Personalized human resource service online shopping mall recommendation method and system

Abstract

The invention belongs to the field of online shopping mall data transmission optimization, and provides a personalized human resource service online shopping mall recommendation method and system, and the method comprises the following steps: screening out a matched human resource server according to request information sent by a user, and obtaining the response aging information of the matched human resource server; calculating the aging load response time through the response aging information; calculating concurrent load feedback time according to the aging load response time; screening out an optimal adaptive human resource server according to the concurrent load feedback time; and switching the user from the current human resource server to the adaptive human resource server. According to the recommendation method provided by the embodiment of the invention, the problem that the response time is obviously delayed due to the fact that individual server resources are excessively used can be effectively solved by introducing the aging load response time and a real-time load management technology, and more intelligent and more efficient personalized human resource service experience is provided for users.

Description

Technical Field

[0001] The invention belongs to the field of network mall data transmission optimization, and in particular relates to a personalized human resource service network mall recommendation method and system. Background Art

[0002] At present, existing methods mainly rely on average response time to evaluate system performance. Average response time is the average time from when a request arrives at the system to when the system returns a response. Although this evaluation method is simple and intuitive, it ignores the actual performance of the system under different load conditions and cannot reflect the performance under extreme loads: especially under extreme load conditions, the system may experience performance degradation or increased latency, such as request queuing, response timeouts, or system crashes. Relying on average response time cannot accurately predict or evaluate these situations because the average response time cannot reflect the actual performance of the system under high load pressure. And because the web page load may change significantly over time and the usage of different human resource servers, static evaluation methods usually only consider performance under a single load condition and cannot dynamically adjust or adapt to the impact of load changes. This leads to poor performance of the system in certain time periods or specific tasks, which affects user experience and system stability.

[0003] Moreover, personalized human resource service online mall recommendations are a scenario where users have high requirements for response speed. Incomplete evaluation of the average response time will cause the system to fail to achieve the response speed expected by users, thus affecting user experience and service quality. This impact is also particularly significant in other e-commerce, online consulting, and real-time interaction applications. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first object of the present invention is to propose a personalized human resource service online mall recommendation method, which can effectively solve the shortcomings of the human resource service system in response capability evaluation and load management by introducing time-sensitive load response time and real-time load management technology;

[0005] The second object of the present invention is to provide a personalized human resource service network mall recommendation system.

[0006] To achieve the above-mentioned purpose, a first embodiment of the present invention provides a personalized human resource service online mall recommendation method, the method comprising the following steps:

[0007] S100, screening out matching human resource servers according to request information sent by a user, and obtaining response timeliness information of the matching human resource servers;

[0008] S200, calculating the time-sensitive load response time through the response time-sensitive information;

[0009] S300, calculating the concurrent load feedback time according to the time-effect load response time;

[0010] S400, selecting the optimal human resource server according to the concurrent load feedback time;

[0011] S500: Switch the user from the current human resources server to an optimal human resources server.

[0012] According to the recommendation method of an embodiment of the present invention, the time-sensitive load response time and real-time load management technology can be introduced to effectively solve the problem of significant response time delay caused by excessive use of individual server resources due to the lack of real-time monitoring and dynamic load adjustment capabilities of the server of the human resources service system, thereby providing users with a smarter and more efficient personalized human resources service experience.

[0013] Furthermore, in step S100; the request information sent by the user includes obtaining the service type, the service type includes online courses, salary reports, recruitment services, job inquiries, or detailed description texts of needs and expectations, the detailed description texts include offline course names, preset salaries, and talent types matching the positions; the response timeliness information of the human resources server includes: the name of the human resources server, the response time of the human resources server, the maximum throughput of the human resources server, and the number of concurrent users of the human resources server.

[0014] Furthermore, the human resource server is a server that transmits human resource service text files in the form of XML files, and a human resource service system is composed of multiple human resource servers.

[0015] Furthermore, in step S100, the user receives the human resources server and sending request information pushed by the server through a mobile device or a client such as a notebook computer or a desktop computer.

[0016] Among them, the human resources server is a server that transmits Web pages in JSON or XML data format, which is used to manage and process service request information related to human resources, and provide a standardized interface or communication protocol to enable different applications or systems to interact and communicate.

