A wireless communication transmission system and method based on intelligent reflective surface

Abstract

The present invention discloses a wireless communication transmission system and method based on a smart reflective surface, and belongs to the technical field of data analysis. The present invention collects the receiving end data in the wireless communication transmission in history, searches for the associated data of the signal abnormality; collects the environmental data when the signal abnormality occurs in the receiving end in history, extracts the features and classifies them; analyzes the environmental data when the smart reflective surface is used in history, defines the features of the environmental data when the smart reflective surface is used as the conditional element, and judges whether each type of environmental data is suitable for using the smart reflective surface after classification; calculates the threshold of the associated data, collects the associated data of the receiving end in real time, judges whether the signal abnormality occurs in the receiving end and issues an early warning; analyzes the real-time environmental data during the early warning, and calculates the similarity between the real-time environment and each type of environmental data; when it is judged that the monitoring area is suitable for placing the smart reflective surface, a signal for using the smart reflective surface is issued.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a wireless communication transmission system and method based on an intelligent reflective surface. Background Art

[0002] In the early stages of radio, it was mainly used in the fields of telegraph, broadcasting and maritime communications. The development of radio technology has enabled communication to break free from the limitations of cables and wires and become a new way of communication. With the advancement of radio technology, mobile communication technology began to emerge in the mid-20th century, allowing people to communicate while on the move. The initial mobile communication system was mainly used for military and emergency communications, and later gradually spread to commercial and civilian fields. With the continuous evolution of wireless communication technology, wireless networks have also experienced development from the first generation to the fifth generation. Each generation of wireless networks has brought higher speeds, lower latency and better user experience, promoting the development of mobile Internet and the Internet of Things. However, when wireless communication is widely used, there are still special circumstances that cause the receiving end in wireless communication to be unable to accurately receive signals, and there are many interference factors that affect the wireless signals of the base station. Therefore, in 2019, the concept of smart reflective surface was proposed. Smart reflective surface is an emerging wireless communication technology that improves the transmission performance of signals by adjusting the phase of electromagnetic waves. It is a passive smart surface, usually composed of reflective units with adjustable phases, which can accurately adjust the phase of the incident signal and project the reflected signal in a specified direction. Smart reflective surface technology can improve signal coverage, enhance signal quality, increase system capacity and enhance privacy and security without increasing transmission power.

[0003] Although smart reflective surfaces can effectively enhance signals in many cases, there are also some cases where smart reflective surface technology may not be used or applicable. The effect of smart reflective surfaces is limited by environmental conditions. For example, some environments may produce large signal interference or hinder the use of reflective surfaces, such as high-rise buildings, dense trees or mountains. In view of the above problems, the present invention proposes a wireless communication transmission system and method based on smart reflective surfaces. Summary of the invention

[0004] The object of the present invention is to provide a wireless communication transmission system and method based on a smart reflective surface to solve the problems raised in the above background technology.

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

[0006] A wireless communication transmission method based on a smart reflective surface, the method comprising the following steps:

[0007] S100, collecting receiving end data in historical wireless communication transmissions, analyzing the receiving end data in the history, and searching for associated data with signal anomalies;

[0008] Furthermore, the specific steps for finding the associated data with signal anomalies are:

[0009] S101, assuming that there are n types of data collected from the receiving end of wireless communication transmission in history, specifically {q1, q2, q3...q n}, q1, q2, q3...q n The first, second, third, ... nth types of data collected from the receiving end of the wireless communication transmission in history, where n is a positive integer; the receiving end data of the wireless base station at m different signal transmission powers in history are collected;

[0010] S102, when the signal transmission power of the wireless base station changes in history, the collected data of n types of receiving ends are analyzed, the signal transmission power of the wireless base station is used as the x-axis independent variable, each data value of the receiving end is used as the y-axis variable, and the signal power curve of each type of receiving end data is drawn using the point drawing method;

[0011] S103, analyzing the signal power curves of each type of data drawn, selecting a linear curve in the curve graph, and a curve showing an upward trend as the independent variable signal transmission power increases as the signal-related data curve, and the corresponding receiving end data type q_x is signal abnormality related data.

