RSSI ranging method, device and electronic equipment

Abstract

The present invention provides an RSSI ranging method, device, and electronic device. The RSSI ranging method includes: establishing a ranging model; determining a critical value of a received signal based on the ranging model; dividing a received signal strength indicator (RSSI) value into multiple intervals based on the critical value; determining the interval to which a first RSSI value obtained belongs; and determining the signal transmission distance between a signal transmitting sensor and a signal receiving sensor based on the interval to which the first RSSI value belongs and the ranging model; wherein the signal receiving sensor is used to obtain the first RSSI value. The present invention divides the RSSI value into intervals and obtains the signal transmission distance based on the intervals and the ranging model, thereby reducing the impact of environmental factors on the RSSI value and improving ranging accuracy.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an RSSI ranging method, device and electronic equipment. Background Art

[0002] The Received Signal Strength Indication (RSSI) indicates the degree of signal attenuation during transmission. Ranging refers to measuring the distance between the signal transmitting sensor and the signal receiving sensor, that is, the signal transmission distance. The RSSI and signal transmission distance model algorithm represents the relationship between RSSI and signal transmission distance. This relationship formula can be used to convert RSSI into signal transmission distance. The RSSI ranging principle converts the RSSI signal obtained by the signal receiving sensor into the signal transmission distance between the signal transmitting sensor and the signal receiving sensor.

[0003] However, to improve measurement accuracy, current RSSI ranging solutions require pre-calibration of model algorithm parameters for the current environment. However, in general ranging environments, this cannot be done in advance, resulting in an exponential increase in ranging errors due to environmental factors. Summary of the Invention

[0004] The embodiments of the present invention provide an RSSI ranging method, device and electronic device to solve the problem of large ranging errors caused by environmental factors in the prior art.

[0005] In order to solve the above technical problems, the embodiments of the present invention provide the following technical solutions:

[0006] An embodiment of the present invention provides an RSSI ranging method, including:

[0007] Establishing a ranging model;

[0008] determining a critical value of a received signal according to the ranging model;

[0009] Dividing the received signal strength indication RSSI value into a plurality of interval segments according to the critical value;

[0010] Determine the interval to which the obtained first RSSI value belongs;

[0011] determining a signal transmission distance between a signal transmitting sensor and a signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model;

[0012] The signal receiving sensor is used to obtain the first RSSI value.

[0013] Optionally, the ranging model includes at least one of the following:

[0014] Logarithmic distance path loss model and Gaussian distance path loss model.

[0015] Optionally, the formula of the logarithmic distance path loss model is:

[0016]

[0017] Wherein, y is the signal transmission distance, x is the RSSI value, A is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, η is the path loss index, and x is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance. δ is a Gaussian random variable.

[0018] Optionally, the formula of the Gaussian distance path loss model is:

[0019]

[0020] Where y is the signal transmission distance, x is the RSSI value, a is the farthest signal transmission distance at which the signal receiving sensor can receive the signal, b is the minimum RSSI value at which the signal receiving sensor can receive the signal, and x is the maximum RSSI value at which the signal receiving sensor can receive the signal. δ is a Gaussian random variable, c is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, and e is a natural exponent.

[0021] Optionally, when the ranging model includes a Gaussian distance path loss model, determining the critical value of the received signal according to the ranging model includes:

[0022] Determining a critical RSSI value in the critical value according to a minimum RSSI value and a Gaussian random variable in the Gaussian distance path loss model;

[0023] The critical signal transmission distance in the critical value is determined according to the farthest signal transmission distance in the Gaussian distance path loss model.

[0024] Optionally, dividing the received signal strength indication RSSI value into a plurality of interval segments according to the critical value includes:

[0025] Dividing the RSSI value into N intervals according to the critical RSSI value and the buffer length of the interval;

[0026] Wherein, N is a positive integer greater than or equal to 2.

[0027] Optionally, determining the interval to which the acquired first RSSI value belongs includes:

[0028] Obtaining the first RSSI value;

[0029] When the first RSSI value reaches a preset value, optimizing the first RSSI value to obtain a first target RSSI value;

[0030] Determine the interval to which the first target RSSI value belongs.

[0031] Optionally, optimizing the first RSSI value to obtain a first target RSSI value includes:

[0032] Performing filtering processing on the preset number of first RSSI values by removing maximum and minimum values;

[0033] Performing mean processing on the first RSSI value after filtering to obtain the first target RSSI value.

[0034] Optionally, determining the interval to which the first target RSSI value belongs includes:

[0035] When the first target RSSI value is less than a critical RSSI value included in the critical value, determining that the first target RSSI value belongs to a first interval;

[0036] When the first target RSSI value is greater than or equal to the critical RSSI value included in the critical value and less than or equal to the sum of the critical RSSI value and the buffer length of the interval, determine that the first target RSSI value belongs to the second interval;

[0037] When the first target RSSI value is greater than the sum of the critical RSSI value included in the critical value and the buffer length of the interval segment, it is determined that the first target RSSI value belongs to the third interval segment.

[0038] Optionally, when the ranging model includes: a logarithmic distance path loss model and a Gaussian distance path loss model, determining the signal transmission distance between the signal transmitting sensor and the signal receiving sensor according to the interval segment to which the first RSSI value belongs and the ranging model includes:

[0039] When the first target RSSI value belongs to the first interval, determining the signal transmission distance by using the logarithmic distance path loss model;

[0040] When the first target RSSI value belongs to the second interval, determining the signal transmission distance by using a logarithmic distance path loss model and the Gaussian distance path loss model;

[0041] When the first target RSSI value belongs to the third interval, the signal transmission distance is determined by using the Gaussian distance path loss model.

[0042] Optionally, the method further includes:

[0043] The test data collected by the signal transmitting sensor with different transmission powers in a test site of a first preset size are used to obtain the RSSI value, the path loss index value and the Gaussian random variable value obtained when the distance between the signal receiving sensor and the signal transmitting sensor in the logarithmic distance path loss model is the first preset distance.

