A hotel management system based on intelligent communication

Abstract

The present invention relates to the field of hotel management, and in particular to a hotel management system based on intelligent communication, comprising: an information collection unit; a plurality of robots; a path planning unit for determining a path planning method according to the total number of hotel robots and the number of leading personnel; a leading analysis unit for determining whether the leading method corresponding to the task information is a midway takeover leading or a single working robot taking over leading according to leading conditions; a takeover setting unit for responding to setting conditions to determine a takeover point setting method when the leading method to be executed is a midway takeover leading; and a takeover selection unit for determining a leading selection method of a takeover point according to the difference between the number of stranded personnel and the number of intermittent robots. The present invention can adaptively adjust the path planning method according to the actual scene conditions in the hotel to improve the path planning efficiency.

Description

Technical Field

[0001] The present invention relates to the field of hotel management, and in particular to a hotel management system based on intelligent communication. Background Art

[0002] In hotel management, robots are often used to lead users to their rooms. However, during the leading process, they are often only guided according to a pre-set route and cannot be adaptively adjusted according to the actual situation of the hotel. As a result, when the number of users is large and the complexity of the passing areas is high, it is impossible to adaptively adjust the setting of the takeover points and the selection of the leading robots, resulting in poor hotel robot leading efficiency. Therefore, how to improve the automatic leading efficiency of hotel robots is a technical problem that needs to be urgently solved by technical personnel in this field.

[0003] Chinese patent publication number CN214560976U discloses a hotel service robot, including: a head, a host part and a mobile part, the head is provided with an electronic camera, the host part is provided with a first mechanical arm, the first mechanical arm is provided with an ID card recognition device, the host part is provided with a second mechanical arm, the second mechanical arm is provided with an infrared body temperature measurement device, the host part is provided with a card issuing mechanism for issuing room cards, the host part is provided with a display, a microphone array, a card outlet of the card issuing mechanism and a speaker on the front, the host part is provided with a control processor, a speech recognizer and a navigation locator, the mobile part is provided with a laser radar on the whole circumference, and the mobile part is provided with a mobile device on the bottom surface. By providing an electronic camera, a laser radar, a mobile device and a navigation locator, the service robot has a mobile navigation function, so that the service robot can guide customers to the corresponding hotel room. It can be seen that the above technical solution has the following problems: the path planning method for the automatic guidance of hotel customers is single, and it is impossible to adaptively adjust the path planning method according to the actual scene in the hotel, resulting in poor path planning efficiency and failure to meet actual service needs. Summary of the invention

[0004] To this end, the present invention provides a hotel management system based on intelligent communication, which is used to overcome the problem that the path planning method in the prior art for automatic guidance of hotel customers is single, and the path planning method cannot be adaptively adjusted according to the actual scene conditions in the hotel, resulting in poor path planning efficiency and inability to meet actual service needs.

[0005] To achieve the above object, the present invention provides a hotel management system based on intelligent communication, comprising:

[0006] An information collection unit, used to collect hotel information;

[0007] A plurality of robots connected to the information collection unit for guiding personnel, wherein each robot is provided with a communication module for real-time communication;

[0008] A path planning unit, which is connected to the information collection unit, and is used to determine the path planning method according to the total number of hotel robots and the number of guides, such as determining the guiding path according to the complexity of the passing area or determining the guiding path according to the road distance;

[0009] A leading analysis unit, which is respectively connected to each robot, the information collection unit and the path planning unit, and is used to determine, according to the leading conditions, whether the leading mode corresponding to the task information is midway takeover leading or single working robot takeover leading;

[0010] A takeover setting unit, which is respectively connected to each robot, the information collection unit and the leading analysis unit, and is used to respond to the setting conditions to determine the takeover point setting method by determining the number of takeover points according to the comprehensive evaluation coefficient and evenly setting the takeover point positions according to the takeover path, or determining the number of takeover points according to the number of feature combinations when the leading method to be executed is midway takeover leading;

[0011] The takeover selection unit is respectively connected to each robot and the takeover setting unit, and is used to determine the leading selection method of the takeover point for regional scheduling according to the difference between the number of stranded personnel and the number of intermittent robots, or to select the robot in the corresponding area of ​​the takeover point.

[0012] Furthermore, the path planning unit determines the path planning method according to the total number of hotel robots and the number of leading personnel;

[0013] If the total number of hotel robots is greater than or equal to the preset total number of hotel robots and the number of guides is less than the preset number of guides, the guidance path is determined according to the complexity of the passing area;

[0014] If the total number of hotel robots is less than the preset total number of hotel robots or the number of guides is greater than or equal to the preset number of guides, the guidance route is determined according to the road distance.

[0015] Furthermore, the path planning method performed by the path planning unit is to determine the leading path according to the complexity of the passing area, identify each moving route corresponding to the task information, and select the moving route with the least complexity of the passing area as the leading path;

[0016] The path planning unit performs a path planning method of determining a leading path according to a road distance, identifying each moving route corresponding to the task information, and selecting a moving route with the shortest road distance as the leading path;

[0017] The complexity of the transit area is determined according to a node threshold and an area restriction reference value. The node threshold is positively correlated with the complexity of the transit area, and the area restriction reference value is positively correlated with the complexity of the transit area.