[0012] Selecting a linear curve in the signal power curve diagram can select the data type that changes with the signal power of the wireless base station, and further selecting data with an upward trend can make the selected data curve have the same change pattern as the signal power of the wireless base station. According to the change pattern, it is determined that this data type can reflect the change pattern of the signal strength at the receiving end.

[0013] S200, collecting historical environmental data when signal anomalies occurred at the receiving end, extracting features from the environmental data, and classifying the data;

[0014] Furthermore, the specific steps of extracting features and classifying environmental data are as follows:

[0015] S201, collect the environmental data when the signal abnormality occurs at the receiving end j times in history, specifically {h1, h2, h3...h j}, h1, h2, h3...h j is the environmental data collected when the 1st, 2nd, 3rd, ...jth signal anomaly occurs at the receiving end, where j is a positive integer;

[0016] S202, suppose that the environmental data h collected each time a signal anomaly occurs at the receiving end has k elements, h = {g1, g2, g3...g k}, g1, g2, g3...g k are the 1st, 2nd, 3rd, ... kth elements in each collected environmental data, where k is a positive integer; calculate the number of identical elements in h, and take the element with the largest number as the feature of environmental data h;

[0017] S203. According to the above method, feature extraction is performed on the environmental data collected for j times of abnormal signals at the receiving end, and environmental data with the same features are classified into the same category, and finally the environmental data are classified into p categories.

[0018] S300, analyzing the environmental data when the smart reflective surface is used to enhance the receiving end signal in the history, defining the characteristics of the environmental data when the smart reflective surface is used as conditional elements, and judging whether each type of environmental data after classification is suitable for using the smart reflective surface according to the conditional elements;

[0019] Furthermore, the specific steps for determining whether each type of environmental data after classification is suitable for using the smart reflective surface are as follows:

[0020] S301, collecting historical environmental data when an abnormal signal occurs at the receiving end and using the intelligent reflective surface to handle the abnormal signal at the receiving end, extracting features of the environmental data of the intelligent reflective surface according to the method of S202, and defining the extracted features as conditional elements for using the intelligent reflective surface;

[0021] S302, after extracting the conditional elements for using the smart reflective surface, the conditional elements are compared with each type of classified environmental data, and the proportion of the conditional elements in each type of environmental data is calculated, and the formula is:

[0022]

[0023] In the above formula, d is the proportion of conditional elements in each environmental data, s_t is the number of conditional elements in each environmental data, and k is the total number of elements in each environmental data;

[0024] S303, after calculating the proportion of conditional elements in each environmental data according to the above formula, when it is determined that d ≥ 50%, the corresponding environmental data is suitable for using the smart reflective surface for signal anomaly processing; conversely, when d < 50%, the corresponding environmental data is not suitable for using the smart reflective surface for signal anomaly processing;

[0025] S304, after p calculations, where p represents the total number of types of classified environmental data, and after determining the proportion of conditional elements in the environmental data, the types of environmental data suitable for using the intelligent reflective surface for signal anomaly processing are obtained among all types of environmental data, and a set B is constructed, where B = {a1, a2, a3 ... a b}, a1, a2, a3...a b Indicates the 1st, 2nd, 3rd, ...bth environmental data types suitable for using smart reflective surfaces, where b is a positive integer and b <p。

[0026] By collecting and analyzing the environmental data when the smart reflector was used to enhance the signal at the receiving end in the past, the characteristics of the environmental data are extracted. This characteristic can represent the environmental condition element for using the smart reflector. By using the environmental condition element to compare and analyze the classified data, it can be determined which environmental data can use the smart reflector.