[0044] Optionally, the method further includes:

[0045] The test data collected by the signal transmitting sensor with different transmission powers in the test site of the second preset size are used to obtain the value of the farthest signal transmission distance that the signal receiving sensor can receive the signal in the Gaussian distance path loss model, the minimum RSSI value that the signal receiving sensor can receive the signal, the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is the second preset distance, and the value of the Gaussian random variable.

[0046] An embodiment of the present invention further provides an RSSI ranging device, comprising:

[0047] Model building module, used to build ranging model;

[0048] A first determining module, configured to determine a critical value of a received signal according to the ranging model;

[0049] An interval segment division module is used to divide the received signal strength indication RSSI value into multiple interval segments according to the critical value;

[0050] A second determining module is used to determine the interval to which the acquired first RSSI value belongs;

[0051] a third determining module, configured to determine a signal transmission distance between a signal transmitting sensor and a signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model;

[0052] The signal receiving sensor is used to obtain the first RSSI value.

[0053] Optionally, the ranging model includes at least one of the following:

[0054] Logarithmic distance path loss model and Gaussian distance path loss model.

[0055] Optionally, the formula of the logarithmic distance path loss model is:

[0056]

[0057] Wherein, y is the signal transmission distance, x is the RSSI value, A is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, η is the path loss index, and x is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance. δ is a Gaussian random variable.

[0058] Optionally, the formula of the Gaussian distance path loss model is:

[0059]

[0060] Where y is the signal transmission distance, x is the RSSI value, a is the farthest signal transmission distance at which the signal receiving sensor can receive the signal, b is the minimum RSSI value at which the signal receiving sensor can receive the signal, and x is the maximum RSSI value at which the signal receiving sensor can receive the signal. δ is a Gaussian random variable, c is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, and e is a natural exponent.

[0061] Optionally, when the ranging model includes a Gaussian distance path loss model, the first determining module includes:

[0062] a first determining unit, configured to determine a critical RSSI value in the critical value according to a minimum RSSI value and a Gaussian random variable in the Gaussian distance path loss model;

[0063] The second determining unit is configured to determine a critical signal transmission distance in the critical value according to a maximum signal transmission distance in the Gaussian distance path loss model.

[0064] Optionally, the interval segment division module includes:

[0065] an interval segment division unit, configured to divide the RSSI value into N interval segments according to the critical RSSI value and the buffer length of the interval segment;

[0066] Wherein, N is a positive integer greater than or equal to 2.

[0067] Optionally, the second determining module includes:

[0068] an acquiring unit, configured to acquire the first RSSI value;

[0069] an optimization unit, configured to optimize the first RSSI value to obtain a first target RSSI value when the first RSSI value reaches a preset value;

[0070] The third determining unit is used to determine the interval to which the first target RSSI value belongs.

[0071] Optionally, the optimization module is specifically configured to:

[0072] Performing filtering processing on the preset number of first RSSI values by removing maximum and minimum values;

[0073] Performing mean processing on the first RSSI value after filtering to obtain the first target RSSI value.

[0074] Optionally, the third determining unit is specifically configured to:

[0075] When the first target RSSI value is less than a critical RSSI value included in the critical value, determining that the first target RSSI value belongs to a first interval;

[0076] When the first target RSSI value is greater than or equal to the critical RSSI value included in the critical value and less than or equal to the sum of the critical RSSI value and the buffer length of the interval, determine that the first target RSSI value belongs to the second interval;

[0077] When the first target RSSI value is greater than the sum of the critical RSSI value included in the critical value and the buffer length of the interval segment, it is determined that the first target RSSI value belongs to the third interval segment.

[0078] Optionally, when the ranging model includes: a logarithmic distance path loss model and a Gaussian distance path loss model, the third determining module includes:

[0079] a fourth determining unit, configured to determine the signal transmission distance by using the logarithmic distance path loss model when the first target RSSI value belongs to the first interval;

[0080] A fifth determining unit is configured to determine the signal transmission distance by using a logarithmic distance path loss model and a Gaussian distance path loss model when the first target RSSI value belongs to the second interval;

[0081] The sixth determining unit is configured to determine the signal transmission distance by using the Gaussian distance path loss model when the first target RSSI value belongs to the third interval.

[0082] Optionally, the model building module further includes:

[0083] The seventh determination unit is used to obtain the RSSI value, the path loss index value and the Gaussian random variable value obtained when the distance between the signal receiving sensor and the signal transmitting sensor in the logarithmic distance path loss model is the first preset distance based on the test data collected by the signal transmitting sensor with different transmission powers in a test site of a first preset size.

[0084] Optionally, the model building module further includes:

[0085] The eighth determination unit is used to obtain the value of the farthest signal transmission distance that the signal receiving sensor can receive the signal in the Gaussian distance path loss model, the minimum RSSI value that the signal receiving sensor can receive the signal, the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is the second preset distance, and the value of the Gaussian random variable based on the test data collected by the signal transmitting sensor with different transmission powers in the test site of the second preset size.

[0086] An embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the RSSI ranging method as described above.

[0087] An embodiment of the present invention further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the RSSI ranging method as described above are implemented.