[0018] Further, the leading analysis unit responds to different leading conditions to determine the leading mode corresponding to the task information;

[0019] The leading condition responded by the leading analysis unit is that the repetition paragraph threshold is greater than or equal to the preset repetition paragraph threshold or the path evaluation coefficient is greater than or equal to the preset path evaluation coefficient, and the leading mode is midway takeover leading;

[0020] The leading condition responded by the leading analysis unit is that the repeated paragraph threshold is less than the preset repeated paragraph threshold and the path evaluation coefficient is less than the preset path evaluation coefficient, and the leading mode is that a single working robot takes over the leading.

[0021] Furthermore, the guidance mode performed by the takeover setting unit is midway takeover guidance, responding to different setting conditions to determine the takeover point setting mode;

[0022] The setting condition of the takeover setting unit response is that the number of busy areas is less than the preset number of busy areas and the area threshold is less than the preset area threshold, the number of takeover points is determined according to the comprehensive evaluation coefficient and the positions of the takeover points are evenly set according to the takeover path;

[0023] The setting condition responded by the takeover setting unit is that the number of busy areas is greater than or equal to the preset number of busy areas or the area threshold is greater than or equal to the preset area threshold. The number of takeover points is determined according to the number of feature combinations and the takeover point position is set at the center position of each feature combination.

[0024] Furthermore, the takeover setting unit is provided with a feature combination determination method, and the determination method includes:

[0025] Perform conventional combination analysis on each moving area that the leading path passes through in order from the earliest to the latest arrival time. When performing conventional combination analysis on a single moving area, record the moving area as the target area, and add the location embedding factor corresponding to the target area and the location embedding factors corresponding to each moving area after the moving direction sequence one by one until the total value of the location embedding factor is greater than the preset total value of the location embedding factor, record each moving area before the arrival time of the moving area corresponding to the last accumulated location embedding factor and the target area as a conventional combination, and continue to perform conventional combination analysis on each moving area that is not recorded as a conventional combination until the preset situation is reached, then stop the conventional combination analysis;

[0026] If the default situation is that all moving areas are regular combinations, then each regular combination is recorded as a characteristic combination;

[0027] If the preset situation is that the total value of the location embedding factor is less than the preset total value of the location embedding factor, the set of moving areas that are not recorded as a regular combination is recorded as a special combination, and each regular combination and special combination is recorded as a feature combination.

[0028] Further, the takeover setting unit determines the location embedding factor according to the sub-area distribution extreme value and the proportion of the crowded sub-area;

[0029] The sub-area distribution extreme value is positively correlated with the location embedding factor, and the crowded sub-area proportion is positively correlated with the location embedding factor.

[0030] Further, the takeover selection unit determines the leading selection method of the takeover point according to the difference between the number of stranded personnel and the number of intermittent robots;

[0031] If the quantity difference is greater than or equal to the standard deviation, regional scheduling is performed;

[0032] If the quantity difference is less than the standard deviation, a robot is selected for the area corresponding to the takeover point.

[0033] Furthermore, the leading selection method performed by the takeover selection unit is to perform regional scheduling, select a mobile area whose location embedding factor is less than a preset location embedding factor as the scheduling area, and select an intermittent robot with the smallest scheduling difficulty threshold in each scheduling area for leading;

[0034] The leading selection method executed by the takeover selection unit is to select robots in the area corresponding to the takeover point, detect the machine reference values ​​corresponding to each intermittent robot in the moving area where the takeover point is located, and select the intermittent robot with the largest machine reference value for leading.

[0035] Furthermore, the leading mode executed by the takeover setting unit is that a single working robot takes over the leading, a takeover point is set at the entrance of the hotel, and for a single leading combination, a robot with the smallest scheduling difficulty threshold is selected to lead to the reserved room corresponding to the leading combination.

[0036] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the path planning unit determines the path planning method according to the total number of hotel robots and the number of guiding personnel, and effectively reflects the current robot's guiding efficiency through the total number of hotel robots and the number of guiding personnel, and then adaptively selects different path planning methods according to the total number of hotel robots and the number of guiding personnel, so that the selection of path planning method is more in line with the actual application scenario, avoiding the problem of a single path setting in the prior art that a suitable path cannot be selected according to actual conditions, and also avoiding the problem of too many stranded personnel and congestion of robots leading the path resulting in too long a guiding time, thereby improving the guiding efficiency of the hotel robot.

[0037] Furthermore, the leading analysis unit in the present invention determines the leading method corresponding to the task information according to the leading conditions. The leading conditions can effectively reflect the repetition of the leading path, and then adaptively select different leading methods, so that the selection of the leading method is more in line with the actual application scenario, avoiding the problem in the prior art that only one leading robot is set for leading, resulting in a long leading time when the complexity of the passed area is high. The leading method can be adjusted according to the actual situation of the hotel, thereby improving the leading efficiency of the hotel robot.

