A data request statistics method, server, terminal and system

Abstract

The present application discloses a data request statistics method applied to a server, a data request statistics method applied to a terminal, a server, a terminal and a data request statistics system; a communication connection is established between the server and the terminal; the method comprises: parsing a data request sent by the terminal through the communication connection to obtain a parsing result; wherein the parsing result comprises at least one header field information contained in the data request; processing the parsing result to determine a triggering mode of the data request; and when the type of the triggering mode is a specified type, counting the data request.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a data request statistics method, a server, a terminal and a system. Background Art

[0002] Page visits refer to the number of visits to a website by users within a unit of time. For example, a website consists of three pages: a.htm, b.htm, and c.htm. Assuming that a visit statistics code is set in each page, and a user visits a.htm twice, b.htm once, and c.htm once in a unit of time, then the user's visits to the website page are four times. In the related art, when any form of page refresh operation is detected, the client immediately sends a page visit statistics request to the server, and the server counts the received page visits. However, this unconditional method of counting the visits to any page increases the pressure on the server and the database for data processing. Summary of the invention

[0003] The present application provides a data request statistics method, a server, a terminal, a data request statistics system and a computer-readable storage medium.

[0004] In the data request statistics method provided in the present application, when the server detects a data request, it can parse any data request and only count the data request when the data request is of a specified type, thereby alleviating the pressure on the server and the database to count each data request.

[0005] The technical solution provided by this application is as follows:

[0006] A data request statistics method is applied to a server; a communication connection is established between the server and a terminal; the method comprises:

[0007] Parsing a data request sent by the terminal through the communication connection to obtain a parsing result; wherein the parsing result includes at least one header field information included in the data request;

[0008] Processing the analysis result to determine a triggering method for the data request;

[0009] When the type of the trigger mode is a specified type, statistics are collected on the data requests.

[0010] In some implementations, the processing of the parsing result to determine the triggering method of the data request includes:

[0011] Determine domain value weight information; wherein the domain value weight information includes the weight of each header domain information;

[0012] The triggering mode is determined based on the domain value weight information and the parsing result.

[0013] In some implementations, determining the triggering mode based on the domain value weight information and the parsing result includes:

[0014] Determine a trigger condition attribute interval corresponding to the data request;

[0015] Obtaining a second threshold value; wherein the second threshold value is a maximum value or a minimum value of a trigger condition attribute interval corresponding to a previous data request of the data request;

[0016] Determine the first threshold based on the first decision tree algorithm, the trigger condition attribute interval and the second threshold; wherein the first threshold is the maximum value or the minimum value of the trigger condition attribute interval corresponding to the data request;

[0017] The triggering mode is determined based on the domain value weight information, the parsing result and the first threshold.

[0018] In some implementations, determining the first threshold based on the first decision tree algorithm, the trigger condition attribute interval, and the second threshold includes:

[0019] In a case where the second threshold is smaller than the minimum value of the trigger condition attribute interval, determining the first threshold as the minimum value of the trigger condition attribute interval based on the first decision tree algorithm;

[0020] In a case where the second threshold is greater than the maximum value of the trigger condition attribute interval, determining the first threshold as the maximum value of the trigger condition attribute interval based on the first decision tree algorithm;

[0021] When the second threshold is within the trigger condition attribute interval, the first threshold is determined to be the second threshold based on the first decision tree algorithm.

[0022] In some implementations, parsing the data request sent by the terminal through the communication connection to obtain a parsing result includes:

[0023] Determine time interval information; wherein the time interval information is the time interval between the triggering moment of the previous data request of the data request and the triggering moment of the data request;

[0024] Determining a time interval threshold based on a type of the data request and / or a triggering time of the data request;

[0025] When the time interval information is greater than the time interval threshold, the data request is parsed to obtain the parsing result.

[0026] The present application also provides a data request statistics method, which is applied to a terminal, and a communication connection is established between the terminal and a server; the method comprises:

[0027] Request for test data;

[0028] When a data request is detected, the data request is sent to the server through the communication connection; wherein the data request is used for the server to parse the data request, obtain a parsing result, and process the parsing result, determine a triggering method of the data request, and perform statistics on the data request when the type of the triggering method is a specified type; the parsing result includes at least one header field information contained in the data request.

[0029] In some implementations, sending the data request to the server includes:

[0030] The data request is judged based on a second decision tree algorithm to obtain a judgment result; when the judgment result indicates that the data request satisfies the first condition or the second condition, the data request is sent to the server through the communication connection; wherein, the first condition indicates that the data request is a like request manually triggered by a user; the second condition indicates that the time interval information is greater than or equal to the time interval threshold, and the data request is a non-like request; the time interval information indicates the time difference between the triggering moment of the previous data request of the data request and the triggering moment of the data request.

[0031] The present application also provides a server, wherein a communication connection is established between the server and the terminal; the server comprises: a first receiving module, a first processing module, and a first statistical module; wherein:

[0032] The first receiving module is used to receive a data request sent by the terminal through the communication connection;

[0033] The first processing module is used to parse the data request to obtain a parsing result; and is also used to process the parsing result to determine a triggering mode of the data request; wherein the parsing result includes at least one header field information included in the data request;

[0034] The first statistical module is used to collect statistics on the data request when the type of the trigger mode is a specified type.

[0035] The present application also provides a server, wherein a communication connection is established between the server and the terminal; the server comprises: a first processor, a first memory and a first communication bus, wherein:

[0036] The first communication bus is used to realize the communication connection between the first processor and the first memory; the first processor is used to implement the computer program stored in the first memory to realize the data request statistics method as described in any of the above.

[0037] The present application also provides a terminal, wherein a communication connection is established between the terminal and the server; the terminal comprises: a second receiving module and a first sending module; wherein:

[0038] The second receiving module is used to detect a data request;

[0039] The first sending module is used to send the data request to the server through the communication connection when the data request is detected; wherein the data request is used for the server to parse the data request, obtain a parsing result, process the parsing result, determine a triggering mode of the data request, and count the data request when the triggering mode is of a specified type; the parsing result includes at least one header field information contained in the data request.

[0040] The present application also provides a terminal, wherein a communication connection is established between the terminal and the server; the terminal comprises: a second processor, a second memory and a second communication bus, wherein:

[0041] The second communication bus is used to realize the communication connection between the second processor and the second memory; the second processor is used to implement the computer program stored in the second memory to realize the data request statistics method as described in any of the above.

