CN114585082B - A wireless positioning method, device and storage medium for power Internet of Things equipment - Google Patents

Abstract

The invention discloses a wireless positioning method, a wireless positioning device and a storage medium of electric power internet of things equipment, and relates to the technical field of internet of things equipment communication. The wireless positioning method of the electric power internet of things equipment comprises the following steps: periodically acquiring wireless positioning signals sent by at least three positioning base stations; according to the wireless positioning signals acquired in each period, channel estimation is respectively carried out on the corresponding positioning base stations; according to the channel estimation result, calculating the distance difference between the Internet of things equipment and different positioning base stations; performing distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods; and calculating the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing. The method can be used for realizing accurate positioning of the nodes of the Internet of things.

Description

A wireless positioning method, device and storage medium for power Internet of Things equipment

Technical Field

The present invention relates to the field of electric power Internet of Things communication technology, and in particular to a method, a device and a storage medium for wireless positioning of electric power Internet of Things equipment.

Background technique

The power Internet of Things connects various types of power grid terminal equipment to exchange and communicate information, and realizes intelligent identification, positioning, tracking, monitoring and management around all links and departments of the power system. It can effectively integrate the infrastructure resources of the power and communication systems, improve the utilization efficiency of smart grid facilities, and provide technical support for the power grid.

In the power Internet of Things, high-precision positioning of node devices is of great significance. Through high-precision positioning technology, the location information of equipment or personnel, and even the activity trajectory, can be obtained in real time, which can improve the fault handling and emergency response capabilities of the power system, improve work efficiency, and reduce personnel and equipment losses. For example, when an equipment failure occurs, the location of the abnormal equipment can be found in time through positioning technology, which is convenient for rapid fault handling. In the inspection system, the working status of the inspection personnel can be tracked in real time. For example, by checking the inspection route, the inspection time of the designated inspection point, etc., it can be judged whether the inspection personnel patrol according to the requirements, and the inspection process can be visualized and intelligentized. In the event of an emergency, the control center can grasp the real-time location of personnel and equipment in the first time, dispatch and control in a timely and rapid manner, and provide road guidance assistance to managers.

Electric power IoT devices have special industry-specific requirements for positioning technology. Different from the application scenarios of conventional IoT positioning technology, the positioning technology of electric power IoT devices is restricted to varying degrees by many factors due to their special use environment and hardware characteristics. For example, since it is difficult to ensure openness in node deployment scenarios, channel conditions and communication conditions are often very complex and diverse. In addition, in the wild environment, nodes are usually powered by batteries, which have limited energy and are not easy to replenish, so positioning algorithms need to be used to save enough power. These characteristics have posed challenges to the positioning technology of the electric power IoT.

The accuracy of wireless positioning strongly depends on the measurement accuracy of the propagation delay of the ranging signal. In complex channel environments, the non-line of sight (NLOS) propagation of ranging signals will lead to large ranging errors, thereby reducing positioning accuracy. Identifying and compensating for NLOS ranging errors is a key technology to improve positioning accuracy.

There are many existing positioning technologies, such as those based on WiFi, Bluetooth, Zigbee, UWB and RFID. However, these technologies are mainly suitable for indoor positioning, and their limited coverage makes it difficult to meet the positioning needs of power IoT devices in a large area. Moreover, these technologies often require the deployment of more nodes, which is costly.

The positioning method based on time of arrival (TOA) is a method of obtaining the final position by measuring the propagation time of wireless signals and then calculating the distance. This method requires three or more base stations with known positions at the same time. The Observed Time Difference of Arrival (OTDOA) positioning method is to locate based on the time difference of wireless signal propagation from the terminal to multiple base stations. The positioning accuracy of the ODTOA method is higher than that of the method based on cell ID, but it is easily affected by the environment. In a complex ranging environment, the probability of signal propagation being blocked by non-line-of-sight NLOS obstacles is greatly increased. The signal is difficult to be effectively detected due to the power reduction caused by obstacles, resulting in a large NLOS ranging error. For example, the positioning accuracy in open areas such as suburbs and rural areas can reach ten meters, but in urban areas, due to the large number of tall buildings, it is difficult for radio wave signals to reach the terminal directly from the base station. Generally, they have to undergo complex refraction or reflection, which will affect the positioning accuracy. The positioning accuracy is about tens of meters to hundreds of meters.

Summary of the invention

The purpose of the present invention is to provide a wireless positioning method, device and storage medium for power Internet of Things equipment, which can realize accurate positioning of Internet of Things nodes. The technical solution adopted by the present invention is as follows.

On the one hand, the present invention provides a wireless positioning method for a power Internet of Things device, comprising:

Periodically acquiring wireless positioning signals sent by at least three positioning base stations;

According to the wireless positioning signals obtained in each period, channel estimation is performed for each positioning base station respectively;

According to the channel estimation results, calculate the distance difference between the IoT device and different positioning base stations;

Based on the distance differences obtained in multiple cycles, Kalman filtering is performed on the distance difference data;

The actual location of the IoT device is calculated based on the distance difference data processed by the Kalman filter.

