A Data Twin-Based Unit Status Visualization Monitoring Method and System

Abstract

The present application relates to a unit status visualization monitoring method and monitoring system based on data twins, the method comprising continuously acquiring data collected by sensors deployed on the unit, recorded as detection data; grouping and processing the detection data in a time series to obtain a detection data group; using the detection data group to generate a detection data curve and decomposing the detection data curve using wavelets to obtain a decomposition curve group; calculating the fluctuation amount of the decomposition curve group to obtain a fluctuation evaluation parameter; selecting a compression method for the detection data group according to the fluctuation evaluation parameter and sending the compressed detection data group to the data twin unit and displaying it at the corresponding position. The unit status visualization monitoring method and monitoring system based on data twins disclosed in the present application use a curve compression method or a combined compression method to compress the data generated by the sensor, which can effectively improve the data transmission speed and enable the unit's data twin model to keep pace with the unit.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a unit status visualization monitoring method and monitoring system based on data twins. Background Art

[0002] Data twin is a digital model created in the virtual world that is completely consistent with the real-world entity. It has obvious advantages in various links such as data collection, processing, analysis, simulation and feedback control. For example, the data twin model of a rotating machinery generator set can display various conditions such as speed, temperature, unit operating status and abnormalities.

[0003] One development direction of the data twin model is to analyze and predict the operating status of the unit based on the data collected by sensors, and then directly display the results on the data twin model so that operation and maintenance personnel can view them directly. Current research focuses on aspects such as data volume, data correlation analysis, and model generalization.

[0004] Another development direction of the data twin model is data transmission. This is because as the number of sensors increases, the amount of monitoring data generated increases exponentially. In addition, local data transmission can rely on optical fiber, and data transmission to the data twin model still needs to rely on wireless transmission. This leads to a significant increase in the speed of data transmission. The data transmission speed directly affects the timeliness of visual monitoring of unit status. Summary of the invention

[0005] The present application provides a unit status visualization monitoring method and monitoring system based on data twins, which uses curve compression or combined compression methods to compress the data generated by the sensor, which can effectively improve the data transmission speed and enable the unit's data twin model to keep synchronized with the unit.

[0006] The above-mentioned purpose of the present application is achieved through the following technical solutions:

[0007] In a first aspect, the present application provides a method for visual monitoring of unit status based on data twins, comprising:

[0008] Continuously obtain the data collected by the sensors deployed on the unit and record it as detection data;

[0009] The detection data are grouped and processed in time series to obtain detection data groups;

[0010] Generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposed curve group, wherein the curve in the decomposed curve group has a starting point and a cut-off point;

[0011] Calculate the fluctuation amount of the decomposed curve group and obtain the fluctuation evaluation parameter;

[0012] Selecting a compression method for the detection data group according to the fluctuation evaluation parameter, when the fluctuation evaluation parameter is greater than or equal to a first set value, compressing the detection data group using a curve compression method, and when the fluctuation evaluation parameter is less than the first set value, compressing the detection data group using a combined compression method;

[0013] The compressed detection data group is sent to the data twin group and displayed at the corresponding position.

[0014] In a possible implementation manner of the first aspect, calculating the fluctuation amount of the decomposition curve group and obtaining the fluctuation evaluation parameter includes:

[0015] Calculate the difference between the maximum amplitude and the minimum amplitude of the curve in the decomposed curve group, and record it as the amplitude difference;

[0016] Calculate the discrete values ​​of the curves in the decomposed curve group;

[0017] The amplitude difference and discrete value are weighted to obtain the fluctuation evaluation parameter;

[0018] The calculation of the discrete values ​​of the curves in the decomposed curve group includes:

[0019] Calculate the length of the overlapping area between any two curves in the decomposed curve group;

[0020] All the lengths of the overlapping areas are accumulated to obtain the accumulated value of the overlapping area lengths, and the ratio of the accumulated value of the overlapping area lengths to the length of the detection data group is calculated to obtain the discrete value of the curve in the decomposed curve group.

[0021] In a possible implementation manner of the first aspect, compressing the detection data group by using a curve compression method includes:

[0022] Generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposed curve group, wherein the curve in the decomposed curve group has a starting point and a cut-off point;

[0023] Each curve in the decomposed curve group is converted into a data group, and a data group includes amplitude, frequency, starting point and cutoff point.

