CN116089250B - Man-machine interaction optimization management system and management method - Google Patents
Man-machine interaction optimization management system and management method Download PDFInfo
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Abstract
The invention discloses a man-machine interaction optimization management system and a man-machine interaction optimization management method, and belongs to the technical field of interaction data management; the method comprises the steps of monitoring a carrier implemented through man-machine interaction, carrying out data analysis on all aspects of data obtained through carrier monitoring, and obtaining and analyzing operation support coefficients through integrating and calculating data items of all aspects; the modularized processing of human-computer interaction is realized by monitoring and counting the reaction data of different content options in the human-computer interaction process, integrating and calculating and analyzing the data of the monitoring and counting; the method comprises the steps that all analysis results of content options in a reference interaction monitoring period are integrated to obtain interaction fluency coefficients, and the fluency of the corresponding content options is integrally evaluated based on the interaction fluency coefficients; the method and the device are used for solving the technical problem that the overall effect of man-machine interaction optimization management is poor in the existing scheme.
Description
Technical Field
The invention relates to the technical field of interactive data management, in particular to a man-machine interaction optimization management system and a man-machine interaction optimization management method.
Background
Man-machine interaction refers to the process of information exchange between a person and a computer for completing a determined task in a certain interaction mode by using a certain dialogue language between the person and the computer.
The existing man-machine interaction optimization management scheme has certain defects in implementation, and the carrier for carrying out man-machine interaction is not monitored and analyzed, so that whether data items in different aspects in the carrier meet the requirement of man-machine interaction cannot be obtained; meanwhile, modularized monitoring analysis is not implemented on man-machine interaction, so that single interaction conditions of different content options cannot be accurately and efficiently obtained, and modularized monitoring analysis results are not integrated to analyze the overall operation conditions of man-machine optimization and implement active optimization prompt, so that the overall effect of man-machine interaction optimization management is poor.
Disclosure of Invention
The invention aims to provide a man-machine interaction optimization management system and a man-machine interaction optimization management method, which are used for solving the technical problems that in the existing scheme, no carrier for carrying out man-machine interaction is monitored and analyzed, modularized monitoring and analysis is carried out on man-machine interaction, modularized monitoring and analysis results are integrated to analyze and optimize and prompt the overall operation condition of man-machine interaction, so that the overall effect of man-machine interaction optimization management is poor.
The aim of the invention can be achieved by the following technical scheme:
the human-computer interaction optimization management system comprises a carrier monitoring and analysis module, a data analysis module and a data analysis module, wherein the carrier monitoring and analysis module is used for monitoring a carrier implemented by human-computer interaction, counting data items affecting the human-computer interaction operation in the aspect of the carrier, and performing data analysis to obtain carrier monitoring and analysis data; comprising the following steps:
acquiring a carrier model implemented by man-machine interaction and implementing monitoring statistics; obtaining a corresponding CPU model, memory model, display card model and hard disk model according to the carrier model;
comparing the obtained CPU model, memory model, display card model and hard disk model with corresponding standard CPU model, standard memory model, standard display card model and standard hard disk model respectively to judge whether the parameter requirements of man-machine interaction operation are met or not, obtaining a judging result containing qualified labels or unqualified labels, and obtaining operation support coefficients corresponding to the carrier according to a plurality of labels in the judging result;
when analyzing and evaluating the carrier operation support capacity of man-machine interaction according to the operation support coefficient, matching the operation support coefficient with a preset operation support threshold value to obtain carrier monitoring analysis data containing a first carrying instruction, a second carrying instruction or a third carrying instruction, and uploading the carrier monitoring analysis data to a cloud platform;
the interaction monitoring analysis module is used for carrying out interaction state monitoring on different interaction contents of human-computer interaction, and carrying out preprocessing and calculation analysis on each item of data obtained by the interaction state monitoring to obtain interaction monitoring analysis data;
and the optimization management prompt module is used for integrating interaction monitoring analysis data of the human-computer interaction in the reference interaction monitoring period to evaluate the fluency of all content options of the human-computer interaction, and prompting maintenance personnel to manage and maintain according to the self-adaptive dynamic prompt of the evaluation result.
Preferably, if the operation parameters corresponding to the CPU model, the memory model, the display card model and the hard disk model are not smaller than the operation parameters corresponding to the standard CPU model, the standard memory model, the standard display card model and the standard hard disk model, the operation parameters corresponding to the models are judged to meet the operation requirements and are associated with qualified labels;
otherwise, judging that the operation parameters of the corresponding signals do not meet the operation requirements and associating unqualified labels; and counting the number of tags corresponding to the CPU model, the memory model, the display card model and the hard disk model, and performing parallel computing to obtain the operation support coefficient corresponding to the human-computer interaction carrier.
