method can also be implemented using a microcontroller or run on a big data processing and analysis system based on the von Neumann architecture. Specifically, the method may include the following steps:

[0028] Step 101: Obtain the layout and process sequence of multiple production equipment.

[0029] In this embodiment of the application, "production equipment" refers to various types of machinery and equipment that perform specific processing, assembly, or testing functions during the product manufacturing process. For example, a machining production line includes processing equipment such as lathes, milling machines, grinding machines, and machining centers; an assembly production line includes assembly equipment such as robots, conveyor belts, and assembly workstations; and a testing line includes testing equipment such as vision inspection equipment and dimensional measurement equipment.

[0030] In this embodiment of the application, the layout location refers to the actual installation coordinates of each production device within the production workshop.

[0031] In this embodiment of the application, the process flow sequence refers to the order in which the product is processed and transferred between various production equipment. The entire production process can be divided into multiple steps, each corresponding to one or more specific production equipment, and the order of the steps is determined according to the product processing requirements.

[0032] Specifically, in actual production, due to the spatial interactions and technological continuity between production equipment, it is necessary to first obtain basic data reflecting these relationships. The layout of each piece of equipment can be obtained from the equipment layout diagram of the production workshop, which can be represented by two-dimensional or three-dimensional coordinates. Simultaneously, the process flow diagram can be used to obtain the sequence of processes, reflecting the order in which the product is processed across different equipment. For example, in a machining production line, the specific installation coordinates of lathes, milling machines, and grinding machines, as well as their sequence of operations in the part processing, can be obtained. By acquiring this basic information, data support is provided for subsequent analysis of the spatial and technological relationships between equipment, thereby enabling accurate grouping of equipment. This data acquisition method based on layout and process sequence overcomes the limitations of traditional analysis focused solely on individual equipment, comprehensively grasping the relationships between equipment from both spatial and technological dimensions, providing a more comprehensive and accurate data foundation for subsequent group analysis and early warning.

[0033] Step 102: Construct a spatial correlation matrix based on each layout location, and construct a process correlation matrix based on the sequence of each process flow.

[0034] In this embodiment, the spatial association matrix refers to an N×N two-dimensional matrix, where N is the total number of production devices, and each element in the matrix represents the degree of spatial association between two corresponding devices. The rows and columns of the matrix correspond to each production device, and the element in the i-th row and j-th column represents the spatial association between device numbered i and device numbered j.

[0035] In this embodiment, the process association matrix is ​​also an N×N two-dimensional matrix used to represent the degree of association between various production equipment in the process flow. The rows and columns of this matrix also correspond to each production equipment, and the element in the i-th row and j-th column of the matrix represents the process association relationship between equipment numbered i and equipment numbered j.

[0036] Specifically, firstly, an N×N initial spatial matrix is ​​constructed based on the total number N of production equipment, and the first reference position corresponding to the equipment type of each production equipment is determined in this initial spatial matrix. Then, based on the layout coordinates of each production equipment, the spatial distance between any two production equipment is calculated. When the spatial distance between two equipment is less than a preset distance threshold, the corresponding first reference position in the initial spatial matrix is ​​set to a first preset value (e.g., 1), indicating that the two equipment have a strong spatial correlation; when the spatial distance is greater than or equal to the distance threshold, the corresponding first reference position is set to a second preset value (e.g., 0), indicating that the two equipment have a weak spatial correlation. In this way, all first reference positions in the initial spatial matrix are set, and the spatial correlation matrix is ​​finally obtained. Similarly, when constructing the process correlation matrix based on the process flow sequence, the process number of each production equipment in the process flow is first determined, then an N×N initial process matrix is ​​constructed, and the difference in process number between any two production equipment is calculated. When the difference in process numbers is less than a preset threshold, an association marker is placed at the corresponding second reference position in the initial process matrix, indicating that the two devices are closely related in terms of process flow. When the difference in process numbers is greater than or equal to the threshold, an unrelated marker is placed. Through this construction method, the resulting spatial association matrix and process association matrix can quantitatively describe the degree of association between devices from two dimensions: spatial distance and process sequence, providing a reliable data foundation for subsequently identifying associated device groups. This matrix-based representation is not only easy for computer processing but also intuitively reflects the multidimensional relationships between devices, helping to more accurately identify device groups that may have a risk of fault propagation.

[0037] Based on the above embodiments, as an optional embodiment, step 102: constructing a spatial association matrix according to each layout position, this step may further include the following steps:

[0038] Step 201: Based on the total number of devices in each production equipment, construct an initial spatial matrix and determine the first reference position corresponding to the device type of each production equipment in the initial spatial matrix.

