Ratio class detection method and related equipment
阅读说明：本技术 比率类检测方法以及相关设备 (Ratio class detection method and related equipment ) 是由 杨思晓 苏婵菲 潘璐伽 于 2020-05-26 设计创作，主要内容包括：本申请实施例公开了一种比率类检测方法。数据处理设备获取比率类数据和数量类数据后,根据比率类数据得到第一参数,并根据数量类数据修正第一参数得到第二参数,数据处理设备根据第二参数进行比率类检测,确定目标比率类是否异常,减少误判。(The embodiment of the application discloses a ratio detection method. After the data processing equipment acquires the ratio data and the quantity data, a first parameter is obtained according to the ratio data, a second parameter is obtained by correcting the first parameter according to the quantity data, the data processing equipment performs ratio detection according to the second parameter, whether the target ratio is abnormal or not is determined, and misjudgment is reduced.)
1. A method for rate class detection, comprising:
the data processing equipment acquires ratio class data and quantity class data, wherein the ratio class data comprises reference ratio class data and target ratio class data, the quantity class data comprises reference quantity class data and target quantity class data, and the target ratio class data and the target quantity class data are data of a target time period to be judged;
the data processing equipment calculates a first parameter, and the first parameter is obtained according to ratio data;
the data processing equipment corrects the first parameter according to the quantity data to obtain a second parameter;
the data processing device determines whether the target ratio class data is abnormal according to a second parameter.
2. The method of claim 1, wherein the first parameters comprise ratio class scores and/or residuals;
the calculation mode of the ratio class score comprises the following steps:
wherein, the zscore (x)t) Is the ratio class score, the [ x ]t-w,xt-w+1,...xt-1]For the reference ratio class data, mean () is the mean function, std () is the standard deviation function, xtIs the target ratio class data;
the calculation mode of the residual error comprises the following steps:
residual(xt)＝mean([xt-w，xt-w+1，...xt-1])-xt；
wherein the residual (x)t) Is a residual error, said [ xt-w,xt-w+1,...xt-1]For the reference ratio class data mean () is the mean function, xtIs the target ratio class data.
3. The method of claim 2, wherein the data processing device modifying the first parameter to obtain the second parameter based on the quantity class data comprises:
when the first parameter is the ratio class parameter, the data processing device calculates a quantity class score;
the data processing equipment corrects the first parameter according to the quantity fraction to obtain the second parameter;
the calculation mode of the quantity class score comprises the following steps:
wherein, the zscore (uc)t) For the number class score, the uctFor the target quantity class data, the [ uc ]t-w，uct-w+1，...，uct-1]Is the reference ratio class data;
the correction mode comprises the following steps:
the above-mentionedAs the second parameter, the α is a real number greater than zero and less than 1;
when the first parameter is the residual error, the mode that the data processing equipment corrects the first parameter according to the quantity data to obtain the second parameter comprises the following steps:
the above-mentionedIs the second parameter, theIs the average of the ratio class data, theThe average value of the reference quantity class data.
4. The method of any of claims 1 to 3, wherein the data processing device determining whether the target ratio class data is abnormal according to a second parameter comprises:
if the second parameter is smaller than an abnormal threshold value, the data processing equipment determines that the target ratio class data is normal;
and if the second parameter is not less than the abnormal threshold value, the data processing equipment determines that the target ratio class data is abnormal.
5. A data processing apparatus, characterized by comprising:
the device comprises an acquisition unit, a judgment unit and a processing unit, wherein the acquisition unit is used for acquiring ratio class data and quantity class data, the ratio class data comprises reference ratio class data and target ratio class data, the quantity class data comprises reference quantity class data and target quantity class data, and the target ratio class data and the target quantity class data are data of a target time period to be judged;
the calculating unit is used for calculating a first parameter, and the first parameter is obtained according to the ratio data;
the correcting unit is used for correcting the first parameter according to the quantity data to obtain a second parameter;
and the determining unit is used for determining whether the target ratio class data is abnormal according to a second parameter.
