Dynamic playback method and device for time series data of Internet of Things and medium

Abstract

The invention discloses a dynamic playback method and device for time series data of the Internet of Things and a medium, and relates to the field of the Internet of Things, and the method comprises the steps: collecting time series data in the operation process of target equipment, and determining key points in the time series data according to a preset rule; obtaining a set segment number, a set key segment number and a set total playing duration input by a user, and dividing the time sequence data; cyclically judging whether the key segment number is greater than a set key segment number; if yes, the number of key points contained in the key segments is obtained, and the key segments are aggregated; and based on a set total playing duration, allocating the aggregated key segments and non-key segments, generating a playback video, and playing the playback video through a video operation interface. According to the method, the segmentation planning of the time sequence data and the playing duration of each segment can be dynamically adjusted according to the distribution and the number of the key points, so that a user can more intuitively and efficiently capture key information in the data.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things, and specifically to a method, device and medium for dynamic playback of Internet of Things time series data. Background Art

[0002] With the continuous development and popularization of Internet of Things technology, more and more devices are connected to the Internet, which enables us to record a large amount of time series data generated during the operation of the equipment in real time. The time series data records the operating status of the equipment, environmental changes and other information, which is of great significance for equipment monitoring, management, optimization and troubleshooting.

[0003] Traditional playback methods usually display data points one by one in order. This method is acceptable when the amount of data is small. However, when faced with massive amounts of data, the huge amount of data makes it difficult for users to achieve comprehensive observation in a short period of time, and it is difficult to identify and aggregate key information. The key information is not highlighted enough, resulting in low efficiency when users observe important information in time series data, which affects users' in-depth understanding of the data and accurate analysis of the data. In addition, the data display method is relatively single and lacks the necessary dynamics and interactivity. Summary of the invention

[0004] In order to solve the above problems, this application proposes a dynamic playback method for IoT time series data, including:

[0005] Collecting time series data during the operation of the target device, and determining key points in the time series data according to preset rules;

[0006] Obtaining the set number of segments, the set number of key segments, and the set total playback duration input by the user, evenly dividing the time series data according to the set number of segments, and obtaining the key segments containing key points in the segmented time series data;

[0007] Determine the key segment quantity corresponding to the key segment, call a loop body, and loop to determine whether the key segment quantity is greater than the set key segment quantity;

[0008] If yes, respectively obtain the number of key points contained in the key segments, aggregate the key segments according to the number of key points and the time sequence between the key segments, and again determine whether the number of aggregated key segments is greater than the set number of key segments, until convergence is determined;

[0009] Based on the set total playback time, the aggregated key segments and non-key segments are allocated, and playback videos are generated from the aggregated key segments and non-key segments based on playback rules, and the playback videos are played through a video operation interface.

[0010] On the other hand, the present application also proposes a dynamic playback device for IoT time series data, including:

[0011] at least one processor; and,

[0012] a memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a dynamic playback method for IoT time series data as described in the above example.

[0014] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as: a dynamic playback method for IoT time series data as described in the above example.

[0015] This application proposes a method for dynamic playback of IoT time series data, which can bring the following beneficial effects:

[0016] Based on the user-set requirements for time series data segmentation and playback, the time series data is segmented, and by determining the key points in the time series data, the segmentation plan of the time series data and the playback duration of each segment are dynamically adjusted according to the distribution and number of key points. This not only optimizes the user's observation experience, allowing users to capture key information in the data more intuitively and efficiently, but also specifically targets time series data from different data sources, different IoT device working states, and different analysis time ranges, ensuring the consistency of the total duration of the generated data playback video. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic diagram of a flow chart of a method for dynamic playback of IoT time series data in an embodiment of the present application;

[0019] Figure 2 This is a schematic diagram of the execution flow of dynamic playback of IoT time series data in an embodiment of the present application;

[0020] Figure 3 A schematic diagram of a scene of a type of key point in an embodiment of the present application;

[0021] Figure 4 This is a schematic diagram of a dynamic playback device for IoT time series data in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0023] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0024] It should be noted that the existing segmented display technology is similar to the automatic projection function of PPT. The display time of each segment can be compared to the "delay time" set during the automatic projection in PPT, and the display method of each time period can be compared to the effect of all time points within the static image display time period in PPT. It is mainly divided into two categories: general and personalized, each with its own advantages and limitations. The general segmentation method realizes basic dynamic display by evenly distributing data points to each paragraph. This method is simple and easy to implement, and can ensure the consistency of the total playback time, but it also has shortcomings: it cannot distinguish between key information and non-key information in the data, resulting in the inability to focus on the display of important data, and the inability to quickly skip the less important data, thus affecting the user's observation efficiency.

