Device deployment method, apparatus, and terminal device

Abstract

This application discloses a device deployment method, apparatus, and terminal equipment, relating to the field of computer technology. The method includes: based on the signal distribution in an indoor area, and considering cost considerations, rationally determining the deployment scheme of the signal optimizer. This not only reduces signal blind spots in the indoor area and decreases the frequency of electronic device disconnections, but also reasonably saves equipment costs.

Description

Technical Field

[0001] This application belongs to the field of communication technology, and in particular relates to equipment deployment methods, apparatus and terminal equipment. Background Technology

[0002] With the development of technology, various household electrical appliances, power generation equipment or energy storage equipment are often used in indoor areas. While providing corresponding functions to users, they also provide real-time feedback on the usage of the equipment through equipment data (such as equipment status data). Therefore, the signal network quality in indoor areas determines the transmission effect.

[0003] In related technologies, staff typically install the equipment on-site according to the actual conditions of the indoor area and connect it to the wireless access device in the indoor area to enable data transmission.

[0004] However, due to the complex indoor environment, the judgment of the installation location by the staff during the installation process is relatively subjective. That is, they combine personal experience to judge the signal strength to determine the deployment location of the equipment. It can be seen that the current equipment deployment location has problems such as poor positioning accuracy, resulting in poor signal transmission efficiency of energy storage equipment, low deployment efficiency and high deployment cost. Summary of the Invention

[0005] This application provides a device deployment method, apparatus, and terminal device that can rationally consider the deployment scheme of the signal optimizer based on the signal distribution in indoor areas and in combination with cost considerations. This not only reduces signal blind spots in indoor areas and decreases the occurrence of electronic device disconnections, but also reasonably saves equipment costs. The technical solution is as follows.

[0006] Firstly, a device deployment method is provided, the method being applied to a terminal device, the method comprising: During data acquisition, the acquisition information corresponding to each sampling point in the target area is obtained. The acquisition information includes the target location information of each sampling point and the signal quality data of each sampling point. The initial signal coverage of the target area is determined based on the acquisition information corresponding to each of the sampling points. An objective function is constructed based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed. The objective function is then optimized to determine the target deployment scheme for the signal optimizer, which includes the actual number of signal optimizers to be deployed.

[0007] In some implementations of the first aspect, the signal quality data of the sampling point includes target signal quality data of the same sampling point at different preset heights; The acquisition of the collection information corresponding to each sampling point within the target area includes: At each of the sampling points at different preset heights, a preset number of initial signal quality data sets are collected. Error elimination is performed on the initial signal quality data of a preset number of groups at the same sampling point and at the same preset height to obtain the target signal quality data of the sampling point at the corresponding preset height.

[0008] In some implementations of the first aspect, obtaining the collection information corresponding to each sampling point within the target area includes: The candidate location information of the sampling points is obtained through at least two positioning methods; The candidate location information is filtered and fused to obtain the target location information of the sampling point.

[0009] The method described in some implementations of the first aspect also includes: During the mapping phase, the target area is determined based on the movement trajectory of the terminal device; or... The target area is determined based on map data imported by the user.

[0010] In some implementations of the first aspect, determining the initial signal coverage of the target area based on the acquisition information corresponding to each of the sampling points includes: The target region is divided into multiple grid regions based on a preset unit grid size; Based on the target location information of each sampling point and the signal quality data of each sampling point in the multiple grid regions, interpolation calculations are performed to obtain the signal quality data corresponding to the multiple grid regions respectively. The grid region whose signal quality data is greater than a preset signal threshold is defined as the target grid. The initial signal coverage of the target area is determined based on the proportion of each target grid to the target area.

[0011] In some implementations of the first aspect, the construction of the objective function based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed includes: A coverage term is constructed based on the initial signal coverage, the coverage increment corresponding to the signal optimizer, and the estimated number of signal optimizers deployed. A cost item is constructed based on the deployment cost of the signal optimizer and the estimated number of signal optimizers to be deployed. The objective function is constructed based on the coverage term and the cost term.

[0012] In some implementations of the first aspect, the optimization of the objective function includes: The objective function is optimized using a genetic algorithm.

[0013] In some implementations of the first aspect, the method further includes: A visualization model is generated based on the signal quality data of each grid region.

[0014] Secondly, a device deployment apparatus is also provided, the apparatus comprising: The acquisition module is used to acquire acquisition information corresponding to each sampling point in the target area during data acquisition. The acquisition information includes the target location information of each sampling point and the signal quality data of each sampling point. The determination module is used to determine the initial signal coverage of the target area based on the acquisition information corresponding to each of the sampling points; The processing module is used to construct an objective function based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed, and to optimize the objective function to determine the target deployment scheme of the signal optimizer, wherein the target deployment scheme includes the actual number of signal optimizers to be deployed.

[0015] Thirdly, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement any of the device deployment methods described in the embodiments of this application above.

[0016] Fourthly, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the device deployment method as described in any of the embodiments of this application above.

[0017] Fifthly, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform any of the device deployment methods described in the above embodiments.

[0018] The beneficial effects of the technical solutions provided in this application include at least the following: The system collects information from each sampling point within the target area, determines the initial signal coverage rate of the target area based on the collected information, and then constructs an objective function by combining the initial signal coverage rate, the deployment cost of the signal optimizer, and the estimated number of deployments. Based on the objective function, the deployment scheme of the signal optimizer is determined. In other words, no dedicated network testing equipment is required. Signals are collected through terminal devices, and the deployment scheme of the signal optimizer is reasonably considered based on the signal distribution in the indoor area and the cost. This not only reduces signal blind spots in the indoor area, reduces the disconnection of electronic devices, and improves the deployment efficiency of the equipment, but also reasonably saves equipment costs. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of an implementation environment provided by an exemplary embodiment of this application; Figure 2 This is a flowchart of a device deployment method provided in an exemplary embodiment of this application; Figure 3 This is a flowchart of a device deployment method provided in another exemplary embodiment of this application; Figure 4 This is a flowchart of a device deployment method provided in an exemplary embodiment of this application; Figure 5 This is a schematic diagram of a device deployment method provided in another exemplary embodiment of this application; Figure 6 This is a schematic diagram of a device deployment method provided in an exemplary embodiment of this application; Figure 7 This is a structural block diagram of a control device for a lighting apparatus provided in another exemplary embodiment of this application; Figure 8 This is a schematic diagram of the structure of a server provided in an exemplary embodiment of this application. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0024] In related technologies, equipment is typically installed on-site by workers based on the actual conditions of the indoor area. This equipment could be energy storage devices, power generation devices, etc. Taking energy storage devices as an example, they are connected to wireless access devices in the indoor area to enable data transmission. However, due to the complex indoor environment, the judgment of installation location by workers is rather subjective; that is, they rely on personal experience to judge signal strength to determine the deployment location of the energy storage device. This results in poor positioning accuracy, leading to poor signal transmission efficiency and low deployment efficiency for current energy storage device deployment methods.