[0039] Specifically, based on the equipment on the production line, the first step is to count the total number of production devices, N. For example, a machining production line might have 10 devices: 3 lathes, 2 milling machines, 2 grinding machines, 2 machining centers, and 1 inspection device. Then, an initial N×N spatial matrix is ​​constructed, where each row and column represents one of the N production devices. In this initial spatial matrix, rows and columns are labeled from 1 to N to distinguish different production devices. For each position in the matrix, its corresponding first reference position is determined; for example, the position in row i and column j is the first reference position for the association between device i and device j. In the initial stage, all first reference positions can be set to null or default values ​​to prepare for subsequent association settings. This matrix construction method provides the basic framework for establishing spatial relationships between devices.

[0040] Step 202: Calculate the spatial distance between any two production devices based on their respective layout locations.

[0041] Specifically, after determining the structure of the initial spatial matrix, it is necessary to calculate the actual spatial distance between any two production devices. Based on the obtained layout coordinates (x, y, z) of each production device, the spatial distance calculation formula is used to calculate the distance between the devices. Specifically, for the coordinates (xi, yi, zi) of device i and the coordinates (xj, yj, zj) of device j, the spatial distance between them can be calculated using the Euclidean distance formula in three-dimensional space: distance = √[(xi-xj)² + (yi-yj)² + (zi-zj)²]. For example, if the coordinates of device 1 are (0, 0, 0) and the coordinates of device 2 are (3, 4, 0), then the spatial distance between them is 5 meters. In this way, the spatial distance between any two devices on the production line can be accurately calculated, providing a quantitative basis for subsequent judgment of the spatial correlation between devices.

[0042] Step 203: For any two production devices, when the spatial distance is less than the distance threshold, set the corresponding first reference position in the initial spatial matrix to the first preset value. When the spatial distance is greater than or equal to the distance threshold, set the corresponding first reference position in the initial spatial matrix to the second preset value. Continue until all first reference positions in the initial spatial matrix are set to obtain the spatial correlation matrix.

[0043] Specifically, after calculating the spatial distance, it is necessary to determine the degree of spatial correlation between each pair of devices based on a pre-set distance threshold (e.g., 5 meters). In practice, each first reference position in the initial spatial matrix is ​​processed sequentially: when the spatial distance between the corresponding two devices is less than the distance threshold, it indicates that the two devices are relatively close in space and may influence each other; in this case, the corresponding first reference position is set to a first preset value (e.g., 1). When the spatial distance is greater than or equal to the distance threshold, it indicates that the two devices are relatively far apart in space and have little mutual influence; in this case, the corresponding first reference position is set to a second preset value (e.g., 0). In this way, the setting of all first reference positions in the initial spatial matrix is ​​gradually completed, ultimately forming a complete spatial correlation matrix. For example, in a 5×5 spatial correlation matrix, if the spatial distance between device 1 and device 2 is 3 meters (less than the 5-meter threshold), then the positions in the first row, second column, and second row, first column of the matrix will both be set to 1, indicating that the two devices have spatial correlation. This matrix format not only visually displays the spatial relationships between devices, but also facilitates subsequent data processing and analysis, providing a basis for identifying potential fault propagation paths.

[0044] Based on the above embodiments, as an optional embodiment, step 102: constructing a process correlation matrix according to the sequence of each process flow, this step may further include the following steps:

[0045] Step 204: Based on the sequence of each process flow, determine the process number of each production equipment in the process flow.

[0046] Specifically, to accurately describe the sequential relationship of production equipment in the technological process, it is necessary to first assign a process number to each piece of equipment according to the process flow sequence. In practice, the equipment is numbered sequentially from the initial process, following the product processing sequence. For example, on a mechanical parts processing production line, rough machining is first performed on lathe A (number 1), then a flat surface is machined on milling machine B (number 2), followed by finish machining on grinding machine C (number 3), and finally quality inspection is performed on inspection equipment D (number 4). This numbering method clearly expresses the processing sequence of each piece of production equipment in the entire process flow, providing a foundation for subsequent analysis of the technological relationships between equipment.

[0047] Step 205: Based on the total number of equipment in each production equipment, construct an initial process matrix and determine the second reference position corresponding to each process number in the initial process matrix; calculate the process number difference between any two production equipment.

[0048] Specifically, after assigning process numbers, an initial N×N process matrix is ​​constructed based on the total number of production equipment N (e.g., N=4 in this case). In this initial process matrix, rows and columns correspond to each production equipment, and each position in the matrix is ​​the second reference position for the process relationship between two pieces of equipment. Next, the process number difference between any two pieces of production equipment is calculated. This difference reflects the degree of interval between the two pieces of equipment in the process flow. For example, for lathe A (number 1) and grinding machine C (number 3) in the above case, the process number difference between them is |3-1|=2; while the process number difference between lathe A (number 1) and testing equipment D (number 4) is |4-1|=3. This difference calculation method can quantitatively represent the relative positional relationship between any two pieces of equipment in the process flow.

