Smart hotel safety early warning method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses a smart hotel safety early warning method, device and equipment and a storage medium, and the method comprises the steps: obtaining the check-in information and first millimeter wave information in real time, and enabling the first millimeter wave information to comprise a falling state; whether early warning information is generated or not is judged according to the tumble state, second millimeter wave information associated with the tumble state is obtained under the condition that the tumble state is tumble, and the second millimeter wave information comprises respiratory frequency data, millimeter wave identification, space coordinates and tumble duration; determining the age of a target resident according to the check-in information, determining an associated normal breathing frequency range according to the age of the target resident, and comparing the normal breathing frequency range with the breathing frequency data; safety parameters are calculated according to the comparison result, the target resident age, the tumble duration and the space coordinates, the early warning level corresponding to the safety parameters is determined, and tumble early warning information is generated based on the early warning level and the millimeter wave identifier. According to the invention, timeliness, integrity and accuracy of early warning information generation are guaranteed.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular to a smart hotel safety warning method, device, equipment and storage medium. Background Art

[0002] With the popularization of the Internet and the rapid development of computer technology, network information technology has been widely used in all walks of life. As a place with high mobility of personnel, it has become a topic of concern to everyone to ensure the safety of hotel personnel through network information technology.

[0003] In the related art, the images in the corridor are usually collected in real time through the electronic cameras installed in the hotel corridor. The hotel staff determines the abnormal situation in the hotel based on the image screen, or determines the abnormal situation in the corresponding room by answering the alarm call in the room, thereby generating an early warning message and notifying the relevant personnel to handle the early warning message in a timely manner. However, this alarm method has a lag, and when the staff receives the alarm information, they cannot quickly determine the specific situation. For example, when the number of people in the hotel room exceeds the limit, a security early warning message is generated, but the relevant staff cannot understand the specific situation in the room, such as theft or fire, and cannot accurately judge the urgency of the early warning, which affects the efficiency of resolving abnormal situations and the check-in experience of the guests. Summary of the invention

[0004] The embodiments of the present application provide a security warning method, device, equipment and storage medium for a smart hotel, which solves the problem of delay in generating security warning information and the problem that staff cannot quickly understand the specific abnormal situation and the degree of urgency based on the warning information, which affects the efficiency of solving the situation and cannot fully guarantee the safety of the guests. By acquiring millimeter wave information and hotel check-in information in real time, corresponding warning information is quickly and timely generated according to different millimeter wave information and different check-in information, thereby ensuring the timeliness, completeness and accuracy of the generated warning information.

[0005] In a first aspect, an embodiment of the present application provides a smart hotel safety early warning method, including:

[0006] Acquiring check-in information and first millimeter wave information in real time, wherein the first millimeter wave information includes a fall status;

[0007] Determine whether to generate warning information according to the fall state, and if the fall state is a fall, obtain second millimeter wave information associated with the fall state, the second millimeter wave information including respiratory rate data, millimeter wave identifier, spatial coordinates and fall duration;

[0008] determining a target resident age according to the check-in information, determining an associated normal respiratory rate range according to the target resident age, and comparing the normal respiratory rate range with the respiratory rate data;

[0009] Safety parameters are calculated according to the comparison result, the age of the target resident, the duration of the fall and the spatial coordinates, and a warning level corresponding to the safety parameters is determined, and fall warning information is generated based on the warning level and the millimeter wave identifier.

[0010] Optionally, the calculating of the safety parameter according to the comparison result, the target resident's age, the fall duration and the spatial coordinates includes:

[0011] Determine the respiratory frequency level according to the comparison result, and determine a first safety factor corresponding to the respiratory frequency according to the respiratory frequency level and the preset safety factor mapping table;

[0012] Determine, according to the preset safety level mapping table, a second safety factor corresponding to the target resident's age, a third safety factor corresponding to the fall duration, and a fourth safety factor corresponding to the spatial coordinates;

[0013] The first safety factor, the second safety factor, the third safety factor and the fourth safety factor are cumulatively calculated to obtain a safety parameter.

[0014] Optionally, the check-in information includes a face picture and ID card number data of the check-in person, and determining the age of the target resident according to the check-in information includes:

[0015] Perform face recognition on the face picture of the occupant to obtain face feature data, determine the target age group based on the face feature data, and determine the age of the target resident within the target age group based on the ID number data.

[0016] Optionally, determining the respiratory rate level according to the comparison result includes:

[0017] In a case where the normal respiratory frequency range is greater than the respiratory frequency data, determining that the respiratory frequency level is a weaker respiratory level;

[0018] In a case where the normal respiratory frequency range is smaller than the respiratory frequency data, determining the respiratory frequency level to be a relatively strong respiratory level;

[0019] When the respiratory frequency data is within the normal respiratory frequency range, the respiratory frequency level is determined to be a normal respiratory level.

[0020] Optionally, the check-in information further includes the number of registered check-ins, the first millimeter wave information further includes the actual number of people in the room and the actual number of people in the corridor, and after the fall warning information is generated based on the warning level and the millimeter wave identifier, it also includes:

[0021] Compare the registered number of people with the actual number of people in the room, and determine whether to generate room abnormality warning information according to the comparison result;

[0022] A corridor population threshold is calculated according to the registered occupants, the actual corridor population is compared with the corridor population threshold, and it is determined whether to generate corridor abnormality warning information according to the comparison result.

[0023] Optionally, the first millimeter wave information further includes the time of abnormal number of people in the room, the time of abnormal number of people in the corridor, the number of abnormal people in the room, and the number of abnormal people in the corridor. The determining whether to generate abnormal room warning information according to the comparison result includes:

[0024] When the number of registered occupants is greater than or equal to the actual number of occupants in the room, no room abnormality warning information is generated;

[0025] In the case where the registered occupants are less than the actual occupants of the room, generating room abnormality warning information according to the abnormal time of the occupants of the room, the abnormal number of occupants of the room and the millimeter wave identifier;

[0026] Correspondingly, judging whether to generate corridor abnormality warning information according to the comparison result includes:

[0027] When the actual number of people in the corridor is less than the threshold number of people in the corridor, no corridor abnormality warning information is generated;

[0028] When the actual number of people in the corridor is greater than or equal to the threshold number of people in the corridor, abnormal corridor warning information is generated according to the abnormal time of the number of people in the corridor, the abnormal number of people in the corridor and the millimeter wave identifier.