Optionally, calculating the distance difference between the IoT device and different positioning base stations according to the channel estimation result includes:

According to the channel results, the propagation delay of each positioning base station in any combination of three positioning base stations is calculated;

Taking the propagation delay of any positioning base station in the combination as a benchmark, calculate the delay difference between the other two positioning base stations and the positioning base station as a benchmark;

The corresponding distance difference is calculated according to the time delay difference to obtain the distance difference between the IoT device and the reference positioning base station and the distance difference between the IoT device and the other two positioning base stations.

Optionally, the distance difference calculation formula is:

d _ij = c × τ _ij

Among _them , _dij represents the difference between the distance of the IoT device to positioning base station i and the distance to positioning base station j; c represents the propagation speed of radio waves in the air; _τij = _τi - _τjτij , represents the delay difference between the delay _τi of positioning base station i and the delay _τj of positioning base station j.

The above-mentioned process of channel estimation and obtaining the base station propagation delay according to the channel estimation result may adopt the existing technology.

Optionally, the distance difference obtained based on multiple cycles is subjected to Kalman filtering, including:

After obtaining the wireless positioning signal in each period, the distance difference data is calculated based on the wireless positioning signal of the period. For the obtained distance difference data, a Kalman filter process is performed based on the pre-built distance difference measurement model to obtain an updated true distance difference estimate;

After each Kalman filter processing, determine whether the Kalman filter algorithm converges. If it converges, the current estimated value of the actual distance difference is used as the actual distance difference. If it does not converge, continue the distance difference calculation and Kalman filter processing after obtaining the wireless positioning signal in the next cycle.

In the present invention, the basis for whether the Kalman filter algorithm converges can be that the iterative calculation reaches a set number of times, or the change in the true distance difference estimate after several updates is less than a preset threshold, which means that the result of the Kalman filter tends to be stable, that is, a stable and accurate distance difference estimate can be obtained.

Optionally, the pre-built distance difference measurement model is:

d _ij (k) = c _ij x _ij (k) + v _ij (k)

where _xij (k)= _aijxij (k-1)+ _wij (k- ₁ )

In the above formula, d _ij (k) represents the difference between the distance from the IoT device to the positioning base station i and the distance from the IoT device to the positioning base station j corresponding to the kth period; c _ij represents the measurement coefficient; and vi _ij (k) represents the measurement error, which has a mean of zero and a variance of Gaussian white noise; a _ij represents the state transition coefficient; w _ij (k-1) represents the excitation signal corresponding to the k-1th period, which has a mean of zero and a variance of /> Gaussian white noise; x _ij (k) represents the true value of the distance difference between the distance from the IoT device to the positioning base station i and the distance to the positioning base station j corresponding to the kth period;

The Kalman filter process is performed once to update the estimated value of the true value of the distance difference according to the following formula:

In the formula, represents the estimated value of the true value of the distance difference corresponding to the kth cycle, and _bij (k) represents the measurement weighting coefficient, which is expressed as the formula:

represents the variance of the measurement error,/> It represents the updated covariance one-step estimate when the Kalman filter is executed in the kth cycle, which is expressed as the formula:

p _ij (k-1) represents the updated covariance coefficient when the Kalman filter is executed in the k-1th cycle, represents the variance of the excitation signal w _ij .

Optionally, the steps of performing the Kalman filter processing in each cycle are:

Based on the covariance coefficient updated during the Kalman filter processing of the previous cycle, the covariance one-step estimate is updated;

Based on the updated covariance one-step estimate, update the measurement weight coefficient;

updating the covariance coefficients based on the updated covariance one-step estimate and the measurement weighting coefficients;

Based on the updated measurement weighting coefficient, the estimated value of the true value of the distance difference obtained by the Kalman filter processing in the previous cycle, and the distance difference calculated in the current cycle, the estimated value of the true value of the distance difference is updated.

Optionally, calculating the actual position of the IoT device according to the distance difference data processed by Kalman filtering includes:

Determine whether the number of positioning base stations of the wireless positioning signal sending end is equal to 3 or greater than 3;

If the number of positioning base stations is equal to 3, the location of the IoT device is calculated based on the two distance difference data obtained;

If the number of positioning base stations is greater than 3, the positioning base stations are grouped to determine the two distance difference data corresponding to each positioning base station combination;

For each positioning base station combination, the location of the IoT device is calculated based on the corresponding distance difference;

According to the positional relationship between each positioning base station and the IoT device in each positioning base station combination, the positioning base station combination with the best layout is determined according to the preset optimal layout selection strategy;

The location of the IoT device calculated based on the optimal positioning base station combination is used as the actual location of the IoT device.

In the above scheme, existing technology can be used to calculate the location of the IoT device based on the distance difference.