[0088] The beneficial effects of the present invention are:

[0089] The solution of the present invention establishes a ranging model; determines a critical value of a received signal according to the ranging model; divides a received signal strength indication RSSI value into multiple intervals according to the critical value, thereby dividing the RSSI value into multiple intervals according to the ranging model; determines the interval to which an acquired first RSSI value belongs; and determines a signal transmission distance between a signal transmitting sensor and a signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model; wherein the signal receiving sensor is used to obtain the first RSSI value, thereby obtaining a signal transmission distance according to different intervals and ranging models, reducing the influence of environmental factors on the RSSI value, and improving measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 Flowchart 1 of the RSSI ranging method provided by an embodiment of the present invention;

[0091] Figure 2A flowchart showing the calculation of the signal transmission distance provided by an embodiment of the present invention;

[0092] Figure 3 Flowchart 2 showing the RSSI ranging method provided by an embodiment of the present invention;

[0093] Figure 4 A schematic diagram showing the structure of an RSSI ranging device provided by an embodiment of the present invention;

[0094] Figure 5 A schematic diagram showing the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0095] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0096] First, the existing ranging method is described as follows:

[0097] The commonly used ranging model algorithm generally adopts the logarithmic distance path loss model algorithm, as shown below:

[0098] y=10 ((A-x) / (10×η))

[0099] Where y is the signal transmission distance between the transmitting and receiving sensors; x is the RSSI value obtained by the corresponding receiving sensor; A is the signal intensity indicator received by the receiving sensor at a distance of 1 meter from the receiving sensor. It represents the impact of the transmitting sensor's transmission power on the model algorithm and is related to the actual environment; η is the path loss exponent, which is also related to the actual environment. The model algorithm parameters A and η are related to the actual environment and are often empirically determined based on different environments. Compared to ideal environments, real environments are subject to many interference factors, such as reflection, diffraction, refraction, and scattering of wireless signals. As a result, A and η will vary in different environments. Even in the same environment, A and η may vary at different locations. Using the same A and η parameters in different environments will result in exponentially increased ranging accuracy errors.

[0100] The present invention aims to solve the problem of large ranging error in the prior art due to the influence of environmental factors and provides an RSSI ranging method, device and electronic equipment.

[0101] like Figure 1 As shown, an embodiment of the present invention provides an RSSI ranging method, including:

[0102] Step 101: Establish a ranging model.

[0103] In the embodiment of the present invention, a distance measurement model is first established to facilitate subsequent determination of the distance between the signal transmitting sensor and the signal receiving sensor based on the distance measurement model.

[0104] Step 102: Determine a critical value of a received signal according to the ranging model.

[0105] In an embodiment of the present invention, a critical value corresponding to a critical point is determined based on a ranging model. The critical point is the point at which the signal receiving sensor is farthest from the signal transmitting sensor while still being able to receive the signal. The critical value is the minimum RSSI value (critical RSSI value) and the farthest signal transmission distance (critical signal transmission distance) corresponding to the critical point.

[0106] Step 103: Divide the received signal strength indication RSSI value into multiple intervals according to the critical value.

[0107] In an embodiment of the present invention, the RSSI value is divided into multiple intervals according to the critical value, so as to facilitate the subsequent determination of the signal transmission distance according to different intervals and ranging models. By dividing the RSSI value into multiple intervals, large measurement errors caused by environmental factors can be avoided.

[0108] It should be noted that the multiple intervals are two or more intervals.

[0109] Step 104: Determine the interval to which the acquired first RSSI value belongs.

[0110] In an embodiment of the present invention, after the interval segments are divided, the interval segment described by the first RSSI value obtained by the signal receiving sensor is determined, so as to facilitate the subsequent selection of the corresponding ranging model or algorithm based on the interval segment to calculate the signal transmission distance, thereby reducing the influence of environmental factors on the measurement error.

[0111] Step 105: determining a signal transmission distance between a signal transmitting sensor and a signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model;

[0112] The signal receiving sensor is used to obtain the first RSSI value.

[0113] In an embodiment of the present invention, after determining the interval to which the first RSSI value belongs, the signal transmission distance is determined according to the ranging model. Different intervals and ranging models can be implemented to obtain the signal transmission distance and improve measurement accuracy.

[0114] Optionally, the ranging model includes at least one of the following:

[0115] Logarithmic distance path loss model and Gaussian distance path loss model.

[0116] In the embodiment of the present invention, the established ranging models mainly include two types: a logarithmic distance path loss model and a Gaussian distance path loss model.

[0117] The ranging model in the embodiment of the present invention may include any one or all of the above two models.

[0118] Optionally, the formula of the logarithmic distance path loss model is:

[0119]

[0120] Wherein, y is the signal transmission distance, x is the RSSI value, A is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, η is the path loss index, and x is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance. δ is a Gaussian random variable.

[0121] The calculation formula of the logarithmic distance path loss model provided in the embodiment of the present invention is as follows:

[0122]

[0123] Wherein, y is the signal transmission distance between the signal transmitting sensor and the signal receiving sensor, x is the RSSI value obtained by the corresponding signal receiving sensor, A is the RSSI value when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, optionally, the first preset distance is 1 meter, η is the path loss index, and x is the RSSI value obtained by the corresponding signal receiving sensor. δ is a Gaussian random variable that describes the transmission loss noise of a specific path, with a mean of zero and a variance of δ.

[0124] Optionally, the formula of the Gaussian distance path loss model is:

[0125]

[0126] Where y is the signal transmission distance, x is the RSSI value, a is the farthest signal transmission distance at which the signal receiving sensor can receive the signal, b is the minimum RSSI value at which the signal receiving sensor can receive the signal, and x is the maximum RSSI value at which the signal receiving sensor can receive the signal. δ is a Gaussian random variable, c is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, and e is a natural exponent.

[0127] The Gaussian distance path loss model provided in the embodiment of the present invention is calculated using the following formula:

[0128]

[0129] Where y is the signal transmission distance between the signal transmitting sensor and the signal receiving sensor, x is the RSSI value obtained by the corresponding signal receiving sensor, a is the farthest signal transmission distance that the signal receiving sensor can receive the signal, b is the minimum RSSI value that the signal receiving sensor can receive the signal, and x is the RSSI value that the signal receiving sensor can receive the signal. δ is a Gaussian random variable used to describe the transmission loss noise of a specific path. Its mean is zero and its variance is δ. c is the RSSI value when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance. Optionally, the second preset distance is 1 meter. e is the natural exponent.

