Dispersion control method and device for human-computer interaction

Abstract

The present invention discloses a dispersal control method and device applied to human-computer interaction, the method comprising: analyzing the danger warning level of the target user according to the human-computer interaction data of the target user, the human-computer interaction data including one or more of user shouting information, user behavior posture information, user gathering information and other user information; judging whether the target user meets the preset dangerous situation conditions according to the danger warning level; if so, generating a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, and controlling the intelligent control equipment to perform corresponding dispersal control operations according to the danger response strategy. It can be seen that the present invention improves the accuracy and reliability of personnel dispersal control, improves the efficiency and convenience of personnel dispersal control, and in addition, when the dangerous situation conditions are met, the corresponding danger response and dispersal control operations are performed, which improves the timeliness and accuracy of personnel's danger response processing, thereby improving the safety of personnel.

Description

Technical Field

[0001] The present invention relates to the field of intelligent device management and security technology, and in particular to a dispersal control method and device applied to human-computer interaction. Background Art

[0002] With the increase in population density and the frequent holding of various activities, there are more and more scenes of people gathering. However, if the control is not timely or inaccurate, it will easily cause congestion and seriously affect the smooth progress of activities. Therefore, crowd dispersal control has become a major research hotspot.

[0003] At present, the main method of personnel dispersal control is for staff to manually analyze the gathering of people in the area where the user is located and then decide whether to disperse them by voice broadcast. Dispersion control based on subjective consciousness will be affected by many factors, such as the poor mental state of staff when conducting regional monitoring and dispersal control, and different staff members have different considerations and focuses on evaluating the gathering situation of the same user. To a certain extent, even the dispersal control plan for the same user gathering situation under the same conditions will have deviations, making the accuracy and determination efficiency of the dispersal control plan low. Therefore, the existing dispersal control method has the problem of low accuracy and efficiency of dispersal control. It can be seen that it is particularly important to provide a method that can improve the accuracy and efficiency of dispersal control. Summary of the invention

[0004] The present invention provides a dispersal control method and device applied to human-computer interaction, which can improve the accuracy and efficiency of dispersal control of human users.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a dispersal control method applied to human-computer interaction, the method comprising:

[0006] Analyzing the danger warning level of the target user according to the detected human-computer interaction data of the target user, wherein the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information and other user information;

[0007] According to the danger warning level, determining whether the target user meets a preset danger situation condition;

[0008] When it is determined that the target user meets the dangerous situation conditions, a corresponding danger response strategy is generated according to the danger warning level and the human-computer interaction data, and according to the danger response strategy, the intelligent management and control device is controlled to perform corresponding dispersal management operations.

[0009] As an optional implementation manner, in the first aspect of the present invention, analyzing the danger warning level of the target user according to the detected human-computer interaction data of the target user includes:

[0010] When the detected human-computer interaction data of the target user includes user shouting information, analyzing the first danger level of the target user based on the user shouting level according to the user shouting information;

[0011] When the detected human-computer interaction data of the target user includes user behavior posture information, analyzing the second danger level of the target user based on the user behavior posture level according to the user behavior posture information;

[0012] When the detected human-computer interaction data of the target user includes user aggregation information, analyzing the third risk level of the target user based on the user aggregation level according to the user aggregation information;

[0013] When the detected human-computer interaction data of the target user includes other user information, analyzing the fourth risk level of the target user based on other levels according to the other user information;

[0014] A danger warning level of the target user is determined according to the first danger level and / or the second danger level and / or the third danger level and / or the fourth danger level.

[0015] As an optional implementation manner, in the first aspect of the present invention, analyzing the first danger level of the target user based on the user shouting level according to the user shouting information includes:

[0016] Determine the shouting content information, shouting volume information and shouting breath information corresponding to the target user according to the user shouting information;

[0017] Determine the shouting purpose type of the target user according to the shouting content information, and determine the shouting demand solution corresponding to the target user according to the shouting content information;

[0018] Determine the breath change of the target user according to the shouting breath information, and determine the volume change of the target user according to the shouting volume information;

[0019] Determining the abnormality of the target user's shouting according to the shouting content information, the breath change and the volume change;

[0020] According to the shouting purpose type, the shouting demand solution and the shouting abnormality level, a first danger level of the target user based on the user shouting level is analyzed.

[0021] As an optional implementation manner, in the first aspect of the present invention, analyzing the second danger level of the target user based on the user behavior posture level according to the user behavior posture information includes:

[0022] Determine, according to the user behavior posture information, an independent embodiment graphic and a comprehensive embodiment graphic formed by the behavior posture of the target user;

[0023] According to the independent embodiment graphic and the comprehensive embodiment graphic, determining whether the target user meets a preset exclusive danger indication condition;

[0024] When it is determined that the target user meets the exclusive danger indication condition, the conventional meaning of the display graphic corresponding to the target user is determined according to the independent embodiment graphic and the comprehensive embodiment graphic; and the second danger level of the target user based on the user behavior posture level is determined according to the conventional meaning of the display graphic;

[0025] When it is determined that the target user does not meet the exclusive danger indication condition, the hand gesture and personal movement of the target user are determined according to the user behavior posture information, and the nature of the target user's behavior posture is determined according to the hand gesture and personal movement; when the nature of the behavior posture is used to indicate the nature of seeking help, the urgency of seeking help corresponding to the target user is determined according to the hand gesture and personal movement; and according to the urgency of seeking help, the second danger level of the target user based on the user behavior posture level is determined.

[0026] As an optional implementation manner, in the first aspect of the present invention, analyzing the third risk level of the target user based on the user aggregation level according to the user aggregation information includes:

[0027] According to the user gathering information, determine the actual number of people gathered, the tolerable number of people gathered, and the real-time increase or decrease in the number of people gathered in the current area where the target user is located;

[0028] Determine the density risk level of the current area location according to the actual number of people gathered, the tolerable number of people gathered, and the increase or decrease in the real-time number of people gathered;

[0029] Determine the location information of the gathering area corresponding to the target user according to the user gathering information, and determine the regional characteristics of the current regional location according to the gathering area location information;

[0030] Analyze the current hidden risk information and risk change trend of the current area location according to the area characteristics and the intensive risk level;

[0031] According to the current hidden risk information and the risk change trend, the third risk level of the target user based on the user aggregation level is determined.

[0032] As an optional implementation manner, in the first aspect of the present invention, judging whether the target user meets a preset dangerous situation condition according to the danger warning level includes:

[0033] Determining whether the danger warning level is greater than or equal to a preset danger warning level threshold;

[0034] When it is determined that the danger warning level is greater than or equal to the danger warning level threshold, determining that the target user meets a preset danger situation condition;

[0035] When it is determined that the danger warning level is less than the danger warning level threshold, it is determined that the target user does not meet the dangerous situation condition.

[0036] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0037] When it is determined that the target user does not meet the dangerous situation condition, determining the noise making situation corresponding to the target user according to the human-computer interaction data;

[0038] According to the noise generation situation, determining whether the target user meets a preset noise processing condition;

[0039] When it is determined that the target user meets the noise processing conditions, a corresponding noise control strategy is generated according to the human-computer interaction data and the noise creation situation, and according to the noise control strategy, the intelligent control device is controlled to perform corresponding noise control operations.

[0040] As an optional implementation, in the first aspect of the present invention, generating a corresponding danger response strategy according to the danger warning level and the human-computer interaction data includes:

[0041] Determining the basic danger response type corresponding to the target user according to the danger warning level;

[0042] When the basic danger response mode type is an automatic alarm type, analyzing the danger alarm related information corresponding to the target user according to the human-computer interaction data; generating a corresponding danger response strategy according to the danger alarm related information and the automatic alarm type;

[0043] When the basic danger response mode type is an automatic monitoring type, determining a predicted duration corresponding to the current danger state of the target user according to the danger warning level and the human-computer interaction data; generating a corresponding danger response strategy according to the predicted duration and the automatic monitoring type;

[0044] When the basic danger response method type is the automatic playback of warning voice type, the warning voice content, playback interval time, and number of playbacks corresponding to the target user are determined according to the danger warning level and the human-computer interaction data; and a corresponding danger response strategy is generated according to the warning voice content, the playback interval time, the number of playbacks, and the automatic playback warning voice type.

[0045] A second aspect of the present invention discloses a dispersal control device for human-computer interaction, the device comprising:

[0046] A danger analysis module, used to analyze the danger warning level of the target user according to the detected human-computer interaction data of the target user, wherein the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information and other user information;

[0047] A judgment module, used to judge whether the target user meets a preset dangerous situation condition according to the danger warning level;

[0048] A strategy generation module, configured to generate a corresponding danger response strategy according to the danger warning level and the human-computer interaction data when the judgment module determines that the target user meets the dangerous situation condition;

[0049] The dispersal control module is used to control the intelligent control equipment to perform corresponding dispersal control operations according to the danger response strategy.