[0038] Furthermore, the guiding method executed by the takeover setting unit in the present invention is mid-way takeover guiding, which responds to different setting conditions to determine the takeover point setting method, and effectively reflects the busyness of the guiding path through the setting conditions, and then adaptively selects different takeover point setting methods, so that the selection of the takeover point setting method is more in line with the actual application scenario, avoiding the problem in the prior art that only one takeover point is set at the entrance of the hotel, resulting in the inability to set the takeover point according to the hotel situation when the regional complexity of the guiding path is high, resulting in too long guiding time, thereby improving the guiding efficiency of the hotel robot.

[0039] Furthermore, the takeover selection unit in the present invention determines the guidance selection method of the takeover point according to the difference between the number of stranded personnel and the number of intermittent robots, and effectively reflects whether there are idle robots in the area where the takeover point is located to guide the leading personnel passing through the takeover point through the difference between the number of stranded personnel and the number of intermittent robots, and then adaptively selects different guidance selection methods, so that the selection of guidance selection method is more in line with the actual application scenario, avoiding the problem of long detention time caused by the inability to dispatch robots when the area is busy, and improving the work efficiency of the robot while optimizing the resource allocation, and can flexibly respond to changes in the busyness of the area and dispatch the robot according to the actual situation, thereby improving the guidance efficiency of the hotel robot.

[0040] Furthermore, the path planning method executed by the path planning unit in the present invention is to determine the guiding path according to the complexity of the passed area, which can ensure that the regional complexity of the guiding path is minimized when the total number of hotel robots is greater than or equal to the preset total number of hotel robots and the number of guiding personnel is less than the preset number of guiding personnel, thereby avoiding the problem of long guiding time due to excessive path complexity, reducing the waiting time of stranded personnel, and thus improving the guiding efficiency of the hotel robots.

[0041] Furthermore, the path planning method executed by the path planning unit in the present invention is to determine the guiding path according to the road distance, and can select the shortest guiding path when the total number of hotel robots is less than the preset total number of hotel robots or the number of guiding personnel is greater than or equal to the preset number of guiding personnel, thereby avoiding the problem of excessively long guiding time and thereby improving the guiding efficiency of the hotel robots. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a unit connection diagram of the hotel management system based on intelligent communication of the present invention;

[0043] Figure 2 This is a flow chart of the present invention for determining a path planning method according to the total number of hotel robots and the number of leading personnel;

[0044] Figure 3 This is a flow chart of the present invention for determining a leading method corresponding to task information according to leading conditions;

[0045] Figure 4 The present invention is a flow chart of a leading selection method for determining a takeover point according to the difference between the number of stranded personnel and the number of intermittent robots. DETAILED DESCRIPTION

[0046] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0048] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0049] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] See also Figures 1 to 4 As shown, the present invention provides a hotel management system based on intelligent communication, comprising:

[0051] An information collection unit, used to collect hotel information;

[0052] A plurality of robots connected to the information collection unit for guiding personnel, wherein each robot is provided with a communication module for real-time communication;

[0053] A path planning unit, which is connected to the information collection unit, and is used to determine the path planning method according to the total number of hotel robots and the number of guides, such as determining the guiding path according to the complexity of the passing area or determining the guiding path according to the road distance;

[0054] A leading analysis unit, which is respectively connected to each robot, the information collection unit and the path planning unit, and is used to determine, according to the leading conditions, whether the leading mode corresponding to the task information is midway takeover leading or single working robot takeover leading;

[0055] A takeover setting unit, which is respectively connected to each robot, the information collection unit and the leading analysis unit, and is used to respond to the setting conditions to determine the takeover point setting method by determining the number of takeover points according to the comprehensive evaluation coefficient and evenly setting the takeover point positions according to the takeover path, or determining the number of takeover points according to the number of feature combinations when the leading method to be executed is midway takeover leading;

[0056] The takeover selection unit is respectively connected to each robot and the takeover setting unit, and is used to determine the leading selection method of the takeover point for regional scheduling according to the difference between the number of stranded personnel and the number of intermittent robots, or to select the robot in the corresponding area of ​​the takeover point.

[0057] The application scenario of the present invention is the field of hotel robot guidance. The robot described in the present invention is a robot with mobile function and can guide people. The specific structure and additional functions are not limited. Each robot is provided with a communication module for real-time communication. The communication module adopts wireless communication technology to communicate with the information collection unit, the guidance analysis unit, the takeover setting unit and the takeover selection unit in real time, so as to guide each leading person to the corresponding reserved room according to the guidance method. The wireless communication technology includes but is not limited to Wi-Fi, Bluetooth and ZigBee. Users can choose according to actual needs. This is easy to understand for people in this field and will not be described in detail.

[0058] Hotel information includes but is not limited to the number of robots, robot power, robot position, number of leading combinations and movable range. A single leading combination corresponds to a reserved room. The number of leading personnel included in a single leading combination does not exceed 5 people. The leading personnel are hotel users who need robots to lead to the reserved room corresponding to the leading personnel when arriving at the hotel. The movable range is the path that the leading personnel can move. The robot includes a working robot and an intermittent robot. The present invention is provided with several robot points for placing intermittent robots. The number and area of ​​the points can be set by the user according to actual needs; the movable range is divided into several equal and adjacent mobile areas, and each mobile area is provided with several robots. The present invention is provided with a continuous cycle monitoring cycle. At the end of each monitoring cycle, the total amount of hotel robots is determined once. The duration of the monitoring cycle can be set according to the needs of the user. The greater the user's demand for the accuracy of the total amount of hotel robots, the shorter the duration of the monitoring cycle. For a single mobile area, if no working robot is monitored at the end of the monitoring cycle, the intermittent robots corresponding to the mobile area are moved to the nearest robot point. A single leading combination corresponds to a task information. After the robot receives the task information, the leading analysis unit executes different leading methods.

