CN110830437B - Data compression method, device, equipment and storage medium for high-frequency service data - Google Patents
Data compression method, device, equipment and storage medium for high-frequency service data Download PDFInfo
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Abstract
The embodiment of the application discloses a data compression method, a device, equipment and a storage medium of high-frequency service data, and relates to the technical field of big data processing. The method comprises the following steps: acquiring target high-frequency service data; judging whether the target high-frequency service data is continuous data or not; if the data is the continuous data, identifying a common conversion field and a differential conversion field in the continuous data; and carrying out effective value interception on the parameter values of the common conversion fields for compression, carrying out numerical value difference calculation on the parameter values of the differential conversion fields for conversion compression, and generating compressed target high-frequency service data. According to the method, one part of data in the high-frequency service data is intercepted, useless data in the high-frequency service data is abandoned, and the numerical value of the other part of data meeting the conversion requirement is converted and compressed by applying the thought of differential calculation, so that the storage space of the data and the bandwidth requirement during transmission are greatly reduced, the pressure of a server is greatly reduced, the resource of the server is saved, and the transmission efficiency is improved.
Description
Technical Field
The present application relates to the field of big data processing technologies, and in particular, to a data compression method, apparatus, device, and storage medium for high-frequency service data.
Background
During processing of the quantitative business, an advanced mathematical model replaces artificial subjective judgment, and various 'high-probability' events which can bring excess income are selected from huge historical data by utilizing a computer technology to make a strategy, so that the influence of mood fluctuation of participants is greatly reduced, and an irrational business decision is avoided under the condition of extreme mania or pessimism of the market. High frequency services involve computerized services that people are unable to utilize in the very short market segment that seeks to make a profit.
The quantitative service has the advantages of dynamic real-time performance, objectivity, plasticity of high and low frequencies, subjective judgment of emotion and psychological fluctuation of immune people and the like, and is rapidly developed in recent years. In particular, high frequency quantization services are particularly concerned and developed because of their advantages such as daily clearing, high frequency, robust revenue recovery performance, etc. The back measurement refers to that a quantitative service platform performs service processing on the designed programmed service strategy simulation historical real asset trend situation to obtain result data after the historical real situation is reproduced so as to evaluate the performance of the strategy.
Because the frequency of high-frequency service data exchange is very high, the sample data during strategy recovery is very huge, and the storage space required during data storage is larger, the pressure of a database is large, and the hardware cost is higher.
Disclosure of Invention
The technical problem to be solved in the embodiments of the present application is to provide a data compression method, apparatus, device and storage medium for high-frequency service data, so as to effectively compress the high-frequency service data and reduce the storage space required by the high-frequency service data.
In order to solve the above technical problem, a data compression method for high-frequency service data according to an embodiment of the present application adopts the following technical solutions:
a data compression method of high-frequency service data comprises the following steps:
monitoring a high-frequency data exchange process in a current exchange period, and acquiring target high-frequency service data which needs to be sent to a target user in the exchange process;
judging whether the target high-frequency service data is continuous data in the current switching period; the continuous data represents target high-frequency service data which is positioned behind initial data in the current switching period, and the initial data represents a first piece of target high-frequency service data in the current switching period;
If the target high-frequency service data is continuous data, analyzing a data structure of the continuous data, identifying a field to be converted in the continuous data, and further identifying a common conversion field and a differential conversion field from the field to be converted;
and carrying out compression on the parameter values of the common conversion fields through effective value interception, and carrying out conversion compression on the parameter values of the differential conversion fields through numerical value differential calculation so as to generate compressed target high-frequency service data.
According to the data compression method of the high-frequency service data, one part of data in the high-frequency service data is intercepted, useless data in the data are abandoned, and the numerical value of the other part of data meeting the conversion requirement is converted and compressed by applying the thought of differential calculation, so that the high compression of the high-frequency service data is realized, the storage space of the high-frequency service data and the bandwidth requirement during data transmission are greatly reduced, the pressure of a server is greatly reduced, the resource of the server is saved, and the transmission efficiency is improved.
Further, the step of determining whether the target high-frequency service data is the continuous data in the current switching period includes:
Identifying a field used for expressing the data state type in the target high-frequency service data, and detecting a parameter value under the field;
if the parameter value is a preset first parameter judgment value, determining the target high-frequency service data as initial data;
and if the parameter value is a preset second parameter judgment value, determining that the target high-frequency service data is continuous data.
Further, after the step of identifying the field to be converted in the connection data, the data compression method for high frequency service data further includes:
identifying other fields except the field to be converted of the continuous data so as to identify a non-sensitive field;
and deleting the non-sensitive field from the continuation data.
