Search request control method and apparatus, device, medium, and product

Abstract

The application discloses a search request control method and device, equipment, medium and product, and the method comprises the following steps: counting access flow data of a plurality of independent stations, checking one or more flow anomaly independent stations in which the access flow data meets a preset flow anomaly rule; listening to the search request response time length of the flow anomaly independent station, checking one or more request anomaly independent stations in which the access flow data meets a preset request anomaly rule; responding to the search request of the request anomaly independent station, calling a preset search flow limiting rule to perform a hash modulo operation on the request characteristic data of the search request, querying the target data slice corresponding to the search service operation result; using the target data slice to perform commodity search processing on the search request, and obtaining the corresponding search result and pushing the search result to the user end corresponding to the search request. The application can position a single independent station to control the search service, so that the stability of the search service of the platform is improved.

Description

Technical Field

[0001] This application relates to the field of online e-commerce, and in particular to a search request control method, as well as the corresponding apparatus, equipment, non-volatile storage medium, and computer program products. Background Technology

[0002] Existing online e-commerce independent website platforms provide product search services to the independent e-commerce websites on their platforms. This allows buyers to search for products on these independent websites. Moreover, the product search service provided by the online e-commerce platform to most of the independent e-commerce websites on the platform is a shared product search service. In other words, all independent e-commerce websites on the platform share the same server cluster to maintain the product search service, which is convenient for the platform to maintain.

[0003] However, when any e-commerce independent website using the shared product search service experiences traffic anomalies, the platform will generally implement traffic throttling on the product search service to control the response speed of search requests and prevent the product search service from crashing. This will also affect the search request response speed of other e-commerce independent websites that are using the product search service and have not experienced traffic anomalies, thus affecting the sales of products by sellers on other e-commerce independent websites and the shopping experience of buyers on those e-commerce independent websites.

[0004] In view of the problems that existing shared product search services serving independent e-commerce websites face in the face of abnormal traffic, the applicant has made corresponding explorations in order to solve this problem. Summary of the Invention

[0005] The purpose of this application is to provide a search request control method to meet user needs, and also relates to the corresponding apparatus, device, non-volatile storage medium and computer program product.

[0006] To achieve the objectives of this application, the following technical solution is adopted:

[0007] A search request control method proposed for the purposes of this application includes the following steps:

[0008] Analyze the access traffic data of multiple independent websites and identify one or more independent websites with abnormal traffic data that meet the preset abnormal traffic rules.

[0009] Monitor the search request response time of each of the aforementioned traffic-abnormal independent websites, and verify one or more of these traffic-abnormal independent websites whose access traffic data meets the preset request abnormality rules.

[0010] In response to the search request from the abnormal independent site, the system calls the preset search rate limiting rules to perform a hash modulo operation on the request feature data of the search request, and retrieves the target data fragment corresponding to the search service operation result.

[0011] The search request is processed using target data shards to obtain the corresponding search results, which are then pushed to the user terminal corresponding to the search request.

[0012] In a further embodiment, the step of statistically analyzing the access traffic data of multiple independent websites and identifying one or more independent websites with abnormal traffic data that meet preset traffic anomaly rules includes the following steps:

[0013] Statistics on the access traffic data of the current independent site within a preset window period;

[0014] Determine whether the latest access traffic data exceeds the preset access traffic threshold;

[0015] When the access traffic threshold is exceeded, the current independent website will be identified as an independent website with abnormal traffic.

[0016] In a further embodiment, the step of monitoring the search request response time of each of the aforementioned traffic-abnormal independent websites and verifying one or more of these traffic-abnormal independent websites whose access traffic data meets preset request abnormality rules includes the following steps:

[0017] Determine the latest search request response from the currently abnormal traffic site, and calculate the search request response time for the search service to complete the response to the search request;

[0018] Determine whether the response time of the search request exceeds a preset abnormal duration threshold. If it does, then the search request is identified as a timed-out search request.

[0019] Determine if the number of timed-out search requests from the current traffic-abnormal independent site exceeds a preset threshold. If it does, then identify the current traffic-abnormal independent site as a request-abnormal independent site.

[0020] In a further embodiment, the step of responding to the search request from the abnormal independent site, invoking a preset search rate limiting rule to perform a hash modulo operation on the request feature data of the search request, and querying the target data shard corresponding to the search service operation result includes the following steps:

[0021] In response to the current request exception, retrieve the latest search request from the independent website and obtain the request characteristic data of that search request;

[0022] Calculate the unique feature code and the feature and value of the request timestamp contained in the request feature data, and determine the number of data shards in the search service;

[0023] Perform a hash operation on the feature and value to obtain the corresponding hash value, and calculate the remainder between the hash value and the number of shards as the shard identifier, where the hash value is the remainder;

[0024] Retrieve the target data fragments with the fragment identifier in the search service.

[0025] In a further embodiment, the step of using target data shards to process the search request for product search and obtaining the corresponding search results to be pushed to the user terminal corresponding to the search request includes the following steps:

[0026] Get the search text contained in the current search request, and extract one or more keywords contained in the search text;

[0027] Query out multiple product values ​​that have one or more of the keywords in the target data segment, and obtain the product objects of each of these product values;

[0028] Based on the product objects stored in the product list, determine the number of keywords for each product object in the product list;

[0029] Obtain the sales quantity of each product object in the product list, calculate the sum of the number of keywords and the sales quantity of each product object, and sort them in descending order based on the sum of each product object;

[0030] The list of products sorted in descending order will be used as the search results, and these results will be pushed to the user client that made the current search request.

[0031] In a further embodiment, the method includes the following post-processing:

[0032] In response to the exception unblocking command applied to the currently requesting abnormal independent site, the access traffic data of the currently requesting abnormal independent site is counted, and it is determined whether the access traffic data is less than the preset traffic unblocking threshold.

[0033] When the access traffic data is less than the preset traffic unblocking threshold, the average response time of search requests from currently abnormal independent websites is calculated.