[0050] As an optional implementation, in the second aspect of the present invention, the danger analysis module analyzes the danger warning level of the target user according to the detected human-computer interaction data of the target user, specifically including:

[0051] When the detected human-computer interaction data of the target user includes user shouting information, analyzing the first danger level of the target user based on the user shouting level according to the user shouting information;

[0052] When the detected human-computer interaction data of the target user includes user behavior posture information, analyzing the second danger level of the target user based on the user behavior posture level according to the user behavior posture information;

[0053] When the detected human-computer interaction data of the target user includes user aggregation information, analyzing the third risk level of the target user based on the user aggregation level according to the user aggregation information;

[0054] When the detected human-computer interaction data of the target user includes other user information, analyzing the fourth risk level of the target user based on other levels according to the other user information;

[0055] A danger warning level of the target user is determined according to the first danger level and / or the second danger level and / or the third danger level and / or the fourth danger level.

[0056] As an optional implementation manner, in the second aspect of the present invention, the manner in which the danger analysis module analyzes the first danger level of the target user based on the user shouting level according to the user shouting information specifically includes:

[0057] Determine the shouting content information, shouting volume information and shouting breath information corresponding to the target user according to the user shouting information;

[0058] Determine the shouting purpose type of the target user according to the shouting content information, and determine the shouting demand solution corresponding to the target user according to the shouting content information;

[0059] Determine the breath change of the target user according to the shouting breath information, and determine the volume change of the target user according to the shouting volume information;

[0060] Determining the abnormality of the target user's shouting according to the shouting content information, the breath change and the volume change;

[0061] According to the shouting purpose type, the shouting demand solution and the shouting abnormality level, a first danger level of the target user based on the user shouting level is analyzed.

[0062] As an optional implementation, in the second aspect of the present invention, the manner in which the risk analysis module analyzes the second risk level of the target user based on the user behavior posture level according to the user behavior posture information specifically includes:

[0063] Determine, according to the user behavior posture information, an independent embodiment graphic and a comprehensive embodiment graphic formed by the behavior posture of the target user;

[0064] According to the independent embodiment graphic and the comprehensive embodiment graphic, determining whether the target user meets a preset exclusive danger indication condition;

[0065] When it is determined that the target user meets the exclusive danger indication condition, the conventional meaning of the display graphic corresponding to the target user is determined according to the independent embodiment graphic and the comprehensive embodiment graphic; and the second danger level of the target user based on the user behavior posture level is determined according to the conventional meaning of the display graphic;

[0066] When it is determined that the target user does not meet the exclusive danger indication condition, the hand gesture and personal movement of the target user are determined according to the user behavior posture information, and the nature of the target user's behavior posture is determined according to the hand gesture and personal movement; when the nature of the behavior posture is used to indicate the nature of seeking help, the urgency of seeking help corresponding to the target user is determined according to the hand gesture and personal movement; and according to the urgency of seeking help, the second danger level of the target user based on the user behavior posture level is determined.

[0067] As an optional implementation manner, in the second aspect of the present invention, the manner in which the risk analysis module analyzes the third risk level of the target user based on the user aggregation level according to the user aggregation information specifically includes:

[0068] According to the user gathering information, determine the actual number of people gathered, the tolerable number of people gathered, and the real-time increase or decrease in the number of people gathered in the current area where the target user is located;

[0069] Determine the density risk level of the current area location according to the actual number of people gathered, the tolerable number of people gathered, and the increase or decrease in the real-time number of people gathered;

[0070] Determine the location information of the gathering area corresponding to the target user according to the user gathering information, and determine the regional characteristics of the current regional location according to the gathering area location information;

[0071] Analyze the current hidden risk information and risk change trend of the current area location according to the area characteristics and the intensive risk level;

[0072] According to the current hidden risk information and the risk change trend, the third risk level of the target user based on the user aggregation level is determined.

[0073] As an optional implementation, in the second aspect of the present invention, the judgment module judges whether the target user meets the preset dangerous situation condition according to the danger warning level, specifically including:

[0074] Determining whether the danger warning level is greater than or equal to a preset danger warning level threshold;

[0075] When it is determined that the danger warning level is greater than or equal to the danger warning level threshold, determining that the target user meets a preset danger situation condition;

[0076] When it is determined that the danger warning level is less than the danger warning level threshold, it is determined that the target user does not meet the dangerous situation condition.

[0077] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0078] The noise control module is used to determine the noise creation situation corresponding to the target user according to the human-computer interaction data when the judgment module determines that the target user does not meet the dangerous situation condition; judge whether the target user meets the preset noise processing condition according to the noise creation situation; when it is judged that the target user meets the noise processing condition, generate a corresponding noise control strategy according to the human-computer interaction data and the noise creation situation, and control the intelligent control device to perform corresponding noise control operations according to the noise control strategy.

[0079] As an optional implementation, in the second aspect of the present invention, the strategy generation module generates a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, specifically including:

[0080] Determining the basic danger response type corresponding to the target user according to the danger warning level;

[0081] When the basic danger response mode type is an automatic alarm type, analyzing the danger alarm related information corresponding to the target user according to the human-computer interaction data; generating a corresponding danger response strategy according to the danger alarm related information and the automatic alarm type;

[0082] When the basic danger response mode type is an automatic monitoring type, determining a predicted duration corresponding to the current danger state of the target user according to the danger warning level and the human-computer interaction data; generating a corresponding danger response strategy according to the predicted duration and the automatic monitoring type;

[0083] When the basic danger response method type is the automatic playback of warning voice type, the warning voice content, playback interval time, and number of playbacks corresponding to the target user are determined according to the danger warning level and the human-computer interaction data; and a corresponding danger response strategy is generated according to the warning voice content, the playback interval time, the number of playbacks, and the automatic playback warning voice type.

[0084] The third aspect of the present invention discloses another dispersal control device for human-computer interaction, the device comprising:

[0085] A memory storing executable program code;

[0086] a processor coupled to the memory;

[0087] The processor calls the executable program code stored in the memory to execute the dispersal control method applied to human-computer interaction disclosed in the first aspect of the present invention.

[0088] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the dispersal control method applied to human-computer interaction disclosed in the first aspect of the present invention.

[0089] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0090] In an embodiment of the present invention, according to the human-computer interaction data of the detected target user, the danger warning level of the target user is analyzed, and the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information and other user information; according to the danger warning level, it is judged whether the target user meets the preset dangerous situation conditions; when it is judged that the target user meets the dangerous situation conditions, according to the danger warning level and the human-computer interaction data, a corresponding danger response strategy is generated, and according to the danger response strategy, the intelligent control device is controlled to perform the corresponding dispersal control operation. It can be seen that the present invention can analyze the danger warning level of the target user according to the human-computer interaction data, further generate a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, and then control the intelligent control device to perform the corresponding dispersal control operation, which is conducive to improving the comprehensiveness, integrity and rationality of the dispersal control method applied to human-computer interaction, and then it is conducive to improving the accuracy and reliability of the dispersal control of personnel users, and it is conducive to improving the efficiency and convenience of the dispersal control of personnel users. In addition, when the dangerous situation conditions are met, the corresponding danger response and dispersal control operations are performed, which is conducive to improving the timeliness and accuracy of the danger response processing of personnel users, thereby improving the safety of personnel users. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0092] Figure 1 It is a flow chart of a dispersal control method applied to human-computer interaction disclosed in an embodiment of the present invention;

[0093] Figure 2 It is a flow chart of another dispersal control method applied to human-computer interaction disclosed in an embodiment of the present invention;

[0094] Figure 3 It is a structural schematic diagram of a dispersal control device applied to human-computer interaction disclosed in an embodiment of the present invention;

[0095] Figure 4 It is a structural schematic diagram of another dispersal control device applied to human-computer interaction disclosed in an embodiment of the present invention;

[0096] Figure 5 It is a structural schematic diagram of another dispersal control device applied to human-computer interaction disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0097] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0098] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0099] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0100] The present invention discloses a dispersal control method and device applied to human-computer interaction, which can analyze the danger warning level of the target user according to the human-computer interaction data, further generate a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, and then control the intelligent control device to perform the corresponding dispersal control operation, which is conducive to improving the comprehensiveness, integrity and rationality of the dispersal control method applied to human-computer interaction, and then is conducive to improving the accuracy and reliability of the dispersal control of the personnel user, and is conducive to improving the efficiency and convenience of the dispersal control of the personnel user. In addition, when the dangerous situation conditions are met, the corresponding danger response and dispersal control operations are performed, which is conducive to improving the timeliness and accuracy of the danger response processing of the personnel user, thereby improving the safety of the personnel user. The following are detailed descriptions.