[0025] This application provides a device deployment method that collects sampling information corresponding to each sampling point in a target area, determines the initial signal coverage of the target area based on the collected information, and then constructs an objective function by combining the initial signal coverage, the deployment cost of the signal optimizer, and the estimated deployment quantity. Based on the objective function, the deployment scheme of the signal optimizer is determined. In other words, the deployment scheme of the signal optimizer is reasonably considered based on the signal distribution in the indoor area and the cost, which can not only reduce signal blind spots in the indoor area and reduce the disconnection of electronic devices, but also reasonably save equipment costs.

[0026] The implementation environment corresponding to this application is described below. The implementation environment includes a signal optimizer, terminal equipment, and a server; please refer to the illustrative examples. Figure 1 The illustration shows an implementation environment provided by an exemplary embodiment of this application, which includes a signal optimizer 110, a terminal device 120 and a server 130, and the signal optimizer 110, the terminal device 120 and the server 130 are connected through a communication network 140.

[0027] In some embodiments, during data acquisition, the terminal device 120 acquires the target location information of each sampling point within the target area, and when the terminal device 120 is at a sampling point, it sends a signal acquisition request to the signal optimizer 110 to request the acquisition of the signal quality data corresponding to the sampling point. After receiving the signal acquisition request, the signal optimizer 110 feeds back the signal quality data to the terminal device 120, and the terminal device 120 uses the signal quality data and target location information corresponding to the sampling point as the sampling information corresponding to the sampling point.

[0028] In some embodiments, the terminal device 120 determines the initial signal coverage of the target area based on the acquisition information corresponding to each sampling point.

[0029] In some embodiments, in one case, the terminal device 120 sends the initial signal coverage of the target area, the deployment cost of the signal optimizer 110, and the estimated deployment number of the signal optimizer 110 as deployment parameters to the server 130. After receiving the deployment parameters, the server 130 constructs an objective function based on the deployment parameters and optimizes the objective function (e.g., taking the minimum value as the output of the objective function). Based on the optimized output of the objective function, the server 130 determines the target deployment scheme of the signal optimizer 110, wherein the target deployment scheme includes the actual deployment number of the signal optimizer 110.

[0030] In some embodiments, the terminal device 120 constructs an objective function based on the initial signal coverage of the target area, the deployment cost of the signal optimizer 110, and the estimated number of signal optimizers 110 to be deployed, and optimizes the objective function (e.g., taking the minimum value as the output of the objective function), and determines the target deployment scheme of the signal optimizer 110 based on the optimized output of the objective function.

[0031] The terminal device 120 can be optional and can be a desktop computer, laptop computer, mobile phone, tablet computer, e-book reader, MP3 (Moving Picture Experts Group Audio Layer III) player, MP4 (Moving Picture Experts Group Audio Layer IV) player, smart TV, smart car, virtual reality (VR), augmented reality (AR) and other terminal devices. This application embodiment does not limit it in this way.

[0032] The signal optimizer 110 can be optional and can be a Wi-Fi signal booster, powerline adapter, router, gateway, or other types of signal optimizer. This application embodiment does not limit this.

[0033] Server 130 includes at least one of a single server, multiple servers, a cloud computing platform, and a virtualization center. Optionally, server 130 undertakes the primary computing work, while terminal device 120 and signal optimizer 110 undertake secondary computing work; or, server 130 undertakes secondary computing work, while terminal device 120 and signal optimizer 110 undertake primary computing work; or, signal optimizer 110, server 130, and terminal device 120 collaborate on computing using a distributed computing architecture.

[0034] It is worth noting that the aforementioned servers can be independent physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0035] It is worth noting that the aforementioned communication network can be implemented as a wired network or a wireless network, and the communication network can be implemented as any one of a local area network, a metropolitan area network, or a wide area network. This application embodiment does not limit this.

[0036] It should be noted that this application may display prompt interfaces, pop-ups, or output voice prompts before and during the collection of user data. These prompt interfaces, pop-ups, or voice prompts are used to inform the user that their data is being collected. This ensures that the application only begins the steps for collecting user data after receiving confirmation from the user regarding the prompt interface or pop-up; otherwise (i.e., without user confirmation), the steps for collecting user data end, meaning no user data is collected. In other words, all user data collected in this application is collected with the user's consent and authorization, and the collection, use, and processing of related user data must comply with the relevant laws, regulations, and standards of the relevant regions.

[0037] Based on the above-described terminology and implementation environment, the device deployment method provided in this application embodiment will be explained, using the example of a terminal device executing the method. For illustrative purposes, please refer to [reference needed]. Figure 2The diagram illustrates a flowchart of a device deployment method provided in an exemplary embodiment of this application, the method comprising the following steps.

[0038] Step 210: During data collection, acquire the collection information corresponding to each sampling point within the target area.

[0039] The collected information includes the target location information of each sampling point and the signal quality data of each sampling point.

[0040] Indicatively, a sampling point refers to a location within the target area used to detect signal quality.

[0041] In this embodiment, the sampling point is the pre-set installation location of the signal optimizer.

[0042] Optionally, the sampling points are pre-defined designated locations. For example, if the target area is a home environment, there are three sampling points: sampling point 1 (center of the living room), sampling point 2 (corner of the master bedroom), and sampling point 3 (center of the bathroom). Alternatively, the sampling points are determined based on the location of the terminal device within the target area. For example, if a user moves the terminal device within the target area and moves to location 1, and stays there for more than 3 seconds, then location 1 is taken as sampling point 1. This embodiment does not limit this.

[0043] Indicative, target location information refers to the location of the sampling point within the target area.

[0044] Optionally, the target location information is a relative location, that is, with the target area as a reference (for example, establishing a three-dimensional coordinate system with a certain location as the origin), the coordinates of the sampling point within the target area are determined according to the range corresponding to the target area, and this is used as the target location information of the sampling point; or, the target location information is a geographical location, that is, the latitude and longitude coordinates of the sampling point are used as the target location information corresponding to the sampling point.