Optionally, the determining of the positioning base station combination with the best layout according to a preset optimal layout selection strategy based on the position relationship between each positioning base station in each positioning base station combination and the Internet of Things device includes:

For any positioning base station combination, according to the calculated location of the IoT device, the angle between the location point of the IoT device and the location points of each base station in the combination is calculated, the maximum angle therein is determined, and the maximum angle corresponding to each positioning base station combination is obtained;

The maximum angles corresponding to all positioning base station combinations are compared, and the positioning base station combination with the smallest maximum angle is selected as the positioning base station combination with the optimal layout.

Optionally, the positioning base station is a positioning base station with a positioning function, or an Internet of Things device with a positioning function, and the positioning function is capable of locating its own position information and sending a wireless positioning signal;

The wireless positioning signal is an ODTOA wireless positioning signal.

In a second aspect, the present invention provides a wireless positioning device for a power Internet of Things device, comprising:

A positioning signal acquisition module, configured to periodically acquire wireless positioning signals sent by at least three positioning base stations;

The channel estimation module is configured to perform channel estimation for each positioning base station according to the wireless positioning signal acquired in each period;

The distance difference calculation module is configured to calculate the distance difference between the IoT device and different positioning base stations according to the channel estimation result;

A Kalman filter module is configured to perform Kalman filter processing on the distance difference data based on the distance difference obtained in multiple cycles;

And, a location determination module is configured to calculate the actual location of the IoT device based on the distance difference data processed by the Kalman filter.

Optionally, the channel estimation module calculates the distance difference between the IoT device and different positioning base stations according to the channel estimation result, including:

According to the channel results, the propagation delay of each positioning base station in any combination of three positioning base stations is calculated;

Taking the propagation delay of any positioning base station in the combination as a benchmark, calculate the delay difference between the other two positioning base stations and the positioning base station as a benchmark;

The corresponding distance difference is calculated according to the time delay difference to obtain the distance difference between the IoT device and the reference positioning base station and the distance difference between the IoT device and the other two positioning base stations.

Optionally, the Kalman filter module performs Kalman filter processing on the distance difference data based on the distance difference obtained in multiple cycles, including:

After obtaining the wireless positioning signal in each period, after calculating the distance difference based on the wireless positioning signal of the period, a Kalman filter process is performed based on the pre-built distance difference measurement model to obtain an updated true distance difference estimate;

After each Kalman filter processing, determine whether the Kalman filter algorithm converges. If it converges, the current estimated value of the actual distance difference is used as the actual distance difference. If it does not converge, continue the distance difference calculation and Kalman filter processing after obtaining the wireless positioning signal in the next cycle.

In a third aspect, the present invention provides an electric power Internet of Things device, which includes the wireless positioning device introduced in the above second aspect, and the wireless positioning device is used to determine the actual location of the electric power Internet of Things device.

In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the wireless positioning method of the electric power Internet of Things device as described in the first aspect is implemented.

Beneficial Effects

The wireless positioning method of the electric power Internet of Things device of the present invention obtains the distance difference measurement value from the node to the positioning base station through channel estimation, and then uses Kalman filtering to filter the distance difference, which can effectively filter out the ranging error and improve the positioning accuracy.

On the basis of obtaining multiple accurate estimates of the distance difference, in order to eliminate the negative impact of an undesirable positioning base station layout on positioning accuracy, the present invention selects three positioning base stations with the best layout from all positioning base stations through a specific optimal layout selection strategy, thereby obtaining the most accurate node positioning result.

The method of the present invention does not require time synchronization between the Internet of Things node and the positioning base station, and is easy to deploy; the algorithm is simple to implement and has a large cost advantage.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram showing a flow chart of an embodiment of a wireless positioning method for a power Internet of Things device according to the present invention;

Figure 2 shows a schematic diagram of the deployment of positioning base stations around IoT devices;

FIG3 is a schematic diagram of positioning principle based on OTDOA;

FIG4 is a schematic diagram showing the principle of selecting the optimal layout of positioning base stations.

Detailed ways

The invention is further described below with reference to the accompanying drawings and specific embodiments.

The technical concept of the present invention is: in order to achieve accurate positioning of IoT devices, firstly, by adopting methods such as channel estimation and pseudorange estimation, the pseudorange estimation between the IoT device to be positioned and multiple positioning base station nodes is obtained; secondly, by modeling the ranging error, the Kalman filter technology is used to filter the ranging result, filter out the NLOS ranging error, and improve the positioning accuracy. For the situation where more than three base stations are deployed in the environment where the IoT device is located, the positioning result determined by the base station combination with the optimal layout is selected as the final positioning result.

Example 1

This embodiment introduces a wireless positioning method for a power Internet of Things device, including:

Periodically acquiring wireless positioning signals sent by at least three positioning base stations;

According to the wireless positioning signals obtained in each period, channel estimation is performed for each positioning base station respectively;

According to the channel estimation results, calculate the distance difference between the IoT device and different positioning base stations;

Based on the distance differences obtained in multiple cycles, Kalman filtering is performed on the distance difference data;

The actual location of the IoT device is calculated based on the distance difference data processed by the Kalman filter.

The following embodiment introduces the specific implementation process of the wireless positioning method of the power Internet of Things device, which can be referred to as shown in Figure 1, involving the following steps.

