Method and apparatus for processing time-associated data table, and computer device and readable storage medium

Abstract

A method and apparatus for processing a time-associated data table, and a computer device, a computer-readable storage medium and a computer program product. The method comprises: acquiring, from a data table to be processed, time data columns arranged according to a time sequence; using sliding windows to slide on the time data columns, to respectively acquire service data corresponding to time data covered by the positions where the sliding windows are located, so as to obtain service data sets; using a service data set, the service data of which meets a data screening condition, as a target service data set; and on the basis of the time data corresponding to the target service data set, determining a state monitoring result of the service data.

Description

Processing method and device of time-related data table, computer device and readable storage medium

[0001] Related applications

[0002] The present application claims priority to the Chinese patent application No. 202411079771.4, filed on August 7, 2024, and entitled "Processing method and device of time-related data table, computer device and readable storage medium", the contents of which are hereby incorporated by reference in its entirety. TECHNICAL FIELD

[0003] The present application relates to the technical field of data analysis, and in particular, to a processing method and device of time-related data table, a computer device, a computer readable storage medium and a computer program product. BACKGROUND

[0004] In the industrial production process, there are often scenarios that require sensors to collect business data. For example, in some scenarios, sensors are placed near chips to determine whether the chip reading data is accurate by comparing the business data read by the chip with the business data collected by the sensor. In other scenarios, the environmental parameters are determined to meet the control requirements according to the business data collected by the sensor.

[0005] Business data usually changes over time, so it is often necessary to filter business data based on time conditions. Currently, the related technology mainly filters business data through excel software. However, as the sensor accuracy becomes higher, the amount of data to be processed for each filtering of business data is large, and the related technology is difficult to realize conditional filtering of time-related business data. SUMMARY

[0006] According to various embodiments of the present application, a processing method and device of time-related data table, a computer device, a computer readable storage medium and a computer program product capable of realizing multi-dimensional filtering of time-related business data are provided.

[0007] In a first aspect, the present application provides a processing method of time-related data table, comprising:

[0008] obtaining a time data column arranged in time sequence from a to-be-processed data table, wherein the to-be-processed data table includes business data corresponding to the time data column;

[0009] determining a filtering condition of the to-be-processed data table, wherein the filtering condition includes a time filtering condition and a data filtering condition;

[0010] A sliding window determined according to the time screening condition is slid on the time data column, and service data corresponding to time data covered by a position of the sliding window is obtained to obtain a service data set, wherein the position of one sliding window corresponds to one service data set;

[0011] The service data set in which the service data meets the data screening condition is taken as a target service data set;

[0012] A state monitoring result of the service data is determined based on time data corresponding to the target service data set.

[0013] In one of the embodiments, the state monitoring result of the service data is determined based on the time data corresponding to the target service data set, including:

[0014] A continuous time section in which the service data meets the screening condition is determined according to the time data corresponding to the target service data set;

[0015] The continuous time section in which the time data belongs to a determined stable change interval is taken as a stable interval result and output.

[0016] In one of the embodiments, the continuous time section in which the service data meets the screening condition is determined according to the time data corresponding to the target service data set, including:

[0017] The time data corresponding to the target service data set is merged to obtain a time data set;

[0018] The time data set is subjected to a deduplication process to obtain a target time data set;

[0019] A continuous time section is determined based on a first time data and a last time data in the target time data set.

[0020] In one of the embodiments, the service data in the to-be-processed data table corresponds to a device that collects the service data, and the state monitoring result of the service data is determined based on the time data corresponding to the target service data set, including:

[0021] Abnormal time information in which the service data does not meet the screening condition is determined according to the time data corresponding to the target service data set;

[0022] Abnormal device information that collects the service data in the to-be-processed data table is determined according to the abnormal time information.

[0023] In one of the embodiments, the data screening condition includes at least one of the following:

[0024] a range of the single-column business data in the business data set is less than a preset first threshold value;

[0025] a deviation value of the single-column business data in the business data set from a preset target value is less than a preset second threshold value;

[0026] a range of all the business data in the business data set is less than a preset third threshold value.