[0025] As disclosed in the patent (announcement) number CN202310156942.8, the patent name is: Data segmentation method and related devices. This patent details an innovative data segmentation method and related devices, which are designed to efficiently process high-frequency time series data generated in the field of the Internet of Things. The method realizes periodic automatic segmentation of data through a series of technical steps, including but not limited to obtaining preset segmentation conditions and target data, evaluating the restrictions on the conditions of the data, determining the set of ontological periods based on the periodic characteristics of the data, determining the target ontological period in the set, and performing data segmentation on the target ontological period to obtain the result. In addition, the technology also includes verification of the validity of the segmentation results, and generating a fast segmentation strategy using historical segmentation records to further optimize the segmentation efficiency. The patent provides an efficient and automated data processing solution for applications such as urban rail transit and power monitoring systems. However, the patent does not have a function point for data playback, and there is no further merging of the segmented data. In addition, the requirements for data may be stricter when segmenting based on the periodicity of high-frequency data.

[0026] Another example is the patent with the publication number CN201910282014.X, the patent name: Time series data segmentation method, device, storage medium and electronic device. This patent proposes an efficient and accurate time series data segmentation method and device, which is suitable for processing panoramic data sequences in electronic devices. The method segments the target time interval by at least two preset time series segmentation rules, merges the segmentation points to generate a second time series, and detects whether the time interval formed by the segmentation time points meets the preset conditions through technologies such as support vector machine classification models, so as to delete the segmentation points that do not meet the conditions. Finally, multiple panoramic data subsequences are generated according to the remaining segmentation time points, which effectively improves the accuracy and efficiency of time series data segmentation. The patent also does not conduct further exploration of the segmented data at the data presentation or visualization level, and because of the use of artificial intelligence models, there may be hidden dangers in the interpretability of this method.

[0027] like Figure 1 As shown, the embodiment of the present application provides a method for dynamic playback of IoT time series data, including:

[0028] S101: Collecting time series data during the operation of a target device, and determining key points in the time series data according to preset rules.

[0029] Specifically, Figure 2 As shown, the IoT device is determined as the target device, and the operating data generated during the operation of the target device is used as the data source, such as sensor data generated by sensor devices (such as temperature, humidity, pressure, etc.) and machine operation data generated by industrial production equipment (such as motor speed, power consumption, etc.).

[0030] During the operation of the target device, the timing data of the target device is collected, wherein the timing data is the device operation data sorted according to the time series in a continuous time. In an embodiment of the present application, the historical timing data corresponding to the collection time can be queried in the database according to the collection time entered by the user to obtain the required timing data.

[0031] Furthermore, the key points in the time series data are determined according to preset rules, wherein the key points include data maximum value points, trend turning points, abnormal value points, event marking points, etc., and the data maximum value points include data maximum value points and data minimum value points.

[0032] Different key points are extracted according to different features of time series data.

[0033] Specifically, for the data extreme value point, the data values ​​of the same type contained in the time series data are compared to obtain the maximum value point and the minimum value point. When the operation data reaches the maximum value or the minimum value, it indicates that the operation state of the physical device at this time may be in a state indicating overload, idle or fault. By extracting the data extreme value point during the operation of the physical device and displaying the corresponding extreme value data, it is possible to provide a more intuitive observation of the actual operation power of the physical device, timely discover the abnormal behavior of the device and take emergency measures.