[0101] Embodiment 1

[0102] See also Figure 1 , Figure 1 : is a flow chart of a method for dispersal control applied to human-computer interaction disclosed in an embodiment of the present invention. Figure 1 The described method may be applied to a dispersal control device, wherein the device may include a server, wherein the server includes a local server or a cloud server, which is not limited in the embodiment of the present invention. Figure 1 As shown, the dispersal control method applied to human-computer interaction includes the following operations:

[0103] 101. Analyze the danger warning level of the target user according to the detected human-computer interaction data of the target user, where the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information, and other user information.

[0104] Optionally, the user's shouting information may include, but is not limited to, one or more of shouting content information, shouting volume information, shouting breath information, shouting method information, shouting frequency information, shouting change rhythm information, shouting pitch information, shouting timbre information, shouting feature information, shouting environment information, shouting time information and other shouting information, and is not limited in the embodiments of the present invention.

[0105] Optionally, user behavior posture information may include, but is not limited to, one or more of the user's specific behavior posture, graphic meaning information embodied by the behavior posture, behavior posture change information, gesture swing information, personnel movement information and other information related to the user's behavior posture, and is not limited in the embodiments of the present invention.

[0106] Optionally, user aggregation information may include, but is not limited to, one or more of information on the number of users gathered, location information on user aggregation areas, user aggregation density information, user aggregation venue information, user aggregation event information, aggregated user feature information, user aggregation activity type information, user aggregation activity scale information, aggregated user age structure information, aggregated user gender ratio information, and other information that can reflect user aggregation-related circumstances, etc., and is not limited in the embodiments of the present invention.

[0107] Optionally, other user information may include, but is not limited to, one or more of user health parameter information, user expression information, user environment data information, user emotion information, user location information, user biometric information, user lip feature information, other information that can reflect whether the user is in a dangerous state, other information that can reflect whether the user is in a panic state, etc., and the embodiments of the present invention are not limited thereto.

[0108] Optionally, the danger warning level of the target user may be one or more of the severity level, injury level, impact level, urgency level required to be addressed, etc. corresponding to the danger currently facing the target user, or it may be one or more of the severity level, injury level, impact level, urgency level required to be addressed, etc. corresponding to the danger hazard currently facing the target user, and the embodiments of the present invention are not limited thereto.

[0109] 102. According to the danger warning level, determine whether the target user meets the preset dangerous situation conditions.

[0110] Optionally, whether the above-mentioned target user meets preset dangerous situation conditions, for example: whether the target user is in a dangerous situation, whether the target user has dangerous safety hazards, whether the current danger of the target user needs to be dealt with and resolved immediately, whether the current dangerous safety hazards of the target user need to be dealt with and resolved immediately, whether the severity or impact of the current danger / dangerous safety hazard of the target user is greater than the corresponding threshold, etc., the embodiments of the present invention are not limited.

[0111] 103. When it is determined that the target user meets the dangerous situation conditions, a corresponding danger response strategy is generated according to the danger warning level and human-computer interaction data, and according to the danger response strategy, the intelligent control device is controlled to perform the corresponding dispersal control operation.

[0112] Optionally, the above-mentioned control of the intelligent control equipment performs corresponding dispersal control operations, for example: controlling the intelligent control equipment to alarm, controlling the intelligent control equipment to perform personnel monitoring and safety monitoring, controlling the intelligent control equipment to perform warning voice broadcasts, controlling the intelligent control equipment to perform other dispersal control operations, etc., which are not limited to the embodiments of the present invention.

[0113] It can be seen that the dispersal control method applied to human-computer interaction described in the embodiment of the present invention can analyze the danger warning level of the target user according to the human-computer interaction data, and further generate a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, and then control the intelligent control equipment to perform the corresponding dispersal control operation, which is beneficial to improving the comprehensiveness, integrity and rationality of the dispersal control method applied to human-computer interaction, and thus is beneficial to improving the accuracy and reliability of the dispersal control of personnel users, as well as improving the efficiency and convenience of the dispersal control of personnel users. In addition, when the dangerous situation conditions are met, the corresponding danger response and dispersal control operations are performed, which is beneficial to improving the timeliness and accuracy of the danger response processing of personnel users, thereby helping to improve the safety of personnel users.

[0114] In an optional embodiment, analyzing the danger warning level of the target user according to the detected human-computer interaction data of the target user may include:

[0115] When the detected human-computer interaction data of the target user includes user shouting information, analyzing the first danger level of the target user based on the user shouting level according to the user shouting information;

[0116] When the detected human-computer interaction data of the target user includes user behavior posture information, analyzing the second danger level of the target user based on the user behavior posture level according to the user behavior posture information;

[0117] When the detected human-computer interaction data of the target user includes user aggregation information, analyzing the third risk level of the target user based on the user aggregation level according to the user aggregation information;

[0118] When the detected human-computer interaction data of the target user includes other user information, analyzing the fourth risk level of the target user based on other levels according to the other user information;

[0119] A danger warning level of the target user is determined according to the first danger level and / or the second danger level and / or the third danger level and / or the fourth danger level.

[0120] Further optionally, determining the danger warning level of the target user according to the first danger level and / or the second danger level and / or the third danger level and / or the fourth danger level may include:

[0121] Determine a danger warning level of the target user according to a preset first weight distribution scheme, a first danger level and / or a second danger level and / or a third danger level and / or a fourth danger level;

[0122] Based on the user's shouting information, a first judgment degree that can reflect dangerous phenomena is determined, and based on the user's behavior and posture information, a second judgment degree that can reflect dangerous phenomena is determined, and based on the user's gathering information, a third judgment degree that can reflect dangerous phenomena is determined, and based on other user information, a fourth judgment degree that can reflect dangerous phenomena is determined; based on the first judgment degree, the second judgment degree, the third judgment degree and the fourth judgment degree, a second weight allocation scheme is determined; based on the second weight allocation scheme, the first danger level and / or the second danger level and / or the third danger level and / or the fourth danger level, the danger warning level of the target user is determined.

[0123] Optionally, the first weight distribution scheme may be that the weights corresponding to the first danger level, the second danger level, the third danger level and the fourth danger level are evenly distributed, that is, the weight value of each danger level is the same, which is not limited in the embodiment of the present invention.

[0124] Optionally, the second weight distribution scheme includes specific weight values ​​corresponding to the first danger level, the second danger level, the third danger level and the fourth danger level; further, the higher the evaluation degree, the higher the corresponding weight value, which is not limited in the embodiment of the present invention.

[0125] Optionally, the first judgment degree that can reflect the dangerous phenomenon is determined based on the user shouting information. For example: the more obvious the existence of the dangerous phenomenon can be determined through the user shouting information, the higher the first judgment degree that can reflect the dangerous phenomenon; when the user shouting information requires in-depth analysis and the existence of the dangerous phenomenon cannot be determined at a glance, the first judgment degree that can reflect the dangerous phenomenon is lower; further, the determination logic of the second judgment degree, the third judgment degree and the fourth judgment degree can refer to but is not limited to the determination logic of the first judgment degree, and the embodiments of the present invention do not limit this.

[0126] It can be seen that this optional embodiment can determine the corresponding danger level for the user shouting information, user behavior posture information, user gathering information and other user information included in the human-computer interaction data, and then determine the danger warning level of the target user, which is conducive to improving the comprehensiveness and integrity of the danger warning level determination method, as well as improving the pertinence and rationality of the danger warning level determination method, and then helping to improve the accuracy and reliability of the determined danger warning level, thereby helping to improve the rationality, timeliness and accuracy of the execution of subsequent related operations based on the determined danger warning level.

[0127] In another optional embodiment, analyzing the first danger level of the target user based on the user shouting level according to the user shouting information may include:

[0128] According to the user's shouting information, determine the shouting content information, shouting volume information and shouting breath information corresponding to the target user;

[0129] Determine the target user's calling purpose type based on the calling content information, and determine the target user's corresponding calling demand solution based on the calling content information;

[0130] Determine the breath change of the target user according to the shouting breath information, and determine the volume change of the target user according to the shouting volume information;

[0131] Determine the abnormality of the target user's shouting based on shouting content information, breath changes, and volume changes;

[0132] According to the type of shouting purpose, shouting demand solution and shouting abnormality, analyze the first danger level of the target user based on the user shouting level.

