Personnel state detection method and device and electronic equipment

Abstract

The invention relates to a personnel state detection method and device and electronic equipment, and the method comprises the steps: obtaining the current passive tag data in a target region; acquiring a ground state parameter set and a disturbance parameter set; the ground state numerical value of each target parameter in the ground state parameter set is determined according to the passive tag data in the target area in the unmanned state; the disturbance numerical value of each target parameter in the disturbance parameter set is determined according to passive tag data in the target area in a manned state; determining a current personnel state in the target area according to the current passive tag data, the ground state numerical value of each target parameter and the disturbance numerical value; wherein the acquisition of the current passive tag data does not capture the privacy information of the personnel, so that the privacy risk is avoided; and moreover, the computing power is low and the processing time is short when the current passive tag data is processed, so that the real-time personnel state detection can be realized, and the personnel state detection efficiency is improved.

Description

Technical Field

[0001] This disclosure relates to the field of mobile communication technology, and in particular to a method, apparatus and electronic device for detecting personnel status. Background Technology

[0002] Currently, solutions for detecting the status of people in a target area include using devices such as cameras to collect images of the target area; and using AI algorithms to analyze the images to capture information such as people and activities in the target area, thereby achieving personnel statistics and behavior analysis.

[0003] In the above-mentioned solutions, the cameras may capture private information, posing a privacy risk to personnel. Furthermore, image processing requires high computing power and takes a long time, affecting the real-time performance of personnel detection and reducing the efficiency of personnel status detection. Summary of the Invention

[0004] This disclosure provides a method, apparatus, and electronic device for detecting personnel status.

[0005] According to a first aspect of the present disclosure, a method for detecting personnel status is provided. The method includes: acquiring current passive tag data within a target area; acquiring a set of ground state parameters and a set of perturbation parameters; wherein the ground state values ​​of each target parameter in the set of ground state parameters are determined based on the passive tag data within the target area in an unoccupied state; and the perturbation values ​​of each target parameter in the set of perturbation parameters are determined based on the passive tag data within the target area in an occupied state; and determining the current personnel status within the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the perturbation values.

[0006] In one embodiment of this disclosure, obtaining the current passive tag data within the target area includes: upon receiving a detection instruction from the platform, sending a radio frequency signal to the target area via an IoT device to excite each passive tag within the target area to respond to the radio frequency signal and transmit passive tag data back; receiving the passive tag data transmitted back by each passive tag via the IoT device; and combining the passive tag data according to the receiving time point to obtain the current passive tag data.

[0007] In one embodiment of this disclosure, the passive tag data includes: Received Signal Strength Indication (RSSI) values ​​and a phase value sequence for the radio frequency signal; the target parameters include at least one of the following: time-domain parameters and frequency-domain parameters; the time-domain parameters include mean-related parameters, standard deviation-related parameters, and range-related parameters of the RSSI, as well as a confidence parameter; the frequency-domain related parameters include amplitude parameters for each frequency component within each time window; the values ​​of the amplitude parameters are determined based on the phase value sequence.

[0008] In one embodiment of this disclosure, the method for determining the set of ground state parameters includes: acquiring a first time period when the target area is unmanned; dividing the first time period according to a time window division strategy to obtain multiple time windows; determining the ground state value of the time domain parameter in the target parameter based on the received signal strength index (RSSI) value in the passive tag data within the multiple time windows; and determining the ground state value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window.

[0009] In one embodiment of this disclosure, the method for determining the set of disturbance parameters includes: obtaining a second time period when the target area is in a manned state; dividing the second time period according to a time window division strategy to obtain multiple time windows; determining the disturbance value of the time domain parameter in the target parameter based on the Received Signal Strength Indication (RSSI) value in the passive tag data within the multiple time windows; and determining the disturbance value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window.

[0010] In one embodiment of this disclosure, the time window segmentation strategy includes a short time window segmentation strategy and a long time window segmentation strategy; the first duration of the time window segmented by the short time window segmentation strategy is less than the second duration of the time window segmented by the long time window segmentation strategy.

[0011] In one embodiment of this disclosure, after acquiring the current passive tag data within the target area, the method further includes: acquiring the collection time period corresponding to the current passive tag data; dividing the collection time period according to a time window division strategy to obtain multiple current time windows; determining whether the parameter set update condition is met based on the passive tag data within the multiple current time windows; and updating the ground state parameter set and the disturbance parameter set if the parameter set update condition is met.

[0012] In one embodiment of this disclosure, the parameter set update condition includes at least one of the following: there are two current time windows, and the difference in the number of RSSI values ​​between the two current time windows is greater than or equal to a difference threshold; the difference between the value of the target parameter determined by passive label data in the first part of the current time window and the value of the target parameter determined by passive label data in the second part of the current time window is greater than or equal to a difference threshold; any one of the current time windows in the first part of the current time window is located before any one of the current time windows in the second part of the current time window.

[0013] In one embodiment of this disclosure, the ground state parameter set includes a first ground state parameter set under a first duration time window and a second ground state parameter set under a second duration time window; the disturbance parameter set includes a first disturbance parameter set under a first duration time window and a second disturbance parameter set under a second duration time window; the first duration is less than the second duration; determining the current personnel status in the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the disturbance values ​​includes: obtaining the historical personnel status of the target area at the most recent historical time point before the current time point; when the historical personnel status is unoccupied, performing time window division processing on the collection time period corresponding to the current passive tag data according to the first duration and determining the each target parameter to obtain the first current value of each target parameter; determining the current personnel status based on the first current value of each target parameter, the ground state values ​​of each target parameter in the first ground state parameter set, and the disturbance values ​​of each target parameter in the first disturbance parameter set.