[0024] In a possible implementation manner of the first aspect, the detection data group is further regrouped according to the amplitude of the curve in the decomposition curve group, and the regrouping includes:

[0025] Calculate the maximum drop value of the curves in the decomposed curve group. The maximum drop value is the difference between the maximum amplitude and the minimum amplitude at a time point in the time series. When the minimum amplitude does not exist, use zero value instead;

[0026] When the maximum drop value is greater than or equal to the second set value, the time point at the position corresponding to the maximum drop value is used as a dividing point to regroup the detection data group;

[0027] The maximum drop value is only located at the starting point of the curve in the decomposition curve group.

[0028] In a possible implementation manner of the first aspect, compressing the detection data group by using a combined compression manner includes:

[0029] Regrouping the detection data in the detection data group, wherein the detection data in one regroup are multiple detection data;

[0030] Select one of the detection data in the regrouping as the reference data;

[0031] Generate a reference line segment using the detection data other than the reference data in the regrouping, wherein the detection data other than the reference data in the regrouping is located on the reference line segment or the difference between the detection data and the reference line segment is within an allowable range;

[0032] Wherein, the reference line segments include straight line segments and oblique line segments;

[0033] The detection data is the maximum detection data or the minimum detection data in a subgroup.

[0034] In a possible implementation of the first aspect, when the difference between the detection data other than the benchmark data in the regrouping and the reference line segment is outside the allowable range, a direct storage method is used for storage or a vertical line segment is generated on the reference line segment, wherein the first segment of the vertical line segment intersects with the reference line segment and the second segment is a free end.

[0035] In a possible implementation of the first aspect, when the difference between the detection data other than the baseline data in the regrouping and the reference line segment is outside the allowed range, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is allowed to be represented on at least the previous detection data group in the time series, and at this time, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is located on the reference line segment of the selected detection data group or the difference with the reference line segment is within the allowed range.

[0036] In a second aspect, the present application provides a unit status visualization monitoring device based on data twin, comprising:

[0037] A data acquisition unit is used to continuously acquire data collected by sensors deployed on the unit, which is recorded as detection data;

[0038] A data grouping unit, used for grouping the detection data in time series to obtain detection data groups;

[0039] A decomposition unit, used to generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposition curve group, wherein the curve in the decomposition curve group has a starting point and a cut-off point;

[0040] The first calculation unit is used to calculate the fluctuation amount of the decomposition curve group to obtain the fluctuation evaluation parameter;

[0041] A data compression unit, used for selecting a compression method for the detection data group according to the fluctuation evaluation parameter, when the fluctuation evaluation parameter is greater than or equal to a first set value, compressing the detection data group using a curve compression method, and when the fluctuation evaluation parameter is less than the first set value, compressing the detection data group using a combined compression method;

[0042] The data transmission unit is used to send the compressed detection data group to the data twin unit and display it at the corresponding position.

[0043] In a third aspect, the present application provides a unit status visualization monitoring system based on data twins, the system comprising:

[0044] one or more memories for storing instructions; and

[0045] One or more processors, used to call and run the instructions from the memory to execute the method as described in the first aspect and any possible implementation of the first aspect.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium comprising:

[0047] Program, when the program is executed by a processor, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0048] In a fifth aspect, the present application provides a computer program product, comprising program instructions. When the program instructions are executed by a computing device, the method described in the first aspect and any possible implementation of the first aspect is executed.

[0049] In a sixth aspect, the present application provides a chip system, which includes a processor for implementing the functions involved in the above aspects, for example, generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0050] The chip system may be composed of chips, or may include chips and other discrete devices.

[0051] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wired or wireless means, or the processor and the memory can also be coupled on the same device. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a schematic flowchart of the steps of a method for visual monitoring of unit status provided in this application.

[0053] Figure 2 This is a schematic diagram of grouping detection data provided by the present application.

[0054] Figure 3 This is a schematic diagram provided by the present application for generating a detection data curve using a detection data group.

[0055] Figure 4 It is a schematic diagram of decomposing a detection data curve provided by the present application.

[0056] Figure 5 It is a schematic diagram of the length of the overlap area provided in this application.