[0034] For trend turning points, the segmentation points are automatically determined by a preset segmentation algorithm, and the time series data is divided into several segments, so that the overall trend is presented in each time series data segment. The preset segmentation algorithm can be a slope threshold method, a segment aggregation approximation algorithm, a dynamic time warping algorithm, etc. After the segmentation is completed, the slope of the segmented time series data segment is obtained by calculating the slope between the corresponding values ​​of adjacent segmentation points. When the slope exceeds the preset slope threshold, the two end points of the time series data segment are determined to be trend turning points, indicating that the trend of the time series data segment exceeds the stable trend and is in a state of large fluctuations. The preset slope threshold can be set according to the stable trend.

[0035] It should be noted that in the embodiment of the present application, for the data trend in each time series data segment, the slope of each segment after the linear approximation of the original data and the time points of the first and last points can be detected to determine whether the trend in each time series data segment is an upward, downward or stable trend. By extracting the turning point of the operation data trend of the physical device and displaying the turning point during the playback process, the user can intuitively understand the changing trend of the equipment performance over time, which helps to formulate a more scientific and reasonable operation plan.

[0036] For the abnormal value points, the normal operating range of the target device is obtained, the normal operating interval is set based on the normal operating range, and the data points in the time series data that are outside the normal operating interval are set as abnormal value points. Among them, in the embodiment of the present application, the normal operating range of the equipment can be determined according to the industry standard or the manual when the equipment leaves the factory, and can also be modified according to the actual needs of the target device. By collecting the abnormal value points of the time series data and displaying the abnormal value points, these abnormal value points can be discovered and displayed in time, which can quickly attract the attention of maintenance personnel, so as to quickly identify faults and handle them, reduce downtime and production losses, and avoid the situation where relevant personnel fail to pay attention to the abnormality in time due to excessive data volume.

[0037] For event marking points, an alarm mechanism is set based on the alarm threshold. When the data value in the time series data is higher or lower than the alarm threshold, an alarm event is responded to, and the start and end points of the alarm event are determined as event marking points.

[0038] In the embodiment of the present application, there are multiple alarm events, such as Figure 3 As shown, there may be an oscillation alarm event. When the data value is lower than the oscillation alarm threshold, an alarm occurs, and the alarm event is monitored to determine the start and end points of the alarm response. At the same time, there are corresponding anti-shake settings for oscillation alarm events to handle them. Within the set anti-shake time, if data oscillation occurs, no alarm will be generated, thereby effectively preventing false alarms.

[0039] S102: Obtain the set segment number, the set key segment number and the set total playback time input by the user, divide the time series data evenly according to the set segment number, and obtain the key segment containing the key point in the segmented time series data.

[0040] Specifically, the number of set segments, the number of set key segments, and the total playback duration input by the user are obtained, and the total number of data points in the time series data, the total number of key points, and the time point corresponding to each key point are determined. The number of set segments is the number of data segments obtained after the time series data is segmented; the number of set key segments is the number of key segments containing key points in the data segments after the time series data is segmented; and the total playback duration is the duration of the playback video obtained after processing the time series data.

[0041] Based on the formula Perform segmented preprocessing on the time series data, where num total is the total number of data points in the time series data, num point is the number of data points contained in each time period after average distribution, and a is the set number of segments.

[0042] More specifically, the total number of data points is compared with the set number of segments. When the total number of data points is not greater than the set number of segments, the time series data is divided according to the total number of data points so that the number of data points in each data segment after division is 1. When the total number of data points is greater than the set number of segments, the time series data is evenly divided according to the set number of segments, and key segments containing key points in the time series data after segmentation are obtained. The number of data points in each data segment after division is the ratio of the total number of data points to the set number of segments rounded down.

[0043] For example, when the total number of time points in the selected time period is not greater than 1000, it means that each divided time period contains only one data point. If the entire selected time range is finally divided into N segments, then (Round up), record the start and end time of the nth segment as start n and end n ,n∈[1,N]. The start of the nth time period n The point in the time series of is (n-1)·num point +1, end nThe point in the time series is num point (when n≤N-1) or num total (When n=N).

[0044] It should be noted that in the embodiment of the present application, in order to avoid not being able to obtain a clear effect due to excessive data volume, it is recommended to set the number of segments to no more than 4000, the number of key segments to no more than 40, and the total playback time to no more than 10 minutes.

