Intelligent distributed signal source automatic configuration method based on POST-INA integrated module

Abstract

The invention discloses an intelligent distributed signal source automatic configuration method based on a POST-INA integrated module, and relates to the technical field of distributed signal source configuration, and the method comprises the following steps: obtaining the equipment information of an intelligent distributed signal source, and determining the equipment density; acquiring network quality data; constructing an intelligent distributed signal source configuration model based on deep learning according to the equipment density evaluation data and the network quality evaluation data, and outputting a configuration scheme group in a configuration parameter feasible value range; obtaining configuration information and corresponding energy consumption information of the intelligent distributed signal source; establishing an intelligent distributed signal source energy consumption evaluation mechanism; and according to an energy consumption evaluation mechanism, screening configuration scheme groups given by the configuration model in combination with actual performance requirements and energy consumption requirements. The method is used for solving the problems that an existing intelligent distributed signal source configuration method is slightly insufficient in research on the aspect of automatically and comprehensively adjusting a configuration scheme in combination with a network environment, and actual performance and energy consumption requirements are not fully considered.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed signal source configuration, and more specifically, to an intelligent distributed signal source automatic configuration method based on a POST-INA integrated module. Background Art

[0002] An intelligent network adapter is a device that can intelligently sense and adjust parameters in a network environment. It adapts to different transmission environments by dynamically adjusting frequency, power, modulation, and other network parameters, thereby improving the effective coverage and transmission stability of the signal source. In areas with weak signals or large interference, the intelligent adapter can automatically increase or adjust the power of the signal source to ensure signal quality; in an environment with dense equipment, the adapter can automatically allocate a more suitable signal source according to demand to optimize transmission efficiency.

[0003] Intelligent distributed signal sources use multiple small signal sources to work simultaneously in different geographical locations, providing signal coverage through intelligent collaboration. This configuration can avoid the coverage blind spots of traditional single signal sources, improve the redundancy and stability of the network, and provide wider, more stable and more efficient signal transmission through optimized configuration and intelligent scheduling.

[0004] For example, the signal link signal quality evaluation method, device, equipment and readable storage medium announced by the invention patent with announcement number: CN109086546A discloses a signal link signal quality evaluation method, which includes: obtaining the target signal link to be evaluated; searching the transmitter model, transmission line model, via model and receiving end model matching the target signal link in the preset model library; connecting the transmitter model, transmission line model, via model and receiving end model to obtain a simulation link; inputting the signal simulation parameters and transmission line length corresponding to the target signal link into the simulation link to obtain the target simulation link; using the target simulation link, obtaining the signal quality evaluation result corresponding to the target signal link. The signal quality evaluation of the target signal link can be completed, further shortening the circuit board design cycle and improving the signal quality of the circuit board design. A signal link signal quality evaluation device, equipment and readable storage medium are also disclosed, which have corresponding technical effects.

[0005] For example, the invention patent with announcement number: CN114218727A announces a method for joint optimization of distribution network framework planning and multi-module intelligent terminal configuration, including: determining the configuration principle of the multi-module intelligent terminal considering differential protection; according to the determined configuration principle, performing reliability analysis on the distribution system of the multi-module intelligent terminal to determine the fault handling time; based on the fault handling time, establishing a collaborative planning model for the distribution network framework and the multi-module intelligent terminal configuration; optimizing the established collaborative planning model to determine the distribution network framework planning scheme, which can obtain the optimal framework planning scheme after configuring the multi-module intelligent terminal under the constraint of system reliability.

[0006] The above disclosed technical solutions have at least the following technical problems:

[0007] The existing intelligent distributed signal source configuration methods are slightly insufficient in terms of research on automatic and comprehensive adjustment of configuration schemes in combination with network environments, and do not fully consider the actual performance and energy consumption requirements in specific environments.