[0130] Optionally, when the ranging model includes a Gaussian distance path loss model, determining the critical value of the received signal according to the ranging model includes:

[0131] Determining a critical RSSI value in the critical value according to a minimum RSSI value and a Gaussian random variable in the Gaussian distance path loss model;

[0132] The critical signal transmission distance in the critical value is determined according to the farthest signal transmission distance in the Gaussian distance path loss model.

[0133] In the embodiment of the present invention, the critical value is determined by first finding the derivative of the Gaussian distance path loss model formula, as shown in the following formula:

[0134]

[0135] Where y is the signal transmission distance between the signal transmitting sensor and the signal receiving sensor, x is the RSSI value obtained by the corresponding signal receiving sensor, a is the farthest signal transmission distance that the signal receiving sensor can receive the signal, b is the minimum RSSI value that the signal receiving sensor can receive the signal, and x is the RSSI value that the signal receiving sensor can receive the signal. δ is a Gaussian random variable used to describe the transmission loss noise of a specific path. Its mean is zero and its variance is δ. c is the RSSI value when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance. Optionally, the second preset distance is 1 meter. e is the natural exponent.

[0136] Then, by the following formula:

[0137]

[0138] The minimum RSSI value at the critical point (critical RSSI value) and the maximum signal transmission distance at the critical point (critical signal transmission distance) are calculated as follows:

[0139]

[0140] Where x is the minimum RSSI value at the critical point (critical RSSI value), which is equal to the difference between the minimum RSSI value at which the signal receiving sensor in the Gaussian distance path loss model can receive the signal and the Gaussian random variable. y is the maximum signal transmission distance at the critical point (critical signal transmission distance), which is equal to the maximum signal transmission distance at which the signal receiving sensor in the Gaussian distance path loss model can receive the signal.

[0141] Optionally, dividing the received signal strength indication RSSI value into a plurality of interval segments according to the critical value includes:

[0142] Dividing the RSSI value into N intervals according to the critical RSSI value and the buffer length of the interval;

[0143] Wherein, N is a positive integer greater than or equal to 2.

[0144] Optionally, N is 3.

[0145] In the embodiment of the present invention, the RSSI value is divided into three intervals according to the critical RSSI value in the critical value and the buffer length of the interval, as shown in the following formula:

[0146]

[0147] in, is the buffer length of the interval segment. key1 and key2 are the boundary points of the three interval segments respectively. The key1 value is equal to the difference between the minimum RSSI value of the signal receiving sensor that can receive the signal in the Gaussian distance path loss model and the Gaussian random variable (critical RSSI value). The key2 value is the sum of the key1 value and the buffer length of the interval segment.

[0148] Optionally, determining the interval to which the acquired first RSSI value belongs includes:

[0149] Obtaining the first RSSI value;

[0150] When the first RSSI value reaches a preset value, optimizing the first RSSI value to obtain a first target RSSI value;

[0151] Determine the interval to which the first target RSSI value belongs.

[0152] In an embodiment of the present invention, the signal receiving sensor receives a first RSSI value and then saves the first RSSI value in an RSSI cache queue rssi_data_list. When the length of the data cache queue reaches a preset requirement n, that is, the number of first RSSI values reaches n, further optimization can be performed. After that, the interval segment to which the optimized first target RSSI value belongs is determined, which can improve the ranging accuracy.

[0153] Preferably, in an embodiment of the present invention, test data is collected in a test field with a size of 110 meters by using signal transmission sensors with various transmission powers, and it is found that the length n of the data cache queue is 20, which is optimal.

[0154] Optionally, optimizing the first RSSI value to obtain a first target RSSI value includes:

[0155] Performing filtering processing on the preset number of first RSSI values by removing maximum and minimum values;

[0156] Performing mean processing on the first RSSI value after filtering to obtain the first target RSSI value.

[0157] In an embodiment of the present invention, when optimizing the first RSSI value, the data in the data cache queue is filtered using the Dixon test method to remove abnormal values in the first RSSI data, wherein the abnormal values are the maximum value and the minimum value. Then, the filtered first RSSI value is subjected to mean filtering. The mean filtering formula for the first RSSI value is as follows:

[0158]

[0159] in, is the first target RSSI value, rssi1, rssi2, rssi3...rssi k is the data in the data cache queue after the outliers in the first RSSI data are removed by performing Dixon test filtering, and k is the number of data in the data cache queue after the outliers in the first RSSI data are removed by performing Dixon test filtering.

[0160] Optionally, determining the interval to which the first target RSSI value belongs includes:

[0161] When the first target RSSI value is less than a critical RSSI value included in the critical value, determining that the first target RSSI value belongs to a first interval;

[0162] When the first target RSSI value is greater than or equal to the critical RSSI value included in the critical value and less than or equal to the sum of the critical RSSI value and the buffer length of the interval, determine that the first target RSSI value belongs to the second interval;

[0163] When the first target RSSI value is greater than the sum of the critical RSSI value included in the critical value and the buffer length of the interval segment, it is determined that the first target RSSI value belongs to the third interval segment.

[0164] In the embodiment of the present invention, the RSSI value is divided into three intervals according to the buffer length of the critical RSSI value interval, and then the first target RSSI value is determined. The interval segment to which it belongs is first As the key value, it is compared with the three interval segments. The judgment method adopts the binary search method. First, use Compared with [key1, key2], if the following

[0165]

[0166] If established, then judge Belongs to the second interval [key1, key2], that is, when the first target RSSI value is greater than or equal to the critical RSSI value and less than or equal to the sum of the critical RSSI value and the buffer length of the interval, it is determined that the first target RSSI value belongs to the second interval; otherwise, Compared with (key2, ~), if the following

[0167]

[0168] If established, it is determined Belongs to the third interval (key2, ~), that is, when the first target RSSI value is greater than the sum of the critical RSSI value and the buffer length of the interval, it is determined that the first target RSSI value belongs to the third interval, otherwise, it is determined Belonging to the first interval (~, key1), that is, when the first target RSSI value is less than the critical RSSI value, the first target RSSI value belongs to the first interval.