Preferably, by the formula
Calculating operation support coefficient corresponding to human-computer interaction carrier>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein XQi is model weight corresponding to a CPU model, a memory model, a display card model and a hard disk model, i=1, 2,3 and 4; BQk is the label weight corresponding to each model-associated label, k=1, 2;
the CPU model, the memory model, the display card model and the hard disk model are respectively matched with the corresponding model weight tables to obtain the model weights corresponding to the CPU model, the memory model, the display card model and the hard disk model.
Preferably, the step of acquiring the interactive monitoring analysis data includes:
in a preset reference interaction monitoring period, when a content option on a man-machine interaction page is clicked, marking the time point when the man-machine interaction page is monitored to be clicked as a first monitoring time, and marking the time point when the fully displayed page after the man-machine interaction page is clicked and jumped to be correspondingly displayed as a second monitoring time;
acquiring the jump time length of the man-machine interaction page according to the second monitoring time and the first monitoring time, marking the jump time length as TS, and simultaneously, matching the clicked content option name with an option weight table pre-stored in a database to acquire a corresponding content option weight and marking the content option weight as XQ; numerical parallel vertical calculation of each item of data of the extraction mark is realized through a formula
Calculation acquisition personInteraction coefficient of machine interaction->
The method comprises the steps of carrying out a first treatment on the surface of the In the formula, TS0 is the standard jump time length corresponding to the content option.
Preferably, when the response state of the corresponding content option is evaluated according to the interaction reaction coefficient, matching the interaction reaction coefficient with the corresponding standard interaction reaction range to obtain option analysis data containing a first reaction instruction and one type of interaction content, a second reaction instruction and two types of interaction content or a third reaction instruction and three types of interaction content;
and arranging and combining option analysis data corresponding to the plurality of content options according to the analysis time sequence to obtain interaction monitoring analysis data of human-computer interaction and uploading the interaction monitoring analysis data to a cloud platform and a database.
Preferably, if the interaction reaction coefficient is smaller than the minimum value of the standard interaction reaction range, generating a first reaction instruction and marking the corresponding content option as one type of interaction content;
if the interaction reaction coefficient is not smaller than the minimum value of the standard interaction reaction range and not larger than the maximum value of the standard interaction reaction range, generating a second reaction instruction and marking the corresponding content option as second-class interaction content;
if the interaction reaction coefficient is larger than the maximum value of the standard interaction reaction range, generating a third reaction instruction and marking the corresponding content options as three types of interaction content.
Preferably, the working steps of the optimization management prompt module include:
traversing the interaction monitoring analysis data, and sequentially evaluating the fluency of the corresponding content options according to a plurality of option analysis data acquired by traversing;
counting all option analysis data of content options in a reference interaction monitoring period, traversing all option analysis data, counting the total number of occurrences of one type of interaction content, two types of interaction content and three types of interaction content, and marking the total number as YJZ, EJZ and SJZ respectively; numerical parallel vertical calculation of each item of data of the extraction mark is realized through a formula
Calculating and obtaining interaction fluency coefficient (corresponding to man-machine interaction content options)>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein, c1 and c2 are constant coefficients greater than zero, and 2×c1=c2.
Preferably, when analyzing the interaction fluency of the corresponding content options according to the interaction fluency coefficient, matching the interaction fluency coefficient with the corresponding standard interaction fluency threshold value to obtain option fluency analysis data containing fluency tags or non-fluency tags;
and when the overall fluency of the human-computer interaction is evaluated according to the total number of the interactive fluency content options of all the content options, comparing and judging the total number of the interactive fluency content options with a preset total number of the human-computer interaction fluency standard content options to obtain an evaluation result containing an interactive fluency instruction or an interactive fluency instruction, uploading the evaluation result to a cloud platform and a database, and prompting a human-computer interaction maintainer to perform optimized maintenance on the corresponding human-computer interaction by the cloud platform according to the interactive fluency instruction in the evaluation result.
