A method for realizing intelligent follow-up of hotel distribution based on enterprise private domain knowledge

Abstract

The present invention belongs to the field of computer technology and proposes a method for realizing intelligent follow-up of hotel distribution based on enterprise private domain knowledge. The method includes the following steps: obtaining the hotel's server information; performing dynamic cache frequency efficiency analysis on the server through the server information to determine a cache-stable server; obtaining the target server by calculating the concurrent response time difference of the cache-stable server; and generating and sending a personalized follow-up notification to the target user through the target server to send an operational instruction for order decision-making. The intelligent follow-up method according to an embodiment of the present invention can effectively optimize the server load management, improve the user experience, enhance the stability and stress resistance of the system, and ensure that the platform can still maintain efficient and stable operation under complex situations such as high concurrency and malicious attacks.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a method for realizing intelligent hotel distribution and order tracking based on enterprise private domain knowledge. Background Art

[0002] Currently, in hotel distribution platforms, server load and response time directly affect users' access experience and order decision-making process; however, when encountering malicious attacks (such as DoS / DDoS attacks, brute force cracking) or improper requests (such as malicious crawlers), the cache may frequently fail and the hit rate will decrease, which will lead to a significant increase in server response time and may even cause the system to crash, seriously affecting the stability and availability of the system; and malicious attacks and crawlers put pressure on the server by sending a large number of invalid requests, which not only consumes valuable resources, but also causes a large number of cache failures, exacerbating the fluctuation of server performance, and thus affecting the user's access experience and the normal operation of the platform. Summary of the Invention

[0003] The present invention aims to solve, at least to a certain extent, one of the technical problems in the related art. To this end, the first object of the present invention is to propose a method for implementing intelligent hotel distribution and tracking based on enterprise private domain knowledge, which can effectively optimize server load management, improve user experience, enhance system stability and stress resistance, and ensure that the platform can maintain efficient and stable operation under complex conditions such as high concurrency and malicious attacks.

[0004] To achieve the above objectives, a first embodiment of the present invention proposes a method for implementing intelligent hotel distribution and order tracking based on enterprise private domain knowledge, the method comprising the following steps:

[0005] S100, obtaining the hotel's server information;

[0006] S200, performing dynamic cache frequency-efficiency analysis on the server based on the server information to determine a cache-stable server;

[0007] S300, obtaining the target server by calculating the concurrent response time difference of the cache stable server;

[0008] S400: Generate and send a personalized order notification to the target user through the target server to send an operation instruction for order decision.

[0009] According to the intelligent trading method of the embodiment of the present invention, it is possible to effectively optimize the server load management, improve the user experience, enhance the stability and stress resistance of the system, and ensure that the platform can maintain efficient and stable operation under complex situations such as high concurrency and malicious attacks.

[0010] Furthermore, in step S100, the hotel's server information is obtained, wherein the hotel's server information includes the server's response time, the number of server requests, the server's concurrency, and the cache hit rate;

[0011] In the hotel distribution platform, the server load and response time directly affect the user's access experience and order decision-making process; however, when encountering malicious attacks (such as DoS / DDoS attacks, brute force cracking) or improper requests (such as malicious crawlers), the cache may frequently fail and the hit rate decreases, which in turn leads to a significant increase in server response time and may even cause the system to crash, seriously affecting the stability and availability of the system; and malicious attacks and crawlers put pressure on the server by sending a large number of invalid requests, which not only consumes valuable resources, but also causes a large number of cache failures, exacerbating the fluctuation of server performance, and thus affecting the user's access experience and the normal operation of the platform. In order to solve the above problems, the present invention proposes step S200.

[0012] Furthermore, in step S200, performing dynamic cache frequency-efficiency analysis on the server through the server information to determine a cache-stable server includes:

[0013] Let j represent the identifier of each server, j = 1, 2, ..., K, where K represents the number of servers. Let r(j) be the maximum cache hit rate of the jth server in the last hour, p(j) be the minimum cache hit rate of the jth server in the last hour, and Ms be the average minimum cache hit rate of all servers in the last hour.