[0059] Specifically, the path planning unit determines the path planning method according to the total number of hotel robots and the number of leading personnel;

[0060] If the total number of hotel robots is greater than or equal to the preset total number of hotel robots and the number of guides is less than the preset number of guides, the guidance path is determined according to the complexity of the passing area;

[0061] If the total number of hotel robots is less than the preset total number of hotel robots or the number of guides is greater than or equal to the preset number of guides, the guidance route is determined according to the road distance.

[0062] Among them, the total number of hotel robots is the number of intermittent robots monitored at the end of a single monitoring cycle; the number of guide personnel is the total number of intermittent robots at the end of a single monitoring cycle;

[0063] The values ​​of the preset total number of hotel robots and the preset number of guiding personnel can be determined by the user according to the actual application scenario. The greater the user's demand for robot guiding efficiency, the greater the value of the preset total number of hotel robots, and the smaller the value of the preset number of guiding personnel. Provided are the values ​​of the preset total number of hotel robots and the preset number of guiding personnel, detect the records corresponding to the guiding path determined according to the road distance in the historical monitoring period, record the average value of the total number of hotel robots corresponding to each record that can meet the user's guiding needs as the preset total number of hotel robots, and record the average value of the number of guiding personnel corresponding to each record that can meet the user's guiding needs as the preset number of guiding personnel. The historical monitoring period is all monitoring periods before the current detection period.

[0064] Specifically, the path planning method performed by the path planning unit is to determine the leading path according to the complexity of the passing area, identify each moving route corresponding to the task information, and select the moving route with the least complexity of the passing area as the leading path;

[0065] The path planning unit performs a path planning method of determining a leading path according to a road distance, identifying each moving route corresponding to the task information, and selecting a moving route with the shortest road distance as the leading path;

[0066] The complexity of the transit area is determined according to a node threshold and an area restriction reference value. The node threshold is positively correlated with the complexity of the transit area, and the area restriction reference value is positively correlated with the complexity of the transit area.

[0067] Among them, a single leading combination corresponds to a reserved room, and the leading combination has several moving routes from the hotel entrance to the corresponding reserved room;

[0068] For a moving route, the complexity of the passing area = (node ​​threshold / preset node threshold) × node threshold coefficient + (regional restriction reference value / preset regional restriction reference value) × restriction coefficient;

[0069] For a moving route corresponding to a booking combination, the node threshold = node quantity coefficient + congestion ratio coefficient, the node quantity coefficient is positively correlated with the node quantity, and the congestion ratio coefficient is positively correlated with the congested node ratio;

[0070] The number of nodes is the number of nodes passed by a single moving route. The node is a path turning point corresponding to a path segment greater than 3. The path segment is the path between two path turning points. The path turning point is the point where the robot needs to change the moving direction during movement. For a moving route, the proportion of crowded nodes = the number of crowded nodes in the moving route / the number of nodes in the moving route. For a node, if the number of people passing through the node in a single monitoring cycle is greater than the preset number of people, the node is recorded as a crowded node. The value of the preset number of people can be determined by the user according to the actual application scenario. It can be understood that the smaller the value of the preset number of people, the greater the probability of determining that the node is a crowded node. A value of the preset number of people is provided, and the preset number of people is 10;

[0071] The way to confirm the distance of the journey is that, for a moving route corresponding to a reservation combination, the shortest distance that the leading combination moves from the hotel entrance to the reserved room along the moving route is recorded as the distance of the journey.

[0072] The relationship between the regional restriction reference value and the path evaluation coefficient is positively correlated. The path evaluation coefficient = the number of people on the path + the number of busy elevators. For a single leading path, the total number of people monitored in a single monitoring cycle is recorded as the number of people on the path. The number of busy elevators is the number of busy elevators corresponding to the leading path. A busy elevator is an elevator with more people than the preset number of people in a single monitoring cycle. The value of the preset number of people in the elevator can be determined by the user according to the actual application scenario. The smaller the value of the preset number of people in the elevator, the greater the probability of determining that the elevator is a busy elevator. A value of the preset number of people in the elevator is provided, and the preset number of people in the elevator is 5.

[0073] The values ​​of the preset node threshold and the preset area restriction reference value can be determined by the user according to the actual application scenario. The greater the user's demand for the robot's leading efficiency, the smaller the values ​​of the preset node threshold and the preset area restriction reference value. Provide a value of the preset node threshold and the preset area restriction reference value, detect the records corresponding to the leading path determined according to the complexity of the passed area in the historical monitoring cycle, and record the average value of the node threshold that can meet the user's leading demand as the preset node threshold, and record the average value of the area restriction reference value that can meet the user's leading demand as the preset area restriction reference value;

[0074] The values ​​of the node threshold coefficient and the restriction coefficient can be obtained by the user through deep learning convolutional neural network learning based on historical records. It can be understood that the present invention reflects the difficulty of the mobile route through the complexity of the passed area. The user can use deep learning through historical user data to obtain the influence of the node threshold coefficient and the restriction coefficient on the difficulty of the mobile route, and then select the corresponding values ​​of the node threshold coefficient and the restriction coefficient, wherein the node threshold coefficient + restriction coefficient = 1, and provide a value of the node threshold coefficient and the restriction coefficient, the node threshold coefficient is 0.6, and the restriction coefficient is 0.4.