[0029] Optionally, generating room abnormality warning information according to the abnormal time of the number of people in the room, the abnormal number of people in the room, and the millimeter wave identifier includes:

[0030] Respectively determine whether the abnormal time of the number of people in the room is within a first preset abnormal time range, and whether the abnormal number of people in the room is greater than a first abnormal number threshold;

[0031] When the abnormal time of the number of people in the room is within the first preset abnormal time range and the number of abnormal people in the room is greater than the first abnormal number threshold, generating first room abnormal warning information according to the millimeter wave identifier;

[0032] When the abnormal time of the number of people in the room is within the first preset abnormal time range or the abnormal number of people in the room is greater than the first abnormal number threshold, generating second room abnormal warning information according to the millimeter wave identifier;

[0033] Correspondingly, generating abnormal corridor warning information according to the abnormal corridor population time, the abnormal corridor population number and the millimeter wave identifier includes:

[0034] Respectively determine whether the abnormal time of the number of people in the corridor is within a second preset abnormal time range, and whether the abnormal number of people in the corridor is greater than a second abnormal number threshold;

[0035] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range and the number of abnormal people in the corridor is greater than the second abnormal number threshold, generating first corridor abnormal warning information according to the millimeter wave identifier;

[0036] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range or the abnormal number of people in the corridor is greater than the second abnormal number threshold, second corridor abnormal warning information is generated according to the millimeter wave identifier.

[0037] In a second aspect, an embodiment of the present application provides a smart hotel safety warning device, including:

[0038] A first information acquisition module, used to acquire check-in information and first millimeter wave information in real time, wherein the first millimeter wave information includes a fall status;

[0039] A fall status judgment module, used to judge whether to generate warning information according to the fall status;

[0040] A second information acquisition module, configured to acquire, when the fall state is a fall, second millimeter wave information associated with the fall state, the second millimeter wave information including respiratory rate data, millimeter wave identifier, spatial coordinates and fall duration;

[0041] a normal breathing frequency range determination module, configured to determine the target resident age according to the check-in information, and determine the associated normal breathing frequency range according to the target resident age;

[0042] A respiratory rate data comparison module, used for comparing the normal respiratory rate range with the respiratory rate data;

[0043] A safety parameter calculation module, used to calculate the safety parameter according to the comparison result, the age of the target resident, the duration of the fall and the spatial coordinates;

[0044] An early warning level determination module, used to determine the early warning level corresponding to the safety parameter;

[0045] The warning information generation module is used to generate fall warning information based on the warning level and the millimeter wave identifier.

[0046] Optionally, a safety parameter calculation module is specifically used to determine the respiratory frequency level according to the comparison result, and determine a first safety factor corresponding to the respiratory frequency according to the respiratory frequency level and the preset safety factor mapping table;

[0047] Determine, according to the preset safety level mapping table, a second safety factor corresponding to the target resident's age, a third safety factor corresponding to the fall duration, and a fourth safety factor corresponding to the spatial coordinates;

[0048] The first safety factor, the second safety factor, the third safety factor and the fourth safety factor are cumulatively calculated to obtain a safety parameter.

[0049] Optionally, a normal breathing frequency range determination module is specifically used to perform facial recognition on the facial image of the resident to obtain facial feature data, determine the target age group based on the facial feature data, and determine the age of the target resident within the target age group based on the ID number data.

[0050] Optionally, a safety parameter calculation module is specifically used to determine that the respiratory frequency level is a weaker respiratory level when the normal respiratory frequency range is greater than the respiratory frequency data;

[0051] In a case where the normal respiratory frequency range is smaller than the respiratory frequency data, determining the respiratory frequency level to be a relatively strong respiratory level;

[0052] When the respiratory frequency data is within the normal respiratory frequency range, the respiratory frequency level is determined to be a normal respiratory level.

[0053] Optionally, the check-in information also includes the number of registered check-ins, the first millimeter wave information also includes the actual number of people in the room and the actual number of people in the corridor, and the warning information generation module is specifically used to compare the number of registered check-ins with the actual number of people in the room, and determine whether to generate room abnormality warning information according to the comparison result;

[0054] A corridor population threshold is calculated according to the registered occupants, the actual corridor population is compared with the corridor population threshold, and it is determined whether to generate corridor abnormality warning information according to the comparison result.

[0055] Optionally, the first millimeter wave information also includes the abnormal time of the number of people in the room, the abnormal time of the number of people in the corridor, the abnormal number of people in the room and the abnormal number of people in the corridor. The warning information generation module is specifically used to not generate room abnormality warning information when the number of registered check-ins is greater than or equal to the actual number of people in the room;

[0056] In the case where the registered occupants are less than the actual occupants of the room, generating room abnormality warning information according to the abnormal time of the occupants of the room, the abnormal number of occupants of the room and the millimeter wave identifier;

[0057] The warning information generating module is specifically used for not generating the abnormal warning information of the corridor when the actual number of people in the corridor is less than the threshold number of people in the corridor;

[0058] When the actual number of people in the corridor is greater than or equal to the threshold number of people in the corridor, abnormal corridor warning information is generated according to the abnormal time of the number of people in the corridor, the abnormal number of people in the corridor and the millimeter wave identifier.

[0059] Optionally, the warning information generation module is further used to determine whether the abnormal time of the number of people in the room is within a first preset abnormal time range, and whether the abnormal number of people in the room is greater than a first abnormal number threshold;

[0060] When the abnormal time of the number of people in the room is within the first preset abnormal time range and the number of abnormal people in the room is greater than the first abnormal number threshold, generating first room abnormal warning information according to the millimeter wave identifier;

[0061] When the abnormal time of the number of people in the room is within the first preset abnormal time range or the abnormal number of people in the room is greater than the first abnormal number threshold, generating second room abnormal warning information according to the millimeter wave identifier;

[0062] Respectively determine whether the abnormal time of the number of people in the corridor is within a second preset abnormal time range, and whether the abnormal number of people in the corridor is greater than a second abnormal number threshold;

[0063] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range and the number of abnormal people in the corridor is greater than the second abnormal number threshold, generating first corridor abnormal warning information according to the millimeter wave identifier;

[0064] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range or the abnormal number of people in the corridor is greater than the second abnormal number threshold, second corridor abnormal warning information is generated according to the millimeter wave identifier.

[0065] In a third aspect, an embodiment of the present application provides an electronic device, comprising: one or more processors; a storage device configured to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the smart hotel safety warning method described in the first aspect.

[0066] In a fourth aspect, an embodiment of the present application provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the smart hotel safety warning method as described in the first aspect.