In order to solve the above problems, the present invention further provides a human-computer interaction optimization management method, which includes:
the method comprises the steps of monitoring a carrier implemented by human-computer interaction, counting data items affecting the operation of the human-computer interaction in the aspect of the carrier, and analyzing the data to obtain carrier monitoring analysis data containing a first carrying instruction, a second carrying instruction or a third carrying instruction, and respectively prompting that the human-computer interaction can not be implemented and prompting that the human-computer interaction can be implemented according to the first carrying instruction, the second carrying instruction and the third carrying instruction in the carrier monitoring analysis data;
performing interaction state monitoring on different interaction contents of human-computer interaction, and performing preprocessing and computational analysis on each item of data obtained by the interaction state monitoring to obtain option analysis data containing a first reaction instruction and one type of interaction contents, a second reaction instruction and two types of interaction contents or a third reaction instruction and three types of interaction contents; the option analysis data corresponding to the content options are arranged and combined according to the time sequence of analysis to obtain interaction monitoring analysis data;
and integrating interaction monitoring analysis data of the human-computer interaction in the reference interaction monitoring period to evaluate fluency of all content options of the human-computer interaction, and prompting maintenance personnel to manage and maintain according to self-adaptive dynamics of evaluation results.
In order to solve the above problems, the present invention also provides a storage medium including at least one processor; and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute a human-computer interaction optimization management system.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the carrier implemented by human-computer interaction is monitored, and the data of each aspect obtained by monitoring the carrier is subjected to data analysis, so that whether the data items of different aspects in the carrier meet the requirement of human-computer interaction can be intuitively and efficiently obtained, the operation support coefficient is obtained by integrating and calculating the data items of each aspect, the operation support condition of the carrier can be intuitively known by analyzing the operation support coefficient, meanwhile, the data support of the carrier aspect can be provided for the analysis of the subsequent human-computer interaction fluency aspect, and the diversity and the accuracy of the human-computer interaction monitoring analysis can be effectively improved.
On the other hand, the invention realizes the modularized processing of man-machine interaction by monitoring and counting the reaction data of different content options in the man-machine interaction process and integrating and calculating and analyzing the data of the monitoring and counting, can intuitively and efficiently acquire single interaction conditions of different content options, can provide reliable data support for the analysis of the whole interaction conditions of the subsequent different content options and the analysis of the whole interaction conditions of the man-machine interaction, and can effectively improve the accuracy and the comprehensiveness of the man-machine interaction.
According to other aspects of the invention, the interaction fluency coefficient is obtained by integrating all analysis results of the content options in the reference interaction monitoring period, and the fluency of the corresponding content options is integrally evaluated based on the interaction fluency coefficient, so that the flow condition of all content options of human-computer interaction can be intuitively and efficiently obtained, the data support in the aspect of content options can be provided for the analysis and evaluation of the overall fluency of the subsequent human-computer interaction, and the local monitoring analysis effect in the human-computer interaction process is effectively improved; by analyzing and classifying the overall fluency of all content options, counting the classified results and analyzing and judging the overall fluency of human-computer interaction according to the counted results, maintenance personnel can be timely and efficiently prompted to maintain, active monitoring and active early warning prompt of human-computer interaction are realized, and compared with the existing scheme, the invention can realize more accurate and efficient maintenance management effect through manual regular or irregular maintenance and optimization.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block diagram of a human-computer interaction optimization management system according to the present invention.
Fig. 2 is a flow chart of a man-machine interaction optimization management method of the invention.
Fig. 3 is a schematic structural diagram of a computer device implementing an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the invention relates to a man-machine interaction optimization management system, which comprises a carrier monitoring and statistics module, an interaction monitoring and analysis module, a cloud platform and a database;
the carrier monitoring and analyzing module is used for monitoring the carrier implemented by human-computer interaction, counting data items affecting the human-computer interaction operation in the aspect of the carrier, and carrying out data analysis to obtain carrier monitoring and analyzing data; comprising the following steps:
acquiring a carrier model implemented by man-machine interaction and implementing monitoring statistics; the carrier refers to equipment capable of running man-machine interaction, and comprises but is not limited to intelligent touch screen equipment;