6. The apparatus of claim 5, wherein the first parameters comprise ratio class scores and/or residuals;
the calculation mode of the ratio class score comprises the following steps:
wherein, the zscore (x)t) Is the ratio class score, the [ x ]t-w,xt-w+1,...xt-1]For the reference ratio class data, mean () is the mean function, std () is the standard deviation function, xtIs the target ratio class data;
the calculation mode of the residual error comprises the following steps:
residual(xt)＝mean([xt-w，xt-w+1，...xt-1])-xt；
wherein the residual (x)t) Is a residual error, said [ xt-w,xt-w+1,...xt-1]For the reference ratio class data mean () is the mean function, xtIs the target ratio class data.
7. The device according to claim 6, wherein the correction unit is specifically configured to:
when the first parameter is the ratio parameter, calculating a quantity score, and correcting the first parameter according to the quantity score to obtain a second parameter;
the calculation mode of the quantity class score comprises the following steps:
wherein, the zscore (uc)t) For the number class score, the uctFor the target quantity class data, the [ uc ]t-w，uct-w+1，...，uct-1]Is the reference ratio class data;
the correction mode comprises the following steps:
the above-mentionedAs the second parameter, the α is a real number greater than zero and less than 1;
when the first parameter is the residual:
the above-mentionedIs the second parameter, theIs the average of the ratio class data, theThe average value of the reference quantity class data.
8. The device according to any one of claims 5 to 7, characterized in that the determination unit is specifically configured to:
if the second parameter is smaller than the abnormal threshold value, determining that the target ratio class data is normal;
and if the second parameter is not smaller than the abnormal threshold value, determining that the target ratio class data is abnormal.
9. A computer storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 4.
10. A computer program product, characterized in that the computer program product, when executed on a computer, causes the computer to perform the method according to any of claims 1 to 4.
Technical Field
The embodiment of the application relates to the field of communication, in particular to a service processing method and a service server.
Background
The quantity data may be data directly obtained by the device, the ratio data may be data that cannot be directly obtained by the device but is obtained by performing an operation on the quantity data, for example, for a server, a Central Processing Unit (CPU) utilization rate, a port access success rate, and the like are the ratio data, and a port request amount and a port request failure amount are the quantity data.
Compared with data type data, ratio type data has the advantages of being more intuitive and more beneficial to analysis, but the existing scheme only takes ratio type data as an abnormal detection object generally and does not consider the variation trend of quantity type data, and a lot of data quantity data, such as the variation of request quantity, has the characteristics of strong period, high noise, large fluctuation and the like.
When the data amount data corresponding to the ratio class data to be judged is less, if the request amount is less, namely the sample is smaller, the ratio class detection only by combining the ratio class data is easy to generate misjudgment.
Disclosure of Invention
The embodiment of the application provides a ratio detection method and data processing equipment, which can reduce misjudgment of the accuracy of the ratio.
A first aspect of the embodiments of the present application provides a ratio-based detection method, including:
the data processing device acquires ratio class data and quantity class data, wherein the ratio class data can comprise reference ratio class data and target ratio class data, and the quantity class data can comprise reference quantity class data and target quantity class data, wherein the target ratio class data and the target quantity class data are data of a target time period to be judged. The data processing equipment calculates a first parameter, then corrects the first parameter according to the quantity data to obtain a second parameter, wherein the first parameter is obtained according to the ratio data, and the data processing equipment determines whether the target ratio data is abnormal or not according to the second parameter.
The data processing equipment judges whether the target ratio data is abnormal or not according to the second parameter corrected by the quantity data, and reduces misjudgment when the data quantity data corresponding to the ratio data to be judged is less, such as the request quantity is less.
Based on the first aspect of the embodiment of the present application, in a first implementation manner of the first aspect of the embodiment of the present application, the first parameter may include a ratio class score and/or a residual error;
the ratio class score may be calculated as:
wherein zscore (x)t) Is a ratio-like score, [ x ]t-w，xt-w+1，...xt-1]For reference to the ratio class data, mean () is the mean function, std () is the standard deviation function, xtIs the target ratio class data.