[0042] The present application also provides a data request statistics system, which includes a server as described in any of the above and a terminal as described in any of the above.

[0043] The present application also provides a computer-readable storage medium, in which a computer program is stored; the computer program can be executed by a processor to implement a data request statistics method applied to a server as described in any of the above, or a data request statistics method applied to a terminal as described in any of the above.

[0044] From the above, it can be seen that the data request statistics method applied to the server provided by the present application, when receiving any data request, first parses the data request to obtain the parsing result, and then determines the triggering mode of the data request based on the parsing result. Only when the triggering mode of the data request is of a specified type, the data request is counted. Therefore, by adopting the data request statistics method applied to the server provided by the present application, the server can parse, process and filter any data request, and only count the data request when the triggering mode of the data request meets the specified conditions. Compared with the method of counting each data request in the related art, it can reduce the pressure on the server and database data update and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a data request statistics method applied to a server provided in this application;

[0046] Figure 2 A flowchart of the first method for counting visit volume record requests applied to a server provided in this application;

[0047] Figure 3 A flowchart of a second method for counting visit volume record requests applied to a server provided in this application;

[0048] Figure 4 A flowchart of a data request statistics method applied to a terminal provided in the present application;

[0049] Figure 5 A flowchart of the first method for counting access record requests applied to a client provided in this application;

[0050] Figure 6 A flowchart of a second method for counting access record requests applied to a client provided in the present application;

[0051] Figure 7 A schematic diagram of the structure of the first server provided for this application;

[0052] Figure 8 A schematic diagram of the structure of the first terminal provided in this application;

[0053] Fig. 9 A schematic diagram of the structure of the data request statistics system provided for this application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0055] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] The present application relates to the field of information technology, and in particular to a data request statistics method, a server, a terminal, a data request statistics system, and a computer-readable storage medium.

[0057] In the related art, when the terminal detects any type of page refresh operation, it immediately initiates a page visit statistics request to the server. This method of unconditionally counting the page visits of any page increases the pressure on the server and database data storage and update. Moreover, this visit statistics method does not eliminate redundant visits and false visits, nor does it distinguish whether the current visits are the result of frequent operations by the same user, and it cannot prevent malicious access to refresh the visit operations.

[0058] Based on this, an embodiment of the present application provides a data request statistics method applied to a server, so that when the server receives any data request, it can first parse the data request, and only when it is determined that the type of triggering method of the data request is a specified type, the data request is counted, thereby reducing the pressure on data storage and updating caused by counting any data request on the server and the database, and can also achieve filtering of redundant access and false access in data requests.

[0059] In the embodiment of the present application, a communication connection is established between the server and the terminal.

[0060] In one implementation, the server may include a database.

[0061] In one implementation, the server end may be an endpoint where the server is located, and may also include a front-end device of the server. Exemplarily, there may be multiple server ends.

[0062] In one implementation, the terminal may be a device with a page display function, and illustratively, may be a device with an application installed.

[0063] In one implementation, the server can establish communication connections with multiple terminals simultaneously.

[0064] It should be noted that the data request statistics method provided in the embodiment of the present application can be implemented by a processor on the server side, and the above-mentioned processor can be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor.

[0065] Figure 1 The following is a flow chart of a data request statistics method applied to a server provided in an embodiment of the present application. Figure 1 As shown, the data request statistics method may include steps 101 to 103:

[0066] Step 101: parse a data request sent by a terminal through a communication connection to obtain a parsing result.

[0067] The parsing result includes at least one header field information included in the data request.

[0068] In one implementation, the data request may be any data request detected by the terminal.

[0069] In one implementation, the analysis result may be obtained by any of the following methods:

[0070] Parse all field data or part of the field data carried by the data request to obtain the parsing result; parse the specified field information in the data request to obtain the parsing result; parse the data request of the target type to obtain the parsing result; parse the data request within the target time period to obtain the parsing result; parse the data request sent by the specified terminal to obtain the parsing result.

[0071] In one implementation, the header field information may include any header field information of General Headers, Request Headers, Response Headers, and Entity Headers.

[0072] In one implementation, at least one header field information included in the parsing result may include one or more of a request method header field, a cache header field, a client header field, a cookie / login header field, an entity header field, a miscellaneous header field, and a transport header field. Exemplarily, in an embodiment of the present application, the above header field information may be recorded as field A to field G in sequence.

[0073] Step 102: Process the analysis result and determine the triggering method of the data request.

[0074] In one embodiment, the triggering method of the data request can be any of the following methods: page refresh triggering, manual triggering, automatic triggering through an automation tool, automatic triggering based on a script, triggering through a specified port, triggering based on a specified terminal type, and triggering through a specified platform.

[0075] In an implementation manner, the triggering method may be determined by quantifying each header field information or designated header field information in the parsing result and then determining the triggering method according to the quantification result.

[0076] Step 103: When the trigger mode type is a specified type, statistics are collected on the data requests.

[0077] In one implementation, when the type of the triggering mode is a non-specified type, statistics on the data requests may not be collected.

[0078] In an implementation manner, when it is determined that statistics are not to be collected for the data request, the server may further return to the terminal the reason for not collecting statistics for the data request, or the reason for the failure of collecting statistics.

[0079] In one embodiment, the specified type indicates that the data request attribute information satisfies any of the following: the trigger frequency is less than the specified frequency, the source of the data request is a specific type of terminal, the port that triggers the data request is a specified port, the platform that triggers the data request is a specified platform, the time period that triggers the data request is a specified time period, manual trigger type, etc.

[0080] In one implementation, statistics on data requests may be collected by sending a page visit update request for the page where the data request is located to a database, and updating the page visit count in the database.

[0081] In the related art, statistics on page visits are usually achieved through statistical management tools provided by network equipment service providers and application system providers. However, the data statistics rules in these statistical management tools are preset by network equipment service providers and application system providers, and cannot be flexibly adjusted according to the actual data statistics needs. Therefore, in order to achieve on-demand statistics of data requests, relevant staff are required to screen and summarize the statistical results, which results in high manpower costs; and, it is easy to miss statistics during manual statistics, so the accuracy of manual statistics is low.