[0017] Among them, the method for screening human resources servers according to request information sent by users is as follows: the human resources service system first receives request information sent by users through clients (such as mobile devices, laptops or desktop computers), and screens human resources servers according to the request information. The human resources servers transmit text files of human resources services in the form of XML files. Human resources services include offline courses, online courses, salary reports, HR products, HR services, workplace value inquiries and recognition and incentive system inquiries; offline courses include vocational training courses; online courses include vocational skills improvement courses on online learning platforms; salary reports include salary levels for positions or industries; HR products include software tools or management platforms; HR services include outsourcing services and recruitment services; workplace value inquiries include the market value of occupations; recognition and incentive system inquiries include the company's incentive and recognition systems for employees.

[0018] Furthermore, filtering the human resources server according to the request information specifically includes: after parsing the user's request information, the system will convert it into corresponding filtering conditions as input for filtering the human resources server, and filter out matching human resources servers from the database; the dimensions considered in the screening process include: the specific service content requested by the user (such as online courses, salary reports, etc.); the database is a Web database stored in the personalized human resources service online mall.

[0019] Furthermore, in step S200, calculating the time-sensitive load response time by using the response time-sensitive information includes:

[0020] After the system selects the matching human resource server from the database based on the user's request information, the time-sensitive load response time is calculated based on the response time of the human resource server after receiving the user's request in the last hour;

[0021] The method for calculating the time-effective load response time through the response time after the human resources server receives the user request in the last hour is as follows: obtain all matching human resources servers and use T(j) to represent the jth human resources server, where j represents the identifier of each matching human resources server, j=1,2,…,K, and K represents the number of matching human resources servers; obtain the response time after each matching human resources server receives the user request in the last hour, and use time(j,i) to represent the response time after the i-th matching human resources server receives the user request in the last hour, where i represents the identifier of the response time, i=1,2,…,G, and G represents the number of response times after the j-th matching human resources server receives the user request in the last hour; record the maximum value of the response time after the j-th human resources server receives the user request in the last hour as tmax(j), and the average value of all response times after the j-th human resources server receives the user request in the last hour is tav(j);

[0022] Since traditional methods usually only consider the average response time and ignore the performance under extreme loads or complex tasks, this incomplete evaluation may cause the system to be unable to effectively respond to user needs under high load, and the web page load may vary significantly over time and on different human resource servers, and the static evaluation of traditional methods cannot effectively adapt to these changes. In order to screen human resource servers according to the changes in web page load over time, it is necessary to calculate the time-sensitive load response time of the human resource server, and further measure the load level of the human resource server through the time-sensitive load response time. The time-sensitive load response time is the response time that incorporates the difference between the maximum response time and the average response time.

[0023] Further calculate the time-efficiency load response time timekp(j) of the jth human resource server, where timekp(j) is tav(j) plus the difference between the maximum response time and the average response time, where the difference between the maximum response time and the average response time is the difference between tmax(j) and tav(j) multiplied by The ratio of G×tav(j).

[0024] Furthermore, tav(j) represents the average response time, which can reflect the processing speed of the human resource server under normal load and the basic response capability of the human resource server; tmax(j) represents the maximum response time, which can reflect the processing capability of the human resource server under extreme load or complex tasks; The accumulation of this part provides a comprehensive measure of the response speed deviation of the human resources server to reflect the gap between the processing capacity of the human resources server and the actual demand; Divide by G×tav(j) to integrate the difference between the maximum response time and the average response time, and finally get the time-dependent load response time timekp(j).

[0025] The beneficial effects of this step are: it solves the problem that the static performance evaluation method only considers the responsiveness of the system under fixed load conditions, while ignoring the impact of dynamic load on system performance, and considers the impact of load changes on responsiveness through the time-sensitive load response time, thereby alleviating the load of the human resources server under high load. And based on the calculation results of the time-sensitive load response time, the system can adjust the service strategy and resource allocation according to different load conditions; for example, when some human resources servers are under high load, the system can give priority to allocating more resources to human resources servers with stronger responsiveness to ensure the overall performance stability of the system; when some human resources servers are under too low load, it can optimize resource utilization and improve the efficiency and cost-effectiveness of the system. This intelligent service optimization solution can better improve the flexibility and response speed of the personalized human resources service network mall.