[0057] Figure 6 It is a schematic diagram of an amplitude difference provided in this application.

[0058] Figure 7 The present application provides a schematic diagram for representing a regrouping using benchmark data and reference line segments.

[0059] Figure 8 This is a schematic diagram of generating a vertical line segment on a reference line segment provided by the present application. DETAILED DESCRIPTION

[0060] The technical solution in this application is further described in detail below in conjunction with the accompanying drawings.

[0061] This application discloses a method for visual monitoring of unit status based on data twins. Figure 1 The unit status visualization monitoring method based on data twin disclosed in this application includes the following steps:

[0062] S101, continuously acquiring data collected by sensors deployed on the unit, recorded as detection data;

[0063] S102, grouping the detection data in time series to obtain detection data groups;

[0064] S103, using the detection data group to generate a detection data curve and decomposing the detection data curve using wavelet to obtain a decomposition curve group, wherein the curve in the decomposition curve group has a starting point and a cut-off point;

[0065] S104, calculating the fluctuation amount of the decomposition curve group to obtain a fluctuation evaluation parameter;

[0066] S105, selecting a compression method for the detection data group according to the fluctuation evaluation parameter, when the fluctuation evaluation parameter is greater than or equal to a first set value, compressing the detection data group using a curve compression method, and when the fluctuation evaluation parameter is less than the first set value, compressing the detection data group using a combined compression method;

[0067] S106, sending the compressed detection data group to the data twin group and displaying it at the corresponding position.

[0068] In general, the problem to be solved by the data twin-based unit status visualization monitoring method disclosed in this application is the synchronization problem between the data twin unit and the unit, where the unit refers to the generator set. It should be noted that in order to obtain sufficient monitoring data, the number of sensors installed on the unit is also increasing. Taking the MEMS fiber optic sensor installed inside the fan blade as an example, a MEMS fiber optic sensor can simultaneously detect three data of the X-axis, Y-axis and Z-axis. Here, it is also necessary to consider the impact of factors such as type, quantity, acquisition frequency and number of units on data increment.

[0069] To solve this problem, an appropriate approach is to use edge computing, but edge computing also requires that the data be first transmitted to the edge calculator, and then sent to the main calculator after processing. The data transmission problem still needs to be considered because the lag generated in this method will still affect the synchronization between the data twin units.

[0070] In step S101, data collected by sensors deployed on the unit are first continuously acquired and recorded as detection data. The detection data are first grouped and processed. Figure 2 As shown, the purpose of the grouping process is to compress the detection data in segments.

[0071] In step S102, the detection data are grouped in time series to obtain detection data groups. Here, the grouping is generally performed in a fixed time length manner. Generally, the time length of a group is controlled at 5-10 seconds. Of course, the time length can also be adjusted for different unit requirements.

[0072] In step S103, a detection data curve is generated using the detection data set. Figure 3As shown, the specific method is to use the order (serial number) of the detection data as the horizontal axis and the value of the detection data as the vertical axis, and then use a curve to connect these discrete points in sequence. The curve obtained at this time is called the detection data curve.

[0073] Then the detection data curve is decomposed using wavelet to obtain a decomposition curve group. Figure 4 As shown, in some possible implementations, the detection data curve is processed using Haar wavelet, and the curves in the decomposition curve group obtained at this time have a starting point and an end point, and the starting point and the end point both correspond to time points, that is, the curves in the decomposition curve group have clear appearance time points and disappearance time points.

[0074] In step S104 and step S105, the fluctuation amount of the decomposition curve group is calculated to obtain the fluctuation evaluation parameter, and then the compression method of the detection data group is selected according to the fluctuation evaluation parameter. The specific rules are as follows:

[0075] When the fluctuation evaluation parameter is greater than or equal to the first set value, the detection data group is compressed using a curve compression method;

[0076] When the fluctuation evaluation parameter is less than the first set value, the detection data group is compressed using a combined compression method.

[0077] Finally, the compressed detection data group is sent to the data twin unit and displayed at the corresponding position, which is the content in step S106. The detection data processed in the above manner can be greatly compressed, and its transmission speed is improved. For the data twin unit, its display content can be synchronized with the actual unit status as much as possible.