[0014] Performing dynamic cache frequency-effect analysis on the server, wherein the steps of performing dynamic cache frequency-effect analysis on the server include:

[0015] S201, calculating the cache loss of the server in the last hour;

[0016] Calculate the cache loss ZG(j) of the jth server in the last hour, where ZG(j) is the difference between r(j) and p(j). Add up all ZG(j) and divide by K to get the stable cache loss HDC.

[0017] Among them, the stable cache loss frequency HDC is used to quantify the changes and unevenness of the server's cache loss frequency in the past hour and exclude servers whose cache has failed and whose cache hit rate has dropped sharply due to DoS / DDoS attacks, brute force cracking, or malicious crawlers. DoS / DDoS attacks are denial of service (DOS) attacks in which attackers consume the target server's cache by sending a large number of invalid requests, resulting in a large number of cache failures. In addition, the behavior of malicious web crawlers is very difficult to accurately identify using existing technologies, especially when these web crawlers simulate normal user behavior. Web crawlers can disguise themselves as browser access and use a more dispersed and intermittent request method, making them almost indistinguishable from real user requests. In addition, the content of web crawler requests usually fully complies with cache rules, but they frequently request non-existent or deleted pages. This abnormal behavior is usually difficult to detect.

[0018] S202, define an integer variable k, set the initial value to 1, and create a blank sequence ZRE;

[0019] S203, performing dynamic cache frequency effect analysis, wherein the dynamic cache frequency effect analysis is as follows: comparing the values ​​of ZG(j) and HDC, and comparing the values ​​of [p(j)+HDC] and Ms. If ZG(j) is less than HDC and [p(j)-HDC] is greater than Ms, then ZG(j) is added to the sequence ZRE;

[0020] S204: Determine whether the dynamic cache frequency effect analysis is complete. The specific determination method is as follows: if the current variable k is less than K, then increase k by 1 and return to step S203 to continue the dynamic cache frequency effect analysis; if the current variable k is equal to K, then the dynamic cache frequency effect analysis is complete and the process goes to step S205;

[0021] S205: Record the servers corresponding to all elements in the sequence ZRE as cache stable servers.

[0022] The dynamic cache frequency-efficiency analysis analyzes the cache hit rate of each server in the past hour, calculates its cache frequency loss, and evaluates the stability of the cache through these frequency loss values. It can not only reflect the changes in the server cache, but also identify cache failures caused by external malicious attacks or improper requests (such as DoS / DDoS attacks, brute force cracking, and malicious crawlers), thereby effectively distinguishing unstable servers. By calculating and comparing cache frequency loss and cache hit rate, servers affected by attacks or web crawlers are excluded. Especially in the case of DoS / DDoS attacks, brute force cracking, malicious crawlers, etc., these attacks and crawlers cause cache failures to the server through a large number of invalid requests. Through the quantitative method of stable cache frequency loss HDC, it is possible to accurately identify which servers have unstable caches due to abnormal requests, and then filter out servers with stable caches.

[0023] Among them, cache frequency loss is used to reflect the fluctuation of cache hit rate, and stable cache frequency loss HDC is used to quantify the fluctuation and unevenness of all server caches of the hotel, which can effectively eliminate incorrect choices caused by abnormal fluctuations.

[0024] The beneficial effects of this step are: By performing dynamic cache frequency-effect analysis on cache hit rates, servers with stable caches can be more accurately selected, avoiding system crashes or response delays caused by cache failure. Furthermore, stable cache servers can maintain low response times even in the face of high concurrency or malicious attacks, avoiding delays and failed requests caused by server cache failures. By identifying and eliminating servers with cache failures caused by malicious attacks or crawlers, the hotel distribution platform's fault tolerance in the face of malicious attacks is effectively enhanced. The system can share the load by selecting stable servers, preventing attackers or crawlers from affecting the platform's normal operation.