[0026] Since the time-efficiency load response time timekp(j) mainly considers the difference between the average response time and the maximum response time, but has not yet considered the direct impact of the actual load of the human resources server on the response time, in order to solve the above problem, it is necessary to calculate the concurrent load feedback time of the human resources server based on the time-efficiency load response time of the matched human resources server.

[0027] Further, in step S300, calculating the concurrent load feedback time according to the time-effect load response time includes:

[0028] Acquire the concurrent user data of the matching human resource server, where the concurrent user data includes the maximum concurrent user number, the current concurrent user number and the total concurrent user number in the last hour, and record the maximum concurrent user number of the j-th human resource server as mthx(j), that is, the maximum number of simultaneously connected users that the human resource server can support; record the current concurrent user number of the j-th human resource server as mth(j), that is, the number of users currently using the human resource server; record the total concurrent user number of the j-th human resource server in the last hour as mthp(j), that is, the number of user requests received by the human resource server in the past hour;

[0029] Calculate the concurrent load feedback time timeact(j): timeact(j) is timekp(j) multiplied by the ratio of the current concurrent total delay KP1 and the total concurrent delay KP2 in the last hour, where the current concurrent total delay KP1 is =G×tav(j)×at; where a is the current concurrency rate, a=mth(j) / mthx(j), and at is the total concurrency rate in the last hour, at=mthp(j) / mth(j);

[0030] The beneficial effects of this step are: introducing the data of the current number of concurrent users and the total number of concurrent users in the last hour through the concurrent load feedback time timeact(j), and further dynamically adjusting the response time measurement through the current concurrency rate and the total concurrency rate in the last hour, which more accurately reflects the processing efficiency of the human resources server under actual load conditions; through this real-time concurrent user data, the system can dynamically adjust the response strategy to cope with resource allocation under different load conditions. This optimization capability makes the human resources service system more intelligent and efficient, and can maintain a stable service level in the face of complex load fluctuations.

[0031] Further, in step S400, the optimal human resource server is selected according to the concurrent load feedback time, including:

[0032] The average value of all concurrent load feedback times is calculated and recorded as tadv, and the human resource server whose concurrent load feedback time is less than tadv is recorded as the optimal human resource server.

[0033] Further, in step S500, switching the user from the current human resource server to the optimal human resource server includes:

[0034] Switch users from the current HR server to the optimal HR server, which can provide faster response services. After the switch is completed, the HR service system will continue to monitor the performance of the optimal HR server to ensure that the optimal HR server can maintain a fast response. When it is found that the optimal HR server is also overloaded, the system will re-evaluate the HR server and perform further switching operations.

[0035] The beneficial effects of the present invention are as follows: by introducing time-sensitive load response time and real-time load management technology, the problem of significant response time delay caused by excessive use of individual server resources due to the lack of real-time monitoring and dynamic load adjustment capacity of the server of the human resources service system is effectively solved, providing users with a smarter and more efficient personalized human resources service experience.

[0036] To achieve the above-mentioned purpose, the second aspect of the present invention also proposes a personalized human resources service network mall recommendation system, which includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in a personalized human resources service network mall recommendation method. The personalized human resources service network mall recommendation system runs on desktop computers, notebooks, PDAs, and computing devices in cloud data centers.

[0037] By implementing the personalized human resource service online mall recommendation method through the personalized human resource service online mall recommendation system, the deficiencies of the human resource service system in response capability evaluation and load management can be effectively solved by introducing time-sensitive load response time and real-time load management technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Shown is a flow chart of a personalized human resource service online mall recommendation method;

[0039] Figure 2 Shown is the structure diagram of the personalized human resource service online mall recommendation system. DETAILED DESCRIPTION

[0040] Embodiments of the present invention are described in detail below, 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 should not be construed as limiting the present invention.

[0041] Figure 1 Shown is a flow chart of a personalized human resource service online mall recommendation method.

[0042] Reference Figure 1 The present invention proposes a personalized human resource service network mall recommendation method, which includes the following steps:

[0043] S100, screening out matching human resource servers according to request information sent by a user, and obtaining response timeliness information of the matching human resource servers;

[0044] S200, calculating the time-sensitive load response time through the response time-sensitive information;

[0045] S300, calculating the concurrent load feedback time according to the time-effect load response time;

[0046] S400, selecting the optimal human resource server according to the concurrent load feedback time;

[0047] S500: Switch the user from the current human resources server to an optimal human resources server.