[0049] Step 206: For any two production equipment, when the difference in process numbers is less than the difference threshold, mark the associated identifier at the corresponding second reference position in the initial process matrix. When the difference in process numbers is greater than or equal to the difference threshold, mark the unassociated identifier at the corresponding second reference position in the initial process matrix. This process continues until all second reference positions in the initial process matrix are marked, thus obtaining the process association matrix.

[0050] Specifically, based on a pre-set difference threshold (e.g., set to 2), the degree of process correlation between each pair of equipment is determined. In practice, each second reference position in the initial process matrix is ​​processed sequentially: when the difference in process numbers between two pieces of equipment is less than the difference threshold, it indicates that the two pieces of equipment are closely related in the process flow and may have process-related influences; in this case, a correlation identifier (e.g., 1) is marked at the corresponding second reference position. When the difference in process numbers is greater than or equal to the difference threshold, it indicates that the two pieces of equipment are far apart in the process flow and have weak process correlation; in this case, a no-correlation identifier (e.g., 0) is marked. In the above example, the difference in process numbers between lathe A and milling machine B is 1 (less than the threshold 2), so the corresponding position in the process correlation matrix is ​​marked as 1; while the difference in process numbers between lathe A and inspection equipment D is 3 (greater than the threshold 2), so the corresponding position is marked as 0. In this way, all second reference positions in the initial process matrix are marked, ultimately forming a complete process correlation matrix. This correlation matrix based on the difference in process numbers not only intuitively reflects the degree of process correlation between equipment but also provides an important basis for subsequent analysis of process-related fault transmission. For example, when a piece of equipment malfunctions, the process correlation matrix can be used to quickly identify the equipment in subsequent processes that may be affected, so that preventive measures can be taken in a timely manner.

[0051] Step 103: Based on the spatial correlation matrix and the process correlation matrix, determine multiple associated equipment groups, and obtain the vibration data, temperature data and current data of each production equipment in each associated equipment group.

[0052] In this embodiment of the application, the associated equipment group refers to a set of equipment with close relationships, determined by analyzing the spatial association matrix and the process association matrix.

[0053] In this embodiment of the application, vibration data refers to vibration-related parameters generated by production equipment during operation, mainly including: time-series data such as vibration frequency, vibration amplitude, and vibration acceleration of key components of the equipment (such as bearings, spindles, and frames). For example, for a lathe, its vibration data may include parameters such as the radial vibration frequency of the spindle, the axial vibration amplitude of the bearings, and the vibration acceleration of the machine tool base.

[0054] In this embodiment of the application, temperature data refers to the temperature changes of various key components of the production equipment during operation, mainly including time-series data such as the spindle temperature, bearing temperature, motor temperature, and machining area temperature. For example, for a milling machine, its temperature data may include parameters such as the temperature rise of the spindle box, the real-time temperature of the bearing housing, and the temperature changes in the cutting area.

[0055] In this embodiment of the application, current data refers to the current parameters of various electrical components during the operation of the production equipment, mainly including: the operating current of the spindle motor, the current value of the feed motor, the operating current of the control system, and other timing data. For example, for a machining center, its current data may include parameters such as the real-time current value of the spindle motor, the current changes of the X / Y / Z axis feed motors, and the operating current of the cooling system.

[0056] Specifically, after obtaining the spatial correlation matrix and the process correlation matrix, it is necessary to identify closely related equipment groups through matrix analysis and collect key operating data of these equipment groups for subsequent fault propagation analysis. In practice, the spatial correlation matrix and the process correlation matrix are first comprehensively analyzed. When the value of the corresponding position of two pieces of equipment in the spatial correlation matrix is ​​a first preset value (e.g., 1), or when the corresponding position in the process correlation matrix is ​​marked with a correlation identifier (e.g., 1), then these two pieces of equipment are assigned to the same related equipment group. For example, on a machining production line, if the spatial distance between lathe A and milling machine B is less than a distance threshold, or the difference in their process numbers is less than a difference threshold, then these two pieces of equipment will be assigned to the same related equipment group. In this way, multiple equipment groups containing interconnected equipment can be formed. The equipment in these groups is either spatially close or closely connected in the process flow. For each identified related equipment group, operating parameters such as vibration data (e.g., equipment vibration frequency and amplitude), temperature data (e.g., temperature changes of key components), and current data (e.g., motor operating current) of each production equipment are collected. For example, for a group of related equipment including lathe A and milling machine B, it is necessary to simultaneously monitor parameters such as bearing vibration frequency, spindle temperature, and motor current of both machines. This correlation-based equipment grouping method and comprehensive data acquisition strategy can more effectively monitor equipment groups that may have fault propagation risks, providing complete data support for subsequent fault propagation feature extraction and early warning, thereby improving the accuracy and timeliness of fault warnings.