[0045] Optionally, the target position information is a two-dimensional coordinate position (e.g., including x-axis coordinates and y-axis coordinates); or, the target position information is a three-dimensional coordinate position (e.g., including x-axis coordinates, y-axis coordinates and z-axis coordinates).

[0046] To illustrate, the same sampling point corresponds to one target location information, while different sampling points correspond to different target location information.

[0047] Indicatively, signal quality data refers to the signal strength corresponding to the terminal device at the sampling point.

[0048] In a illustrative scenario, at least one signal optimizer has been deployed in the target area, and the signal quality data refers to the strength of the signal transmitted by the signal optimizer received by the terminal device when it is located at the sampling point.

[0049] In an illustrative scenario, where no signal optimizer is deployed in the target area, the signal quality data refers to the signal transmission and reception strength at the sampling point where the terminal device is located.

[0050] Optionally, the signal quality data includes at least one of Received Signal Strength Indication (RSSI) or Channel Quality Indicator (CQI).

[0051] Among them, a signal optimizer is a device used to provide a signal source for terminal devices within a target area, and the signal optimizer can improve the data transmission efficiency of terminal devices within the target area.

[0052] Optionally, if the signal optimizer is a Wi-Fi router, then RSSI refers to the RSSI value corresponding to the Wi-Fi router, for example, -30 dBm, or RSSI refers to the RSSI range corresponding to the Wi-Fi router, for example, -30 dBm to -90 dBm; if the signal optimizer is a Bluetooth gateway, then RSSI refers to the RSSI value corresponding to the Bluetooth gateway, or RSSI refers to the RSSI range corresponding to the Bluetooth gateway. This application embodiment does not limit this.

[0053] Optionally, if a signal optimizer has been deployed and installed in the current target area, or if no signal optimizer has been installed in the current target area, the installer may place at least one signal optimizer in a designated location within the target area to collect the acquisition information corresponding to each sampling point through the signal optimizer.

[0054] Optionally, for the same sampling point, there may be one collection information (sampling once) or multiple collection information (sampling multiple times).

[0055] Indicatively, the target area refers to the area corresponding to the environment in which the terminal device is located. For example, if the terminal device is located in the bedroom, then the target area is the bedroom area, or the target area is the entire indoor area of ​​the home where the bedroom is located.

[0056] Step 220: Determine the initial signal coverage of the target area based on the acquisition information corresponding to each sampling point.

[0057] Indicatively, initial signal coverage refers to the signal distribution within the target area, where signal distribution refers to the signal quality corresponding to each sampling point.

[0058] Schematic, the initial signal coverage is used to evaluate the signal quality of each sampling point within the target area under the current deployment scheme corresponding to the signal optimizer. If the signal quality of a sampling point is lower than a preset signal threshold, it indicates that the sampling point belongs to a signal dead zone. The device deployment method provided in this embodiment is precisely to reduce the generation of signal dead zones.

[0059] To illustrate, the initial signal coverage is determined by the sampling points. That is, if the sampling points are different, the target location information and signal quality data corresponding to the sampling points will also be different. Therefore, the initial signal coverage in the target area will also be different.

[0060] For illustration, the initial signal coverage rate within the target area is determined using a pre-defined signal propagation model, as shown in Formula 1 below.

[0061] Formula 1: RSSI(d, h) = RSSI(d0, h0) - 10nlog10(d / d0) - Xσ + W + Hc Where RSSI(d, h) represents the received signal strength, i.e., the initial signal coverage; d represents the distance between the signal source (the signal sent by the signal optimizer) and the terminal device; h represents the height of the terminal device above the ground of the target area; d0 represents the preset reference distance (e.g., 1 meter from the signal source); h0 represents the preset reference height (e.g., 1 meter above the ground); n represents the path loss exponent (used to describe the rate attenuation of the signal with distance, e.g., n is 2 in free space, 3 to 4 in indoor environments, and 4 to 5 in environments with multiple walls); Xσ represents shadow fading (used to represent random attenuation caused by obstructions in the target area); W represents multipath effect (used to represent short-term signal fluctuations caused by signal reflection or refraction); and Hc represents the height correction factor (used to represent the impact of changes in the height of the terminal device on the signal).

[0062] For illustrative purposes, the calculation method for the high correction factor can be referenced in Formula 2 below.

[0063] Formula 2: Hc(h) = k×log10(h / h0) Where Hc(h) represents the height correction factor, h represents the actual height of the terminal device from the ground (e.g., 1.8 meters), h0 represents the reference height of the terminal device from the ground (e.g., 1 meter), and k is the height coefficient (e.g., a value of 2-4 dB).

[0064] In one example, given d=3.5m, h=1.5m, n=3.2, Xσ=8dB, W=2dB, and k=2, substituting these values ​​into Formula 1 and Formula 2 above, we get: RSSI(3.5, 1.5) = -45dBm - 10×3.2×log10(3.5 / 1) -8dB +2dB + 2×log10(1.5 / 1)dBm≈ -68dBm.

[0065] Therefore, after obtaining the signal reception strength corresponding to each sampling point according to Formula 1 and Formula 2, the initial signal coverage rate corresponding to the target area is formed.

[0066] Step 230: Construct an objective function based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed, and optimize the objective function to determine the target deployment scheme of the signal optimizer.

[0067] The target deployment plan includes the actual number of signal optimizers deployed.

[0068] For illustrative purposes, the deployment cost of a signal optimizer refers to the cost of the signal optimizer during the installation phase, such as the purchase cost of the signal optimizer and the installation cost for the installers.

[0069] For illustrative purposes, the estimated number of signal optimizers to be deployed refers to the pre-set number of signal optimizers to be deployed. For example, if 3 signal optimizers are pre-set to be deployed for indoor area 1, the estimated number of deployments is 3. If 5 signal optimizers are pre-set to be deployed for outdoor area 2, the estimated number of deployments is 5.

[0070] For illustrative purposes, the actual number of signal optimizers deployed refers to the number of signal optimizers ultimately deployed within the target area.

[0071] Optionally, the actual number of deployments may be greater than the estimated number of deployments, or the actual number of deployments may be equal to the estimated number of deployments, or the actual number of deployments may be less than the estimated number of deployments. This application embodiment does not limit this.

[0072] Indicatively, the objective function is used to evaluate the rationale for the signal optimizer in the current deployment scenario by combining the deployment cost of the signal optimizer with the signal coverage in the target area.

[0073] Indicatively, the target deployment scheme refers to the actual deployment strategy of the final signal optimizer within the target area.

