CN112632154B - Method and device for determining parallel service quantity and time interval based on time data - Google Patents
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Abstract
The invention provides a method and a device for determining the number of parallel services and time intervals by time data, which convert the traditional complex computing mode of minimum time granularity slicing into a binary characteristic digital sequential accumulation computing mode, accumulate all time data to be processed meeting specified characteristics before a certain time node to obtain the number of parallel services on the time node, and determine the time interval with the maximum number of parallel services after carrying out statistical analysis on the number of parallel services of each time node.
Description
Technical Field
The present invention relates to the field of data mining technologies, and in particular, to a method and apparatus for determining a parallel service number and a time interval based on time data.
Background
Big data mining refers to a data processing mode of mining data conforming to specified characteristics from mass data and analyzing the data obtained by mining. In the field of big data mining, it is often necessary to perform mining analysis on massive time data with time as a dimension, where the content of the mining analysis includes: and determining the quantity of service data meeting the specified characteristics before a certain time node, and determining a time interval meeting two screening conditions meeting the specified characteristics and having the maximum quantity of the specified characteristics at the same time so as to provide data support for subsequent decisions. The mass time data basically exist in the unit of seconds, and the uncertain amount of time data to be processed is generated every day, every hour and every minute, so how to quickly determine the amount of service data and the time interval meeting the requirement from the mass time data is always a technical problem to be solved in the field of data mining.
The current general method is to determine the minimum time granularity first, then divide the time interval to be analyzed according to the minimum granularity, for example, the first time interval from 0 point in 1 month of 2020 to 1 point in 1 month of 2020, the second time interval from 1 point in 1 month of 2020 to 1 point in 1 day of 2020, divide the time data to be processed into the above time intervals according to the time field content of the time data to be processed of massive time data, finally find out the number of the time data to be processed which accords with the characteristics in each time interval, compare the calculated number in all time intervals, and then need to consider the merging condition of the adjacent time intervals, finally obtain the required time interval.
Specifically, the current method for determining the time interval is to analyze the time interval in a slicing manner, however, there are various different situations when the time interval is sliced, for example: the situation that the starting time and the ending time are in the time interval, the starting time is not in the time interval, the ending time is not in the time interval, the starting time is not in the time interval, and the starting time and the ending time are not in the time interval leads to complex and complicated whole calculation logic. In this way, if the interval between time intervals is reduced, the calculated amount is increased by a multiple of the number of steps, the calculated amount per minute is 60 times that per minute, the calculated amount per second is 60 times that per minute, and the calculated amount per hour is 3600 times that per hour, that is, the smaller the interval between time intervals is, the larger the corresponding calculated amount is, and the more complicated the calculation logic is. Moreover, the method can only count according to the appointed granularity of the fragments, can not analyze the time range according to the actual occurrence time, can only be realized through programming language codes such as java, and has high technical threshold.
Taking the time data generated by the online conference system as an example for analysis, it is often necessary to analyze the time interval in which the maximum parallel conference (online conference at the same time, conference which has not yet ended yet) occurs, if the number of data of the parallel conference in each time interval is found in the conventional manner, it is necessary to consider various situations such as the conference start time is not in the current time interval, the conference start time is in the current time interval, the conference end time is not in the current time interval, and the conference end time is in the current time interval, which causes confusion and inefficiency in the system operation logic.
Disclosure of Invention
The invention aims to provide a method and a device for determining the number of parallel services and time intervals based on time data, the method converts the traditional complex computing mode of minimum time granularity fragmentation into a binary feature digital sequential accumulation computing mode, the number of the parallel services on a certain time node can be obtained by accumulating all time data to be processed meeting specified features before the time node, and the time interval with the maximum number of the parallel services can be determined after the statistical analysis of the number of the parallel services of each time node.
In order to achieve the above object, the present technical solution provides a method for determining the number of parallel services based on time data, including the following steps:
acquiring time data to be processed in the time data;
acquiring service time and service characteristic marks corresponding to specific services in the to-be-processed time data, wherein the service characteristic marks are forward characteristics or reverse characteristics representing relative forward and reverse meanings, and the to-be-processed time data representing the relative forward and reverse meanings corresponding to the same service are stored in the same data table;
converting the service feature mark into a feature value, wherein the superposition of the feature values corresponding to the forward feature and the reverse feature is counteracted;
and selecting the time data to be processed before the current time node by taking the current time node as a reference, and accumulating the characteristic values corresponding to all the time data to be processed to obtain the parallel task number corresponding to the current time node.