[0008] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0009] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an automatic configuration method for an intelligent distributed signal source based on a POST-INA integrated module, provides an automatic configuration plan by evaluating device density and network quality data, and establishes an energy consumption evaluation mechanism to optimize the configuration plan, so as to solve the above-mentioned technical problems.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] An automatic configuration method for an intelligent distributed signal source based on a POST-INA integrated module includes the following steps: obtaining device information of the intelligent distributed signal source, and determining device density based on the device information; obtaining network quality data, wherein the network quality data includes network traffic and signal interference intensity; constructing an intelligent distributed signal source configuration model based on deep learning according to the device density assessment data and the network quality assessment data, and outputting an intelligent distributed signal source configuration scheme group within a feasible value range of configuration parameters; obtaining configuration information and corresponding energy consumption information of the intelligent distributed signal source; establishing an intelligent distributed signal source energy consumption assessment mechanism based on the configuration information and the corresponding energy consumption information; and screening the configuration scheme group provided by the intelligent distributed signal source configuration model according to the intelligent distributed signal source energy consumption assessment mechanism and in combination with actual performance requirements and energy consumption requirements.

[0012] In a preferred embodiment, the device information of the intelligent distributed signal source includes: specific location information of the device, network topology information between devices and communication frequency information between devices; the device density includes device space density and device network density; the device density assessment data includes device density impact coefficient; the network quality assessment data includes network quality impact coefficient.

[0013] In a preferred embodiment, the device density is determined based on the device information, and the specific determination method is as follows: construct a signal source area distribution map based on the acquired device specific location information, and the signal source area distribution map marks the specific location of each intelligent distributed signal source in the area; determine the device space density in the area based on the specific location of each distributed signal source in the area; determine the device network density based on the acquired network topology structure information between devices and the communication frequency information between devices; weight the device space density and device network density in the area to obtain the device density.

[0014] In a preferred embodiment, the device density influence coefficient is specifically obtained by the following method: based on a preset area limitation principle, a specific area range is limited; combining the weighted device space density and the weighted device network density in the area, a preset device spacing influence empirical constant is given to obtain the device density coefficient; according to the device density coefficient, based on the channel capacity variation of the Shannon formula, combined with a preset device density influence coefficient calculation formula, the device density influence coefficient for evaluating the intelligent distributed signal source in the area is obtained.

[0015] In a preferred embodiment, the network quality impact coefficient is specifically obtained by the following method: according to a preset time period division principle, divide at least one time period and process the network quality data in each time period; according to the relationship between the maximum network carrying capacity and the current network capacity, obtain the network flow impact coefficient within the time period; according to the preset empirical constant for adjusting the specific impact of signal interference, evaluate the signal interference impact coefficient within the time period; quantify the interaction between network flow and signal interference intensity, and adjust the influence weights of network flow and signal interference intensity; according to the network flow impact coefficient within the time period and the signal interference impact coefficient within the time period, calculate the network quality impact coefficient within the time period based on the preset network quality impact coefficient calculation formula.

[0016] In a preferred embodiment, the intelligent distributed signal source configuration model is constructed based on deep learning based on the device density assessment data and the network quality assessment data, specifically: based on the preset area limitation principle and the preset time period division principle, the device density historical data and the network quality historical data of the limited area and limited time period are obtained, the device density historical data includes the device space density historical data and the device network density historical data, and the network quality historical data includes the network traffic historical data and the signal interference intensity historical data; according to the obtained historical data, the device density influence coefficient and the network quality influence coefficient of the limited area and limited time period are obtained; the processed data are divided into training sets, and the model parameters are adjusted based on the deep learning training model in combination with the preset intelligent distributed signal source transmission power, transmission frequency and transmission time slot empirical weight coefficients, and weights are respectively assigned to the device density influence coefficient and the network quality influence coefficient to construct the intelligent distributed signal source configuration model of the limited area and limited time period; the intelligent distributed signal source configuration model of each limited area and limited time period is integrated to obtain the intelligent distributed signal source configuration model.