[0169] Optionally, when the ranging model includes: a logarithmic distance path loss model and a Gaussian distance path loss model, determining the signal transmission distance between the signal transmitting sensor and the signal receiving sensor according to the interval segment to which the first RSSI value belongs and the ranging model includes:

[0170] When the first target RSSI value belongs to the first interval, determining the signal transmission distance by using the logarithmic distance path loss model;

[0171] When the first target RSSI value belongs to the second interval, determining the signal transmission distance by using a logarithmic distance path loss model and the Gaussian distance path loss model;

[0172] When the first target RSSI value belongs to the third interval, the signal transmission distance is determined by using the Gaussian distance path loss model.

[0173] The following combination Figure 2 , specifically explains that when the ranging model includes a logarithmic distance path loss model and a Gaussian distance path loss model, the corresponding ranging model is selected according to different interval segments to obtain the process of the signal transmission distance.

[0174] If the first target RSSI value Belonging to the first interval (~, key1), the logarithmic path loss model is used for calculation The corresponding signal transmission distance is taken as the optimal signal transmission distance; if Belonging to the second interval [key1, key2], the logarithmic path loss model is first used to calculate The corresponding first signal transmission distance y1 is then calculated using the Gaussian distance path loss model The corresponding second signal transmission distance y2, and then use the mean filter formula y = (y1 + y2) / 2 to get the final The corresponding signal transmission distance y is taken as the optimal signal transmission distance; if Belonging to the third interval (key2, ~), the Gaussian distance path loss model is used for calculation The corresponding signal transmission distance is taken as the optimal signal transmission distance.

[0175] The following combination Figure 3 , specifically describes the process of the RSSI ranging method of an embodiment of the present invention.

[0176] Establish a ranging model, which includes a logarithmic distance path loss model and a Gaussian distance path loss model. Determine the critical value parameter of the critical point according to the Gaussian distance path loss model. Divide the RSSI value into three intervals according to the critical value and the buffer length of the interval. Receive the first RSSI value, optimize the first RSSI value, obtain the first target RSSI value, determine the interval where the first target RSSI value is located, select the corresponding ranging model according to the interval, calculate the signal transmission distance, and determine whether it is ended. If not, return to the step of receiving the first RSSI value.

[0177] Optionally, the method further includes:

[0178] The test data collected by the signal transmitting sensor with different transmission powers in a test site of a first preset size are used to obtain the RSSI value, the path loss index value and the Gaussian random variable value obtained when the distance between the signal receiving sensor and the signal transmitting sensor in the logarithmic distance path loss model is the first preset distance.

[0179] In an embodiment of the present invention, by collecting test data in advance using signal transmission sensors with multiple different transmission powers in a test field of a first preset size, the general parameters of the logarithmic distance path loss model can be fitted, as shown in the following formula:

[0180]

[0181] Wherein, y is the signal transmission distance between the signal transmitting sensor and the signal receiving sensor, x is the RSSI value obtained by the corresponding signal receiving sensor, A is the RSSI value when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, optionally, the first preset distance is 1 meter, η is the path loss index, and x is the RSSI value obtained by the corresponding signal receiving sensor. δ is a Gaussian random variable.

[0182] Optionally, the first preset size is 110 meters.

[0183] Optionally, the method further includes:

[0184] The test data collected by the signal transmitting sensor with different transmission powers in the test site of the second preset size are used to obtain the value of the farthest signal transmission distance that the signal receiving sensor can receive the signal in the Gaussian distance path loss model, the minimum RSSI value that the signal receiving sensor can receive the signal, the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is the second preset distance, and the value of the Gaussian random variable.

[0185] In an embodiment of the present invention, by collecting test data in advance using signal transmission sensors with multiple different transmission powers in a test field of a second preset size, the general parameters of the Gaussian distance path loss model can be fitted, as shown in the following formula:

[0186]

[0187] Where y is the signal transmission distance between the signal transmitting sensor and the signal receiving sensor, x is the RSSI value obtained by the corresponding signal receiving sensor, a is the farthest signal transmission distance that the signal receiving sensor can receive the signal, b is the minimum RSSI value that the signal receiving sensor can receive the signal, and x is the RSSI value that the signal receiving sensor can receive the signal. δ is a Gaussian random variable, c is the RSSI value when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, optionally, the second preset distance is 1 meter, and e is a natural exponent.

[0188] Optionally, in an embodiment of the present invention, by collecting test data in advance using signal transmission sensors with multiple different transmission powers in a test field of a second preset size, a universal parameter for dividing the interval segments can be fitted, as shown in the following formula:

[0189]

[0190] in, is the buffer length of the interval segment, key1 and key2 are the boundary points of the three interval segments respectively, the key1 value is equal to the difference between the minimum RSSI value of the signal receiving sensor that can receive the signal in the Gaussian distance path loss model and the Gaussian random variable (critical RSSI value), the key2 value is the sum of the key1 value and the buffer length of the interval segment, b is the minimum RSSI value of the signal receiving sensor that can receive the signal in the Gaussian distance path loss model, x δ The Gaussian distance path loss model is a Gaussian random variable.

[0191] In an embodiment of the present invention, a critical value is determined based on a Gaussian distance path loss model in a ranging model, the RSSI value is divided into three interval segments according to the critical value, the received first RSSI value is optimized, the interval segment to which the first target RSSI value belongs is determined, the corresponding ranging model is selected according to the interval segment, and the signal transmission distance is calculated. This can reduce the influence of environmental factors and improve measurement accuracy. Moreover, by collecting data in an actual environment and fitting universal parameters, the ranging accuracy can be further improved.