1. Acquisition of wireless positioning signals

To achieve the positioning of IoT devices, several positioning base stations should be pre-deployed in the area where the devices are located. The positioning base station is a special base station that needs to know its own precise location information, and needs to send positioning auxiliary information including its own precise location information to the IoT nodes in the area through appropriate information transmission methods, that is, it needs to be able to send wireless positioning signals. For a certain target area, the distribution of positioning base stations should enable all IoT nodes in the area to receive wireless positioning signals sent by at least three positioning base stations. In this embodiment, the wireless positioning signal sent by the positioning base station can be a wireless positioning signal based on ODTOA, and the ODTOA wireless positioning signal and transceiver technology can refer to the existing technology.

As shown in reference figure 2, there are multiple IoT device nodes in the target area, and a total of four positioning base stations are deployed, namely "positioning base station 1" to "positioning base station 4". This deployment enables any IoT device node to receive positioning signals from at least three positioning base stations.

For any IoT node, when there are not enough three positioning base stations in the area, the IoT node that has obtained its own precise location information can be used as a positioning base station to expand the service scope of the high-precision positioning service.

For any IoT node in the target area where sufficient positioning base stations have been deployed, when positioning is required, it periodically listens for wireless positioning signals sent by at least three positioning base stations.

2. Channel Estimation and Distance Difference Calculation

After each interception of wireless positioning signals sent by multiple positioning base stations, channel estimation is performed based on the wireless positioning signals sent by each positioning base station, and then the difference between the distances of the IoT device node and the corresponding positioning base station is calculated based on the channel estimation results. The method of using positioning signals for channel estimation can refer to the prior art.

When calculating the distance difference between the positioning base station and the IoT device node, the propagation delay of the positioning base station is first determined according to the channel estimation result. As shown in Figure 3, for IoT node _N1 and three positioning base stations - base station 1, base station 2, and base station 3. Based on the channel estimation results, node _N1 measures the propagation delays to the three positioning base stations as _τ1 , _τ2 , and _τ3 , respectively. When calculating the distance difference, the propagation delay _τ1 of base station 1 is used as the benchmark to calculate the delay difference between base station 2 and base station 1 _τ21 = _τ2 - _τ1 , and the delay difference between base station 3 and base station 1 _τ31 = _τ3 - _τ1 . Then convert the delay difference into the distance difference, that is, the distance between node _N1 and base station 2 and the distance between node _N1 and base station 1, the difference between the two is _d21 = c× _τ21 , and similarly, _d31 = c× _τ31 , where c represents the propagation speed of radio waves in the air.

From the above, it can be deduced that for any IoT device, the calculation formula for the distance difference between it and different positioning base stations is:

d _ij = c × τ _ij

Wherein, d _ij represents the difference between the distance of the IoT device to positioning base station i and the distance to positioning base station j, τ _ij =τ _i -τ _j τ _ij represents the delay difference between the delay τ _i of positioning base station i and the delay τ _j of positioning base station j.

According to the above implementation, each IoT device that can receive at least three wireless positioning signals can calculate at least two distance difference data.

3. Kalman filter processing of distance difference data

According to the link attenuation of obstacles in typical power scenarios, the distance measurement error is compensated to improve the positioning accuracy. In order to filter out the error of the distance difference estimation value obtained in the above step 2, this embodiment performs Kalman filtering on the estimated distance difference.

Kalman filter is a linear filter that uses the linear system state equation to optimally estimate the system state through the system input and output observation data. The estimation of state variables is completed through the estimated value at the previous moment and the observed value at the current moment. Kalman filter can be applied to any dynamic system containing uncertain information, effectively resisting the interference of noise and taking the state variable as the optimal estimate. Therefore, it is very suitable for filtering out noise signals in ranging in positioning methods, improving ranging accuracy and thus improving positioning accuracy.

The technical concept of performing Kalman filtering on the distance difference in this embodiment is as follows: after obtaining the wireless positioning signal in each period, the distance difference data is calculated based on the wireless positioning signal obtained in the period, and then a Kalman filtering process is performed on the obtained distance difference data based on a pre-built distance difference measurement model to obtain an updated true distance difference estimation value;

After each Kalman filter processing, determine whether the Kalman filter algorithm converges. If it converges, the current estimated value of the actual distance difference is used as the actual distance difference. If it does not converge, continue the distance difference calculation and Kalman filter processing after obtaining the wireless positioning signal in the next cycle.

The basis for whether the Kalman filter algorithm converges can be that the iterative calculation reaches the set number of times, or the change in the true distance difference estimate after several updates is less than the preset threshold, which means that the result of the Kalman filter tends to be stable, that is, a stable and accurate distance difference estimate can be obtained.