[0017] In a preferred embodiment, the feasible value range of the configuration parameters is specifically as follows: the lower limit of the transmission power is the minimum transmission power value for maintaining the signal transmission of the intelligent distributed signal source, and the upper limit is the maximum transmission power value limited by the hardware performance of the intelligent distributed signal source; the lower limit of the transmission frequency is the minimum transmission frequency value limited by the hardware performance of the intelligent distributed signal source and the spectrum management, and the upper limit is the maximum transmission frequency value limited by the hardware performance of the intelligent distributed signal source and the spectrum management; the transmission time slot value range is limited to the time slot range that complies with the regulations and does not cause signal conflicts among the signal sources; the intelligent distributed signal source configuration scheme group includes: an intelligent distributed signal source signal transmission power configuration scheme; an intelligent distributed signal source signal transmission frequency configuration scheme; an intelligent distributed signal source signal transmission time slot configuration scheme.

[0018] In a preferred embodiment, the intelligent distributed signal source energy consumption assessment mechanism is specifically as follows: according to a preset energy consumption level classification standard, the energy consumption information is graded and divided into no less than one energy consumption level; and a corresponding relationship between the configuration information and the energy consumption level is established.

[0019] In a preferred embodiment, the configuration scheme group given by the intelligent distributed signal source configuration model is screened in combination with the actual performance requirements and energy consumption requirements, specifically: determining the performance limit and the energy consumption limit based on the actual performance requirements and energy consumption requirements; quantifying the performance limit and the energy consumption limit to obtain the performance limit level and the energy consumption limit level; selecting the configuration scheme group that meets the energy consumption limit level and has the configuration information corresponding to the energy consumption level obtained based on the intelligent distributed signal source energy consumption evaluation mechanism; eliminating the configuration scheme group that does not meet the performance limit level to obtain the intelligent distributed signal source configuration scheme group that meets the actual performance requirements and energy consumption requirements.

[0020] The technical effects and advantages of the intelligent distributed signal source automatic configuration method based on the POST-INA integrated module of the present invention are as follows:

[0021] 1. The present invention obtains the device information of the intelligent distributed signal source and the network quality data, and evaluates the network quality data and the device density determined based on the device information, constructs an intelligent distributed signal source configuration model based on machine learning, and outputs an intelligent distributed signal source configuration scheme group within the feasible value range of the configuration parameters, thereby achieving the purpose of automatically and comprehensively adjusting the configuration of the intelligent distributed signal source in combination with the network environment, effectively reducing the manual operation cost, and significantly improving the data transmission rate and anti-interference ability between the intelligent distributed signal sources. In addition, the device density influence coefficient and the network quality influence coefficient are used to dynamically evaluate the network environment by time period and region, and the signal configuration parameters of the intelligent distributed signal source in the region within the time period are adjusted in a targeted manner, effectively improving the accuracy of the automatic configuration of the intelligent distributed signal source in response to changes in the network environment.

[0022] 2. The present invention establishes an intelligent distributed signal source energy consumption evaluation mechanism by acquiring the configuration information and corresponding energy consumption information of the intelligent distributed signal source, and screens the configuration scheme group given by the intelligent distributed signal source configuration model in combination with the actual performance requirements and energy consumption requirements, thereby taking the actual performance and energy consumption requirements into consideration, fully ensuring the system performance requirements while reducing unnecessary energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a structural schematic diagram of the intelligent distributed signal source automatic configuration method based on the POST-INA integrated module of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0025] Embodiment 1, Figure 1 The present invention provides an intelligent distributed signal source automatic configuration method based on the POST-INA integrated module, comprising the following steps:

[0026] S1, obtaining device information of the intelligent distributed signal source, and determining the device density according to the device information.

[0027] S2, obtaining network quality data, where the network quality data includes network traffic and signal interference intensity.

[0028] In this embodiment, the device information of the intelligent distributed signal source includes: specific location information of the device, network topology information between devices, and communication frequency information between devices;

[0029] The device density includes device space density and device network density.

[0030] In this embodiment, the device density is determined based on the device information, and the specific determination method is:

[0031] According to the acquired specific location information of the device, a signal source area distribution map is constructed, wherein the signal source area distribution map marks the specific location of each intelligent distributed signal source in the area;

[0032] Determine the spatial density of devices in the area based on the specific locations of each distributed signal source in the area;

[0033] Determine the device network density based on the acquired network topology information between devices and the communication frequency information between devices;

[0034] The device density is obtained by weighting the device space density and device network density in the area.