[0078] In some examples, calculating the fluctuation amount of the decomposition curve group and obtaining the fluctuation evaluation parameter includes:

[0079] S201, calculating the difference between the maximum amplitude and the minimum amplitude of the curves in the decomposed curve group, and recording it as the amplitude difference;

[0080] S202, calculating the discrete values ​​of the curves in the decomposed curve group;

[0081] S203, performing weighted calculation on the amplitude difference and the discrete value to obtain a fluctuation evaluation parameter;

[0082] The calculation of the discrete values ​​of the curves in the decomposed curve group includes:

[0083] Calculate the length of the overlapping area between any two curves in the decomposed curve group;

[0084] The length of all overlapping areas is accumulated to obtain the length of the overlapping area ( Figure 5The accumulated values ​​are calculated (as shown) and the ratio of the accumulated value of the overlapped area length to the length of the detection data group is calculated to obtain the discrete values ​​of the curves in the decomposed curve group.

[0085] In step S201 to step S203, the amplitude difference is calculated respectively ( Figure 6 As shown in the figure, the amplitude difference and the discrete value both characterize the fluctuation of the decomposed curve group. The amplitude difference refers to the difference between the maximum amplitude and the minimum amplitude of the curve in the curve group. The discrete value of the curve in the decomposed curve group refers to the cumulative value of the length of the overlapping area and calculates the ratio of the cumulative value of the length of the overlapping area to the length of the detection data group.

[0086] The smaller the amplitude difference is, the smaller the fluctuation degree of the curves in the curve group is, and the smaller the discreteness is, the longer the overlapping part of the curves in the curve group is, and there is no frequent fluctuation.

[0087] Finally, the amplitude difference and discrete value are weighted to obtain the fluctuation evaluation parameter. The weighting here is that the sum of the weighted value of the amplitude difference and the weighted value of the amplitude difference is one. The specific distribution method is not limited in this application.

[0088] In some examples, compressing the detection data set using the curve compression method includes:

[0089] Generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposed curve group, wherein the curve in the decomposed curve group has a starting point and a cut-off point;

[0090] Each curve in the decomposed curve group is converted into a data group, and a data group includes amplitude, frequency, starting point and cutoff point.

[0091] Compression using curve compression means describing the detection data curve using several data groups. When generating the detection data group in reverse, first use the data group to generate the detection data curve, then take points on the detection data curve, and the values ​​obtained by taking the points are the detection data at the corresponding time points.

[0092] Furthermore, the method further includes regrouping the detection data group according to the amplitude of the curve in the decomposition curve group, and the purpose of the regrouping is to further improve the data compression rate.

[0093] For example, when there is a large amplitude difference in a detection data group, according to the above description, it needs to be compressed using curve compression. However, if the detection data group is regrouped at this time, the part in front of the large amplitude difference can be compressed using combined compression.

[0094] The advantage of compression is that there is no need to generate the detection data curve again.

[0095] The sub-grouping is as follows:

[0096] S301, calculating the maximum drop value of the curves in the decomposed curve group, where the maximum drop value is the difference between the maximum amplitude and the minimum amplitude at a time point in the time series, and when the minimum amplitude does not exist, a zero value is used instead;

[0097] S302, when the maximum drop value is greater than or equal to the second set value, using the time point at the position corresponding to the maximum drop value as a dividing point to regroup the detection data group;

[0098] The maximum drop value is only located at the starting point of the curve in the decomposition curve group.

[0099] In some examples, compressing the detection data set using a combination of compression methods includes:

[0100] S401, regrouping the detection data in the detection data group, wherein a plurality of detection data are included in one regroup;

[0101] S402, selecting one of the detection data in the regrouping as the reference data;

[0102] S403, generating a reference line segment using the detection data other than the reference data in the regrouping, wherein the detection data other than the reference data in the regrouping is located on the reference line segment or the difference between the detection data and the reference line segment is within an allowable range;

[0103] Wherein, the reference line segments include straight line segments and oblique line segments;

[0104] The detection data is the maximum detection data or the minimum detection data in a subgroup.

[0105] The contents in step S401 to step S403 are compressed by grouping similar detection data together. At this time, one subgroup can be represented by one reference data and one reference line segment. Figure 7 shown.