[0067] The embodiment of the present application obtains check-in information and first millimeter wave information in real time, the first millimeter wave information includes the fall status; determines whether to generate warning information according to the fall status, and obtains the second millimeter wave information associated with the fall status when the fall status is a fall, the second millimeter wave information includes respiratory rate data, millimeter wave identification, spatial coordinates and fall duration; determines the target resident age according to the check-in information, determines the associated normal respiratory rate range according to the target resident age, and compares the normal respiratory rate range with the respiratory rate data; calculates the safety parameters according to the comparison result, the target resident age, the fall duration and the spatial coordinates, and determines the warning level corresponding to the safety parameters, and generates fall warning information based on the warning level and millimeter wave identification. It is possible to quickly and timely generate corresponding warning information according to different millimeter wave information and different check-in information by acquiring millimeter wave information and hotel check-in information in real time, thereby ensuring the timeliness, completeness and accuracy of the generated warning information. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of a safety warning method for a smart hotel provided by an embodiment of the present application;

[0069] Figure 2 is a flow chart of a method for calculating a security parameter provided in an embodiment of the present application;

[0070] Figure 3 It is a flow chart of another smart hotel safety early warning method provided by an embodiment of the present application;

[0071] Figure 4 It is a structural diagram of a smart hotel safety warning device provided in an embodiment of the present application;

[0072] Figure 5 It is a structural diagram of a smart hotel safety warning device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only the part related to the present application but not all the contents are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0074] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0075] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0076] In the following, in conjunction with the accompanying drawings, the smart hotel safety warning method, device, equipment and medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0077] The smart hotel security warning method provided in the embodiment of the present application can be applied to the scenario of hotel security management, and timely warning is provided when a security incident occurs in the hotel, such as timely warning of a fire incident in the hotel, a fall incident of a person in a hotel room, and a theft incident in the hotel. Based on the above application scenario, it can be understood that the execution subject of the present application can be a terminal device installed with a security warning system, such as a laptop computer and a tablet computer.

[0078] Figure 1: is a flow chart of a safety warning method for a smart hotel provided in an embodiment of the present application, such as Figure 1 As shown, including:

[0079] Step S101: Acquire check-in information and first millimeter wave information in real time, where the first millimeter wave information includes a fall status.

[0080] Among them, the check-in information can be the information of the hotel check-in personnel collected by the hotel check-in machine installed in the hotel, and can include facial information and identity information, etc. The first millimeter wave information can be used to represent the current state information of the target person, such as walking state or upright state, etc., wherein the first millimeter wave information can be calculated based on the data collected by the millimeter wave installed in the hotel and the preset algorithm, such as based on the collected temperature data, smoke concentration data and the corresponding algorithm for calculation, and whether a fire occurs is determined based on the calculation result. Optionally, the first millimeter wave information includes a fall state, wherein the human body posture data collected by the first millimeter wave and the preset algorithm can be used to calculate whether the current human body posture data is within the preset normal posture range. If it is not within the preset normal posture range, the fall state of the target to be detected is determined to be "falling", that is, the first millimeter wave information is a fall state, and if it is within the preset normal posture range, the fall state of the target to be detected is determined to be "not falling", that is, the first millimeter wave information is a non-falling state.

[0081] Step S102: determine whether to generate warning information according to the fall state, and if the fall state is a fall, obtain second millimeter wave information associated with the fall state, wherein the second millimeter wave information includes respiratory rate data, millimeter wave identification, spatial coordinates and fall duration.

[0082] Among them, the early warning information is a warning information used to indicate the current safety situation. The early warning information may include the content of the early warning, the location of the early warning, and the current safety status of the personnel. For example, a fire broke out in room 8001 of the hotel. There are 2 people in the room and they are currently in a safe state. The second millimeter wave information is used to indicate the relevant data information for judging the abnormal state, such as the indoor temperature data and indoor concentration data of the current room collected when judging the fire. Optionally, the second millimeter wave information includes respiratory rate data, millimeter wave identification, spatial coordinates, and fall duration. Among them, the respiratory rate data is used to indicate the number of times a human body breathes per minute, and the normal respiratory rate of an adult is 12 to 20 times per minute. The millimeter wave identification is the unique identification information of each millimeter wave in the hotel. The millimeter wave identification can be the name of each millimeter wave or the number of each millimeter wave, etc., or the room number of the room where the millimeter wave is located. The spatial coordinates are used to determine the position of any point in space by introducing a spatial rectangular coordinate system in space. The spatial coordinates can also indicate the area to which a certain position in space belongs, such as a toilet, bathroom, or bedroom. The fall duration can be calculated based on the time when the warning information is generated and the time when the first millimeter wave information with the fall status of "fall" is first received.

[0083] In one embodiment, it is determined whether to generate warning information according to the fall state in the acquired first millimeter wave information. When the first millimeter wave information of the "fall" state is received, the corresponding warning information is generated, and the respiratory rate data, millimeter wave identifier, spatial coordinates and fall duration associated with the first millimeter wave information are obtained. When the first millimeter wave information of the "no fall" state is received, there is no need to generate warning information.

[0084] Step S103: determining the target resident age according to the check-in information, determining the associated normal respiratory rate range according to the target resident age, and comparing the normal respiratory rate range with the respiratory rate data.

[0085] In one embodiment, the age of the target resident may be the actual age of the target resident or the age stage to which the target resident belongs. The age of the target resident may be calculated based on the ID number or the age stage to which the target resident belongs may be determined by face recognition based on the face image collected by the hotel check-in machine. The target resident may be a registered resident in a hotel room associated with a millimeter wave identifier. After determining the age of the target resident, the normal breathing frequency range corresponding to the age of the target resident is determined. For example, the normal breathing frequency of a pre-set infant is 20-30 times / minute, the normal breathing frequency of a child is 18-25 times / minute, and the normal breathing frequency of an adult is 12-20 times / minute. If the age of the target resident is determined to be 30 years old, the target resident may be determined to be an adult, and the normal breathing frequency range associated with the age of the target resident is 12-20 times / minute. The breathing frequency data contained in the second millimeter wave information is compared with the normal breathing range to determine whether the age of the target resident is within the normal breathing range.

[0086] In one embodiment, the check-in information includes a face picture and ID number data of the check-in person, and determining the age of the target resident according to the check-in information includes:

[0087] Perform face recognition on the face picture of the occupant to obtain face feature data, determine the target age group based on the face feature data, and determine the age of the target resident within the target age group based on the ID number data.

[0088] Among them, facial feature data refers to various feature information on the face extracted by face recognition technology, including facial feature points and feature descriptions. Facial feature points include the position and shape of eyes, nose, mouth and other parts. These feature points can be used to locate the key parts of the face. The features describe the geometric relationship and texture information between the various parts of the face, such as the distance between the eyes, the shape of the nose, etc.