[0027] In one of the embodiments, the time precision of the time data includes at least one of the following: millisecond level, microsecond level or nanosecond level.

[0028] In one of the embodiments, the business data includes temperature data.

[0029] In one of the embodiments, the to-be-processed data table includes an excel table.

[0030] In a second aspect, the application further provides a processing device for a time-related data table, including:

[0031] a data acquisition module, configured to acquire a time data column arranged in time sequence from a to-be-processed data table, the to-be-processed data table including business data corresponding to the time data column;

[0032] a screening condition determination module, configured to determine a screening condition of the to-be-processed data table, the screening condition including a time screening condition and a data screening condition;

[0033] a time screening module, configured to slide a sliding window determined according to the time screening condition on the time data column, respectively acquire business data corresponding to time data covered by a position of the sliding window, and obtain a business data set, wherein the position of one sliding window corresponds to one business data set;

[0034] a data screening module, configured to take a business data set in which the business data satisfies the data screening condition as a target business data set;

[0035] a result analysis module, configured to determine a state monitoring result of the business data based on time data corresponding to the target business data set.

[0036] In one of the embodiments, the result analysis module includes:

[0037] a time section determination submodule, configured to determine a continuous time section in which the business data meets the screening condition according to time data corresponding to the target business data set;

[0038] The first result output sub-module is configured to output, as a stable interval result, a continuous time interval in which the time data belongs to a determined stable variation interval.

[0039] In one of the embodiments, the time interval determination sub-module comprises:

[0040] The merging unit is configured to merge the time data corresponding to the target service data set to obtain a time data set.

[0041] The deduplication unit is configured to perform deduplication processing on the time data set to obtain a target time data set.

[0042] The time interval determination unit is configured to determine a continuous time interval based on a first time data and a last time data in the target time data set.

[0043] In one of the embodiments, the device collecting the service data corresponding to a column of service data in the to-be-processed data table, and the result analysis module comprises:

[0044] The time information determination sub-module is configured to determine, according to the time data corresponding to the target service data set, abnormal time information in which the service data does not meet the screening condition.

[0045] The second result output sub-module is configured to determine, according to the abnormal time information, abnormal device information collecting the service data in the to-be-processed data table.

[0046] In one of the embodiments, the data screening condition comprises at least one of the following:

[0047] A range of a single column of service data in the service data set is less than a preset first threshold value;

[0048] A deviation value of the single column of service data in the service data set from a preset target value is less than a preset second threshold value;

[0049] A range of all service data in the service data set is less than a preset third threshold value.

[0050] In one of the embodiments, the time precision of the time data comprises at least one of the following: millisecond level, microsecond level or nanosecond level.

[0051] In one of the embodiments, the service data comprises temperature data.

[0052] In one of the embodiments, the to-be-processed data table comprises an excel table.

[0053] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method according to any one of the preceding aspects.

[0054] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the method according to any one of the preceding aspects.

[0055] In a fifth aspect, the present application provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the method according to any one of the preceding aspects.

[0056] The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the application will be apparent from the description and drawings, and from the claims. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the accompanying drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on the disclosed drawings.

[0058] Fig. 1 is a flow diagram of processing of a time-related data table in an embodiment;

[0059] Fig. 2 is a flow diagram of step S110 in an embodiment;

[0060] Fig. 3 is a flow diagram of step A1 in an embodiment;

[0061] Fig. 4 is a flow diagram of step S110 in another embodiment;

[0062] Fig. 5 is a block diagram of a structure of a time-related data table processing device in an embodiment;

[0063] Fig. 6 is an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only represent some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0065] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0066] At present, many data are collected by various sensor devices continuously or intermittently, and then stored in an excel table according to the time sequence of the collected data to form a time-related data table, which can be used for subsequent data processing. In many data collection business scenarios, the data amount of the obtained time-related data table is usually large. For example, the time-related data table to be processed can include more than one million rows of business data. The excel software cannot open the data table with a large amount of data, for example, more than 65536 rows of data, and also cannot analyze the running state of the business according to the business data in the data table. The business can be the adjustment of the temperature, humidity, pressure, illumination and other environmental parameters of the device monitored by the sensor, or a certain process in the production process. Based on the current demand of computer devices for data processing of time-related tables, the present disclosure provides a method for processing a time-related data table.