[0048] According to the recommendation method of an embodiment of the present invention, the time-sensitive load response time and real-time load management technology can be introduced to effectively solve the problem of significant response time delay caused by excessive use of individual server resources due to the lack of real-time monitoring and dynamic load adjustment capabilities of the server of the human resources service system, thereby providing users with a smarter and more efficient personalized human resources service experience.

[0049] Furthermore, in step S100; the request information sent by the user includes obtaining the service type, the service type includes online courses, salary reports, recruitment services, job inquiries, or detailed description texts of needs and expectations, the detailed description texts include offline course names, preset salaries, and talent types matching the positions; the response timeliness information of the human resources server includes: the name of the human resources server, the response time of the human resources server, the maximum throughput of the human resources server, and the number of concurrent users of the human resources server.

[0050] Furthermore, the human resource server is a server that transmits human resource service text files in the form of XML files, and a human resource service system is composed of multiple human resource servers.

[0051] Furthermore, in step S100, the user receives the human resources server and sending request information pushed by the server through a mobile device or a client such as a notebook computer or a desktop computer.

[0052] Among them, the human resources server is a server that transmits Web pages in JSON or XML data format, which is used to manage and process service request information related to human resources, and provide a standardized interface or communication protocol to enable different applications or systems to interact and communicate.

[0053] Among them, the method for screening human resources servers according to request information sent by users is as follows: the human resources service system first receives request information sent by users through clients (such as mobile devices, laptops or desktop computers), and screens human resources servers according to the request information. The human resources servers transmit text files of human resources services in the form of XML files. Human resources services include offline courses, online courses, salary reports, HR products, HR services, workplace value inquiries and recognition and incentive system inquiries; offline courses include vocational training courses; online courses include vocational skills improvement courses on online learning platforms; salary reports include salary levels for positions or industries; HR products include software tools or management platforms; HR services include outsourcing services and recruitment services; workplace value inquiries include the market value of occupations; recognition and incentive system inquiries include the company's incentive and recognition systems for employees.

[0054] Furthermore, filtering the human resources server according to the request information specifically includes: after parsing the user's request information, the system will convert it into corresponding filtering conditions as input for filtering the human resources server, and filter out matching human resources servers from the database; the dimensions considered in the screening process include: the specific service content requested by the user (such as online courses, salary reports, etc.); the database is a Web database stored in the personalized human resources service online mall.

[0055] Furthermore, in step S200, calculating the time-sensitive load response time by using the response time-sensitive information includes:

[0056] After the system selects the matching human resource server from the database based on the user's request information, the time-sensitive load response time is calculated based on the response time of the human resource server after receiving the user's request in the last hour;

[0057] The method for calculating the time-effective load response time through the response time after the human resources server receives the user request in the last hour is as follows: obtain all matching human resources servers and use T(j) to represent the jth human resources server, where j represents the identifier of each matching human resources server, j=1,2,…,K, and K represents the number of matching human resources servers; obtain the response time after each matching human resources server receives the user request in the last hour, and use time(j,i) to represent the response time after the i-th matching human resources server receives the user request in the last hour, where i represents the identifier of the response time, i=1,2,…,G, and G represents the number of response times after the j-th matching human resources server receives the user request in the last hour; record the maximum value of the response time after the j-th human resources server receives the user request in the last hour as tmax(j), and the average value of all response times after the j-th human resources server receives the user request in the last hour is tav(j);

[0058] Since traditional methods usually only consider the average response time and ignore the performance under extreme loads or complex tasks, this incomplete evaluation may cause the system to be unable to effectively respond to user needs under high load, and the web page load may vary significantly over time and on different human resource servers, and the static evaluation of traditional methods cannot effectively adapt to these changes. In order to screen human resource servers according to the changes in web page load over time, it is necessary to calculate the time-sensitive load response time of the human resource server, and further measure the load level of the human resource server through the time-sensitive load response time. The time-sensitive load response time is the response time that incorporates the difference between the maximum response time and the average response time.

[0059] Further calculate the time-efficiency load response time timekp(j) of the jth human resource server, where timekp(j) is tav(j) plus the difference between the maximum response time and the average response time, where the difference between the maximum response time and the average response time is the difference between tmax(j) and tav(j) multiplied by The ratio of G×tav(j).