[0014] In one embodiment of this disclosure, determining the current personnel status within the target area based on the current passive tag data, the ground-state values ​​of each target parameter, and the perturbation values ​​further includes: when the historical personnel status is "occupied," performing time window division processing on the collection time period corresponding to the current passive tag data according to a second duration, and determining the each target parameter to obtain the second current value of each target parameter; determining the current personnel status based on the second current values ​​of each target parameter, the ground-state values ​​of each target parameter in the second ground-state parameter set, and the perturbation values ​​of each target parameter in the second perturbation parameter set.

[0015] In one embodiment of this disclosure, the method further includes: controlling the Internet of Things (IoT) devices within the target area based on the current personnel status.

[0016] According to a second aspect of the present disclosure, a personnel status detection device is also provided. The device includes: a first acquisition module, configured to acquire current passive tag data within a target area; a second acquisition module, configured to acquire a set of ground state parameters and a set of disturbance parameters; wherein the ground state values ​​of each target parameter in the set of ground state parameters are determined based on the passive tag data within the target area in an unoccupied state; and the disturbance values ​​of each target parameter in the set of disturbance parameters are determined based on the passive tag data within the target area in an occupied state; and a first determination module, configured to determine the current personnel status within the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the disturbance values.

[0017] According to a third aspect of the present disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to: implement the steps of the personnel status detection method as described above.

[0018] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is also provided, which, when executed by a processor, enables the processor to perform the personnel status detection method as described above.

[0019] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: By acquiring the current passive tag data within the target area; acquiring the set of ground-state parameters and the set of perturbation parameters; the ground-state values ​​of each target parameter in the set of ground-state parameters are determined based on the passive tag data within the target area in an unmanned state; the perturbation values ​​of each target parameter in the set of perturbation parameters are determined based on the passive tag data within the target area in a manned state; and determining the current personnel status within the target area based on the current passive tag data, the ground-state values ​​of each target parameter, and the perturbation values; the acquisition of the current passive tag data does not capture personnel privacy information, avoiding privacy risks; and the computational power required to process the current passive tag data is low, the processing time is short, enabling real-time personnel status detection, thereby improving the efficiency of personnel status detection.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0022] Figure 1This is a flowchart of a personnel status detection method according to an embodiment of the present disclosure; Figure 2 This is a flowchart of a personnel status detection method according to another embodiment of this disclosure; Figure 3 This is a flowchart of a personnel status detection method according to another embodiment of this disclosure; Figure 4 This is a schematic diagram illustrating the acquisition of current passive label data; Figure 5 This is a schematic diagram of the structure of a personnel status detection device according to an embodiment of the present disclosure; Figure 6 This is a structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0024] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0025] Currently, solutions for detecting the status of people in a target area include using devices such as cameras to collect images of the target area; and using AI algorithms to analyze the images to capture information such as people and activities in the target area, thereby achieving personnel statistics and behavior analysis.

[0026] In the above-mentioned solutions, the cameras may capture private information, posing a privacy risk to personnel. Furthermore, image processing requires high computing power and takes a long time, affecting the real-time performance of personnel detection and reducing the efficiency of personnel status detection.

[0027] Figure 1 This is a flowchart of a personnel status detection method according to an embodiment of the present disclosure. It should be noted that the personnel status detection method of this embodiment can be applied to a personnel status detection device, which can be configured in an electronic device to enable the electronic device to perform personnel status detection functions.

[0028] The electronic device can be any device with computing capabilities, such as a terminal device, a server, or other device that communicates and connects with IoT devices. The following embodiments use an electronic device as an example for illustration.

[0029] like Figure 1 As shown, the method includes the following steps: Step 101: Obtain the current passive label data within the target area.

[0030] In this embodiment of the disclosure, the target area can be an area where personnel status detection is required, such as an indoor area.

[0031] In one example of this disclosure, the electronic device performing step 101 may, for example, send a radio frequency signal to the target area via an IoT device upon receiving a detection instruction from the platform, thereby stimulating each passive tag in the target area to respond to the radio frequency signal and transmit passive tag data back; receive the passive tag data transmitted back by each passive tag via the IoT device; and combine and process the passive tag data according to the receiving time point to obtain the current passive tag data.

[0032] It should be noted that the passive tag data can correspond to a specific collection period. Within this period, IoT devices periodically send radio frequency signals to the target area. Correspondingly, each passive tag can receive and respond to radio frequency signals multiple times within this period. Therefore, the passive tag data transmitted back by each passive tag can represent passive tag data from multiple points in time within the collection period.

[0033] In another example, the electronic device performing step 101 may include receiving passive tag data transmitted back from each passive tag by the IoT device; combining the passive tag data according to the received time point to obtain the current passive tag data. Specifically, upon receiving a detection command from the platform, the IoT device may send a radio frequency signal to the target area to stimulate each passive tag within the target area to respond to the radio frequency signal and transmit passive tag data back.

[0034] In this embodiment of the disclosure, the passive tag data may include: a Received Signal Strength Indicator (RSSI) value for the radio frequency signal and a phase value sequence.

[0035] Step 102: Obtain the set of ground state parameters and the set of perturbation parameters. The ground state values ​​of each target parameter in the set of ground state parameters are determined based on the passive tag data in the target area under unmanned conditions. The perturbation values ​​of each target parameter in the set of perturbation parameters are determined based on the passive tag data in the target area under manned conditions.