[0035] S3, based on the device density assessment data and network quality assessment data, builds an intelligent distributed signal source configuration model based on deep learning, and outputs an intelligent distributed signal source configuration solution group within the feasible value range of the configuration parameters.

[0036] In this embodiment, the device density assessment data includes a device density impact coefficient; and the network quality assessment data includes a network quality impact coefficient.

[0037] In this embodiment, the device density influence coefficient is obtained in the following specific method:

[0038] According to the preset regional limitation principle, the specific regional scope is limited;

[0039] Combine the weighted device space density and weighted device network density in the area, give the preset device spacing influence empirical constant, and obtain the device density coefficient;

[0040] According to the device density coefficient, based on the channel capacity variation of the Shannon formula, combined with a preset device density influence coefficient calculation formula, the device density influence coefficient for evaluating the intelligent distributed signal source in the area is obtained.

[0041] In this embodiment, the network quality impact coefficient is specifically obtained in the following manner:

[0042] According to the preset time period division principle, divide the time period into no less than one, and process the network quality data in the time period;

[0043] According to the relationship between the maximum network carrying capacity and the current network capacity, the network capacity impact coefficient within the time period is obtained;

[0044] Based on the preset empirical constants for adjusting the specific impact of signal interference, the signal interference impact coefficient within the evaluation period is evaluated;

[0045] Quantify the interaction between network traffic and signal interference strength, and adjust the impact weights of network traffic and signal interference strength;

[0046] The network quality impact coefficient within the time period is calculated based on the preset network quality impact coefficient calculation formula according to the network traffic impact coefficient within the time period and the signal interference impact coefficient within the time period.

[0047] In this embodiment, the intelligent distributed signal source configuration model is constructed based on deep learning according to the device density evaluation data and the network quality evaluation data, specifically:

[0048] According to a preset area limitation principle and a preset time period division principle, historical device density data and historical network quality data of a limited area and limited time period are obtained, wherein the historical device density data includes historical device space density data and historical device network density data, and the historical network quality data includes historical network traffic data and historical signal interference intensity data;

[0049] According to the historical data obtained, the device density influence coefficient and the network quality influence coefficient of the limited area and limited time period are obtained;

[0050] The processed data is divided into training sets. Based on the deep learning training model, the model parameters are adjusted in combination with the preset empirical weight coefficients of the intelligent distributed signal source transmission power, transmission frequency and transmission time slot. The device density influence coefficient and the network quality influence coefficient are weighted respectively to build an intelligent distributed signal source configuration model for a limited area and a limited time period.

[0051] The intelligent distributed signal source configuration model of each limited area and limited time period is integrated to obtain the intelligent distributed signal source configuration model.

[0052] In this embodiment, the specific calculation formula of the device density influence coefficient is as follows:

[0053]

[0054] In the formula, is the device density influence coefficient, B is the bandwidth, P is the signal power, and k is the preset device spacing influence empirical constant. is the equipment space density weighting coefficient, is the device network density weighting coefficient, is the device space density, is the device network density.

[0055] The specific calculation formula of the network quality impact coefficient is as follows:

[0056]

[0057] In the formula, is the network quality impact coefficient, is the network traffic weight within the time period, is the signal interference strength weight within the time period, is the network traffic in the current period, is the maximum network carrying capacity, and is the empirical constant for the specific effect of the preset regulation signal interference, It is the signal interference intensity of the current period.

[0058] The specific formula of the intelligent distributed signal source configuration model is as follows:

[0059]

[0060] In the formula, is the weight of the equipment density influence coefficient, is the network quality impact coefficient weight, is the empirical weight coefficient of the transmission power of the intelligent distributed signal source, is the empirical weight coefficient of the transmission frequency of the intelligent distributed signal source, is the empirical weight coefficient of the transmission time slot of the intelligent distributed signal source, is the transmission power, is the transmitting frequency, is the transmission time slot, is the equipment density influence coefficient, is the network quality impact coefficient, is the base of natural logarithms.