[0060] Furthermore, tav(j) represents the average response time, which can reflect the processing speed of the human resource server under normal load and the basic response capability of the human resource server; tmax(j) represents the maximum response time, which can reflect the processing capability of the human resource server under extreme load or complex tasks; The accumulation of this part provides a comprehensive measure of the response speed deviation of the human resources server to reflect the gap between the processing capacity of the human resources server and the actual demand; Divide by G×tav(j) to integrate the difference between the maximum response time and the average response time, and finally get the time-dependent load response time timekp(j).

[0061] The beneficial effects of this step are: it solves the problem that the static performance evaluation method only considers the responsiveness of the system under fixed load conditions, while ignoring the impact of dynamic load on system performance, and considers the impact of load changes on responsiveness through the time-sensitive load response time, thereby alleviating the load of the human resources server under high load. And based on the calculation results of the time-sensitive load response time, the system can adjust the service strategy and resource allocation according to different load conditions; for example, when some human resources servers are under high load, the system can give priority to allocating more resources to human resources servers with stronger responsiveness to ensure the overall performance stability of the system; when some human resources servers are under too low load, it can optimize resource utilization and improve the efficiency and cost-effectiveness of the system. This intelligent service optimization solution can better improve the flexibility and response speed of the personalized human resources service network mall.

[0062] Since the time-efficiency load response time timekp(j) mainly considers the difference between the average response time and the maximum response time, but has not yet considered the direct impact of the actual load of the human resources server on the response time, in order to solve the above problem, it is necessary to calculate the concurrent load feedback time of the human resources server based on the time-efficiency load response time of the matched human resources server.

[0063] Further, in step S300, calculating the concurrent load feedback time according to the time-effect load response time includes:

[0064] Acquire the concurrent user data of the matching human resource server, where the concurrent user data includes the maximum concurrent user number, the current concurrent user number and the total concurrent user number in the last hour, and record the maximum concurrent user number of the j-th human resource server as mthx(j), that is, the maximum number of simultaneously connected users that the human resource server can support; record the current concurrent user number of the j-th human resource server as mth(j), that is, the number of users currently using the human resource server; record the total concurrent user number of the j-th human resource server in the last hour as mthp(j), that is, the number of user requests received by the human resource server in the past hour;

[0065] Calculate the concurrent load feedback time timeact(j): timeact(j) is timekp(j) multiplied by the ratio of the current concurrent total delay KP1 and the total concurrent delay KP2 in the last hour, where the current concurrent total delay KP1 is KP2 is G×tav(j)×at; where a is the current concurrency rate, a=mth(j) / mthx(j), and at is the total concurrency rate in the last hour, at=mthp(j) / mth(j);

[0066] The beneficial effects of this step are: introducing the data of the current number of concurrent users and the total number of concurrent users in the last hour through the concurrent load feedback time timeact(j), and further dynamically adjusting the response time measurement through the current concurrency rate and the total concurrency rate in the last hour, which more accurately reflects the processing efficiency of the human resources server under actual load conditions; through this real-time concurrent user data, the system can dynamically adjust the response strategy to cope with resource allocation under different load conditions. This optimization capability makes the human resources service system more intelligent and efficient, and can maintain a stable service level in the face of complex load fluctuations.

[0067] Further, in step S400, the optimal human resource server is selected according to the concurrent load feedback time, including:

[0068] The average value of all concurrent load feedback times is calculated and recorded as tadv, and the human resource server whose concurrent load feedback time is less than tadv is recorded as the optimal human resource server.

[0069] Further, in step S500, switching the user from the current human resource server to the optimal human resource server includes:

[0070] Switch users from the current HR server to the optimal HR server, which can provide faster response services. After the switch is completed, the HR service system will continue to monitor the performance of the optimal HR server to ensure that the optimal HR server can maintain a fast response. When it is found that the optimal HR server is also overloaded, the system will re-evaluate the HR server and perform further switching operations.

[0071] The beneficial effects of the present invention are as follows: by introducing time-sensitive load response time and real-time load management technology, the problem of significant response time delay caused by excessive use of individual server resources due to the lack of real-time monitoring and dynamic load adjustment capabilities of the human resources service system server is effectively solved, providing users with a smarter and more efficient personalized human resources service experience.

[0072] Figure 2 Shown is the structure diagram of the personalized human resource service online mall recommendation system.