[0082] From the above, it can be seen that the data request statistics method applied to the server provided by the embodiment of the present application, when receiving any data request, first parses the data request to obtain the parsing result, and then determines the triggering mode of the data request based on the parsing result. Only when the triggering mode of the data request is of a specified type, the data request is counted. Therefore, by adopting the data request statistics method applied to the server provided by the embodiment of the present application, the server can parse, process and filter any data request, and only count the data request when the triggering mode of the data request meets the specified conditions. Compared with the method of counting each data request in the related art, it can reduce the pressure of data update and storage on the server and the database.

[0083] In the embodiment of the present application, step 101 can be implemented by steps A1 to A3:

[0084] Step A1: Determine time interval information.

[0085] The time interval information is the time interval between the triggering time of the previous data request and the triggering time of the data request.

[0086] Step A2: Determine the time interval threshold.

[0087] In the embodiment of the present application, the time interval threshold may be determined based on the type of data request and / or the triggering time of the data request.

[0088] In the embodiment of the present application, the order of step A1 and step A2 can be adjusted successively, and the embodiment of the present application does not limit this.

[0089] Step A3: When the time interval information is greater than the time interval threshold, the data request is parsed to obtain a parsing result.

[0090] Correspondingly, when the time interval information is less than or equal to the time interval threshold, the parsing result may not be processed. Exemplarily, the reason for not processing the data request may also be returned to the terminal.

[0091] In the embodiment of the present application, if the data request is initiated by the same user and the time interval information is less than or equal to the time interval threshold, it can be determined that the data request is a redundant data request.

[0092] In an embodiment of the present application, if the time interval information is greater than the time interval threshold, it may indicate that the data request is most likely manually triggered by the user. Thus, by judging the time interval information, a portion of data requests triggered by scripts or automated testing tools can be filtered out.

[0093] In the embodiment of the present application, after step A3, the following operations may also be performed:

[0094] The time interval threshold is sent to the terminal through the communication connection.

[0095] The time interval threshold is sent to the terminal for the terminal to use for preliminary filtering of data requests.

[0096] In the embodiment of the present application, step A1 can also be implemented through steps B1 to B3:

[0097] Step B1: Obtain the address of the data request and the user identity information corresponding to the data request.

[0098] In one implementation, the address of the data request may be any of the following:

[0099] The port number that initiates the data request, the Internet Protocol (IP) address that initiates the data request, the device identifier of the terminal that initiates the data request, etc.

[0100] In one implementation, the user identity information corresponding to the data request may be carried in a specified field of the data request or stored in a cookie. For example, when the server receives a data request sent by any terminal, in order to identify the initiator of the data request, it will perform session tracking and then establish a cookie.

[0101] Step B2: Determine whether the data request is credible based on the address of the data request and the user identity information.

[0102] In one implementation, whether the data request is an unconditional trust request may be determined based on the address of the data request and user identity information corresponding to the data request.

[0103] For example, in the server, for each data request address and the user identity information corresponding to the data request, an identification bit can be set to indicate whether it is an unconditional trust request. After receiving any data request, the identification bit corresponding to the data request can be found according to the data request address and the user identity information corresponding to the data request, and the identification bit can be used to determine whether the data request is an unconditional trust request.

[0104] Exemplarily, if the data request is an unconditional trust request, step B3 may be executed; correspondingly, if the data request is not an unconditional trust request, the current execution process may be terminated and information indicating that the data request is not an unconditional trust request may be returned to the terminal.

[0105] In this way, through preliminary screening based on the address of the data request and the user identity information, it can be determined whether to perform subsequent parsing and statistical operations on the data request, thereby saving computing resources on the server side.

[0106] Step B3: When the data request is a trusted data request, determine the time interval information.

[0107] Accordingly, in the case where the data request is an untrusted data request, it is not necessary to determine the time interval information, and the current processing flow of the data request can be terminated. Exemplarily, a message indicating that the data request is not counted or fails to be counted can be returned to the terminal, and the message can also carry the reason data for the data request not being counted or failing to be counted.

[0108] Accordingly, based on the above description, in the embodiment of the present application, determining whether a data request is a redundant data request can also be implemented in the following manner:

[0109] The server counts the historical address and historical user identity information corresponding to each data request. For example, the historical address and historical user identity information can be the historical IP+Cookie. When a new data request is received, the current address and current user identity information of the new data request are determined, such as the current IP+Cookie. The server matches the current IP+Cookie with the historical IP+Cookie to determine the number of successful matches. When the number of successful matches within a unit time is greater than or equal to a specified number, the current data request is determined to be a redundant data request. Accordingly, when the number of successful matches within a unit time is less than a specified number, the current data request is determined to be a non-redundant data request. In an embodiment of the present application, step 102 can be implemented by steps C1 to C2:

[0110] Step C1, determine the domain value weight information.

[0111] The domain value weight information includes the weight of each header domain information.

[0112] In one implementation, the threshold weight information may be determined based on a first decision tree algorithm.

[0113] Exemplarily, the number of state nodes, decision points, and result nodes included in the first decision tree algorithm may be obtained by analyzing the historical data request sent by the terminal.

[0114] In one implementation, the adjustment and training of the first decision tree algorithm may be performed based on the data request samples sent from the terminal.

[0115] In one embodiment, in the initial stage of adjustment and training, the accuracy of the first decision tree algorithm when processing historical data requests sent by the terminal may be relatively low, but as the number of data requests sent by the terminal increases, the first decision tree algorithm is trained through these data requests, and the decision accuracy of the first decision tree algorithm will gradually improve.

[0116] In one implementation, the domain value weight information may be flexibly adjusted according to the need for statistics of data requests of the terminal. For example, the sum of the weights of each header domain information in the domain value weight information may be a constant, such as 1.

[0117] In the embodiment of the present application, when the data request is a non-like request, the domain value weight information is determined. For example, since the like request has a higher priority, when the data request is a like type, the like request can be directly counted, and when the data request is a non-like request, the domain value weight information is determined.

[0118] In this way, the data request statistics method applied to the server provided in the embodiment of the present application can make a quick judgment on the high priority of the like request, thereby shortening the processing time of the like request.

[0119] Step C2: Determine the triggering method based on the domain value weight information and the analysis result.

[0120] In one implementation, before determining the triggering mode, each domain value information in the parsing result may be quantified.

[0121] In an implementation manner, each header field information of the parsing result may also include only one type of information. For example, if field A is a single factor, the header field information corresponding to field A includes only one item of information.