[0089] In one embodiment, face recognition is performed based on the facial image information of the resident in the check-in information to obtain facial feature data, and the age stage of the resident is determined based on the facial feature data, such as children, teenagers, middle-aged and elderly people, and the specific age of the target resident is determined based on the ID number in the check-in information.

[0090] The above method determines the age of the target resident based on the facial feature data obtained by face recognition and the ID card number, thereby avoiding the problem of inconsistency between the facial feature data and the actual age and the age calculation error caused by incorrect input of the ID card number, thereby ensuring the accuracy of the target resident's age.

[0091] Step S104: Calculate safety parameters according to the comparison result, the target resident's age, the fall duration and the spatial coordinates, determine the warning level corresponding to the safety parameters, and generate fall warning information based on the warning level and the millimeter wave identifier.

[0092] Among them, the safety parameter is used to indicate the degree of danger corresponding to the currently generated warning information. The higher the safety parameter, the more dangerous the current situation is, and the higher the warning level is. Conversely, the lower the safety parameter, the lower the warning level is. In one embodiment, the weight values ​​corresponding to different risk factors and the mapping relationship between the safety parameter range and the warning level are pre-set. Exemplarily, if the comparison result is that the age of the target resident is not within the normal breathing range, the safety parameter value is +1, if the age of the target resident is a teenager or an elderly person, the safety parameter value is +1, if the fall time is greater than 3 minutes, the safety parameter value is +1, if the fall time is greater than 5 minutes, the safety parameter value is +2, and so on. For every 2 minutes of increase in the fall time, the corresponding safety parameter value is +1, if the area to which the spatial coordinates belong is the restroom, the safety parameter value is +1, if the area to which the spatial coordinates belong is the bathroom, the safety parameter value is +2, and finally the cumulative value of the safety parameter value is calculated, and the warning level is determined according to the mapping relationship between the safety parameter range and the preset level. For example, if the warning level corresponding to the safety parameter range [0, 3] is level 1, the warning level corresponding to the safety parameter range [4, 7] is level 2, and the warning level corresponding to the safety parameter range [8, 11] is level 3. If the safety parameter calculated as 5 based on the comparison result, the target resident's age, the duration of the fall, and the spatial coordinates, the warning level is determined to be level 2, and a warning message containing the warning level and the millimeter wave identifier is generated, so that relevant personnel can quickly understand the degree of danger of the warning information based on the warning information, and can quickly determine the specific location where the warning information is issued based on the millimeter wave identifier.

[0093] In the above, by obtaining the check-in information and the first millimeter wave information in real time, the first millimeter wave information includes the fall status; judging whether to generate warning information according to the fall status, obtaining the second millimeter wave information associated with the fall status when the fall status is a fall, and the second millimeter wave information includes respiratory rate data, millimeter wave identification, spatial coordinates and fall duration; determining the target resident age according to the check-in information, determining the associated normal respiratory rate range according to the target resident age, and comparing the normal respiratory rate range with the respiratory rate data; calculating the safety parameters according to the comparison result, the target resident age, the fall duration and the spatial coordinates, and determining the warning level corresponding to the safety parameters, and generating the fall warning information based on the warning level and the millimeter wave identification. By obtaining the millimeter wave information and the hotel check-in information in real time, the corresponding warning information can be quickly and timely generated according to different millimeter wave information and different check-in information, ensuring the timeliness, completeness and accuracy of the generated warning information, so that the relevant personnel can quickly determine the dangerous degree of the warning information according to the warning information, and can quickly determine the specific location of the warning information according to the millimeter wave identification, accordingly, improving the user's experience and fully ensuring the safety of the people in the hotel.

[0094] Figure 2 is a flow chart of a method for calculating a security parameter provided in an embodiment of the present application. Figure 2 As shown, including:

[0095] Step S201: judging the respiratory frequency level according to the comparison result, and determining a first safety factor corresponding to the respiratory frequency according to the respiratory frequency level and the preset safety factor mapping table.

[0096] Among them, the respiratory rate level is used to indicate the normal or abnormal situation of the respiratory rate, and may include a weaker abnormal level and a stronger abnormal level. The first safety factor is used to indicate the degree of influence of the respiratory rate on the safety parameters. The larger the first safety factor, the greater the influence of the respiratory rate on the safety parameters. In one embodiment, it can be determined whether the respiratory rate is within the normal respiratory rate range based on the comparison result. If it is not within the normal respiratory rate range, the difference between the currently acquired respiratory rate data and the normal respiratory rate range is determined. If the difference is less than or equal to the preset difference range, the respiratory rate level is determined to be a weaker abnormal level, and the first safety factor corresponding to the weaker abnormal level is determined according to the preset safety factor mapping table. If the difference is greater than the preset difference range, the respiratory rate level is determined to be a stronger abnormal level, and the first safety factor corresponding to the stronger abnormal level is determined according to the preset safety factor mapping table.

[0097] In one embodiment, determining the respiratory rate level according to the comparison result includes:

[0098] In a case where the normal respiratory frequency range is greater than the respiratory frequency data, determining that the respiratory frequency level is a weaker respiratory level;

[0099] In a case where the normal respiratory frequency range is smaller than the respiratory frequency data, determining the respiratory frequency level to be a relatively strong respiratory level;

[0100] When the respiratory frequency data is within the normal respiratory frequency range, the respiratory frequency level is determined to be a normal respiratory level.

[0101] The breathing rate level can also include a weaker breathing level, a stronger breathing level and a normal breathing level. Strenuous exercise may also cause the breathing rate to be too high, but the person is not in a dangerous state at this time. Therefore, the weaker breathing level has a greater impact on the safety parameters than the stronger breathing level.

[0102] As described above, by comparing the respiratory frequency with the normal respiratory frequency range, the respiratory frequency level is determined to be a weak respiratory level, a strong respiratory level, or a normal respiratory level. It is possible to directly determine whether the breathing is abnormal based on the respiratory frequency level, and the specific abnormal reason is strong breathing or weak breathing, thereby ensuring the integrity and accuracy of the subsequent generation of warning information.

[0103] Step S202: determine the second safety factor corresponding to the target resident's age, the third safety factor corresponding to the fall duration, and the fourth safety factor corresponding to the spatial coordinates according to the preset safety level mapping table.