obtaining a corresponding CPU model, memory model, display card model and hard disk model according to the carrier model;
comparing the obtained CPU model, memory model, display card model and hard disk model with corresponding standard CPU model, standard memory model, standard display card model and standard hard disk model respectively to judge whether the parameter requirements of man-machine interaction operation are met;
the standard CPU model, the standard memory model, the standard display card model and the standard hard disk model can be obtained based on big data designed by the man-machine interaction system;
if the operation parameters corresponding to the CPU model, the memory model, the display card model and the hard disk model are not smaller than the operation parameters corresponding to the standard CPU model, the standard memory model, the standard display card model and the standard hard disk model, judging that the operation parameters corresponding to the models meet the operation requirements and associating qualified labels; for example, the running parameters of the CPU model are the main frequency; the running parameter of the memory model is the storage speed; the operating parameter of the display card model is the core frequency; the operation parameter of the hard disk model is the hard disk capacity; the operation parameters are compared by extracting the numerical values thereof;
otherwise, judging that the operation parameters of the corresponding signals do not meet the operation requirements and associating unqualified labels; counting tag weights correspondingly associated with CPU model, memory model, display card model and hard disk model, and performing parallel vertical calculation through a formula
Calculating and obtaining an operation support coefficient corresponding to the human-computer interaction carrier>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein XQi is model weight corresponding to a CPU model, a memory model, a display card model and a hard disk model, i=1, 2,3 and 4; BQk is the label weight corresponding to each model-associated label, k=1, 2;
the CPU model, the memory model, the display card model and the hard disk model are respectively matched with the corresponding model weight tables to obtain model weights corresponding to the CPU model, the memory model, the display card model and the hard disk model;
the CPU model, the memory model, the display card model and the model weight table corresponding to the hard disk model are constructed in advance; for example, the CPU model weight table corresponding to the CPU model contains a plurality of different CPU models and corresponding CPU model weights, one corresponding CPU model weight is preset for different CPU models, and the specific numerical value of the CPU model weight can be set according to the main frequency of the specific numerical value; the construction mode of the model weight table corresponding to the memory model, the display card model and the hard disk model is the same as that of the CPU model weight table;
in addition, the label weights corresponding to the qualified labels and the unqualified labels can be defined by a man-machine interaction system designer;
it should be noted that, the operation support coefficient is a numerical value for integrating the data items of the carrier that affect the man-machine interaction operation to integrally evaluate the operation support capability; the larger the operation support coefficient is, the stronger the operation support capability is;
when analyzing and evaluating the carrier operation support capacity of man-machine interaction according to the operation support coefficient, matching the operation support coefficient with a preset operation support threshold; the operation support threshold value can be obtained through big data designed by a man-machine interaction system;
if the operation support coefficient is smaller than the operation support threshold, judging that the operation support capability of the corresponding carrier does not meet the operation requirement and generating a first carrying instruction;
if the operation support coefficient is not smaller than the operation support threshold value and not larger than Y of the operation support threshold value, judging that the operation support capacity of the corresponding carrier meets the operation requirement and is in a normal state and generating a second carrying instruction; y is a real number greater than one hundred;
if the operation support coefficient is larger than Y of the operation support threshold, judging that the operation support capacity of the corresponding carrier meets the operation requirement and is in an excellent state, and generating a third carrying instruction;
the operation support coefficient and the corresponding first carrying instruction, second carrying instruction or third carrying instruction form carrier monitoring analysis data and are uploaded to the cloud platform, and the cloud platform respectively prompts that the man-machine interaction can not be implemented and prompts that the man-machine interaction can be implemented according to the first carrying instruction, the second carrying instruction and the third carrying instruction in the carrier monitoring analysis data;
in the embodiment of the invention, the carrier implemented by human-computer interaction is monitored, and the data of each aspect obtained by monitoring the carrier is subjected to data analysis, so that whether the data items of different aspects in the carrier meet the requirement of human-computer interaction can be intuitively and efficiently obtained, the operation support coefficient is obtained by integrating and calculating the data items of each aspect, the operation support condition of the carrier can be intuitively known by analyzing the operation support coefficient, meanwhile, the data support of the carrier aspect can be provided for the analysis of the subsequent human-computer interaction fluency aspect, and the diversity and the accuracy of the human-computer interaction monitoring analysis can be effectively improved.