The pre-built distance difference measurement model described above is:

d _ij (k) = c _ij x _ij (k) + v _ij (k)

where _xij (k)= _aijxij (k-1)+ _wij (k- ₁ )

In the above formula, d _ij (k) represents the difference between the distance from the IoT device to the positioning base station i and the distance from the IoT device to the positioning base station j corresponding to the kth period; c _ij represents the measurement coefficient; and vi _ij (k) represents the measurement error, which has a mean of zero and a variance of Gaussian white noise; a _ij represents the state transition coefficient; w _ij (k-1) represents the excitation signal corresponding to the k-1th period, which has a mean of zero and a variance of /> Gaussian white noise; x _ij (k) represents the true value of the distance difference between the distance from the IoT device to the positioning base station i and the distance to the positioning base station j corresponding to the kth period.

Each time the Kalman filter is executed, the estimated value of the true value of the distance difference is updated according to the following formula

In the formula, represents the estimated value of the true value of the distance difference corresponding to the kth cycle, and _bij (k) represents the measurement weighting coefficient, which is expressed as the formula:

represents the variance of the measurement error,/> It represents the updated covariance one-step estimate when the Kalman filter is executed in the kth cycle, expressed as the formula:

p _ij (k-1) represents the updated covariance coefficient when the Kalman filter is executed in the k-1th cycle, represents the variance of the excitation signal w _ij .

Therefore, in this embodiment, taking the Kalman filtering process performed on the distance difference calculated after the k-th acquisition of the wireless positioning signal as an example, the steps of the Kalman filtering process performed are:

Based on the covariance coefficient p _ij (k-1) updated during the previous Kalman filter processing, update the covariance one-step estimate

One-step estimate based on the updated covariance Update the measurement weight coefficient b _ij (k);

One-step estimate based on the updated covariance And the measurement weight coefficient b _ij (k) updates the covariance coefficient p _ij (k) for the next Kalman filter processing;

Based on the updated measurement weight coefficient b _ij (k) and the estimated value of the true value of the distance difference obtained by the Kalman filter processing in the previous cycle And the distance difference d _ij (k) calculated in the current cycle, update the estimated value of the true value of the distance difference

Continuing to refer to FIG3 , taking the distance difference d ₂₁ measured by IoT node N ₁ for the kth time to base station BS ₁ and base station BS ₂ as an example, the distance difference d ₂₁ is generated by a first-order autoregressive signal x ₂₁ excited by white noise w ₂₁ , and x ₂₁ represents the true value of the distance difference between N ₁ and base stations BS ₂ and BS _1. The measurement model of the distance difference d ₂₁ can be expressed as:

d ₂₁ (k)＝c ₂₁ x ₂₁ (k)+v ₂₁ (k)

The Kalman filter is used to filter the measured distance difference d ₂₁ to obtain an estimate of the actual distance difference x _21. The process includes:

First, update the one-step estimate of the covariance:

Next, update the measurement weighting coefficients:

The third step is to update the covariance coefficient:

Finally, the estimate of x _ij (k) is updated:

Through the above process, after each periodic measurement of the distance difference d ₂₁ , the node implements a Kalman filter to obtain an updated estimate of the true value of the accurate distance difference After several iterations, the Kalman filter algorithm converges and a stable and accurate estimate of the true value of the distance difference can be obtained, which can be used as the true distance difference x ₂₁ from the IoT device N ₁ to the base station BS ₂ and the base station BS ₁ .

Using the same method as above, Kalman filtering is performed on the distance difference d ₃₁ to obtain a stable and accurate real distance difference x ₃₁ between the IoT device N ₁ and the base station BS ₃ and the base station BS ₁ , and the same applies to the others.

4. Determine the actual location of IoT devices

Referring to FIG. 1 , in this embodiment, the actual position of the IoT device is calculated based on the distance difference data processed by the Kalman filter, including:

Determine whether the number of positioning base stations of the wireless positioning signal sending end is equal to 3 or greater than 3;

If the number of positioning base stations is equal to 3, the location of the IoT device is calculated based on the two distance difference data obtained;

If the number of positioning base stations is greater than 3, the positioning base stations are grouped to determine the two distance difference data corresponding to each positioning base station combination;

For each positioning base station combination, the location of the IoT device is calculated according to the corresponding distance difference. Alternatively, after determining the positioning base station combination with the optimal layout, the location of the IoT device is calculated only for the distance difference obtained for the optimal positioning base station combination.

According to the positional relationship between each positioning base station and the IoT device in each positioning base station combination, the positioning base station combination with the best layout is determined according to the preset optimal layout selection strategy;

The location of the IoT device calculated based on the optimal positioning base station combination is used as the actual location of the IoT device.

The following is a detailed introduction.

4.1 To achieve OTDOA positioning, at least three positioning base stations are required, that is, two distance difference data. At the same time, the relative layout of the positioning base stations will also affect the positioning accuracy of the node. Since each IoT device may receive more than three wireless positioning signals, in order to further improve the positioning accuracy, this embodiment determines the location of the IoT device by selecting a positioning base station combination with the optimal layout and determining the actual distance difference calculated by the combination when the IoT device can receive more than three (not including three) wireless positioning signals.

Referring to Figure 1, first determine whether the wireless positioning signal received by the IoT device comes from more than three positioning base stations. If it comes from only three positioning base stations, the location of the IoT device is directly calculated based on the true distance difference after Kalman filtering. If there are four or more wireless positioning signals, the positioning base stations need to be grouped to determine the optimal layout of the positioning base station combination.