[0061] In this embodiment, the feasible value range of the configuration parameter is specifically as follows:

[0062] The lower limit of the transmission power is the minimum transmission power value for maintaining the signal transmission of the intelligent distributed signal source, and the upper limit is the maximum transmission power value limited by the hardware performance of the intelligent distributed signal source;

[0063] The lower limit of the transmission frequency is the lowest transmission frequency value limited by the hardware performance of the intelligent distributed signal source and the spectrum management, and the upper limit is the highest transmission frequency value limited by the hardware performance of the intelligent distributed signal source and the spectrum management;

[0064] The transmission time slot value range is limited to the time slot range that complies with regulations and does not cause signal conflicts among signal sources;

[0065] The intelligent distributed signal source configuration scheme group includes:

[0066] Intelligent distributed signal source signal transmission power configuration scheme;

[0067] Intelligent distributed signal source signal transmission frequency configuration scheme;

[0068] Intelligent distributed signal source signal transmission time slot configuration scheme.

[0069] It should be noted that the area defined by the preset regional limitation principle should include the area where the intelligent distributed signal source as the research object is located, and the intelligent distributed signal sources in the area should be correlated; the preset time period division principle can be based on equal time division or based on the stable time period of the network environment, and the divided time as a whole should be a complete cycle, so as to ensure the regularity of the collection of research results for each time period.

[0070] Furthermore, according to the intelligent distributed signal source configuration model, the intelligent distributed signal source configuration scheme group output within the feasible value range of the configuration parameters is a non-unique scheme group.

[0071] This embodiment obtains the device information of the intelligent distributed signal source and the network quality data, and evaluates the network quality data and the device density determined based on the device information, builds an intelligent distributed signal source configuration model based on machine learning, and outputs an intelligent distributed signal source configuration solution group within the feasible value range of the configuration parameters, thereby achieving the purpose of automatically and comprehensively adjusting the configuration of the intelligent distributed signal source in combination with the network environment, effectively reducing the manual operation cost, and significantly improving the data transmission rate and anti-interference ability between the intelligent distributed signal sources. In addition, the device density influence coefficient and the network quality influence coefficient are used to dynamically evaluate the network environment by time period and region, and the signal configuration parameters of the intelligent distributed signal source in the region within the time period are adjusted in a targeted manner, effectively improving the accuracy of the automatic configuration of the intelligent distributed signal source in response to changes in the network environment.

[0072] Embodiment 2, S4, obtaining configuration information and corresponding energy consumption information of the intelligent distributed signal source.

[0073] S5, establishing an intelligent distributed signal source energy consumption evaluation mechanism according to the configuration information and corresponding energy consumption information.

[0074] In this embodiment, the intelligent distributed signal source energy consumption evaluation mechanism is specifically:

[0075] According to a preset energy consumption level classification standard, the energy consumption information is classified into at least one energy consumption level;

[0076] A correspondence between the configuration information and the energy consumption level is established.

[0077] S6, according to the intelligent distributed signal source energy consumption evaluation mechanism, combined with actual performance requirements and energy consumption requirements, the configuration solution group given by the intelligent distributed signal source configuration model is screened.

[0078] In this embodiment, the configuration scheme group provided by the intelligent distributed signal source configuration model is screened in combination with the actual performance requirements and energy consumption requirements, specifically:

[0079] Determine the performance limit and energy consumption limit based on actual performance requirements and energy consumption requirements;

[0080] Quantify the performance limit and energy consumption limit, and obtain the performance limit level and energy consumption limit level;

[0081] Selecting a configuration solution group that meets the energy consumption limit level and has configuration information corresponding to the energy consumption level obtained based on the intelligent distributed signal source energy consumption assessment mechanism;

[0082] The configuration scheme groups that do not meet the performance limit level are eliminated to obtain the intelligent distributed signal source configuration scheme group that meets the actual performance requirements and energy consumption requirements.