[0073] Reference Figure 2The present invention also proposes a personalized human resource service network mall recommendation system 20, the personalized human resource service network mall recommendation system 20 includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, the processor implements the steps in a personalized human resource service network mall recommendation method when executing the computer program, and the personalized human resource service network mall recommendation system 20 runs on a desktop computer, a notebook, a PDA, and a computing device in a cloud data center.

[0074] The recommendation system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the recommendation system:

[0075] A matching unit 21, configured to filter out a matching human resource server according to the request information sent by the user, and obtain response time information of the matching human resource server;

[0076] A first calculation unit 22 is used to calculate the time-sensitive load response time through the response time-sensitive information;

[0077] The second calculation unit 23 is used to calculate the concurrent load feedback time according to the time-effect load response time;

[0078] A screening unit 24, configured to screen out optimal human resource servers according to concurrent load feedback time;

[0079] The switching unit 25 is used to switch the user from the current human resource server to the optimal human resource server.

[0080] The personalized human resource service network mall recommendation system can be run on computing devices such as desktop computers, notebooks, PDAs, and cloud servers. The personalized human resource service network mall recommendation system can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the example is only an example of a personalized human resource service network mall recommendation system 20, and does not constitute a limitation on a personalized human resource service network mall recommendation system 20, which can include more or fewer components than the example, or combine certain components, or different components. For example, the personalized human resource service network mall recommendation system can also include input and output devices, network access devices, buses, etc.

[0081] By implementing the personalized human resource service online mall recommendation method through the personalized human resource service online mall recommendation system 20, it is possible to effectively solve the deficiencies of the human resource service system in terms of response capability evaluation and load management by introducing time-sensitive load response time and real-time load management technology.

[0082] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, 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 in other suitable ways if necessary, and then stored in a computer memory.

[0083] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0084] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0085] In the description of the present invention, it is to 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”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0086] In addition, the terms "first", "second", etc. 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 the present embodiment. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present invention can explicitly or implicitly indicate that the embodiment includes at least one of the features. In the description of the present invention, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0087] In the present invention, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that 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 the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific implementation situation.

[0088] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0089] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A personalized human resource service online mall recommendation method, characterized in that: The method comprises the following steps: S100, screening out matching human resource servers according to request information sent by a user, and obtaining response timeliness information of the matching human resource servers; S200, calculating the time-sensitive load response time through the response time-sensitive information; S300, calculating the concurrent load feedback time according to the time-effect load response time; S400, selecting the optimal human resource server according to the concurrent load feedback time; S500: Switch the user from the current human resources server to an optimal human resources server.

2. A personalized human resource service online mall recommendation method according to claim 1, characterized in that: The human resource server is a server that transmits human resource service text files in the form of XML files. A human resource service system is composed of multiple human resource servers.

3. A personalized human resource service online mall recommendation method according to claim 2, characterized in that: The human resources server is a server that transmits web pages in JSON or XML data format. It is used to manage and process service request information related to human resources and provide a standardized interface or communication protocol to enable different applications or systems to interact and communicate.

4. A personalized human resource service online mall recommendation method according to claim 3, characterized in that: The human resources server transmits text files of human resources services in the form of XML files. Human resources services include offline courses, online courses, salary reports, HR products, HR services, workplace value inquiries, and recognition and incentive system inquiries.

5. A personalized human resource service online mall recommendation method according to claim 1, characterized in that: In step S200, the time-sensitive load response time is the response time obtained by incorporating the difference level ratio between the maximum response time and the average response time.

6. A personalized human resource service online mall recommendation method according to claim 1, characterized in that: The concurrent load feedback time is calculated by the time-sensitive load response time, the current concurrent rate, and the total concurrent rate in the last hour.

7. A personalized human resource service online mall recommendation method according to claim 1, characterized in that: In step S400, the optimal human resource servers selected according to the concurrent load feedback time include: The average value of all concurrent load feedback times is calculated and recorded as tadv, and the human resource server whose concurrent load feedback time is less than tadv is recorded as the optimal human resource server.

8. A personalized human resources service online mall recommendation system, characterized in that: The personalized human resource service network mall recommendation system includes: a processor, a memory, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps in the personalized human resource service network mall recommendation method described in any one of claims 1 to 7 are implemented. The personalized human resource service network mall recommendation system runs on a desktop computer, a laptop computer, a PDA or a cloud data center.

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