[0074] Optionally, the target deployment scheme includes at least one of the following schemes: The first type is the actual number deployed, which represents the number of signal optimizers in the target area; The second type is the actual deployment location, which indicates the installation location of the signal optimizer in the target area (including installation height and location). The third is the antenna angle, which refers to the angle of the antenna in the signal optimizer. Different antenna angles determine different interference rejection ratios, thus affecting the signal quality of the signal optimizer.

[0075] It is worth noting that the above-described target deployment scheme is merely an illustrative example, and the embodiments of this application do not limit it.

[0076] In illustrative terms, the optimization of the objective function is used to determine the final target deployment scheme. Based on the output value of the optimized objective function, the target deployment scheme of the signal optimizer is determined.

[0077] Optionally, the optimization process may include taking the maximum value, minimum value, or any pre-defined specified value of the objective function.

[0078] In summary, the device deployment method provided in this application collects the collection information corresponding to each sampling point in the target area, determines the initial signal coverage of the target area based on the collection information, and then constructs an objective function by combining the initial signal coverage, the deployment cost of the signal optimizer, and the estimated deployment quantity. Based on the objective function, the deployment scheme of the signal optimizer is determined. In other words, the deployment scheme of the signal optimizer is reasonably considered based on the signal distribution in the indoor area and the cost, which can not only reduce signal blind spots in the indoor area and reduce the disconnection of electronic devices, but also reasonably save equipment costs.

[0079] This illustration provides a detailed explanation of the signal optimizer deployment process. Please refer to [link / reference]. Figure 3 It illustrates a flowchart of a device deployment method provided in an exemplary embodiment of this application, such as Figure 3 As shown, step 210 also includes steps 211 to 212, and step 220 also includes steps 221 to 224. The method includes the following steps.

[0080] Step 211: At different preset heights of each sampling point, a preset number of initial signal quality data sets are collected.

[0081] The signal quality data of the sampling points includes the target signal quality data of the same sampling point at different preset heights; To illustrate, the sampling point is implemented as a two-dimensional coordinate system. For example, sampling point a is (10, 23). Therefore, there are different heights for the same sampling point. The height refers to the distance of the sampling point from the ground, such as 0.5 meters, 1 meter, or 1.5 meters.

[0082] In this embodiment, the example is taken where the same sampling point corresponds to target signal quality data at different preset heights.

[0083] In this embodiment, taking the same sampling point as an example, a predetermined number of initial signal quality data sets are collected at a predetermined height. For example, 3 sets of initial signal quality data sets are collected at a height of 1 meter, and 5 sets of initial signal quality data sets are collected at a height of 1.5 meters.

[0084] Optionally, taking the same sampling point as an example, the number of preset groups corresponding to different preset heights may be the same or different; this application embodiment does not limit this.

[0085] To illustrate, taking the same sampling point as an example, at the same preset height, signal quality data is collected once at a specified time interval to obtain multiple sets of initial signal quality data.

[0086] Step 212: Perform error elimination on the initial signal quality data of the same sampling point at the same preset height for a preset number of groups to obtain the target signal quality data of the sampling point at the corresponding preset height.

[0087] To illustrate, taking the same sampling point as an example, error elimination processing is performed on multiple sets of initial signal quality data at the same preset height to reduce the interference of outliers on the signal quality data, thereby obtaining the target signal quality data corresponding to the sampling point at the preset height. The specific calculation method can be found in Formula 3 below.

[0088] Formula 3: Target signal quality data = (Σ sample value × weighting coefficient) / n Wherein, the sample value is the initial signal data, the weight coefficient = 1 / (sample variance + ε), n is the preset number of groups, and ε is a minimal constant (to avoid the denominator being zero).

[0089] In some embodiments, during the mapping phase, the target area is determined based on the movement trajectory of the terminal device; or, the target area is determined based on map data imported by the user.

[0090] As an illustration, there are two ways to determine the target area, which will be described in detail below.

[0091] The first method is to determine the target area based on the movement trajectory of the terminal device.

[0092] The user moves their handheld device along the boundary of the environment. The system records the trajectory using the gyroscope and step count data built into the device, and finally determines the target area based on the movement trajectory.

[0093] This is illustrative; please refer to it. Figure 4 It illustrates a schematic diagram of a target region generation method provided in an exemplary embodiment of this application, such as... Figure 4 As shown, multiple movement trajectories (including path 1, path 2, path 3 and path 4) corresponding to the terminal device are currently displayed. The final target area 400 is determined based on the movement trajectory of the terminal device.

[0094] The second method involves determining the target area based on map data imported by the user.

[0095] Taking a house as an example, when a house is constructed, there are corresponding auxiliary design drawings (such as CAD drawings), or the user can manually draw the map content corresponding to the house. The auxiliary design drawings or manually drawn map content can be imported into the terminal device to generate the target area corresponding to the house.

[0096] To illustrate, if there is a sudden change in signal strength during the data acquisition process (e.g., RSSI change > 15dBm within 1-3 seconds), the wall location will be identified and marked based on the sudden change.

[0097] To illustrate, during the data acquisition process, signal blind spots and areas with severe multipath effects are automatically marked based on changes in signal strength.

[0098] In this embodiment, the terminal device runs an application with data acquisition function. The application prompts the user to collect data through voice and animation, such as: "Please walk along the grid line to the first sampling point" (based on a 1.5m×1.5m preset grid), or "Please hold your phone at a height of 1.5m for 5 seconds" (triggering automatic sampling).

[0099] In this embodiment, at each grid point, the user needs to collect signals at heights of 0.5m (the terminal device is located at the user's waist), 1.5m (the terminal device is located at the user's chest), and 2.0m (the terminal device is located above the user's head) as prompted. The application automatically records: the average RSSI value of WIFI detected at each height (averaged after 10 samplings), Bluetooth signal strength (including AoA angle data), and the three-dimensional coordinates of the sampling point (calculated by gyroscope trajectory).

[0100] In this embodiment, if the data at a certain sampling point is abnormal (e.g., variance > 5dB), the application prompts "Please re-collect at the current location" until the data is qualified.

[0101] In some embodiments, candidate location information of sampling points is obtained through at least two positioning methods; the candidate location information is filtered and fused to obtain the target location information of the sampling points.