In a second aspect, there is provided a method of determining a time interval based on time data, comprising the steps of:
acquiring time data to be processed in the time data;
acquiring service time and service characteristic marks corresponding to specific services in the to-be-processed time data, wherein the service characteristic marks are forward characteristics or reverse characteristics representing relative forward and reverse meanings, and the to-be-processed time data representing the relative forward and reverse meanings corresponding to the same service are stored in the same data table;
converting the service feature mark into a feature value, wherein the superposition of the feature values corresponding to the forward feature and the reverse feature is counteracted;
selecting the time data to be processed before the service time by taking the service time as a reference, accumulating the characteristic values corresponding to the time data to be processed to obtain accumulated values corresponding to the service time, and circularly obtaining the accumulated values corresponding to each service time;
selecting the time data to be processed with the largest parallel task number as selected time data, and determining a time interval according to the service time of the selected time data.
In a third aspect, an apparatus for determining a number of parallel services based on time data is provided, and the method for determining a number of parallel services based on time data is performed.
In a fourth aspect, an apparatus for determining a time interval based on time data is provided, and the method for determining a time interval based on time series data is performed.
In a fifth aspect, a processor is provided; and a memory in which is stored computer program instructions which, when executed by the processor, cause the processor to perform the above method.
Compared with the prior art, the technical scheme has the following characteristics and beneficial effects:
redefining the specified characteristics: converting the specified feature into binary feature content with opposite meaning so as to convert complex analysis logic into opposite binary digital calculation, for example, the specified feature can be converted into binary feature content with satisfaction/unsatisfied or yes/no, in/out, start/end and the like; standardized conversion of time data to be processed: recording the to-be-processed time data with opposite binary characteristic contents in the same recording table, at least recording the service time and the characteristic mark field of the to-be-processed time data, converting the characteristic mark field into the binary characteristic contents, and digitizing the characteristic mark field according to the contents to obtain characteristic mark numbers, wherein the characteristic mark numbers representing opposite meanings can be overlapped and offset; and superposing the feature tag numbers of all the time data to be processed in front of the service time node, so as to obtain all the parallel service quantity in front of the current service time node. And carrying out statistical analysis on the parallel service quantity, and determining a time interval which meets the maximum quantity of the parallel service.
The method and the device take the start and the stop of the service as references, convert the start and the stop of a single service into the computable numerical values, specifically convert the time data to be processed of the corresponding service into characteristic sign numbers capable of canceling each other, and easily acquire the number of parallel services before a cut-off time point through simple digital accumulation calculation, thereby solving the problem of low efficiency of determining the minimum granularity, time slicing and a large number of cyclic calculation of the data in the traditional time interval analysis process. The method has wide application range, can be suitable for any time interval analysis problem which can be converted into binary characteristics, and can be used for solving the determination of the time interval which can rapidly meet the maximum specified characteristic service quantity, such as calculating the maximum remaining parking spaces, the maximum occupancy and the maximum turnover rate in the time range of the parking lot according to the vehicle entry and exit records of the parking lot; the conference system calculates the maximum simultaneous meeting when the conference system is running according to the start and end records of the conference; and calculating various practical problems such as maximum vehicles on the expressway in a certain time range according to the vehicle records of the expressway in-out buckle.
Drawings
Fig. 1 is a flow chart of a method for determining the number of parallel services based on time data according to the present solution.
Fig. 2 is a flow chart of a method for determining a time interval based on time data according to the present scheme.
Fig. 3 to 5 are schematic diagrams of applications of the present scheme for determining a time interval based on time data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which are derived by a person skilled in the art based on the embodiments of the invention, fall within the scope of protection of the invention.
The scheme provides a method for determining the number of parallel services based on time data and determining a time interval based on the time data, wherein the time data refers to at least two time data to be processed with time as a sequence, the same service generates serial time data to be processed with time as a distribution sequence in the operation process, different services generate different time data to be processed of service content fields, and a plurality of time data to be processed form the time data. The time data to be processed at least comprises a service time field and a service characteristic mark, wherein the service time field is used for determining the generation time of the time data to be processed, and the service characteristic mark is used for determining the service content of the service. The "parallel traffic" determined herein refers to traffic satisfying the specified characteristics, and the "time interval" determined herein refers to a time interval satisfying the maximum number of parallel traffic.