[0075] Specifically, the leading analysis unit responds to different leading conditions to determine the leading mode corresponding to the task information;

[0076] The leading condition responded by the leading analysis unit is that the repetition paragraph threshold is greater than or equal to the preset repetition paragraph threshold or the path evaluation coefficient is greater than or equal to the preset path evaluation coefficient, and the leading mode is midway takeover leading;

[0077] The leading condition responded by the leading analysis unit is that the repeated paragraph threshold is less than the preset repeated paragraph threshold and the path evaluation coefficient is less than the preset path evaluation coefficient, and the leading mode is that a single working robot takes over the leading.

[0078] The leading condition includes a first leading condition and a second leading condition, the first leading condition is that the repeated paragraph threshold is greater than or equal to the preset repeated paragraph threshold or the path evaluation coefficient is greater than or equal to the preset path evaluation coefficient, and the second leading condition is that the repeated paragraph threshold is less than the preset repeated paragraph threshold and the path evaluation coefficient is less than the preset path evaluation coefficient;

[0079] Repeated paragraph threshold = repeated paragraph length coefficient + maximum repeated paragraph coefficient, the repeated paragraph length coefficient is positively correlated with the repeated paragraph length, and the maximum repeated paragraph coefficient is positively correlated with the maximum repeated paragraph length; the repeated paragraph length is confirmed by recording the length of the path that is repeated between the leading path and other leading paths as the repeated paragraph length for a single leading path;

[0080] The method for confirming the maximum repeated segment length is that, for a single leading path, the sum of the lengths of the reference repeated paths is recorded as the maximum repeated segment length. The method for confirming the reference repeated path is that, for a single path segment, the path segment whose number of leading paths passing through the path segment is greater than a preset number is recorded as a reference repeated path. The value of the preset number can be determined according to the actual application scenario. The smaller the value of the preset number, the greater the probability of determining that the path segment is a reference repeated path. A value of the preset number is provided, and the preset number is 3.

[0081] The values ​​of the preset repeated paragraph threshold and the preset path evaluation coefficient can be determined by the user according to the actual application scenario. The greater the user's demand for the robot's leading efficiency, the smaller the values ​​of the preset repeated paragraph threshold and the preset path evaluation coefficient. A value of a preset repeated paragraph threshold and a preset path evaluation coefficient is provided, and the records corresponding to the leading mode of taking over the leading in the middle of the historical monitoring period are detected. The average value of the repeated paragraph threshold corresponding to the records that can meet the user's leading needs is recorded as the preset repeated paragraph threshold, and the average value of the path evaluation coefficient corresponding to the records that can meet the user's leading needs is recorded as the preset path evaluation coefficient.

[0082] Specifically, the guidance method performed by the takeover setting unit is midway takeover guidance, responding to different setting conditions to determine the takeover point setting method;

[0083] The setting condition of the takeover setting unit response is that the number of busy areas is less than the preset number of busy areas and the area threshold is less than the preset area threshold, the number of takeover points is determined according to the comprehensive evaluation coefficient and the positions of the takeover points are evenly set according to the takeover path;

[0084] The setting condition responded by the takeover setting unit is that the number of busy areas is greater than or equal to the preset number of busy areas or the area threshold is greater than or equal to the preset area threshold. The number of takeover points is determined according to the number of feature combinations and the takeover point position is set at the center position of each feature combination.

[0085] The setting condition includes a first setting condition and a second setting condition, the first setting condition is that the number of busy areas is less than the preset number of busy areas and the area threshold is less than the preset area threshold, and the second setting condition is that the number of busy areas is greater than or equal to the preset number of busy areas or the area threshold is greater than or equal to the preset area threshold;

[0086] Under the first setting condition, the number of takeover points is positively correlated with the comprehensive evaluation coefficient;

[0087] Under the second setting condition, the number of takeover points is equal to the number of feature combinations;

[0088] The number of busy areas is the number of busy areas corresponding to a single leading path. The busy area is a mobile area where the number of working robots is greater than the preset number of working robots. The value of the preset number of working robots can be determined by the user according to the actual application scenario. It can be understood that the larger the value of the preset number of working robots, the busier the mobile area is. A value of the preset number of working robots is provided, and the number of working robots corresponding to each mobile area in the historical monitoring period is detected. The average value of the number of working robots that can meet the user's leading needs is recorded as the preset number of working robots;

[0089] The method for confirming the regional threshold is to detect the regional evaluation value of each moving area passed by the single guiding path, and record the largest regional evaluation value as the regional threshold; for a single moving area, the regional evaluation value = the number of people monitored in the moving area in the current monitoring period + the number of elevators in the moving area;