[0067] It should be noted that the embodiments provided by the present disclosure are not limited to data processing in the excel scenario, and the embodiments of the present disclosure can also be performed for data processing of other time-related tables. In one embodiment, as shown in FIG. 1, a method for processing a time-related data table is provided, and the present embodiment is exemplarily described by taking the method applied to a server. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and can be realized through the interaction of the terminal and the server. In the present embodiment, the method includes the following steps:

[0068] In step S102, time data arranged in time sequence is obtained from a data table to be processed, and the data table to be processed includes business data corresponding to the time data column.

[0069] Exemplarily, the data table to be processed can include a time data column and a business data column, and the time data and the business data in the same row correspond to each other. The time data can be used to represent the collection time of the corresponding business data. The time data in the time data column can be arranged in time sequence, for example, arranged in the time sequence of collecting the data. The time accuracy of the time data can be microsecond level. The business data can be used to represent the running state of the business. The business data column can be multiple columns, and the business data in different business data columns can be collected by different devices. In some implementation scenarios, different devices can be located at different positions of the same device, or can be located at different devices, or can be the same or different devices collected by the same device.

[0070] The to-be-processed data table can be obtained in various ways, such as being directly transmitted and stored into a data table of a server through collected data, or being obtained from a database or other system. For example, a data processing tool can be called to read the time data column in the to-be-processed data table. Here, the data processing tool can be an extension library Pandas of Python language. In actual application, the time data can be composed of date data and time point data, and the date data column and the time point data column arranged in time sequence are formed in the to-be-processed data table. At this time, after the date data column and the time point data column are read out, the date data column and the time point data column can be merged into one column, and the merged date data and time point data are converted into a preset time format to obtain the pre-processed time data column.

[0071] In step S104, a filtering condition of the to-be-processed data table is determined, and the filtering condition includes a time filtering condition and a data filtering condition.

[0072] The filtering condition can be determined according to the data processing requirement of the business. In some embodiments of the present disclosure, the filtering condition can include a time filtering condition of the time dimension and a data filtering condition of the specific business data filtering on the data content. In some application scenarios, the filtering condition can also be determined according to different target running states, such as whether the data acquisition device of the business data is in a just-started running state or in a stable monitoring state. For example, the filtering condition can be that the business data is zero and lasts for a certain time, or whether the monitoring business data fluctuates more than a set threshold in a certain time period. In some application scenarios of the present disclosure, when the target running state is to control the stable environment parameters of the device, the filtering condition can be that the fluctuation of the business data is within a preset range and lasts for a certain time.

[0073] For example, the time filtering condition can include a start time, an end time, a time span, a periodic condition, etc. The data filtering condition can include a numerical range, whether missing, a statistical feature range, etc.

[0074] In step S106, a sliding window determined according to the time filtering condition is slid on the time data column, and the business data corresponding to the time data covered by the position of the sliding window is obtained to obtain a business data set, wherein the position of one sliding window corresponds to one business data set.

[0075] Exemplarily, the initial position of the sliding window can be set to point to the first time data in the time data column, and the ending position can be set to point to the last time data in the time data column, i.e., the sliding window can start from the first time data in the time data sequence and end at the last time data in the time data sequence. In the current embodiment, the sliding window can also be set to perform sliding window filtering on one or more intervals in the time data sequence. The size of the sliding window can be set such that the time data covered by the sliding window at each position satisfies the time filtering condition. When the sliding window slides, the sliding window can slide by a row as the sliding step, or the sliding window can slide by time as the sliding step. When the sliding step is set by time, the sliding step of the sliding window is usually not greater than the size of the sliding window, so that the sliding window slides through the time data column.

[0076] In an embodiment, the sliding window can be set by the rolling method in Pandas. In a possible implementation, the sliding window can be set to slide by a row as the sliding step, and the sliding step of the sliding window can be set to 1, i.e., the sliding window moves one row each time. For example, in an example, the size of the sliding window is 10 seconds, and if the sliding window covers the time data from the first row to the tenth row at the initial position, the sliding window covers the time data from the second row to the eleventh row after sliding once.