[0106] Of course, during the regrouping process, the detection data other than the benchmark data in the regrouping needs to be located on the reference line segment or the difference with the reference line segment is within the allowable range, that is, a certain amount of error is allowed to minimize the number of groups during regrouping.

[0107] Of course, some detection data may not be included in the grouping. In this case, use the following method to handle it:

[0108] When the difference between the detection data other than the reference data in the regrouping and the reference line segment is outside the allowable range, the data is stored directly or a vertical line segment is generated on the reference line segment. The first segment of the vertical line segment intersects the reference line segment, and the second segment is a free end. Figure 8 shown.

[0109] When the difference between the detection data other than the baseline data and the reference line segment in the regrouping is outside the allowed range, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is allowed to be represented on at least the previous detection data group in the time series. At this time, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is located on the reference line segment of the selected detection data group or the difference with the reference line segment is within the allowed range.

[0110] The present application also provides a unit status visualization monitoring device based on data twin, including:

[0111] A data acquisition unit is used to continuously acquire data collected by sensors deployed on the unit, which is recorded as detection data;

[0112] A data grouping unit, used for grouping the detection data in time series to obtain detection data groups;

[0113] A decomposition unit, used to generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposition curve group, wherein the curve in the decomposition curve group has a starting point and a cut-off point;

[0114] The first calculation unit is used to calculate the fluctuation amount of the decomposition curve group to obtain the fluctuation evaluation parameter;

[0115] A data compression unit, used for selecting a compression method for the detection data group according to the fluctuation evaluation parameter, when the fluctuation evaluation parameter is greater than or equal to a first set value, compressing the detection data group using a curve compression method, and when the fluctuation evaluation parameter is less than the first set value, compressing the detection data group using a combined compression method;

[0116] The data transmission unit is used to send the compressed detection data group to the data twin unit and display it at the corresponding position.

[0117] Furthermore, it also includes:

[0118] The second calculation unit is used to calculate the difference between the maximum amplitude and the minimum amplitude of the curves in the decomposed curve group, which is recorded as the amplitude difference;

[0119] A third calculation unit, used for calculating discrete values ​​of curves in the decomposed curve group;

[0120] A fourth calculation unit, used for performing weighted calculation on the amplitude difference and the discrete value to obtain a fluctuation evaluation parameter;

[0121] The calculation of the discrete values ​​of the curves in the decomposed curve group includes:

[0122] A fifth calculation unit, used for calculating the length of the overlapped area between any two curves in the decomposed curve group;

[0123] The sixth calculation unit is used to accumulate all the lengths of the overlapping areas to obtain the accumulated value of the overlapping area lengths and calculate the ratio of the accumulated value of the overlapping area lengths to the length of the detection data group to obtain the discrete value of the curve in the decomposition curve group.

[0124] Furthermore, it also includes:

[0125] A decomposition unit, used to generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposition curve group, wherein the curve in the decomposition curve group has a starting point and a cut-off point;

[0126] The group processing unit is used to convert each curve in the decomposed curve group into a data group, wherein a data group includes amplitude, frequency, starting point and cut-off point.

[0127] Furthermore, it also includes:

[0128] A seventh calculation unit is used to calculate the maximum drop value of the curves in the decomposition curve group, where the maximum drop value is the difference between the maximum amplitude and the minimum amplitude at a time point in the time series, and when the minimum amplitude does not exist, a zero value is used instead;

[0129] A first grouping unit, configured to regroup the detection data group using a time point at a position corresponding to the maximum drop value as a dividing point when the maximum drop value is greater than or equal to a second set value;

[0130] The maximum drop value is only located at the starting point of the curve in the decomposition curve group.

[0131] Furthermore, it also includes:

[0132] A second grouping unit, used for regrouping the detection data in the detection data group, wherein a plurality of detection data are included in one regroup;

[0133] A selection unit, used for selecting one of the detection data in the regrouping as the reference data;

[0134] A reference line segment generating unit, used to generate a reference line segment using the detection data other than the reference data in the regrouping, wherein the detection data other than the reference data in the regrouping is located on the reference line segment or the difference between the detection data and the reference line segment is within an allowable range;

[0135] Wherein, the reference line segments include straight line segments and oblique line segments;

[0136] The detection data is the maximum detection data or the minimum detection data in a subgroup.