[0045] S103: Determine the key segment quantity corresponding to the key segment, call a loop body, and loop to determine whether the key segment quantity is greater than the set key segment quantity.

[0046] Specifically, after the time series data is segmented, the key segments containing the key points are determined, the number of key segments is counted, and the loop body is called to perform a cyclic judgment on whether the number of key segments is greater than the set number of key segments. When the number of key segments is greater than the set number of key segments, the key segments are aggregated to ensure that the playback time of the generated playback video is within the set total playback time.

[0047] If not, that is, the number of key segments is not greater than the set number of key segments, there is no need to aggregate the key segments, and they are directly re-encoded to execute step S105.

[0048] S104: If yes, then respectively obtain the number of key points contained in the key segments, aggregate the key segments according to the number of key points and the time sequence between the key segments, and again determine whether the number of aggregated key segments is greater than the set number of key segments, until convergence is determined.

[0049] Specifically, if the number of key segments is greater than the set number of key segments, it means that the number of key segments after segmentation is too large, which may cause the duration of the generated playback video to exceed the set playback duration, so the excess key segments need to be aggregated.

[0050] More specifically, the key segments adjacent in time sequence are set as a pair of data groups, and the number of non-key segments in the data group is determined, the sum of the number of key points in the data group is obtained, and the cost coefficient is constructed based on the sum of the number of key points and the number of non-key segments. The formula k is obtained according to the cost coefficient. i , i+1 =(key i +key i+1 )·unkey i,i+1 , where k i,i+1 is the cost coefficient, key i +key i+1 is the total number of key points in the data set, unkey i,i+1The number of non-key segments in the data set is determined based on the above formula according to the number of key points and the time sequence between the key segments, so as to aggregate the key segments based on the minimum cost coefficient.

[0051] Further, a key data group whose total number of key points is higher than a preset threshold is determined, and a part of the key data group that is preferentially merged is obtained, and then the number of non-key segments in the key data group that is preferentially merged is compared to determine the minimum number of non-key segments, and the key data group corresponding to the minimum number of non-key segments is determined. Then, the key group is composed of two key segments with a large number of key points and a close distance, and the two key segments in the key data group corresponding to the minimum number of non-key segments are preferentially merged. In an embodiment of the present application, it can be set according to the mean value of the number of key points contained in the key data group.

[0052] Among them, based on the minimum cost coefficient, the key segment aggregation model formula is as follows:

[0053]

[0054] st

[0055] x i,i+1 ∈{0,1}

[0056]

[0057] Among them, I is the number of key segments, including the number of key segments before aggregation and the number of key segments after aggregation, b is the set number of key segments, x i,i+1 ∈{0,1} by introducing the cost coefficient k i，i+1 , two key segments with more key points and closer distance are merged first, so that all the key segments finally aggregated contain the least number of non-key segments divided in the original time series.

[0058] It should be noted that when the variable x i,i+1 When it is 0, it means that the key segments i and i+1 are not merged, and when it is 1, it means that the key segments i and i+1 are merged. Formula It is ensured that the number of key segments after merging is the set number of key segments, which means that the number of key segments to be merged is the difference between the number of key segments before aggregation and the set number of key segments.

[0059] It should be noted that when the result of the key segment aggregation model formula is x1, 2 = 1 and x2, 3 = 1, based on our merging principle, it means that the three consecutive key segments numbered i 1, 2, and 3 need to be merged into a new key segment. Similarly, 4, 5, and other consecutive key segments need to be aggregated into a new key segment.

[0060] S105: allocating the aggregated key segments and non-key segments based on the set total playback time, generating a playback video from the aggregated key segments and the non-key segments based on a playback rule, and playing the playback video through a video operation interface

[0061] Specifically, the total playback time will be set, and the aggregated key segments and non-key segments will be evenly distributed to determine the key segment duration and non-key segment duration. Based on the number of aggregated key segments, the number of aggregated non-key segments will be determined. Based on the number of aggregated key segments and the key segment duration, the playback speed of the key segments will be determined. Based on the number of aggregated non-key segments and the non-key segment duration, the playback speed of the non-key segments will be determined.