Further, in the data compression method of high-frequency service data, the step of compressing the parameter value of the common conversion field by valid value interception includes:
and intercepting the 16-bit data or 24-bit data of the lower bits in the parameter values of each ordinary conversion field to be used as the parameter values of the corresponding ordinary conversion field in the compressed target information data.
Further, before the step of performing transform compression on the parameter values of the differential transform field through numerical difference calculation, the data compression method for high-frequency service data further includes:
And identifying the parameter value type of each field in the differential conversion field, and dividing the differential conversion field into an asset value differential field and a time differential field.
Further, in the data compression method for high-frequency service data, the step of identifying the parameter value type of each field in the differential conversion field and dividing the differential conversion field into an asset value differential field and a time differential field includes:
acquiring a plurality of continuous historical target information data in a historical exchange period;
counting parameter values of all fields in corresponding differential conversion fields in the plurality of historical target information data, and identifying the change trend of the parameter values;
if the change trend of the parameter value is incremental change, dividing a differential conversion field corresponding to the parameter value into time differential fields;
and if the change trend of the parameter value is fluctuation change, dividing the differential conversion field corresponding to the parameter value into asset value differential fields.
Further, in the data compression method of high-frequency service data, the step of performing transform compression on the parameter values of the differential transform field through numerical difference calculation includes:
Reading the minimum variable asset value and the high-frequency interval time;
calculating the difference value of the parameter value under each identical asset value differential field between the continuous data and the initial data, and dividing the difference value corresponding to each asset value differential field by the minimum variation asset value to be used as the parameter value of the asset value differential field corresponding to the compressed continuous data;
and calculating the difference value of the parameter values under each same time difference field between the continuous data and the initial data, and dividing the difference value corresponding to each time difference field by the high-frequency interval time to be used as the parameter value of the time difference field corresponding to the compressed continuous data.
In order to solve the above technical problem, an embodiment of the present application further provides a data compression apparatus for high-frequency service data, which adopts the following technical solutions:
a data compression apparatus for high frequency service data, comprising:
the data acquisition module is used for monitoring a high-frequency data exchange process in a current exchange period and acquiring target information data which needs to be sent to a target user in the exchange process;
the data type judging module is used for judging whether the target information data is initial data or continuous data in the current exchange period; the initial data represents a first piece of target information data in the current exchange period, and the continuing data represents target information data positioned behind the initial data in the current exchange period;
The field identification module is used for analyzing the data structure of the continuous data after the target information data is confirmed to be the continuous data, identifying a field to be converted in the continuous data, and further identifying a common conversion field and a differential conversion field from the field to be converted;
and the data compression module is used for intercepting and compressing the parameter values of the common conversion fields through effective values, and performing conversion and compression on the parameter values of the differential conversion fields through numerical difference calculation so as to generate compressed target information data.
The data compression device for the high-frequency service data intercepts a part of data in the high-frequency service data, discards useless data in the data, and converts and compresses the numerical value of the other part of data meeting the conversion requirement by applying the thought of differential calculation, thereby realizing high compression of the high-frequency service data, greatly reducing the storage space of the high-frequency service data and the bandwidth requirement during data transmission, greatly reducing the pressure of a server, saving the resource of the server and improving the transmission efficiency.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
A computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the data compression method for high-frequency service data according to any one of the above technical solutions when executing the computer program.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the data compression method for high-frequency traffic data according to any one of the above-mentioned technical solutions.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
the embodiment of the application discloses a data compression method, a device, equipment and a storage medium of high-frequency service data, and the data compression method of the high-frequency service data obtains target high-frequency service data to be sent to a target user by monitoring a high-frequency data exchange process; judging whether the target high-frequency service data is continuous data or not; if the data is the continuous data, identifying a field to be converted in the continuous data, and further identifying a common conversion field and a differential conversion field from the field to be converted; and finally, carrying out effective value interception on the parameter values of the common conversion fields for compression, and carrying out numerical value difference calculation on the parameter values of the differential conversion fields for conversion compression, thereby generating compressed target high-frequency service data. According to the method, one part of data in the high-frequency service data is intercepted, useless data in the high-frequency service data is abandoned, and the numerical value of the other part of data meeting the conversion requirement is converted and compressed by applying the thought of differential calculation, so that the high-frequency service data is highly compressed, the storage space of the high-frequency service data and the bandwidth requirement during data transmission are greatly reduced, the pressure of a server is greatly reduced, the resource of the server is saved, and the transmission efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is an exemplary system architecture diagram to which embodiments of the present application may be applied;
fig. 2 is a flowchart of an embodiment of a data compression method for high-frequency service data in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an embodiment of a data compression apparatus for high-frequency service data in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an embodiment of a computer device in the embodiment of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It should be noted that the terms "comprises," "comprising," and "has" and any variations thereof in the description and claims of this application and in the above-described drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. In the claims, the description and the drawings of the specification of the present application, relational terms such as "first" and "second", and the like, may be used solely to distinguish one entity/action/object from another entity/action/object without necessarily requiring or implying any actual such relationship or order between such entities/actions/objects.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein may be combined with other embodiments.