4.2 Selection of the optimal layout and positioning base station combination

4.2.1 Group the positioning base stations from which the received wireless positioning signals are sourced. The IoT node groups all measurable positioning base stations into groups of three and lists all combinations. As shown in FIG4 , IoT device node 1 can receive positioning signals from four base stations, namely, BS ₁ , BS ₂ , BS ₃ and BS ₄ . Theoretically, there are four combinations of any three base stations. Two of the combinations are shown in the example in FIG4 , namely, base station combination 1, including BS ₁ , BS ₂ and BS ₃ ; and base station combination 2, including BS ₁ , BS ₃ and BS ₄ .

4.2.2 IoT device node 1 uses the three positioning base stations {BS _i ,BS _j ,BS _k } in each combination, corresponding to two distance differences, to solve the node position, and the solved node position is recorded as p _m . In this embodiment, base station combination 1 uses the corresponding distance differences d ₂₁ and d ₃₁ to solve the corresponding position, recorded as p ₁ ; base station combination 2 uses the corresponding distance differences d ₃₁ and d ₄₁ to solve the corresponding position, recorded as p ₂ . In FIG4 , it is assumed that the positions of p ₁ and p ₂ are very close, and are not separately marked, which does not affect the description of subsequent steps.

4.2.3 For all M combinations, use the solved node positions to calculate the angles between the node and the positioning base station. There are three angles in total. Find the largest angle z _m in the combination.

In this embodiment, for base station combination 1 (BS ₁ , BS ₂ and BS ₃ ), node 1 uses the positions of the three base stations and the solved position p ₁ to calculate the three angles between the node and the base stations, namely angle 1: BS ₁ - node 1 - BS ₂ ; angle 2: BS ₁ - node 1 - BS ₃ ; angle 3: BS ₂ - node 1 - BS ₃ . Then, the largest angle is selected, which is marked as "maximum angle 1" in FIG. 4 .

For base station combination 2 (BS ₁ , BS ₃ and BS ₄ ), node 1 uses the positions of the three base stations and the solved position p ₂ to calculate the three angles between the node and the base stations, namely angle 1: BS ₁ , node 1, BS ₃ ; angle 2: BS ₁ , node 1, BS ₄ ; angle 3: BS ₃ , node 1, BS ₄ . Then, the largest angle is selected. In Figure 4 , it is marked as "maximum angle 2".

Repeat this step to obtain the maximum angle corresponding to each combination.

4.2.4 Among all the M angles, find the smallest angle. The corresponding positioning base station combination {BS _i ,BS _j ,BS _k } is the positioning node combination with the optimal layout.

In the two combinations shown in FIG4 , the smallest angle is “maximum angle 1”. If the angles of other combinations are larger than “maximum angle 1”, the base station combination corresponding to maximum angle 1 is the optimal base station combination, namely, base station combination 1.

Therefore, the three base stations (BS1, BS2 and BS3) of combination 1 are selected as the three base stations that can obtain the optimal node positioning. According to the true value of the distance difference after the combination filtering process, the corresponding IoT device node positioning position is calculated as p ₁ .

At this point, node 1 completes the high-precision positioning process.

Example 2

Based on the same inventive concept as that of Example 1, this embodiment introduces a wireless positioning device for a power Internet of Things device, including:

A positioning signal acquisition module, configured to periodically acquire wireless positioning signals sent by at least three positioning base stations;

The channel estimation module is configured to perform channel estimation for each positioning base station according to the wireless positioning signal acquired in each period;

The distance difference calculation module is configured to calculate the distance difference between the IoT device and different positioning base stations according to the channel estimation result;

A Kalman filter module is configured to perform Kalman filter processing on the distance difference data based on the distance difference obtained in multiple cycles;

And, a location determination module is configured to calculate the actual location of the IoT device based on the distance difference data processed by the Kalman filter.

The specific functional implementation of each of the above functional modules refers to the relevant content in Example 1, such as the following introduction to the channel estimation module and the Kalman filter module.

The channel estimation module calculates the distance difference between the IoT device and different positioning base stations based on the channel estimation results, including:

According to the channel results, the propagation delay of each positioning base station in any combination of three positioning base stations is calculated;

Taking the propagation delay of any positioning base station in the combination as a benchmark, calculate the delay difference between the other two positioning base stations and the positioning base station as a benchmark;

The corresponding distance difference is calculated according to the time delay difference to obtain the distance difference between the IoT device and the reference positioning base station and the distance difference between the IoT device and the other two positioning base stations.

The Kalman filter module performs Kalman filter processing on the distance difference data based on the distance difference obtained in multiple cycles, including:

After obtaining the wireless positioning signal in each period, after calculating the distance difference based on the wireless positioning signal of the period, a Kalman filter process is performed based on the pre-built distance difference measurement model to obtain an updated true distance difference estimate;

After each Kalman filter processing, determine whether the Kalman filter algorithm converges. If it converges, the current estimated value of the actual distance difference is used as the actual distance difference. If it does not converge, continue the distance difference calculation and Kalman filter processing after obtaining the wireless positioning signal in the next cycle.