[0034] If the average response time of the search request is less than the preset unblocking time, the independent site with the current request exception will be allowed to open traffic, so that multiple data shards can be used to process the search requests of the independent site with the current request exception for product search.

[0035] A search request control device proposed for the purposes of this application includes:

[0036] The access traffic verification module is used to count the access traffic data of multiple independent websites and verify one or more independent websites whose access traffic data meets the preset traffic anomaly rules.

[0037] The response time determination module is used to monitor the response time of search requests of each of the aforementioned traffic abnormal independent sites, and to verify one or more of these traffic abnormal independent sites whose access traffic data meets the preset request abnormality rules.

[0038] The target shard determination module is used to respond to the search request of the abnormal independent site, call the preset search rate limiting rules to perform a hash modulo operation on the request feature data of the search request, and query the target data shard corresponding to the search service operation result;

[0039] The product request response module is used to process the search request for products using target data shards, and push the corresponding search results to the user terminal corresponding to the search request.

[0040] In a further embodiment, the access traffic verification module includes:

[0041] The access traffic statistics submodule is used to collect access traffic data for the current standalone site within a preset window period;

[0042] The traffic threshold judgment submodule is used to determine whether the latest statistical access traffic data exceeds the preset access traffic threshold.

[0043] The Traffic Anomaly Determination Submodule is used to determine the current independent website as a traffic anomaly independent website when the access traffic threshold is exceeded.

[0044] In a further embodiment, the response duration determination module includes:

[0045] The response time calculation submodule is used to determine the latest response to the search request of the independent site with abnormal traffic, and to calculate the search request response time for the search service to complete the response to the search request;

[0046] The duration threshold judgment submodule is used to determine whether the response time of the search request exceeds a preset abnormal duration threshold. If it does, the search request is determined to be a timed-out search request.

[0047] The request anomaly determination submodule is used to determine whether the number of timed-out search requests of the current traffic anomaly independent site exceeds a preset threshold. If it does, the current traffic anomaly independent site is determined to be a request anomaly independent site.

[0048] In a further embodiment, the target fragment determination module includes:

[0049] The feature data acquisition submodule is used to respond to the latest search request from the current request exception independent website and obtain the request feature data of the search request.

[0050] The feature and value calculation submodule is used to calculate the feature and value of the unique feature code and the request timestamp contained in the request feature data, and to determine the number of data shards in the search service;

[0051] The distribution identifier determination submodule is used to perform hash operations on features and values ​​to obtain the corresponding hash values, and calculate the remainder between the hash value and the number of shards as the shard identifier, where the hash value is used as the remainder;

[0052] The target shard query submodule is used to query the target data shards in the search service that have the shard identifier.

[0053] In a further embodiment, the product request response module includes:

[0054] The keyword extraction submodule is used to obtain the search text contained in the current search request and extract one or more keywords contained in the search text;

[0055] The product object retrieval submodule is used to query multiple product values ​​that have one or more of the keywords in the target data segment, and retrieve the product objects of each of these product values.

[0056] The keyword quantity determination submodule is used to determine the keyword quantity of each product object in the product list based on the product objects stored in the product list.

[0057] The product sorting submodule is used to obtain the sales quantity of each product object in the product list, calculate the sum of the number of keywords and the sales quantity of each product object, and sort the product objects in descending order based on the sum of each product object.

[0058] The search results push submodule is used to take the product list that has been sorted in descending order as the search results and push the search results to the user terminal of the current search request.

[0059] To address the aforementioned technical problems, this application also provides a computer device, including a memory and a processor. The memory stores computer-readable instructions, which, when executed by the processor, cause the processor to perform the steps of the search request control method described above.

[0060] To address the aforementioned technical problems, this application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the search request control method described above.

[0061] To address the aforementioned technical problems, this application also provides a computer program product, including a computer program and computer instructions. When the computer program and computer instructions are executed by a processor, the processor performs the steps of the search request control method described above.

[0062] Compared with existing technologies, the advantages of this application are as follows:

[0063] This application can control the search service of any e-commerce independent website on a network e-commerce independent website platform, preventing the platform's shared search service from crashing due to handling high-concurrency search requests from e-commerce independent websites with abnormal traffic, thus affecting the product search efficiency of other e-commerce independent websites with normal traffic on the platform. It detects independent websites with abnormal access traffic on the network e-commerce independent website platform and determines the time it takes for the search service to process search requests from these independent websites. This helps determine if the search service is under computational pressure when processing search requests from independent websites with abnormal traffic. Furthermore, it evenly distributes the search requests from independent websites affecting the search service to the data shards of the search service, using a single data shard corresponding to each search request for product search processing. This reduces the computational pressure on the search service when handling high-concurrency search requests from independent websites with abnormal traffic, ensuring the stability of the search service and preventing it from crashing. This prevents other independent websites using the shared search service from being unable to provide product search functionality to their buyers, thus affecting the shopping experience of buyers on these independent websites. Attached Figure Description

[0064] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0065] Figure 1 A typical network deployment architecture diagram related to the implementation of the technical solution of this application;

[0066] Figure 2 This is a flowchart illustrating a typical embodiment of the search request control method of this application;

[0067] Figure 3 This is a flowchart illustrating the specific implementation method for verifying whether the access traffic data of an independent website meets the preset traffic anomaly rules in this application.

[0068] Figure 4This is a flowchart illustrating the specific implementation method of this application for verifying whether the response time of a search request from an independent website with abnormal traffic meets the preset request abnormality rules.

[0069] Figure 5 This is a flowchart illustrating the specific implementation method for determining the target data fragments corresponding to search requests from abnormal independent websites in this application;

[0070] Figure 6 This is a flowchart illustrating the specific implementation method of using target data fragmentation to process abnormal search requests in an independent website for product search in this application.