The interaction monitoring analysis module is used for carrying out interaction state monitoring on different interaction contents of human-computer interaction, and carrying out preprocessing and calculation analysis on each item of data obtained by the interaction state monitoring to obtain interaction monitoring analysis data; comprising the following steps:
in a preset reference interaction monitoring period, the unit of the reference interaction monitoring period is day, specifically may be 7 days, when a content option on a man-machine interaction page is clicked, the time point when the man-machine interaction page is monitored to be clicked is marked as a first monitoring time, and the time point when a page which is completely displayed after the man-machine interaction page is clicked and jumped is marked as a second monitoring time; wherein the units of the first monitoring time and the second monitoring time are accurate to seconds;
acquiring the jump time length of the man-machine interaction page according to the second monitoring time and the first monitoring time, and marking the jump time length as TS; the unit of the jump time length is seconds, and meanwhile, the clicked content option name is matched with an option weight table prestored in a database to obtain a corresponding content option weight and marked as XQ;
the selected weight table comprises all content options of human-computer interaction and corresponding content option weights, one corresponding content option weight is preset for different content options, and the specific numerical value of the content option weight can be obtained through big data designed by a human-computer interaction system;
it should be noted that, in the embodiment of the invention, the network operated by the man-machine interaction carrier is a wired network, the wired network has strong stability, and the network can not influence the stability of the operation in the default man-machine interaction process;
numerical parallel vertical calculation of each item of data of the extraction mark is realized through a formula
Calculating and obtaining interaction reaction coefficient of human-computer interaction>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein TS0 is the standard jump time length corresponding to the content option, and the specific value of the standard jump time length can be obtained through big data designed by a man-machine interaction system;
it should be noted that, the interaction reaction coefficient is a numerical value for integrating various data in terms of content option reaction in man-machine interaction to integrally evaluate the reaction state thereof; the larger the interactive response coefficient is, the less good the response state of the corresponding content option is;
when the reaction state of the corresponding content option is evaluated according to the interaction reaction coefficient, matching the interaction reaction coefficient with the corresponding standard interaction reaction range; the standard interaction reaction range can be obtained through big data designed by a man-machine interaction system;
if the interaction reaction coefficient is smaller than the minimum value of the standard interaction reaction range, judging that the reaction state of the corresponding content option is normal, generating a first reaction instruction, and marking the corresponding content option as one type of interaction content according to the first reaction instruction;
if the interaction reaction coefficient is not smaller than the minimum value of the standard interaction reaction range and not larger than the maximum value of the standard interaction reaction range, judging that the reaction state of the corresponding content option is slightly abnormal, generating a second reaction instruction, and marking the corresponding content option as the second type of interaction content according to the second reaction instruction;
if the interaction reaction coefficient is larger than the maximum value of the standard interaction reaction range, judging that the reaction state of the corresponding content option is seriously abnormal, generating a third reaction instruction, and marking the corresponding content option as three types of interaction content according to the third reaction instruction;
the interaction reaction coefficient and the corresponding first reaction instruction and one type of interaction content, the second reaction instruction and two types of interaction content or the third reaction instruction and three types of interaction content form option analysis data corresponding to content options;
the method comprises the steps that option analysis data corresponding to a plurality of content options are arranged and combined according to an analysis time sequence to obtain interaction monitoring analysis data of man-machine interaction and upload the interaction monitoring analysis data to a cloud platform and a database;
in the embodiment of the invention, the modularized processing of the man-machine interaction is realized by carrying out monitoring statistics on the reaction data of different content options in the man-machine interaction process and integrating and calculating and analyzing the monitored and counted data, so that the single interaction condition of different content options can be intuitively and efficiently obtained, reliable data support can be provided for the analysis of the whole interaction condition of different content options and the analysis of the whole interaction condition of the man-machine interaction, and the accuracy and the comprehensiveness of the man-machine interaction can be effectively improved.
Example 2
On the basis of the embodiment 1, the method further comprises the following steps:
the optimization management prompting module is used for integrating interaction monitoring analysis data of human-computer interaction in the reference interaction monitoring period to evaluate fluency of all content options of the human-computer interaction, and prompting maintenance personnel to manage and maintain in a self-adaptive dynamic mode according to an evaluation result; comprising the following steps:
traversing the interaction monitoring analysis data, and sequentially evaluating the fluency of the corresponding content options according to a plurality of option analysis data acquired by traversing;
counting all option analysis data of content options in a reference interaction monitoring period, traversing all option analysis data, counting the total number of occurrences of one type of interaction content, two types of interaction content and three types of interaction content, and marking the total number as YJZ, EJZ and SJZ respectively; numerical parallel vertical calculation of each item of data of the extraction mark is realized through a formula
Calculating and obtaining interaction fluency coefficient (corresponding to man-machine interaction content options)>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein, c1 and c2 are constant coefficients larger than zero, and 2×c1=c2, c1 can take a value of 0.537, and c2 can take a value of 1.074;
it should be noted that, the interaction fluency coefficient is a numerical value for integrating all analysis results of the content options in the reference interaction monitoring period to integrally evaluate the interaction fluency of the content options; the larger the interaction fluency coefficient is, the poorer the interaction fluency of the corresponding content options is;
when analyzing the interaction fluency of the corresponding content options according to the interaction fluency coefficient, matching the interaction fluency coefficient with a corresponding standard interaction fluency threshold; the standard interaction fluency threshold value can be obtained through big data designed by a man-machine interaction system;
if the interaction fluency coefficient is smaller than the standard interaction fluency threshold, judging interaction fluency and associated fluency labels of the corresponding content options in the reference interaction monitoring period, and adding one to the total number of the interaction fluency content options;
if the interaction fluency coefficient is not smaller than the standard interaction fluency threshold, judging that the interaction of the corresponding content option in the reference interaction monitoring period is not fluent and associating a fluent label, and adding one to the total number of the interaction fluent content options;
the interaction fluency coefficient and the corresponding fluency label or non-fluency label form option fluency analysis data;
in the embodiment of the invention, the interaction fluency coefficient is obtained by integrating all analysis results of the content options in the reference interaction monitoring period, and the fluency of the corresponding content options is integrally evaluated based on the interaction fluency coefficient, so that the flow condition of all content options of man-machine interaction can be intuitively and efficiently obtained, the data support in the aspect of content options can be provided for the subsequent analysis and evaluation of the overall fluency of man-machine interaction, and the local monitoring analysis effect in the man-machine interaction process is effectively improved.