Example 3

This embodiment introduces an electric power Internet of Things device, which includes the wireless positioning device introduced in Example 2, and determines its actual position through the wireless positioning device.

In addition, the power Internet of Things device can also use its own processor to execute the wireless positioning method introduced in Example 1 to determine the actual location of the power Internet of Things device itself.

Example 4

This embodiment introduces a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the wireless positioning method of the power Internet of Things device as described in Example 1 is implemented.

In summary, the present invention combines Kalman filtering and a specific positioning base station layout optimization technology to achieve accurate calculation of the distance difference between an IoT device and the positioning base stations with the optimal layout, thereby obtaining an accurate device location.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims (12)

1. The wireless positioning method of the electric power internet of things equipment is characterized by comprising the following steps of:

periodically acquiring wireless positioning signals sent by at least three positioning base stations;

according to the wireless positioning signals acquired in each period, channel estimation is respectively carried out on the corresponding positioning base stations;

According to the channel estimation result, calculating the distance difference between the Internet of things equipment and different positioning base stations;

performing distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;

Calculating the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing;

The calculating the actual position of the internet of things device according to the distance difference data after the Kalman filtering processing comprises the following steps: judging whether the number of the positioning base stations of the wireless positioning signal transmitting end is equal to 3 or more than 3; if the number of the positioning base stations is equal to 3, calculating the position of the Internet of things equipment according to the obtained 2 distance difference data; if the number of the positioning base stations is more than 3, grouping the positioning base stations, and determining 2 distance difference data corresponding to each positioning base station combination; aiming at each positioning base station combination, respectively calculating the position of the equipment of the Internet of things according to the corresponding distance difference; determining the positioning base station combination with the optimal layout according to a preset optimal layout selection strategy according to the position relation between each positioning base station in each positioning base station combination and the Internet of things equipment; taking the position of the Internet of things equipment obtained by combining and calculating according to the optimal positioning base station as the actual position of the Internet of things equipment; wherein,

The determining the positioning base station combination with the optimal layout according to the position relation between each positioning base station in each positioning base station combination and the internet of things equipment and the preset optimal layout selection strategy comprises the following steps: for any positioning base station combination, calculating the included angle of a connecting line between the position point of the Internet of things equipment and the position point of each base station in the combination according to the calculated position of the Internet of things equipment, and determining the maximum included angle of the connecting line to obtain the maximum included angle corresponding to each positioning base station combination; and comparing the maximum included angles corresponding to all the positioning base station combinations, selecting the positioning base station combination with the minimum maximum included angle, and taking the positioning base station combination as the positioning base station combination with the optimal layout.

2. The method according to claim 1, wherein the calculating the distance difference between the internet of things device and the different positioning base stations according to the channel estimation result comprises:

according to the channel result, calculating the propagation delay of each positioning base station in the combination of any three positioning base stations;

Calculating the time delay difference between the other two positioning base stations and the positioning base station serving as the reference by taking the propagation time delay of any one of the positioning base stations in the combination as the reference;

And calculating a corresponding distance difference according to the time delay difference to obtain the distance difference between the Internet of things equipment and the reference positioning base station and the distance difference between the Internet of things equipment and the other two positioning base stations respectively.

3. The method of claim 2, wherein the distance difference calculation formula is:

；

Wherein, Representing arrival of an Internet of things device at a positioning base station/>Distance to positioning base station/>Is the difference between the distances of (2); /(I)Representing the propagation velocity of radio waves in the air; /(I) ，/>Representing positioning base station/>Time delay/>And locating base station/>Time delay/>And a time delay difference between them.

4. A method according to claim 3, wherein said subjecting the distance difference data to a kalman filter process based on the distance differences obtained over a plurality of cycles comprises:

after the wireless positioning signals are acquired in each period, calculating to obtain distance difference data based on the wireless positioning signals in the period, and performing Kalman filtering processing on the obtained distance difference data once based on a pre-constructed distance difference measurement model to obtain an updated real distance difference estimated value;

After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.

5. The method of claim 4, wherein the pre-constructed distance difference measurement model is:

；

Wherein, ；

In the above-mentioned method, the step of,Represents the/>Internet of things equipment corresponding to each period arrives at a positioning base station/>Distance to positioning base station/>Is the difference between the distances of (2); /(I)Representing the measurement coefficient; /(I)Indicating the measurement error, which is zero mean and varianceIs white gaussian noise; /(I)Representing state transition coefficients; /(I)Represents the/>Excitation signals corresponding to each period are zero mean and variance/>Is white gaussian noise; /(I)Represents the/>Internet of things equipment corresponding to each period arrives at a positioning base station/>Distance to positioning base station/>A distance difference true value between the distances of (a);

the primary Kalman filtering is performed by updating the estimated value of the true value of the distance difference according to the following formula ：

；

In the method, in the process of the invention,Representation of the corresponding/>Estimated value of the true value of the distance difference of each period,/>The measurement weighting coefficient is expressed as the formula:

；

representing the variance of the measurement error,/> Represents the/>The covariance one-step estimation value updated when the kalman filtering is performed in each period is expressed as the formula:

；

Represents the/> Updated covariance coefficient when Kalman filtering is performed in each cycle,/>Representing the excitation signal/>Is a variance of (c).