[0071] Figure 7 This is a flowchart illustrating the specific implementation method for handling abnormal unblocking of independent websites with abnormal requests in this application;

[0072] Figure 8 This is a schematic block diagram of a typical embodiment of the search request control device of this application;

[0073] Figure 9 This is a basic structural block diagram of a computer device according to an embodiment of this application. Detailed Implementation

[0074] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0075] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0076] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0077] Those skilled in the art will understand that the terms "client," "terminal," and "terminal device" as used herein include both devices that receive wireless signals, devices that only possess wireless signal receiver capabilities without transmission capabilities, and devices with receiving and transmitting hardware, devices that have receiving and transmitting hardware capable of bidirectional communication over a bidirectional communication link. Such devices may include: cellular or other communication devices such as personal computers or tablets, having single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service) that can combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant) that may include a radio frequency receiver, pager, internet / intranet access, web browser, notepad, calendar, and / or GPS (Global Positioning System) receiver; and traditional laptops and / or handheld computers or other devices that have and / or include radio frequency receivers. As used herein, "client," "terminal," and "terminal device" can be portable, transportable, installed in a means of transportation (air, sea, and / or land), or suitable and / or configured to operate locally and / or in a distributed manner, operating in any other location on Earth and / or in space. "Client," "terminal," and "terminal device" as used herein can also be a communication terminal, an internet access terminal, or a music / video playback terminal, such as a PDA, a MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or a smart TV, set-top box, etc.

[0078] The hardware referred to by the names "server," "client," and "work node" in this application is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components revealed by the von Neumann architecture, such as a central processing unit (including an arithmetic logic unit and a control unit), memory, input devices, and output devices. The computer program is stored in its memory, and the central processing unit loads the program stored in the secondary storage into the main memory to run it, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0079] It should be noted that the concept of "server" used in this application can also be extended to the case of server clusters. Based on the network deployment principles understood by those skilled in the art, the servers should be logically divided. Physically, these servers can be independent of each other but accessible through interfaces, or they can be integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method in this application.

[0080] Please see Figure 1 The hardware infrastructure required for implementing the technical solutions of this application can be deployed according to the architecture shown in the figure. The server 80 mentioned in this application is deployed in the cloud and acts as an online server. It can further connect to relevant data servers and other servers providing related support, thereby forming a logically related service cluster to provide services to relevant terminal devices such as the smartphone 81 and personal computer 82 shown in the figure, or third-party servers (not shown). Both the smartphone and personal computer can access the Internet through known network access methods and establish a data communication link with the cloud server 80 to run terminal applications related to the services provided by the server.

[0081] For servers, the application is usually built as a service process, with corresponding program interfaces exposed for remote calls by applications running on various terminal devices. The relevant technical solutions in this application that are suitable for running on servers can be implemented in servers in this way.

[0082] The application mentioned refers to an application running on a server or terminal device. This application implements the relevant technical solutions of this application in a programmed manner. Its program code can be stored in a non-volatile storage medium that can be recognized by a computer in the form of computer-executable instructions, and is loaded into memory by the central processing unit for execution. The relevant device of this application is constructed by the operation of the application on the computer.

[0083] For servers, the application is usually built as a service process, with corresponding program interfaces exposed for remote calls by applications running on various terminal devices. The relevant technical solutions in this application that are suitable for running on servers can be implemented in servers in this way.

[0084] Those skilled in the art will understand that although the various methods in this application are described based on the same concept and thus present commonality among them, they can be performed independently unless otherwise specified. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept; therefore, concepts expressed in the same way, as well as concepts that are appropriately changed for convenience but are expressed differently, should be understood equivalently.

[0085] Please see Figure 2 A search request control method according to this application, in a typical embodiment, includes the following steps:

[0086] Step S11: Analyze the access traffic data of multiple independent websites and identify one or more independent websites with abnormal traffic that meet the preset traffic anomaly rules.

[0087] The aforementioned independent website refers to an independent e-commerce website within an online e-commerce platform. Unlike traditional online e-commerce shops, an independent e-commerce website possesses its own independent online e-commerce services. It can be viewed as a traditional online e-commerce platform, where the buyers of an independent website are its own user base, unlike the buyers of individual online shops on a traditional platform who are the platform's users. However, the online e-commerce platform provides corresponding e-commerce functions to the independent websites within it, such as a search function. Each independent website can use this search function to provide search services for products within its own user base.

[0088] The access traffic data refers to the number of user visits to the independent website. The server usually counts the access traffic data of each independent website in minutes, with a typical statistical duration of 5 to 10 minutes. Of course, those skilled in the art can also set the statistical duration to 30 to 60 seconds in seconds, or 1 to 2 hours in hours. Correspondingly, the access traffic data can also refer to the number of requests to access the search service, specifically the access traffic data for the search service that requires subsequent search request control.

[0089] The server monitors the access traffic data of all independent websites on the platform in real time to verify whether the access traffic data of each independent website meets the preset traffic anomaly rules. Independent websites that meet the traffic anomaly rules are characterized by their access traffic exceeding the limit, that is, the number of user accesses to these independent websites has exceeded their traffic load capacity, or the number of search requests from buyer users to the search service has exceeded the traffic load capacity of the search service. Independent websites that meet the search traffic anomaly rules will be identified as traffic anomaly independent websites, so as to determine whether the excessive access traffic data of these traffic anomaly independent websites has affected the request response speed of the search service.

[0090] The aforementioned traffic anomaly rule is used to verify whether the access traffic data of an independent website has exceeded a preset access traffic threshold. After the server counts the latest access traffic data of any independent website, it will determine whether the access traffic exceeds the preset access traffic threshold. When the access traffic threshold is exceeded, the independent website is identified as the traffic anomaly independent website.

[0091] Step S12: Monitor the search request response time of each of the aforementioned traffic-abnormal independent websites, and verify one or more of these traffic-abnormal independent websites whose access traffic data meets the preset request abnormality rules:

[0092] After the server identifies the independent websites with abnormal traffic on the platform, it will monitor the search request response time of these independent websites with abnormal traffic. The search request response time refers to the time consumed by the search service to determine the corresponding search results when a buyer's client on an independent website searches for products on that independent website.