In addition, the scheme utilizes SQL technology (structured query language, structured Query Language, is a programming language with special purposes, is a database query and programming language and is used for accessing data, querying, updating and managing a relational database system, and is also an extension of a database script file), the quick query function of SQL is applied, the data of the time to be processed meeting the specified characteristics is searched from a standardized data table and is counted in an accumulated mode, and in the scheme, the minimum granularity definition and segmentation of time are not needed in advance, each data is not needed to be independently circulated for statistics, and development and compiling are not needed by using a high-level programming language.
Specifically, the SQL technology can play the parallel query performance of the distributed data query engine by using the index which is created in advance in the data table through the data query engine, a window function query optimization mechanism is built in, and the complex calculation according to the minimum granularity is converted into the sequential accumulation calculation through batch preprocessing and accumulation calculation of a large amount of data to be analyzed, so that the scheme can analyze and mine the massive data efficiently and omnidirectionally, and the problem of determining the number of parallel services and time intervals in the large data item is solved.
In a first aspect, the present disclosure provides a method for determining the number of parallel services based on time data, where the method converts time data to be processed corresponding to a service into a computable binary number according to start-stop content of the service, and determines the number of parallel services by accumulating the binary number. The method and the system represent the start and the stop of the service by clear binarization numbers, and when a certain service is stopped, the accumulation quantity is counteracted by a negative value, so that the quantity of the parallel service is acquired by skillfully utilizing a superposition counteraction digital calculation method. In a second aspect, the present solution provides a method for quickly determining a time interval in a massive amount of time data, where the method counts the number of parallel services based on obtaining the number of parallel services, so as to determine a time interval with the maximum number of parallel services.
In a first aspect, the present solution introduces a method for determining the number of parallel services based on time data, including the following steps:
acquiring time data to be processed in the time data;
acquiring service time and service characteristic marks corresponding to specific services in the to-be-processed time data, wherein the service characteristic marks are forward characteristics or reverse characteristics representing relative forward and reverse meanings, and the to-be-processed time data representing the relative forward and reverse meanings corresponding to the same service are stored in the same data table;
converting the service feature mark into a feature value, wherein the superposition of the feature values corresponding to the forward feature and the reverse feature is counteracted;
and selecting the time data to be processed before the current time node by taking the current time node as a reference, and accumulating the characteristic values corresponding to all the time data to be processed to obtain the parallel task number corresponding to the current time node.
The scheme is suitable for mining and analyzing massive time data, and has the advantages of small pressure, high efficiency and the like when being applied to analysis of massive time data due to simple calculation logic.
Correspondingly, the scheme can acquire massive time data to be analyzed from any type of data source, and can be various relational databases such as Mysql, distributed databases such as PostgreSQL, databases such as Hive, and the like, and the databases are connected to the data source according to the service address, port, user name, password and database instance information of the databases, so that the analyzed time data need to be mined from the data source.
It should be noted that the to-be-processed time data in the scheme includes service time and service feature marks corresponding to the specific service. In some embodiments, the time to process data is processed according to the analysis characteristics to obtain the service time and the service characteristic mark.
Correspondingly, the scheme comprises the steps of processing the time data to be processed according to analysis features to obtain the business feature mark, wherein the analysis features express task content in relative positive and negative feature content. The field content of the analysis feature comprises paired feature words representing opposite positive and negative meanings of the task content.
Further, if the time data to be processed corresponds to a forward meaning in the analysis feature, the service feature flag is the forward feature, and if the time data to be processed corresponds to a reverse meaning in the analysis feature, the service feature flag is the reverse feature.
In some embodiments, the forward feature selects a feature word whose forward meaning is represented by the analysis feature, and the reverse feature selects a feature word whose reverse meaning is represented by the analysis feature. Of course, the forward and reverse features may be represented by words other than feature words.
The paired feature words of the analysis feature may be: meeting/not meeting certain conditions, or yes/no, in/out, start/end, etc.