[0077] Of course, as mentioned above, the sliding window can also be time-sliding step. In the case that the time filtering condition includes a time span, the size of the sliding window can be determined according to the time span, so that the difference between the last time data and the first time data in the sliding window is equal to the time span. For example, in an example, the data in the time data column starts from 21:00:00:000000 on August 1, 2024, and the time filtering condition is 10 seconds. The initial position of the sliding window can be set to the first time data of the time data column, and at this time the first row of the sliding window covers 21:00:00:000000 on August 1, 2024, and the last row of the sliding window covers 21:00:10:000000 on August 1, 2024. At this time, the 10-second sliding window can include all time data from 21:00:00:000000 on August 1, 2024 to 21:00:10:000000 on August 1, 2024, which can be more than 10 rows of data, or less than 10 rows of data. The sliding step of the time dimension can be set according to the demand, and in some embodiments, the sliding step of the time dimension can be set to be less than the sliding window, for example, the sliding window is 10 seconds, and the sliding step is 1 second, which can be set to slide from the previous window position to the next second window position each time, for example, the first row of the sliding window slides from 21:00:00:000000 on August 1, 2024 to 21:00:01:000000 on August 1, 2024. In this way, the accuracy of data filtering can be improved. In other embodiments, the sliding step of the time dimension can be set to be equal to the sliding window, for example, the sliding window is 10 seconds, and the sliding step is 10 seconds. Referring to the above example, the first row of the sliding window slides from 21:00:00:000000 on August 1, 2024 to 21:00:10:000000 on August 1, 2024, which can improve the speed of data filtering.

[0078] In the process of sliding the sliding window on the time data column, the business data corresponding to the time data covered by the sliding window at the current position can be obtained respectively, and the position of a sliding window corresponds to a business data set, and a plurality of business data sets of the sliding window at different positions can be obtained.

[0079] In step S108, the business data set in which the business data satisfies the data filtering condition is taken as the target business data set.

[0080] In some embodiments of the present disclosure, the data judgment condition is not required to include time-related screening conditions in some implementations due to the separately set time judgment condition. The data screening condition can include various conditions and various types of screening requirements, such as containing a specific key character in the business data, one or more data values themselves meeting / not meeting a set threshold, a result obtained by statistics on the business data meeting / not meeting a set condition, and the like. The specific data judgment can be set according to the screening requirements of the business data. For example, in some embodiments of the present disclosure, the data screening condition can include at least one of the following: range condition, extreme value condition, average value condition, and variance condition.

[0081] The business data set meeting the data screening condition is obtained by further screening the business data set obtained according to the time screening condition according to the pre-set data screening condition. For ease of description, the business data set meeting the data screening condition can be referred to as a target business data set.

[0082] Step S110, determining the state monitoring result of the business data based on the time data corresponding to the target business data set.

[0083] Exemplarily, the state monitoring result can include state monitoring information of the object corresponding to the business data, which can be fed back or represented according to the processing of the data in the to-be-processed data table. The state monitoring result of the business data is usually associated with time information, and can include state information representing the device collecting the business data at a certain time point or interval, or data state information representing the business data itself at a certain time point or interval, such as the average value or range of the collected business data in a certain time interval being greater than a threshold. Of course, the state monitoring result in some temperature monitoring implementation scenarios of the present disclosure can include, for example, a business running normally result, a business running abnormally result, a business running normally time, and a business running abnormally time. The business running normally result can include a stable interval result, a change interval result, and a process interval result. The business running abnormally result can include abnormal time information, an abnormal device, and an abnormal process.