[0093] The search service mentioned refers to the product search service provided by the e-commerce independent website platform to each independent website under the platform. This search service is generally a shared search service, that is, search requests from each of the independent websites are responded to by the same search service, which determines and pushes the corresponding search results. Buyers on the independent websites can search for products available on the independent websites through the search service. Specifically, after a buyer enters the corresponding search text through the search function of their respective independent website, a search request containing the search text is generated and pushed to the server. The server uses the search service to obtain one or more product objects corresponding to the search text as search results and pushes them to the buyer's client. After receiving these product objects, the buyer's client outputs them to the graphical user interface for display, allowing the buyer to browse these product objects and make online purchases.

[0094] The aforementioned request anomaly rule is used to verify that the search request response time consumed by the search service in processing search requests from independent websites with abnormal traffic has timed out. Specifically, the server listens for the search request response time consumed by the search service in responding to the latest search request from the independent website with abnormal traffic and outputting the corresponding search result. This process continues, continuously monitoring the search request response time of each search request from the independent website with abnormal traffic, to calculate the average response time of these search requests, and to determine whether the average response time exceeds a preset average response time threshold. If it does, the independent website with abnormal traffic will be identified as the request anomaly independent website. The average response time threshold is generally set within a time range of 1 to 2 seconds, or within a time range of 10 to 50 milliseconds. Of course, those skilled in the art can flexibly design the average response time threshold, which will not be elaborated upon here.

[0095] In one embodiment, the server monitors the response time of the search service in responding to the latest search request from the traffic-abnormal independent website and outputting the corresponding search results. It determines whether the response time exceeds a preset abnormal duration threshold. If it does, the search request is identified as a timed-out request. The server continuously checks whether the response times of subsequent search requests from the traffic-abnormal independent website exceed the preset abnormal duration threshold to identify timed-out search requests. It also counts the number of timed-out search requests from the traffic-abnormal independent website. When the number of timed-out search requests from the traffic-abnormal independent website exceeds the preset threshold, the current traffic-abnormal independent website is identified as the request-abnormal independent website. The abnormal duration threshold is generally set within a time range of 1 to 2 seconds, or within a time range of 10 to 50 milliseconds. Of course, those skilled in the art can flexibly design the abnormal duration threshold, which will not be elaborated upon here.

[0096] Step S13: In response to the search request from the abnormal independent site, the preset search rate limiting rules are invoked to perform a hash modulo operation on the request feature data of the search request, and the target data fragment corresponding to the search service operation result is retrieved.

[0097] When processing search requests from the abnormal independent websites, the server will perform search degradation processing on the abnormal independent websites to prevent the high concurrency of search requests from the abnormal independent websites from affecting the system stability of the search service. The server will evenly distribute the search requests from the abnormal independent websites to the data shards in the search service, and use only one data shard to process a single search request for a product search.

[0098] The search service comprises multiple data shards, each storing all product search indexes. Specifically, the search service is typically built upon the Elasticsearch search engine, a full-text search engine with distributed multi-user capabilities. The search service built upon this Elasticsearch engine will have multiple data shards storing all product search indexes, each index consisting of keywords and product values. Because the search service distributes all product objects from each independent website across its data shards to build distributed capabilities, although each data shard contains all the product search indexes, The product objects stored in each data shard do not represent all product objects available on their corresponding independent websites. Therefore, for regular independent websites that are not experiencing traffic or request anomalies, the search service will use multiple data shards to process their search requests. This ensures that the returned search results contain all product objects from the regular independent website corresponding to the search request. However, for independent websites experiencing request anomalies, the search service needs to be downgraded. Only one data shard will be used to process the search requests from these websites. This prevents the search service from crashing due to the use of multiple data shards to process multiple high-volume search requests, which would prevent all independent websites on the platform from using the search service to provide product search functionality to their users.

[0099] When a server responds to a search request from an independent website that has experienced a search request anomaly, it performs a hash modulo operation on the request feature data contained in the search request to determine the target data shard for that search request within the search service. Specifically, the request feature data includes a unique identifier and a request timestamp. The request timestamp generally refers to the response time or push time of the search request. The server calculates the sum of the unique identifier and the request timestamp of the current search request, then hashes this sum to obtain its corresponding hash value. This hash value is then used as the remainder and moduloed by the number of data shards in the search service. The resulting remainder is used as a shard identifier, which represents the storage order of the data shards in the service. Finally, the server retrieves the data shard corresponding to this shard identifier from the search service as the target data shard for the search request.

[0100] In one embodiment, hashing can be performed solely based on the unique feature code contained in the data feature data to obtain the corresponding hash value as the remainder. This remainder is then used to perform a remainder operation with the number of data shards in the search service. The remainder is then used as a shard identifier to query the corresponding data shard as the target data shard.

[0101] Step S14: Use target data shards to process the search request for product search, and push the corresponding search results to the user terminal corresponding to the search request.

[0102] After the server determines the target data segment of the search request, it will use the target data segment to perform product search processing on the search request to obtain the corresponding search results for push feedback. Specifically, the server obtains the search text contained in the search request of the determined target data segment, extracts one or more keywords contained in the search text, and queries multiple product values ​​with one or more of the keywords from the target data segment. Then, it obtains the product objects of each of these product values. After obtaining each product object, it stores these product objects in a product list, determines the number of keywords that each product object in the product list matches with the keywords of the search text, and obtains the sales quantity of each product object in the product list. Then, it calculates the sum of the number of keywords and the sales quantity of each product object, sorts them in descending order according to the sum of each product object, and finally uses the product list after descending sorting as the search results. The search results are pushed to the user terminal of the current search request so that the user terminal can output the product objects in the product list in order to the graphical user interface for the buyer user to browse.