[0102] To illustrate, if a Bluetooth gateway is deployed in the target area, the azimuth angle is calculated using the Bluetooth standard's AoA (Angle of Arrival) / AoD (Angle of Departure) technology by receiving signals from multiple array antennas. This is then combined with the Wi-Fi RSSI fingerprint database to achieve three-dimensional spatial positioning and determine the candidate location information of the sampling point. In the absence of a Bluetooth gateway, the initial coordinate point is set using the gyroscope built into the terminal device. Three-dimensional spatial positioning is then achieved through the wireless access point and the Wi-Fi RSSI fingerprint database, thereby determining the candidate location information of the sampling point.

[0103] The following section provides a detailed explanation of how to construct an RSSI fingerprint database for Wi-Fi.

[0104] 1. Offline Data Acquisition Phase: Sampling points are divided into a 1.5m × 1.5m grid within the target area. Three sets of Wi-Fi signal data (including AP MAC address, RSSI signal strength, and acquisition timestamp) are collected from each sampling point to form a basic fingerprint database. The specific calculation method is as follows: Fingerprint database F = { (x_i, y_i, h_i), { (MAC_j, RSSI_ij, t_ij)}_{j=1 to m}}_{i=1 to n}, where (x_i, y_i, h_i) are the three-dimensional coordinates, m is the number of visible wireless access points, and n is the total number of sampling points.

[0105] 2. Online matching stage: Real-time acquisition of WIFI signal features at the current sampling point location, and calculation of Euclidean distance with each sampling point in the fingerprint database using the K-Nearest Neighbor (KNN) algorithm: d_k = Σ| RSSI_current_j - RSSI_kj | (j=1 to m; The weighted average of the three sampling points with the smallest distance is selected to obtain the positioning result, which is used as candidate location information.

[0106] 3. Dynamic update mechanism: The fingerprint database is manually calibrated by the user. The fingerprint database is corrected by the signal changes in the user's daily movement trajectory (such as the update of wall attenuation coefficient and the update of obstruction), so as to maintain the positioning accuracy (error < ±50cm) in the long term.

[0107] In this embodiment, the candidate location information is filtered and fused using the Extended Kalman Filter (EKF) method to obtain the final target location information of the sampling points.

[0108] EKF achieves error control by fusing GPS, gyroscope, and candidate location information through the following steps.

[0109] (1) Definition of state variables: The terminal device's position (x, y, h), movement speed (vx, vy, vh), and sensor bias (b_gps, b_gyro) are defined as a 10-dimensional state vector: X = [x, y, h, vx, vy, vh, b_gps, b_gyro, b_bluetooth, b_wifi]^T (2) Prediction stage: Based on the angular velocity and acceleration measured by the gyroscope, predict the state at the next moment: X_pred = F × X_current + B × u Where F is the state transition matrix (describing the position-velocity relationship), and u is the gyroscope measurement input.

[0110] (3) Update phase: Correct the prediction results using measurements from GPS, Bluetooth, and WIFI: K = P_pred × H^T × (H × P_pred × H^T + R)^-1 X_update = X_pred + K × (Z_measured - H × X_pred) Where K is the Kalman gain, P is the covariance matrix (representing state uncertainty), and R is the sensor noise matrix. The error control method is explained below.

[0111] (1) Dynamic noise distribution: GPS: Outdoor ambient noise R=1m², indoor ambient noise R=5m² (uncertainty increases due to signal attenuation). Bluetooth AoA: Noise R=0.1m² (high weighting for high-precision positioning) Gyroscope: Accumulates noise over time, increasing by 0.05 m² every 10 seconds. (2) Outlier removal: When the deviation between the sensor measurement and the predicted value exceeds 3 times the standard deviation, it is marked as an outlier and its weight is reduced (e.g., it is temporarily excluded from the update when GPS changes).

[0112] (3) Initial calibration: The initial GPS position is used as the initial value of the state vector X. The covariance matrix P is trained using the data from the first 30 seconds, which enables the filter to converge quickly.

[0113] The following describes the definition and function of R in Kalman filtering (EKF): In the EKF algorithm, R represents the Measurement Noise Covariance Matrix, a parameter that quantifies the "unreliability" of sensor measurements. Its physical meaning is: A larger R value indicates greater noise in the sensor data (and lower reliability). A smaller R value indicates more stable sensor data (higher reliability).

[0114] R is in square meters (m²), corresponding to the variance of the positioning error.

[0115] The Kalman gain K (weighting coefficient) is affected during the update phase by the following formula: K = P_pred × H^T × (H × P_pred × H^T + R)^-1 When R increases (e.g., when indoor GPS noise increases), the denominator in the above formula increases, resulting in a decrease in the calculated K, indicating that the correction effect of the sensor measurement on the final result is weakened.

[0116] When R decreases (such as with Bluetooth high-precision positioning), the denominator in the above formula decreases, resulting in an increase in the calculated K, which enhances the correction effect of the sensor measurement on the final result.

[0117] 4. Dynamic adjustment strategy The system automatically switches the R value according to the environment. For example, when the number of GPS satellites is less than 4, the R value is automatically adjusted from 1m² to 5m². Another example is when the Bluetooth signal strength is greater than -65dBm, the R value is reduced from 0.1m² to 0.05m² (to enhance reliability).

[0118] Step 221: Divide the target area based on the preset unit grid size to obtain multiple grid areas.

[0119] To illustrate, a unit grid (e.g., 1.5m × 1.5m) is pre-defined, and the target area is divided into multiple grid areas based on the unit grid.

[0120] In this embodiment, the target area is divided into grids by a pre-defined unit grid to determine the relative position of each sampling point in the target area. For example, sampling point 1 is located at grid point a, and sampling point 2 is located at grid point b.

[0121] This is illustrative; please refer to it. Figure 5 It illustrates a schematic diagram of a target location information determination method provided in an exemplary embodiment of this application, such as... Figure 5 As shown, the method includes the following steps.

[0122] During the signal input process 510, the terminal device collects signal data corresponding to the sampling point while moving, determines the data type of the signal data, and if the signal data is sensor data (obtained by sensors), it determines the candidate location information corresponding to the sampling point by combining gyroscope or GPS fusion. If the signal data is a WIFI signal, it determines the candidate location information corresponding to the sampling point by RSSI fingerprint matching. If the signal data is a Bluetooth signal, it determines the candidate location information corresponding to the sampling point by AoA angle calculation. After determining the candidate location information corresponding to the sampling point, Kalman filtering is performed on the candidate location information to obtain the target location information 520 corresponding to the sampling point.

[0123] Step 222: Based on multiple grid regions, interpolation calculations are performed on the target location information of each sampling point and the signal quality data of each sampling point to obtain the signal quality data corresponding to each of the multiple grid regions.