[0084] In the processing method of the time-related data table, the time data column in the to-be-processed data table is obtained, a sliding window is slid on the time data column, corresponding business data is obtained from the to-be-processed data table according to the time data in the sliding window, and a plurality of sets of business data of time window interval segments are obtained, so that business data in different time segments can be flexibly filtered out. According to the data characteristics of the business data, the business data set is further filtered, so that the business data meeting the data dimension condition can be filtered out from the business data meeting the time dimension condition. Since the business data is associated with the business time dimension, the running state of the business in a specific time segment can be determined according to the filtered business data. According to the time data corresponding to the filtered business data, the time when the business is in a specific running state can be determined, and then the state of the object corresponding to the business data can be determined, so as to facilitate the business personnel to trace back and analyze.

[0085] In some embodiments provided by the present disclosure, the stability of the control can be determined according to the business data. Specifically, as shown in FIG. 2, the step S110 can include:

[0086] In step A1, the continuous time segment in which the business data meets the filtering condition is determined according to the time data corresponding to the target business data set.

[0087] For example, the minimum value and the maximum value in the time data corresponding to the target business data set can be filtered out to obtain the start time and the end time of the continuous time segment, respectively. It can be understood that the number of target business data sets can be one or more, and the number of continuous time segments can also be one or more. In the case where the time segments corresponding to the target business data sets are adjacent, the adjacent continuous time segments can be merged.

[0088] In step A2, the continuous time segment in which the time data belongs to the determined stable change interval is output as a stable interval result.

[0089] The time data can be used to determine the stable change interval to represent that the business data corresponding to the time data fluctuates within a preset range. For example, in the case where the business data is temperature data, the stable change interval can be used to represent the time when the temperature remains stable. The output can include obtaining / determining the stable interval, or visualizing the obtained / determined stable interval through a chart, or feeding back the obtained / determined stable interval to another processing object.

[0090] For example, the stable interval result can include the presence / absence of a stable change interval, and / or the start time and the end time of the stable change interval.

[0091] Optionally, the continuous time section of the time data belonging to the determined non-stable change interval can be output as a non-stable interval result. The non-stable change interval can be an interval in the time data sequence other than the stable change interval. For example, in the case of temperature data as the business data, the non-stable change interval can be used to represent the time of temperature change.

[0092] In this embodiment, according to the time data corresponding to the business data meeting the filtering condition, the running state and duration of the business can be determined, and further, in the case of needing to judge the stability of the control, the results such as whether the stable control can be achieved and the time section that can be stably controlled are displayed.

[0093] In one possible implementation, as shown in FIG. 3, the above step A1 can include:

[0094] Step A11, merging the time data corresponding to the target business data set to obtain a time data set.

[0095] Step A12, performing a deduplication processing on the time data set to obtain a target time data set.

[0096] Step A13, determining a continuous time section based on the first time data and the last time data in the target time data set.

[0097] Exemplarily, when the step size of the sliding window is smaller than the size of the sliding window, there is an overlapping case of the continuous time section corresponding to the target business data set, at this time, the time data corresponding to the target business data set can be merged and deduplicated to form one or more target data sets, so that the time data in the target time data set is continuous in the time data sequence.

[0098] In this embodiment, by merging and deduplicating the time data set corresponding to the target business data set, the overlapping time sections can be combined into a continuous time section.

[0099] In one embodiment, a device (for example, a temperature sensor) collecting business data corresponding to a column of business data in the to-be-processed data table, as shown in FIG. 4, the above step S110 can include:

[0100] Step B1, determining abnormal time information of the business data not meeting the filtering condition according to the time data corresponding to the target business data set.

[0101] The abnormal time information can include a time point and / or a time section. Exemplarily, time data other than the time data corresponding to the target business data set can be filtered out from the time data sequence, and the abnormal time information is determined according to these data.

[0102] Step B2, determining abnormal device information of the collected business data in the to-be-processed data table according to the abnormal time information.

[0103] In some scenarios, time information that does not need to be warned in the abnormal time information can be excluded first (for example, fluctuation of business data is allowed in some time sections). Here, the abnormal time information can generally represent that the business data in the to-be-processed data table does not meet the data screening condition at a certain time point or interval, and the business data is abnormal. Therefore, the corresponding abnormal business data can be further found according to the abnormal time information, and the specific abnormal device information of collecting the abnormal business data is determined. The abnormal device information can be output to the business personnel. In order to facilitate the analysis of abnormal conditions, the automation processing of finding abnormal time information and / or abnormal device information according to the condition screening of the time dimension and the data dimension of the business data is greatly improved, and the abnormal device is monitored and processed more effectively, quickly and accurately.