[0103] In one embodiment, after the search service retrieves the product objects corresponding to the search request and stores them in the product list, it sorts the product objects in the search product list in descending order based solely on the number of keywords that each product object matches the keywords in the search text, so that the product objects displayed at the top of the graphical user interface are the product objects that the user most wants to search for.

[0104] In another embodiment, after the search service retrieves the product objects corresponding to the search request and stores them in the product list, it will sort the product objects in the search product list in descending order based solely on their sales volume, so that the product objects displayed at the top of the graphical user interface are the best-selling products on the independent website.

[0105] As can be seen from the typical embodiments of this method, this method can control the search service of any e-commerce independent website in the online e-commerce independent website platform, preventing the platform's shared search service from crashing due to handling high-concurrency search requests from e-commerce independent websites with abnormal traffic, thus affecting the product search efficiency of other e-commerce independent websites with normal traffic in the platform; it detects independent websites with abnormal access traffic in the online e-commerce independent website platform and determines the time it takes for the search service to process the search requests of these independent websites, in order to determine whether the search service is under computational pressure in processing the search requests of independent websites with abnormal traffic, and then evenly distributes the search requests of independent websites affecting the search service to the data shards of the search service, so that the product search is processed using the single data shard corresponding to the search request, thereby reducing the computational pressure on the search service to process the high-concurrency search requests pushed by independent websites with abnormal traffic, ensuring the stability of the search service, and preventing the search service from crashing, which would prevent other independent websites using the shared search service in the platform from using the search service to provide product search functions for their buyers, thus affecting the shopping experience of buyers in independent websites.

[0106] The above typical embodiments and their variations fully disclose the implementation scheme of the search request control method of this application. However, various variations of the method can still be derived by changing and expanding some technical means. Other embodiments are briefly described below:

[0107] In one embodiment, please refer to Figure 3 The process of analyzing access traffic data from multiple independent websites and identifying one or more independent websites with abnormal traffic that meet preset traffic anomaly rules includes the following steps:

[0108] Step S111: Calculate the access traffic data of the current independent site within a preset window time:

[0109] The window time is generally set within a range of 5 to 10 minutes. Of course, those skilled in the art can also set the window time to 30 to 60 seconds or 1 to 2 hours.

[0110] The server collects access traffic data for the current independent site within the window period. The access traffic data refers to the number of user visits to the independent site to improve the versatility of traffic verification, or the number of search service requests to target the access traffic data of search services that require subsequent search request control.

[0111] Step S112: Determine whether the latest access traffic data exceeds the preset access traffic threshold.

[0112] The access traffic threshold is set according to the window time. For example, if the window time is generally set to 5 to 10 minutes in minutes, the access traffic threshold will be set to between 45,000,000 and 9,000,000 times.

[0113] Step S113: When the access traffic threshold is exceeded, the current independent website is identified as an independent website with abnormal traffic.

[0114] The server identifies independent websites whose access traffic data exceeds the access traffic threshold as traffic abnormal independent websites. That is, the access traffic data of such independent websites meets the preset traffic abnormality rules, indicating that the number of user accesses to such independent websites has exceeded their traffic load capacity, or the number of search requests from buyer users of such independent websites to access the search service has exceeded the traffic load capacity of the search service.

[0115] In this embodiment, access traffic data is used to verify whether an independent website is an independent website with abnormal traffic, so as to determine whether the excessive access traffic data of the independent website with abnormal traffic has affected the request response speed of the search service.

[0116] In one embodiment, please refer to Figure 4 The step of monitoring the search request response time of each of the aforementioned traffic-abnormal independent websites and verifying one or more of these traffic-abnormal independent websites whose access traffic data meets the preset request abnormality rules includes the following steps:

[0117] Step S121: Determine the latest search request response from the currently abnormal traffic independent site, and calculate the search request response time for the search service to complete the response to the search request.

[0118] The search request response time refers to the time consumed by the search service to match the corresponding search results based on the search text contained in the search request.

[0119] Step S122: Determine whether the response time of the search request exceeds a preset abnormal duration threshold. If it does, then the search request is identified as a timed-out search request.

[0120] The preset abnormal duration threshold for the search is generally set within a time range of 1 to 2 seconds, or within a time range of 10 to 50 milliseconds. Of course, those skilled in the art can flexibly design the abnormal duration threshold, which will not be elaborated here.

[0121] When the response time of the search request exceeds the preset abnormal duration threshold, it indicates that the traffic abnormality of the independent website to which the search request belongs may have affected the request response speed of the search service. Therefore, the search request needs to be identified as a timed-out search request and recorded.

[0122] Step S123: Determine whether the number of timed-out search requests from the current traffic-abnormal independent website exceeds a preset threshold. If it does, then identify the current traffic-abnormal independent website as a request-abnormal independent website.

[0123] The system continuously checks whether multiple search requests from an independent website with abnormal traffic are timed-out search requests, determines the number of timed-out search requests for that website, and identifies the website as having abnormal traffic when the number of timed-out search requests exceeds a preset threshold.

[0124] In this embodiment, by determining the number of timed-out search requests from traffic-abnormal independent stations, it is verified whether there is a response timeout problem for the traffic-abnormal independent stations. Traffic-abnormal independent stations with response timeout problems are identified as request-abnormal independent stations, and then the search service of the request-abnormal independent stations is degraded to prevent their high-concurrency requests from affecting the system stability of the search service.

[0125] In one embodiment, please refer to Figure 5 The steps involved in responding to a search request from an abnormal independent website, invoking preset search rate limiting rules to perform a hash modulo operation on the request feature data of the search request, and querying the target data shard corresponding to the search service operation result, include the following steps:

[0126] Step S131: Respond to the latest search request from the current request exception independent website and obtain the request characteristic data of the search request:

[0127] For the aforementioned independent websites with abnormal requests, the server will downgrade its search requests, that is, only use one data shard from the search service to process the product search requests from the independent websites with abnormal requests, in order to ensure that the search service can handle the high concurrency requests from the independent websites with abnormal requests.