For example, if the task content is "online meeting online at the same time", the corresponding analysis feature is "meeting start but not end", the corresponding forward feature is "start", and the corresponding reverse feature is: "end"; if the task content is "the number of parks in the parking lot", the corresponding analysis features are: "enter parking lot but not exit parking lot", the corresponding forward feature is "enter", and the corresponding reverse feature is "exit". If the task content is "number of audience in concert", the corresponding analysis feature is "audience enters into the concert venue but does not come out from the concert venue", the corresponding forward feature is "enter", and the reverse feature is "come out".
It should be noted that if the acquired task content itself is already represented by a content representing a meaning of a relatively positive and negative sense, the task content does not need to be converted. If not, the task feature of the task content needs to be converted into the task feature of the task content into the analysis feature composed of the content in a relatively positive and negative sense. Examples as described above, no further explanation is made here.
The purpose of the "the time data to be processed representing the relative positive and negative meaning corresponding to the same service is stored in the same data table" is to facilitate the SQL language to extract the data source data from the data table. Because the acquired time data to be processed is defined by a plurality of structures, the subsequent processing of the time data to be processed is inconvenient, the time data to be processed representing relative positive and negative meanings are summarized into the same data table, and the schematic diagram of the field structure of the data table is shown in the following table one:
table-structural field schematic of data table
| Business time | Service feature identification |
| XX year x1 month | Forward feature |
| XX year x2 month | Forward feature |
| XX year x3 month | Reverse feature |
| XX year x4 month | Reverse feature |
Taking the waiting time data of an online conference as an example, there may be: the conference start time and the conference end time are in the same record; or the conference starting time is in one table, the conference ending time is in the other table, and the conference ending time is matched through the conference number; or the conference start record and the conference end record are recorded in a table, and the standards of the conference start record and the conference end record are unified through the above modes according to various conditions such as conference number matching.
It should be noted that the time data to be processed of the opposite service characteristic mark is recorded in the same data table, and the time data source at least comprises the service time and the service characteristic mark. And arranging the time data to be processed in the data table according to the sequence of the service time. The parallel service can be analyzed by only acquiring the starting time and the ending time of the service without knowing the specific service content, and the calculation cost of the server is reduced from the side.
The step of converting the service feature mark into a feature value includes uniformly converting the service feature mark into the feature value by using standard keywords provided by SQL, wherein the service feature mark can be expressed in various forms such as Chinese characters, english, characters, numbers and the like, and uniformly converting the service feature mark into the feature value which is binarized by SQL. The characteristic values satisfy: feature value obtained by single forward feature conversion+feature value obtained by single backward feature conversion=0.
In an embodiment of the present solution, the positive features are converted to positive features, and the negative features are converted to negative features. For example, positive values may be characterized as 1 and negative values may be characterized as-1.
The current time node may or may not be a service node. The values of the characteristic values corresponding to all the time data to be processed are the number of parallel tasks.
In addition, in this scheme, the time data to be processed may be screened and then analyzed, where before "selecting the time data to be processed before the service time" includes: the time data to be processed is screened for specified characteristics, where the specified characteristics correspond to characteristics of the service content of the service itself, different from the analytical characteristics thereon. In the scheme, the standard keyword WHERE provided by SQL (structured query language) performs data screening from massive time data to be processed according to the requirement of actual business analysis mining, so that the calculation range of the data is reduced, and the filtering condition needs to select indexed fields so as to accelerate the calculation efficiency.
In a second aspect, the present solution provides a method for determining a time interval based on time data, comprising the steps of:
acquiring time data to be processed in the time data;
acquiring service time and service characteristic marks corresponding to specific services in the to-be-processed time data, wherein the service characteristic marks are forward characteristics or reverse characteristics representing relative forward and reverse meanings, and the to-be-processed time data representing the relative forward and reverse meanings corresponding to the same service are stored in the same data table;
converting the service feature mark into a feature value, wherein the superposition of the feature values corresponding to the forward feature and the reverse feature is counteracted;
selecting the time data to be processed before the service time by taking the service time as a reference, accumulating the characteristic values corresponding to the time data to be processed to obtain accumulated values corresponding to the service time, and circularly obtaining the accumulated values corresponding to each service time;
selecting the time data to be processed with the largest parallel task number as selected time data, and determining a time interval according to the service time of the selected time data.
