Intelligent antenna scheduling method, device, terminal and storage medium

Abstract

The present application discloses a smart antenna scheduling method, device, terminal and storage medium, the method comprising: configuring multiple antenna states for the smart antenna; calculating multiple weighted averages corresponding to the multiple antenna states using a preset method; determining a target weighted average based on the multiple weighted averages, and selecting the antenna state corresponding to the target weighted average as the target antenna state. The present invention measures the performance of the smart antenna in the antenna state by calculating the weighted average of the negotiation rates of all clients of the wireless AP corresponding to the smart antenna, so as to select the optimal antenna state (i.e., the target antenna state). This method does not need to consider the adaptation and compatibility between the wireless AP and the terminal, nor does it need to provide additional channel expenses, avoiding the problems of high hardware power consumption and heavy network burden, conveniently realizing smart antenna scheduling, and improving the efficiency of smart antenna scheduling.

Description

Technical Field

[0001] The present application relates to the field of antenna scheduling technology, and in particular to an intelligent antenna scheduling method, device, terminal and storage medium. Background Art

[0002] With the continuous development of WIFI technology, the physical layer rate of WIFI has been rapidly improved, which has promoted the rapid development of smart antenna technology. Smart antenna technology mainly includes two major technical points, one is the design of smart antenna hardware, and the other is the smart antenna scheduling method. Among them, the quality of the smart antenna scheduling method is related to the actual benefits brought by the smart antenna to the system.

[0003] At present, the implementation method of the intelligent antenna scheduling mechanism of major manufacturers is: the wireless AP (WIFI AP) sends a test frame to the terminal, and the terminal feeds back information such as signal-to-noise ratio, RSSI, bit error rate, etc. to the wireless AP based on the test frame. Then the wireless AP performs weighted calculation on the above information to obtain the comprehensive performance impact factor α, and polls various antenna states, and then compares and analyzes the size of the comprehensive performance impact factor α corresponding to various antenna states, and uses the antenna state corresponding to the optimal value of α as the target antenna state.

[0004] However, the above-mentioned intelligent antenna scheduling method not only requires the adaptation and compatibility of the wireless AP and the terminal, but also requires the provision of additional channel expenses, resulting in high hardware power consumption and heavy network burden. Summary of the invention

[0005] The main purpose of the present application is to provide an intelligent antenna scheduling method, device, terminal and storage medium to solve the problems of high hardware power consumption and heavy network burden existing in the related technology.

[0006] In order to achieve the above objectives, in a first aspect, the present application provides a smart antenna scheduling method, comprising:

[0007] Configure multiple antenna states for smart antennas;

[0008] Calculate multiple weighted averages corresponding to multiple antenna states by using a preset method, wherein the multiple antenna states correspond to the multiple weighted averages one by one, and the weighted average is a weighted average of the negotiation rates of all clients of the wireless AP corresponding to the smart antenna;

[0009] Based on the multiple weighted averages, determining a target weighted average;

[0010] The antenna state corresponding to the target weighted average value is selected as the target antenna state.

[0011] In a possible implementation, a plurality of weighted average values ​​corresponding to a plurality of antenna states are calculated using a preset method, including:

[0012] Calculate the weighted average value corresponding to each antenna state in the plurality of antenna states by using a preset method;

[0013] The weighted average values ​​corresponding to each antenna state are aggregated to obtain multiple weighted average values.

[0014] In a possible implementation, calculating a weighted average value corresponding to each antenna state in a plurality of antenna states by using a preset method includes:

[0015] For each antenna state, determining a negotiation rate for each client among all clients of the wireless AP;

[0016] Based on the negotiation rate and weighted value of each client, a weighted average value corresponding to all clients is obtained.

[0017] In a possible implementation, all clients include a first client and a second client;

[0018] Determine the negotiated rate for each of the wireless AP's clients, including:

[0019] Configuring a corresponding first negotiation rate for the first client;

[0020] Consulting a roaming database and mapping target data in the roaming database to obtain a second negotiated rate corresponding to the second client;

[0021] A negotiated rate for each client is obtained based on the first negotiated rate and the second negotiated rate.

[0022] In a possible implementation, based on the negotiation rate and weighted value of each client, a weighted average value corresponding to all clients is obtained, including:

[0023] Determine a weighted value for each client;

[0024] Based on the number of clients, the negotiation rate of each client, and the weighted value, a weighted average value corresponding to all clients is obtained.