In order to make those skilled in the art better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the relevant drawings in the embodiments of the present application.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is the medium used to provide communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may use terminal devices 101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), a laptop portable computer, a desktop computer, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that the data compression method for the high-frequency service data provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the data compression apparatus for the high-frequency service data is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continuing reference to fig. 2, a flowchart of an embodiment of a data compression method for high-frequency service data according to an embodiment of the present application is shown. The data compression method of the high-frequency service data comprises the following steps:
step 201: monitoring a high-frequency data exchange process in the current exchange period, and acquiring target high-frequency service data which needs to be sent to a target user in the exchange process.
Due to the characteristics of the high-frequency service data exchange and strategy retest system, the high-frequency service data exchange and strategy retest system has extremely high requirements on the sampling rate and the data quality of a data source, and most of the high-frequency service data exchange and strategy retest systems require tick-level original data storage. In the embodiment of the application, the target high-quality service data sent to the target user generally refers to tick-level raw data.
The Tick data is data stored with exchange details during data exchange, and in the process of high-frequency data exchange, a server intercepts snapshot data every n seconds and sends the snapshot data as Tick data to a target user. Generally, the value of n in the high-frequency data exchange is 0.5, that is, the server sends target high-frequency service data of tick level to each target user at a frequency of twice per second, and when the target high-frequency service data of tick level is stored, the target high-frequency service data is stored in a database of the server in a snapshot data structure.
When high-frequency data is exchanged, because the data exchange amount generated during the high-frequency data exchange is very large, the server generally presets an exchange cycle, and the exchange data in the period of time is counted by taking the exchange cycle as a unit. In the embodiment of the present application, the switching period is generally set to one day.
In this embodiment, an electronic device (for example, the server/terminal device shown in fig. 1) on which the data compression method for high-frequency service data operates may receive target high-frequency service data that needs to be sent to a target user through a wired connection manner or a wireless connection manner. It should be noted that the above-mentioned wireless connection means may include, but is not limited to, 3G/4G connection, WiFi connection, bluetooth connection, WiMAX connection, Zigbee connection, uwb (ultra wideband) connection, and other now known or later developed wireless connection means.
Step 202: judging whether the target high-frequency service data is continuous data in the current exchange period or not; and the continuous data represents target high-frequency service data positioned after initial data in the current switching period, and the initial data represents the first piece of target high-frequency service data in the current switching period.
One compression means used in compressing the target high-frequency service data in the present application is based on the idea of differential calculation. When compressing the target high-frequency service data in the current switching period through differential calculation, specifically, performing differential calculation on the first target high-frequency service data and other target high-frequency service data in the current switching period, and replacing parameter values of fields in other target high-frequency service data with the result of the differential calculation, wherein the length of the parameter values calculated based on the differential calculation is greatly reduced compared with the length of the original other target high-frequency service data.
Then, first, setting the first piece of target high-frequency service data in the current exchange period as initial data, recording other pieces of target high-frequency service data in the current exchange period as continuous data, and after determining that the target high-frequency service data is the initial data or the continuous data, further executing subsequent steps. The initial data is stored in the database and transmitted to the target user completely in the original snapshot data format.
In this embodiment of the present application, the step 202 includes:
identifying a field used for expressing the data state type in the target high-frequency service data, and detecting a parameter value under the field;
if the parameter value is a preset first parameter judgment value, determining the target high-frequency service data as initial data;
and if the parameter value is a preset second parameter judgment value, determining the target high-frequency service data as continuous data.
In order to determine whether the data state type of the target high-frequency service data is initial data or continuous data, a parameter for representing the data state type may be selected from parameters in a data structure of the target high-frequency service data in advance, different data state types respectively represented by specific parameter values under the parameter are specified, and the data state type to which the parameter belongs is marked by setting the specific parameter values under the parameter.
By way of example, it is further understood that, in the snapshot data structure of the target high-frequency service data, the data state type represented by the target high-frequency service data may be represented by setting a specific field, such as an order _ type field, specifically, a specific value of 2 bits taken under the order _ type field represents a different data state type, for example, in a specific embodiment, the highest 2 bits may be set to be a value of 11 to represent initial data, and the highest 2 bits may be set to be a value of 00 to represent subsequent data.
Thus, by identifying the specific parameter value in the order _ type field, the data state type of the target high-frequency service data can be identified, and the target high-frequency service data is judged to be initial data or continuous data.
Step 203: and if the target high-frequency service data is continuous data, analyzing the data structure of the continuous data, identifying a field to be converted in the continuous data, and further identifying a common conversion field and a differential conversion field from the field to be converted.