[0104] In actual application, whether the environmental parameter meets the control requirement can be determined according to the business data. In one embodiment of the business data processing of the temperature data in the present disclosure, the data screening condition can include at least one of the following:

[0105] The range of the single-column business data in the business data set is less than a preset first threshold.

[0106] The deviation value of the single-column business data in the business data set from a preset target value is less than a preset second threshold.

[0107] The range of all business data in the business data set is less than a preset third threshold.

[0108] The embodiment provided by the present disclosure can perform data screening from the range of the single-column business data, the deviation of the single-column business data from the target value, and the range of all business data in all columns (target business data) in a period of time, which can more accurately reflect the change of the business data and more accurately screen the business data meeting the condition. Exemplarily, each column of business data can be collected by a sensor at different positions in the device. The range of the single-column business data can be used to represent the single-point fluctuation amplitude of each sensor in a preset time span. The deviation value of the single-column business data from the preset target value can be used to represent the deviation of each sensor from the corresponding target value. In the same environment, the target values corresponding to the sensors can be the same. At this time, the business data set is a two-dimensional array. For each business data set, each column in the business data set can be traversed, the range of the single-column business data and the deviation value of the single-column business data from the preset target value are calculated, and then the range of all business data is calculated to determine the business data set meeting the data screening condition.

[0109] For example, in the case where the business data is temperature data and the filtering condition is used to determine a stable change interval, the time filtering condition can include a preset time span of 10 seconds, the data filtering condition can include a preset first threshold of 0.1 degrees Celsius, a preset target value between 20 degrees Celsius and 21 degrees Celsius, a preset second threshold of 0.3 degrees Celsius, and a preset third threshold of 0.3 degrees Celsius.

[0110] In this embodiment, by combining the time dimension and the data dimension to calculate the data characteristics of the business data in the business data set, it can be determined whether the environmental parameter meets the control requirement according to the business data.

[0111] In some embodiments, the business data can include temperature data, and / or the to-be-processed data table can include an excel table. In actual applications, the time accuracy of the business data is usually high. As described above, in some scenarios, the data accuracy of temperature collection can reach microsecond or nanosecond level, and the requirement for processing such data is extremely high, and the conventional excel processing may not be able to process data at such level of accuracy. Therefore, in some embodiments provided by the present disclosure, the time accuracy of the time data includes at least one of the following: millisecond level, microsecond level or nanosecond level. In an embodiment, Pandas can be used to process time data at microsecond level.

[0112] For example, in the case where the business data is temperature data and the filtering condition is used to determine a stable change interval, the time filtering condition can include a preset time span of 10 seconds, the data filtering condition can include a preset first threshold of 0.1 degrees Celsius, a preset target value between 20 degrees Celsius and 21 degrees Celsius, a preset second threshold of 0.3 degrees Celsius, and a preset third threshold of 0.3 degrees Celsius.

[0113] To sum up, the processing method, device, computer device, computer readable storage medium and computer program product of the time-related data table can obtain the time data column in the to-be-processed data table, slide the sliding window on the time data column, and obtain the corresponding business data from the to-be-processed data table according to the time data in the sliding window, so as to flexibly filter out the business data in different time sections. The business data set is further filtered according to the data characteristics of the business data, so as to filter out the business data that meets the data dimension condition from the business data that meets the time dimension condition. Since the business data is related to the business time dimension, the running state of the business in a specific time period can be determined according to the filtered business data. The time when the business is in a specific running state can be determined according to the time data corresponding to the filtered business data, and then the state of the object corresponding to the business data can be determined, so as to facilitate the business personnel to trace back and analyze.

[0114] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps.

[0115] Based on the same inventive concept, the disclosure also provides a time-related data table processing device for implementing the above-mentioned time-related data table processing method. The problem-solving implementation scheme provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more time-related data table processing device embodiments provided below can refer to the limitations of the time-related data table processing method in the above text, which will not be repeated here.