[0133] The type of shouting purpose is optional, for example: shouting for help, shouting for happiness, shouting to release stress, calling others in a safe state, calling others in a dangerous state, etc., which is not limited in the embodiment of the present invention.

[0134] Optionally, the shouting demand solution and the first danger level are illustrated by way of example: the danger level corresponding to the shouting demand solution of shouting for police rescue is different from the danger level corresponding to the shouting demand solution of simply shouting for a user to go to another location to play from the current location. That is, the corresponding danger level is determined based on the actual situation of the shouting demand solution, and the embodiments of the present invention do not limit this.

[0135] Optionally, the above-mentioned determination of the abnormal degree of the target user's shouting is based on the shouting content information, breath changes and volume changes. For example: the shouting content information is used to express the meaning of asking for help or panic and fear, and / or the breath changes are used to indicate that the larger the amplitude of the target user's breath fluctuation is, or the breath changes are less consistent with conventional breath changes or the breath is weaker, and / or the volume changes are used to indicate that the target user's volume changes are less consistent with conventional volume changes or the target user's real-time volume is higher than the normal human volume, then it is determined that the target user's shouting is more abnormal. Other situations are similarly applicable and are not limited to the embodiments of the present invention.

[0136] Optionally, the above-mentioned first danger level of the target user based on the user shouting level is analyzed according to the shouting purpose type, the shouting demand solution and the shouting abnormality level. For example: the shouting purpose type is used to represent the shouting type for help or the shouting type of panic and fear, and / or the shouting demand solution is used to represent the more urgent the alarm rescue / rescue, and / or the higher the shouting abnormality level, then it is determined that the first danger level of the target user based on the user shouting level is higher, and other situations are similarly obtainable, and the embodiments of the present invention are not limited thereto.

[0137] It can be seen that this optional embodiment can provide a method for determining the first danger level at the user shouting level, determine the type of shouting purpose, the shouting demand solution and the degree of shouting abnormality according to the user shouting information, and then determine the first danger level, which is conducive to improving the comprehensiveness and integrity of the first danger level determination method, and thus is conducive to improving the pertinence, matching and rationality of the first danger level determination method, and is conducive to improving the diversity, flexibility and pertinence of the determination parameters used to determine the first danger level, thereby helping to improve the accuracy and reliability of the determined first danger level at the user shouting level.

[0138] In yet another optional embodiment, the analyzing the second risk level of the target user based on the user behavior posture level according to the user behavior posture information may include:

[0139] According to the user behavior posture information, determine the independent embodiment graph and the comprehensive embodiment graph formed by the behavior posture of the target user;

[0140] According to the independent manifestation graphics and the comprehensive manifestation graphics, determine whether the target user meets the preset exclusive hazard indication conditions;

[0141] When it is determined that the target user meets the exclusive danger indication condition, the conventional meaning of the display graphic corresponding to the target user is determined based on the independent manifestation graphic and the comprehensive manifestation graphic; the second danger level of the target user based on the user behavior posture level is determined based on the conventional meaning of the display graphic;

[0142] When it is determined that the target user does not meet the exclusive danger indication conditions, the target user's hand gestures and movements are determined based on the user's behavior posture information, and the nature of the target user's behavior posture is determined based on the hand gestures and movements; when the nature of the behavior posture is used to indicate the nature of a call for help, the urgency of the call for help corresponding to the target user is determined based on the hand gestures and movements; based on the urgency of the call for help, the second danger level of the target user based on the user's behavior posture level is determined.

[0143] Optionally, an independent representation graphic may be understood as a graphic displayed by a single individual or a partial body part of a target user, and a comprehensive representation graphic may be understood as a graphic displayed by a plurality of target users in combination or a graphic displayed by the entire body, which is not limited in the embodiments of the present invention.

[0144] Optionally, examples are given for independent manifestation graphics and comprehensive manifestation graphics and the second danger level: for example, when the independent manifestation graphics and / or the comprehensive manifestation graphics are used to represent the "SOS" graphic, it is determined that the target user is in a dangerous state or the target user is in a state of asking for help, and then the second danger level is determined according to the preset danger level value of the "SOS" graphic. For example, if the danger level value corresponding to the preset "SOS" graphic is level one danger, then the second danger level is determined to be level one danger, and the second danger level can also be directly determined to be a high danger level; for example, when the independent manifestation graphics and / or the comprehensive manifestation graphics are used to represent the "flower" graphic for a literary and artistic report performance, it is highly likely that the target user is not in a dangerous state or the target user is in a state of asking for help, and the second danger level is further determined to be a low danger level or no danger level, which is not limited in the embodiments of the present invention.

[0145] Optionally, the conventional meaning of the display graphic is used, for example: when an independent display graphic and / or a comprehensive display graphic is used to represent an "SOS" graphic, the conventional meaning of the display graphic is used to represent an "emergency distress signal" or "save us", and the second danger level is further determined to be a high danger level, or the second danger level is determined based on the danger level value corresponding to a preset "SOS" graphic, which is not limited in the embodiments of the present invention.

[0146] Further optionally, the above-mentioned judging whether the target user satisfies the preset exclusive danger indication condition based on the independent manifestation graphic and the comprehensive manifestation graphic is illustrated by way of example: judging whether the independent manifestation graphic and / or the comprehensive manifestation graphic has a special danger indication meaning, if so, it is determined that the target user satisfies the preset exclusive danger indication condition, if not, it is determined that the target user does not satisfy the preset exclusive danger indication condition; further, for example, the "SOS" graphic has a special meaning and is a special danger indication meaning, which is not limited in the embodiments of the present invention.

[0147] Optionally, the target user's gesture waving situation may include, but is not limited to, one or more of the target user's gesture waving amplitude information, gesture waving speed information, gesture waving mode information, gesture waving trajectory information, gesture waving direction information, gesture waving shape information, gesture waving time information, gesture waving acceleration and deceleration information, gesture waving posture angle information and other information that can reflect the gesture waving situation, and is not limited in the embodiments of the present invention.

[0148] Optionally, the movement of the target user may include, but is not limited to, one or more of the target user's movement type (such as walking type, running type, staying in place type, etc.), personnel movement location information, personnel movement trajectory information, personnel movement speed information, personnel movement acceleration and deceleration information, personnel movement mode information, personnel movement time information and other information that can reflect the movement of the personnel, and is not limited in the embodiments of the present invention.

[0149] Optionally, the above-mentioned determination of the nature of the target user's behavior posture based on the gesture waving situation and the person's movement situation is used as an example: when the gesture waving situation is used to indicate an abnormal gesture waving or the target user is waving the gesture towards the alarm monitor, and the person's movement situation is used to indicate that the target user has been performing abnormal behavior towards the alarm monitor or has been following the monitoring device for movement, etc., then it can be determined that the nature of the target user's behavior posture is a nature of asking for help. Other situations are applicable in the same way, and the embodiments of the present invention are not limited thereto.

[0150] Optionally, the above-mentioned determination of the urgency of the target user's request for help according to the gesture waving situation and the movement situation of the person is provided for example: when the gesture waving situation is used to indicate that the gesture waving amplitude of the target user is higher than the normal waving amplitude and the larger the waving amplitude, the gesture waving speed is higher than the normal waving speed and the larger the waving speed, the more abnormal the gesture waving situation, then the higher the urgency of the target user's request for help, and the same applies to other situations; further, for example, the target user is just shouting at the sky or greeting the sky, and the gesture waving speed amplitude and the person's movement speed trajectory corresponding to the target user shouting for help at an airplane passing through the sky are different. Furthermore, shouting at the sky or greeting the sky usually takes place while not moving, while shouting for help at an airplane passing through the sky may follow the flight direction of the airplane while shouting for help, and the embodiments of the present invention do not limit this.

[0151] Optionally, the above-mentioned second danger level of the target user based on the user behavior posture level is determined according to the urgency of the rescue. For example: when the urgency of the rescue is greater, the level of the second danger level of the target user based on the user behavior posture level is higher / used to indicate more dangerous. Other situations are similarly obtainable and are not limited to the embodiments of the present invention.

[0152] It can be seen that this optional embodiment can provide a method for determining the second danger level at the user behavior posture level. When the exclusive danger representation conditions are met, the second danger level is determined based on the general meaning of the display graphics according to the independent manifestation image and the comprehensive manifestation graphics. When the exclusive danger representation conditions are not met, the second danger level is determined based on the determined target user's gesture waving and personnel movement conditions. This is conducive to improving the comprehensiveness, integrity and rationality of the method for determining the second danger level, and thus is conducive to improving the diversity, flexibility, pertinence and selectivity of the method for determining the second danger level, and is conducive to improving the pertinence and matching of the determination parameters used to determine the second danger level, thereby helping to improve the accuracy and reliability of the determined second danger level at the user behavior posture level.