The residual error may be calculated as:
residual(xt)＝mean([xt-w，xt-w+1，...xt-1])-xt；
wherein residual (x)t) Is a residual, [ x ]t-w，xt-w+1，...xt-1]For reference to the ratio class data, mean () is a mean function, xtIs the target ratio class data.
In the embodiment of the application, multiple possible situations of the first parameter are provided, and the flexibility of the scheme is improved.
Based on the first implementation manner of the first aspect of the embodiment of the present application, in the second implementation manner of the first aspect of the embodiment of the present application, when the first parameter is a ratio-based parameter, the data processing device calculates a quantity-based score, and the data processing device corrects the first parameter according to the quantity-based score to obtain a second parameter, where the calculation manner of the quantity-based score includes:
zscore(uct) As a number class score, uctFor the target quantity class data, [ uct-w，uct-w+1，...，uct-1]For reference to the ratio class data.
The correction method comprises the following steps:
wherein the content of the first and second substances,alpha is a second parameter, larger than zeroReal numbers at 1.
When the first parameter is a residual error, the method for obtaining the second parameter by the data processing equipment according to the quantity data correction first parameter comprises the following steps:
wherein the content of the first and second substances,as the second parameter, the parameter is,is the average of the ratio-class data,is the average of the reference quantity class data.
Based on any one of the first aspect of the embodiment of the present application and the second implementation manner of the first aspect of the embodiment of the present application, in a first single implementation manner of the first aspect of the embodiment of the present application, if the second parameter is smaller than the anomaly threshold, the data processing device determines that the target ratio class data is normal, and if the second parameter is not smaller than the anomaly threshold, the data processing device determines that the target ratio class data is abnormal.
In this embodiment of the application, the method for determining, by the data processing device, whether the target ratio class data is abnormal according to the second parameter may be determined according to a magnitude relationship between the second parameter and an abnormality threshold.
A second aspect of embodiments of the present application provides a data processing apparatus, which executes the method of the first aspect.
A third aspect of embodiments of the present application provides a computer storage medium having instructions stored therein, which when executed on a computer, cause the computer to perform the method of the first aspect.
A fourth aspect of embodiments of the present application provides a computer software product, which when executed on a computer causes the computer to perform the method of the first aspect.
Drawings
FIG. 1 is a schematic flow chart of a ratio class detection method according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application;
fig. 3 is another schematic structural diagram of a data processing device in the embodiment of the present application.
Detailed Description
The embodiment of the application provides a ratio detection method and data processing equipment, which can reduce misjudgment of the accuracy of the ratio.
In the embodiment of the present application, an embodiment of the ratio class detection method includes:
101. the data processing equipment acquires ratio data and quantity data;
the data acquired by the data processing device may be data of the data processing device, and may also be data of other network devices, which is not limited herein.
Referring to table 1, t is a time sequence, the time sequence generally includes a timestamp (timestamp) that changes and a value corresponding to the timestamp, when timestamp information is unknown, a subscript (indicator) is used as the timestamp, a target time period is t 21, a reference time period is t 1 to 20, a reference time length w is 1 to 20, x is a ratio class, c is a request quantity, uc is a failure quantity, c and uc are quantity class data, and x is ratio class data. When the target time period is t-21, the corresponding data x21 is target ratio data, x 21-0.900000 can be obtained from the following table, c21 and uc21 are both target data, c 21-20 can be obtained from the following table, and uc 21-2; when the reference time period is 1-20, the corresponding data x 1-x 21 are reference ratio data, and c 1-c 20 and uc 1-uc 20 are reference data.
TABLE 1
In the embodiment of the present application, the model of the ratio-based detection method may be an autoregressive integrated moving average model (ARIMA), a Z-score (Z-score) model, a threshold based on a same ratio and a ring ratio, and the like, which is not limited herein.
102. The data processing device calculates a first parameter;
when the model of the ratio class detection method is a z-score model, the first parameter may be a ratio class score, and the ratio class score may be calculated by:
wherein zscore (x)t) Is a ratio-like score, [ x ]t-w，xt-w+1，…xt-1]For reference to the ratio class data, mean () is the mean function and std () is the standard deviation function, xtIs the target ratio class data.