[0036] In this embodiment of the disclosure, the target parameters include at least one of the following: time-domain parameters and frequency-domain parameters; the time-domain parameters include the mean correlation parameter, standard deviation correlation parameter, and range correlation parameter of RSSI, as well as the confidence parameter; the frequency-domain correlation parameters include the amplitude parameters of each frequency component within each time window; the values ​​of the amplitude parameters are determined based on the phase value sequence.

[0037] The RSSI parameters include: mean-related parameters (e.g., mean range, mean weight); standard deviation-related parameters (e.g., standard deviation range, standard deviation weight); and range-related parameters (e.g., range, range weight). The confidence level parameter is a confidence score calculated by combining the aforementioned ranges and weights.

[0038] Step 103: Determine the current status of personnel within the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the disturbance values.

[0039] In this embodiment of the disclosure, the electronic device may perform step 103 as follows: determine the current value of each target parameter based on the current passive tag data; determine the tendency result of the current passive tag data based on the current value of each target parameter, the ground state value, and the disturbance value; the tendency result indicates a tendency towards the ground state parameter set or the disturbance parameter set; if the tendency result indicates a tendency towards the ground state parameter set, determine the current personnel status as unmanned; if the tendency result indicates a tendency towards the disturbance parameter set, determine the current personnel status as manned.

[0040] The tendency of the current passive label data can be determined by combining the first difference between the current value and the ground state value of each target parameter, and the second difference between the current value and the perturbation value of each target parameter. For example, if the first difference is less than or equal to the first difference threshold and the second difference is greater than the second difference threshold, the tendency indicates a preference for the ground state parameter set. Similarly, if the first difference is greater than the second difference threshold and the second difference is less than or equal to the first difference threshold, the tendency indicates a preference for the perturbation parameter set. Furthermore, if both the first and second differences are greater than the first difference threshold and less than or equal to the second difference threshold, then if the first difference is less than the second difference, the tendency indicates a preference for the ground state parameter set; if the first difference is greater than the second difference, the tendency indicates a preference for the perturbation parameter set.

[0041] The first degree of difference can be determined, for example, based on the overlap between the current value and the ground state value of each target parameter, or based on the proportion of the difference between the current value and the ground state value of the target parameter. The proportion of the difference can be the ratio of the difference to the current value.

[0042] In the personnel status detection method of this disclosure embodiment, the following steps are taken: acquiring current passive tag data within a target area; acquiring a set of ground state parameters and a set of perturbation parameters; the ground state values ​​of each target parameter in the set of ground state parameters are determined based on the passive tag data within the target area in an unmanned state; the perturbation values ​​of each target parameter in the set of perturbation parameters are determined based on the passive tag data within the target area in a manned state; and determining the current personnel status within the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the perturbation values. The acquisition of the current passive tag data does not capture personnel privacy information, thus avoiding privacy risks. Furthermore, the computational power required to process the current passive tag data is low, and the processing time is short, enabling real-time personnel status detection and improving the efficiency of personnel status detection.

[0043] Figure 2 This is a flowchart illustrating a personnel status detection method according to another embodiment of the present disclosure. It should be noted that the personnel status detection method of this embodiment can be applied to a personnel status detection device, which can be configured in an electronic device to enable the electronic device to perform personnel status detection functions.

[0044] The electronic device can be any device with computing capabilities, such as a terminal device, a server, or other device that communicates and connects with IoT devices. The following embodiments use an electronic device as an example for illustration.

[0045] like Figure 2As shown, the method includes the following steps: Step 201: Obtain the first time period when the target area is unoccupied.

[0046] Step 202: Divide the first time period into multiple time windows according to the time window division strategy.

[0047] In this embodiment of the disclosure, the time window segmentation strategy includes a short time window segmentation strategy and a long time window segmentation strategy; the first duration of the time window segmented by the short time window segmentation strategy is shorter than the second duration of the time window segmented by the long time window segmentation strategy.

[0048] Specifically, the electronic device can divide the first time period according to a short time window division strategy to obtain multiple time windows of the first duration; then, the electronic device can divide the first time period according to a long time window division strategy to obtain multiple time windows of the second duration.

[0049] Step 203: Determine the ground state value of the time domain parameter in the target parameters based on the received signal strength index (RSSI) values ​​in the passive tag data within multiple time windows.

[0050] In this embodiment of the disclosure, the electronic device may perform step 203 as follows: for multiple first time windows, determine the first ground state value of the time domain parameter in the target parameter based on the received signal strength index (RSSI) value in the passive tag data within the multiple first time windows; and for multiple second time windows, determine the second ground state value of the time domain parameter in the target parameter based on the received signal strength index (RSSI) value in the passive tag data within the multiple second time windows.

[0051] Taking the first time window as an example, the process by which the electronic device determines the first ground state value of the time-domain parameter in the target parameter based on the Received Signal Strength Indication (RSSI) values ​​in the passive tag data within multiple first time windows can be as follows: For each time window, the mean value is calculated for each RSSI value within that time window; the standard deviation value is calculated for each RSSI value within that time window; the range value is calculated for each RSSI value within that time window; the mean range is determined based on the mean value within each time window; the standard deviation range is determined based on the standard deviation value within each time window; the range range is determined based on the range value within each time window; the size of each of the three ranges is determined based on the size of the three ranges; then, the mean weight, standard deviation weight, and range weight are determined based on the size of the three ranges; and finally, the confidence parameter value is determined based on the three ranges and the three weights.