Selecting the to-be-processed time data before the service time by taking the service time as a reference, accumulating the characteristic value corresponding to the to-be-processed time data, and obtaining an accumulated value corresponding to the service time, wherein the step of accumulating the to-be-processed time data comprises the following steps: and selecting the service time as the current service time, finding out the to-be-processed time data smaller than the service time, and accumulating the feature values corresponding to all the to-be-processed time data to obtain the service still existing when the current service time is cut off. If the accumulated value is 1, the first service still exists; if the accumulated value is 0, no service is indicated. That is, the accumulated value corresponds to the number of services in the forward feature that cut off the current service time.
The step of circularly obtaining the accumulated value corresponding to each service time comprises the following steps: the time data to be processed are ordered according to the time sequence, and the service time is selected from the front to the back for accumulation calculation until all the time data to be processed are accumulated.
The accumulation operation of the feature values can use a window function SUM provided by the database to accumulate, and the SUM (feature tag number) over (feature by 1order by service date) can fully utilize parallel computing resources of the database, so that the computing speed of mass data is accelerated. After accumulation, a new accumulation number field is formed, which indicates the number of parallel services up to the current time node.
The step of selecting the time data to be processed with the largest parallel task number as the selected time data and determining a time interval by the service time of the selected time data comprises the following steps: selecting the time data to be processed corresponding to the nearest forward feature before the selected time data as starting time data, taking the service time of the starting selected time data as the starting time of the time interval, and taking the selected time data as the ending time of the time interval.
And the selected time data is the time point with the maximum number of parallel tasks, and correspondingly, the time point of starting the latest service before the time point is acquired, so that the time interval with the maximum number of parallel tasks compared with other time periods can be acquired.
In a third aspect, the present disclosure provides an application for determining a time interval based on time data, which is described by taking an online conference system as an example, if a time interval with a maximum parallel task of an online conference needs to be confirmed:
firstly, determining analysis characteristics to be analyzed, wherein the analysis characteristics are as follows: the conference starts but does not end;
normalizing the time data to be processed of the online conference according to the analysis characteristics to obtain a data table as shown in fig. 3, wherein the time data to be processed is expressed as: (1) service time: 2020-11-02 08:59:53 traffic signature: starting; (2) service time: 2020-11-02 09:18:37 traffic signature: ending; (3) service time: 2020-11-0212:10:41 service feature flags: starting; (4) service time: 2020-11-02 15:03:52 service characteristic flags: starting; (5) service time: 2020-11-02 21:02:13 service characteristic flag: and (5) ending.
Converting the service feature mark into a feature value to obtain a data table as shown in fig. 4, wherein the time data to be processed is expressed as (1) service time: 2020-11-02 08:59:53 traffic signature: starting; characteristic value: 1(2) service time: 2020-11-02 09:18:37 traffic signature: ending; characteristic value: 1(3) service time: 2020-11-0212:10:41 service feature flags: starting; characteristic value: 1(4) service time: 2020-11-02 15:03:52 service characteristic flags: starting; characteristic value: 1(5) service time: 2020-11-02 21:02:13 service characteristic flag: ending, feature values: -1.
Accumulating the characteristic values by taking the service time as a reference to obtain accumulated values, and obtaining a data table as shown in fig. 5, (1) service time: 2020-11-02 08:59:53 traffic signature: starting; characteristic value: 1, a step of; accumulating values: 1(2) service time: 2020-11-02 09:18:37 traffic signature: ending; characteristic value: -1; accumulating values: 0(3) service time: 2020-11-0212:10:41 service feature flags: starting; characteristic value: 1, a step of; accumulating values: 1(4) service time: 2020-11-02 15:03:52 service characteristic flags: starting; characteristic value: 1, a step of; accumulating values: 2(5) service time: 2020-11-02 21:02:13 service characteristic flag: ending, feature values: -1; accumulating values: 1.
obtaining the maximum number according to the accumulated numerical statistics, and back-pushing the time interval based on the maximum number: the accumulation number 2 in fig. 5 is the number of simultaneous conferences in maximum, the time 2020-11-02-15:03:52 corresponding to the accumulation number 2 is the end time, the last service time 2020-11-0212:10:41 of the end time is the start time, the number of simultaneous conferences in the time range 2020-11-02-12:10:41 to 2020-11-02-15:03:52 is 2, and the number of simultaneous conferences in maximum in the statistical analysis range is the number of simultaneous conferences in maximum.