[0090] The values ​​of the preset number of busy areas and the preset area threshold can be determined by the user according to the actual application scenario. The greater the user's demand for providing robot leading efficiency, the smaller the values ​​of the preset number of busy areas and the preset area threshold are. Provide a value of the preset number of busy areas and the preset area threshold. The preset number of busy areas is 50% of the total number of moving areas passed by the current leading path. The method for confirming the preset area threshold is to detect the records corresponding to the leading paths with the same number of busy areas in the historical monitoring period, and record the average value of the area thresholds corresponding to the records that can meet the user's leading needs as the preset area threshold;

[0091] Comprehensive evaluation coefficient = busy area quantity coefficient + regional threshold coefficient, the busy area quantity coefficient is positively correlated with the busy area quantity, and the regional threshold coefficient is positively correlated with the regional threshold;

[0092] The center position of the feature combination is the midpoint of the shortest route from one end of the path segment corresponding to the feature combination to the other end of the path segment corresponding to the feature combination.

[0093] Specifically, the takeover setting unit is provided with a determination method of feature combination, and the determination method includes:

[0094] Perform conventional combination analysis on each moving area that the leading path passes through in order from the earliest to the latest arrival time. When performing conventional combination analysis on a single moving area, record the moving area as the target area, and add the location embedding factor corresponding to the target area and the location embedding factors corresponding to each moving area after the moving direction sequence one by one until the total value of the location embedding factor is greater than the preset total value of the location embedding factor, record each moving area before the arrival time of the moving area corresponding to the last accumulated location embedding factor and the target area as a conventional combination, and continue to perform conventional combination analysis on each moving area that is not recorded as a conventional combination until the preset situation is reached, then stop the conventional combination analysis;

[0095] If the default situation is that all moving areas are regular combinations, then each regular combination is recorded as a characteristic combination;

[0096] If the preset situation is that the total value of the location embedding factor is less than the preset total value of the location embedding factor, the set of moving areas that are not recorded as a regular combination is recorded as a special combination, and each regular combination and special combination is recorded as a feature combination.

[0097] The preset situation includes that all mobile areas are conventional combinations and the total value of the location embedding factor is less than the total value of the preset location embedding factor; the total value of the location embedding factor is the sum of the location embedding factor corresponding to the target area and the location embedding factors corresponding to each accumulated area;

[0098] The value of the total value of the preset location embedding factor can be determined according to the actual application scenario. It can be understood that the smaller the value of the total value of the preset location embedding factor, the more takeover points are set. A value of the total value of the preset location embedding factor is provided, and the total value of the location embedding factor corresponding to each feature combination corresponding to the historical monitoring period is detected. The average value of the total value of the location embedding factor that can meet the user's leadership needs is recorded as the total value of the preset location embedding factor.

[0099] Specifically, the takeover setting unit determines the location embedding factor according to the sub-area distribution extreme value and the proportion of the crowded sub-area;

[0100] The sub-area distribution extreme value is positively correlated with the location embedding factor, and the crowded sub-area proportion is positively correlated with the location embedding factor.

[0101] Among them, location embedding factor = (extreme value of sub-region distribution / extreme value of preset sub-region distribution) × extreme value coefficient + (proportion of crowded sub-region / proportion of preset crowded sub-region) × coefficient of crowded sub-region proportion;

[0102] The method for confirming the extreme value of sub-area distribution is as follows: for a single moving area, the moving area is divided into several adjacent sub-areas with the same path length. For a single sub-area, the sub-area distribution reference value = the number of people passing through the sub-area monitored in the current monitoring period / the area of ​​the sub-area. The sub-area distribution reference values ​​corresponding to each sub-area corresponding to the area are detected, and the largest sub-area distribution reference value is recorded as the sub-area distribution extreme value;

[0103] The method for confirming the proportion of crowded sub-areas is as follows: for a single mobile area, a sub-area whose sub-area distribution reference value is greater than a preset sub-area distribution reference value in the mobile area is recorded as a crowded sub-area, and the proportion of crowded sub-areas = the number of crowded sub-areas / the number of all sub-areas; the value of the preset sub-area distribution reference value can be determined by the user according to the actual application scenario. It can be understood that the smaller the value of the preset sub-area distribution reference value, the greater the probability of judging that the sub-area is a crowded sub-area, and a value of the preset sub-area distribution reference value is provided. The preset sub-area distribution reference value is the average value of the sub-area distribution reference values ​​corresponding to each sub-area that can meet the user's guidance needs in the historical monitoring period;

[0104] The values ​​of the preset sub-area distribution extreme value and the preset crowded sub-area ratio can be determined by the user according to the actual application scenario. It can be understood that the larger the values ​​of the preset sub-area distribution extreme value and the preset crowded sub-area ratio are, the more difficult it is to pass the leading path, which leads to poor robot leading efficiency. A value of the preset sub-area distribution extreme value and the preset crowded sub-area ratio is provided, and the average value of the sub-area distribution extreme value corresponding to each area that can meet the user's leading needs in the historical monitoring period is recorded as the preset sub-area distribution extreme value, and the average value of the crowded sub-area ratio corresponding to each mobile area that can meet the user's leading needs in the historical monitoring period is recorded as the preset crowded sub-area ratio;

[0105] The values ​​of the extreme value coefficient and the crowded sub-area proportion coefficient can be obtained by the user through deep learning convolutional neural network learning on historical records. It can be understood that the present invention reflects the complexity of the leading path through the location embedding factor. The user can use deep learning through historical user data to obtain the influence of the sub-area distribution extreme value and the crowded sub-area proportion on the complexity of the leading path, and then select the values ​​of the extreme value coefficient and the crowded sub-area proportion coefficient accordingly, wherein the extreme value coefficient + the crowded sub-area proportion coefficient = 1, and provide the values ​​of the extreme value coefficient and the crowded sub-area proportion coefficient, the extreme value coefficient is 0.5, and the crowded sub-area proportion coefficient is 0.5.