[0052] The formulas for determining the first ground state value of each time domain parameter can be, for example, as shown in formulas (1) to (14) below.

[0053] (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) in, This represents the j-th RSSI value within a time window; N represents the number of RSSI values ​​within a time window. It represents the average value of each RSSI value within a time window; and The meanings are the same; This represents the standard deviation of each RSSI value within a time window. This represents the maximum RSSI value within a time window. This represents the minimum RSSI value within a time window; Represents the range value within a time window; This represents the maximum value within the range of the mean. This represents the minimum value within the range of the mean. Indicates the size of the range of the mean; This represents the maximum value within the range of standard deviations. This represents the minimum value within the range of standard deviations; This indicates the size of the range of standard deviation. This represents the maximum value within the range of range values; This represents the minimum value within the range of range values; This indicates the size of the range of range values; This represents the sum of the three ranges mentioned above; Indicates mean weight; Indicates the standard deviation weight; Indicates the range weight; Represents the mean score; Represents the standard deviation score; Represents the range as a fraction; This represents the confidence level parameter.

[0054] Step 204: Determine the ground state value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window.

[0055] In this embodiment of the disclosure, the frequency domain correlation parameters include the amplitude parameters of each frequency component within each time window. The electronic device performing step 204 can, for example, involve determining, for multiple first-duration time windows, the first ground-state value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window; and for multiple second-duration time windows, determining the second ground-state value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window.

[0056] For example, taking the first time window as an example, the first ground state value of the frequency domain parameter can be determined as shown in the following formula (15).

[0057] (15) in, This represents the sequence of phase values ​​at the nth time point within the i-th time window; This represents the k-th frequency component; This represents the amplitude parameter of the k-th frequency component within the i-th time window.

[0058] Step 205: Determine the set of ground state parameters based on the ground state values ​​of the time-domain parameters and the ground state values ​​of the frequency-domain parameters within each time window.

[0059] Step 206: Obtain the second time period when the target area is occupied.

[0060] Step 207: Divide the second time period according to the time window division strategy to obtain multiple time windows.

[0061] Step 208: Determine the perturbation value of the time-domain parameter in the target parameter based on the Received Signal Strength Indication (RSSI) values ​​in the passive tag data within multiple time windows.

[0062] Step 209: Determine the perturbation value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window.

[0063] Step 210: Determine the set of perturbation parameters based on the perturbation values ​​of the time-domain parameters and the perturbation values ​​of the frequency-domain parameters within each time window.

[0064] In the embodiments of this disclosure, the calculation formulas for the perturbation values ​​of the time-domain parameters and the frequency-domain parameters during the determination of the perturbation parameter set can be referred to formulas (1) to (15), and will not be described in detail here.

[0065] Step 211: Obtain the current passive label data within the target area.

[0066] Step 212: Obtain the set of ground state parameters and the set of perturbation parameters.

[0067] Step 213: Determine the current personnel status within the target area based on the current passive tag data, the ground state values ​​of each target parameter in the ground state parameter set, and the perturbation values ​​of each target parameter in the perturbation parameter set.

[0068] It should be noted that for details of steps 211 to 213, please refer to [the relevant documentation / reference]. Figure 1 Steps 101 to 103 in the illustrated embodiment will not be described in detail here.

[0069] In the personnel status detection method of this embodiment, a first time period when the target area is unoccupied is obtained; the first time period is divided according to a time window division strategy to obtain multiple time windows; the ground state value of the time domain parameter in the target parameter is determined based on the received signal strength index (RSSI) value in the passive tag data within the multiple time windows; the ground state value of the frequency domain parameter in the target parameter is determined based on the phase value sequence in the passive tag data within each time window; a ground state parameter set is determined based on the ground state value of the time domain parameter and the ground state value of the frequency domain parameter within each time window; a second time period when the target area is occupied is obtained; the second time period is divided according to a time window division strategy to obtain multiple time windows; the received signal strength index (RSSI) value in the passive tag data within the multiple time windows is determined based on the phase value sequence in the passive tag data within the multiple time windows; the ground state parameter set is determined based on the phase value sequence in the passive tag data within the multiple time windows; a second time period when the target area is occupied is obtained; the second time period is divided according to a time window division strategy to obtain multiple time windows; the ground state parameter set is determined based on the received signal strength index (RSSI) value in the passive tag data within the multiple time windows; the ground state parameter set is determined based on the phase value sequence ... The signal strength index (RSSI) value is used to determine the perturbation values ​​of the time-domain parameters in the target parameters. Based on the phase value sequence in the passive tag data within each time window, the perturbation values ​​of the frequency-domain parameters in the target parameters within each time window are determined. Based on the perturbation values ​​of the time-domain parameters and the frequency-domain parameters within each time window, a set of perturbation parameters is determined. Current passive tag data within the target area is acquired. The set of ground-state parameters and the set of perturbation parameters are acquired. Based on the current passive tag data, the ground-state values ​​of each target parameter in the ground-state parameter set, and the perturbation values ​​of each target parameter in the perturbation parameter set, the current personnel status within the target area is determined. The determination process for the ground-state parameter set and the perturbation parameter set does not involve personnel privacy information and requires relatively low computational power, further reducing the cost of personnel status detection.

[0070] Figure 3 This is a flowchart illustrating a personnel status detection method according to another embodiment of the present disclosure. It should be noted that the personnel status detection method of this embodiment can be applied to a personnel status detection device, which can be configured in an electronic device to enable the electronic device to perform personnel status detection functions.