[0025] Furthermore, in step S300, obtaining the target server by calculating the concurrent response time difference of the cache stable server includes the following steps:

[0026] S301, calculating the concurrent response time difference of the cache stable server;

[0027] Set an empty sequence STS as the response time set of the cache stable server, where the sequence takes the response time of the cache stable server as the element of the set STS, and uses STS(i) to represent the average response time of the cache stable server with sequence number i in the last hour, where i represents the sequence number of the cache stable server, i = 1, 2, ..., G, G is the number of cache stable servers; get the shortest response time in STS and record it as STSs; get the longest response time in STS and record it as STSx; get the average value of all response times in STS as STSm, set an empty sequence as the cache stable server The minimum concurrency set KPT of the cache stable server in the last hour is set, wherein the sequence takes the minimum concurrency of the cache stable server in the last hour as the element of the set KPT, and uses KPT(i) to represent the minimum concurrency of the cache stable server with sequence number i in the last hour; an empty sequence is set as the maximum concurrency set KPU of the cache stable server in the last hour, wherein the sequence takes the maximum concurrency of the cache stable server in the last hour as the element of the set KPU, and uses KPU(i) to represent the maximum concurrency of the cache stable server with sequence number i in the last hour; the average concurrency in KPU is obtained and recorded as KPUs.

[0028] The minimum concurrent number of cache stability servers in the last hour indicates the lowest number of concurrent requests received by the cache stability servers in the last hour. The maximum concurrent number of cache stability servers in the last hour indicates the highest number of concurrent requests received by the cache stability servers in the last hour.

[0029] The concurrent response time difference includes the first concurrent response time difference and the second concurrent response time difference. The difference between STSx and STSm is taken as the first concurrent response time difference SE; the difference between STSm and STSs is taken as the second concurrent response time difference SR.

[0030] S302, calculating the concurrent load request duration of the cache stable server by the concurrent response time difference of the cache stable server;

[0031] Because server response time is not only affected by hardware performance, but also closely related to factors such as request type, system workload, and external dependencies, in high-concurrency and high-load environments, server response time often exhibits nonlinear fluctuations. This is especially true when there are large differences in the number of concurrent requests and user behavior. It is impossible to predict whether a server is functioning properly based solely on changes in response time itself. Furthermore, different servers have different hardware configurations and load processing capabilities, so even under the same load conditions, server performance and response time may vary. By combining response time stability with concurrent load capacity to calculate the duration of concurrent load requests, we can more accurately understand the processing power and load of each server, enabling intelligent scheduling.

[0032] Furthermore, an empty sequence POT is set to store the concurrent load request duration of the cache stable server, wherein the sequence POT uses the concurrent load request duration POT(i) of the cache stable server as an element of the sequence POT, wherein i represents the serial number of the cache stable server, i=1,2,…,G, G is the number of cache stable servers, and POT(i) represents the concurrent load request duration of the cache stable server with serial number i. The calculation method of the concurrent load request duration POT(i) is:

[0033] POT(i)=STS(i)+|(SE-SR)|×(KPT(i)+KPU(i)) / KPUs;

[0034] Among them, || is the absolute value symbol. The principle of concurrent load request duration POT(i) is: based on the server's own response time STS(i), the fluctuation of the server response time is considered through the concurrent response time difference (SE-SR): if SE is much larger than SR or much smaller than SR, it means that the maximum response time is far from the average value, while the minimum response time is close to the average value, indicating that the server performance degrades significantly under high load; if SE is close to SR, it means that the server performance is relatively stable under high load and low load, and is suitable as a target server. The concurrent load capacity of the server is considered by (KPT(i)+KPU(i)) / KPUs: if the number of concurrent requests KPT(i) and KPU(i) of a server are both high, it means that the server is under greater pressure. If the concurrent load of the server is low, the POT(i) value is small, which means that the server can give priority to processing new requests.

[0035] S303: Determine the target server according to the concurrent load request duration of the cache stabilization server.

[0036] Specifically, the cache stable servers are sorted from small to large according to the concurrent load request duration POT(i). The sequence number corresponding to the largest value in the load duration POT(i) is G, and the sequence number corresponding to the smallest value in the load duration POT(i) is 1. According to the sequence number of the cache stable server, the cache stable server with a smaller sequence number is preferentially selected as the target server.