Taking table 1 as an example, when t is 21 and w is 20, the average value of the reference ratio class data is 0.95, and the ratio class score is present
When the model of the ratio detection method is an ARIMA model, the first parameter may be a residual error, and the calculation method of the residual error may be:
residual(xt)＝mean([xt-w，xt-w+1，...xt-1])-xt；
residual(xt) Is a residual, [ x ]t-w，xt-w+1，…xt-1]For reference to the ratio class data, mean () is a mean function, xtIs the target ratio class data.
Taking table 1 as an example, when t is 21 and w is 3, the average value of the reference ratio data is 0.956, and the residual is residual (x)21)＝0.956-0.9＝0.056。
103. The data processing equipment corrects the first parameter according to the quantity data to obtain a second parameter;
when the model of the ratio-class detection method is a z-score model, the data processing device corrects the first parameter according to the quantity-class score to obtain the second parameter, and the calculation mode of the quantity-class score may be:
zscore(uct) As a number class score, uctFor the target quantity class data, [ uct-w，uct-w+1，...，uct-1]For reference to the ratio class data.
Taking table 1 as an example, when t is 21 and w is 20, the number class score is:
the correction method comprises the following steps:
wherein the content of the first and second substances,as the second parameter, α is a weighting coefficient, a specific numerical value of α may be a real number greater than zero and smaller than 1, and is not limited herein, for example, α may be 0.5 or 0.6, and in this embodiment, α ═ 0.5 is taken as an example for description.
I.e. the second parameter is 1.2.
When the model of the ratio-based detection method is an ARIMA model, the first parameter is a residual error, and the manner in which the data processing device corrects the first parameter according to the quantity-based data to obtain the second parameter may be:
wherein the content of the first and second substances,as the second parameter, the parameter is,is the average of the ratio-class data,is the average of the reference quantity class data.
Taking table 1 as an example, when t is 21,at this time:
i.e. the second parameter is 1.083.
104. The data processing apparatus determines whether the target ratio class data is abnormal according to the second parameter.
If the second parameter is smaller than the abnormal threshold, the data processing equipment determines that the target ratio data is normal, otherwise, if the second parameter is not smaller than the abnormal threshold, the data processing equipment determines that the target ratio data is abnormal. The abnormal threshold is not limited, and may be 3, for example, or another value such as 2.8, and the specific value may be set according to the actual operation.
Taking the anomaly threshold value as 3 as an example, in step 103, taking table 1 as an example, the second parameters obtained by the two models are 1.2 and 1.083 respectively, both of which are smaller than the anomaly threshold value, that is, when t is 21, the target ratio class data (x is the ratio of the target ratio class data to the anomaly threshold value of the target ratio class data (x is the ratio of the anomaly threshold value of the target ratio class data to the anomaly threshold value of the anomaly data of the target ratio class data)210.900000) no anomaly occurred.
In the above description of the ratio class detection method in the embodiment of the present application, referring to fig. 2, the following description of the data processing device in the embodiment of the present application, where an embodiment of the data processing device in the embodiment of the present application includes:
an obtaining unit 201 is configured to obtain the ratio class data and the quantity class data.
A calculating unit 202, configured to calculate a first parameter, where the first parameter is obtained according to the ratio class data.
And the correcting unit 203 is configured to correct the first parameter according to the quantity data to obtain a second parameter.
A determining unit 204, configured to determine whether the target ratio class data is abnormal according to a second parameter.
In this embodiment, operations performed by each unit in the data processing apparatus are similar to those described in the embodiment shown in fig. 1, and are not described again here.
Fig. 3 is a schematic structural diagram of a data processing device according to an embodiment of the present disclosure, where the data processing device 300 may include one or more processors 301 and a memory 305, and one or more applications or data are stored in the memory 305.
Memory 305 may be volatile storage or persistent storage, among other things. The program stored in memory 305 may include one or more modules, each of which may include a sequence of instructions operating on a data processing device. Still further, the processor 301 may be configured to communicate with the memory 305 to execute a series of instruction operations in the memory 305 on the data processing device 300.