[0137] Furthermore, when the difference between the detection data other than the benchmark data in the regrouping and the reference line segment is outside the allowable range, it is stored in a direct storage manner or a vertical line segment is generated on the reference line segment, the first segment of the vertical line segment intersects with the reference line segment, and the second segment is a free end.

[0138] Further, when the difference between the detection data other than the baseline data and the reference line segment in the regrouping is outside the allowed range, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is allowed to be represented on at least the previous detection data group in the time series. At this time, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is located on the reference line segment of the selected detection data group or the difference with the reference line segment is within the allowed range.

[0139] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASICs), or, one or more digital signal processors (DSPs), or, one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0140] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0141] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. 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.

[0144] 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.

[0145] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0146] It should also be understood that in various embodiments of the present application, the first, second, etc. are only used to indicate that multiple objects are different. For example, the first time window and the second time window are only used to indicate different time windows. They should not have any impact on the time window itself, and the first, second, etc. mentioned above should not impose any limitations on the embodiments of the present application.

[0147] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0148] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a computer-readable storage medium, including 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 described in each embodiment of the present application. The aforementioned computer-readable storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0149] The present application also provides a unit status visualization monitoring system based on data twin, the system comprising:

[0150] one or more memories for storing instructions; and

[0151] One or more processors are used to call and run the instructions from the memory to execute the method as described above.

[0152] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the terminal device and the network device perform operations of the terminal device and the network device corresponding to the above method.

[0153] The present application also provides a chip system, which includes a processor for implementing the functions involved in the above content, such as generating, receiving, sending, or processing the data and / or information involved in the above method.

[0154] The chip system may be composed of chips, or may include chips and other discrete devices.

[0155] The processor mentioned in any of the above places can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for executing programs for controlling the above-mentioned feedback information transmission method.

[0156] In a possible design, the chip system also includes a memory, which is used to store necessary program instructions and data. The processor and the memory can be decoupled and respectively set on different devices, connected by wire or wireless means to support the chip system to implement various functions in the above embodiments. Alternatively, the processor and the memory can also be coupled on the same device.

[0157] Optionally, the computer instructions are stored in a memory.

[0158] Optionally, the memory is a storage unit within the chip, such as a register, a cache, etc. The memory can also be a storage unit within the terminal located outside the chip, such as a ROM or other types of static storage devices that can store static information and instructions, RAM, etc.

[0159] It can be understood that the memory in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0160] The non-volatile memory may be a ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory.

[0161] The volatile memory may be a RAM, which is used as an external cache. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus RAM.

[0162] The embodiments of this specific implementation method are all preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, all equivalent changes made based on the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A unit status visualization monitoring method based on data twin, characterized in that: include: Continuously obtain the data collected by the sensors deployed on the unit and record it as detection data; The detection data are grouped and processed in time series to obtain detection data groups; Generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposed curve group, wherein the curve in the decomposed curve group has a starting point and a cut-off point; Calculate the fluctuation amount of the decomposed curve group and obtain the fluctuation evaluation parameter; Selecting a compression method for the detection data group according to the fluctuation evaluation parameter, when the fluctuation evaluation parameter is greater than or equal to a first set value, compressing the detection data group using a curve compression method, and when the fluctuation evaluation parameter is less than the first set value, compressing the detection data group using a combined compression method; Send the compressed detection data group to the data twin unit and display it at the corresponding position; The curve compression method used to compress the detection data group includes: Generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposed curve group, wherein the curve in the decomposed curve group has a starting point and a cut-off point; Convert each curve in the decomposed curve group into a data group, wherein the data group includes amplitude, frequency, starting point and cutoff point; Compression of the detection data group using a combination of compression methods includes: Regrouping the detection data in the detection data group, wherein the detection data in one regroup are multiple detection data; Select one of the detection data in the regrouping as the reference data; Generate a reference line segment using the detection data other than the reference data in the regrouping, wherein the detection data other than the reference data in the regrouping is located on the reference line segment or the difference between the detection data and the reference line segment is within an allowable range; Wherein, the reference line segments include straight line segments and oblique line segments; The detection data is the maximum detection data or the minimum detection data in a subgroup.