[0122] In one implementation, each header field of the parsing result may include multiple types of information. For example, field B may include several subcategories such as if-Modified-since, if-None-Match, Pragma, and Cache-Control; field C may include several subcategories such as Accept, Accept-Encode, Accept-Language, User-Accept, and Accept-chareset. At this time, corresponding weight data may be set for different subcategories of information. For example, the sum of the weights corresponding to each subcategories in each header field is 1, and each weight data is in the interval (0,1].

[0123] For example, in the embodiment of the present application, the domain value weight information can be used to process the parsing result of the data request, so as to obtain the trigger condition parameter of the data request, as shown in formula (1):

[0124]

[0125] In formula (1), μ is the trigger condition parameter of data request; as well as are the jth header field information of the Ath field to the Gth field respectively; in formula (1), 0.4, 0.05, 0.05, 0.35, 0.05, 0.05 and 0.05 are the field value weight information corresponding to the Ath field to the Gth field respectively.

[0126] In one implementation, the triggering mode may also be determined by analyzing and judging the triggering condition parameters based on the state nodes, decision points, and result nodes in the first decision tree algorithm.

[0127] In the embodiment of the present application, step C2 can be implemented in the following manner:

[0128] A triggering method is determined based on the domain value weight information, the parsing result, and the first threshold.

[0129] Exemplarily, the first threshold may be obtained from the first decision tree algorithm. Exemplarily, the first threshold in the first decision tree algorithm may be flexibly adjusted according to the type of the terminal, the trigger port of the data request, and the like.

[0130] In the embodiment of the present application, the first threshold value can be achieved through steps D1 to D3:

[0131] Step D1: Determine the trigger condition attribute interval corresponding to the data request.

[0132] In one implementation, the trigger condition attribute interval may be an interval pre-set in the first decision tree algorithm. Exemplarily, the trigger condition attribute interval may be written into a configuration file when the server system is started and continuously adjusted during the adjustment of the first decision tree algorithm.

[0133] In one implementation, the trigger condition attribute interval may be read from a configuration file of the server. Exemplarily, the trigger condition attribute interval may be flexibly set according to the needs of the server for data request analysis.

[0134] Step D2: Obtain a second threshold.

[0135] The second threshold is the maximum value or the minimum value of the trigger condition attribute interval corresponding to the previous data request of the data request.

[0136] In one embodiment, the second threshold may be a ratio between the first data and the second data; wherein the second data represents the total number of data requests from the first data request to the previous data request when the server receives the previous data request; and the first data represents the number of data requests of non-specified types contained in the second data.

[0137] Exemplarily, the second threshold can be calculated by formula (2):

[0138]

[0139] In formula (2), P2 is the second threshold, d1 is the first data, and d2 is the second data.

[0140] In the embodiment of the present application, the order of step D1 and step D2 can be adjusted, and the embodiment of the present application does not limit this.

[0141] Step D3: Determine a first threshold based on the first decision tree algorithm, the trigger condition attribute interval and the second threshold.

[0142] The first threshold is the maximum value or the minimum value of the trigger condition attribute interval corresponding to the data request.

[0143] In one implementation, the first threshold may be determined by comparing the trigger condition attribute interval and the second threshold based on a first decision tree algorithm.

[0144] In one implementation, the first threshold may be determined by updating the second threshold based on the first decision tree algorithm according to the trigger condition attribute interval.

[0145] Exemplarily, step D3 may be implemented in the following manner:

[0146] When the second threshold is less than the minimum value of the trigger condition attribute interval, based on the first decision tree algorithm, the first threshold is determined to be the minimum value of the trigger condition attribute interval; when the second threshold is greater than the maximum value of the trigger condition attribute interval, based on the first decision tree algorithm, the first threshold is determined to be the maximum value of the trigger condition attribute interval; when the second threshold is within the trigger condition attribute interval, based on the first decision tree algorithm, the first threshold is determined to be the second threshold.

[0147] In the embodiment of the present application, when the number of data requests sent by the terminal is small, the first threshold corresponding to the current data request can be dynamically intervened and adjusted according to the trigger condition attribute interval and the second threshold corresponding to the historical data request; and when the number of data requests sent by the terminal is large, the accuracy of the first decision tree algorithm will be improved. Exemplarily, after the first decision tree algorithm is adjusted, each first threshold and each second threshold can be stabilized within the trigger condition attribute interval.

[0148] In the embodiment of the present application, when the trigger condition parameter is less than the first threshold, the trigger mode of the data request can be determined to be a specified type. Accordingly, when the trigger condition parameter is greater than or equal to the first threshold, the trigger mode of the data request can be determined to be a non-specified type.

[0149] Figure 2 The first method for counting visit volume record requests applied to the server is a flow chart of the first method provided in the embodiment of the present application. Figure 2 As shown, the data request statistics process may include the following steps:

[0150] Step 201, the process starts.

[0151] Exemplarily, the start phase of the process may include initialization of the server-side software and hardware environment, and initialization operations of various parameters in the first decision tree algorithm in the server.

[0152] Step 202: The server receives a request for recording page views and performs IP+Cookie filtering on the request.

[0153] The pageview record request in step 202 may be the data request described in the above embodiment.

[0154] Exemplarily, performing IP+Cookie screening on the request may be based on the address of the data request and the user identity information as described in the above embodiment to determine whether the access record request is a redundant request.

[0155] Step 203: Determine whether it is a redundant request.

[0156] Exemplarily, the determination of whether the access record request is a redundant request in step 203 is performed based on the screening result in step 202. If the access record request is determined to be a redundant request, step 204 is executed; otherwise, step 205 is executed.

[0157] Step 204: The process ends.

[0158] Step 205: Determine the type of triggering mode.

[0159] Exemplarily, the type of triggering method may be determined by processing the parsing result of the pageview record request through the first decision tree algorithm in the aforementioned embodiment.

[0160] Exemplarily, after the type of the triggering mode is determined, step 206 may be performed.

[0161] Step 206: Determine whether to perform statistics.

[0162] Exemplarily, when the type of the trigger mode is a specified type, if it is determined that statistics are required, step 207 is executed; otherwise, step 204 is executed.

[0163] Step 207: Statistics are stored in the warehouse.

[0164] Exemplarily, the statistics storage may be to collect statistics on the current access record requests, and update the statistical results into the database, and then execute step 208 .

[0165] Step 208: The process ends and new necessary parameters are returned.

[0166] Exemplarily, the new necessary parameter may include the time interval threshold as described in the above embodiment.