[0192] like Figure 4 As shown, an embodiment of the present invention further provides an RSSI ranging device, comprising:

[0193] Model building module 401, used to build a distance measurement model;

[0194] A first determining module 402 is configured to determine a critical value of a received signal according to the ranging model;

[0195] An interval segment division module 403 is configured to divide the received signal strength indication RSSI value into a plurality of interval segments according to the critical value;

[0196] A second determining module 404 is configured to determine the interval to which the acquired first RSSI value belongs;

[0197] A third determining module 405 is configured to determine a signal transmission distance between a signal transmitting sensor and a signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model;

[0198] The signal receiving sensor is used to obtain the first RSSI value.

[0199] In an embodiment of the present invention, a ranging model is established; a critical value of a received signal is determined according to the ranging model; a received signal strength indication RSSI value is divided into multiple intervals according to the critical value, so that the RSSI value can be divided into multiple intervals according to the ranging model; the interval to which the obtained first RSSI value belongs is determined; and a signal transmission distance between a signal transmitting sensor and a signal receiving sensor is determined according to the interval to which the first RSSI value belongs and the ranging model; wherein the signal receiving sensor is used to obtain the first RSSI value, so that the signal transmission distance can be obtained according to different intervals and ranging models, thereby improving measurement accuracy.

[0200] Optionally, the ranging model includes at least one of the following:

[0201] Logarithmic distance path loss model and Gaussian distance path loss model.

[0202] Optionally, the formula of the logarithmic distance path loss model is:

[0203]

[0204] Wherein, y is the signal transmission distance, x is the RSSI value, A is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, η is the path loss index, and x is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance. δ is a Gaussian random variable.

[0205] Optionally, the formula of the Gaussian distance path loss model is:

[0206]

[0207] Where y is the signal transmission distance, x is the RSSI value, a is the farthest signal transmission distance at which the signal receiving sensor can receive the signal, b is the minimum RSSI value at which the signal receiving sensor can receive the signal, and x is the maximum RSSI value at which the signal receiving sensor can receive the signal. δ is a Gaussian random variable, c is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, and e is a natural exponent.

[0208] Optionally, when the ranging model includes a Gaussian distance path loss model, the first determining module 402 includes:

[0209] a first determining unit, configured to determine a critical RSSI value in the critical value according to a minimum RSSI value and a Gaussian random variable in the Gaussian distance path loss model;

[0210] The second determining unit is configured to determine a critical signal transmission distance in the critical value according to a maximum signal transmission distance in the Gaussian distance path loss model.

[0211] Optionally, the interval segment division module 403 includes:

[0212] an interval segment division unit, configured to divide the RSSI value into N interval segments according to the critical RSSI value and the buffer length of the interval segment;

[0213] Wherein, N is a positive integer greater than or equal to 2.

[0214] Optionally, the second determining module 404 includes:

[0215] an acquiring unit, configured to acquire the first RSSI value;

[0216] an optimization unit, configured to optimize the first RSSI value to obtain a first target RSSI value when the first RSSI value reaches a preset value;

[0217] The third determining unit is used to determine the interval to which the first target RSSI value belongs.

[0218] Optionally, the third determining unit is specifically configured to:

[0219] When the first target RSSI value is less than a critical RSSI value included in the critical value, determining that the first target RSSI value belongs to a first interval;

[0220] When the first target RSSI value is greater than or equal to the critical RSSI value included in the critical value and less than or equal to the sum of the critical RSSI value and the buffer length of the interval, determine that the first target RSSI value belongs to the second interval;

[0221] When the first target RSSI value is greater than the sum of the critical RSSI value included in the critical value and the buffer length of the interval segment, it is determined that the first target RSSI value belongs to the third interval segment.

[0222] Optionally, the optimization module is specifically configured to:

[0223] Performing filtering processing on the preset number of first RSSI values by removing maximum and minimum values;

[0224] Performing mean processing on the first RSSI value after filtering to obtain the first target RSSI value.

[0225] Optionally, when the ranging model includes: a logarithmic distance path loss model and a Gaussian distance path loss model, the third determining module 405 includes:

[0226] a fourth determining unit, configured to determine the signal transmission distance by using the logarithmic distance path loss model when the first target RSSI value belongs to the first interval;

[0227] a fifth determining unit, configured to determine the signal transmission distance by using a logarithmic distance path loss model and a Gaussian distance path loss model when the first target RSSI value belongs to a second interval;

[0228] A sixth determining unit is configured to determine the signal transmission distance by using the Gaussian distance path loss model when the first target RSSI value belongs to a third interval.

[0229] Optionally, the model building module 401 further includes:

[0230] The seventh determination unit is used to obtain the RSSI value, the path loss index value and the Gaussian random variable value obtained when the distance between the signal receiving sensor and the signal transmitting sensor in the logarithmic distance path loss model is the first preset distance based on the test data collected by the signal transmitting sensor with different transmission powers in a test site of a first preset size.

[0231] Optionally, the model building module 401 further includes:

[0232] The eighth determination unit is used to obtain the value of the farthest signal transmission distance that the signal receiving sensor can receive the signal in the Gaussian distance path loss model, the minimum RSSI value that the signal receiving sensor can receive the signal, the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is the second preset distance, and the value of the Gaussian random variable based on the test data collected by the signal transmitting sensor with different transmission powers in the test site of the second preset size.

[0233] It should be noted that the RSSI ranging device of the embodiment of the present invention is a device capable of executing the above-mentioned RSSI ranging method, and all embodiments of the above-mentioned RSSI ranging method are applicable to the device and can achieve the same or similar technical effects.

[0234] like Figure 5As shown, an embodiment of the present invention also provides an electronic device, including: a processor 500; and a memory 510 connected to the processor 500 through a bus interface, wherein the memory 510 is used to store programs and data used by the processor 500 when performing operations, and the processor 500 calls and executes the programs and data stored in the memory 510.

[0235] The electronic device further includes a transceiver 520 , which is connected to the bus interface and is used to receive and send data under the control of the processor 500 ; the processor 500 is used to read the program in the memory 510 .