[0062] Set the non-critical segment playback time to The key segment playback time is set to T is the total playback time, based on the formula Calculate the playback duration of each non-key segment. The time allocation ratio of each key point is 1+lnkey m , the average playing time allocated to each key point is The playback time allocated to the mth key segment is period key,m =(1+lnkey m )·time key .

[0063] Furthermore, all the time periods after aggregation are re-encoded, and the start and end time points of all the time periods are determined, the data corresponding to each time period is smoothed to obtain smoothed data points, the start and end time points are set as smoothed time points corresponding to the smoothed data points, and a dynamic line chart is generated based on the smoothed data points and the smoothed time points.

[0064] Determine the key data segments and non-key data segments in the dynamic line graph respectively, generate a playback video for the dynamic line graph according to the playback speed of the key segments and the playback speed of the non-key segments, play the playback video through the video operation interface, and when the data point running in the dynamic line graph is a key point, the data information and time information corresponding to the key point will pop up.

[0065] Based on the user-set requirements for time series data segmentation and playback, the time series data is segmented, and by determining the key points in the time series data, the segmentation plan of the time series data and the playback duration of each segment are dynamically adjusted according to the distribution and number of key points. This not only optimizes the user's observation experience, allowing users to capture key information in the data more intuitively and efficiently, but also specifically targets time series data from different data sources, different IoT device working states, and different analysis time ranges, ensuring the consistency of the total duration of the generated data playback video.

[0066] like Figure 4 As shown, the embodiment of the present application also proposes a dynamic playback device for IoT time series data, including:

[0067] at least one processor; and,

[0068] a memory communicatively connected to the at least one processor; wherein,

[0069] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a dynamic playback method for IoT time series data as described in any of the above embodiments.

[0070] An embodiment of the present application also provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to be: a method for dynamic playback of IoT time series data as described in any of the above embodiments.

[0071] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0072] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0073] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0079] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0081] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A dynamic playback method for IoT time series data, characterized in that: include: Collecting time series data during the operation of the target device, and determining key points in the time series data according to preset rules; Obtaining the set number of segments, the set number of key segments, and the set total playback duration input by the user, evenly dividing the time series data according to the set number of segments, and obtaining the key segments containing key points in the segmented time series data; Determine the key segment quantity corresponding to the key segment, call a loop body, and loop to determine whether the key segment quantity is greater than the set key segment quantity; If yes, respectively obtain the number of key points contained in the key segments, aggregate the key segments according to the number of key points and the time sequence between the key segments, and again determine whether the number of aggregated key segments is greater than the set number of key segments, until convergence is determined; Based on the set total playback time, the aggregated key segments and non-key segments are allocated, and playback videos are generated from the aggregated key segments and non-key segments based on playback rules, and the playback videos are played through a video operation interface.

2. According to claim 1, a dynamic playback method for IoT time series data is characterized in that: The key points include data maximum value points, trend turning points, abnormal value points, and event marking points; Determining the key points in the time series data according to the preset rules specifically includes: Compare the data values ​​of the time series data to obtain data maximum value points, wherein the data maximum value points include maximum value points and minimum value points; Automatically determine the segmentation point by using a preset segmentation algorithm, divide the time series data into several segments, calculate the slope of the segmented time series data segment, and when the slope exceeds a preset slope threshold, determine the segmentation point corresponding to the time series data segment as a trend turning point; Determine a normal operating range of the target device, and set data points outside the normal operating range as outlier points; Based on a preset alarm mechanism, in response to an alarm event, the start and end points of the alarm event are determined as event marking points.

3. According to claim 1, a dynamic playback method for IoT time series data is characterized in that: Before evenly dividing the time series data according to the set number of segments, the method further includes: Determine the total number of data points in the time series data, and compare the total number of data points with the set number of segments; When the total number of data points is not greater than the set number of segments, the time series data is divided according to the total number of data points so that the number of data points in each data segment after the division is 1.