[0106] Specifically, the takeover selection unit determines the leading selection method of the takeover point according to the difference between the number of stranded personnel and the number of intermittent robots;

[0107] If the quantity difference is greater than or equal to the standard deviation, regional scheduling is performed;

[0108] If the quantity difference is less than the standard deviation, a robot is selected for the area corresponding to the takeover point.

[0109] Among them, the standard deviation is 0, the quantity difference = the number of stranded personnel - the number of intermittent robots. For a takeover point, the number of stranded personnel is the number of leading combinations located at the takeover point in a single monitoring cycle;

[0110] The number of intermittent robots is determined based on the number of moving areas that the takeover point passes through;

[0111] If the number of moving areas that the takeover point passes through is 1, the number of intermittent robots is the number of intermittent robots in the area where the takeover point is located;

[0112] If the number of moving areas that the takeover point passes through is greater than 1, the number of intermittent robots is the sum of the number of intermittent robots in each moving area that the takeover point passes through;

[0113] The area corresponding to the takeover point is the moving area through which the takeover point passes.

[0114] Specifically, the leading selection method performed by the takeover selection unit is to perform regional scheduling, select a mobile area whose location embedding factor is less than a preset location embedding factor as the scheduling area, and select an intermittent robot with the smallest scheduling difficulty threshold in each scheduling area for leading;

[0115] The leading selection method executed by the takeover selection unit is to select robots in the area corresponding to the takeover point, detect the machine reference values ​​corresponding to each intermittent robot in the moving area where the takeover point is located, and select the intermittent robot with the largest machine reference value for leading.

[0116] Among them, the value of the preset location embedding factor can be determined according to the actual application scenario. The smaller the value of the preset location embedding factor, the greater the probability of determining the mobile area as the scheduling area. A value of the preset location embedding factor is provided, and the average value of the location embedding factors corresponding to the mobile areas that can meet the user's guidance needs in the historical monitoring period is recorded as the preset location embedding factor;

[0117] For a single intermittent robot, the scheduling difficulty threshold = path elevator number coefficient + robot movement coefficient - power coefficient, the relationship between the combined elevator number coefficient and the combined elevator number is positively correlated, the relationship between the robot movement coefficient and the robot movement distance is positively correlated, and the relationship between the power coefficient and the robot power is positively correlated; the path elevator number is the number of elevators passed by a single intermittent robot to the takeover point, the robot movement distance is the shortest distance a single intermittent robot moves to the takeover point, and the robot power is the power of a single intermittent robot in the current monitoring cycle; the relationship between the machine reference value and the robot power is positively correlated.

[0118] Specifically, the leading mode executed by the takeover setting unit is that a single working robot takes over the leading, a takeover point is set at the entrance of the hotel, and for a single leading combination, a robot with the smallest scheduling difficulty threshold is selected to lead the robot to the reserved room corresponding to the leading combination.