[0104] Among them, the second safety factor is used to indicate the degree of influence of the target resident's age on the safety parameters. The third safety factor is used to indicate the degree of influence of the duration of the fall on the safety parameters. The fourth safety factor is used for the degree of influence of the current spatial coordinates of the target user on the safety parameters. In one embodiment, the second safety factor corresponding to the target resident's age is determined by querying a preset security level mapping table. The preset security level mapping table is used to indicate the mapping relationship between the data contained in the second millimeter wave information and the safety factor. Exemplarily, the second safety factor corresponding to 0-6 years old is 3, the second safety factor corresponding to 6-18 years old is 2, the second safety factor corresponding to 18-40 years old is 1, the second safety factor corresponding to 40-70 years old is 2, and the second safety factor corresponding to over 70 years old is 3. If the duration of the fall is less than 2 minutes, the corresponding third safety factor is 1, if the duration of the fall is greater than 2 minutes and less than 5 minutes, the corresponding third safety factor is 2, and if the duration of the fall is greater than or equal to 5 minutes, the corresponding third safety factor is 3. If the area to which the spatial coordinates belong is a bedroom, the corresponding fourth safety factor is 1; if the area to which the spatial coordinates belong is a restroom, the corresponding fourth safety factor is 2; and if the area to which the spatial coordinates belong is a bathroom, the corresponding fourth safety factor is 3.

[0105] Step S203: Accumulate and calculate the first safety factor, the second safety factor, the third safety factor and the fourth safety factor to obtain a safety parameter.

[0106] In one embodiment, the first safety factor, the second safety factor, the third safety factor and the fourth safety factor corresponding to the comparison result, the target resident's age, the fall time and the spatial coordinates are determined respectively, and the first safety factor, the second safety factor, the third safety factor and the fourth safety factor are accumulated to obtain a final safety parameter.

[0107] In the above, the respiratory rate level is determined according to the comparison result, and the first safety factor corresponding to the respiratory rate is determined according to the respiratory rate level and the preset safety factor mapping table; the second safety factor corresponding to the target resident's age, the third safety factor corresponding to the fall duration, and the fourth safety factor corresponding to the spatial coordinates are determined according to the preset safety level mapping table; the first safety factor, the second safety factor, the third safety factor, and the fourth safety factor are cumulatively calculated to obtain the safety parameters. In the process of calculating the safety parameters, the influence of the target resident's age, fall time, respiratory rate status, and current position on the safety parameters can be fully considered, so that when the alarm information is generated based on the safety parameters in the future, it can be convenient for relevant personnel to quickly and accurately determine the current safety status of the target resident according to the alarm information, so as to take targeted measures.

[0108] Figure 3 is a flow chart of another smart hotel safety warning method provided by an embodiment of the present application, such as Figure 3 As shown, including:

[0109] Step S301: compare the registered number of people with the actual number of people in the room, and determine whether to generate room abnormality warning information based on the comparison result.

[0110] Optionally, the check-in information also includes the number of registered check-ins, and the first millimeter wave information also includes the actual number of people in the room and the actual number of people in the corridor. Among them, the number of registered check-ins may include the number of registered check-ins in each room, and the total number of registered check-ins in each room on each floor. One millimeter wave is installed in each room, and multiple millimeter waves are pre-installed in the corridor. The first millimeter wave information may include room data information collected by the millimeter wave in each room, and may also include corridor data information collected by multiple millimeter waves in the corridor, and each millimeter wave collects corridor data information in the corresponding area. The actual number of people in the room is the actual number of people in the room collected by the millimeter wave in the room, and the actual number of people in the corridor is the sum of the actual number of people in each area in the corridor collected by multiple millimeter waves in the corridor.

[0111] In one embodiment, the number of registered occupants in each room is compared with the actual number of people in the room collected by millimeter wave data in each room. If the number of registered occupants is less than the actual number of people in the room, it is considered that an abnormal situation has occurred in the room, such as the presence of strangers in the room, theft may have occurred, etc. At this time, abnormal warning information corresponding to the room can be generated, where the warning information may include room number information, the actual number of people in the room, etc.

[0112] Step S302: Calculate a corridor population threshold according to the registered occupants, compare the actual corridor population with the corridor population threshold, and determine whether to generate corridor abnormality warning information according to the comparison result.

[0113] Among them, the corridor population threshold is used to indicate the number of people that can appear in the corridor at the same time. In one embodiment, the total number of registered occupants on each floor is calculated based on the number of registered occupants in each room, and the corridor population threshold is calculated based on the ratio of the total number of registered occupants to the preset number of people. Exemplarily, if the preset number ratio is 50%, and the total number of registered occupants on one floor is 100, then according to the calculation, the corridor population threshold is 50, and the sum of the actual number of people in each area of ​​the corridor collected by each millimeter wave on the floor is compared with the corridor population threshold. If it is greater than the corridor population threshold, it is considered that an abnormal situation may occur on the floor, such as a fire, and an early warning information corresponding to the floor is generated at this time, wherein the early warning information may include the floor number and the actual number of people in the corridor.

[0114] In the above, by comparing the number of registered occupants with the actual number of people in the room, it is determined whether to generate room abnormality warning information based on the comparison result; the threshold of the number of people in the corridor is calculated based on the number of registered occupants, and the actual number of people in the corridor is compared with the threshold of the number of people in the corridor, and it is determined whether to generate corridor abnormality warning information based on the comparison result. By comparing the actual number of people in the room and the actual number of people in the corridor with the number of registered occupants, it is possible to quickly determine abnormal situations in the room or corridor, fully ensuring the personal safety and property safety of hotel occupants.

[0115] In one embodiment, the first millimeter wave information further includes the abnormal time of the number of people in the room, the abnormal time of the number of people in the corridor, the abnormal number of people in the room and the abnormal number of people in the corridor, and the determination of whether to generate room abnormality warning information according to the comparison result includes:

[0116] When the number of registered occupants is greater than or equal to the actual number of occupants in the room, no room abnormality warning information is generated;

[0117] In the case where the registered occupants are less than the actual occupants of the room, generating room abnormality warning information according to the abnormal time of the occupants of the room, the abnormal number of occupants of the room and the millimeter wave identifier;

[0118] Correspondingly, judging whether to generate corridor abnormality warning information according to the comparison result includes:

[0119] When the actual number of people in the corridor is less than the threshold number of people in the corridor, no corridor abnormality warning information is generated;

[0120] When the actual number of people in the corridor is greater than or equal to the threshold number of people in the corridor, abnormal corridor warning information is generated according to the abnormal time of the number of people in the corridor, the abnormal number of people in the corridor and the millimeter wave identifier.

[0121] The abnormal time of the number of people in the room can be the time when the abnormal number of people is detected, or the duration of the abnormal number of people. The abnormal number of people in the room is used to indicate the number of people exceeding the registered check-in number. The abnormal number of people in the corridor is used to indicate the number of people in the corridor that exceeds the corridor number threshold.