[0128] After responding to a search request from an abnormal independent website, the server will obtain the request feature data contained in the search request. The request feature data includes a unique feature code that characterizes the uniqueness of the data in the search request and a request timestamp that characterizes the push time of the search request.

[0129] Step S132: Calculate the sum of the unique feature code and the request timestamp contained in the request feature data, and determine the number of data shards in the search service:

[0130] The search service has multiple data shards, each storing all the product search indexes of the search service. Although each data shard contains all the product search indexes, the product objects stored in each data shard are not all the product objects of its corresponding independent website.

[0131] The unique feature code and the requested timestamp are calculated to perform a hash operation on the feature and value in a subsequent process.

[0132] Step S133: Perform a hash operation on the feature and value to obtain the corresponding hash value, and calculate the remainder between the hash value and the number of shards as the shard identifier, where the hash value is used as the remainder:

[0133] After performing a hash operation on the features and values ​​to obtain a hash value, the hash value is used as the remainder and the number of data shards of the search service is used to calculate the remainder. The calculated remainder is used as the shard identifier.

[0134] Step S134: Query the target data shards with the shard identifier in the search service:

[0135] The data shards in the search service generally have storage sequence numbers that represent their storage order. Therefore, the data shard with the storage sequence number that remains the same can be queried in the service as the target data shard by using the remainder of the shard identifier.

[0136] In this embodiment, the target data shard corresponding to the search request is determined by the unique feature code and request timestamp of the search request for the abnormal independent website and the number of data shards of the search service. This allows the search request for the abnormal independent website to be evenly distributed among the data shards of the search service, so that the corresponding data shards can be used to process the search request for products in the subsequent search, thereby reducing the computational pressure on the search service to handle high-concurrency requests.

[0137] In one embodiment, please refer to Figure 6 The step of processing the search request for products using target data shards and pushing the corresponding search results to the user terminal corresponding to the search request includes the following steps:

[0138] Step S141: Obtain the search text contained in the current search request, and extract one or more keywords contained in the search text:

[0139] The search text is text edited and entered by the buyer on the user side through the independent website page, in order to search for the product objects on the independent website that correspond to the search text and browse and shop online.

[0140] Keywords can be extracted from search text using text keyword extraction methods such as TF-IDF or YAKE. For example, for the search text "Xiaomi smartphone 12th generation", the keywords that can be extracted are ["Xiaomi", "smartphone", "12th generation"].

[0141] Step S142: Query multiple product values ​​in the target data segment that have one or more of the keywords, and obtain the product objects of each of these product values:

[0142] The data shard has multiple search indexes consisting of keywords as key data and one or more product names as value data. The product value refers to the value data. The search service will query multiple product values ​​that correspond to the keywords used as key data in the search text and obtain the product objects corresponding to each of these product values.

[0143] The product object is generally the product object possessed by the independent website that experienced the request exception. The product object contains product characteristic information such as product name, product price, product image, and product description.

[0144] Step S143: Based on the product objects stored in the product list, determine the number of keywords for each product object in the product list:

[0145] After the server retrieves multiple product objects corresponding to the search request through the search service, it stores these product objects in the product list and determines the number of keywords matched by each product object. Since the product name as value data is not unique in the data shard, each search index in the data shard will generally have product values ​​with the same product name as value data. Therefore, a product object may match multiple keywords contained in the search text. The server will determine the number of keywords matched by each retrieved product object.

[0146] Step S144: Obtain the sales quantity of each product object in the product list, calculate the sum of the keyword count and sales quantity of each product object, and sort them in descending order based on the sum of each product object.

[0147] In addition to determining the number of keywords for each product object, the sales quantity of each product object will also be obtained to calculate the sum of the number of keywords and the sales quantity of each product object. Then, the product objects in the product list will be sorted in descending order according to the sum of the number of keywords and the sales quantity of each product object, that is, the product objects with higher sums of keyword count and sales quantity will be adjusted to the front of the product list.

[0148] Step S145: The product list that has been sorted in descending order is used as the search result, and the search result is pushed to the user terminal of the current search request:

[0149] The user terminal receives the search results of the search request it pushes, and outputs them to the graphical user interface of the page in order of the sorting position of each product object in the product list contained in the search results, so that the buyer user on the user terminal can browse these product objects and make online purchases.

[0150] In this embodiment, the server performs product search processing on search requests from abnormal independent websites using a single data shard. It only uses a specific data shard corresponding to the current search request to process the request and obtain the corresponding search results for feedback and push. This reduces the pressure of high-concurrency request processing on the search service. Furthermore, the searched product objects will be sorted by keyword hit and sales volume, prioritizing the output of products that better match the user's search intentions and are more popular, thereby improving the online shopping experience for buyers.

[0151] In one embodiment, please refer to Figure 7 This method includes the following post-processing:

[0152] Step S15: Respond to the exception unblocking command applied to the currently requesting abnormal independent website, count the access traffic data of the currently requesting abnormal independent website, and determine whether the access traffic data is less than the preset traffic unblocking threshold:

[0153] The abnormal unblocking instruction can be a timed instruction to periodically verify whether the search service degradation control of the abnormal independent website can be lifted. Of course, the abnormal unblocking instruction can also be triggered and pushed by the management terminal of the e-commerce independent website platform.

[0154] After the server responds to the exception unblocking command applied to the independent website with the current request exception, it will first count the access traffic data of the independent website with the request exception and determine whether the access traffic data is less than the traffic unblocking threshold in order to determine whether the traffic exception issue of the independent website with the request exception has been resolved.

[0155] Step S16: When the access traffic data is less than the preset traffic unblocking threshold, calculate the average response time of search requests for currently abnormal independent websites:

[0156] The average response time for the search request is obtained by the server through statistical analysis of the average time consumed by the search service in responding to each search request of the currently abnormal traffic independent website within a time window. The time window is generally set within a time range of 1 to 5 minutes, in minutes. Of course, designers in the art can design the time window according to the actual application scenario.