[0057] Based on the above embodiments, as an optional embodiment, step 102, which involves determining multiple associated equipment groups based on the spatial correlation matrix and the process correlation matrix, may further include the following steps:

[0058] Step 301: Obtain the spatial association weight of the first preset value in the spatial association matrix, and the process association weight of the association identifier in the process association matrix.

[0059] Specifically, to comprehensively consider the impact of spatial and technological correlations on the degree of equipment correlation, it is necessary to first determine the corresponding weighting coefficients. For the first preset value in the spatial correlation matrix (e.g., the position with a value of 1), its spatial correlation weight is set to w1 (e.g., 0.4); for the correlation marker in the technological correlation matrix (e.g., the position with a value of 1), its technological correlation weight is set to w2 (e.g., 0.6). This weighting method reflects that in actual production, technological correlation may have a more significant impact than spatial correlation. These weight values ​​can be set based on historical data and can be dynamically adjusted according to the actual application results.

[0060] Step 302: Perform a weighted summation of the spatial correlation matrix and the process correlation matrix based on the spatial correlation weight and the process correlation weight to obtain the weighted correlation matrix.

[0061] Specifically, after obtaining the weighting coefficients, the spatial correlation matrix and the process correlation matrix need to be weighted and combined to obtain a weighted correlation matrix that comprehensively reflects the degree of correlation between the equipment. For any two equipment i and j, the element value corresponding to them in the weighted correlation matrix can be calculated using the following formula: Weighted value = w1 × spatial correlation matrix (i, j) + w2 × process correlation matrix (i, j). For example, if the value of equipment 1 and equipment 2 is 1 in both the spatial correlation matrix and the process correlation matrix, then the element value corresponding to them in the weighted correlation matrix is ​​0.4 × 1 + 0.6 × 1 = 1. Through this weighted calculation method, a new N × N matrix can be obtained, in which the element values ​​range from 0 to 1, and the larger the value, the higher the comprehensive correlation between the two equipment.

[0062] Step 303: Determine the association threshold based on the element values ​​corresponding to each production equipment in the weighted association matrix; when the element values ​​corresponding to any two production equipment in the weighted association matrix are greater than the association threshold, divide any two production equipment into the same associated equipment group.

[0063] Specifically, after obtaining the weighted correlation matrix, a suitable correlation threshold needs to be determined based on the distribution of element values ​​in the matrix to classify related equipment groups. An appropriate threshold (e.g., 0.5) can be selected by statistically analyzing the distribution characteristics of each element value in the weighted correlation matrix. When the element values ​​corresponding to any two equipment i and j in the weighted correlation matrix are greater than this correlation threshold, it indicates that these two equipment have a strong correlation both spatially and in the process flow, and therefore they are classified into the same related equipment group. For example, if the weighted correlation value between equipment 1 and equipment 2 is 0.7 (greater than the threshold 0.5), the weighted correlation value between equipment 2 and equipment 3 is 0.6 (greater than the threshold 0.5), and the weighted correlation value between equipment 1 and equipment 3 is 0.3 (less than the threshold 0.5), then equipment 1, equipment 2, and equipment 3 can be classified into the same related equipment group because they form an indirect correlation relationship through equipment 2. This grouping method based on weighted correlation values ​​can more accurately identify sets of devices with close relationships, providing a more reliable basis for subsequent fault propagation analysis, while also avoiding grouping bias that may be caused by considering only a single correlation factor.

[0064] Based on the above embodiments, as an optional embodiment, step 303: determining the association threshold based on the element values ​​corresponding to each production device in the weighted association matrix, may further include the following steps:

[0065] Step 313: Construct a numerical distribution curve based on the element values ​​corresponding to each production equipment.

[0066] Specifically, to determine the association threshold, it is necessary to first analyze the distribution pattern of the element values ​​in the weighted association matrix. In practice, all non-zero element values ​​are extracted from the weighted association matrix and sorted in ascending order of value. Then, a numerical distribution curve is constructed with the element value as the ordinate and the corresponding index as the abscissa. For example, on a production line with 10 devices, the weighted association matrix may contain 45 non-zero element values ​​(because the matrix is ​​symmetric and diagonal elements are usually not considered). Sort these values ​​from smallest to largest to obtain an increasing curve. This curve can intuitively reflect the overall distribution characteristics of the association degree between devices, and the positions where the slope changes significantly often represent the natural boundary points of the association degree.

[0067] Step 323: Obtain the inflection point value in the numerical distribution curve and use the inflection point value as the correlation threshold.