[0025] In a possible implementation, determining a weighted value of each client includes:

[0026] Based on the target data, determine the negotiation rate of each client at different signal strengths;

[0027] A weighted value for each client is determined based on the negotiated rate of each client at different signal strengths.

[0028] In a possible implementation, determining a target weighted average value based on a plurality of weighted average values ​​includes:

[0029] The maximum value among multiple weighted averages is selected as the target weighted average.

[0030] In a second aspect, an embodiment of the present invention provides a smart antenna scheduling device, including:

[0031] A configuration module, used for configuring multiple antenna states for the smart antenna;

[0032] A weighted calculation module, used to calculate multiple weighted average values ​​corresponding to multiple antenna states by using a preset method, wherein the multiple antenna states correspond to the multiple weighted average values ​​one by one, and the weighted average value is a weighted average value of the negotiation rates of all clients of the wireless AP corresponding to the smart antenna;

[0033] A weighted average value determination module, used for determining a target weighted average value based on a plurality of weighted average values;

[0034] The target antenna state determination module is used to select the antenna state corresponding to the target weighted average value as the target antenna state.

[0035] In a third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above intelligent antenna scheduling methods when executing the computer program.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above intelligent antenna scheduling methods are implemented.

[0037] The embodiment of the present invention provides a smart antenna scheduling method, device, terminal and storage medium, including: configuring multiple antenna states for the smart antenna, then using a preset method to calculate multiple weighted averages corresponding to the multiple antenna states, then based on the multiple weighted averages, determining a target weighted average, and selecting the antenna state corresponding to the target weighted average as the target antenna state. The present invention measures the performance of the smart antenna in the antenna state by calculating the weighted average of the negotiation rates of all clients of the wireless AP corresponding to the smart antenna, so as to select the optimal antenna state (i.e., the target antenna state). This method does not need to consider the adaptation and compatibility between the wireless AP and the terminal, nor does it need to provide additional channel expenses, avoiding the problems of high hardware power consumption and heavy network burden, conveniently realizing smart antenna scheduling, and improving the efficiency of smart antenna scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0039] Figure 1 is a flow chart of an implementation of a smart antenna scheduling method provided by an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of a PAT curve provided by an embodiment of the present invention;

[0041] Figure 3 It is a structural schematic diagram of an intelligent antenna scheduling device provided by an embodiment of the present invention;

[0042] Figure 4 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0045] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0046] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0047] It should be understood that in the present invention, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0048] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based only on A, but B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0049] Depending on the context, "if" as used herein may be interpreted as "when" or "when" or "in response to determining" or "in response to detecting."

[0050] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0051] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0052] In one embodiment, Figure 1 As shown, a smart antenna scheduling method is provided, comprising the following steps:

[0053] Step S101: configuring multiple antenna states for a smart antenna;

[0054] Step S102: Calculate multiple weighted average values ​​corresponding to multiple antenna states using a preset method.

[0055] The present invention configures multiple antenna states for smart antennas and calculates the weighted average of all clients of the corresponding wireless AP based on each antenna state, wherein the weighted average is the weighted average of the negotiation rate.

[0056] When configuring multiple antenna states for a smart antenna, the antenna states are mainly distinguished based on the signal transmission angle of the smart antenna. Specifically, assuming that the antenna state of the smart antenna is set to 8, the 8 antenna states are based on dividing the horizontal plane into 8 angles, and the interval between adjacent angles is 45°, that is, the antenna state of the smart antenna is divided into 8 based on the 8 signal transmission angles of the smart antenna. In addition, no matter how many antenna states there are, the interval between adjacent signal transmission angles should be a multiple of 45°, specifically, 90°, 135°, etc.

[0057] After the antenna state is configured for the smart antenna, a plurality of weighted average values ​​corresponding to the plurality of antenna states need to be calculated using a preset method. Specifically, the weighted average value corresponding to each of the plurality of antenna states needs to be calculated using a preset method, and then the weighted average values ​​corresponding to each antenna state are aggregated to obtain a plurality of weighted average values.

[0058] The weighted average value corresponding to each antenna state in the plurality of antenna states is calculated by using a preset method. Specifically, the plurality of antenna states are switched, and then the weighted average value corresponding to each antenna state is calculated by using a preset method.