[0236] Specifically, the processor 500 is used to establish a ranging model; and, based on the ranging model, determine a critical value of the received signal; and, based on the critical value, divide the received signal strength indication RSSI value into multiple interval segments; and, determine the interval segment to which the obtained first RSSI value belongs; and, based on the interval segment to which the first RSSI value belongs and the ranging model, determine the signal transmission distance between the signal transmitting sensor and the signal receiving sensor; wherein, the signal receiving sensor is used to obtain the first RSSI value.

[0237] Optionally, the ranging model includes at least one of the following:

[0238] Logarithmic distance path loss model and Gaussian distance path loss model.

[0239] Optionally, the formula of the logarithmic distance path loss model is:

[0240]

[0241] Wherein, y is the signal transmission distance, x is the RSSI value, A is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, η is the path loss index, and x is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance. δ is a Gaussian random variable.

[0242] Optionally, the formula of the Gaussian distance path loss model is:

[0243]

[0244] Where y is the signal transmission distance, x is the RSSI value, a is the farthest signal transmission distance at which the signal receiving sensor can receive the signal, b is the minimum RSSI value at which the signal receiving sensor can receive the signal, and x is the maximum RSSI value at which the signal receiving sensor can receive the signal. δ is a Gaussian random variable, c is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, and e is a natural exponent.

[0245] Optionally, the processor 500 is specifically used to determine the critical RSSI value in the critical value based on the minimum RSSI value and the Gaussian random variable in the Gaussian distance path loss model; and to determine the critical signal transmission distance in the critical value based on the farthest signal transmission distance in the Gaussian distance path loss model.

[0246] Optionally, the processor 500 is specifically configured to divide the RSSI value into N interval segments according to the critical RSSI value and the buffer length of the interval segment;

[0247] Wherein, N is a positive integer greater than or equal to 2.

[0248] Optionally, the transceiver 520 is configured to obtain the first RSSI value;

[0249] Optionally, the processor 500 is specifically configured to optimize the first RSSI value to obtain a first target RSSI value when the first RSSI value reaches a preset number; and determine the interval to which the first target RSSI value belongs.

[0250] Optionally, the processor 500 is specifically configured to perform filtering processing on the preset number of first RSSI values to remove maximum and minimum values; and perform averaging processing on the filtered first RSSI values to obtain the first target RSSI value.

[0251] Optionally, the processor 500 is specifically used to determine that the first target RSSI value belongs to the first interval segment when the first target RSSI value is less than the critical RSSI value included in the critical value; and, when the first target RSSI value is greater than or equal to the critical RSSI value included in the critical value, and less than or equal to the sum of the critical RSSI value and the buffer length of the interval segment, determine that the first target RSSI value belongs to the second interval segment; and, when the first target RSSI value is greater than the sum of the critical RSSI value included in the critical value and the buffer length of the interval segment, determine that the first target RSSI value belongs to the third interval segment.

[0252] Optionally, the processor 500 is specifically used to determine the signal transmission distance by using the logarithmic distance path loss model when the first target RSSI value belongs to the first interval segment; and, when the first target RSSI value belongs to the second interval segment, determine the signal transmission distance by using the logarithmic distance path loss model and the Gaussian distance path loss model; and, when the first target RSSI value belongs to the third interval segment, determine the signal transmission distance by using the Gaussian distance path loss model.

[0253] Optionally, the processor 500 is further specifically used to obtain the RSSI value, the path loss index value and the Gaussian random variable value obtained when the distance between the signal receiving sensor and the signal transmitting sensor in the logarithmic distance path loss model is a first preset distance through the test data collected by the signal transmitting sensor with different transmission powers in a test site of a first preset size.

[0254] Optionally, the processor 500 is also specifically used to obtain the value of the farthest signal transmission distance that the signal receiving sensor can receive the signal in the Gaussian distance path loss model, the minimum RSSI value of the signal receiving sensor that can receive the signal, the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, and the value of the Gaussian random variable through the test data collected by the signal transmitting sensor with different transmission powers in a test site of a second preset size.

[0255] Among them, Figure 5 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 500 and memory represented by memory 510. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 520 may be a plurality of components, i.e., a transmitter and a transceiver, providing a unit for communicating with various other devices on a transmission medium. For different terminals, the user interface 530 may also be an interface capable of connecting external or internal devices as required, including but not limited to a keypad, display, speaker, microphone, joystick, etc. The processor 500 is responsible for managing the bus architecture and general processing, and the memory 510 may store data used by the processor 500 when performing operations.

[0256] An embodiment of the present invention further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the RSSI ranging method as described above are implemented.

[0257] In addition, it should be noted that, in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0258] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0259] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary personnel in this technical field, several improvements and modifications can be made without departing from the principles described in the present invention. These improvements and modifications are also within the scope of protection of the present invention.

Claims

1. An RSSI ranging method, characterized in that: include: Establishing a ranging model; Determine, according to the ranging model, a critical value of the received signal, the critical value including a critical RSSI value and a critical signal transmission distance corresponding to a critical point, the critical RSSI value being the minimum RSSI value corresponding to the critical point, the critical signal transmission distance being the farthest signal transmission distance corresponding to the critical point, the critical point being the point at which the signal receiving sensor is farthest from the signal transmitting sensor while still being able to receive the signal; Dividing the received signal strength indication RSSI value into a plurality of interval segments according to the critical value; Determine the interval to which the obtained first RSSI value belongs; determining a signal transmission distance between a signal transmitting sensor and a signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model; The signal receiving sensor is used to obtain the first RSSI value; The RSSI value is divided into a plurality of intervals according to the critical value, including: Divide the RSSI value into N intervals according to the critical RSSI value and the buffer length of the interval, where N is a positive integer greater than or equal to 3; The step of determining the interval to which the acquired first RSSI value belongs includes: Determining a first target RSSI value interval, wherein the first target RSSI value is obtained by optimizing the first RSSI value; The step of determining the interval to which the first target RSSI value belongs includes: When the first target RSSI value is less than a critical RSSI value included in the critical value, determining that the first target RSSI value belongs to a first interval; When the first target RSSI value is greater than or equal to the critical RSSI value included in the critical value and less than or equal to the sum of the critical RSSI value and the buffer length of the interval, determine that the first target RSSI value belongs to the second interval; When the first target RSSI value is greater than the sum of the critical RSSI value included in the critical value and the buffer length of the interval segment, determining that the first target RSSI value belongs to the third interval segment; The ranging model includes: Logarithmic distance path loss model and Gaussian distance path loss model; The determining of the signal transmission distance between the signal transmitting sensor and the signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model includes: When the first target RSSI value belongs to the first interval, determining the signal transmission distance by using the logarithmic distance path loss model; When the first target RSSI value belongs to the second interval, determining the signal transmission distance by using a logarithmic distance path loss model and the Gaussian distance path loss model; When the first target RSSI value belongs to the third interval, the signal transmission distance is determined by using the Gaussian distance path loss model.