2. The unit status visualization monitoring method based on data twin according to claim 1 is characterized in that: Calculate the fluctuation amount of the decomposed curve group and obtain the fluctuation evaluation parameters including: Calculate the difference between the maximum amplitude and the minimum amplitude of the curve in the decomposed curve group, and record it as the amplitude difference; Calculate the discrete values ​​of the curves in the decomposed curve group; The amplitude difference and discrete value are weighted to obtain the fluctuation evaluation parameter; The calculation of the discrete values ​​of the curves in the decomposed curve group includes: Calculate the length of the overlapping area between any two curves in the decomposed curve group; All the lengths of the overlapping areas are accumulated to obtain the accumulated value of the overlapping area lengths, and the ratio of the accumulated value of the overlapping area lengths to the length of the detection data group is calculated to obtain the discrete value of the curve in the decomposed curve group.

3. The unit status visualization monitoring method based on data twin according to claim 1 is characterized in that: The method further includes regrouping the detection data group according to the amplitude of the curve in the decomposition curve group, wherein the regrouping includes: Calculate the maximum drop value of the curves in the decomposed curve group. The maximum drop value is the difference between the maximum amplitude and the minimum amplitude at a time point in the time series. When the minimum amplitude does not exist, use zero value instead; When the maximum drop value is greater than or equal to the second set value, the time point at the position corresponding to the maximum drop value is used as a dividing point to regroup the detection data group; The maximum drop value is only located at the starting point of the curve in the decomposition curve group.

4. The unit status visualization monitoring method based on data twin according to claim 1 is characterized in that: When the difference between the detection data other than the benchmark data in the regrouping and the reference line segment is outside the allowable range, it is stored in a direct storage manner or a vertical line segment is generated on the reference line segment, the first segment of the vertical line segment intersects with the reference line segment, and the second segment is a free end.

5. The unit status visualization monitoring method based on data twin according to claim 1 is characterized in that: When the difference between the detection data other than the baseline data and the reference line segment in the regrouping is outside the allowed range, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is allowed to be represented on at least the previous detection data group in the time series. At this time, the detection data other than the baseline data whose difference with the reference line segment is outside the allowed range is located on the reference line segment of the selected detection data group or the difference with the reference line segment is within the allowed range.

6. A visual monitoring device for unit status based on data twin, characterized in that: include: A data acquisition unit is used to continuously acquire data collected by sensors deployed on the unit, which is recorded as detection data; A data grouping unit, used for grouping the detection data in time series to obtain detection data groups; A decomposition unit, used to generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposition curve group, wherein the curve in the decomposition curve group has a starting point and a cut-off point; The first calculation unit is used to calculate the fluctuation amount of the decomposition curve group to obtain the fluctuation evaluation parameter; A data compression unit, used for selecting a compression method for the detection data group according to the fluctuation evaluation parameter, when the fluctuation evaluation parameter is greater than or equal to a first set value, compressing the detection data group using a curve compression method, and when the fluctuation evaluation parameter is less than the first set value, compressing the detection data group using a combined compression method; A data transmission unit, used for sending the compressed detection data group to the data twin unit and displaying it at a corresponding position; The curve compression method used to compress the detection data group includes: Generate a detection data curve using the detection data group and decompose the detection data curve using wavelet to obtain a decomposed curve group, wherein the curve in the decomposed curve group has a starting point and a cut-off point; Convert each curve in the decomposed curve group into a data group, wherein the data group includes amplitude, frequency, starting point and cutoff point; Compression of the detection data group using a combination of compression methods includes: Regrouping the detection data in the detection data group, wherein the detection data in one regroup are multiple detection data; Select one of the detection data in the regrouping as the reference data; Generate a reference line segment using the detection data other than the reference data in the regrouping, wherein the detection data other than the reference data in the regrouping is located on the reference line segment or the difference between the detection data and the reference line segment is within an allowable range; Wherein, the reference line segments include straight line segments and oblique line segments; The detection data is the maximum detection data or the minimum detection data in a subgroup.

7. A unit status visualization monitoring system based on data twin, characterized in that: The system comprises: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer readable storage medium comprises: The program, when the program is executed by a processor, the method according to any one of claims 1 to 5 is executed.

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