When the target high-frequency service data is identified as initial data in the current switching period, the target high-frequency service data is completely stored and sent in an original snapshot data format without compressing the data size, so that the subsequent implementation steps of the method in the application are further executed only when the target high-frequency service data is continuous data.
In some embodiments of the present application, when dividing the field type under the target high-frequency service data, firstly, a part of the fields are divided into fields that do not need to be compressed, another part of the fields are fields that need to be compressed, and the fields that need to be compressed are the fields to be converted. Different compression means are required to be adopted for different fields of the same target high-frequency service data, so after the target high-frequency service is determined to be continuous data and the field to be converted which needs to be compressed is further identified, the type of the field to be converted is required to be distinguished, and different compression means are implemented.
In the embodiment of the present application, a field that needs to be compressed by means of differential calculation in a field to be converted is regarded as a differential conversion field, and fields other than the differential conversion field can be regarded as common conversion fields.
The target user can judge in advance based on the snapshot data format of the target high-frequency service data, and set which fields in the snapshot data format are fields needing to be converted according to the user requirements, and which fields under the fields needing to be converted are common conversion fields and which are differential conversion fields.
In a specific embodiment, the target user may configure a conversion instruction including its conversion requirement, and when the field to be converted is identified and the normal conversion field and the differential conversion field are identified from the field to be converted, by receiving the conversion instruction and analyzing the conversion requirement of the target user in the conversion instruction, it can be determined which fields in the following data belong to the field to be converted and which fields belong to the normal conversion field and the differential conversion field under the field to be converted.
In some embodiments of the present application, after step 203, the method for compressing data of high frequency service data further includes:
Identifying other fields except the field to be converted of the continuous data so as to identify a non-sensitive field;
and deleting the non-sensitive field from the continuous data.
And all the fields in the data structure of the continuous data except the field to be converted are regarded as fields without compression processing. And in the field which does not need to be compressed, data which is not interesting or is not needed by the target user exists in part of the field, and the target user is not sensitive to the data, so that the data is meaningless to send to the target user, and the data represented by the non-sensitive field is deleted from the continuous data, so that the data can be effectively compressed, and the size of the data is reduced.
Step 204: and carrying out compression on the parameter values of the common conversion fields through effective value interception, and carrying out conversion compression on the parameter values of the differential conversion fields through numerical value differential calculation so as to generate compressed target high-frequency service data.
In the embodiment of the present application, the data type of each field in the snapshot data structure is a double-precision floating point type, that is, the length is 8 bytes (64 bits). However, for snapshot data in the current exchange cycle, if the parameter values in each field of the snapshot data represent unsigned shaping data, 2 bytes are generally completely able to satisfy the current requirement, and if signed shaping data is required, the corresponding data may be represented with a length of 3 bytes.
Generally, the value size of the normal conversion field in the fields to be converted of the connection data does not need to be represented by all 64 bits of the parameter value, so that for the normal conversion field, the parameter value of the normal conversion field can be effectively compressed by intercepting the valid value in the bits of the normal conversion field and removing the invalid bit represented by 0 in the bits.
In general, the value of the differential conversion field in the field to be converted of the subsequent data does not need to use all 64 bits of the parameter value, a relative value is calculated by performing differential calculation on the parameter value of the same field in the initial data, and the original parameter value of the differential conversion field is replaced by the relative value, so that the length of the differential conversion field can be effectively reduced.
In a specific implementation manner of the embodiment of the present application, specific operation modes of the effective value interception and the difference calculation may be preset in a compression rule, and the compression rule is called to compress the target high-frequency service data based on the compression rule.
In a specific embodiment, it is further understood that the compression process in the above steps, for example, the order _ type field may be divided into the general conversion field in step 203, and when the field is compressed in step 204, only the 2-bit value of the most significant bit of the field needs to be truncated, so that the length of the order _ type field after being compressed is 1 byte. In order to facilitate the target user to locate the field to determine whether the received target high-frequency service data is the initial data or the continuous data after implementing the data compression method for the high-frequency service data, the order _ type field may be moved to be used as the first field of the target high-frequency service data.
In some embodiments of the present application, the step 204 of compressing the parameter value of the generic transformation field by valid value truncation includes:
and intercepting the 16-bit data or 24-bit data of the lower bits in the parameter values of each ordinary conversion field to be used as the parameter values of the corresponding ordinary conversion field in the compressed target information data.
When the parameter value in the normal conversion field is used for representing unsigned shaping data, the numerical value of the upper 48 bits in 2 bytes (64 bits) is 0, the invalid data is cut off, and the numerical value of the lower 16 bits is reserved as the parameter value of the normal conversion field, so that the data length of the normal conversion field can be effectively compressed while the normal use of the data is not influenced.