[0122] In one embodiment, when the number of registered check-ins is greater than or equal to the actual number of people in the room, it can be considered that it is in a safe state at this time, and no abnormal warning information for the room is generated. When the actual number of people in the corridor is less than the threshold number of people in the corridor, it can be considered that the number of people in the corridor is the normal flow of people, and no abnormal situation occurs in the hotel. At this time, no abnormal warning information for the corridor can be generated. When the number of registered check-ins is less than the actual number of people in the room, it can be considered that there are strangers or unregistered people in the room. At this time, the corresponding room number can be determined according to the millimeter wave identification, and the room abnormal warning information is generated according to the abnormal time of the number of people in the room, the abnormal number of people in the room and the room number. When the actual number of people in the corridor is greater than the threshold number of people in the corridor, it can be considered that there is a gathering of people in the corridor at this time, and a fire or other emergency may occur. At this time, the number of floors is determined according to the identification of each millimeter wave installed in the corridor, and the abnormal warning information for the corridor is generated according to the abnormal time of the number of people in the corridor, the abnormal number of people in the corridor and the number of floors.

[0123] As described above, the corresponding types of alarm information are generated based on the comparison results of the registered check-in number and the actual number of people in the room, as well as the comparison results of the actual number of people in the corridor and the threshold number of people in the corridor, so that relevant personnel can quickly determine the current abnormal situation based on the alarm information and take corresponding measures.

[0124] In one embodiment, it is determined whether the abnormal time of the number of people in the room is within a first preset abnormal time range, and whether the abnormal number of people in the room is greater than a first abnormal number threshold;

[0125] When the abnormal time of the number of people in the room is within the first preset abnormal time range and the number of abnormal people in the room is greater than the first abnormal number threshold, generating first room abnormal warning information according to the millimeter wave identifier;

[0126] When the abnormal time of the number of people in the room is within the first preset abnormal time range or the abnormal number of people in the room is greater than the first abnormal number threshold, generating second room abnormal warning information according to the millimeter wave identifier;

[0127] Correspondingly, generating abnormal corridor warning information according to the abnormal corridor population time, the abnormal corridor population number and the millimeter wave identifier includes:

[0128] Respectively determine whether the abnormal time of the number of people in the corridor is within a second preset abnormal time range, and whether the abnormal number of people in the corridor is greater than a second abnormal number threshold;

[0129] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range and the number of abnormal people in the corridor is greater than the second abnormal number threshold, generating first corridor abnormal warning information according to the millimeter wave identifier;

[0130] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range or the abnormal number of people in the corridor is greater than the second abnormal number threshold, second corridor abnormal warning information is generated according to the millimeter wave identifier.

[0131] Among them, the first room abnormal warning information is used to indicate emergency room abnormal warning information, and the second room abnormal warning information is used to indicate non-emergency room abnormal warning information. The first corridor abnormal warning information is used to indicate emergency corridor abnormal warning information, and the second corridor abnormal warning information is used to indicate non-emergency corridor abnormal warning information. The first preset abnormal time range is used to indicate the duration range of the abnormal number of people in the room, and the second preset abnormal time range is used to indicate the duration range of the abnormal number of people in the corridor. The first abnormal number threshold is used to indicate the maximum abnormal number of people in the room, and the second abnormal number threshold is used to indicate the maximum abnormal number of people in the corridor.

[0132] In one embodiment, when the abnormal time of the number of people in the room is within the first preset abnormal time range and the abnormal number of people in the room is greater than the first abnormal number threshold, there may be a large safety hazard, and emergency room abnormal warning information is generated. When the abnormal time of the number of people in the room is within the first preset abnormal time range or the abnormal number of people in the room is greater than the first abnormal number threshold, non-emergency room abnormal warning information is generated. When the abnormal time of the number of people in the corridor is within the second preset abnormal time range and the abnormal number of people in the corridor is greater than the second abnormal number threshold, there may be a large safety hazard, and emergency corridor abnormal warning information is generated. When the abnormal time of the number of people in the corridor is within the second preset abnormal time range or the abnormal number of people in the corridor is greater than the second abnormal number threshold, non-emergency corridor abnormal warning information is generated.

[0133] In the above, the emergency conditions of the room warning information and the corridor warning information are judged based on the first preset abnormal time range, the first preset abnormal time range, the first abnormal number threshold and the second abnormal number threshold, which fully ensures the detail and accuracy of the warning information, facilitates relevant personnel to collect targeted measures, and improves user satisfaction.

[0134] Figure 4 is a structural diagram of a smart hotel safety warning device provided in an embodiment of the present application, such as Figure 4 As shown, including:

[0135] A first information acquisition module 41 is used to acquire check-in information and first millimeter wave information in real time, where the first millimeter wave information includes a fall status;

[0136] A fall state judgment module 42, used to judge whether to generate warning information according to the fall state;

[0137] A second information acquisition module 43 is used to acquire second millimeter wave information associated with the fall state when the fall state is a fall, wherein the second millimeter wave information includes respiratory rate data, millimeter wave identification, spatial coordinates and fall duration;

[0138] A normal breathing rate range determination module 44 is used to determine the target resident age according to the check-in information, and determine the associated normal breathing rate range according to the target resident age;

[0139] A respiratory rate data comparison module 45, for comparing the normal respiratory rate range with the respiratory rate data;

[0140] A safety parameter calculation module 46, for calculating safety parameters according to the comparison result, the target resident's age, the fall duration and the spatial coordinates;

[0141] An early warning level determination module 47, used to determine the early warning level corresponding to the safety parameter;

[0142] The warning information generating module 48 is used to generate fall warning information based on the warning level and the millimeter wave identifier.

[0143] In the above, by obtaining the check-in information and the first millimeter wave information in real time, the first millimeter wave information includes the fall status; judging whether to generate warning information according to the fall status, obtaining the second millimeter wave information associated with the fall status when the fall status is a fall, and the second millimeter wave information includes respiratory rate data, millimeter wave identification, spatial coordinates and fall duration; determining the target resident age according to the check-in information, determining the associated normal respiratory rate range according to the target resident age, and comparing the normal respiratory rate range with the respiratory rate data; calculating the safety parameters according to the comparison result, the target resident age, the fall duration and the spatial coordinates, and determining the warning level corresponding to the safety parameters, and generating the fall warning information based on the warning level and the millimeter wave identification. By obtaining the millimeter wave information and the hotel check-in information in real time, the corresponding warning information can be quickly and timely generated according to different millimeter wave information and different check-in information, ensuring the timeliness, completeness and accuracy of the generated warning information, so that the relevant personnel can quickly determine the dangerous degree of the warning information according to the warning information, and can quickly determine the specific location of the warning information according to the millimeter wave identification, accordingly, improving the user's experience and fully ensuring the safety of the people in the hotel.