[0071] The electronic device can be any device with computing capabilities, such as a terminal device, a server, or other device that communicates and connects with IoT devices. The following embodiments use an electronic device as an example for illustration.

[0072] like Figure 3 As shown, the method includes the following steps: Step 301: Obtain the current passive label data within the target area.

[0073] In this embodiment of the disclosure, in order to ensure the accuracy of the ground state parameter set and the perturbation parameter set, it can be determined whether the parameter set update condition is met; if the parameter set update condition is met, the ground state parameter set and the perturbation parameter set are updated.

[0074] The parameter set update conditions may include at least one of the following: (1) Reaching the cycle time point. (2) There are two current time windows, and the difference in the number of RSSI values ​​between the two current time windows is greater than or equal to the difference threshold. (3) The difference between the value of the target parameter determined by the passive label data in the first current time window and the value of the target parameter determined by the passive label data in the second current time window is greater than or equal to the difference threshold; any current time window in the first current time window is located before any current time window in the second current time window.

[0075] In one example, the parameter set update condition may include reaching a periodic time point. The number of periodic time points can be one or more. Correspondingly, the electronic device can update the ground state parameter set and the disturbance parameter set upon reaching the periodic time point.

[0076] The process of updating the ground state parameter set involves first acquiring the first time period when the target area is unoccupied, and then re-determining the ground state parameter set based on the passive tag data within the first time period. Similarly, the process of updating the disturbance parameter set involves first acquiring the second time period when the target area is occupied, and then re-determining the disturbance parameter set based on the passive tag data within the second time period.

[0077] In another example, the parameter set update condition may include at least one of the following: (1) There are two current time windows, and the difference in the number of RSSI values ​​between the two current time windows is greater than or equal to the difference threshold. (2) The difference between the value of the target parameter determined by the passive label data in the first current time window and the value of the target parameter determined by the passive label data in the second current time window is greater than or equal to the difference threshold; any current time window in the first current time window is located before any current time window in the second current time window.

[0078] The formula for calculating the quantity difference can be shown in formulas (16) and (17) below.

[0079] (16) (17) in, This represents the number of RSSI values ​​in the i-th time window within each time window of the first duration. This represents the number of RSSI values ​​in the j-th time window within each time window of the first duration. The threshold representing the difference in the number of time windows within the first duration; This represents the number of RSSI values ​​in the i-th time window within each time window of the second duration; This represents the number of RSSI values ​​in the j-th time window within each time window of the second duration; This represents the threshold value for the difference in the number of time windows in the second duration.

[0080] The degree of difference can be determined based on the overlap between the target parameter values ​​determined by the passive label data in the first part of the current time window and the target parameter values ​​determined by the passive label data in the second part of the current time window; or, it can be determined based on the percentage difference between the target parameter values ​​determined by the passive label data in the first part of the current time window and the target parameter values ​​determined by the passive label data in the second part of the current time window.

[0081] Step 302: Obtain the ground state parameter set and the disturbance parameter set; the ground state values ​​of each target parameter in the ground state parameter set are determined based on the passive tag data in the target area under unmanned conditions; the disturbance values ​​of each target parameter in the disturbance parameter set are determined based on the passive tag data in the target area under manned conditions; the ground state parameter set includes a first ground state parameter set under a first time window and a second ground state parameter set under a second time window; the disturbance parameter set includes a first disturbance parameter set under a first time window and a second disturbance parameter set under a second time window; the first time window is shorter than the second time window.

[0082] Step 303: Obtain the historical personnel status of the target area at the most recent historical time point before the current time point.

[0083] Step 304: When the historical personnel status is unmanned, perform time window division processing on the collection time period corresponding to the current passive tag data according to the first duration and determine the target parameters to obtain the first current value of each target parameter.

[0084] The process of determining the first current value of each target parameter can be referred to the process of determining the ground state value of each target parameter in the ground state parameter set, and will not be described in detail here.

[0085] Step 305: Determine the current personnel status based on the first current value of each target parameter, the ground state value of each target parameter in the first ground state parameter set, and the disturbance value of each target parameter in the first disturbance parameter set.

[0086] The target parameters may include the mean range, standard deviation range, range, confidence level parameter, and amplitude parameters for each frequency component within each time window. For each of these five parameters, the degree of difference is determined based on its first current value and ground-state value. These degree of difference are then summed and averaged to obtain a first-processed degree of difference. A second-processed degree of difference is then determined by combining the first current value and perturbation value of each of the five parameters. The first and second-processed degree of difference are compared to determine the current personnel status.

[0087] For example, if the difference after the first processing is less than the difference after the second processing, the current personnel status is determined to be unmanned; if the difference after the first processing is greater than the difference after the second processing, the current personnel status is determined to be occupied.

[0088] Specifically, for the amplitude parameters of each frequency component within each time window, before determining the degree of difference, frequency points with large amplitude parameter values ​​due to non-human activity can be filtered out. These frequency points with large amplitude parameter values ​​due to non-human activity are those exceeding a preset frequency threshold, while those with large amplitude parameter values ​​due to human activity are those below the preset frequency threshold.

[0089] After filtering out frequency points with large amplitude parameter values ​​due to non-human activities, the degree of difference is determined based on the current value of the amplitude parameter of each frequency component within each time window and the ground state value of the amplitude parameter of each frequency component within each time window.

[0090] In this embodiment of the disclosure, after step 303, the electronic device may further perform the following process: when the historical personnel status is a person status, perform time window division processing and target parameter determination processing on the collection time period corresponding to the current passive tag data according to the second duration to obtain the second current value of each target parameter; determine the current personnel status based on the second current value of each target parameter, the ground state value of each target parameter in the second ground state parameter set, and the disturbance value of each target parameter in the second disturbance parameter set.