4. According to claim 1, a dynamic playback method for IoT time series data is characterized in that: Aggregating the key segments according to the number of key points and the time sequence between the key segments specifically includes: Setting the key segments adjacent in time sequence as a pair of data groups, and determining the number of non-key segments in the data groups; Obtaining a total number of key points of the data group, and constructing a cost coefficient based on the total number of key points and the number of non-key segments; A minimum cost coefficient is determined according to the number of key points and the time sequence between the key segments, and the key segments are aggregated based on the minimum cost coefficient.

5. According to claim 4, a dynamic playback method for IoT time series data is characterized in that: Determining the minimum cost coefficient according to the number of key points and the time sequence between the key segments specifically includes: Determine a key data group whose total number of key points is higher than a preset threshold; Comparing the numbers of non-critical segments in the critical data groups, determining a minimum number of non-critical segments, and determining a critical data group corresponding to the minimum number of non-critical segments; The key data groups corresponding to the minimum number of non-key segments are merged preferentially.

6. According to claim 1, a method for dynamic playback of IoT time series data is characterized in that: The allocating the aggregated key segments and non-key segments based on the set total playback duration specifically includes: The set total playback time is evenly distributed to the aggregated key segments and non-key segments to determine the key segment time and non-key segment time; Determining the number of non-critical segments after aggregation according to the number of critical segments after aggregation; The key segment playback speed is determined based on the aggregated number of key segments and the key segment duration, and the non-key segment playback speed is determined based on the aggregated number of non-key segments and the non-key segment duration.

7. The method for dynamic playback of IoT time series data according to claim 6, characterized in that: Before generating a playback video from the aggregated key segments and the non-key segments based on the playback rule, the method includes: Re-encode all the aggregated time periods and determine the start and end time points of all the time periods; Smoothing the data corresponding to each time period to obtain smoothed data points, and setting the start and end time points as smoothed time points corresponding to the smoothed data points; A dynamic line graph is generated based on the smoothed data points and the smoothed time points.

8. The method for dynamic playback of IoT time series data according to claim 7, characterized in that: The generating a playback video from the aggregated key segments and the non-key segments based on the playback rule specifically includes: Determine the key data segments and non-key data segments in the dynamic line graph respectively, and generate a playback video from the dynamic line graph according to the key segment playback speed and the non-key segment playback speed; Playing the playback video through the video operation interface specifically includes: The playback video is played through the video operation interface, and when the data point running in the dynamic line graph is a key point, the data information and time information corresponding to the key point are popped up.

9. A dynamic playback device for IoT time series data, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform, for example: Collecting time series data during the operation of the target device, and determining key points in the time series data according to preset rules; Obtaining the set number of segments, the set number of key segments, and the set total playback duration input by the user, evenly dividing the time series data according to the set number of segments, and obtaining the key segments containing key points in the segmented time series data; Determine the key segment quantity corresponding to the key segment, call a loop body, and loop to determine whether the key segment quantity is greater than the set key segment quantity; If yes, respectively obtain the number of key points contained in the key segments, aggregate the key segments according to the number of key points and the time sequence between the key segments, and again determine whether the number of aggregated key segments is greater than the set number of key segments, until convergence is determined; Based on the set total playback time, the aggregated key segments and non-key segments are allocated, and playback videos are generated from the aggregated key segments and non-key segments based on playback rules, and the playback videos are played through a video operation interface.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Collecting time series data during the operation of the target device, and determining key points in the time series data according to preset rules; Obtaining the set number of segments, the set number of key segments, and the set total playback duration input by the user, evenly dividing the time series data according to the set number of segments, and obtaining the key segments containing key points in the segmented time series data; Determine the key segment quantity corresponding to the key segment, call a loop body, and loop to determine whether the key segment quantity is greater than the set key segment quantity; If yes, respectively obtain the number of key points contained in the key segments, aggregate the key segments according to the number of key points and the time sequence between the key segments, and again determine whether the number of aggregated key segments is greater than the set number of key segments, until convergence is determined; Based on the set total playback time, the aggregated key segments and non-key segments are allocated, and playback videos are generated from the aggregated key segments and non-key segments based on playback rules, and the playback videos are played through a video operation interface.

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