[0068] Specifically, based on the constructed numerical distribution curve, it is necessary to identify the critical point that effectively distinguishes between high and low correlation. In practice, this is achieved by analyzing the slope changes of the numerical distribution curve and calculating the rate of change of the slope between adjacent points on the curve. When the rate of change of the slope reaches its maximum value, the corresponding value is the inflection point, which is used as the correlation threshold. For example, if the slope change is found to be maximum at 0.45 on the numerical distribution curve, it indicates a significant jump in the correlation between devices at this point, and 0.45 can be determined as the correlation threshold. This method of determining the threshold based on data distribution characteristics reflects the natural boundary of the correlation between devices. When the threshold is determined to be 0.45, device pairs with a weighted correlation value greater than 0.45 are considered to have significant correlation characteristics and should be grouped into the same associated device group, while device pairs with a correlation value less than 0.45 can be grouped into different associated device groups, thus achieving more reasonable device grouping.

[0069] Step 104: For each associated equipment group, determine the equipment status value of the associated equipment group by combining the vibration data, temperature data and current data of each production equipment.

[0070] In this embodiment of the application, the equipment status value refers to the status evaluation index obtained by comprehensively analyzing the operating parameters of each production equipment in the associated equipment group.

[0071] Specifically, after identifying the associated equipment groups, to assess the overall operating status of each group, it is necessary to comprehensively analyze the vibration, temperature, and current data of each production device within the group, thereby obtaining equipment status values ​​reflecting the overall operating condition of the entire equipment group. In practice, the operating parameters of each production device are first normalized, mapping vibration data (such as spindle vibration frequency, bearing vibration amplitude, etc.), temperature data (such as spindle temperature, bearing temperature, etc.), and current data (such as spindle motor current, feed motor current, etc.) to a range of 0 to 1. Then, based on these normalized data, a weighted average is used to calculate the comprehensive status index of each device. For example, for a lathe in the equipment group, its normalized vibration, temperature, and current indices can be assigned weights (such as 0.4, 0.3, 0.3), and the status index of that device can be obtained by weighted summation. Finally, the status indices of all devices within the associated equipment group are again weighted and averaged to obtain the equipment status value of the entire equipment group. Different weights can be set according to the importance of each device in the process flow. This multi-level weighted calculation method considers the impact of various operating parameters of individual devices, as well as the contribution of different devices to the overall status of the equipment group, thus providing a more comprehensive and accurate reflection of the overall operating status of the associated equipment group.

[0072] Based on the above embodiments, as an optional embodiment, step 104, which combines the vibration data, temperature data, and current data of each production device to determine the equipment status value of the associated equipment group, may further include the following steps:

[0073] Step 401: For each production equipment, based on vibration data, determine the vibration frequency within a preset time window; based on temperature data, determine the temperature change rate within a preset time window; and based on current data, determine the current change rate within a preset time window.

[0074] Specifically, to accurately assess the operating status of each production piece of equipment, it is necessary to analyze the key operating parameters of the equipment within a preset time window (e.g., 10 minutes). First, based on vibration data collected by vibration sensors, the main vibration frequency components of the equipment within this time window are calculated using a Fast Fourier Transform (FFT). For example, the fundamental frequency (e.g., 50Hz) and its harmonic frequencies of the spindle can be identified. Second, using temperature data collected by temperature sensors, the rate of temperature change over time is calculated, i.e., the speed at which the temperature rises or falls. This can be calculated using the formula (T2-T1) / Δt, where T2 and T1 are the temperature values ​​at the end and beginning of the time window, respectively, and Δt is the length of the time window. Similarly, based on current data collected by current sensors, the rate of current change (I2-I1) / Δt is calculated, where I2 and I1 are the current values ​​at the end and beginning of the time window, respectively. The calculation results of these parameters can reflect the performance change trend of the equipment during dynamic operation.

[0075] Step 402: Perform weighted calculations on the vibration frequency, temperature change rate, and current change rate of each production equipment to obtain the equipment status value of the associated equipment group.

[0076] Specifically, after obtaining the various parameters, it is necessary to synthesize these parameters with different dimensions into a unified state assessment value through a reasonable weighted calculation method. First, the vibration frequency, temperature change rate, and current change rate are normalized, mapping them to the range of 0-1. For example, assuming the vibration frequency of a certain device is 45Hz (normal range 40-60Hz), its normalized value can be set to 0.8; the temperature change rate is 2℃ / min (normal range 0-5℃ / min), with a normalized value of 0.6; and the current change rate is 0.5A / min (normal range 0-2A / min), with a normalized value of 0.75. Then, according to the degree of influence of each parameter on the device state, corresponding weight coefficients are assigned (e.g., vibration frequency weight is 0.4, temperature change rate weight is 0.3, and current change rate weight is 0.3). Through weighted summation, the state value of the device is calculated as 0.4×0.8+0.3×0.6+0.3×0.75=0.725. Finally, the status values ​​of all devices within the associated equipment group are weighted and averaged again. The weight of each device can be determined based on its importance in the production process, thus obtaining the overall equipment status value of the associated equipment group. This multi-level weighted calculation method considers both the differences in the importance of different operating parameters and the contribution of each device to the overall status of the equipment group, and can more accurately reflect the operating status of the associated equipment group.