[0091] In one embodiment of this disclosure, after step 305, the electronic device may further perform the following process: control the Internet of Things devices within the target area based on the current personnel status.

[0092] The target area includes IoT devices such as air conditioners, lighting equipment, and refrigerators. The process involves controlling these IoT devices, for example, turning them off when no one is present and turning them on when someone is present.

[0093] This includes shutdown control processes, such as turning off lighting equipment and air conditioning. Turning on control processes, such as turning on lighting equipment and air conditioning.

[0094] In another example, after step 305, the electronic device may also perform the following process: providing the current personnel status to the platform, so that the platform can control the IoT devices in the target area based on the current personnel status.

[0095] In the personnel status detection method of this disclosure embodiment, the following steps are taken: acquiring current passive tag data within a target area; acquiring a set of ground state parameters and a set of disturbance parameters; the ground state values ​​of each target parameter in the set of ground state parameters are determined based on the passive tag data within the target area in an unmanned state; the disturbance values ​​of each target parameter in the set of disturbance parameters are determined based on the passive tag data within the target area in a manned state; the set of ground state parameters includes a first set of ground state parameters under a first time window and a second set of ground state parameters under a second time window; the set of disturbance parameters includes a first set of disturbance parameters under a first time window and a second set of disturbance parameters under a second time window; the first time window is less than the second time window; and the most recent historical data of the target area before the current time point is acquired. The system calculates the historical personnel status at historical time points. When the historical personnel status is unoccupied, it performs time windowing on the current passive tag data collection period according to a first duration, and determines the first current value of each target parameter. Based on the first current value of each target parameter, the ground state value of each target parameter in the first ground state parameter set, and the perturbation value of each target parameter in the first perturbation parameter set, the current personnel status is determined. Specifically, when the historical personnel status is unoccupied, performing the first current value of each target parameter and determining the current personnel status according to a shorter first duration allows for timely detection of changes in personnel status, further ensuring the real-time nature of personnel status detection and thus improving its efficiency.

[0096] The following example illustrates this. For example... Figure 4 The image shown illustrates the acquisition of current passive label data. Figure 4 This may include the following steps: Step 401: The platform system (i.e., the platform) issues monitoring instructions (i.e., detection instructions) to the IoT devices.

[0097] Step 402: The IoT device sends radio frequency signals to each passive tag in order to receive reflected passive tag data.

[0098] Step 403: The IoT device sends the passive tag data to the data processing module (i.e., the electronic device).

[0099] Step 404: The electronic device constructs a ground state model (i.e., a set of ground state parameters) based on the passive tag data in the target area under unmanned conditions; and constructs a disturbance model (i.e., a set of disturbance parameters) based on the passive tag data in the target area under manned conditions.

[0100] Step 405: Combine the ground state model, the disturbance model, and the passive tag data received from the Internet of Things to perform time domain data analysis and frequency domain data analysis (target parameter analysis), and then jointly determine the updated status (personnel status).

[0101] Figure 5 This is a schematic diagram of the structure of a personnel status detection device according to an embodiment of the present disclosure.

[0102] like Figure 5 As shown, the personnel status detection device may include: a first acquisition module 501, a second acquisition module 502, and a first determination module 503.

[0103] The system includes a first acquisition module 501 for acquiring current passive tag data within a target area; a second acquisition module 502 for acquiring a set of ground-state parameters and a set of disturbance parameters; the ground-state values ​​of each target parameter in the ground-state parameter set are determined based on the passive tag data within the target area in an unmanned state; the disturbance values ​​of each target parameter in the disturbance parameter set are determined based on the passive tag data within the target area in a manned state; and a first determination module 503 for determining the current personnel status within the target area based on the current passive tag data, the ground-state values ​​of each target parameter, and the disturbance values.

[0104] In one embodiment of this disclosure, the first acquisition module 501 is specifically configured to: upon receiving a detection instruction from the platform, send a radio frequency signal to the target area via an IoT device to excite each passive tag in the target area to respond to the radio frequency signal and transmit passive tag data back; receive the passive tag data transmitted back by each passive tag via the IoT device; and combine and process each passive tag data according to the receiving time point to obtain the current passive tag data.

[0105] In one embodiment of this disclosure, the passive tag data includes: Received Signal Strength Indication (RSSI) values ​​and a phase value sequence for the radio frequency signal; the target parameters include at least one of the following: time-domain parameters and frequency-domain parameters; the time-domain parameters include mean-related parameters, standard deviation-related parameters, and range-related parameters of the RSSI, as well as a confidence parameter; the frequency-domain related parameters include amplitude parameters for each frequency component within each time window; the values ​​of the amplitude parameters are determined based on the phase value sequence.

[0106] In one embodiment of this disclosure, the method for determining the set of ground state parameters includes: acquiring a first time period when the target area is unmanned; dividing the first time period according to a time window division strategy to obtain multiple time windows; determining the ground state value of the time domain parameter in the target parameter based on the received signal strength index (RSSI) value in the passive tag data within the multiple time windows; and determining the ground state value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window.

[0107] In one embodiment of this disclosure, the method for determining the set of disturbance parameters includes: obtaining a second time period when the target area is in a manned state; dividing the second time period according to a time window division strategy to obtain multiple time windows; determining the disturbance value of the time domain parameter in the target parameter based on the Received Signal Strength Indication (RSSI) value in the passive tag data within the multiple time windows; and determining the disturbance value of the frequency domain parameter in the target parameter within each time window based on the phase value sequence in the passive tag data within each time window.