In a fourth aspect, the present disclosure provides an apparatus for determining a parallel service number based on time data, where the apparatus is configured to perform the method for determining a parallel service number based on time data, and the method includes:
the data acquisition unit is used for acquiring time data to be processed in the time data, service time corresponding to specific service in the time data to be processed and service characteristic marks, wherein the service characteristic marks represent forward characteristics or reverse characteristics in relative positive and negative meanings;
the data table storage unit is used for storing the time data to be processed representing relative positive and negative meanings corresponding to the same service;
the conversion unit is used for converting the service characteristic mark into a characteristic value, and the superposition of the characteristic values corresponding to the forward characteristic and the reverse characteristic is counteracted;
and the quantity determining unit is used for selecting the time data to be processed before the current time node by taking the current time node as a reference, and accumulating the characteristic values corresponding to all the time data to be processed to obtain the quantity of parallel tasks corresponding to the current time node.
The applicant does not make excessive description here regarding the operation method of the apparatus for determining the number of parallel services based on time data, see the statements made above regarding the method for determining the number of parallel services based on time data.
In a fifth aspect, the present disclosure provides an apparatus for determining a time interval based on a time sequence, where the apparatus is configured to perform the method for determining a time interval based on time data, and the method includes:
the data acquisition unit is used for acquiring time data to be processed in the time data, service time corresponding to specific service in the time data to be processed and service characteristic marks, wherein the service characteristic marks represent forward characteristics or reverse characteristics in relative positive and negative meanings;
the data table storage unit is used for storing the time data to be processed representing relative positive and negative meanings corresponding to the same service;
the conversion unit is used for converting the service characteristic mark into a characteristic value, and the superposition of the characteristic values corresponding to the forward characteristic and the reverse characteristic is counteracted;
the quantity determining unit is used for circularly determining parallel task data of service time, selecting the time data to be processed before the service time by taking the service time as a reference, and accumulating the characteristic value corresponding to the time data to be processed to obtain the parallel task quantity corresponding to the service time;
and the interval determining unit is used for selecting the time data to be processed with the largest parallel task number as selected time data and determining a time interval according to the service time of the selected time data.
Similarly, the applicant does not make excessive description here regarding the method of operation of the device for determining parallel time intervals based on time data, see the statements made above regarding the method of determining time intervals based on time data.
The computer system of the server for implementing the method of determining the number of parallel traffic and time interval of time data of the embodiment of the present invention includes a Central Processing Unit (CPU) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage section into a Random Access Memory (RAM). In the RAM, various programs and data required for the system operation are also stored. The CPU, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method for determining the number of parallel traffic and time intervals from time data shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The method of the invention for determining the number of parallel traffic and the time interval of time data is performed when the computer program is executed by a Central Processing Unit (CPU).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software, or may be implemented in hardware, and the described modules may also be disposed in a processor.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by one of the devices, cause the device to perform the flow steps corresponding to the method for determining the number of parallel services and the time interval from the time data.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (13)
1. A method for determining the number of concurrent services based on time data, comprising the steps of: acquiring time data to be processed in the time data;
acquiring service time and service characteristic marks corresponding to specific services in the to-be-processed time data, wherein the service characteristic marks are forward characteristics or reverse characteristics representing relative forward and reverse meanings, and the to-be-processed time data representing the relative forward and reverse meanings corresponding to the same service are stored in the same data table;
converting the service feature mark into a feature value, wherein the superposition of the feature values corresponding to the forward feature and the reverse feature is counteracted;
and selecting the time data to be processed before the current time node by taking the current time node as a reference, and accumulating the characteristic values corresponding to all the time data to be processed to obtain the parallel task number corresponding to the current time node.
2. The method for determining the number of parallel services based on time data according to claim 1, comprising: and processing the time data to be processed according to analysis features to obtain the service feature mark, wherein the analysis features express task content in relative positive and negative feature content.