[0077] Step 105: When the device status value of any associated device group exceeds the standard status value range, generate the corresponding warning information for the associated device group.

[0078] In this embodiment, the standard status value range refers to a normal operating range determined based on historical equipment operating data and technical specifications, used to determine whether the operating status of the associated equipment group is at a normal level. This range is typically expressed as an interval value, such as [0.6, 0.8], where 0.6 is the lower limit and 0.8 is the upper limit. When the equipment status value of the associated equipment group remains within this range, it indicates that the equipment group is in normal operating condition; if it exceeds this range, it indicates that the equipment group may have an abnormality or failure risk.

[0079] In this embodiment of the application, the warning information refers to the alert information automatically generated by the system when the equipment status value of an associated equipment group exceeds the standard status value range. The warning information includes the following key contents: the unique identifier of the associated equipment group (such as the equipment group number), the current equipment status value, the specific circumstances of exceeding the range (whether it is below the lower limit or above the upper limit), the key operating parameters of each production equipment in the group (including vibration frequency, temperature change rate, current change rate, etc.), and possible causes of abnormality and suggested maintenance measures.

[0080] Specifically, to promptly detect and prevent equipment failures, real-time monitoring of the operating status of associated equipment groups is necessary. First, a standard status value range needs to be set. This range can be determined based on historical operating data and equipment technical specifications; for example, 0.6 to 0.8 could be set as the standard status value range. Then, the equipment status values ​​of each associated equipment group are monitored in real time, and the currently calculated equipment status value is compared with the preset standard status value range. When the equipment status value of an associated equipment group is detected to exceed the standard range (e.g., below 0.6 or above 0.8), an early warning mechanism is triggered, and the system automatically generates an early warning message for that associated equipment group. The early warning message includes the associated equipment group number, the current equipment status value, the specific circumstances of exceeding the range (too high or too low), and key parameters of each production device within the group (vibration frequency, temperature change rate, current change rate), etc. For example, when the status value of a group of interconnected equipment consisting of lathes and milling machines drops to 0.45, the system will generate an early warning message containing information such as "Equipment group A001 is in an abnormal state, current status value is 0.45, lower than the standard lower limit of 0.6, it is recommended to check the lathe vibration frequency (current value 58Hz) and the milling machine temperature change rate (current value 4.2℃ / min)." This early warning mechanism based on the overall status of the equipment group can not only promptly detect potential equipment failures, but also help determine the propagation path and scope of the failure through correlation analysis, thereby providing more targeted maintenance suggestions for equipment maintenance personnel and effectively preventing cascading failures.

[0081] Based on the above embodiments, as an optional embodiment, in step 105: generating warning information for the corresponding associated device group, this step may further include the following steps:

[0082] Step 106: Obtain historical warning records for the associated device group; based on the historical warning records, determine the warning frequency for the associated device group.

[0083] Specifically, to analyze the operational status trends of associated equipment groups and take timely maintenance measures, it is necessary to perform statistical analysis on the early warning status of the equipment groups. The system first reads historical early warning records for the associated equipment group from the database over a past period (e.g., the last 30 days). These records include the time of each early warning, the equipment status value at the time the warning was triggered, and specific abnormal parameters. By analyzing these historical early warning records, the number of times the equipment group triggered early warnings within that time period is calculated, i.e., the early warning frequency. For example, if an associated equipment group triggered 15 early warnings in the past 30 days, its early warning frequency is 15 times / month.

[0084] Step 107: When the alarm frequency of the associated equipment group exceeds the frequency threshold, increase the data collection frequency of the associated equipment group and send maintenance suggestion information of the associated equipment group to the target department.

[0085] Specifically, once the warning frequency of a related equipment group is determined, the system compares this frequency with a preset frequency threshold (e.g., 10 times / month). When the warning frequency exceeds the threshold, it indicates that the equipment group may have a serious potential for failure or performance degradation. In this case, the system takes two measures: First, it increases the data collection frequency for the related equipment group, for example, increasing the frequency from once per minute to once every 30 seconds. This allows for more detailed equipment operation data, helping to more accurately analyze the cause of the failure. Second, the system automatically generates and sends maintenance suggestion information to the target department (e.g., the equipment maintenance department). This suggestion information includes basic information about the related equipment group (e.g., equipment group number, equipment composition within the group), warning frequency statistics (e.g., the distribution of warnings over the past 30 days), abnormal parameter analysis (e.g., which parameters frequently exceed limits and their trends), and suggested maintenance plans (e.g., recommended equipment components and maintenance cycles). For example: "Equipment group A001 has issued 15 warnings in the past 30 days, exceeding the threshold 10 times. It is recommended to conduct a comprehensive overhaul of the lathe spindle and milling machine cooling system, with an overhaul cycle not exceeding 7 days." This approach not only strengthens the monitoring of high-risk equipment groups but also guides maintenance personnel to carry out targeted maintenance work, thereby improving equipment reliability and production efficiency.