6. The method of claim 5, wherein the step of performing the kalman filter process for each cycle comprises:

updating a covariance one-step estimation value based on the covariance coefficient updated in the previous period of Kalman filtering processing;

Updating the measurement weighting coefficient based on the updated covariance one-step estimation value;

Updating the covariance coefficient based on the updated covariance one-step estimation value and the measurement weighting coefficient;

And updating the estimated value of the distance difference true value based on the updated measurement weighting coefficient, the estimated value of the distance difference true value obtained by the Kalman filtering process of the previous period and the distance difference obtained by the calculation of the current period.

7. The method of claim 1, wherein the positioning base station is a positioning base station with a positioning function or an internet of things device with a positioning function, and the positioning function is capable of positioning self-position information and transmitting a wireless positioning signal;

the wireless positioning signal is an OTDOA wireless positioning signal.

8. A wireless positioning device of electric power internet of things equipment is characterized by comprising:

The positioning signal acquisition module is configured to periodically acquire wireless positioning signals sent by at least three positioning base stations;

The channel estimation module is configured to perform channel estimation respectively for each positioning base station according to the wireless positioning signals acquired in each period;

The distance difference calculation module is configured to calculate the distance difference between the Internet of things equipment and different positioning base stations according to the channel estimation result;

the Kalman filtering module is configured to perform distance difference data Kalman filtering processing based on the distance differences obtained in a plurality of periods;

the position determining module is configured to calculate the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing;

The position determining module calculates the actual position of the Internet of things equipment according to the distance difference data after Kalman filtering processing, and the position determining module comprises the following steps: judging whether the number of the positioning base stations of the wireless positioning signal transmitting end is equal to 3 or more than 3; if the number of the positioning base stations is equal to 3, calculating the position of the Internet of things equipment according to the obtained 2 distance difference data; if the number of the positioning base stations is more than 3, grouping the positioning base stations, and determining 2 distance difference data corresponding to each positioning base station combination; aiming at each positioning base station combination, respectively calculating the position of the equipment of the Internet of things according to the corresponding distance difference; determining the positioning base station combination with the optimal layout according to a preset optimal layout selection strategy according to the position relation between each positioning base station in each positioning base station combination and the Internet of things equipment; taking the position of the Internet of things equipment obtained by combining and calculating according to the optimal positioning base station as the actual position of the Internet of things equipment; wherein,

The determining the positioning base station combination with the optimal layout according to the position relation between each positioning base station in each positioning base station combination and the internet of things equipment and the preset optimal layout selection strategy comprises the following steps: for any positioning base station combination, calculating the included angle of a connecting line between the position point of the Internet of things equipment and the position point of each base station in the combination according to the calculated position of the Internet of things equipment, and determining the maximum included angle of the connecting line to obtain the maximum included angle corresponding to each positioning base station combination; and comparing the maximum included angles corresponding to all the positioning base station combinations, selecting the positioning base station combination with the minimum maximum included angle, and taking the positioning base station combination as the positioning base station combination with the optimal layout.

9. The wireless positioning device of the electric power internet of things device according to claim 8, wherein the channel estimation module calculates a distance difference between the internet of things device and different positioning base stations according to a channel estimation result, and the wireless positioning device comprises:

according to the channel result, calculating the propagation delay of each positioning base station in the combination of any three positioning base stations;

Calculating the time delay difference between the other two positioning base stations and the positioning base station serving as the reference by taking the propagation time delay of any one of the positioning base stations in the combination as the reference;

And calculating a corresponding distance difference according to the time delay difference to obtain the distance difference between the Internet of things equipment and the reference positioning base station and the distance difference between the Internet of things equipment and the other two positioning base stations respectively.

10. The wireless positioning device of the power internet of things device according to claim 8, wherein the kalman filter module performs a distance difference data kalman filter process based on the distance differences obtained in a plurality of periods, and the wireless positioning device comprises:

after the wireless positioning signals are acquired in each period, calculating to obtain distance difference data based on the wireless positioning signals in the period, and performing Kalman filtering processing on the obtained distance difference data once based on a pre-constructed distance difference measurement model to obtain an updated real distance difference estimated value;

After each Kalman filtering process, judging whether the Kalman filtering algorithm is converged, if so, taking the currently obtained true distance difference estimated value as the true distance difference, and if not, continuing the distance difference calculation and the Kalman filtering process after the wireless positioning signal is acquired in the next period.

11. An electric power internet of things device, characterized by comprising the wireless positioning device according to any one of claims 8-10, the wireless positioning device being configured to determine an actual position of the electric power internet of things device.

12. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a wireless location method of an electric internet of things device according to any of claims 1-7.