[0108] In one embodiment of this disclosure, the time window segmentation strategy includes a short time window segmentation strategy and a long time window segmentation strategy; the first duration of the time window segmented by the short time window segmentation strategy is less than the second duration of the time window segmented by the long time window segmentation strategy.

[0109] In one embodiment of this disclosure, the apparatus further includes: a third acquisition module, a partitioning processing module, a second determination module, and an update processing module; the third acquisition module is used to acquire the collection time period corresponding to the current passive tag data; the partitioning processing module is used to partition the collection time period according to a time window partitioning strategy to obtain multiple current time windows; the second determination module is used to determine whether the parameter set update condition is met based on the passive tag data within the multiple current time windows; the update processing module is used to update the ground state parameter set and the disturbance parameter set if the parameter set update condition is met.

[0110] In one embodiment of this disclosure, the parameter set update condition includes at least one of the following: there are two current time windows, and the difference in the number of RSSI values ​​between the two current time windows is greater than or equal to a difference threshold; the difference between the value of the target parameter determined by passive label data in the first part of the current time window and the value of the target parameter determined by passive label data in the second part of the current time window is greater than or equal to a difference threshold; any one of the current time windows in the first part of the current time window is located before any one of the current time windows in the second part of the current time window.

[0111] In one embodiment of this disclosure, the ground state parameter set includes a first ground state parameter set under a first duration time window and a second ground state parameter set under a second duration time window; the disturbance parameter set includes a first disturbance parameter set under a first duration time window and a second disturbance parameter set under a second duration time window; the first duration is less than the second duration; the first determining module 503 is specifically used to: obtain the historical personnel status of the target area at the most recent historical time point before the current time point; when the historical personnel status is unmanned, perform time window division processing on the collection time period corresponding to the current passive tag data according to the first duration and determine the various target parameters to obtain the first current value of each target parameter; determine the current personnel status based on the first current value of each target parameter, the ground state value of each target parameter in the first ground state parameter set, and the disturbance value of each target parameter in the first disturbance parameter set.

[0112] In one embodiment of this disclosure, the first determining module 503 is further configured to, when the historical personnel status is a resident status, perform time window division processing on the collection time period corresponding to the current passive tag data according to the second duration and determine the various target parameters to obtain the second current value of each target parameter; and determine the current personnel status based on the second current value of each target parameter, the ground state value of each target parameter in the second ground state parameter set, and the disturbance value of each target parameter in the second disturbance parameter set.

[0113] In one embodiment of this disclosure, the device further includes a control processing module, configured to control the Internet of Things (IoT) devices within the target area based on the current personnel status.

[0114] In the personnel status detection device of this embodiment, the current passive tag data in the target area is acquired; a set of ground state parameters and a set of disturbance parameters are acquired; the ground state values ​​of each target parameter in the set of ground state parameters are determined based on the passive tag data in the target area under unmanned conditions; the disturbance values ​​of each target parameter in the set of disturbance parameters are determined based on the passive tag data in the target area under manned conditions; the current personnel status in the target area is determined based on the current passive tag data, the ground state values ​​of each target parameter, and the disturbance values; wherein, the acquisition of the current passive tag data does not capture the personnel's privacy information, avoiding privacy risks; and the computing power required to process the current passive tag data is low, the processing time is short, and real-time personnel status detection can be achieved, thereby improving the efficiency of personnel status detection.

[0115] According to a third aspect of the present disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to implement the personnel status detection method as described above.

[0116] To implement the above embodiments, this disclosure also proposes a storage medium.

[0117] When the instructions in the storage medium are executed by the processor, the processor is able to execute the personnel status detection method as described above.

[0118] To implement the above embodiments, this disclosure also provides a computer program product.

[0119] When the computer program product is executed by the processor of the electronic device, it enables the electronic device to perform the above-described method.

[0120] Figure 6 This is a structural block diagram of an electronic device according to an exemplary embodiment. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0121] like Figure 6As shown, the electronic device 1000 includes a processor 111, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 112 or a program loaded from memory 116 into random access memory (RAM) 113. The RAM 113 also stores various programs and data required for the operation of the electronic device 1000. The processor 111, ROM 112, and RAM 113 are interconnected via a bus 114. An input / output (I / O) interface 115 is also connected to the bus 114.

[0122] The following components are connected to I / O interface 115: memory 116 including hard disks, etc.; and communication section 117 including network interface cards such as local area network (LAN) cards, modems, etc., communication section 117 performs communication processing via a network such as the Internet; and driver 118 is also connected to I / O interface 115 as needed.

[0123] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 117. When the computer program is executed by processor 111, it performs the functions defined in the methods of this disclosure.

[0124] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, which can be executed by the processor 111 of the electronic device 1000 to perform the above-described method. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0125] In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0126] Furthermore, the term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept in a concrete manner. As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from the context, “X applies A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this application and the appended claims are generally understood to mean “one or more.”

[0127] Similarly, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of this disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations, as may be desired and advantageous to any given or particular application. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0128] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0129] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for detecting personnel status, characterized in that, The method includes: Obtain the current passive label data within the target area; Obtain the set of ground state parameters and the set of perturbation parameters; the ground state values ​​of each target parameter in the set of ground state parameters are determined based on the passive tag data in the target area under unmanned conditions; the perturbation values ​​of each target parameter in the set of perturbation parameters are determined based on the passive tag data in the target area under manned conditions. Based on the current passive tag data, the ground state values ​​of each target parameter, and the perturbation values, the current status of personnel within the target area is determined.