[0167] After the operations of the above processes, the access record request method applied to the server provided in the embodiment of the present application can perform multiple screening and judgment on the access record request when receiving any access record request. On the one hand, it reduces the amount of data that the server needs to update for each access record request, and on the other hand, it can also realize flexible statistics of access record requests.

[0168] Figure 3 A flow chart of a second method for counting visit volume record requests applied to a server provided in an embodiment of the present application. Figure 3 In the above, the impurity type may be the non-specified type described in the above embodiments. Figure 3 Said statistical method may include the following steps:

[0169] Step 301, the process starts.

[0170] Exemplarily, step 301 may include an initialization operation on the first decision tree algorithm.

[0171] Step 302: Obtain configuration data corresponding to IP+Cookie.

[0172] Exemplarily, IP+Cookie can be the address of the data request and the user identity information described in the aforementioned embodiment. In the embodiment of the present application, IP+Cookie can be obtained from the access record request under the condition that the server receives the access record request sent by the terminal. Accordingly, the configuration data corresponding to IP+Cookie can be the IP+Cookie configuration corresponding to the access record request when the server first receives any access record request.

[0173] Step 303: Determine whether to trust unconditionally.

[0174] Exemplarily, whether it is unconditional trust can be determined by the configuration data corresponding to IP+Cookie. If the access record request is unconditional trust, the current statistical request can be responded to through step 308. If the access record request is not unconditional trust, step 304 can be executed.

[0175] Step 304: Determine whether it is a like request.

[0176] In actual applications, since the priority of a like request is higher, if the pageview record request is a like request, step 308 can be executed; if the pageview record request is not a like request, step 305 is executed.

[0177] Step 305: Determine whether it is an impurity type.

[0178] In step 305, the impurity type may indicate that the access record request is of a non-specified type. Accordingly, the data request of the specified type in the embodiment of the present application may be a non-impurity request, and the non-impurity request may indicate a data request that the server can receive and expect to count. Exemplarily, when several specified fields of a data request carry target data, the data request may be a non-impurity request; accordingly, a data request that does not carry a target field in several fields may be classified as an impurity request.

[0179] Exemplarily, the access record requests received by the server can also be analyzed and filtered based on the operating system used by the terminal set by the server, or the version of the terminal, etc.; the access record requests sent from the specified version or operating system can also be determined as non-impurity requests, and the access record requests sent from non-specified versions or non-specified operating systems can be determined as impurity requests. That is, in the embodiment of the present application, by setting the impurity type filtering condition, flexible filtering of any data request can be achieved.

[0180] When the access volume record request is of the impurity type, step 307 may be executed; when the access volume statistical record is of the non-impurity type, step 306 may be executed.

[0181] Step 306: Determine whether the request time interval is greater than the time interval threshold.

[0182] When the request time interval is less than or equal to the time interval threshold, it may indicate that the frequency of sending the current access record request is too high, and step 307 may be executed at this time; and when the request time interval is greater than the time interval threshold, it may indicate that the frequency of sending the current access record request is low, and step 308 may be executed at this time.

[0183] Step 307: Do not respond to the current statistics request.

[0184] Exemplarily, in the case where it is determined not to respond to the current statistics request, the reason for not responding to the current statistics request may also be returned to the terminal, and then step 309 is executed.

[0185] Step 308: respond to the current statistics request.

[0186] Exemplarily, when it is determined to respond to the current statistics request, a request for updating the page view quantity may be sent to the database, and then step 309 is executed.

[0187] Step 309: The process ends.

[0188] After the processing of the current pageview record request is completed, the first decision tree algorithm of the server waits for the next pageview record request.

[0189] It should be noted that steps 301 to 309 can all be implemented by using the first decision tree algorithm.

[0190] Through the above steps, the access record request statistics method applied to the server provided in the embodiment of the present application can realize layer-by-layer filtering and screening of any access record statistics request sent to the server, thereby realizing targeted statistics of the access record requests and reducing the amount of calculation.

[0191] Based on the above embodiments, the embodiments of the present application provide a data request statistics method applied to a terminal. A communication connection is established between the terminal and the server. It should be noted that the data request statistics method applied to a terminal provided in the embodiments of the present application can be implemented by a processor of the terminal. The processor can be at least one of an ASIC, a DSP, a DSPD, a PLD, an FPGA, a CPU, a controller, a microcontroller, and a microprocessor.

[0192] Figure 4 The present invention provides a flow chart of a method for counting data requests applied to a terminal according to an embodiment of the present invention. Figure 4 As shown, the data request statistics method may include steps 401 and 402:

[0193] Step 401: Detect data request.

[0194] Step 402: When a data request is detected, the data request is sent to the server via a communication connection.

[0195] Among them, the data request is used for the server to parse the data request, obtain the parsing result, and process the parsing result, determine the triggering method of the data request, and count the data request when the triggering method type is a specified type; the parsing result includes at least one header field information contained in the data request; the specified type includes at least a manual trigger type.

[0196] In one embodiment, when a data request is detected, the terminal can immediately send the data request to the server through a communication connection. For example, when the terminal detects a data request, the data request can be judged first, and whether to send the data request to the server is determined based on the result of the judgment.

[0197] From the above, it can be seen that the data request statistics method applied to the terminal provided in the embodiment of the present application is that after the data request detected by the terminal is sent to the server, the server can parse the data request, and then determine the triggering method of the data request based on the analysis result. Only when the type of the triggering method is a specified type, the data request is counted, thereby realizing conditional statistics of data requests and reducing the amount of calculation for statistics on any data request.

[0198] In the embodiment of the present application, step 402 can be implemented through steps E1 to E2.

[0199] Step E1: judging the data request based on the second decision tree algorithm.

[0200] In the embodiment of the present application, the second decision tree algorithm can be obtained by training based on historical data requests received by the terminal and time interval thresholds.

[0201] In one implementation, the number and hierarchical structure of state nodes, decision points, and result nodes in the second decision tree algorithm may be obtained based on historical data requests received by the terminal and time interval threshold training.

[0202] Step E2: When the data request satisfies the first condition or the second condition, the data request is sent to the server through the communication connection.

[0203] Exemplarily, the data request statistics method applied to a terminal provided in an embodiment of the present application may further include the following steps:

[0204] When the time interval threshold sent by the server is not received, the time interval threshold is set to the initial time interval; when the time interval threshold sent by the server is received, the time interval threshold is set to the time interval threshold sent by the server; and the second decision tree algorithm is adjusted based on the historical data request received by the terminal and the time interval threshold.