[0157] When the server determines that the access traffic data of the currently requesting independent site is less than the preset traffic unblocking threshold, it will calculate the average response time of the search request for that independent site.

[0158] Step S17: Determine whether the average response time of the search request is less than the preset unblocking time. If it is less, the independent site with the current request exception will be allowed to open up traffic, so that multiple data shards can be used to process the search requests of the independent site with the current request exception for product search.

[0159] If the average response time of the search request for the currently abnormal independent site is less than the preset unblocking time, it indicates that the search service can handle the search request of the abnormal independent site relatively easily. Therefore, the degradation control of the search service applied to the abnormal independent site is lifted, and the search service is allowed to use multiple data shards to handle the search request of the abnormal independent site.

[0160] In this embodiment, by verifying the access traffic and average response time of the abnormal request independent site, it is determined whether the access traffic of the abnormal request independent site has returned to normal and the search request processing capability of the search service is assessed, so as to determine whether it is necessary to lift the search service degradation control of the abnormal request independent site and restore the search function capability of the abnormal request independent site.

[0161] Furthermore, a search request control device of this application can be constructed by functionalizing the various steps in the methods disclosed in the above embodiments. Following this approach, please refer to... Figure 8 In one typical embodiment, the device includes: an access traffic verification module 11, used to count the access traffic data of multiple independent websites and verify one or more independent websites with abnormal access traffic data that meet preset traffic abnormality rules; a response time judgment module 12, used to monitor the response time of search requests of each of the independent websites with abnormal access traffic data and verify one or more independent websites with abnormal request access traffic data that meet preset request abnormality rules; a target shard determination module 13, used to respond to the search requests of the independent websites with abnormal access traffic data, call preset search rate limiting rules to perform a hash modulo operation on the request feature data of the search request, and query the target data shard corresponding to the search service operation result; and a product request response module 14, used to process the search request for products using the target data shard, obtain the corresponding search results and push them to the user terminal corresponding to the search request.

[0162] In one embodiment, the access traffic verification module 11 includes: an access traffic statistics submodule, used to count the access traffic data of the current independent station within a preset window time; a traffic threshold judgment submodule, used to determine whether the latest statistical access traffic data exceeds a preset access traffic threshold; and a traffic anomaly determination submodule, used to determine the current independent station as a traffic anomaly independent station when the access traffic threshold is exceeded.

[0163] In one embodiment, the response duration determination module 12 includes: a response duration calculation submodule, used to determine the latest response search request from the currently abnormal traffic independent site and calculate the search request response duration for the search service to complete the response to the search request; a duration threshold determination submodule, used to determine whether the search request response duration exceeds a preset abnormal duration threshold, and if it does, determine the search request as a timed-out search request; and a request abnormality determination submodule, used to determine whether the number of timed-out search requests from the currently abnormal traffic independent site exceeds a preset threshold, and if it does, determine the currently abnormal traffic independent site as a request abnormal independent site.

[0164] In one embodiment, the target shard determination module 13 includes: a feature data acquisition submodule, used to respond to the latest search request from the current request exception independent site and acquire the request feature data of the search request; a feature and value calculation submodule, used to calculate the feature and value of the unique feature code and request timestamp contained in the request feature data, and determine the number of data shards in the search service; a distribution identifier determination submodule, used to perform a hash operation on the feature and value to obtain the corresponding hash value, and calculate the remainder of the hash value and the number of shards as the shard identifier, wherein the hash value is used as the remainder; and a target shard query submodule, used to query the target data shards in the search service that have the shard identifier.

[0165] In one embodiment, the product request response module 14 includes: a keyword extraction submodule, used to obtain the search text contained in the current search request and extract one or more keywords contained in the search text; a product object acquisition submodule, used to query multiple product values ​​with one or more of the keywords in the target data segment and obtain the product objects of each of the product values; a keyword quantity determination submodule, used to determine the keyword quantity of each product object in the product list based on the product objects stored in the product list; a product sorting submodule, used to obtain the sales quantity of each product object in the product list, calculate the sum of the keyword quantity and sales quantity of each product object, and sort them in descending order based on the sum of each product object; and a search result push submodule, used to push the product list that has been sorted in descending order as the search result to the user terminal to which the current search request belongs.

[0166] To address the aforementioned technical problems, this application also provides a computer device for running a computer program implemented according to the search request control method. Please refer to the following for details. Figure 9 , Figure 9 This is a basic structural block diagram of the computer device in this embodiment.

[0167] like Figure 9 The diagram shows the internal structure of a computer device. The computer device includes a processor, non-volatile storage medium, memory, and a network interface connected via a system bus. The non-volatile storage medium stores the operating system, a database, and computer-readable instructions. The database may store control information sequences. When the computer-readable instructions are executed by the processor, they enable the processor to implement a search request control method. The processor provides computing and control capabilities, supporting the operation of the entire computer device. The memory stores computer-readable instructions, which, when executed by the processor, enable the processor to implement a search request control method. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0168] In this embodiment, the processor executes the specific functions of each module / submodule in the search request control device of this application, and the memory stores the program code and various types of data required to execute the above modules. The network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules / submodules in the search request control device, and the server can call the server's program code and data to execute the functions of all submodules.

[0169] This application also provides a non-volatile storage medium in which the search request control method is written as a computer program and stored in the storage medium in the form of computer-readable instructions. When the computer-readable instructions are executed by one or more processors, it means that the program is running in the computer, thereby causing one or more processors to perform the steps of the search request control method of any of the above embodiments.

[0170] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0171] In summary, this application can target a single independent website for search service control, thereby improving the stability of the platform's search service.

[0172] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0173] Those skilled in the art will understand that the steps, measures, and solutions in the various operations, methods, and processes discussed in this application can be alternated, modified, combined, or deleted. Furthermore, other steps, measures, and solutions in the various operations, methods, and processes discussed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted. Furthermore, steps, measures, and solutions in the prior art that are similar to those disclosed in this application can also be alternated, modified, rearranged, decomposed, combined, or deleted.