Acquiring option fluency analysis data corresponding to all the man-machine interaction content options, when evaluating the overall fluency of man-machine interaction according to the total number of interactive non-fluency content options of all the content options, marking the total number of interactive non-fluency content options as N, and comparing and judging with a preset total number threshold N0 of man-machine interaction fluency standard content options; the total number threshold of the man-machine interaction fluency standard content options can be set through the total number of all content options of man-machine interaction;
if N is less than N0, judging that the overall fluency of the man-machine interaction is normal and generating an interaction fluency instruction;
if N is more than or equal to N0, judging that the overall fluency of the man-machine interaction is normal and generating an interaction non-fluency instruction;
the interactive fluency instructions or the interactive non-fluency instructions form an evaluation result and are uploaded to a cloud platform and a database, and the cloud platform prompts a man-machine interaction maintainer to optimize and maintain the corresponding man-machine interaction according to the interactive non-fluency instructions in the evaluation result.
In the embodiment of the invention, the overall fluency of all content options is analyzed and classified, the classified results are counted, and the overall fluency of man-machine interaction is analyzed and judged according to the counted results, so that maintenance personnel can be timely and efficiently prompted to carry out maintenance, active monitoring and active early warning prompt of the man-machine interaction are realized, and compared with the conventional scheme, the embodiment of the invention can realize more accurate and efficient maintenance management effects through manual regular or irregular maintenance and optimization;
in addition, the formulas related in the above are all formulas with dimensions removed and numerical values calculated, and are a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and the proportionality coefficient in the formulas and each preset threshold value in the analysis process are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data; the size of the scaling factor is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the scaling factor depends on the number of sample data and the corresponding processing coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
Example 3
As shown in fig. 2, the method for optimizing and managing human-computer interaction includes:
the method comprises the steps of monitoring a carrier implemented by human-computer interaction, counting data items affecting the operation of the human-computer interaction in the aspect of the carrier, and analyzing the data to obtain carrier monitoring analysis data containing a first carrying instruction, a second carrying instruction or a third carrying instruction, and respectively prompting that the human-computer interaction can not be implemented and prompting that the human-computer interaction can be implemented according to the first carrying instruction, the second carrying instruction and the third carrying instruction in the carrier monitoring analysis data;
performing interaction state monitoring on different interaction contents of human-computer interaction, and performing preprocessing and computational analysis on each item of data obtained by the interaction state monitoring to obtain option analysis data containing a first reaction instruction and one type of interaction contents, a second reaction instruction and two types of interaction contents or a third reaction instruction and three types of interaction contents; the option analysis data corresponding to the content options are arranged and combined according to the time sequence of analysis to obtain interaction monitoring analysis data;
and integrating interaction monitoring analysis data of the human-computer interaction in the reference interaction monitoring period to evaluate fluency of all content options of the human-computer interaction, and prompting maintenance personnel to manage and maintain according to self-adaptive dynamics of evaluation results.
Example 4
Fig. 3 is a schematic structural diagram of a computer device for implementing a man-machine interaction optimization management system according to an embodiment of the present invention.
The computer device may include a processor, a memory, and a bus, and may also include a computer program stored in the memory and executable on the processor, such as a program for a human interaction optimization management system.
The memory includes at least one type of readable storage medium, including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory may in some embodiments be an internal storage unit of a computer device, such as a removable hard disk of the computer device. The memory may also be an external storage device of the computer device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. that are provided on the computer device. Further, the memory may also include both internal storage units and external storage devices of the computer device. The memory may be used not only for storing application software installed in a computer device and various types of data, such as codes of a program of a man-machine interaction optimization management system, but also for temporarily storing data that has been output or is to be output.
The processor may in some embodiments be comprised of integrated circuits, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor is a Control Unit (Control Unit) of the computer device, connects various components of the entire computer device using various interfaces and lines, and executes various functions of the computer device and processes data by running or executing programs or modules stored in a memory (for example, a program of a man-machine interaction optimization management system, etc.), and calling data stored in the memory.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between said memory and at least one processor or the like.