When the parameter value in the ordinary conversion field is used for representing the signed shaping data, at most, the numerical value of the lower 3 bytes (namely, 24 bits) is required to be intercepted as the parameter value of the ordinary conversion field, so as to compress the ordinary conversion field.
In some embodiments of the present application, before the step of performing transform compression on the parameter values of the differential transform field through numerical differential calculation in step 204, the data compression method for high-frequency service data further includes:
And identifying the parameter value type of each field in the differential conversion field, and dividing the differential conversion field into an asset value differential field and a time differential field.
For the compression processing of the parameter values of the differential conversion field, two compression manners based on differential calculation are adopted in the embodiment of the present application, wherein one compression manner is when the data represented by the parameter values of the differential conversion field is an asset value (such as price), and the other compression manner is when the data represented by the parameter values of the differential conversion field is time, so that it is necessary to further confirm which compression manners are asset value differential fields and which compression manners are time differential fields in the identified differential conversion field.
In a further specific embodiment, the step of identifying the parameter value type of each field in the differential conversion field, and dividing the differential conversion field into an asset value differential field and a time differential field includes:
acquiring a plurality of continuous historical target information data in a historical exchange period;
counting parameter values of each field in corresponding differential conversion fields in the plurality of historical target information data, and identifying the variation trend of the parameter values;
If the change trend of the parameter value is incremental change, dividing a differential conversion field corresponding to the parameter value into time differential fields;
and if the change trend of the parameter value is fluctuation change, dividing the differential conversion field corresponding to the parameter value into asset value differential fields.
If the parameter values in each field in the Tick data are the parameter values representing time, the change mode of the parameter values in the Tick data continuously tends to increase gradually in the high-frequency data exchange process, but the parameter values representing asset values continuously fluctuate irregularly between an upper value limit and a lower value limit. Therefore, the type of the differential conversion field can be judged more effectively in this way, and especially when the reference data volume is large, the judgment accuracy rate is very high.
In a further specific embodiment, the step of performing transform compression on the parameter values of the differential transform field through numerical differential calculation includes:
reading the minimum variable asset value and the high-frequency interval time;
calculating the difference value of the parameter value under each identical asset value differential field between the continuous data and the initial data, and dividing the difference value corresponding to each asset value differential field by the minimum variation asset value to be used as the parameter value of the asset value differential field corresponding to the compressed continuous data;
And calculating the difference value of the parameter values under each same time difference field between the continuous data and the initial data, and dividing the difference value corresponding to each time difference field by the high-frequency interval time to be used as the parameter value of the time difference field corresponding to the compressed continuous data.
In the high-frequency service data exchange, the asset value fluctuation has a minimum calculation fluctuation unit, the asset value fluctuation is carried out by integral multiple of the unit, and the minimum calculation fluctuation unit when the asset value fluctuates is represented by a preset minimum fluctuating asset value in the proposal. The asset value in the high-frequency service data exchange is limited by fluctuation and drop every day, so that the fluctuation range of the asset value in the limited exchange period is a limited discrete asset value interval. This interval or the range of this fluctuation is relatively small, and the parameter value of the differential conversion field in the target high-frequency traffic data is a double-precision floating-point type of 8 bytes to represent the asset numerical data.
And calculating the difference value between the parameter value of the asset value differential field and the parameter value of the same asset value differential field in the initial data by differentiating the continuous data and the initial data, dividing the difference value by the minimum variation asset value to obtain a scatter value, and taking the scatter value as the parameter value of the asset value differential field corresponding to the continuous data, thereby compressing the continuous data. Based on the scatter value and the known initial data, the parameter value under the asset value difference field in the continuous data before compression can be easily deduced, so that the length of 8 bytes is obviously not needed to store the related asset value data, the data length can be greatly reduced, and the storage space is saved.
In the foregoing, the server intercepts a snapshot data every n seconds and sends the snapshot data as tick data to the target user, and for the asset value difference field, the high frequency interval time in this application refers to the interval time for sending data to the target user, which is represented by the n seconds, and in general, the value of n in the high frequency service data exchange is 0.5.
And similarly, the continuous data and the initial data are differentiated, the difference value between the parameter value of the time difference field and the parameter value of the same time difference field in the initial data is calculated, a relative time is obtained, the relative time is divided by the high-frequency interval time, and the final time offset is obtained and used as the parameter value of the corresponding time difference field under the continuous data, so that the continuous data is compressed. Based on the time offset and the known initial data, the parameter value under the price difference field in the continuous data before compression can be easily deduced, so that the length of 8 bytes is not needed to be used for storing the relevant time data, the data length can be greatly reduced, and the storage space is saved.