[0205] In one implementation, a preset initial time interval is stored in the terminal.

[0206] Exemplarily, when the terminal receives a small number of data requests, the accuracy of the second decision tree algorithm in processing the data requests is insufficient. However, as the number of data requests received by the terminal increases, the accuracy of the second decision tree algorithm in analyzing and considering the data requests will improve.

[0207] Exemplarily, in the embodiment of the present application, the terminal may be a client.

[0208] Figure 5 The first method for counting access volume record requests applied to a client is a flow chart of the first method provided in the embodiment of the present application. Figure 5 As shown, the method may include steps 501 to 506:

[0209] Step 501, the process starts.

[0210] In step 501, the terminal and the second decision tree algorithm may be initialized.

[0211] Step 502: When the client receives a data request for the first time, it immediately sends a request for a visit record to the server.

[0212] In one embodiment, whether the data request received by the client is the first data request received can be determined by judging a configuration parameter in the client. For example, the configuration parameter can be of boolean type. In the embodiment of the present application, the configuration parameter can be recorded as isVisited. When the client is started for the first time, the value of isVisited is false. After the client receives the first data request and sends the visit record request to the server, the value of isVisited can be set to true.

[0213] Exemplarily, after receiving the access record request, the server parses the access record request to obtain a parsing result, and determines whether the trigger mode type of the data request corresponding to the access record request is a specified type based on the parsing result. If it is a specified type, the access record request is counted and the time interval threshold is returned to the client.

[0214] Step 503: The client receives necessary parameters and stores them locally.

[0215] Exemplarily, in step 503, the necessary parameters received by the client may include a time interval threshold returned by the server.

[0216] Exemplarily, when the client does not receive the time interval threshold returned by the server, the initial time interval may be used as the time interval threshold as described in the above embodiment.

[0217] Step 504: Receive the pageview request record again.

[0218] Step 505: Determine whether to send a pageview record request.

[0219] Exemplarily, the second decision tree algorithm can be used to determine the time and request type of the visit record request to determine whether the visit record request should be sent to the server. Exemplarily, the second decision tree algorithm here can be in a state where adjustment has been completed.

[0220] If it is necessary to send a visit record request to the server, then execute step 506; otherwise, execute step 504 to continue detecting and waiting for the next data request.

[0221] Step 506: Send the request.

[0222] Exemplarily, step 506 may be sending a request to a server.

[0223] Through the above method, when the client receives a data request for the first time, it directly sends the data request to the server. When receiving subsequent data requests, the client first performs preliminary filtering and judgment on the data requests, thereby reducing the number of data requests sent to the server and alleviating the pressure on the server to analyze and count data requests.

[0224] Based on the foregoing embodiments, the embodiments of the present application provide a process for a client to filter and determine data requests. Figure 6 The second method for counting access volume record requests applied to a client provided in the embodiment of the present application is a flow chart. Figure 6 As shown, the process may include the following steps:

[0225] Step 601, the process starts.

[0226] In step 601, the terminal and the second decision tree algorithm may be initialized.

[0227] Step 602: Determine whether it is a like request.

[0228] In the embodiment of the present application, since the like request is a request with a higher priority, it is first determined whether the data request is a like request.

[0229] Exemplarily, if it is a like request, execute step 603; if it is not a like request, execute step 607.

[0230] Step 603: Determine whether it is triggered by the user.

[0231] In one embodiment, the client can determine whether the like request is manually triggered by the user through the Event.isTrusted property of JavaScript. Among them, Event.isTrusted is a Boolean type variable. When the like request is manually triggered by the user, the value of the variable is true; conversely, when the like request is created, modified by a script, or dispatched through EventTarget.dispatchEvent(), the value of the variable is false. Exemplarily, if it is triggered by the user, execute step 604; if it is not triggered by the user, execute step 605.

[0232] Step 604: Send a request to the server.

[0233] Exemplarily, after receiving the request, the server may analyze and process the request to determine whether the triggering mode of the request is of a specified type.

[0234] Step 605: Do not send the request.

[0235] Step 606: The process ends.

[0236] Step 607: Determine whether the time interval information is less than the time interval threshold.

[0237] Exemplarily, whether the time interval information is less than the time interval threshold may be determined by determining the size relationship between the time interval information and the time interval threshold as described in the above embodiment.

[0238] Exemplarily, if the time interval information is less than the time interval threshold, step 605 is executed; otherwise, step 604 is executed.

[0239] It should be noted that all the judgments from step 601 to step 607 can be implemented by the second decision tree algorithm.

[0240] It can be seen from the above implementation methods that the access record request judgment and filtering method provided in the embodiment of the present application should be used for the client. When the client detects any access record request, the access record request can be preliminarily filtered, and the appropriate time to report the access volume can be automatically calculated, thereby reducing the pressure on the server and the database.

[0241] As can be seen from the above, the data request statistics method applied to the terminal provided by the embodiment of the present application can filter and screen the detected data requests based on the second decision tree algorithm, and then determine whether to send the data request to the server, thereby reducing the pressure on the server for data request statistics. In addition, the adjustment conditions can be flexibly set during the adjustment process of the second decision tree algorithm, so that manual intervention can be infiltrated during the filtering process of the data request, so that the terminal can filter the data request more flexibly, and improve the pertinence and flexibility of the filtering and screening of the data request.

[0242] Based on the above embodiments, the present application also provides a first server 7, Figure 7 This is a schematic diagram of the structure of a server 7 provided in an embodiment of the present application. Figure 7 As shown, the server 7 may include: a first receiving module 701, a first processing module 702, and a first statistical module 703; wherein:

[0243] The first receiving module 701 is used to receive a data request sent by a terminal through a communication connection;

[0244] The first processing module 702 is used to parse the data request to obtain a parsing result; and is also used to process the parsing result to determine a triggering method for the data request; wherein the parsing result includes at least one header field information included in the data request;

[0245] The first statistics module 703 is used to collect statistics on data requests when the type of the trigger mode is a specified type.

[0246] In some implementations, the first processing module 702 is configured to determine field value weight information; wherein the field value weight information includes a weight of each header field information.

[0247] The first processing module 702 is further used to determine a triggering method based on the domain value weight information and the analysis result.