2. The RSSI ranging method according to claim 1, wherein: The formula for the logarithmic distance path loss model is: Wherein, y is the signal transmission distance, x is the RSSI value, A is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance, η is the path loss index, and x is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a first preset distance. δ is a Gaussian random variable.

3. The RSSI ranging method according to claim 1, wherein: The formula of the Gaussian distance path loss model is: Where y is the signal transmission distance, x is the RSSI value, a is the farthest signal transmission distance at which the signal receiving sensor can receive the signal, b is the minimum RSSI value at which the signal receiving sensor can receive the signal, and x is the maximum RSSI value at which the signal receiving sensor can receive the signal. δ is a Gaussian random variable, c is the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is a second preset distance, and e is a natural exponent.

4. The RSSI ranging method according to claim 1, wherein: Determining the critical value of the received signal according to the ranging model includes: Determining a critical RSSI value in the critical value according to a minimum RSSI value and a Gaussian random variable in the Gaussian distance path loss model; The critical signal transmission distance in the critical value is determined according to the farthest signal transmission distance in the Gaussian distance path loss model.

5. The RSSI ranging method according to claim 1, wherein: The determining the interval to which the acquired first RSSI value belongs includes: Obtaining the first RSSI value; When the first RSSI value reaches a preset value, optimizing the first RSSI value to obtain a first target RSSI value; Determine the interval to which the first target RSSI value belongs.

6. The RSSI ranging method according to claim 5, wherein: The optimizing the first RSSI value to obtain a first target RSSI value includes: Performing filtering processing on the preset number of first RSSI values by removing maximum and minimum values; Performing mean processing on the first RSSI value after filtering to obtain the first target RSSI value.

7. The RSSI ranging method according to claim 2, wherein: The method further comprises: The test data collected by the signal transmitting sensor with different transmission powers in a test site of a first preset size are used to obtain the RSSI value, the path loss index value and the Gaussian random variable value obtained when the distance between the signal receiving sensor and the signal transmitting sensor in the logarithmic distance path loss model is the first preset distance.

8. The RSSI ranging method according to claim 3, wherein: The method further comprises: The test data collected by the signal transmitting sensor with different transmission powers in the test site of the second preset size are used to obtain the value of the farthest signal transmission distance that the signal receiving sensor can receive the signal in the Gaussian distance path loss model, the minimum RSSI value that the signal receiving sensor can receive the signal, the RSSI value obtained when the distance between the signal receiving sensor and the signal transmitting sensor is the second preset distance, and the value of the Gaussian random variable.

9. An RSSI ranging device, characterized in that: include: Model building module, used to build ranging model; A first determination module is configured to determine a critical value of a received signal according to the ranging model, the critical value including a critical RSSI value and a critical signal transmission distance corresponding to a critical point, the critical RSSI value being a minimum RSSI value corresponding to the critical point, the critical signal transmission distance being a maximum signal transmission distance corresponding to the critical point, and the critical point being the point at which the signal receiving sensor is farthest from the signal transmitting sensor while still being able to receive the signal; An interval segment division module is used to divide the received signal strength indication RSSI value into multiple interval segments according to the critical value; A second determining module is used to determine the interval to which the acquired first RSSI value belongs; a third determining module, configured to determine a signal transmission distance between a signal transmitting sensor and a signal receiving sensor according to the interval to which the first RSSI value belongs and the ranging model; The signal receiving sensor is used to obtain the first RSSI value; The interval segment division module includes: an interval segment division unit, configured to divide the RSSI value into N interval segments according to the critical RSSI value and the buffer length of the interval segment, where N is a positive integer greater than or equal to 3; The second determining module includes: a third determining unit, configured to determine a range to which a first target RSSI value belongs, wherein the first target RSSI value is obtained by optimizing the first RSSI value; The third determining unit is specifically configured to: When the first target RSSI value is less than a critical RSSI value included in the critical value, determining that the first target RSSI value belongs to a first interval; When the first target RSSI value is greater than or equal to the critical RSSI value included in the critical value and less than or equal to the sum of the critical RSSI value and the buffer length of the interval, determine that the first target RSSI value belongs to the second interval; When the first target RSSI value is greater than the sum of the critical RSSI value included in the critical value and the buffer length of the interval segment, determining that the first target RSSI value belongs to the third interval segment; The ranging model includes: Logarithmic distance path loss model and Gaussian distance path loss model; Wherein, the third determination module includes: a fourth determining unit, configured to determine the signal transmission distance by using the logarithmic distance path loss model when the first target RSSI value belongs to the first interval; a fifth determining unit, configured to determine the signal transmission distance by using a logarithmic distance path loss model and a Gaussian distance path loss model when the first target RSSI value belongs to a second interval; A sixth determining unit is configured to determine the signal transmission distance by using the Gaussian distance path loss model when the first target RSSI value belongs to a third interval.

10. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of the RSSI ranging method according to any one of claims 1 to 8 are implemented.

11. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the RSSI ranging method according to any one of claims 1 to 8 are implemented.

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