In the specific implementation manner of the present application, the lengths of the asset value difference field and the time difference field after compression are both 2 bytes, the maximum decimal data size corresponding to 2 bytes is 65536, and according to the data statistical experience of high-frequency service data exchange, the data size represented by 2 bytes is enough to satisfy most high-frequency service data exchange scenarios. However, for some high-frequency service data exchange scenarios, the parameter value in the asset value difference field may exceed the maximum data size of 2 bytes, and the corresponding data may be represented by a data length of 3 bytes.
Based on the tick data compression method in the application, for the high-frequency service data exchange scene in which the field length is compressed into 3 bytes in the above embodiment, the data volume can be compressed from 120 bytes of one tick data to 14 bytes, and the compression degree is about 8.5 times; for the high frequency service data exchange scenario in which the field length is compressed to 2 bytes in the foregoing embodiment, the data size may be compressed to 11 bytes from 120 bytes of one tick data, and the compression degree is about 10.9 times.
According to the data compression method of the high-frequency service data, one part of data in the high-frequency service data is intercepted, useless data in the data are abandoned, and the numerical value of the other part of data meeting the conversion requirement is converted and compressed by applying the thought of differential calculation, so that the high compression of the high-frequency service data is realized, the storage space of the high-frequency service data and the bandwidth requirement during data transmission are greatly reduced, the pressure of a server is greatly reduced, the resource of the server is saved, and the transmission efficiency is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, fig. 3 is a schematic structural diagram of an embodiment of a data compression apparatus for high-frequency service data according to the embodiment of the present application. As an implementation of the method shown in fig. 2, the present application provides an embodiment of a data compression apparatus for high-frequency service data, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various electronic devices in particular.
As shown in fig. 3, the data compression apparatus for high frequency service data according to this embodiment includes:
a data acquisition module 301; the method is used for monitoring the high-frequency data exchange process in the current exchange period and acquiring target information data which needs to be sent to a target user in the exchange process.
A data type determination module 302; the data exchange module is used for judging whether the target information data is initial data or continuous data in the current exchange period; the initial data represents the first piece of target information data in the current exchange period, and the subsequent data represents the target information data positioned after the initial data in the current exchange period.
A field identification module 303; and after the target information data is confirmed to be the continuous data, analyzing the data structure of the continuous data, identifying a field to be converted in the continuous data, and further identifying a common conversion field and a differential conversion field from the field to be converted.
A data compression module 304; and the system is used for carrying out compression on the parameter values of the common conversion fields through effective value interception and carrying out conversion compression on the parameter values of the differential conversion fields through numerical value differential calculation so as to generate compressed target information data.
In this embodiment of the present application, the data type determining module 202 is configured to identify a field used for representing a data state type in the target high-frequency service data, and detect a parameter value in the field; if the parameter value is a preset first parameter judgment value, determining the target high-frequency service data as initial data; and if the parameter value is a preset second parameter judgment value, determining the target high-frequency service data as continuous data.
In some embodiments of the present application, the data compression apparatus for high frequency service data further includes: and the non-sensitive field identifies the module. After the field identification module 203 identifies a field to be converted in the continuous data, the non-sensitive field identification module is configured to identify other fields of the continuous data except the field to be converted so as to identify a non-sensitive field; and deleting the non-sensitive field from the continuous data.
In some embodiments of the present application, the data compression module 204 comprises: and intercepting the compression submodule. And the interception and compression submodule is used for intercepting low-order 16-bit data or 24-bit data in the parameter value of each common conversion field to be used as the parameter value of the corresponding common conversion field in the compressed target information data.
In some embodiments of the present application, the data compression module 204 comprises: and a differential field classification submodule. Before the data compression module 204 performs transform compression on the parameter values of the differential transform field through numerical difference calculation, the differential field classification submodule is configured to identify the parameter value types of the fields in the differential transform field, and divide the differential transform field into an asset numerical difference field and a time differential field.
In a further specific embodiment, the differential field classification submodule is configured to obtain a plurality of pieces of historical target information data that are consecutive within one historical exchange period; counting parameter values of each field in corresponding differential conversion fields in the plurality of historical target information data, and identifying the variation trend of the parameter values; if the change trend of the parameter value is incremental change, dividing a differential conversion field corresponding to the parameter value into time differential fields; and if the change trend of the parameter value is fluctuation change, dividing the differential conversion field corresponding to the parameter value into asset value differential fields.
In a further specific embodiment, the data compression module 204 further includes: and a differential conversion submodule. The differential conversion submodule is used for reading the minimum variable asset value and the high-frequency interval time; calculating the difference value of the parameter value under each identical asset value differential field between the continuous data and the initial data, and dividing the difference value corresponding to each asset value differential field by the minimum variation asset value to be used as the parameter value of the asset value differential field corresponding to the compressed continuous data; and calculating the difference value of the parameter values under each same time difference field between the continuous data and the initial data, and dividing the difference value corresponding to each time difference field by the high-frequency interval time to be used as the parameter value of the time difference field corresponding to the compressed continuous data.