[0153] In yet another optional embodiment, the above-mentioned analyzing the third risk level of the target user based on the user aggregation level according to the user aggregation information may include:

[0154] According to the user gathering information, determine the actual number of people gathered in the current area where the target user is located, the tolerable number of people gathered, and the real-time increase or decrease in the number of people gathered;

[0155] Determine the density risk level of the current area based on the actual number of people gathered, the tolerable number of people gathered, and the real-time increase or decrease in the number of people gathered;

[0156] Determine the location information of the gathering area corresponding to the target user according to the user gathering information, and determine the regional characteristics of the current regional location according to the gathering area location information;

[0157] Analyze the current hidden risk information and risk change trends of the current regional location based on regional characteristics and intensive risk levels;

[0158] According to the current hidden risk information and risk change trends, determine the third risk level of target users based on the user aggregation level.

[0159] Optionally, the real-time increase or decrease in the number of people gathered may include, but is not limited to, one or more of the following information: the real-time increase or decrease value of the real-time number of people gathered, the real-time increase or decrease amplitude of the real-time number of people gathered, the real-time increase or decrease speed of the real-time number of people gathered, the real-time increase or decrease ratio of the real-time number of people gathered, the real-time flow of the real-time number of people gathered, the real-time structure of the real-time number of people gathered, and other information that can reflect the increase in the real-time number of people gathered, and the embodiments of the present invention are not limited thereto.

[0160] Optionally, the above-mentioned determination of the density risk level of the current area location based on the actual number of people gathered, the tolerable number of people gathered and the increase or decrease in the real-time number of people gathered is provided as follows: for example, when the actual number of people gathered is greater than the tolerable number of people gathered, the higher the density risk level of the current area location is / the greater the risk is; for example, when the increase in the real-time number of people gathered is used to indicate that the increase rate of the real-time number of people gathered in the current area location exceeds the safe increase rate and the more it exceeds, the higher the density risk level of the current area location is / the greater the risk is; for example, when the actual number of people gathered is lower than the tolerable number of people gathered but the increase or decrease in the real-time number of people gathered is used to indicate that the real-time number of people gathered is constantly increasing, then the density risk level of the current area location is constantly increasing / the risk is constantly increasing. Other situations are similarly applicable and are not limited to the embodiments of the present invention.

[0161] Optionally, the regional characteristics of the current regional location include, for example: confined space characteristics (such as a small utility room, a small corner, etc.), open space characteristics (such as a track and field, etc.), dangerous edge characteristics (such as a cliff edge, a railing edge, etc.), etc., which are not limited in the embodiments of the present invention.

[0162] Optionally, the current hidden risk information may include, but is not limited to, one or more of the following: specific hidden risk content, hidden risk type, degree of danger of hidden risk, hazard information of hidden risk, impact information of hidden risk, cause of hidden risk, target object of hidden risk, predicted time / predicted trigger point for hidden risk to become actual danger, and other information about the current hidden risk, etc., and the embodiments of the present invention are not limited thereto.

[0163] Optionally, the risk change trend may include, but is not limited to, one or more of the following: specific risk content, risk change time, risk change rate, impact of risk prediction, harm caused by risk prediction, relevant information on the conversion of risk into actual harm, predicted risk changes, etc., and is not limited in the embodiments of the present invention.

[0164] Optionally, examples are given for regional characteristics, density risk levels, current hidden risk information, risk change trends and the third danger level: for example, when the density risk level is used to indicate that the denser the gathering of people in the current regional location, and / or the regional characteristics are used to indicate that the gathering location is closer to the cliff, then it can be determined that the third danger level is higher / used to indicate that it is more dangerous; for another example, the hidden risk present in target users gathering in an open large playground is smaller than that in target users gathering in a closed small room, and the third danger levels of the two are different, that is, the danger level of gathering in an open large playground is lower than the danger level of gathering in a closed small space; for another example, the third danger level corresponding to the situation where people gather in the same place but there are still many loose spaces is different from the crowded situation, that is, the danger level of the loose space is lower than the danger level of the crowded space, and other situations are similarly applicable, and the embodiments of the present invention are not limited thereto.

[0165] It can be seen that this optional embodiment can provide a method for determining the third danger level at the user aggregation level, and determine the density risk level and regional characteristics of the current area location based on the user aggregation information to further determine the third danger level. This is conducive to improving the comprehensiveness and integrity of the method for determining the third danger level, and thus is conducive to improving the pertinence and rationality of the method for determining the third danger level, as well as improving the pertinence, matching, diversity and flexibility of the determination parameters used to determine the third danger level, thereby helping to improve the accuracy and reliability of the determined third danger level at the user aggregation level.

[0166] Embodiment 2

[0167] See also Figure 2 , Figure 2 is a flow chart of another method for dispersal control applied to human-computer interaction disclosed in an embodiment of the present invention. Figure 2 The described method can be applied to a dispersal control method, wherein the device may include a server, wherein the server includes a local server or a cloud server, which is not limited in the embodiment of the present invention. Figure 2 As shown, the dispersal control method applied to human-computer interaction includes the following operations:

[0168] 201. Analyze the danger warning level of the target user according to the detected human-computer interaction data of the target user, where the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information, and other user information.

[0169] 202. According to the danger warning level, determine whether the target user meets the preset danger situation condition. When the determination result is yes, execute step 203; when the determination result is no, execute step 204.

[0170] 203. Generate a corresponding danger response strategy based on the danger warning level and human-computer interaction data, and control the intelligent control equipment to perform corresponding dispersal control operations based on the danger response strategy.

[0171] 204. Determine the noise making situation corresponding to the target user based on the human-computer interaction data.

[0172] Optionally, the noise creation situation corresponding to the target user may include, but is not limited to, one or more of the noise content created by the target user, noise decibel volume information, noise frequency and sound quality information, noise source information, noise duration, the interference effect of noise on the outside world, noise generation method information, noise generation object, cause of noise generation, and other information that can reflect the situation of the noise created, etc., and the embodiment of the present invention is not limited thereto.

[0173] 205. According to the noise generation situation, determine whether the target user meets the preset noise processing conditions.

[0174] Further optionally, judging whether the target user meets the preset noise processing condition according to the noise generation situation may include:

[0175] According to the noise generation situation, determine the interference impact of the noise generated by the target user on the outside world;

[0176] Determine whether the interference impact degree is greater than or equal to a preset interference impact degree threshold;

[0177] When the judgment result is yes, it is determined that the target user meets the preset noise processing condition;

[0178] When the judgment result is no, it is determined that the target user does not meet the preset noise processing condition.

[0179] Optionally, the interference impact of the noise generated by the target user on the outside world may be the interference impact of the noise generated by the target user on other people and / or the interference impact of the noise generated by the target user on the surrounding environment, which is not limited in this embodiment of the present invention.

[0180] 206. When it is determined that the target user meets the noise processing conditions, a corresponding noise control strategy is generated according to the human-computer interaction data and the noise generation situation, and according to the noise control strategy, the intelligent control device is controlled to perform the corresponding noise control operation.

[0181] Optionally, the above-mentioned noise control strategy may include but is not limited to notifying staff to remind target personnel to stop noise generation, playing a prompt voice prohibiting noise making, starting signal shielding equipment, etc. to forcibly control and suppress noise-generating machines and equipment, etc., and one or more other methods and strategies that can achieve noise control, which are not limited in the embodiments of the present invention.

[0182] In the embodiment of the present invention, for other descriptions of step 201 to step 206, please refer to other detailed descriptions of step 101 to step 103 in embodiment 1, and the embodiment of the present invention will not be repeated here.

[0183] It can be seen that the embodiment of the present invention can analyze the danger warning level of the target user according to the human-computer interaction data, further generate a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, and then control the intelligent control equipment to perform the corresponding dispersal control operation, which is conducive to improving the comprehensiveness, integrity and rationality of the dispersal control method applied to human-computer interaction, and thus is conducive to improving the accuracy and reliability of the dispersal control of personnel users, as well as improving the efficiency and convenience of the dispersal control of personnel users. In addition, when the dangerous situation conditions are met, the corresponding danger response and dispersal control operations are performed, which is conducive to improving the timeliness and accuracy of the danger response processing of personnel users, thereby helping to improve the safety of personnel users; and, it can also provide a noise control method, perform corresponding noise control operations according to the human-computer interaction data, which is conducive to improving the comprehensiveness and integrity of the dispersal control method applied to human-computer interaction, and thus is conducive to improving the intelligence and functional diversity of the dispersal control device, as well as improving the accuracy and reliability of the noise control of personnel users, and improving the efficiency and convenience of the noise control of personnel users, thereby helping to reduce the noise generation and noise impact of the current environmental area.