[0059] For the switching of multiple antenna states, the present invention uses the binary method to improve the speed of antenna switching and reduce the performance fluctuations caused by the antenna switching process. Assume that the antenna state of the smart antenna is set to 8. These 8 antenna states are based on dividing the horizontal plane into 8 angles, and the interval between adjacent angles is 45°. First, the weighted average of all clients of the wireless AP in the current antenna state is calculated. When the smart antenna is switched to the remaining 7 states one by one, the weighted average of all clients of the wireless AP in each state is calculated respectively, and the weighted average corresponding to each antenna state in the multiple antenna states can be obtained. In addition, the preset method used in this application is a method of setting the objective function, and the objective function is as follows:

[0060]

[0061] Among them, S ANT is the weighted average value corresponding to all clients of the wireless AP, N is the number of clients accessing the wireless AP, N is a positive integer greater than 1, DR i is the negotiation rate of the i-th client, w i is the weighted value of the i-th client.

[0062] Based on the above formula, it can be known that to calculate the weighted average value corresponding to each antenna state, it is necessary to first determine the negotiation rate and weighted value of each client among all clients of the wireless AP for each antenna state, and then based on the negotiation rate and weighted value of each client, obtain the weighted average value corresponding to all clients, that is, the weighted average value corresponding to all clients of the wireless AP in each antenna state.

[0063] Since the clients among all the clients corresponding to the wireless AP are divided into two categories, namely the first client and the second client, wherein the first client is a client connected to the wireless AP with traffic transmission, and the second client is a client connected to the wireless AP without traffic transmission. For the above two types of clients, the methods of determining the negotiation rate are different. Specifically, the negotiation rate (first negotiation rate) of the first client is determined and can be obtained by directly looking it up; the negotiation rate (second negotiation rate) of the second client is obtained by looking up the roaming database and mapping the target data in the roaming database. After obtaining the first negotiation rate and the second negotiation rate, the negotiation rate of each client is determined, and the negotiation rate of all the clients corresponding to the wireless AP is obtained.

[0064] After calculating the negotiation rates of all clients corresponding to the wireless AP, it is necessary to determine the weighted value of each client. Specifically, first determine the negotiation rate of each client under different signal strengths based on the target data, and then determine the weighted value of each client based on the negotiation rate of each client under different signal strengths.

[0065] Furthermore, combined with Figure 2 The steps for determining the weighted value for each client are described below. i The setting will be based on the target data (PAT data) obtained from the roaming database, and then the target data will be divided into two-dimensional grids to obtain the PAT curve under concentrated performance. After that, the segmented slope of each PAT curve is calculated, and the slope is divided into N intervals. The settings belonging to the same interval are placed in a box, and different boxes correspond to different weighted values ​​w. i .

[0066] Specifically, the PAT curve is obtained through long-term statistics of wireless AP intelligent roaming, so the data does not depend on external clients (terminals). It is a statistical analysis of all terminal devices at different RSSI (signal strength) and different Thrput (negotiation rate) levels. Figure 2 The three PAT curves from top to bottom in the figure represent PAT curves of different speed levels. Due to differences in terminal device hardware, the rates negotiated between the AP and the terminal are divided into different levels. The AP uses intelligent roaming to calculate the PAT curves of various speed levels.

[0067] The 2D meshing algorithm for the PAT curve in the figure above is fixed, so as long as the PAT curve is fixed, the authority is fixed. Among them, the principle of PAT meshing depends on the change of the slope of the curve. Because our goal is to improve the overall throughput performance, the weight of the location with a large slope is large, and the weight of the location with a small slope is small. Therefore, we will calculate the segmented slope of each PAT curve, divide the slope into N interval segments, and then Figure 2 The items belonging to the same interval are set in one box, and different boxes correspond to different weight values ​​w. i . Through the weighted values ​​w corresponding to different boxes i , we can know the weighted value w of each client i .

[0068] It should be noted that the above PAT curve does not depend on the number of terminal clients. The data is a statistical cumulative value and also the value of the wireless AP's intelligent roaming statistics. In theory, new clients will affect the PAT curve, but PAT data is a cumulative data, so the impact of individual terminal access in a short period of time is very small.

[0069] After determining the negotiation rate and weighted value of each client among all clients of the wireless AP in the above manner, since the number of clients accessing the wireless AP is fixed, the weighted average value corresponding to all clients can be obtained based on the number of clients and the negotiation rate and weighted value of each client. That is, the weighted average value corresponding to all clients of the wireless AP under different antenna states can be calculated by substituting the number of clients and the negotiation rate and weighted value of each client into the above formula.