The data compression device for the high-frequency service data intercepts a part of data in the high-frequency service data, discards useless data in the data, and converts and compresses the numerical value of the other part of data meeting the conversion requirement by applying the thought of differential calculation, thereby realizing high compression of the high-frequency service data, greatly reducing the storage space of the high-frequency service data and the bandwidth requirement during data transmission, greatly reducing the pressure of a server, saving the resource of the server and improving the transmission efficiency.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user in a keyboard mode, a mouse mode, a remote controller mode, a touch panel mode or a voice control equipment mode.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various application software, such as program codes of a data compression method of high-frequency service data. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically arranged to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute the program code stored in the memory 61 or process data, for example, execute the program code of the data compression method of the high-frequency service data.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The present application provides yet another embodiment, which is to provide a computer readable storage medium storing a data compression program of high frequency service data, the data compression program of high frequency service data being executable by at least one processor to cause the at least one processor to perform the steps of the data compression method of high frequency service data as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
In the above embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one type of logical functional division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The modules or components may or may not be physically separate, and components displayed as modules or components may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network elements. Some or all of the modules or components can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The present application is not limited to the above-mentioned embodiments, the above-mentioned embodiments are preferred embodiments of the present application, and the present application is only used for illustrating the present application and not for limiting the scope of the present application, it should be noted that, for those skilled in the art, it is still possible to make several improvements and modifications to the technical solutions described in the foregoing embodiments or to make equivalent replacement for some technical features thereof without departing from the principle of the present application. All equivalent structures made by using the contents of the specification and the drawings of the present application can be directly or indirectly applied to other related technical fields, and the same should be considered to be included in the protection scope of the present application.
It should be understood that the above-described embodiments are merely exemplary of some, and not all, embodiments of the present application, and that the drawings illustrate preferred embodiments of the present application without limiting the scope of the claims appended hereto. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All other embodiments that can be obtained by a person skilled in the art based on the embodiments in this application without any creative effort and all equivalent structures made by using the contents of the specification and the drawings of this application can be directly or indirectly applied to other related technical fields and are within the scope of protection of the present application.
Claims (10)
1. A method for compressing high frequency service data, comprising:
monitoring a high-frequency data exchange process in a current exchange period, and acquiring target high-frequency service data which needs to be sent to a target user in the exchange process;
Judging whether the target high-frequency service data is continuous data in the current switching period; the continuous data represents target high-frequency service data which is positioned behind initial data in the current switching period, and the initial data represents a first piece of target high-frequency service data in the current switching period;
if the target high-frequency service data is continuous data, analyzing a data structure of the continuous data, identifying a field to be converted in the continuous data, and further identifying a common conversion field and a differential conversion field from the field to be converted;
and carrying out compression on the parameter values of the common conversion fields through effective value interception, and carrying out conversion compression on the parameter values of the differential conversion fields through numerical value differential calculation so as to generate compressed target high-frequency service data.
2. The data compression method of high-frequency service data according to claim 1, wherein the step of determining whether the target high-frequency service data is the subsequent data in the current switching cycle comprises:
identifying a field used for expressing the data state type in the target high-frequency service data, and detecting a parameter value under the field;
If the parameter value is a preset first parameter judgment value, determining the target high-frequency service data as initial data;
and if the parameter value is a preset second parameter judgment value, determining that the target high-frequency service data is continuous data.
3. The method for compressing high frequency traffic data according to claim 1, wherein after the step of identifying the field to be converted in the continuous data, the method further comprises:
identifying other fields except the field to be converted of the continuous data so as to identify a non-sensitive field;
and deleting the non-sensitive field from the continuation data.
4. The data compression method of high-frequency service data according to claim 1, wherein the step of compressing the parameter value of the normal conversion field by valid value truncation comprises:
and intercepting the 16-bit data or 24-bit data of the lower bits in the parameter values of each ordinary conversion field to be used as the parameter values of the corresponding ordinary conversion field in the compressed target information data.
5. The data compression method for high-frequency service data according to claim 1, wherein before the step of performing transform compression on the parameter values of the differential transform field by numerical differential calculation, the method further comprises:
And identifying the parameter value type of each field in the differential conversion field, and dividing the differential conversion field into an asset value differential field and a time differential field.
6. The method of claim 5, wherein the step of identifying the parameter value type of each field in the differential transform field and dividing the differential transform field into an asset value differential field and a time differential field comprises:
acquiring a plurality of continuous historical target information data in a historical exchange period;
counting parameter values of all fields in corresponding differential conversion fields in the plurality of historical target information data, and identifying the change trend of the parameter values;
if the change trend of the parameter value is incremental change, dividing a differential conversion field corresponding to the parameter value into time differential fields;
and if the change trend of the parameter value is fluctuation change, dividing the differential conversion field corresponding to the parameter value into asset value differential fields.