[0037] The beneficial effects of this step are: through POT(i) calculation, the system can dynamically evaluate the load of each server, rather than relying solely on traditional indicators such as current CPU usage and memory occupancy. By combining the stability of response time and concurrent load capacity, the POT(i) calculation method can effectively screen out high-performance, low-load servers and improve the rationality of request allocation; since servers with larger POT(i) values ​​usually mean higher loads or unstable response times, the system will give priority to servers with smaller POT(i) values ​​to process requests. By giving priority to servers with smaller POT(i) values, user requests can be processed faster, reducing page loading time, improving the smoothness of user access, and avoiding sudden delays or crashes caused by excessive server pressure. When encountering a large-scale DoS / DDoS attack, the calculation of POT(i) can effectively identify high-load servers and automatically assign new requests to more stable servers, thereby reducing the impact of the attack on the entire system; and because web crawlers usually cause the server's cache hit rate to drop and the number of concurrent requests to increase abnormally, the calculation of POT(i) can reflect these abnormal situations and avoid assigning critical business requests to servers that may be affected by crawlers.

[0038] S400: Generate and send a personalized order notification to the target user through the target server to send an operation instruction for order decision.

[0039] Specifically, in step S400, the target server analyzes the user's potential needs based on their historical booking behavior, browsing history, preferences, and real-time market trends, and generates targeted order notifications, such as price change alerts, room shortage warnings, and limited-time discount notifications. Furthermore, the target server sends personalized order notifications to the target user via multiple channels (such as SMS, email, or app push), prompting the user to make an order decision as quickly as possible, thereby improving booking conversion rates and optimizing the user experience.

[0040] By implementing a system that realizes intelligent hotel distribution and follow-up based on enterprise private domain knowledge, the method of realizing intelligent hotel distribution and follow-up based on enterprise private domain knowledge can effectively optimize server load management, improve user experience, enhance system stability and stress resistance, and ensure that the platform can maintain efficient and stable operation under complex situations such as high concurrency and malicious attacks. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Shown is a flowchart of a method for realizing intelligent hotel distribution and follow-up based on enterprise private domain knowledge. DETAILED DESCRIPTION

[0042] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0043] Figure 1 Shown is a flowchart of a method for realizing intelligent hotel distribution and follow-up based on enterprise private domain knowledge.

[0044] Reference Figure 1 The present invention proposes a method for realizing intelligent hotel distribution and follow-up based on enterprise private domain knowledge, and the method comprises the following steps:

[0045] S100, obtaining the hotel's server information;

[0046] S200, performing dynamic cache frequency-efficiency analysis on the server based on the server information to determine a cache-stable server;

[0047] S300, obtaining the target server by calculating the concurrent response time difference of the cache stable server;

[0048] S400: Generate and send a personalized order notification to the target user through the target server to send an operation instruction for order decision.

[0049] According to the intelligent trading method of the embodiment of the present invention, it is possible to effectively optimize the server load management, improve the user experience, enhance the stability and stress resistance of the system, and ensure that the platform can maintain efficient and stable operation under complex situations such as high concurrency and malicious attacks.

[0050] Furthermore, in step S100, the hotel's server information is obtained, wherein the hotel's server information includes the server's response time, the number of server requests, the server's concurrency, and the cache hit rate;

[0051] Furthermore, in step S200, performing dynamic cache frequency-efficiency analysis on the server through the server information to determine a cache-stable server includes:

[0052] Let j represent the identifier of each server, j = 1, 2, ..., K, where K represents the number of servers. Let r(j) be the maximum cache hit rate of the jth server in the last hour, p(j) be the minimum cache hit rate of the jth server in the last hour, and Ms be the average minimum cache hit rate of all servers in the last hour.

[0053] Performing dynamic cache frequency-effect analysis on the server, wherein the steps of performing dynamic cache frequency-effect analysis on the server include:

[0054] S201, calculating the cache loss of the server in the last hour;

[0055] Calculate the cache loss ZG(j) of the jth server in the last hour, where ZG(j) is the difference between r(j) and p(j). Add up all ZG(j) and divide by K to get the stable cache loss HDC.