[0144] In a possible embodiment, the safety parameter calculation module 46 is used to:

[0145] Determine the respiratory frequency level according to the comparison result, and determine a first safety factor corresponding to the respiratory frequency according to the respiratory frequency level and the preset safety factor mapping table;

[0146] Determine, according to the preset safety level mapping table, a second safety factor corresponding to the target resident's age, a third safety factor corresponding to the fall duration, and a fourth safety factor corresponding to the spatial coordinates;

[0147] The first safety factor, the second safety factor, the third safety factor and the fourth safety factor are cumulatively calculated to obtain a safety parameter.

[0148] In a possible embodiment, the normal respiratory rate range determination module 44 is used to:

[0149] Perform face recognition on the face picture of the occupant to obtain face feature data, determine the target age group based on the face feature data, and determine the age of the target resident within the target age group based on the ID number data.

[0150] In a possible embodiment, the safety parameter calculation module 46 is used to:

[0151] In a case where the normal respiratory frequency range is greater than the respiratory frequency data, determining that the respiratory frequency level is a weaker respiratory level;

[0152] In a case where the normal respiratory frequency range is smaller than the respiratory frequency data, determining the respiratory frequency level to be a relatively strong respiratory level;

[0153] When the respiratory frequency data is within the normal respiratory frequency range, the respiratory frequency level is determined to be a normal respiratory level.

[0154] In a possible embodiment, the check-in information further includes the number of registered check-ins, the first millimeter wave information further includes the actual number of people in the room and the actual number of people in the corridor, and the warning information generation module 48 is further used to:

[0155] Compare the registered number of people with the actual number of people in the room, and determine whether to generate room abnormality warning information according to the comparison result;

[0156] A corridor population threshold is calculated according to the registered occupants, the actual corridor population is compared with the corridor population threshold, and it is determined whether to generate corridor abnormality warning information according to the comparison result.

[0157] In a possible embodiment, the first millimeter wave information further includes the abnormal time of the number of people in the room, the abnormal time of the number of people in the corridor, the abnormal number of people in the room and the abnormal number of people in the corridor, and the warning information generation module 48 is further used to:

[0158] When the number of registered occupants is greater than or equal to the actual number of occupants in the room, no room abnormality warning information is generated;

[0159] In the case where the registered occupants are less than the actual occupants of the room, generating room abnormality warning information according to the abnormal time of the occupants of the room, the abnormal number of occupants of the room and the millimeter wave identifier;

[0160] The warning information generating module 48 is further used for: not generating the abnormal warning information of the corridor when the actual number of people in the corridor is less than the threshold number of people in the corridor;

[0161] When the actual number of people in the corridor is greater than or equal to the threshold number of people in the corridor, abnormal corridor warning information is generated according to the abnormal time of the number of people in the corridor, the abnormal number of people in the corridor and the millimeter wave identifier.

[0162] In a possible embodiment, the warning information generating module 48 is further used to:

[0163] Respectively determine whether the abnormal time of the number of people in the room is within a first preset abnormal time range, and whether the abnormal number of people in the room is greater than a first abnormal number threshold;

[0164] When the abnormal time of the number of people in the room is within the first preset abnormal time range and the number of abnormal people in the room is greater than the first abnormal number threshold, generating first room abnormal warning information according to the millimeter wave identifier;

[0165] When the abnormal time of the number of people in the room is within the first preset abnormal time range or the abnormal number of people in the room is greater than the first abnormal number threshold, generating second room abnormal warning information according to the millimeter wave identifier;

[0166] Respectively determine whether the abnormal time of the number of people in the corridor is within a second preset abnormal time range, and whether the abnormal number of people in the corridor is greater than a second abnormal number threshold;

[0167] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range and the number of abnormal people in the corridor is greater than the second abnormal number threshold, generating first corridor abnormal warning information according to the millimeter wave identifier;

[0168] When the abnormal time of the number of people in the corridor is within the second preset abnormal time range or the abnormal number of people in the corridor is greater than the second abnormal number threshold, second corridor abnormal warning information is generated according to the millimeter wave identifier.

[0169] An embodiment of the present application also provides an electronic device, which can integrate a smart hotel safety warning device provided in an embodiment of the present application. Figure 5 This is a schematic diagram of the structure of a smart hotel safety warning device provided by an embodiment of the present application. Figure 5 The electronic device includes: an input device 53, an output device 54, a memory 52 and one or more processors 51; the memory 52 is used to store one or more programs; when one or more programs are executed by one or more processors 51, the one or more processors 51 implement the smart hotel safety warning method provided in the above embodiment. The input device 53, the output device 54, the memory 52 and the processor 51 can be connected by a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0170] The memory 52, as a computing device readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the smart hotel safety warning method provided in any embodiment of the present application. The memory 52 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to the use of the device, etc. In addition, the memory 52 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 52 may further include a memory remotely arranged relative to the processor 51, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0171] The input device 53 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 54 may include a display device such as a display screen.

[0172] The processor 51 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 52, thereby realizing the above-mentioned smart hotel safety warning method.

[0173] The smart hotel safety warning device, equipment and computer provided above can be used to execute the smart hotel safety warning method provided in any of the above embodiments, and have corresponding functions and beneficial effects.

[0174] The embodiment of the present application also provides a storage medium storing computer executable instructions. When the computer executable instructions are executed by a computer processor, they are used to execute the smart hotel safety early warning method provided in the above embodiment. The smart hotel safety early warning method includes:

[0175] Acquiring check-in information and first millimeter wave information in real time, wherein the first millimeter wave information includes a fall status;

[0176] Determine whether to generate warning information according to the fall state, and if the fall state is a fall, obtain second millimeter wave information associated with the fall state, the second millimeter wave information including respiratory rate data, millimeter wave identifier, spatial coordinates and fall duration;

[0177] determining a target resident age according to the check-in information, determining an associated normal respiratory rate range according to the target resident age, and comparing the normal respiratory rate range with the respiratory rate data;

[0178] Safety parameters are calculated according to the comparison result, the age of the target resident, the duration of the fall and the spatial coordinates, and a warning level corresponding to the safety parameters is determined, and fall warning information is generated based on the warning level and the millimeter wave identifier.

[0179] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disk or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system, which is connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations (for example, in different computer systems connected by a network). The storage medium may store program instructions (for example, embodied as a computer program) that can be executed by one or more processors.

[0180] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present application, whose computer executable instructions are not limited to the smart hotel safety warning method described above, can also execute related operations in the smart hotel safety warning method provided in any embodiment of the present application.

[0181] The smart hotel safety warning device, equipment and storage medium provided in the above embodiments can execute the smart hotel safety warning method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, please refer to the smart hotel safety warning method provided in any embodiment of the present application.