2. The method according to claim 1, characterized in that, The acquisition of current passive tag data within the target area includes: Upon receiving a detection instruction from the platform, an IoT device sends a radio frequency signal to the target area to stimulate each passive tag in the target area to respond to the radio frequency signal and transmit passive tag data back. The IoT device receives the passive tag data transmitted back by each passive tag; The passive tag data is combined and processed according to the receiving time point to obtain the current passive tag data.

3. The method according to claim 1 or 2, characterized in that, The passive tag data includes: the Received Signal Strength Indicator (RSSI) value and the phase value sequence for the radio frequency signal; The target parameters include at least one of the following: time-domain parameters and frequency-domain parameters; the time-domain parameters include the mean correlation parameter, standard deviation correlation parameter, and range correlation parameter of the RSSI, as well as the confidence level parameter; The frequency domain correlation parameters include amplitude parameters for each frequency component within each time window; the values ​​of the amplitude parameters are determined based on the phase value sequence.

4. The method according to claim 1, characterized in that, The method for determining the set of ground state parameters includes: Obtain the first time period when the target area is unoccupied; The first time period is divided according to the time window segmentation strategy to obtain multiple time windows; Based on the Received Signal Strength Indication (RSSI) values ​​in the passive tag data within the multiple time windows, determine the ground state value of the time-domain parameter in the target parameter; Based on the phase value sequence in the passive tag data within each time window, determine the ground state value of the frequency domain parameter in the target parameter within each time window.

5. The method according to claim 1, characterized in that, The method for determining the set of disturbance parameters includes: Obtain the second time period when the target area is occupied; The second time period is divided according to the time window segmentation strategy to obtain multiple time windows; Based on the Received Signal Strength Indication (RSSI) values ​​in the passive tag data within the multiple time windows, the perturbation values ​​of the time-domain parameters in the target parameters are determined. Based on the phase value sequence in the passive tag data within each time window, determine the perturbation value of the frequency domain parameter in the target parameter within each time window.

6. The method according to claim 4 or 5, characterized in that, The time window segmentation strategy includes a short time window segmentation strategy and a long time window segmentation strategy; The first duration of the time window obtained by the short time window segmentation strategy is less than the second duration of the time window obtained by the long time window segmentation strategy.

7. The method according to claim 1, characterized in that, After acquiring the current passive label data within the target area, the method further includes: Obtain the collection time period corresponding to the current passive tag data; The collection time period is divided according to the time window segmentation strategy to obtain multiple current time windows; Based on the passive label data within the multiple current time windows, determine whether the parameter set update conditions are met; If the parameter set update conditions are met, the ground state parameter set and the disturbance parameter set are updated.

8. The method according to claim 7, characterized in that, The parameter set update condition includes at least one of the following: There are two current time windows, and the difference in the number of RSSI values ​​between the two current time windows is greater than or equal to a threshold value. The difference between the value of the target parameter determined by the passive label data in the first part of the current time window and the value of the target parameter determined by the passive label data in the second part of the current time window is greater than or equal to the difference threshold; any current time window in the first part of the current time window is located before any current time window in the second part of the current time window.

9. The method according to claim 1, characterized in that, The ground state parameter set includes a first ground state parameter set under a first time window and a second ground state parameter set under a second time window; the perturbation parameter set includes a first perturbation parameter set under a first time window and a second perturbation parameter set under a second time window. The first duration is less than the second duration; determining the current personnel status within the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the perturbation values ​​includes: Obtain the historical personnel status of the target area at the most recent historical time point before the current time point; When the historical personnel status is unmanned, the time window is divided according to the first duration of the current passive tag data collection time period and the determination of each target parameter is performed to obtain the first current value of each target parameter. The current personnel state is determined based on the first current value of each target parameter, the ground state value of each target parameter in the first ground state parameter set, and the disturbance value of each target parameter in the first disturbance parameter set.

10. The method according to claim 9, characterized in that, The step of determining the current personnel status within the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the perturbation values ​​further includes: When the historical personnel status is "occupied", the time window processing of the collection time period corresponding to the current passive tag data is divided according to the second duration, and the determination processing of each target parameter is performed to obtain the second current value of each target parameter; The current personnel state is determined based on the second current value of each target parameter, the ground state value of each target parameter in the second ground state parameter set, and the disturbance value of each target parameter in the second disturbance parameter set.

11. The method according to claim 1, 9, or 10, characterized in that, The method further includes: Based on the current personnel status, control and process the IoT devices within the target area.

12. A personnel status detection device, characterized in that, The device includes: The first acquisition module is used to acquire the current passive label data within the target area; The second acquisition module is used to acquire a set of ground state parameters and a set of perturbation parameters; the ground state values ​​of each target parameter in the set of ground state parameters are determined based on passive tag data in the target area under unmanned conditions; the perturbation values ​​of each target parameter in the set of perturbation parameters are determined based on passive tag data in the target area under manned conditions. The first determining module is used to determine the current status of personnel within the target area based on the current passive tag data, the ground state values ​​of each target parameter, and the perturbation values.

13. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured as follows: The steps of implementing the personnel status detection method as described in any one of claims 1 to 11.

14. A non-transitory computer-readable storage medium, wherein instructions in the storage medium, when executed by a processor, enable the processor to perform the personnel status detection method as described in any one of claims 1 to 11.

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