[0184] In an optional embodiment, judging whether the target user meets the preset dangerous situation condition according to the danger warning level may include:

[0185] Determine whether the danger warning level is greater than or equal to a preset danger warning level threshold;

[0186] When it is determined that the danger warning level is greater than or equal to the danger warning level threshold, it is determined that the target user meets the preset danger situation condition;

[0187] When it is determined that the danger warning level is less than the danger warning level threshold, it is determined that the target user does not meet the dangerous situation condition.

[0188] It can be seen that this optional embodiment can determine the results of satisfaction of dangerous situation conditions by comparing the danger warning level with the size of the corresponding threshold value, which is conducive to improving the comprehensiveness and integrity of the method for determining the results of satisfaction of dangerous situation conditions, and further helps to improve the accuracy and reliability of the determined results of satisfaction of dangerous situation conditions, thereby helping to improve the timeliness, rationality and accuracy of the execution of subsequent corresponding operations based on the results of satisfaction of dangerous situation conditions.

[0189] In another optional embodiment, the above-mentioned generation of corresponding danger response strategies according to the danger warning level and human-computer interaction data may include:

[0190] According to the danger warning level, determine the basic danger response type corresponding to the target user;

[0191] When the basic danger response type is an automatic alarm type, the danger alarm related information corresponding to the target user is analyzed according to the human-computer interaction data; and the corresponding danger response strategy is generated according to the danger alarm related information and the automatic alarm type;

[0192] When the basic danger response type is the automatic monitoring type, the predicted duration corresponding to the current danger state of the target user is determined according to the danger warning level and the human-computer interaction data; and the corresponding danger response strategy is generated according to the predicted duration and the automatic monitoring type;

[0193] When the basic danger response method type is the automatic playback of warning voice type, the warning voice content, playback interval time, and number of playbacks corresponding to the target user are determined according to the danger warning level and human-computer interaction data; based on the warning voice content, playback interval time, number of playbacks, and the automatic playback warning voice type, a corresponding danger response strategy is generated.

[0194] Further optionally, the above-mentioned determining the basic danger response type corresponding to the target user according to the danger warning level may include:

[0195] When the danger warning level meets the preset first level requirement, the basic danger response type corresponding to the target user is determined to be an automatic alarm type;

[0196] When the danger warning level meets the preset second level requirement, the basic danger response method type corresponding to the target user is determined to be an automatic monitoring type;

[0197] When the danger warning level meets the preset third level requirement, it is determined that the basic danger response method type corresponding to the target user is the automatic playback warning voice type.

[0198] Optionally, the danger alarm related information corresponding to the above-mentioned target user may include, but is not limited to, one or more of the content information that needs to be conveyed when making a danger alarm, specific danger alarm objects (for example, different danger types correspond to different police types and alarm types), specific danger alarm methods, specific danger alarm response times, specific danger alarm reply requirements and other danger alarm-related information, etc., and the embodiments of the present invention are not limited thereto.

[0199] Optionally, the above-mentioned generation of a corresponding danger response strategy based on the predicted duration and the automatic monitoring type can be understood as: predicting the duration of the target user being in the current dangerous state, and monitoring the target user within the duration, which is not limited in the embodiments of the present invention.

[0200] Optionally, the above-mentioned generation of a corresponding danger response strategy based on the danger alarm related information and the automatic alarm type can be understood as: issuing an alarm based on the determined danger alarm related information, which is not limited in the embodiment of the present invention.

[0201] Optionally, the above-mentioned generation of a corresponding danger response strategy based on the warning voice content, playback interval time and number of playbacks and the type of automatically played warning voice can be understood as: playing the warning voice based on the warning voice content, playback interval time and number of playbacks, which is not limited in the embodiments of the present invention.

[0202] Optionally, the warning voice content, playback interval and playback times corresponding to the target user may be understood as: the playback interval between two playbacks of the warning voice content and the total required playback times for the warning content, which is not limited in the embodiment of the present invention.

[0203] It can be seen that this optional embodiment can determine the basic danger response method type according to the danger warning level, and execute the corresponding danger response strategy generation method for different basic danger response method types, which is conducive to improving the comprehensiveness, integrity and rationality of the danger response strategy generation method, and thus is conducive to improving the diversity, flexibility and pertinence of the danger response strategy generation method, thereby helping to improve the accuracy and reliability of the determined danger response strategy. In addition, it is conducive to improving the diversity, flexibility and pertinence of the determined basic danger response method types, thereby helping to improve the effectiveness and accuracy of the danger handling effect formed based on the basic danger response method types and danger response strategies.

[0204] Embodiment 3

[0205] See also Figure 3 , Figure 3 : is a schematic diagram of a dispersal control device for human-computer interaction disclosed in an embodiment of the present invention. Figure 3 The described device may include a server, wherein the server includes a local server or a cloud server, which is not limited in the embodiment of the present invention. Figure 3 As shown, the dispersal control device applied to human-computer interaction may include:

[0206] The danger analysis module 301 is used to analyze the danger warning level of the target user according to the detected human-computer interaction data of the target user, where the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information and other user information.

[0207] The judgment module 302 is used to judge whether the target user meets the preset dangerous situation conditions according to the danger warning level.

[0208] The strategy generation module 303 is used to generate a corresponding danger response strategy according to the danger warning level and human-computer interaction data when the judgment module determines that the target user meets the dangerous situation conditions.

[0209] The dispersal control module 304 is used to control the intelligent control equipment to perform corresponding dispersal control operations according to the danger response strategy.

[0210] It can be seen that implementation Figure 3 The dispersal control device applied to human-computer interaction described above can analyze the danger warning level of the target user according to the human-computer interaction data, further generate a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, and then control the intelligent control equipment to perform the corresponding dispersal control operation, which is beneficial to improving the comprehensiveness, integrity and rationality of the dispersal control method applied to human-computer interaction, and thus is beneficial to improving the accuracy and reliability of the dispersal control of personnel users, as well as improving the efficiency and convenience of the dispersal control of personnel users. In addition, when the dangerous situation conditions are met, the corresponding danger response and dispersal control operations are performed, which is beneficial to improving the timeliness and accuracy of the danger response processing of personnel users, thereby improving the safety of personnel users.

[0211] In an optional embodiment, the danger analysis module 301 analyzes the danger warning level of the target user according to the detected human-computer interaction data of the target user in the following manner:

[0212] When the detected human-computer interaction data of the target user includes user shouting information, analyzing the first danger level of the target user based on the user shouting level according to the user shouting information;

[0213] When the detected human-computer interaction data of the target user includes user behavior posture information, analyzing the second danger level of the target user based on the user behavior posture level according to the user behavior posture information;

[0214] When the detected human-computer interaction data of the target user includes user aggregation information, analyzing the third risk level of the target user based on the user aggregation level according to the user aggregation information;

[0215] When the detected human-computer interaction data of the target user includes other user information, analyzing the fourth risk level of the target user based on other levels according to the other user information;

[0216] A danger warning level of the target user is determined according to the first danger level and / or the second danger level and / or the third danger level and / or the fourth danger level.

[0217] It can be seen that the implementation Figure 4 The described device can determine the corresponding danger level for the user shouting information, user behavior posture information, user gathering information and other user information included in the human-computer interaction data, and then determine the danger warning level of the target user, which is conducive to improving the comprehensiveness and integrity of the danger warning level determination method, as well as improving the pertinence and rationality of the danger warning level determination method, and then helping to improve the accuracy and reliability of the determined danger warning level, thereby helping to improve the rationality, timeliness and accuracy of the subsequent execution of related operations based on the determined danger warning level.

[0218] In another optional embodiment, the method in which the danger analysis module 301 analyzes the first danger level of the target user based on the user shouting level according to the user shouting information specifically includes:

[0219] According to the user's shouting information, determine the shouting content information, shouting volume information and shouting breath information corresponding to the target user;

[0220] Determine the target user's calling purpose type based on the calling content information, and determine the target user's corresponding calling demand solution based on the calling content information;

[0221] Determine the breath change of the target user according to the shouting breath information, and determine the volume change of the target user according to the shouting volume information;

[0222] Determine the abnormality of the target user's shouting based on shouting content information, breath changes, and volume changes;

[0223] According to the type of shouting purpose, shouting demand solution and shouting abnormality, analyze the first danger level of the target user based on the user shouting level.