[0124] To illustrate, interpolation refers to combining the signal quality data corresponding to sparse sampling points to complete the signal quality data corresponding to multiple grid regions.

[0125] That is, the number of grid regions is greater than the number of sampling points. By using the signal quality data corresponding to the sampling points, the signal quality data of the grid regions corresponding to the non-sampling points can be deduced.

[0126] First, the target area is divided into three-dimensional grid units (x: 1.5m, y: 1.5m, h: 0.5m), forming a three-dimensional sampling space that includes the height dimension. For example, a 90㎡ family scene is divided into 120 grid units (h range 0.5m-2.0m, planar sampling points = 90 / (1.5×1.5) = 40, height 0.5m, 1m, 1.5m, so 40×3 = 120 grid units).

[0127] Secondly, the user walks along the grid xy plane with a handheld terminal device, and locates the coordinates of each sampling point by using a gyroscope and step count statistics; at key sampling points (such as signal inflection points and obstacle edges), a telescopic bracket is used to collect RSSI values ​​at three heights of 0.5m / 1.0m / 1.5m, with 5 sets of samples collected at each height as a sample set.

[0128] Wherein, the sample set S = { (x_i, y_i, h_k), { RSSI_ijk(t)}_{t=1 to 5}} Where i = 1..n (xy grid index), k = 1..3 (height index) The interpolation calculation will be explained in detail below.

[0129] 1. Use the Kriging interpolation algorithm to perform spatial interpolation on sparse sampling points. For details, please refer to Formula 5 below.

[0130] Formula 5: RSSI(x, y, h) = μ + Σλ_i(RSSI_i - μ) + ε Where μ is the global mean, λ_i is the weighting coefficient, and ε is the error term.

[0131] 2. Construct a three-dimensional signal intensity matrix: M[x][y][h] = Interpolated RSSI value (dBm) In some embodiments, a visualization model is generated based on signal quality data of each grid region.

[0132] An illustrative, visual model is used to show users the signal quality data corresponding to each grid area within a target region.

[0133] Optionally, signal quality data can be represented in color, numerical value, or symbol form. For example, different colors can be used to display the signal strength of the signal quality data (red = good signal, blue = poor signal, the darker the red, the stronger the signal, or the darker the blue, the weaker the signal).

[0134] In one example, volume rendering is used to show the distribution of signals in three-dimensional space, with red representing strong signals (>-55dBm) and blue representing weak signals (<-85dBm). In this embodiment, model optimization and verification also exist, which will be described in detail below.

[0135] 1. Cross-validation: Randomly retain 20% of the sampling points; models with a prediction error RMSE < 4dB are considered valid. 2. Dynamic update: The model is automatically iterated every 10 new sampling points, and the interpolation weights are adjusted by Bayesian update rules.

[0136] Steps for constructing a 3D signal model: 1. Grid division: Divide the room into small cubes of 1.5m × 1.5m × 0.5m; 2. Multi-point measurement: Measure the signal at the corner of the grid corresponding to the key locations at the edge of the room or building and around obstacles, and measure 5 times at each height (0.5m, 1.5m, 2m); 3. Intelligent Completion: Based on the measured data, the computer automatically calculates the signal strength of the untested cells between the center and edge cells (untested cells will lose accuracy, and the obtained value is a calculated value, the disadvantage of which is that there will be a certain error with the actual value, the advantage is that it reduces the number of test items. Users who need high-precision positioning need to complete the basic cell coverage, while in outdoor unobstructed scenarios, only the actual measurement of the edge cells needs to be completed, and the other untested cells can be obtained according to the WIFI signal propagation attenuation algorithm).

[0137] Step 223: The grid area where the signal quality data is greater than the preset signal threshold is determined as the target grid.

[0138] To illustrate, after obtaining the signal quality data corresponding to each grid region, a signal threshold is preset. If there is signal quality data that is greater than the signal threshold, the grid region corresponding to that signal quality data is determined as the target grid.

[0139] Here, the target grid refers to the location where the signal quality is good. Therefore, if there is a grid region where the signal quality data is less than or equal to the signal threshold, it indicates that the signal quality at that location is poor.

[0140] Step 224: Determine the initial signal coverage of the target area based on the proportion of each target grid to the target area.

[0141] To illustrate, after determining the target grid, the initial signal coverage of the target area is determined based on the proportional relationship between the number of target grids and the number of all grid areas within the target area.

[0142] In some embodiments, a coverage term is constructed based on the initial signal coverage, the coverage increment corresponding to the signal optimizer, and the estimated number of signal optimizers deployed; a cost term is constructed based on the deployment cost of the signal optimizer and the estimated number of signal optimizers deployed; and an objective function is constructed based on the coverage term and the cost term.

[0143] For illustrative purposes, the specific objective function can be referenced in Formula Six below: Formula 6: Objective Function = (Coverage Area Compliance Rate × 40%) + (Signal Stability Score × 40%) + (1 - Cost Increment Percentage) × 20% The coverage area compliance rate (i.e., the coverage rate item) refers to the actual signal coverage area / the preset expected coverage area (i.e., the initial signal coverage rate). For example, in a home scenario, the expected coverage is 90%, and the coverage area compliance rate is 0-1, where 1 represents 100% coverage and the weight is 40%. The specific calculation method can be found in Formula 6 below.

[0144] Formula 6: Coverage area = Σ(grid cell i) × 0.25㎡ (cell i signal ≥ -85dBm) Signal stability score is a comprehensive score calculated based on the standard deviation of signal fluctuation and outage probability within a certain time range (the time can be configured by the user, arbitrarily selected between 1 minute and 1 hour, and multiple network measurements are performed through the terminal device, with one of the measurements taken as the measurement result). For details, please refer to Formula 7 below.

[0145] Formula 7: Signal stability score = (1 - σ / 10) × 0.6 + (1 - P) × 0.4 Where σ represents the standard deviation, σ = √[Σ(RSSI_i - μ)² / N] (μ is the mean, N=3600 samples); P represents the outage probability, P = duration of sample value <-85dBm / total duration.

[0146] For illustration, the deployment cost of the signal optimizer and the estimated number of signal optimizers to be deployed constitute the cost item. The calculation method for this cost item can be referred to in Formula 8 below.

[0147] Formula 8: C_total = C_base + Σ(C_accessory × N) + C_install Where C_base represents the basic cost of the energy storage equipment, C_accessory represents the unit price of the signal optimizer, N represents the number of auxiliary equipment (determined by the number of signal blind spots), and C_install represents the installation labor cost.

