AnQiCMS Help Document and User Guide
When releasing a security announcement for trojan horse programs, check four places before upgrading: the running directory of write and cache, pseudo-static rules and site rewriting configuration, abnormal files in the upload and template directories, and the scheduled tasks and background login logs. Collect evidence first, clean up, and then update the version. Directly covering the upgrade will retain the trojan horse and also destroy the clues for subsequent investigation.
The release of a security version out of the normal release cycle for the operating environment indicates that the problem is not within the regular release period. Before upgrading, check in the following order: first, confirm that there is an available rollback point and complete the backup of static files; then, verify the current version with the affected range in the announcement; then, run the form, backend login, and key pages in the test environment; finally, peak load online for observation, and record the results in the version ledger.
Based on the day's self-hosted operation standards, mainstream self-hosted content systems need to process patches from high to medium risk levels every six to eight weeks, accumulating eight to ten times a year. Each time, it involves announcement verification, assessment, backup, update and regression, plus on-site and backup drills, the hidden labor cost is often higher than the monthly fee of the host. This article provides the breakdown of working hours and the sections that can be saved.
When the system email cannot be sent out, should we first look at the bounce rate, which directly reflects the quality of the address and whether the delivery is rejected; the complaint rate reflects whether the content has been judged as spam, usually after the delivery is successful. This article gives the troubleshooting sequence according to the delivery index threshold verified on the day and explains the division of labor in maintaining the email reminder and email subscription list triggered by the event
When forms are flooded by bots, the field collection is done first followed by human verification. According to the samples checked that day, in the first eight days, the proportion of real people among the hundreds of contact form submissions was very low. The pricing of captcha service at a scale of thousands indicates that bypassing verification has already become industrialized, while edge-side condition filtering can block about three to five percentage points of the bottom flow. The order of processing is to tighten fields and condition filtering, then integrate reCAPTCHA, and finally leave a content review.
A share refers to the proportion of answers containing the brand out of all answers, based on the statistics of the day, the model share is the proportion of the brand appearing in the artificial intelligence answers; the citation rate looks at the frequency of the brand appearing in the source of the answer. The two sets of standards answer different operational issues and cannot be substituted for each other, the data must be able to fall on the interface or background data for verification.
According to the guidelines checked on the day, the visibility of content with statistical figures in AI answers is increased by 30% to 40%, but this is not an income that can be obtained simply by adding numbers. The numbers must have a scope and a verifiable source, and the paragraph should be able to stand independently; otherwise, isolated numbers will actually reduce credibility. The focus on content is determined by the keyword library on what questions to write, and content review ensures consistency in the scope of reference.
The change of reference source every round is the norm, not an exception. According to the criteria checked that day, only 30% of brands can remain visible between two consecutive answers, and 86% of the references come from brand-owned or nearby sources. There are still three handles on the content side: write paragraphs as independent extractable units, concentrate the pages worth reading in the owned domain and use llms.txt for navigation, maintain consistency and freshness with content review and scheduled publishing.
Rank fluctuation after garbage update, check the collection first and then the content. According to the October site owner report on the day, the garbage update in September is still fermenting in October, and at the same time, about a hundred publishers have obtained a small proportion of ad revenue in the artificial intelligence contribution pilot. The self-check order is: crawling and indexing status, invalid addresses in the site map, keyword library and anchor text distribution, and finally the ranking of specific word groups.
The proportion of blocked crawler training is rising. According to the samples checked on the day, the statistics of the robots.txt files of nearly five thousand top-level sites show that 14.3% are blocking GPTBot, and 19.2% are blocking at least one AI training crawler. It is recommended to divide the grouping writing by the purpose of crawling: search crawling, retrieval citation, and model training should be authorized separately, expressed by different user group rules, and llms.txt is only responsible for navigation without participating in authorization.