3. The method according to claim 2, wherein the traffic signature is the forward signature if the time data to be processed corresponds to a forward sense within the analysis signature, and the traffic signature is the reverse signature if the time data to be processed corresponds to a reverse sense within the analysis signature.
4. The method for determining the number of parallel services based on time data according to claim 1, wherein the time data to be processed is arranged in the data table in a sequential order of the service times.
5. The method for determining the number of parallel services based on time data according to claim 1, wherein said service signature is converted into said binarized signature value using SQL, said signature value satisfying: feature value obtained by single forward feature conversion+feature value obtained by single backward feature conversion=0.
6. The method for determining the number of parallel traffic based on time data according to claim 1, characterized in that before "selecting the time data to be processed before the traffic time" comprises: and screening the time data to be processed, which accords with the specified characteristics, wherein the specified characteristics correspond to the characteristics of the business content.
7. A method for determining a time interval based on time data, comprising the steps of:
acquiring time data to be processed in the time data;
acquiring service time and service characteristic marks corresponding to specific services in the to-be-processed time data, wherein the service characteristic marks are forward characteristics or reverse characteristics representing relative forward and reverse meanings, and the to-be-processed time data representing the relative forward and reverse meanings corresponding to the same service are stored in the same data table;
converting the service feature mark into a feature value, wherein the superposition of the feature values corresponding to the forward feature and the reverse feature is counteracted;
selecting the time data to be processed before the service time by taking the service time as a reference, and accumulating the characteristic values corresponding to the time data to be processed to obtain the parallel task quantity corresponding to the service time;
selecting the time data to be processed with the largest parallel task number as selected time data, and determining a time interval according to the service time of the selected time data.
8. The method of determining a time interval based on time data of claim 7, wherein the accumulating of the characteristic values is performed using a database-provided window function SUM.
9. The method according to claim 7, wherein selecting the time data to be processed with the largest number of parallel tasks as selected time data and determining a time interval with a service time of the selected time data comprises: selecting the time data to be processed corresponding to the nearest forward feature before the selected time data as starting time data, taking the service time of the starting selected time data as the starting time of the time interval, and taking the selected time data as the ending time of the time interval.
10. An apparatus for determining the number of concurrent services based on time data, comprising: the data acquisition unit is used for acquiring time data to be processed in the time data, service time corresponding to specific service in the time data to be processed and service characteristic marks, wherein the service characteristic marks represent forward characteristics or reverse characteristics in relative positive and negative meanings;
the data table storage unit is used for storing the time data to be processed representing relative positive and negative meanings corresponding to the same service;
the conversion unit is used for converting the service characteristic mark into a characteristic value, and the superposition of the characteristic values corresponding to the forward characteristic and the reverse characteristic is counteracted;
and the quantity determining unit is used for selecting the time data to be processed before the current time node by taking the current time node as a reference, and accumulating the characteristic values corresponding to all the time data to be processed to obtain the quantity of parallel tasks corresponding to the current time node.
11. An apparatus for determining a time interval based on a time sequence, comprising:
the data acquisition unit is used for acquiring time data to be processed in the time data, service time corresponding to specific service in the time data to be processed and service characteristic marks, wherein the service characteristic marks represent forward characteristics or reverse characteristics in relative positive and negative meanings;
the data table storage unit is used for storing the time data to be processed representing relative positive and negative meanings corresponding to the same service;
the conversion unit is used for converting the service characteristic mark into a characteristic value, and the superposition of the characteristic values corresponding to the forward characteristic and the reverse characteristic is counteracted;
the quantity determining unit is used for circularly determining parallel task data of service time, selecting the time data to be processed before the service time by taking the service time as a reference, and accumulating the characteristic value corresponding to the time data to be processed to obtain the parallel task quantity corresponding to the service time;
and the interval determining unit is used for selecting the time data to be processed with the largest parallel task number as selected time data and determining a time interval according to the service time of the selected time data.
12. An electronic device, comprising:
a processor; and
a memory in which is stored a computer memory in which is stored computer program instructions which, when executed by the processor, cause the processor to perform the method of determining the number of parallel traffic based on time data according to any one of claims 1-6.
13. An electronic device, comprising:
a processor; and
memory in which a computer memory is stored, in which computer program instructions are stored which, when executed by the processor, cause the processor to perform the method of determining a time interval based on time data according to any one of claims 7-9.
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