[0182] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A smart hotel safety early warning method, characterized in that: include: Acquiring check-in information and first millimeter wave information in real time, wherein the first millimeter wave information includes a fall status; Determine whether to generate warning information according to the fall state, and if the fall state is a fall, obtain second millimeter wave information associated with the fall state, the second millimeter wave information including respiratory rate data, millimeter wave identifier, spatial coordinates and fall duration; determining a target resident age according to the check-in information, determining an associated normal respiratory rate range according to the target resident age, and comparing the normal respiratory rate range with the respiratory rate data; Safety parameters are calculated according to the comparison result, the age of the target resident, the duration of the fall and the spatial coordinates, and a warning level corresponding to the safety parameters is determined, and fall warning information is generated based on the warning level and the millimeter wave identifier.

2. The smart hotel safety early warning method according to claim 1 is characterized in that: The calculating of the safety parameters according to the comparison result, the target resident's age, the fall duration and the spatial coordinates includes: Determine the respiratory frequency level according to the comparison result, and determine a first safety factor corresponding to the respiratory frequency according to the respiratory frequency level and the preset safety factor mapping table; Determine, according to the preset safety level mapping table, a second safety factor corresponding to the target resident's age, a third safety factor corresponding to the fall duration, and a fourth safety factor corresponding to the spatial coordinates; The first safety factor, the second safety factor, the third safety factor and the fourth safety factor are cumulatively calculated to obtain a safety parameter.

3. The smart hotel safety early warning method according to claim 1 is characterized in that: The check-in information includes a face picture and ID number data of the check-in person, and determining the age of the target resident based on the check-in information includes: Perform face recognition on the face picture of the occupant to obtain face feature data, determine the target age group based on the face feature data, and determine the age of the target resident within the target age group based on the ID number data.

4. The smart hotel safety early warning method according to claim 2 is characterized in that: Determining the respiratory frequency level according to the comparison result includes: In a case where the normal respiratory frequency range is greater than the respiratory frequency data, determining that the respiratory frequency level is a weaker respiratory level; In a case where the normal respiratory frequency range is smaller than the respiratory frequency data, determining the respiratory frequency level to be a relatively strong respiratory level; When the respiratory frequency data is within the normal respiratory frequency range, the respiratory frequency level is determined to be a normal respiratory level.

5. The smart hotel safety early warning method according to claim 1 is characterized in that: The check-in information also includes the number of registered check-ins, the first millimeter wave information also includes the actual number of people in the room and the actual number of people in the corridor, and after the fall warning information is generated based on the warning level and the millimeter wave identifier, it also includes: Compare the registered number of people with the actual number of people in the room, and determine whether to generate room abnormality warning information according to the comparison result; A corridor population threshold is calculated according to the registered occupants, the actual corridor population is compared with the corridor population threshold, and it is determined whether to generate corridor abnormality warning information according to the comparison result.

6. The smart hotel safety early warning method according to claim 5 is characterized in that: The first millimeter wave information also includes the abnormal time of the number of people in the room, the abnormal time of the number of people in the corridor, the abnormal number of people in the room and the abnormal number of people in the corridor. The determination of whether to generate room abnormality warning information according to the comparison result includes: When the number of registered occupants is greater than or equal to the actual number of occupants in the room, no room abnormality warning information is generated; In the case where the registered occupants are less than the actual occupants of the room, generating room abnormality warning information according to the abnormal time of the occupants of the room, the abnormal number of occupants of the room and the millimeter wave identifier; Correspondingly, judging whether to generate corridor abnormality warning information according to the comparison result includes: When the actual number of people in the corridor is less than the threshold number of people in the corridor, no corridor abnormality warning information is generated; When the actual number of people in the corridor is greater than or equal to the threshold number of people in the corridor, abnormal corridor warning information is generated according to the abnormal time of the number of people in the corridor, the abnormal number of people in the corridor and the millimeter wave identifier.

7. The smart hotel safety early warning method according to claim 6 is characterized in that: The generating room abnormality warning information according to the abnormal time of the number of people in the room, the abnormal number of people in the room and the millimeter wave identifier includes: Respectively determine whether the abnormal time of the number of people in the room is within a first preset abnormal time range, and whether the abnormal number of people in the room is greater than a first abnormal number threshold; When the abnormal time of the number of people in the room is within the first preset abnormal time range and the number of abnormal people in the room is greater than the first abnormal number threshold, generating first room abnormal warning information according to the millimeter wave identifier; When the abnormal time of the number of people in the room is within the first preset abnormal time range or the abnormal number of people in the room is greater than the first abnormal number threshold, generating second room abnormal warning information according to the millimeter wave identifier; Correspondingly, generating abnormal corridor warning information according to the abnormal corridor population time, the abnormal corridor population number and the millimeter wave identifier includes: Respectively determine whether the abnormal time of the number of people in the corridor is within a second preset abnormal time range, and whether the abnormal number of people in the corridor is greater than a second abnormal number threshold; When the abnormal time of the number of people in the corridor is within the second preset abnormal time range and the number of abnormal people in the corridor is greater than the second abnormal number threshold, generating first corridor abnormal warning information according to the millimeter wave identifier; When the abnormal time of the number of people in the corridor is within the second preset abnormal time range or the abnormal number of people in the corridor is greater than the second abnormal number threshold, second corridor abnormal warning information is generated according to the millimeter wave identifier.

8. A smart hotel safety warning device, characterized in that: include: A first information acquisition module, used to acquire check-in information and first millimeter wave information in real time, wherein the first millimeter wave information includes a fall status; A fall status judgment module, used to judge whether to generate warning information according to the fall status; A second information acquisition module, configured to acquire, when the fall state is a fall, second millimeter wave information associated with the fall state, the second millimeter wave information including respiratory rate data, millimeter wave identifier, spatial coordinates and fall duration; a normal breathing frequency range determination module, configured to determine the target resident age according to the check-in information, and determine the associated normal breathing frequency range according to the target resident age; A respiratory rate data comparison module, used for comparing the normal respiratory rate range with the respiratory rate data; A safety parameter calculation module, used to calculate the safety parameter according to the comparison result, the age of the target resident, the duration of the fall and the spatial coordinates; An early warning level determination module, used to determine the early warning level corresponding to the safety parameter; The warning information generation module is used to generate fall warning information based on the warning level and the millimeter wave identifier.

9. An electronic device, comprising: one or more processors; A storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the smart hotel safety warning method as described in any one of claims 1-7.

10. A storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the smart hotel safety warning method as described in any one of claims 1 to 7 when executed by a computer processor.

Images