[0116] In one exemplary embodiment, as shown in FIG. 5, a time-related data table processing device 200 is provided, which includes a data acquisition module 202, a filtering condition determination 204, a time filtering module 206, a data filtering module 208 and a result analysis module 210, wherein:

[0117] The data acquisition module 202 is configured to obtain a time data column arranged in time sequence from a to-be-processed data table, and the to-be-processed data table includes business data corresponding to the time data column.

[0118] The screening condition determination module 204 is configured to determine a screening condition of the to-be-processed data table, and the screening condition comprises a time screening condition and a data screening condition.

[0119] The time screening module 206 is configured to slide on the time data column through a sliding window determined according to the time screening condition, and obtain service data corresponding to time data covered by a position of the sliding window to obtain a service data set, wherein the position of one sliding window corresponds to one service data set.

[0120] The data screening module 208 is configured to take a service data set in which service data satisfy the data screening condition as a target service data set.

[0121] The result analysis module 210 is configured to determine a state monitoring result of the service data based on time data corresponding to the target service data set.

[0122] In an exemplary embodiment, the result analysis module 210 comprises:

[0123] The time section determination sub-module is configured to determine a continuous time section in which the service data satisfy the screening condition according to time data corresponding to the target service data set.

[0124] The first result output sub-module is configured to take the continuous time section in which the time data belongs to the determined stable variation interval as a stable interval result and output the stable interval result.

[0125] In an exemplary embodiment, the time section determination sub-module comprises:

[0126] The merging unit is configured to merge the time data corresponding to the target service data set to obtain a time data set.

[0127] The deduplication unit is configured to perform a deduplication process on the time data set to obtain a target time data set.

[0128] The time section determination unit is configured to determine the continuous time section based on a first time data and a last time data in the target time data set.

[0129] In an exemplary embodiment, one column of service data in the to-be-processed data table corresponds to one device collecting the service data, and the result analysis module 210 comprises:

[0130] The time information determination sub-module is configured to determine abnormal time information in which the service data do not satisfy the screening condition according to time data corresponding to the target service data set.

[0131] The second result output sub-module is configured to determine abnormal device information of the device collecting the service data in the to-be-processed data table according to the abnormal time information.

[0132] In an example embodiment, the data screening condition comprises at least one of the following:

[0133] a range of the single column of business data in the business data set is less than a preset first threshold value;

[0134] a deviation value of the single column of business data in the business data set from a preset target value is less than a preset second threshold value;

[0135] a range of all business data in the business data set is less than a preset third threshold value.

[0136] In an example embodiment, the time precision of the time data comprises at least one of the following: millisecond level, microsecond level or nanosecond level.

[0137] In an example embodiment, the business data comprises temperature data, and / or the to-be-processed data table comprises an excel table.

[0138] Each module in the processing apparatus of the time-related data table can be implemented by software, hardware and combinations thereof, in whole or in part. Each module can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0139] In an example embodiment, a computer device is provided, which can be a server, and an internal structure diagram thereof can be as shown in FIG. 6. The computer device comprises a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store a to-be-processed data table. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a time-related data table processing method.

[0140] Those skilled in the art can understand that the structure shown in FIG. 6 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0141] In an exemplary embodiment, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0142] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0143] In an embodiment, a computer program product is provided, comprising a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0144] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0145] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above-mentioned embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0146] The above embodiments only express several implementation ways of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation to the patent scope of the application. It should be pointed out that for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, which all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method of processing a time-related data table, characterized by, The method comprises: obtaining a time data column arranged in time sequence from a to-be-processed data table, the to-be-processed data table comprising business data corresponding to the time data column; determining a screening condition of the to-be-processed data table, the screening condition comprising a time screening condition and a data screening condition; sliding a sliding window determined according to the time screening condition on the time data column to obtain business data corresponding to time data covered by a position of the sliding window, thereby obtaining a business data set, wherein the position of one sliding window corresponds to one business data set; taking a business data set in which the business data meets the data screening condition as a target business data set; determining a state monitoring result of the business data based on time data corresponding to the target business data set.