[0086] Reference Figure 2 This application provides a big data processing and analysis system, which includes: a data acquisition module, an association matrix construction module, a device status value determination module, and an early warning module, wherein:

[0087] The data acquisition module is used to acquire the layout location and process flow sequence of multiple production equipment;

[0088] The association matrix construction module is used to construct a spatial association matrix based on each layout position and a process association matrix based on the sequence of each process flow.

[0089] The equipment status value determination module is used to determine multiple associated equipment groups based on the spatial correlation matrix and the process correlation matrix, and to obtain the vibration data, temperature data and current data of each production equipment in each associated equipment group; for each associated equipment group, the module determines the equipment status value of the associated equipment group by combining the vibration data, temperature data and current data of each production equipment.

[0090] The early warning module is used to generate early warning information for the corresponding associated device group when the device status value of any associated device group exceeds the standard status value range.

[0091] Based on the above embodiments, the association matrix construction module is also used to construct an initial spatial matrix based on the total number of devices in each production device, and determine the first reference position corresponding to the device type of each production device in the initial spatial matrix; calculate the spatial distance between any two production devices based on each layout position; for any two production devices, when the spatial distance is less than the distance threshold, set the corresponding first reference position in the initial spatial matrix to a first preset value, and when the spatial distance is greater than or equal to the distance threshold, set the corresponding first reference position in the initial spatial matrix to a second preset value, until all first reference positions in the initial spatial matrix are set, and a spatial association matrix is ​​obtained.

[0092] Based on the above embodiments, the association matrix construction module is also used to determine the process number of each production equipment in the process flow based on the sequence of each process flow; construct an initial process matrix based on the total number of equipment in each production equipment, and determine the second reference position corresponding to each process number in the initial process matrix; calculate the process number difference between any two production equipment; for any two production equipment, when the process number difference is less than the difference threshold, mark the corresponding second reference position in the initial process matrix with an association identifier; when the process number difference is greater than or equal to the difference threshold, mark the corresponding second reference position in the initial process matrix with an unassociation identifier, until all second reference positions in the initial process matrix are marked, and the process association matrix is ​​obtained.

[0093] Based on the above embodiments, the equipment status value determination module is further used to obtain the spatial association weight of the first preset value in the spatial association matrix and the process association weight of the association identifier in the process association matrix; to perform a weighted summation of the spatial association matrix and the process association matrix according to the spatial association weight and the process association weight to obtain a weighted association matrix; to determine the association threshold based on the element values ​​corresponding to each production equipment in the weighted association matrix; and to classify any two production equipment into the same associated equipment group when the element values ​​corresponding to any two production equipment in the weighted association matrix are greater than the association threshold.

[0094] Based on the above embodiments, the equipment status value determination module is also used to construct a numerical distribution curve based on the element values ​​corresponding to each production equipment; obtain the inflection point value in the numerical distribution curve, and use the inflection point value as an association threshold.

[0095] Based on the above embodiments, the equipment status value determination module is also used to determine the vibration frequency within a preset time window based on vibration data, the temperature change rate within a preset time window based on temperature data, and the current change rate within a preset time window based on current data for each production equipment; and to perform weighted calculation on the vibration frequency, temperature change rate, and current change rate of each production equipment to obtain the equipment status value of the associated equipment group.

[0096] Based on the above embodiments, the early warning module is also used to obtain historical early warning records of the associated equipment group; determine the early warning frequency of the associated equipment group based on the historical early warning records; when the early warning frequency of the associated equipment group is greater than the frequency threshold, increase the data collection frequency of the associated equipment group, and send maintenance suggestion information of the associated equipment group to the target department.

[0097] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0098] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0099] The communication bus 302 is used to enable communication between these components.

[0100] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0101] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0102] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface graphics, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0103] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for big data processing and analysis.

[0104] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a big data processing and analysis method. When executed by one or more processors 301, the electronic device 300 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0105] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0106] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0109] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0110] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practical disclosure.

[0111] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only.