Data processing apparatus 300 may also include one or more power supplies 302, one or more wired or wireless network interfaces 303, one or more input-output interfaces 304, and/or one or more operating systems, such as any of the microsoft (Windows) systems, the Android system (Android), the apple operating system (Mac OS), the Unix (Unix), the linax (Linux).
The processor 301 may perform the operations performed by the data processing apparatus in the embodiment shown in fig. 1, which are not described herein again.
The present application provides a data processing device, which is coupled to a memory and configured to read and execute instructions stored in the memory, so that the data processing device implements the steps of the method executed by the data processing device in any of the embodiments corresponding to fig. 1. In one possible design, the data processing device is a chip or a system on a chip.
The present application provides a chip system comprising a processor for enabling a data processing device to carry out the functions referred to in the above aspects, e.g. to send or process data and/or information referred to in the above methods. In one possible design, the system-on-chip further includes a memory, the memory being used to hold the necessary program instructions and data. The chip system may be formed by a chip, or may include a chip and other discrete devices.
In another possible design, when the system of chips is a chip within a data processing device or the like, the chip comprises: a processing unit, which may be, for example, a processor, and a communication unit, which may be, for example, an input/output interface, a pin or a circuit, etc. The processing unit may execute computer-executable instructions stored by the storage unit to cause a chip within the data processing apparatus or the like to perform the steps of the method performed by the data processing apparatus in any of the embodiments corresponding to fig. 1. Alternatively, the storage unit may be a storage unit in a chip, such as a register, a cache, and the like, and the storage unit may also be a storage unit located outside the chip in the UE or the base station, such as a read-only memory (ROM) or another type of static storage device that can store static information and instructions, a Random Access Memory (RAM), and the like.
The embodiments of the present application further provide a processor, coupled to the memory, for performing the method and functions related to the data processing device in any of the embodiments.
The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a computer, implements the method flow related to the data processing device in any of the above method embodiments. Correspondingly, the computer may be the data processing device described above.
It should be understood that the processor mentioned in the data processing device, the chip system, etc. in the above embodiments of the present application, or the processor provided in the above embodiments of the present application, may be a Central Processing Unit (CPU), and may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It should also be understood that the number of processors in the data processing device, the chip system, and the like in the above embodiments in the present application may be one or more, and may be adjusted according to practical application scenarios, and this is merely an exemplary illustration and is not limited. The number of the memories in the embodiment of the present application may be one or multiple, and may be adjusted according to an actual application scenario, and this is merely an exemplary illustration and is not limited.
It should also be understood that the memory or the readable storage medium and the like mentioned in the data processing device, the chip system and the like in the above embodiments of the present application may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate SDRAM, enhanced SDRAM, SLDRAM, Synchronous Link DRAM (SLDRAM), and direct rambus RAM (DR RAM).
It should be further noted that, when the data processing device includes a processor (or a processing unit) and a memory, the processor in this application may be integrated with the memory, or the processor and the memory are connected through an interface, which may be adjusted according to an actual application scenario, and is not limited.
The present application further provides a computer program or a computer program product including a computer program, where the computer program, when executed on a computer, causes the computer to implement the method flow of any one of the above method embodiments and data processing apparatus. Correspondingly, the computer may be the data processing device described above.
In the embodiment corresponding to fig. 1, all or part of the above may be implemented by software, hardware, firmware or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, e.g., the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium may be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application, which is a part of or contributes to the prior art in essence, or all or part of the technical solution may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or other network devices) to execute all or part of the steps of the method in fig. 1 of the present application. And the storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely descriptive of the various embodiments of the application and how objects of the same nature can be distinguished. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The names of the messages/frames/information, modules or units, etc. provided in the embodiments of the present application are only examples, and other names may be used as long as the roles of the messages/frames/information, modules or units, etc. are the same.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the embodiments of the present application, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that in the description of the present application, unless otherwise indicated, "/" indicates a relationship where the objects associated before and after are an "or", e.g., a/B may indicate a or B; in the present application, "and/or" is only an association relationship describing an associated object, and means that there may be three relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural.
The word "if" or "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.