[0119] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0120] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A hotel management system based on intelligent communication, characterized in that: include: An information collection unit, used to collect hotel information; A plurality of robots connected to the information collection unit for guiding personnel, wherein each robot is provided with a communication module for real-time communication; A path planning unit, which is connected to the information collection unit, and is used to determine the path planning method according to the total number of hotel robots and the number of guides, such as determining the guiding path according to the complexity of the passing area or determining the guiding path according to the road distance; A leading analysis unit, which is respectively connected to each robot, the information collection unit and the path planning unit, and is used to determine, according to the leading conditions, whether the leading mode corresponding to the task information is midway takeover leading or single working robot takeover leading; A takeover setting unit, which is respectively connected to each robot, the information collection unit and the leading analysis unit, and is used to respond to the setting conditions to determine the takeover point setting method by determining the number of takeover points according to the comprehensive evaluation coefficient and evenly setting the takeover point positions according to the takeover path, or determining the number of takeover points according to the number of feature combinations when the leading method to be executed is midway takeover leading; A takeover selection unit, which is connected to each robot and the takeover setting unit, and is used to determine the leading selection mode of the takeover point according to the difference between the number of stranded personnel and the number of intermittent robots, such as regional scheduling, or selecting the regional robot corresponding to the takeover point; The leading analysis unit responds to different leading conditions to determine the leading mode corresponding to the task information; The leading condition responded by the leading analysis unit is that the repetition paragraph threshold is greater than or equal to the preset repetition paragraph threshold or the path evaluation coefficient is greater than or equal to the preset path evaluation coefficient, and the leading mode is midway takeover leading; The leading condition responded by the leading analysis unit is that the repetition paragraph threshold is less than the preset repetition paragraph threshold and the path evaluation coefficient is less than the preset path evaluation coefficient, and the leading mode is that a single working robot takes over the leading; Repeated paragraph threshold = repeated paragraph length coefficient + maximum repeated paragraph coefficient, the repeated paragraph length coefficient is positively correlated with the repeated paragraph length, and the maximum repeated paragraph coefficient is positively correlated with the maximum repeated paragraph length; the repeated paragraph length is confirmed by recording the length of the path that is repeated between the leading path and other leading paths as the repeated paragraph length for a single leading path; The maximum repetitive segment length is confirmed by, for a single leading path, recording the sum of the lengths of the reference repetitive paths as the maximum repetitive segment length. The reference repetitive path is confirmed by, for a single path segment, recording the path segment whose number of leading paths passing through the path segment is greater than a preset number as the reference repetitive path. The guidance mode performed by the takeover setting unit is midway takeover guidance, responding to different setting conditions to determine the takeover point setting mode; The setting condition of the takeover setting unit response is that the number of busy areas is less than the preset number of busy areas and the area threshold is less than the preset area threshold, the number of takeover points is determined according to the comprehensive evaluation coefficient and the positions of the takeover points are evenly set according to the takeover path; The takeover setting unit responds to the setting condition that the number of busy areas is greater than or equal to the preset number of busy areas or the area threshold is greater than or equal to the preset area threshold, determines the number of takeover points according to the number of feature combinations and sets the takeover point position at the center position of each feature combination; The takeover setting unit is provided with a feature combination determination method, and the determination method includes: Perform conventional combination analysis on each moving area that the leading path passes through in order from the earliest to the latest arrival time. When performing conventional combination analysis on a single moving area, record the moving area as the target area, and add the location embedding factor corresponding to the target area and the location embedding factors corresponding to each moving area after the moving direction sequence one by one until the total value of the location embedding factor is greater than the preset total value of the location embedding factor, record each moving area before the arrival time of the moving area corresponding to the last accumulated location embedding factor and the target area as a conventional combination, and continue to perform conventional combination analysis on each moving area that is not recorded as a conventional combination until the preset situation is reached, then stop the conventional combination analysis; If the default situation is that all moving areas are regular combinations, then each regular combination is recorded as a characteristic combination; If the preset situation is that the total value of the location embedding factor is less than the preset total value of the location embedding factor, the set of moving areas that are not recorded as a regular combination is recorded as a special combination, and each regular combination and special combination is recorded as a feature combination.

2. The hotel management system based on intelligent communication according to claim 1 is characterized in that: The path planning unit determines the path planning method according to the total number of hotel robots and the number of leading personnel; If the total number of hotel robots is greater than or equal to the preset total number of hotel robots and the number of guides is less than the preset number of guides, the guidance path is determined according to the complexity of the passing area; If the total number of hotel robots is less than the preset total number of hotel robots or the number of guides is greater than or equal to the preset number of guides, the guidance route is determined according to the road distance.

3. The hotel management system based on intelligent communication according to claim 2 is characterized in that: The path planning method performed by the path planning unit is to determine the leading path according to the complexity of the passing area, identify each moving route corresponding to the task information, and select the moving route with the least complexity of the passing area as the leading path; The path planning unit performs a path planning method of determining a leading path according to a road distance, identifying each moving route corresponding to the task information, and selecting a moving route with the shortest road distance as the leading path; The complexity of the transit area is determined according to a node threshold and an area restriction reference value. The node threshold is positively correlated with the complexity of the transit area, and the area restriction reference value is positively correlated with the complexity of the transit area.

4. The hotel management system based on intelligent communication according to claim 1 is characterized in that: The takeover setting unit determines the location embedding factor according to the sub-area distribution extreme value and the proportion of the crowded sub-area; The sub-area distribution extreme value is positively correlated with the location embedding factor, and the crowded sub-area proportion is positively correlated with the location embedding factor.

5. The hotel management system based on intelligent communication according to claim 1 is characterized in that: The takeover selection unit determines the leading selection method of the takeover point according to the difference between the number of stranded personnel and the number of intermittent robots; If the quantity difference is greater than or equal to the standard deviation, regional scheduling is performed; If the quantity difference is less than the standard deviation, a robot is selected for the area corresponding to the takeover point.

6. The hotel management system based on intelligent communication according to claim 5 is characterized in that: The leading selection method performed by the takeover selection unit is to perform regional scheduling, select a mobile area whose location embedding factor is less than a preset location embedding factor as the scheduling area, and select an intermittent robot with the smallest scheduling difficulty threshold in each scheduling area for leading; The leading selection method executed by the takeover selection unit is to select robots in the area corresponding to the takeover point, detect the machine reference values ​​corresponding to each intermittent robot in the moving area where the takeover point is located, and select the intermittent robot with the largest machine reference value for leading.

7. The hotel management system based on intelligent communication according to claim 1 is characterized in that: The leading mode executed by the takeover setting unit is that a single working robot takes over the leading, a takeover point is set at the entrance of the hotel, and a robot with the smallest scheduling difficulty threshold is selected for a single leading combination to lead to the reserved room corresponding to the leading combination.

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