[0148] The optimization objective for the cost component is to minimize C_total under the constraints of coverage area ≥90% and signal stability ≥8.5.

[0149] In some embodiments, the objective function is optimized using a genetic algorithm.

[0150] In this embodiment, the maximum value of the objective function is taken as the optimization objective corresponding to the objective function.

[0151] Taking the home scenario as an example, the coverage area compliance rate = actual coverage area 82.8㎡ / expected coverage area 90㎡ = 0.92, that is; signal stability score = 8.5 points (0.85); cost increment ratio = 0.3.

[0152] Therefore, the optimal score = 0.92×0.4 + 0.85×0.4 + (1-0.3)×0.2 = 0.368 + 0.34 +0.14 = 0.848 (out of 1.0) If the score is ≥0.8: this deployment plan will be adopted first; if the score is 0.6≤score<0.8: cost or stability needs to be optimized; if the score is <0.6: the plan is rejected and a new deployment plan will be generated.

[0153] This is illustrative; please refer to it. Figure 6 It illustrates a device deployment method according to this application, such as... Figure 6 As shown, the method includes the following:

[0154] Signal acquisition module 610: integrates a WIFI / Bluetooth dual-mode radio frequency unit, allowing users to collect information from various sampling points through the terminal device while the terminal device is in motion.

[0155] In this embodiment, we take the example of a user who has already deployed a router and a Bluetooth gateway.

[0156] Multi-source localization fusion unit 620: used to determine the target location information corresponding to each sampling point.

[0157] Environment Modeling Engine 630: Used to determine the target area and to divide the target area into multiple grid regions.

[0158] Signal analysis and error correction module 640: used to eliminate errors in the acquired initial signal quality data, thereby obtaining the target signal quality data corresponding to the sampling point.

[0159] Optimization suggestion generation module 650: It is used to generate an objective function based on the initial signal coverage, the deployment cost of the signal optimizer and the preset deployment quantity, and determine the deployment scheme of the signal optimizer by optimizing the objective function.

[0160] Pre-installation - Simulated Location Recommendation Module 660: Provides a simulated deployment plan, including the simulated installation location of the signal optimizer, before deploying the signal optimizer.

[0161] Post-installation - Actual location matching verification module 670: After the signal optimizer is deployed, it provides the matching degree between the actual deployment location and the simulated installation location.

[0162] In summary, the device deployment method provided in this application collects the collection information corresponding to each sampling point in the target area, determines the initial signal coverage of the target area based on the collection information, and then constructs an objective function by combining the initial signal coverage, the deployment cost of the signal optimizer, and the estimated deployment quantity. Based on the objective function, the deployment scheme of the signal optimizer is determined. In other words, the deployment scheme of the signal optimizer is reasonably considered based on the signal distribution in the indoor area and the cost, which can not only reduce signal blind spots in the indoor area and reduce the disconnection of electronic devices, but also reasonably save equipment costs.

[0163] Example 1: Implementation steps of optimized deployment method for energy storage on a residential balcony: 1. Environmental Map Construction (1) In a home setting, the user walks around the balcony-living room boundary twice with a terminal device that supports Bluetooth 5.3 (track length 32m). The system records the trajectory through a gyroscope (sampling rate 100Hz) and identifies the location of the concrete wall (thickness 0.2m) and the glass window (attenuation 3dB) by combining the RSSI mutation point (change amount >18dBm).

[0164] (2) Divide the family area into grids, generate a 1.5m×1.5m grid map, and mark 3 signal blind spots (behind the sofa, corner of the refrigerator, and corner of the balcony).

[0165] 2. Multi-dimensional signal acquisition (1) At the candidate installation points on the balcony (east / west sides), signal data were collected at heights of 1.0m / 1.5m / 2.0m respectively. Five sets of samples were collected at each height after a 5-second pause.

[0166] (2) Applying the error elimination algorithm (dynamic weighted average), the 1.5m height on the east side was selected as the optimal sampling point because it had the smallest variance.

[0167] The static sampled data, after being processed by weighted averaging, will be used as the input for EKF filtering (see 5.3 for details), realizing full-process control of 'sampling error → positioning error'.

[0168] 3. Signal model optimization (1) Substitute into the signal propagation model: RSSI(3.5m, 1.5m) = -45dBm - 10×3.2log10(3.5 / 1) - 8dB + 2dB + 0.6dB =-65dBm Where n=3.2 is the measured path loss index, and Hc=0.6dB is the height correction factor.

[0169] (2) The predicted signal strength in the central area of ​​the living room is >-70dBm (meets the communication threshold). 4. Construction guidance generation (1) Output installation coordinates: 0.5m from the wall on the east side of the balcony, 1.5m high, antenna facing a 30° elevation angle (avoiding reflection from the glass window); (2) Frequency band selection recommendation: 2.4GHz channel 6 (interference index <15%).

[0170] In summary, the device deployment method provided in this application collects sampling information corresponding to each sampling point within a target area, determines the initial signal coverage rate of the target area based on the collected information, and then constructs an objective function by combining the initial signal coverage rate, the deployment cost of the signal optimizer, and the estimated deployment quantity. Based on the objective function, a deployment scheme for the signal optimizer is determined. In other words, by considering the signal distribution in the indoor area and taking into account cost, a reasonable deployment scheme for the signal optimizer can be considered. This not only reduces signal blind spots and minimizes the occurrence of electronic device disconnections in the indoor area but also reasonably saves equipment costs. For illustrative purposes, please refer to... Figure 7 The diagram illustrates a structural diagram of a device deployment apparatus provided in an exemplary embodiment of this application, wherein the device deployment apparatus may specifically include the following modules: The acquisition module 710 is used to acquire acquisition information corresponding to each sampling point in the target area during data acquisition. The acquisition information includes target location information of each sampling point and signal quality data of each sampling point. The determination module 720 is used to determine the initial signal coverage of the target area based on the acquisition information corresponding to each of the sampling points; The processing module 730 is configured to construct an objective function based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed, and to optimize the objective function to determine the target deployment scheme of the signal optimizer, wherein the target deployment scheme includes the actual number of signal optimizers to be deployed.

[0171] Optionally, the signal quality data of the sampling point includes target signal quality data of the same sampling point at different preset heights; The acquisition module 710 is further configured to acquire a preset number of initial signal quality data at different preset heights of each sampling point; and to perform error elimination on the preset number of initial signal quality data of the same sampling point at the same preset height to obtain the target signal quality data of the sampling point at the corresponding preset height.