Fig. 3 shows only a computer device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the computer device and may include fewer or more components than shown, or may combine some of the components, or a different arrangement of components.
For example, although not shown, the computer device may further include a power source (such as a battery) for powering the various components, preferably the power source may be logically connected to the at least one processor by a power management device, such that charge management, discharge management, and power consumption management functions are performed by the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device may also include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described in detail herein.
Further, the computer device may also include a network interface, which may optionally include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the computer device and other computer devices.
The computer device may optionally further comprise a user interface, which may be a Display, an input unit such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the computer device and for displaying a visual user interface.
It should be understood that the above-described embodiments are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
A program of a human-computer interaction optimization management system stored in a memory in a computer device is a combination of a plurality of instructions.
In particular, the specific implementation method of the above instruction by the processor may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
Further, the modules/units integrated with the computer device may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of a computer device, causes a computer to perform the method of the invention.
In the several embodiments provided in the present invention, it should be understood that the disclosed method may be implemented in other manners. For example, the above-described embodiments of the invention are merely illustrative, and for example, the division of modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. The man-machine interaction optimization management system is characterized by comprising a carrier monitoring and analysis module, wherein the carrier monitoring and analysis module is used for monitoring a carrier implemented by man-machine interaction, counting data items affecting the man-machine interaction operation in the aspect of the carrier and carrying out data analysis to obtain carrier monitoring and analysis data; comprising the following steps:
acquiring a carrier model implemented by man-machine interaction and implementing monitoring statistics; obtaining a corresponding CPU model, memory model, display card model and hard disk model according to the carrier model;
comparing the obtained CPU model, memory model, display card model and hard disk model with corresponding standard CPU model, standard memory model, standard display card model and standard hard disk model respectively to judge whether the parameter requirements of man-machine interaction operation are met or not, and obtaining judgment of inclusion of qualified labels or unqualified labelsAs a result, obtaining the operation support coefficient corresponding to the carrier according to a plurality of labels in the judgment result
；
According to the operation support coefficient
To analyze and evaluate the carrier operation support capability of man-machine interaction, the operation support coefficient is +.>
Matching with a preset operation support threshold value to obtain carrier monitoring analysis data containing a first carrying instruction, a second carrying instruction or a third carrying instruction, and uploading the carrier monitoring analysis data to a cloud platform; the first carrying instruction, the second carrying instruction and the third carrying instruction respectively prompt that the man-machine interaction can not be implemented and prompt that the man-machine interaction can be implemented;
the interaction monitoring analysis module is used for carrying out interaction state monitoring on different interaction contents of human-computer interaction, and carrying out preprocessing and calculation analysis on each item of data obtained by the interaction state monitoring to obtain interaction monitoring analysis data; comprising the following steps:
in a preset reference interaction monitoring period, when a content option on a man-machine interaction page is clicked, marking the time point when the man-machine interaction page is monitored to be clicked as a first monitoring time, and marking the time point when the fully displayed page after the man-machine interaction page is clicked and jumped to be correspondingly displayed as a second monitoring time;
acquiring the jump time length of the man-machine interaction page according to the second monitoring time and the first monitoring time, marking the jump time length as TS, and simultaneously, matching the clicked content option name with an option weight table pre-stored in a database to acquire a corresponding content option weight and marking the content option weight as XQ; numerical parallel vertical calculation of each item of data of the extraction mark is realized through a formula
Calculating and obtaining interaction reaction coefficient of human-computer interaction>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein TS0 is the standard jump time length corresponding to the content option;
when the response state of the corresponding content option is evaluated according to the interaction reaction coefficient, matching the interaction reaction coefficient with a corresponding standard interaction reaction range to obtain option analysis data containing a first reaction instruction and one type of interaction content, a second reaction instruction and two types of interaction content or a third reaction instruction and three types of interaction content; if the interaction reaction coefficient is smaller than the minimum value of the standard interaction reaction range, generating a first reaction instruction and marking the corresponding content option as one type of interaction content;
if the interaction reaction coefficient is not smaller than the minimum value of the standard interaction reaction range and not larger than the maximum value of the standard interaction reaction range, generating a second reaction instruction and marking the corresponding content option as second-class interaction content;
if the interaction reaction coefficient is larger than the maximum value of the standard interaction reaction range, generating a third reaction instruction and marking the corresponding content options as three types of interaction content;
the method comprises the steps that option analysis data corresponding to a plurality of content options are arranged and combined according to an analysis time sequence to obtain interaction monitoring analysis data of man-machine interaction and upload the interaction monitoring analysis data to a cloud platform and a database;
and the optimization management prompt module is used for integrating interaction monitoring analysis data of the human-computer interaction in the reference interaction monitoring period to evaluate the fluency of all content options of the human-computer interaction, and prompting maintenance personnel to manage and maintain according to the self-adaptive dynamic prompt of the evaluation result.