[0070] Step S103: determining a target weighted average value based on multiple weighted average values;

[0071] Step S104: Selecting the antenna state corresponding to the target weighted average value as the target antenna state.

[0072] After obtaining multiple weighted average values ​​corresponding to multiple antenna states, in order to select the target (optimal) antenna state, it is necessary to compare the multiple weighted average values, and select the maximum value among the multiple weighted average values ​​as the target weighted average value after comparison. Through the target weighted average value, the antenna state corresponding to the target weighted average value can be directly found, and this antenna state is used as the target antenna state.

[0073] Since the weighted value w of each client in the above formula iIt is a set fixed value. Therefore, when switching the antenna state, the negotiation rate of all clients of the wireless AP in each antenna state can be recorded, and the target antenna state can be determined by comparing the negotiation rates of all clients of the wireless AP in different antenna states.

[0074] The embodiment of the present invention provides a smart antenna scheduling method, including: configuring multiple antenna states for the smart antenna, then calculating multiple weighted averages corresponding to the multiple antenna states using a preset method, then determining a target weighted average based on the multiple weighted averages, and selecting the antenna state corresponding to the target weighted average as the target antenna state. The present invention measures the performance of the smart antenna in the antenna state by calculating the weighted average of the negotiation rates of all clients of the wireless AP corresponding to the smart antenna, so as to select the optimal antenna state (i.e., the target antenna state). This method does not need to consider the adaptation and compatibility of the wireless AP and the terminal, nor does it need to provide additional channel expenses, avoids the problems of high hardware power consumption and heavy network burden, conveniently realizes smart antenna scheduling, and improves the efficiency of smart antenna scheduling. In addition, the smart antenna scheduling method not only minimizes the performance fluctuation caused by the scheduling process, greatly reduces the performance impact caused by the algorithm, and adopts a low-complexity algorithm, which is conducive to the real-time implementation of scheduling, and can maximize and optimize the overall performance rather than the performance of a single user.

[0075] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0076] The following is an embodiment of the device of the present invention. For details not described in detail, reference may be made to the corresponding method embodiments described above.

[0077] Figure 3 A schematic diagram of the structure of an intelligent antenna scheduling device provided by an embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown. An intelligent antenna scheduling device includes a configuration module 31, a weighted calculation module 32, a weighted average determination module 33 and a target antenna state determination module 34, which are specifically as follows:

[0078] A configuration module 31, configured to configure multiple antenna states for the smart antenna;

[0079] A weighted calculation module 32, configured to calculate a plurality of weighted average values ​​corresponding to a plurality of antenna states by using a preset method, wherein the plurality of antenna states correspond one to one to the plurality of weighted average values, and the weighted average value is a weighted average value of negotiation rates of all clients of the wireless AP corresponding to the smart antenna;

[0080] A weighted average value determination module 33, configured to determine a target weighted average value based on a plurality of weighted average values;

[0081] The target antenna state determination module 34 is used to select the antenna state corresponding to the target weighted average value as the target antenna state.

[0082] In a possible implementation, the weighted calculation module 32 includes:

[0083] A weighted calculation submodule, used to calculate a weighted average value corresponding to each antenna state in a plurality of antenna states by using a preset method;

[0084] The weighted summarization submodule is used to summarize the weighted average values ​​corresponding to each antenna state to obtain multiple weighted average values.

[0085] In a possible implementation, the weighted calculation submodule includes:

[0086] A negotiation rate calculation unit, configured to determine a negotiation rate of each client among all clients of the wireless AP for each antenna state;

[0087] The weighted calculation unit is used to obtain the weighted average value corresponding to all clients based on the negotiation rate and weight value of each client.

[0088] In a possible implementation, all clients include a first client and a second client;

[0089] The negotiation rate calculation unit includes:

[0090] A first negotiation rate calculation subunit, configured to configure a corresponding first negotiation rate for the first client;

[0091] A second negotiated rate calculation subunit is used to consult the roaming database and map the target data in the roaming database to obtain a second negotiated rate corresponding to the second client;

[0092] The negotiation rate calculation subunit is used to obtain the negotiation rate of each client based on the first negotiation rate and the second negotiation rate.