[0083] It should be noted that the energy consumption levels divided according to the preset energy consumption level classification standards include multiple groups of configuration information under a single energy consumption level, and a single group of configuration information includes multiple configuration parameters, and the multiple configuration parameters are the transmission power, transmission frequency and transmission time slot of the intelligent distributed signal source.

[0084] Furthermore, the classification standard of the energy consumption limit level is consistent with the preset energy consumption level classification standard; the performance limit level is comprehensively determined based on the transmission power, transmission frequency and transmission time slot of the intelligent distributed signal source.

[0085] This embodiment establishes an intelligent distributed signal source energy consumption evaluation mechanism by acquiring the configuration information and corresponding energy consumption information of the intelligent distributed signal source, and screens the configuration scheme group provided by the intelligent distributed signal source configuration model in combination with the actual performance requirements and energy consumption requirements, thereby taking the actual performance and energy consumption requirements into consideration, fully ensuring the system performance requirements while reducing unnecessary energy waste.

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0088] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0089] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent distributed signal source automatic configuration method based on POST-INA integrated module, characterized in that: The steps include: Obtain device information of intelligent distributed signal sources, and determine device density based on the device information; Acquiring network quality data, wherein the network quality data includes network traffic and signal interference intensity; Based on the device density assessment data and network quality assessment data, an intelligent distributed signal source configuration model is constructed based on deep learning, and within the feasible value range of the configuration parameters, an intelligent distributed signal source configuration solution group is output; Obtain configuration information and corresponding energy consumption information of intelligent distributed signal sources; Establishing an intelligent distributed signal source energy consumption assessment mechanism based on the configuration information and corresponding energy consumption information; According to the energy consumption evaluation mechanism of intelligent distributed signal sources, combined with actual performance requirements and energy consumption requirements, the configuration scheme group given by the intelligent distributed signal source configuration model is screened.

2. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 1 is characterized in that: The device information of the intelligent distributed signal source includes: specific location information of the device, network topology information between devices, and communication frequency information between devices; The device density includes device space density and device network density; The equipment density assessment data includes equipment density impact coefficient; The network quality evaluation data includes a network quality impact coefficient.

3. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 2 is characterized in that: The device density is determined based on the device information, and the specific determination method is: According to the acquired specific location information of the device, a signal source area distribution map is constructed, wherein the signal source area distribution map marks the specific location of each intelligent distributed signal source in the area; Determine the spatial density of devices in the area based on the specific locations of each distributed signal source in the area; Determine the device network density based on the acquired network topology information between devices and the communication frequency information between devices; The device density is obtained by weighting the device space density and device network density in the area.

4. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 3 is characterized in that: The specific method for obtaining the device density influence coefficient is as follows: According to the preset regional limitation principle, the specific regional scope is limited; Combine the weighted device space density and weighted device network density in the area, give the preset device spacing influence empirical constant, and obtain the device density coefficient; According to the device density coefficient, based on the channel capacity variation of the Shannon formula, combined with a preset device density influence coefficient calculation formula, the device density influence coefficient for evaluating the intelligent distributed signal source in the area is obtained.

5. The method for automatic configuration of intelligent distributed signal sources based on the POST-INA integrated module according to claim 4, characterized in that: The network quality impact coefficient is specifically obtained as follows: According to the preset time period division principle, divide the time period into no less than one, and process the network quality data in the time period; According to the relationship between the maximum network carrying capacity and the current network capacity, the network capacity impact coefficient within the time period is obtained; Based on the preset empirical constants for adjusting the specific impact of signal interference, the signal interference impact coefficient within the evaluation period is evaluated; Quantify the interaction between network traffic and signal interference strength, and adjust the impact weights of network traffic and signal interference strength; The network quality impact coefficient within the time period is calculated based on the preset network quality impact coefficient calculation formula according to the network traffic impact coefficient within the time period and the signal interference impact coefficient within the time period.

6. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 5 is characterized in that: The intelligent distributed signal source configuration model is constructed based on deep learning according to the device density assessment data and the network quality assessment data, specifically: According to a preset area limitation principle and a preset time period division principle, historical device density data and historical network quality data of a limited area and limited time period are obtained, wherein the historical device density data includes historical device space density data and historical device network density data, and the historical network quality data includes historical network traffic data and historical signal interference intensity data; According to the historical data obtained, the device density influence coefficient and the network quality influence coefficient of the limited area and limited time period are obtained; The processed data is divided into training sets. Based on the deep learning training model, the model parameters are adjusted in combination with the preset empirical weight coefficients of the intelligent distributed signal source transmission power, transmission frequency and transmission time slot. The device density influence coefficient and the network quality influence coefficient are weighted respectively to build an intelligent distributed signal source configuration model for a limited area and a limited time period. The intelligent distributed signal source configuration model of each limited area and limited time period is integrated to obtain the intelligent distributed signal source configuration model.

7. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 6 is characterized in that: The feasible value range of the configuration parameters is as follows: The lower limit of the transmission power is the minimum transmission power value for maintaining the signal transmission of the intelligent distributed signal source, and the upper limit is the maximum transmission power value limited by the hardware performance of the intelligent distributed signal source; The lower limit of the transmission frequency is the lowest transmission frequency value limited by the hardware performance of the intelligent distributed signal source and the spectrum management, and the upper limit is the highest transmission frequency value limited by the hardware performance of the intelligent distributed signal source and the spectrum management; The transmission time slot value range is limited to the time slot range that complies with regulations and does not cause signal conflicts among signal sources; The intelligent distributed signal source configuration scheme group includes: Intelligent distributed signal source signal transmission power configuration scheme; Intelligent distributed signal source signal transmission frequency configuration scheme; Intelligent distributed signal source signal transmission time slot configuration scheme.

8. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 7 is characterized in that: The intelligent distributed signal source energy consumption evaluation mechanism is specifically: According to a preset energy consumption level classification standard, the energy consumption information is classified into at least one energy consumption level; A correspondence between the configuration information and the energy consumption level is established.

9. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 8 is characterized in that: The configuration scheme group provided by the intelligent distributed signal source configuration model is screened in combination with the actual performance requirements and energy consumption requirements, specifically: Determine the performance limit and energy consumption limit based on actual performance requirements and energy consumption requirements; Quantify the performance limit and energy consumption limit, and obtain the performance limit level and energy consumption limit level; Selecting a configuration solution group that meets the energy consumption limit level and has configuration information corresponding to the energy consumption level obtained based on the intelligent distributed signal source energy consumption assessment mechanism; The configuration scheme groups that do not meet the performance limit level are eliminated to obtain the intelligent distributed signal source configuration scheme group that meets the actual performance requirements and energy consumption requirements.

10. The intelligent distributed signal source automatic configuration method based on the POST-INA integrated module according to claim 9 is characterized in that: The specific calculation formula of the equipment density influence coefficient is as follows: ， In the formula, is the device density influence coefficient, B is the bandwidth, P is the signal power, and k is the preset device spacing influence empirical constant. is the equipment space density weighting coefficient, is the device network density weighting coefficient, is the device space density, is the device network density; The specific calculation formula of the network quality impact coefficient is as follows: ， In the formula, is the network quality impact coefficient, is the network traffic weight within the time period, is the signal interference strength weight within the time period, is the network traffic in the current period, is the maximum network carrying capacity, and is the empirical constant for the specific effect of the preset regulation signal interference, is the signal interference intensity of the current period; The specific formula of the intelligent distributed signal source configuration model is as follows: ， In the formula, is the weight of the equipment density influence coefficient, is the network quality impact coefficient weight, is the empirical weight coefficient of the transmission power of the intelligent distributed signal source, is the empirical weight coefficient of the transmission frequency of the intelligent distributed signal source, is the empirical weight coefficient of the transmission time slot of the intelligent distributed signal source, is the transmission power, is the transmitting frequency, is the transmission time slot, is the equipment density influence coefficient, is the network quality impact coefficient, is the base of natural logarithms.

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