2. The human-computer interaction optimization management system according to claim 1, wherein if the operation parameters corresponding to the CPU model, the memory model, the graphics card model and the hard disk model are not smaller than the operation parameters corresponding to the standard CPU model, the standard memory model, the standard graphics card model and the standard hard disk model, the operation parameters corresponding to the models are judged to meet the operation requirements and the qualified labels are associated;
otherwise, judging that the operation parameters of the corresponding signals do not meet the operation requirements and associating unqualified labels; and counting the number of tags corresponding to the CPU model, the memory model, the display card model and the hard disk model, and performing parallel computing to obtain the operation support coefficient corresponding to the human-computer interaction carrier.
3. The human-computer interaction optimization management system according to claim 2, wherein the human-computer interaction optimization management system is characterized by the formula
Calculating operation support coefficient corresponding to human-computer interaction carrier>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein XQi is model weight corresponding to a CPU model, a memory model, a display card model and a hard disk model, i=1, 2,3 and 4; BQk is the label weight corresponding to each model-associated label, k=1, 2;
the CPU model, the memory model, the display card model and the hard disk model are respectively matched with the corresponding model weight tables to obtain the model weights corresponding to the CPU model, the memory model, the display card model and the hard disk model.
4. The human-computer interaction optimization management system according to claim 1, wherein the operation step of the optimization management prompting module comprises:
traversing the interaction monitoring analysis data, and sequentially evaluating the fluency of the corresponding content options according to a plurality of option analysis data acquired by traversing;
counting all option analysis data of content options in a reference interaction monitoring period, traversing all option analysis data, counting the total number of occurrences of one type of interaction content, two types of interaction content and three types of interaction content, and marking the total number as YJZ, EJZ and SJZ respectively; numerical parallel vertical calculation of each item of data of the extraction mark is realized through a formula
Calculating and obtaining interaction fluency coefficient (corresponding to man-machine interaction content options)>
The method comprises the steps of carrying out a first treatment on the surface of the Wherein, c1 and c2 are constant coefficients greater than zero, and 2×c1=c2.
5. The human-computer interaction optimization management system according to claim 4, wherein when analyzing the interaction fluency of the corresponding content options according to the interaction fluency coefficient, the interaction fluency coefficient is matched with the corresponding standard interaction fluency threshold value to obtain option fluency analysis data containing fluency tags or non-fluency tags;
and when the overall fluency of the human-computer interaction is evaluated according to the total number of the interactive fluency content options of all the content options, comparing and judging the total number of the interactive fluency content options with a preset total number of the human-computer interaction fluency standard content options to obtain an evaluation result containing an interactive fluency instruction or an interactive fluency instruction, uploading the evaluation result to a cloud platform and a database, and prompting a human-computer interaction maintainer to perform optimized maintenance on the corresponding human-computer interaction by the cloud platform according to the interactive fluency instruction in the evaluation result.
6. A man-machine interaction optimization management method applied to the man-machine interaction optimization management system of any one of claims 1 to 5, comprising:
the method comprises the steps of monitoring a carrier implemented by human-computer interaction, counting data items affecting the operation of the human-computer interaction in the aspect of the carrier, and analyzing the data to obtain carrier monitoring analysis data containing a first carrying instruction, a second carrying instruction or a third carrying instruction, and respectively prompting that the human-computer interaction can not be implemented and prompting that the human-computer interaction can be implemented according to the first carrying instruction, the second carrying instruction and the third carrying instruction in the carrier monitoring analysis data;
performing interaction state monitoring on different interaction contents of human-computer interaction, and performing preprocessing and computational analysis on each item of data obtained by the interaction state monitoring to obtain option analysis data containing a first reaction instruction and one type of interaction contents, a second reaction instruction and two types of interaction contents or a third reaction instruction and three types of interaction contents; the option analysis data corresponding to the content options are arranged and combined according to the time sequence of analysis to obtain interaction monitoring analysis data;
and integrating interaction monitoring analysis data of the human-computer interaction in the reference interaction monitoring period to evaluate fluency of all content options of the human-computer interaction, and prompting maintenance personnel to manage and maintain according to self-adaptive dynamics of evaluation results.
7. A storage medium comprising at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a human interaction optimization management system according to any one of claims 1 to 5.
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