[0224] It can be seen that the implementation Figure 4The described device can also provide a method for determining the first danger level at the user's shouting level, and determine the type of shouting purpose, shouting demand solution and shouting abnormality level based on the user's shouting information to further determine the first danger level. This is conducive to improving the comprehensiveness and integrity of the method for determining the first danger level, and thus is conducive to improving the pertinence, matching and rationality of the method for determining the first danger level, as well as improving the diversity, flexibility and pertinence of the determination parameters used to determine the first danger level, thereby facilitating improving the accuracy and reliability of the determined first danger level at the user's shouting level.

[0225] In another optional embodiment, the method in which the risk analysis module 301 analyzes the second risk level of the target user based on the user behavior posture level according to the user behavior posture information specifically includes:

[0226] According to the user behavior posture information, determine the independent embodiment graph and the comprehensive embodiment graph formed by the behavior posture of the target user;

[0227] According to the independent manifestation graphics and the comprehensive manifestation graphics, determine whether the target user meets the preset exclusive hazard indication conditions;

[0228] When it is determined that the target user meets the exclusive danger indication condition, the conventional meaning of the display graphic corresponding to the target user is determined based on the independent manifestation graphic and the comprehensive manifestation graphic; the second danger level of the target user based on the user behavior posture level is determined based on the conventional meaning of the display graphic;

[0229] When it is determined that the target user does not meet the exclusive danger indication conditions, the target user's hand gestures and movements are determined based on the user's behavior posture information, and the nature of the target user's behavior posture is determined based on the hand gestures and movements; when the nature of the behavior posture is used to indicate the nature of a call for help, the urgency of the call for help corresponding to the target user is determined based on the hand gestures and movements; based on the urgency of the call for help, the second danger level of the target user based on the user's behavior posture level is determined.

[0230] It can be seen that implementation Figure 4The described device can also provide a method for determining the second danger level at the user behavior posture level. When the exclusive danger indication conditions are met, the second danger level is determined based on the general meaning of the display graphics according to the independent manifestation image and the comprehensive manifestation graphics. When the exclusive danger indication conditions are not met, the second danger level is determined based on the determined target user's gesture waving and personnel movement conditions. This is conducive to improving the comprehensiveness, integrity and rationality of the method for determining the second danger level, and thus is conducive to improving the diversity, flexibility, pertinence and selectivity of the method for determining the second danger level, and is conducive to improving the pertinence and matching of the determination parameters used to determine the second danger level, thereby facilitating improving the accuracy and reliability of the determined second danger level at the user behavior posture level.

[0231] In another optional embodiment, the risk analysis module 301 analyzes the third risk level of the target user based on the user aggregation level according to the user aggregation information, specifically including:

[0232] According to the user gathering information, determine the actual number of people gathered in the current area where the target user is located, the tolerable number of people gathered, and the real-time increase or decrease in the number of people gathered;

[0233] Determine the density risk level of the current area based on the actual number of people gathered, the tolerable number of people gathered, and the real-time increase or decrease in the number of people gathered;

[0234] Determine the location information of the gathering area corresponding to the target user according to the user gathering information, and determine the regional characteristics of the current regional location according to the gathering area location information;

[0235] Analyze the current hidden risk information and risk change trends of the current regional location based on regional characteristics and intensive risk levels;

[0236] According to the current hidden risk information and risk change trends, determine the third risk level of target users based on the user aggregation level.

[0237] It can be seen that implementation Figure 4 The described device can also provide a method for determining the third danger level at the user aggregation level, and determine the density risk level and regional characteristics of the current area location based on the user aggregation information to further determine the third danger level. This is conducive to improving the comprehensiveness and integrity of the method for determining the third danger level, and thus is conducive to improving the pertinence and rationality of the method for determining the third danger level, as well as improving the pertinence, matching, diversity and flexibility of the determination parameters used to determine the third danger level, thereby helping to improve the accuracy and reliability of the determined third danger level at the user aggregation level.

[0238] In another optional embodiment, the determination module 302 determines whether the target user meets the preset dangerous situation condition according to the danger warning level, specifically including:

[0239] Determine whether the danger warning level is greater than or equal to a preset danger warning level threshold;

[0240] When it is determined that the danger warning level is greater than or equal to the danger warning level threshold, it is determined that the target user meets the preset danger situation condition;

[0241] When it is determined that the danger warning level is less than the danger warning level threshold, it is determined that the target user does not meet the dangerous situation condition.

[0242] It can be seen that the implementation Figure 4 The described device can also determine the results of dangerous situation conditions being met by comparing the danger warning level with the corresponding threshold value, which is conducive to improving the comprehensiveness and integrity of the method for determining the results of dangerous situation conditions being met, and further helps to improve the accuracy and reliability of the determined results of dangerous situation conditions being met, thereby helping to improve the timeliness, rationality and accuracy of subsequent execution of corresponding operations based on the results of dangerous situation conditions being met.

[0243] In yet another optional embodiment, Figure 4 As shown, the device may also include:

[0244] The noise control module 305 is used to determine the noise creation situation corresponding to the target user based on the human-computer interaction data when the judgment module 302 determines that the target user does not meet the dangerous situation conditions; based on the noise creation situation, determine whether the target user meets the preset noise processing conditions; when it is determined that the target user meets the noise processing conditions, generate a corresponding noise control strategy based on the human-computer interaction data and the noise creation situation, and control the intelligent control device to perform corresponding noise control operations based on the noise control strategy.

[0245] It can be seen that the implementation Figure 4 The described device can also provide a noise control method and perform corresponding noise control operations according to human-computer interaction data, which is conducive to improving the comprehensiveness and integrity of the dispersal control method applied to human-computer interaction, and thus is conducive to improving the intelligence and functional diversity of the dispersal control device, as well as improving the accuracy and reliability of noise control for human users, and is conducive to improving the efficiency and convenience of noise control for human users, thereby helping to reduce noise generation and noise impact in the current environmental area.

[0246] In another optional embodiment, the strategy generation module 303 generates a corresponding danger response strategy according to the danger warning level and the human-computer interaction data, specifically including:

[0247] According to the danger warning level, determine the basic danger response type corresponding to the target user;

[0248] When the basic danger response type is an automatic alarm type, the danger alarm related information corresponding to the target user is analyzed according to the human-computer interaction data; and the corresponding danger response strategy is generated according to the danger alarm related information and the automatic alarm type;

[0249] When the basic danger response type is the automatic monitoring type, the predicted duration corresponding to the current danger state of the target user is determined according to the danger warning level and human-computer interaction data; and the corresponding danger response strategy is generated according to the predicted duration and the automatic monitoring type;

[0250] When the basic danger response method type is the automatic playback of warning voice type, the warning voice content, playback interval time, and number of playbacks corresponding to the target user are determined according to the danger warning level and human-computer interaction data; based on the warning voice content, playback interval time, number of playbacks, and the automatic playback warning voice type, a corresponding danger response strategy is generated.

[0251] It can be seen that the implementation Figure 4 The described device can also determine the basic danger response method type according to the danger warning level, and execute corresponding danger response strategy generation methods for different basic danger response method types, which is conducive to improving the comprehensiveness, integrity and rationality of the danger response strategy generation method, and thus is conducive to improving the diversity, flexibility and pertinence of the danger response strategy generation method, thereby helping to improve the accuracy and reliability of the determined danger response strategy. In addition, it is conducive to improving the diversity, flexibility and pertinence of the determined basic danger response method types, thereby helping to improve the effectiveness and accuracy of the danger handling effect formed based on the basic danger response method types and danger response strategies.

[0252] Embodiment 4

[0253] See also Figure 5 , Figure 5 is a structural diagram of another dispersal control device for human-computer interaction disclosed in an embodiment of the present invention. Figure 5 The described device may include a server, wherein the server includes a local server or a cloud server, which is not limited in the embodiment of the present invention. Figure 5 As shown, the device may include:

[0254] A memory 401 storing executable program codes;

[0255] a processor 402 coupled to the memory 401;

[0256] Furthermore, it may also include an input interface 403 and an output interface 404 coupled to the processor 402;

[0257] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the dispersal control method applied to human-computer interaction described in the first or second embodiment.

[0258] Embodiment 5

[0259] An embodiment of the present invention discloses a computer storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the dispersal control method applied to human-computer interaction described in Embodiment 1 or Embodiment 2.

[0260] Embodiment 6

[0261] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the dispersal control method applied to human-computer interaction described in Example 1 or Example 2.