[0056] S202, define an integer variable k, set the initial value to 1, and create a blank sequence ZRE;

[0057] S203, performing dynamic cache frequency effect analysis, wherein the dynamic cache frequency effect analysis is as follows: comparing the values ​​of ZG(j) and HDC, and comparing the values ​​of [p(j)+HDC] and Ms. If ZG(j) is less than HDC and [p(j)-HDC] is greater than Ms, then ZG(j) is added to the sequence ZRE;

[0058] S204: Determine whether the dynamic cache frequency effect analysis is complete. The specific determination method is as follows: if the current variable k is less than K, then increase k by 1 and return to step S203 to continue the dynamic cache frequency effect analysis; if the current variable k is equal to K, then the dynamic cache frequency effect analysis is complete and the process goes to step S205;

[0059] S205: Record the servers corresponding to all elements in the sequence ZRE as cache stable servers.

[0060] The dynamic cache frequency-efficiency analysis analyzes the cache hit rate of each server in the past hour, calculates its cache frequency loss, and evaluates the stability of the cache through these frequency loss values. It can not only reflect the changes in the server cache, but also identify cache failures caused by external malicious attacks or improper requests (such as DoS / DDoS attacks, brute force cracking, and malicious crawlers), thereby effectively distinguishing unstable servers. By calculating and comparing cache frequency loss and cache hit rate, servers affected by attacks or web crawlers are excluded. Especially in the case of DoS / DDoS attacks, brute force cracking, malicious crawlers, etc., these attacks and crawlers cause cache failures to the server through a large number of invalid requests. Through the quantitative method of stable cache frequency loss HDC, it is possible to accurately identify which servers have unstable caches due to abnormal requests, and then filter out servers with stable caches.

[0061] Among them, cache frequency loss is used to reflect the fluctuation of cache hit rate, and stable cache frequency loss HDC is used to quantify the fluctuation and unevenness of all server caches of the hotel, which can effectively eliminate incorrect choices caused by abnormal fluctuations.

[0062] Furthermore, in step S300, obtaining the target server by calculating the concurrent response time difference of the cache stable server includes the following steps:

[0063] S301, calculating the concurrent response time difference of the cache stable server;

[0064] Set an empty sequence STS as the response time set of the cache stable server, where the sequence takes the response time of the cache stable server as the element of the set STS, and uses STS(i) to represent the average response time of the cache stable server with sequence number i in the last hour, where i represents the sequence number of the cache stable server, i = 1, 2, ..., G, G is the number of cache stable servers; get the shortest response time in STS and record it as STSs; get the longest response time in STS and record it as STSx; get the average value of all response times in STS as STSm, set an empty sequence as the cache stable server The minimum concurrency set KPT of the cache stable server in the last hour, where the sequence takes the minimum concurrency of the cache stable server in the last hour as the element of the set KPT, and uses KPT(i) to represent the minimum concurrency of the cache stable server with sequence number i in the last hour; set an empty sequence as the maximum concurrency set KPU of the cache stable server in the last hour, where the sequence takes the maximum concurrency of the cache stable server in the last hour as the element of the set KPU, and uses KPU(i) to represent the maximum concurrency of the cache stable server with sequence number i in the last hour; obtain the average concurrency in KPU and record it as KPUs.

[0065] The minimum concurrent number of cache stability servers in the last hour indicates the lowest number of concurrent requests received by the cache stability servers in the last hour. The maximum concurrent number of cache stability servers in the last hour indicates the highest number of concurrent requests received by the cache stability servers in the last hour.

[0066] The concurrent response time difference includes the first concurrent response time difference and the second concurrent response time difference. The difference between STSx and STSm is taken as the first concurrent response time difference SE; the difference between STSm and STSs is taken as the second concurrent response time difference SR.