[0174] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A search request control method characterized by comprising: The method comprises the following steps: Statistical data of multiple independent station access traffic, check out the access traffic data in the one or more traffic anomaly independent station of the independent station meets the preset traffic anomaly rule; Listen to each of the search request response time of the traffic anomaly independent station, check out the access traffic data in the one or more request anomaly independent station of the traffic anomaly independent station meets the preset request anomaly rule; Response search request of the request anomaly independent station, call the preset search flow limiting rule to the request characteristic data of the search request Hash remainder operation, query out the target data slice corresponding to the search service operation result, including: response the latest search request of the current request anomaly independent station, get the request characteristic data of the search request; Calculate the unique feature code and request timestamp in the request characteristic data Feature and value, and determine the number of data slices of the search service; Hash operation is carried out on the feature and value to obtain the corresponding hash value, and the remainder of the hash value and the number of slices is taken as the slice identifier, wherein the hash value is taken as the remainder; Query the target data slice with the slice identifier in the search service; Using target data slice to search for goods for the search request, get the corresponding search result and push it to the user terminal corresponding to the search request; The search service refers to the goods search service provided by the network e-commerce independent station platform to each independent station under the platform; The search service has a plurality of data slices, and each data slice stores all the goods search indexes of the search service.

2. The method of claim 1, wherein, In the step of statistical data of multiple independent station access traffic, check out the one or more traffic anomaly independent station of the independent station meets the preset traffic anomaly rule, comprising the following steps: Statistical data of current independent station in the preset window time; Determine whether the current latest statistical access traffic data exceeds the preset access traffic threshold; When the access traffic threshold is exceeded, the current independent station is determined as a traffic anomaly independent station.

3. The method of claim 1, wherein, In the step of listening to each of the search request response time of the traffic anomaly independent station, check out the one or more request anomaly independent station of the traffic anomaly independent station meets the preset request anomaly rule, comprising the following steps: Determine the latest search request of the current traffic anomaly independent station, calculate the search request response time of the search service to complete the response of the search request; Determine whether the search request response time exceeds the preset abnormal time threshold, if it exceeds, the search request is determined as a timeout search request; Determine whether the number of timeout search requests of the current traffic anomaly independent station exceeds the preset threshold, if it exceeds, the current traffic anomaly independent station is determined as a request anomaly independent station.

4. The method of claim 1, wherein, In the step of using target data slice to search for goods for the search request, get the corresponding search result and push it to the user terminal corresponding to the search request, comprising the following steps: Get the search text contained in the current search request, extract one or more keywords contained in the search text; Query a plurality of commodity values in the target data shard that have one or more of the keywords, obtain the commodity object of each of the commodity values; According to the storage of each commodity object in the commodity list, determine the number of keywords of each commodity object in the commodity list; Obtain the sales quantity of each commodity object in the commodity list, calculate the sum of the keyword quantity and the sales quantity of each of the commodity objects, and sort the sum of each of the commodity objects in descending order; The commodity list after descending order sorting is taken as the search result, and the search result is pushed to the user end to which the current search request belongs.

5. The method of claim 1, wherein, The method comprises the following post-processing: In response to an exception unblocking instruction acting on the current request exception independent station, the access traffic data of the current request exception independent station is counted, and it is determined whether the access traffic data is less than a preset traffic unblocking threshold; When the access traffic data is less than the preset traffic unblocking threshold, the average response time of search requests of the current request exception independent station is counted; It is determined whether the average response time of search requests is less than a preset unblocking exception time, and if so, the current request exception independent station is subjected to traffic opening processing, and the search requests of the current request exception independent station are subjected to commodity search processing using a plurality of data shards.

6. A search request control device characterized by comprising: It comprises: An access traffic verification module is configured to count the access traffic data of a plurality of independent stations, and verify one or more traffic exception independent stations in which the access traffic data satisfies a preset traffic exception rule; A response time judgment module is configured to listen to the response time of search requests of each of the traffic exception independent stations, and verify one or more request exception independent stations in which the access traffic data satisfies a preset request exception rule; A target shard determination module is configured to respond to the search requests of the request exception independent stations, perform a hash modulo operation on the request feature data of the search requests using a preset search flow limiting rule, query a target data shard corresponding to a search service operation result, and comprises: in response to the latest search request of the current request exception independent station, obtaining the request feature data of the search request; calculating the feature sum value of the unique feature code and the request timestamp contained in the request feature data, and determining the number of shards of the data shards possessed by the search service; performing a hash operation on the feature sum value to obtain a corresponding hash value, and calculating the remainder of the hash value and the number of shards as a shard identifier, wherein the hash value is the remainder; query the target data shard in the search service that has the shard identifier; A commodity request response module is configured to use the target data shard to perform commodity search processing on the search requests, and obtain the corresponding search results and push them to the user end corresponding to the search requests. The search service refers to the commodity search service provided by the network e-commerce independent station platform to each independent station under the platform; the search service has a plurality of data shards, and each data shard stores all the commodity search indexes of the search service.

7. The search request control device according to claim 6, wherein The access traffic verification module comprises: An access traffic counting submodule is configured to count the access traffic data of the current independent station within a preset window time; The flow threshold judging submodule is configured to judge whether the current latest statistical access flow data exceeds a preset access flow threshold. The flow anomaly determining submodule is configured to determine the current independent station as a flow anomaly independent station when the access flow threshold is exceeded.

8. An electronic device comprising a central processing unit and a memory, characterized in that The central processing unit is configured to call and run a computer program stored in the memory to perform the steps of the method according to any one of claims 1 to 5.

9. A non-volatile storage medium, comprising: The computer program is stored in the form of computer readable instructions, and when the computer program is called and run by a computer, the steps included in the method are performed.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the steps of the method according to any one of claims 1 to 5.

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