[0262] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0263] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, a magnetic disk storage, a magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0264] Finally, it should be noted that the dispersal control method and device for human-computer interaction disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dispersal control method applied to human-computer interaction, characterized in that: The method comprises: Analyzing the danger warning level of the target user according to the detected human-computer interaction data of the target user, wherein the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information and other user information; According to the danger warning level, determining whether the target user meets a preset danger situation condition; When it is determined that the target user meets the dangerous situation condition, a corresponding danger response strategy is generated according to the danger warning level and the human-computer interaction data, and according to the danger response strategy, the intelligent control device is controlled to perform a corresponding dispersal control operation; And, analyzing the danger warning level of the target user according to the detected human-computer interaction data of the target user includes: When the detected human-computer interaction data of the target user includes user behavior posture information, analyzing the second danger level of the target user based on the user behavior posture level according to the user behavior posture information; Determining a danger warning level of the target user according to the second danger level; And, analyzing the second risk level of the target user based on the user behavior posture level according to the user behavior posture information includes: According to the user behavior posture information, determine the independent embodiment graphic and the comprehensive embodiment graphic formed by the behavior posture of the target user; the independent embodiment graphic is a graphic displayed by a single individual or a partial body part of the target user; the comprehensive embodiment graphic is a graphic displayed by a plurality of the target users in combination or a graphic displayed by the complete body; According to the independent embodiment graphic and the comprehensive embodiment graphic, determining whether the target user meets a preset exclusive danger indication condition; When it is determined that the target user meets the exclusive danger indication condition, the conventional meaning of the display graphic corresponding to the target user is determined according to the independent embodiment graphic and the comprehensive embodiment graphic; and the second danger level of the target user based on the user behavior posture level is determined according to the conventional meaning of the display graphic; When it is determined that the target user does not meet the exclusive danger indication condition, the hand gesture and personal movement of the target user are determined according to the user behavior posture information, and the nature of the target user's behavior posture is determined according to the hand gesture and personal movement; when the nature of the behavior posture is used to indicate the nature of seeking help, the urgency of seeking help corresponding to the target user is determined according to the hand gesture and personal movement; and according to the urgency of seeking help, the second danger level of the target user based on the user behavior posture level is determined.

2. The method for dispersal control applied to human-computer interaction according to claim 1, characterized in that: The step of analyzing the danger warning level of the target user according to the detected human-computer interaction data of the target user further includes: When the detected human-computer interaction data of the target user includes user shouting information, analyzing the first danger level of the target user based on the user shouting level according to the user shouting information; When the detected human-computer interaction data of the target user includes user aggregation information, analyzing the third risk level of the target user based on the user aggregation level according to the user aggregation information; When the detected human-computer interaction data of the target user includes other user information, analyzing the fourth risk level of the target user based on other levels according to the other user information; A danger warning level of the target user is determined according to the first danger level and / or the third danger level and / or the fourth danger level.

3. The method for dispersal control applied to human-computer interaction according to claim 2 is characterized in that: The analyzing, according to the user shouting information, a first danger level of the target user based on the user shouting level includes: Determine the shouting content information, shouting volume information and shouting breath information corresponding to the target user according to the user shouting information; Determine the shouting purpose type of the target user according to the shouting content information, and determine the shouting demand solution corresponding to the target user according to the shouting content information; Determine the breath change of the target user according to the shouting breath information, and determine the volume change of the target user according to the shouting volume information; Determining the abnormality of the target user's shouting according to the shouting content information, the breath change and the volume change; According to the shouting purpose type, the shouting demand solution and the shouting abnormality level, a first danger level of the target user based on the user shouting level is analyzed.

4. The method for dispersal control applied to human-computer interaction according to claim 2 is characterized in that: The analyzing, according to the user aggregation information, the third risk level of the target user based on the user aggregation level includes: According to the user gathering information, determine the actual number of people gathered, the tolerable number of people gathered, and the real-time increase or decrease in the number of people gathered in the current area where the target user is located; Determine the density risk level of the current area location according to the actual number of people gathered, the tolerable number of people gathered, and the increase or decrease in the real-time number of people gathered; Determine the location information of the gathering area corresponding to the target user according to the user gathering information, and determine the regional characteristics of the current regional location according to the gathering area location information; Analyze the current hidden risk information and risk change trend of the current area location according to the area characteristics and the intensive risk level; According to the current hidden risk information and the risk change trend, the third risk level of the target user based on the user aggregation level is determined.

5. The method for dispersal control applied to human-computer interaction according to any one of claims 1 to 4, characterized in that: The step of judging whether the target user meets a preset dangerous situation condition according to the danger warning level includes: Determining whether the danger warning level is greater than or equal to a preset danger warning level threshold; When it is determined that the danger warning level is greater than or equal to the danger warning level threshold, determining that the target user meets a preset danger situation condition; When it is determined that the danger warning level is less than the danger warning level threshold, determining that the target user does not meet the dangerous situation condition; And, the method further comprises: When it is determined that the target user does not meet the dangerous situation condition, determining the noise making situation corresponding to the target user according to the human-computer interaction data; According to the noise generation situation, determining whether the target user meets a preset noise processing condition; When it is determined that the target user meets the noise processing conditions, a corresponding noise control strategy is generated according to the human-computer interaction data and the noise creation situation, and according to the noise control strategy, the intelligent control device is controlled to perform corresponding noise control operations.

6. The method for dispersal control applied to human-computer interaction according to claim 5 is characterized in that: Generating a corresponding danger response strategy according to the danger warning level and the human-computer interaction data includes: Determining the basic danger response type corresponding to the target user according to the danger warning level; When the basic danger response mode type is an automatic alarm type, analyzing the danger alarm related information corresponding to the target user according to the human-computer interaction data; generating a corresponding danger response strategy according to the danger alarm related information and the automatic alarm type; When the basic danger response mode type is an automatic monitoring type, determining a predicted duration corresponding to the current danger state of the target user according to the danger warning level and the human-computer interaction data; generating a corresponding danger response strategy according to the predicted duration and the automatic monitoring type; When the basic danger response method type is the automatic playback of warning voice type, the warning voice content, playback interval time, and number of playbacks corresponding to the target user are determined according to the danger warning level and the human-computer interaction data; and a corresponding danger response strategy is generated according to the warning voice content, the playback interval time, the number of playbacks, and the automatic playback warning voice type.

7. A dispersal control device for human-computer interaction, characterized in that: The device comprises: A danger analysis module, used to analyze the danger warning level of the target user according to the detected human-computer interaction data of the target user, wherein the human-computer interaction data includes one or more of user shouting information, user behavior posture information, user gathering information and other user information; A judgment module, used to judge whether the target user meets a preset dangerous situation condition according to the danger warning level; A strategy generation module, configured to generate a corresponding danger response strategy according to the danger warning level and the human-computer interaction data when the judgment module determines that the target user meets the dangerous situation condition; A dispersal control module, used to control the intelligent control equipment to perform corresponding dispersal control operations according to the risk response strategy; Furthermore, the danger analysis module analyzes the danger warning level of the target user according to the detected human-computer interaction data of the target user, specifically including: When the detected human-computer interaction data of the target user includes user behavior posture information, analyzing the second danger level of the target user based on the user behavior posture level according to the user behavior posture information; Determining a danger warning level of the target user according to the second danger level; And, the danger analysis module analyzes the second danger level of the target user based on the user behavior posture level according to the user behavior posture information, specifically including: According to the user behavior posture information, determine the independent embodiment graphic and the comprehensive embodiment graphic formed by the behavior posture of the target user; the independent embodiment graphic is a graphic displayed by a single individual or a partial body part of the target user; the comprehensive embodiment graphic is a graphic displayed by a plurality of the target users in combination or a graphic displayed by the complete body; According to the independent embodiment graphic and the comprehensive embodiment graphic, determining whether the target user meets a preset exclusive danger indication condition; When it is determined that the target user meets the exclusive danger indication condition, the conventional meaning of the display graphic corresponding to the target user is determined according to the independent embodiment graphic and the comprehensive embodiment graphic; and the second danger level of the target user based on the user behavior posture level is determined according to the conventional meaning of the display graphic; When it is determined that the target user does not meet the exclusive danger indication condition, the hand gesture and personal movement of the target user are determined according to the user behavior posture information, and the nature of the target user's behavior posture is determined according to the hand gesture and personal movement; when the nature of the behavior posture is used to indicate the nature of seeking help, the urgency of seeking help corresponding to the target user is determined according to the hand gesture and personal movement; and according to the urgency of seeking help, the second danger level of the target user based on the user behavior posture level is determined.

8. A dispersal control device applied to human-computer interaction, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the dispersal control method applied to human-computer interaction as described in any one of claims 1-6.

9. A computer storage medium, characterized in that The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the dispersal control method applied to human-computer interaction as described in any one of claims 1-6.

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