[0067] S302, calculating the concurrent load request duration of the cache stable server by the concurrent response time difference of the cache stable server;

[0068] Furthermore, an empty sequence POT is set to store the concurrent load request duration of the cache stable server, wherein the sequence POT uses the concurrent load request duration POT(i) of the cache stable server as an element of the sequence POT, wherein the concurrent load request duration POT(i) is calculated as follows:

[0069] POT(i)=STS(i)+|(SE-SR)|×(KPT(i)+KPU(i)) / KPUs;

[0070] Among them, || is the absolute value symbol. The principle of concurrent load request duration POT(i) is: based on the server's own response time STS(i), the fluctuation of the server response time is considered through the concurrent response time difference (SE-SR): if SE is much larger than SR or much smaller than SR, it means that the maximum response time is far from the average value, while the minimum response time is close to the average value, indicating that the server performance degrades significantly under high load; if SE is close to SR, it means that the server performance is relatively stable under high load and low load, and is suitable as a target server. The concurrent load capacity of the server is considered by (KPT(i)+KPU(i)) / KPUs: if the number of concurrent requests KPT(i) and KPU(i) of a server are both high, it means that the server is under greater pressure. If the concurrent load of the server is low, the POT(i) value is small, which means that the server can give priority to processing new requests.

[0071] S303: Determine the target server according to the concurrent load request duration of the cache stabilization server.

[0072] Specifically, the cache stable servers are sorted from small to large according to the concurrent load request duration POT(i). The sequence number corresponding to the largest value in the load duration POT(i) is G, and the sequence number corresponding to the smallest value in the load duration POT(i) is 1. According to the sequence number of the cache stable server, the cache stable server with a smaller sequence number is preferentially selected as the target server.

[0073] S400: Generate and send a personalized order notification to the target user through the target server to send an operation instruction for order decision.

[0074] Specifically, in step S400, the target server analyzes the user's potential needs based on their historical booking behavior, browsing history, preferences, and real-time market trends, and generates targeted order notifications, such as price change alerts, room shortage warnings, and limited-time discount notifications. Furthermore, the target server sends personalized order notifications to the target user via multiple channels (such as SMS, email, or app push), prompting the user to make an order decision as quickly as possible, thereby improving booking conversion rates and optimizing the user experience.

[0075] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0076] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0077] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0078] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0079] In addition, the terms "first" and "second" used in the embodiments of the present invention are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in this embodiment. Therefore, the features defined by the terms "first" and "second" in the embodiments of the present invention can explicitly or implicitly indicate that the embodiment includes at least one of such features. In the description of the present invention, the word "plurality" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0080] In the present invention, unless otherwise clearly specified or limited in the embodiments, the terms "installed," "connected," "connect," and "fixed" appearing in the embodiments should be understood in a broad sense. For example, the connection may be a fixed connection, a detachable connection, or an integral connection. It can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be internal communication between two elements, or an interaction between two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood based on the specific implementation.