[0093] In a possible implementation, the weighted calculation unit includes:

[0094] A weighted determination subunit, used to determine a weighted value for each client;

[0095] The weighted calculation subunit is used to obtain the weighted average value corresponding to all clients based on the number of clients, the negotiation rate of each client and the weighted value.

[0096] In a possible implementation, the weighted determination subunit includes: determining the negotiation rate of each client at different signal strengths based on target data; and determining the weighted value of each client based on the negotiation rate of each client at different signal strengths.

[0097] In a possible implementation, the weighted average value determination module 33 includes:

[0098] The weighted average value determination submodule is used to select the maximum value among multiple weighted average values ​​as the target weighted average value.

[0099] Figure 4 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 4 As shown, the terminal 4 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41. When the processor 41 executes the computer program 43, the steps in the above-mentioned smart antenna scheduling method embodiments are implemented, for example Figure 1 Alternatively, when the processor 41 executes the computer program 43, the functions of each module / unit in the above-mentioned smart antenna scheduling device embodiments are realized, for example Figure 3 Functionality of the modules / units 31 to 34 shown.

[0100] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the smart antenna scheduling method provided by the above-mentioned various implementation modes.

[0101] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, abbreviated as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0102] The present invention also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the smart antenna scheduling method provided by the various embodiments described above.

[0103] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0104] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A smart antenna scheduling method, characterized in that: include: Configure multiple antenna states for smart antennas; Calculate multiple weighted averages corresponding to the multiple antenna states by using a preset method, wherein the multiple antenna states correspond to the multiple weighted averages one by one, and the weighted average is a weighted average of the negotiation rates of all clients of the wireless AP corresponding to the smart antenna; Based on the multiple weighted averages, determining a target weighted average; An antenna state corresponding to the target weighted average value is selected as a target antenna state.

2. The smart antenna scheduling method according to claim 1, characterized in that: The calculating a plurality of weighted average values ​​corresponding to the plurality of antenna states by using a preset method includes: Calculating a weighted average value corresponding to each antenna state in the plurality of antenna states by using the preset method; The weighted average values ​​corresponding to each antenna state are aggregated to obtain the multiple weighted average values.

3. The smart antenna scheduling method according to claim 2, characterized in that: The using the preset method to calculate the weighted average value corresponding to each antenna state in the multiple antenna states includes: For each antenna state, determining a negotiation rate for each client among all clients of the wireless AP; Based on the negotiation rate and weighted value of each client, a weighted average value corresponding to all the clients is obtained.

4. The smart antenna scheduling method according to claim 3, characterized in that: The all clients include a first client and a second client; The determining of the negotiation rate of each client among all clients of the wireless AP includes: Configuring a corresponding first negotiation rate for the first client; Consulting a roaming database and mapping target data in the roaming database to obtain a second negotiated rate corresponding to the second client; The negotiation rate of each client is obtained based on the first negotiation rate and the second negotiation rate.

5. The smart antenna scheduling method according to claim 4, characterized in that: The obtaining, based on the negotiation rate and weighted value of each client, a weighted average value corresponding to all clients, comprises: Determining a weighted value for each client; Based on the number of clients, the negotiation rate of each client, and the weighted value, a weighted average value corresponding to all the clients is obtained.

6. The smart antenna scheduling method according to claim 5, characterized in that: The determining of the weighted value of each client includes: Determine, based on the target data, a negotiation rate of each client at different signal strengths; A weighted value of each client is determined based on the negotiation rate of each client under different signal strengths.

7. The smart antenna scheduling method according to any one of claims 1 to 6, characterized in that: Determining a target weighted average value based on the multiple weighted average values ​​comprises: The maximum value among the multiple weighted average values ​​is selected as the target weighted average value.

8. An intelligent antenna scheduling device, characterized in that: include: A configuration module, used for configuring multiple antenna states for the smart antenna; A weighted calculation module, configured to calculate a plurality of weighted average values ​​corresponding to the plurality of antenna states by using a preset method, wherein the plurality of antenna states correspond one-to-one to the plurality of weighted average values, and the weighted average value is a weighted average value of negotiation rates of all clients of the wireless AP corresponding to the smart antenna; A weighted average value determination module, configured to determine a target weighted average value based on the multiple weighted average values; The target antenna state determination module is used to select the antenna state corresponding to the target weighted average value as the target antenna state.

9. A terminal 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, the steps of the smart antenna scheduling method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the smart antenna scheduling method according to any one of claims 1 to 7 are implemented.

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