[0172] Optionally, the acquisition module 710 is further configured to acquire candidate location information of the sampling point through at least two positioning methods; and filter and fuse each candidate location information to obtain target location information of the sampling point.

[0173] Optionally, the acquisition module 710 is further configured to determine the target area based on the movement trajectory of the terminal device during the mapping stage; or, to determine the target area based on map data imported by the user.

[0174] Optionally, the determining module 720 is further configured to divide the target area based on a preset unit grid size to obtain multiple grid areas; perform interpolation calculations on the target location information of each sampling point and the signal quality data of each sampling point based on the multiple grid areas to obtain the signal quality data corresponding to each of the multiple grid areas; determine the grid area whose signal quality data is greater than a preset signal threshold as the target grid; and determine the initial signal coverage rate of the target area based on the proportion of each target grid to the target area.

[0175] Optionally, the processing module 730 is further configured to construct a coverage term based on the initial signal coverage, the coverage increment corresponding to the signal optimizer, and the estimated number of deployments of the signal optimizer; construct a cost term based on the deployment cost of the signal optimizer and the estimated number of deployments of the signal optimizer; and construct the objective function based on the coverage term and the cost term.

[0176] Optionally, the processing module 730 is further configured to optimize the objective function using a genetic algorithm.

[0177] Optionally, the processing module 730 is further configured to generate a visualization model based on the signal quality data of each of the grid regions.

[0178] In summary, the device deployment apparatus provided in this application collects the collection information corresponding to each sampling point in the target area, determines the initial signal coverage of the target area based on the collection information, and then constructs an objective function by combining the initial signal coverage, the deployment cost of the signal optimizer, and the estimated deployment quantity. Based on the objective function, the deployment scheme of the signal optimizer is determined. In other words, the deployment scheme of the signal optimizer is reasonably considered based on the signal distribution in the indoor area and the cost, which can not only reduce signal blind spots in the indoor area and reduce the disconnection of electronic devices, but also reasonably save equipment costs.

[0179] See Figure 8 This illustration shows a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 8 As shown, the computer device 1000 of this embodiment includes: at least one processor 1010 ( Figure 8 (Only one is shown in the image) a processor, a memory 1020, and a computer program 1021 stored in the memory 1020 and executable on at least one processor 1010, wherein the processor 1010 executes the computer program 1021 to implement the steps in the above-described location hinting method embodiment.

[0180] Computer device 1000 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This terminal device may include, but is not limited to, processor 1010 and memory 1020. Those skilled in the art will understand that... Figure 8 This is merely an example of computer device 1000 and does not constitute a limitation on computer device 1000. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0181] The processor 1010 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0182] In some embodiments, memory 1020 may be an internal storage unit of computer device 1000, such as a hard disk or memory of computer device 1000. In other embodiments, memory 1020 may be an external storage device of computer device 1000, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on computer device 1000. Furthermore, memory 1020 may include both internal and external storage units of computer device 1000. Memory 1020 is used to store operating system, program content, boot loader, data, and other programs, such as program code of computer programs. Memory 1020 may also be used to temporarily store data that has been output or will be output.

[0183] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0184] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0185] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0186] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0189] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, swivel hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0190] The implementation of all or part of the processes in the methods of the above embodiments can also be accomplished by a computer program product. When the computer program product is run on a computer device, the computer device can implement the steps in the various method embodiments described above.

[0191] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for deploying equipment, characterized in that, The method is applied to a terminal device, and the method includes: During data acquisition, the acquisition information corresponding to each sampling point in the target area is obtained. The acquisition information includes the target location information of each sampling point and the signal quality data of each sampling point. The initial signal coverage of the target area is determined based on the acquisition information corresponding to each of the sampling points. An objective function is constructed based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed. The objective function is then optimized to determine the target deployment scheme for the signal optimizer, which includes the actual number of signal optimizers to be deployed.

2. The method according to claim 1, characterized in that, The signal quality data of the sampling point includes the target signal quality data of the same sampling point at different preset heights; The acquisition of the collection information corresponding to each sampling point within the target area includes: At each of the sampling points at different preset heights, a preset number of initial signal quality data sets are collected. Error elimination is performed on the initial signal quality data of a preset number of groups at the same sampling point and at the same preset height to obtain the target signal quality data of the sampling point at the corresponding preset height.

3. The method according to claim 1, characterized in that, The acquisition of the collection information corresponding to each sampling point within the target area includes: The candidate location information of the sampling points is obtained through at least two positioning methods; The candidate location information is filtered and fused to obtain the target location information of the sampling point.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: During the mapping phase, the target area is determined based on the movement trajectory of the terminal device; or... The target area is determined based on map data imported by the user.

5. The method according to claim 1, characterized in that, Determining the initial signal coverage of the target area based on the acquisition information corresponding to each of the sampling points includes: The target region is divided into multiple grid regions based on a preset unit grid size; Based on the target location information of each sampling point and the signal quality data of each sampling point in the multiple grid regions, interpolation calculations are performed to obtain the signal quality data corresponding to the multiple grid regions respectively. The grid region whose signal quality data is greater than a preset signal threshold is defined as the target grid. The initial signal coverage of the target area is determined based on the proportion of each target grid to the target area.

6. The method according to claim 1, characterized in that, The objective function constructed based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed includes: A coverage term is constructed based on the initial signal coverage, the coverage increment corresponding to the signal optimizer, and the estimated number of signal optimizers deployed. A cost item is constructed based on the deployment cost of the signal optimizer and the estimated number of signal optimizers to be deployed. The objective function is constructed based on the coverage term and the cost term.

7. The method according to claim 1, characterized in that, The optimization process for the objective function includes: The objective function is optimized using a genetic algorithm.

8. The method according to claim 5, characterized in that, The method further includes: A visualization model is generated based on the signal quality data of each of the grid regions.

9. A device deployment apparatus, characterized in that, The device includes: The acquisition module is used to acquire acquisition information corresponding to each sampling point in the target area during data acquisition. The acquisition information includes the target location information of each sampling point and the signal quality data of each sampling point. The determination module is used to determine the initial signal coverage of the target area based on the acquisition information corresponding to each of the sampling points; The processing module is used to construct an objective function based on the initial signal coverage, the deployment cost of the signal optimizer, and the estimated number of signal optimizers to be deployed, and to optimize the objective function to determine the target deployment scheme of the signal optimizer, wherein the target deployment scheme includes the actual number of signal optimizers to be deployed.

10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 8.

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