[0081] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0082] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for realizing intelligent hotel distribution and follow-up based on enterprise private domain knowledge, characterized in that: The method comprises the following steps: S100, obtaining the hotel's server information; S200, performing dynamic cache frequency effect analysis on the server through server information to determine the cache stable server; wherein, step S200 includes: using j to represent the identifier of each server, j=1,2,…,K, K represents the number of servers; recording the maximum cache hit rate of the j-th server in the last hour as r(j), the minimum cache hit rate of the j-th server in the last hour as p(j), and the average value of the minimum cache hit rate of all servers in the last hour as Ms; performing dynamic cache frequency effect analysis on the server, wherein the step of performing dynamic cache frequency effect analysis on the server includes: S201, calculating the cache frequency loss of the server in the last hour; calculating the cache frequency loss ZG(j) of the j-th server in the last hour, wherein ZG(j) is the difference between r(j) and p(j), and adding all ZG(j) and dividing by K to obtain the stable cache frequency loss HDC; S202, define an integer variable k, set the initial value to 1, and create a blank sequence ZRE; S203, perform dynamic cache frequency effect analysis, where the dynamic cache frequency effect analysis is as follows: compare the values ​​of ZG(j) and HDC, and compare the values ​​of [p(j)+HDC] and Ms. If ZG(j) is less than HDC and [p(j)-HDC] is greater than Ms, then add ZG(j) to the sequence ZRE; S204, determine whether the dynamic cache frequency effect analysis is completed. The specific determination method is as follows: if the current variable k is less than K, increase k by 1, and return to step S203 to continue the dynamic cache frequency effect analysis; if the current variable k is equal to K, it means that the dynamic cache frequency effect analysis has been completed, and go to step S205; S205, record the servers corresponding to all elements in the sequence ZRE as cache stable servers; S300, obtaining a target server by calculating the concurrent response time difference of the cache stable server; wherein step S300 includes: S301, calculating the concurrent response time difference of the cache stable server; wherein the method for calculating the concurrent response time difference of the cache stable server includes: Set an empty sequence STS as the response time set of the cache stable server, where the sequence takes the response time of the cache stable server as the element of the set STS, and uses STS(i) to represent the average response time of the cache stable server with sequence number i in the last hour, where i represents the sequence number of the cache stable server, i=1,2,…,G, G is the number of cache stable servers; get the shortest response time in STS and record it as STSs; get the longest response time in STS and record it as STSx; get the average value of all response times in STS as STSm, set an empty sequence as the cache stable server The minimum concurrent number set KPT of the cache stable server in the last hour is used as the element of the set KPT, and KPT(i) is used to represent the minimum concurrent number of the cache stable server in the last hour; an empty sequence is set as the maximum concurrent number set KPU of the cache stable server in the last hour, wherein the sequence takes the maximum concurrent number of the cache stable server in the last hour as the element of the set KPU, and KPU(i) is used to represent the maximum concurrent number of the cache stable server in the last hour; the average number of requests in KPU is recorded as KPUs; The minimum concurrent number of the cache stable server in the last hour indicates the lowest number of concurrent requests received by the cache stable server in the past hour; the maximum concurrent number of the cache stable server in the last hour indicates the highest number of concurrent requests received by the cache stable server in the past hour. The concurrent response time difference includes the first concurrent response time difference and the second concurrent response time difference. The difference between STSx and STSm is taken as the first concurrent response time difference SE; the difference between STSm and STSs is taken as the second concurrent response time difference SR. S302, calculating the concurrent load request duration of the cache stable server by the concurrent response time difference of the cache stable server; S303, determining the target server according to the concurrent load request duration of the cache stable server; S400: Generate and send a personalized order notification to the target user through the target server to send an operation instruction for order decision.

2. The method for realizing intelligent hotel distribution and order tracking based on enterprise private domain knowledge according to claim 1 is characterized in that: In step S100 , the server information of the hotel is obtained, wherein the server information includes the response time of the server, the number of requests to the server, the number of concurrent requests to the server, and the cache hit rate.

3. The method for realizing intelligent hotel distribution and order tracking based on enterprise private domain knowledge according to claim 1 is characterized in that: In step S302, calculating the concurrent load request duration of the cache stabilization server by using the concurrent response time difference of the cache stabilization server includes: An empty sequence POT is set to store the concurrent load request duration of the cache stable server, wherein the sequence POT uses the concurrent load request duration POT(i) of the cache stable server as an element of the sequence POT.

4. The method for realizing intelligent hotel distribution and order tracking based on enterprise private domain knowledge according to claim 3 is characterized in that: In step S303, determining the target server according to the concurrent load request duration of the cache stabilization server includes: The cache stable servers are sorted from small to large according to the concurrent load request duration POT(i). The sequence number corresponding to the largest value in the load duration POT(i) is G, and the sequence number corresponding to the smallest value in the load duration POT(i) is 1. According to the sequence number of the cache stable server, the cache stable server with a smaller sequence number is preferentially selected as the target server.

5. The method for realizing intelligent hotel distribution and order tracking based on enterprise private domain knowledge according to claim 1 is characterized in that: In step S400, a personalized follow-up notification is generated and sent to the target user through the target server to send an operation instruction for order decision, including: the target server analyzes the user's potential needs based on the user's historical booking behavior, browsing history, preference settings and real-time market dynamics, and generates a targeted follow-up notification.

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