[0248] In some embodiments, the first processing module 702 is used to determine the trigger condition attribute interval corresponding to the data request; obtain the second threshold; determine the first threshold based on the first decision tree algorithm, the trigger condition attribute interval and the second threshold; wherein the second threshold is the maximum value or minimum value of the trigger condition attribute interval corresponding to the previous data request of the data request; the first threshold is the maximum value or minimum value of the trigger condition attribute interval corresponding to the data request.

[0249] The first processing module 702 is used to determine a triggering method based on the domain value weight information, the analysis result and the first threshold.

[0250] In some embodiments, the first processing module 702 is used to determine that the first threshold is the minimum value of the trigger condition attribute interval based on the first decision tree algorithm when the second threshold is less than the minimum value of the trigger condition attribute interval; determine that the first threshold is the maximum value of the trigger condition attribute interval based on the first decision tree algorithm when the second threshold is greater than the maximum value of the trigger condition attribute interval; and determine that the first threshold is the second threshold based on the first decision tree algorithm when the second threshold is within the trigger condition attribute interval.

[0251] In some implementations, the first processing module 702 is used to determine time interval information; wherein the time interval information is the time interval between the triggering moment of the previous data request of the data request and the triggering moment of the data request;

[0252] The first processing module 702 is further used to determine a time interval threshold based on the type of data request and / or the triggering time of the data request; when the time interval information is greater than the time interval threshold, parse the data request to obtain a parsing result.

[0253] It should be noted that the first receiving module 701, the first processing module 702 and the first statistical module 703 can be implemented using a processor in an electronic device, and the above processor can be at least one of ASIC, DSP, DSPD, PLD, FPGA, CPU, controller, microcontroller, and microprocessor.

[0254] From the above, it can be seen that the server 7 provided in the embodiment of the present application, when receiving any data request, first parses the data request to obtain the parsing result, and then determines the triggering mode of the data request based on the parsing result. Only when the triggering mode of the data request is of a specified type, the data request is counted. Therefore, by using the server 7 provided in the embodiment of the present application, any data request can be parsed, processed and filtered, and the data request is counted only when the triggering mode of the data request meets the specified conditions. Compared with the method of counting each data request in the related art, the pressure of data update and storage on the server and the database can be reduced.

[0255] Based on the above embodiments, the present application provides a second server. The server may include: a first processor, a first memory, and a first communication bus, wherein:

[0256] A first communication bus, used to realize a communication connection between the first processor and the first memory;

[0257] The first processor is used to implement the computer program stored in the first memory to implement the data request statistics method applied to the server as described in any of the previous embodiments.

[0258] The first processor may be at least one of an application specific integrated circuit ASIC, a DSP, a DSPD, a PLD, an FPGA, a CPU, a controller, a microcontroller, and a microprocessor. It is understandable that the electronic device used to implement the functions of the processor may also be other, and the embodiment of the present invention does not specifically limit this.

[0259] The first memory may be a volatile memory, such as a RAM; or a non-volatile memory, such as a ROM, a flash memory, a hard disk (HDD) or a solid-state drive (SSD); or a combination of the above types of memories, and provide instructions and data to the processor.

[0260] Based on the above embodiments, the present application embodiment provides a first terminal 8, Figure 8 This is a schematic diagram of the structure of the first terminal 8 provided in the embodiment of the present application. Figure 8 As shown, the terminal 8 may include: a second receiving module 801 and a first sending module 802. Among them:

[0261] The second receiving module 801 is used to detect a data request.

[0262] The first sending module 802 is used to send the data request to the server through the communication connection when a data request is detected; wherein the data request is used for the server to parse the data request, obtain the parsing result, and process the parsing result, determine the triggering method of the data request, and count the data request when the triggering method is of a specified type; the parsing result includes at least one header field information contained in the data request.

[0263] In one implementation, the terminal 8 further includes a second processing module; wherein the second processing module is configured to judge the data request based on a second decision tree algorithm.

[0264] The first sending module 802 is used to send the data request to the server through a communication connection when the data request meets the first condition or the second condition; wherein, the first condition indicates that the data request is a like request manually triggered by the user; the second condition indicates that the time interval information is greater than or equal to the time interval threshold, and the data request is a non-like request; the time interval information indicates the time difference between the triggering moment of the previous data request and the triggering moment of the data request.

[0265] It should be noted that the second receiving module 801 and the first sending module 802 can be implemented using a processor in an electronic device, and the above processor can be at least one of ASIC, DSP, DSPD, PLD, FPGA, CPU, controller, microcontroller, and microprocessor.

[0266] From the above, it can be seen that the terminal 8 provided in the embodiment of the present application can filter and screen the detected data requests based on the trained second decision tree algorithm, and then send the screened data requests to the server, thereby reducing the pressure on the server to count the data requests. In addition, the training conditions can be flexibly set during the adjustment process of the second decision tree algorithm, so that manual intervention can be infiltrated in the process of filtering data requests, so that the terminal can filter the data requests more flexibly, and improve the pertinence and flexibility of the filtering and screening of data requests.

[0267] Based on the above embodiment, the embodiment of the present application further provides a second terminal. The terminal includes: a second processor, a second memory and a second communication bus, wherein:

[0268] A second communication bus, used to realize the communication connection between the second processor and the second memory;

[0269] The second processor is used to implement the computer program stored in the second memory to implement the data request statistics method applied to the terminal as described in any of the previous embodiments.

[0270] The second processor may be at least one of an application specific integrated circuit ASIC, a DSP, a DSPD, a PLD, an FPGA, a CPU, a controller, a microcontroller, and a microprocessor. It is understandable that the electronic device used to implement the functions of the processor may also be other, and the embodiment of the present invention does not specifically limit this.

[0271] The second memory may be a volatile memory, such as RAM; or a non-volatile memory, such as ROM, flash memory, HDD or solid-state drive SSD; or a combination of the above types of memory, and provide instructions and data to the processor.

[0272] Based on the above-mentioned embodiments, the present application also provides a data request statistics system 9. Fig. 9 This is a schematic diagram of the structure of the data request statistics system 9 provided in the embodiment of the present application. Fig. 9 As shown, the data request statistics system 9 may include a server 7 and a terminal 8 as described in the aforementioned embodiment.

[0273] Exemplarily, when the terminal 8 detects a data request, after making a preliminary judgment on the data request, the data request that meets the first condition or the second condition is sent to the server 7. The server 7 parses the data request, obtains the parsing result, processes the parsing result, determines the triggering method of the data request, and counts the data requests when the type of the triggering method is a specified type.