2. The method of claim 1, wherein, The determination of the state monitoring result of the business data based on the time data corresponding to the target business data set comprises: determining a continuous time section in which the business data meets the screening condition according to the time data corresponding to the target business data set; outputting a continuous time section in which the time data belongs to a determined stable change interval as a stable interval result.

3. The method of claim 2, wherein, The determination of the continuous time section in which the business data meets the screening condition according to the time data corresponding to the target business data set comprises: merging the time data corresponding to the target business data set to obtain a time data set; performing a deduplication process on the time data set to obtain a target time data set; determining a continuous time section based on a first time data and a last time data in the target time data set.

4. The method of claim 1, wherein, The determination of the state monitoring result of the business data based on the time data corresponding to the target business data set comprises: determining abnormal time information in which the business data does not meet the screening condition according to the time data corresponding to the target business data set; determining abnormal device information collecting the business data in the to-be-processed data table according to the abnormal time information.

5. The method of claim 1, wherein, The data screening condition comprises at least one of the following: a range of business data in a single column in the business data set is less than a preset first threshold value; a deviation value of the business data in the single column in the business data set from a preset target value is less than a preset second threshold value; a range of all business data in the business data set is less than a preset third threshold value.

6. The method of claim 1, wherein, The time accuracy of the time data comprises at least one of the following: millisecond level, microsecond level or nanosecond level.

7. The method according to any one of claims 1 to 6, characterized in that, The business data comprises temperature data.

8. The method according to any one of claims 1 to 6, characterized in that, The to-be-processed data table comprises an excel table.

9. A processing device of a time-related data table, characterized by, The device comprises: a data acquisition module configured to obtain a time data column arranged in time sequence from a to-be-processed data table, the to-be-processed data table comprising business data corresponding to the time data column; a screening condition determination module configured to determine a screening condition of the to-be-processed data table, the screening condition comprising a time screening condition and a data screening condition; The time screening module is configured to slide on the time data column through a sliding window determined according to the time screening condition, and obtain service data corresponding to time data covered by the sliding window to obtain a service data set, wherein a position of a sliding window corresponds to a service data set. The data screening module is configured to take a service data set satisfying the data screening condition as a target service data set. The result analysis module is configured to determine a state monitoring result of the service data based on time data corresponding to the target service data set.

10. The apparatus of claim 9, wherein, The result analysis module includes: The time section determination submodule is configured to determine a continuous time section in which the service data meets the screening condition based on time data corresponding to the target service data set. The first result output submodule is configured to take a continuous time section in which the time data belongs to a determined stable change interval as a stable interval result and output the stable interval result.

11. The apparatus of claim 10, wherein, The time section determination submodule includes: The merging unit is configured to merge time data corresponding to the target service data set to obtain a time data set. The deduplication unit is configured to perform deduplication processing on the time data set to obtain a target time data set. The time section determination unit is configured to determine a continuous time section based on a first time data and a last time data in the target time data set.

12. The apparatus of claim 9, wherein, The result analysis module includes: The time information determination submodule is configured to determine abnormal time information in which the service data does not meet the screening condition based on time data corresponding to the target service data set. The second result output submodule is configured to determine abnormal device information collecting the service data in the to-be-processed data table based on the abnormal time information.

13. The apparatus of claim 9, wherein, The data screening condition includes at least one of the following: A range of service data in a single column in the service data set is less than a preset first threshold value. A deviation value of service data in a single column in the service data set from a preset target value is less than a preset second threshold value. A range of all service data in the service data set is less than a preset third threshold value.

14. The apparatus of claim 9, wherein, The time precision of the time data includes at least one of the following: millisecond level, microsecond level, or nanosecond level.

15. The apparatus of any one of claims 9 to 14, wherein, The service data includes temperature data.

16. The apparatus of any one of claims 9 to 14, wherein, The to-be-processed data table includes an excel table. 17.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-16. The processor executes the computer program to implement steps of the method in any one of claims 1 to 8.

18. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement steps of the method in any one of claims 1 to 8.

19. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement steps of the method in any one of claims 1 to 8.

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