7. The data compression method for high-frequency service data according to claim 5, wherein the step of performing transform compression on the parameter values of the differential transform field by numerical differential calculation comprises:
Reading the minimum variable asset value and the high-frequency interval time;
calculating the difference value of the parameter value under each identical asset value differential field between the continuous data and the initial data, and dividing the difference value corresponding to each asset value differential field by the minimum variation asset value to be used as the parameter value of the asset value differential field corresponding to the compressed continuous data;
and calculating the difference value of the parameter values under each same time difference field between the continuous data and the initial data, and dividing the difference value corresponding to each time difference field by the high-frequency interval time to be used as the parameter value of the time difference field corresponding to the compressed continuous data.
8. An apparatus for compressing high frequency service data, comprising:
the data acquisition module is used for monitoring a high-frequency data exchange process in a current exchange period and acquiring target information data which needs to be sent to a target user in the exchange process;
the data type judging module is used for judging whether the target information data is initial data or continuous data in the current exchange period; the initial data represents a first piece of target information data in the current exchange period, and the continuing data represents target information data positioned behind the initial data in the current exchange period;
A field identification module, configured to, after it is determined that the target information data is continuous data, analyze a data structure of the continuous data, identify a field to be converted in the continuous data, and further identify a common conversion field and a differential conversion field from the field to be converted;
and the data compression module is used for carrying out compression on the parameter values of the common conversion fields through effective value interception and carrying out conversion compression on the parameter values of the differential conversion fields through numerical value differential calculation so as to generate compressed target information data.
9. A computer arrangement comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the steps of the method for data compression of high frequency traffic data according to any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for data compression of high-frequency traffic data according to any one of claims 1 to 7.
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| PCT/CN2019/117697 WO2021056737A1 (en) | 2019-09-25 | 2019-11-12 | Data compression method and apparatus for high-frequency service data, device, and storage medium |
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| CN111522710B (en) * | 2020-04-16 | 2021-02-26 | 星环信息科技(上海)股份有限公司 | Data compression method, device and medium based on big data |
| CN111835759B (en) * | 2020-07-10 | 2023-02-03 | 广东源泉科技有限公司 | Network flow control method based on data compression |
| CN111917745B (en) * | 2020-07-16 | 2022-07-22 | 上海星秒光电科技有限公司 | Data compression method |
| CN114257656A (en) * | 2021-12-22 | 2022-03-29 | 深圳锂安技术有限公司 | Compression processing method and device for battery system data and electronic equipment |
| CN115145496B (en) * | 2022-09-05 | 2023-01-06 | 中国汽车技术研究中心有限公司 | Simulation result data processing method, device and storage medium |
| CN115589436B (en) * | 2022-12-14 | 2023-03-28 | 三亚海兰寰宇海洋信息科技有限公司 | Data processing method, device and equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108874962A (en) * | 2018-06-07 | 2018-11-23 | 武汉理工大学 | A kind of finance big data processing method |
| CN109271411A (en) * | 2018-09-28 | 2019-01-25 | 中国平安财产保险股份有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN109783487A (en) * | 2018-12-07 | 2019-05-21 | 广州经传多赢投资咨询有限公司 | The storage method of securities data |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020001315A1 (en) * | 2000-03-21 | 2002-01-03 | Tran Hung V. | Method and apparatus for compressing IP/UDP/RTP headers in a lossy environment |
| CN107295036B (en) * | 2016-03-31 | 2021-02-23 | 华为技术有限公司 | Data sending method and data merging equipment |
| US9742434B1 (en) * | 2016-12-23 | 2017-08-22 | Mediatek Inc. | Data compression and de-compression method and data compressor and data de-compressor |
| US11635908B2 (en) * | 2017-06-22 | 2023-04-25 | Adobe Inc. | Managing digital assets stored as components and packaged files |
| CN107749815B (en) * | 2017-09-21 | 2020-02-18 | 京信通信系统（中国）有限公司 | Data transmission method, data processing method and data transmission device |
| CN109379376B (en) * | 2018-11-30 | 2021-10-08 | 成都德辰博睿科技有限公司 | Radio monitoring equipment and system based on data compression and data compression method |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108874962A (en) * | 2018-06-07 | 2018-11-23 | 武汉理工大学 | A kind of finance big data processing method |
| CN109271411A (en) * | 2018-09-28 | 2019-01-25 | 中国平安财产保险股份有限公司 | Report form generation method, device, computer equipment and storage medium |
| CN109783487A (en) * | 2018-12-07 | 2019-05-21 | 广州经传多赢投资咨询有限公司 | The storage method of securities data |
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