[0274] From the above, it can be seen that the data request statistics system 9 provided in the embodiment of the present application can realize preliminary statistics and filtering of data requests at the terminal 8, and can realize re-analysis and screening of data requests at the server 7, and finally count the data requests with a triggering type of a specified type, thereby reducing the amount of data counted by the server 7 and the terminal 8 for the data requests.

[0275] Based on the foregoing embodiments, the embodiments of the present application also provide a computer-readable storage medium, which stores a computer program; the computer program can be executed by a processor to implement a data request statistics method applied to a server as in any of the previous embodiments, or a data request statistics method applied to a terminal as in any of the previous embodiments.

[0276] The description of each embodiment above tends to emphasize the differences between the various embodiments, and the same or similar aspects can be referenced to each other. For the sake of brevity, this article will not go into details. The methods disclosed in the various method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments. The features disclosed in the various product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments. The features disclosed in the various method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0277] It should be noted that the above-mentioned computer-readable storage medium can be ROM, programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), magnetic random access memory (Ferromagnetic Random Access Memory, FRAM), flash memory (Flash Memory), magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM) and other memories; it can also be various electronic devices including one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0278] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0279] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0280] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus necessary general hardware nodes, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can 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 a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0281] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0282] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0283] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0284] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data request statistics method, the method is applied in a server; A communication connection is established between the server and the terminal; it is characterized in that: The method comprises: Parsing a data request sent by the terminal through the communication connection to obtain a parsing result; wherein the parsing result includes at least one header field information included in the data request; Determine domain value weight information; wherein the domain value weight information includes the weight of each header domain information; Determining a triggering method for the data request based on the domain value weight information and the parsing result; When the type of the trigger mode is a specified type, statistics are collected on the data requests.

2. The method according to claim 1, characterized in that: The determining, based on the domain value weight information and the parsing result, a triggering mode of the data request includes: Determine a trigger condition attribute interval corresponding to the data request; Obtaining a second threshold value; wherein the second threshold value is a maximum value or a minimum value of a trigger condition attribute interval corresponding to a previous data request of the data request; Determine a first threshold based on a first decision tree algorithm, the trigger condition attribute interval and the second threshold; wherein the first threshold is a maximum value or a minimum value of the trigger condition attribute interval corresponding to the data request; The triggering mode is determined based on the domain value weight information, the parsing result and the first threshold.

3. The method according to claim 2, characterized in that The determining the first threshold based on the first decision tree algorithm, the trigger condition attribute interval and the second threshold includes: In a case where the second threshold is smaller than the minimum value of the trigger condition attribute interval, determining the first threshold as the minimum value of the trigger condition attribute interval based on the first decision tree algorithm; In a case where the second threshold is greater than the maximum value of the trigger condition attribute interval, determining the first threshold as the maximum value of the trigger condition attribute interval based on the first decision tree algorithm; When the second threshold is within the trigger condition attribute interval, the first threshold is determined to be the second threshold based on the first decision tree algorithm.

4. The method according to claim 1, characterized in that: The parsing the data request sent by the terminal through the communication connection to obtain a parsing result includes: Determine time interval information; wherein the time interval information is the time interval between the triggering moment of the previous data request of the data request and the triggering moment of the data request; Determining a time interval threshold based on a type of the data request and / or a triggering time of the data request; When the time interval information is greater than the time interval threshold, the data request is parsed to obtain the parsing result.

5. A data request statistics method, the method is applied to a terminal, and a communication connection is established between the terminal and a server; characterized in that: The method comprises: Detection data request; When a data request is detected, the data request is sent to the server through the communication connection; wherein the data request is used for the server to parse the data request, obtain a parsing result, determine and based on the domain value weight information and the parsing result, determine a triggering mode of the data request, and when the type of the triggering mode is a specified type, perform statistics on the data request; the parsing result includes at least one header field information included in the data request; the domain value weight information includes a weight of each header field information.

6. The method according to claim 5, characterized in that The sending the data request to the server includes: Based on the second decision tree algorithm, the data request is judged to obtain a judgment result; When the judgment result indicates that the data request satisfies the first condition or the second condition, the data request is sent to the server through the communication connection; wherein, the first condition indicates that the data request is a like request manually triggered by a user; the second condition indicates that the time interval information is greater than or equal to the time interval threshold, and the data request is a non-like request; the time interval information indicates the time difference between the triggering moment of the previous data request of the data request and the triggering moment of the data request.

7. A server, characterized in that: A communication connection is established between the server and the terminal; the server comprises: a first receiving module, a first processing module, and a first statistical module; wherein: The first receiving module is used to receive a data request sent by the terminal through the communication connection; The first processing module is used to parse the data request to obtain a parsing result; and is also used to determine and determine a triggering mode of the data request based on domain value weight information and the parsing result; wherein the parsing result includes at least one header field information included in the data request; the domain value weight information includes a weight of each header field information; The first statistical module is used to collect statistics on the data request when the type of the trigger mode is a specified type.

8. A server, characterized in that: A communication connection is established between the server and the terminal; the server includes: a first processor, a first memory and a first communication bus, wherein: The first communication bus is used to implement a communication connection between the first processor and the first memory; The first processor is used to implement the computer program stored in the first memory to implement the data request statistics method as described in any one of claims 1 to 4.

9. A terminal, characterized in that: A communication connection is established between the terminal and the server; the terminal comprises: a second receiving module and a first sending module; wherein: The second receiving module is used to detect a data request; The first sending module is used to send the data request to the server through the communication connection when a data request is detected; wherein the data request is used for the server to parse the data request, obtain a parsing result, determine and determine a triggering mode of the data request based on domain value weight information and the parsing result, and perform statistics on the data request when the triggering mode is of a specified type; the parsing result includes at least one header field information included in the data request; the domain value weight information includes a weight of each header field information.

10. A terminal, characterized in that: A communication connection is established between the terminal and the server; the terminal includes: a second processor, a second memory and a second communication bus, wherein: The second communication bus is used to realize the communication connection between the second processor and the second memory; The second processor is used to implement the computer program stored in the second memory to implement the data request statistics method as described in any one of claims 5 to 6.

11. A data request statistics system, characterized in that: The system comprises the server as claimed in claim 7 or 8, and at least one terminal as claimed in claim 9 or 10.

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