Claims

1. A big data processing and analysis method, characterized in that, include: Obtain the layout location and process flow sequence of multiple production equipment; Based on the layout positions described, a spatial correlation matrix is ​​constructed, and based on the process flow sequence described, a process correlation matrix is ​​constructed. Based on the spatial correlation matrix and the process correlation matrix, multiple associated equipment groups are determined, and vibration data, temperature data and current data of each production equipment in each associated equipment group are obtained. For each of the associated equipment groups, the equipment status value of the associated equipment group is determined by combining the vibration data, temperature data and current data of each of the production equipment. When the device status value of any associated device group exceeds the standard status value range, a warning message for the corresponding associated device group is generated.

2. The big data processing and analysis method according to claim 1, characterized in that, The step of constructing a spatial association matrix based on the respective layout positions includes: Based on the total number of devices of each of the aforementioned production equipment, an initial spatial matrix is ​​constructed, and the first reference position corresponding to the device type of each of the aforementioned production equipment is determined in the initial spatial matrix; Based on the aforementioned layout positions, calculate the spatial distance between any two production devices; For any two production devices, when the spatial distance is less than a distance threshold, the corresponding first reference position in the initial spatial matrix is ​​set to a first preset value. When the spatial distance is greater than or equal to the distance threshold, the corresponding first reference position in the initial spatial matrix is ​​set to a second preset value. This process continues until all first reference positions in the initial spatial matrix are set, resulting in a spatial correlation matrix.

3. The big data processing and analysis method according to claim 1, characterized in that, The step of constructing a process correlation matrix based on the sequence of each process flow includes: Based on the sequence of each process flow, the process number of each production equipment in the process flow is determined; Based on the total number of equipment in each of the aforementioned production equipment, an initial process matrix is ​​constructed, and the second reference position corresponding to each of the aforementioned process numbers is determined in the initial process matrix; Calculate the difference in process number between any two production equipment; For any two production devices, when the difference in process numbers is less than the difference threshold, an association identifier is marked at the corresponding second reference position in the initial process matrix. When the difference in process numbers is greater than or equal to the difference threshold, an unassociated identifier is marked at the corresponding second reference position in the initial process matrix. This process continues until all second reference positions in the initial process matrix are marked, thus obtaining the process association matrix.

4. The big data processing and analysis method according to claim 1, characterized in that, The determination of multiple associated equipment groups based on the spatial correlation matrix and the process correlation matrix includes: Obtain the spatial association weight of the first preset value in the spatial association matrix, and the process association weight of the association identifier in the process association matrix; The spatial correlation matrix and the process correlation matrix are weighted and summed according to the spatial correlation weight and the process correlation weight to obtain the weighted correlation matrix; Based on the element values ​​corresponding to each production equipment in the weighted correlation matrix, the correlation threshold is determined. When the element values ​​corresponding to any two production devices in the weighted association matrix are greater than the association threshold, the two production devices are classified into the same associated device group.

5. The big data processing and analysis method according to claim 4, characterized in that, The step of determining the association threshold based on the element values ​​corresponding to each production device in the weighted association matrix includes: Based on the element values ​​corresponding to each of the aforementioned production equipment, a numerical distribution curve is constructed; Obtain the inflection point value in the numerical distribution curve and use the inflection point value as the correlation threshold.

6. The big data processing and analysis method according to claim 1, characterized in that, The process of determining the equipment status value of the associated equipment group by combining the vibration data, temperature data, and current data of each of the aforementioned production equipment includes: For each of the aforementioned production equipment, the vibration frequency within a preset time window is determined based on the vibration data, the temperature change rate within the preset time window is determined based on the temperature data, and the current change rate within the preset time window is determined based on the current data. The vibration frequency, temperature change rate, and current change rate of each of the aforementioned production equipment are weighted and calculated to obtain the equipment status value of the associated equipment group.

7. The big data processing and analysis method according to claim 1, characterized in that, After generating the warning information for the corresponding associated device group, the method further includes: Obtain the historical warning records of the associated device group; Based on the historical early warning records, the early warning frequency of the associated device group is determined; When the alarm frequency of the associated equipment group exceeds the frequency threshold, the data collection frequency of the associated equipment group is increased, and maintenance suggestion information of the associated equipment group is sent to the target department.

8. A big data processing and analysis system, characterized in that, The system includes: The data acquisition module is used to acquire the layout location and process sequence of multiple production equipment; The association matrix construction module is used to construct a spatial association matrix according to each of the layout positions, and to construct a process association matrix according to each of the process flow sequences; The equipment status value determination module is used to determine multiple associated equipment groups based on the spatial correlation matrix and the process correlation matrix, and to obtain vibration data, temperature data and current data of each production equipment in each associated equipment group; for each associated equipment group, the module determines the equipment status value of the associated equipment group by combining the vibration data, temperature data and current data of each production equipment. The early warning module is used to generate early warning information for the corresponding associated device group when the device status value of any associated device group exceeds the standard status value range.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the big data processing and analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the big data processing and analysis method as described in any one of claims 1-7.

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