[0027] S400, analyzing the historical associated data and calculating the threshold of the associated data;

[0028] Furthermore, the specific steps for calculating the threshold of the associated data are:

[0029] S401, collect the values ​​of the associated data when the receiving end signal is abnormal in the history {z1, z2, z3...z c}, z1, z2, z3...z c is the 1st, 2nd, 3rd, ... cth data of the receiving end in the historical wireless communication transmission collected, where c is a positive integer;

[0030] S402, collecting the number w of receiving-end devices in different regions, and calculating the density of receiving-end devices in different regions, the formula is:

[0031]

[0032] In the formula, ρ represents the receiving device density in different regions, w represents the number of receiving devices in different regions, and ar represents the area of ​​different regions. After calculating the receiving devices in each region, the required signal strength of each region is graded according to the different receiving device densities, and the standard deviation of the receiving device density is calculated. The region with the lowest receiving device density is selected as the first level of signal strength. After that, when the receiving device density increases by one standard deviation, the corresponding signal strength increases by one level, and then it is analogized to {1, 2, 3...H}, where 1, 2, 3...H means that different regions are divided into H levels according to the receiving device density, and H is a positive integer.

[0033] S403, calculating the average value and standard deviation of all the collected associated data values, and calculating the threshold of the associated data according to the calculated average value and standard deviation, the formula is:

[0034] q_y = z_p - H i ×z_s

[0035] In the above formula, q_y represents the threshold of associated data, z_p represents the average value of associated data, and z_s

[0036] represents the standard deviation of associated data, and H i represents the signal strength grading of the area where the receiving end for calculating the threshold of associated data is located, 1 ≤ H i ≤ H.

[0037] The required signal strength varies in different regions. And within the region, the higher the density of receiving end devices, the higher the required signal strength to ensure normal wireless communication. By calculating the density of receiving end devices in the region, the required signal strength in the corresponding region can be reflected.

[0038] S500. Collect the associated data of the receiving end in real time, and judge whether the receiving end has signal anomalies according to the threshold and give an early warning;

[0039] Further, the specific steps for judging whether the receiving end has signal anomalies according to the threshold and giving an early warning are as follows:

[0040] S501. Collect in real time the associated data values of all receiving ends in the area where the required signal strength is H i as {q_x1, q_x2, q_x3...q_x L}, where q_x1, q_x2, q_x3...q_x L represent the associated data values of the 1st, 2nd, 3rd...Lth receiving ends collected, and L is a positive integer;

[0041] S502. Judge the associated data of each receiving end in the area where the signal strength is H i . When q_x r < q_y, it is judged that the corresponding receiving end has signal anomalies. On the contrary, when q_x r ≥ q_y, it is judged that the signal of the corresponding receiving end is normal, 1 ≤ r ≤ L;

[0042] S503. According to the above method, judge all the associated data of the L receiving ends collected, and finally obtain the receiving ends with signal anomalies as {J1, J2, J3...J K}, where J1, J2, J3...J K represent the 1st, 2nd, 3rd...Kth receiving ends judged to have signal anomalies, and K is a positive integer;

[0043] S504. After judging that the receiving end has signal anomalies, give a signal anomaly early warning to each receiving end respectively.

[0044] S600. After the receiving end is warned, the real-time environmental data at the time of the warning is analyzed, and the similarity between the real-time environment and each type of environmental data is calculated; the corresponding environmental data type is selected according to the similarity, and the proportion of environmental data types in the entire area is calculated. Then, when it is determined that the monitoring area is suitable for placing an intelligent reflective surface based on the proportion, a signal for using the intelligent reflective surface is issued.

[0045] Furthermore, the specific steps for calculating the similarity between the real-time environment and each type of environmental data are as follows:

[0046] S601, after the signal abnormality warning is performed on the receiving end, the environmental data of the receiving end is collected, and the similarity between the environmental data of the receiving end for signal abnormality warning and each type of environmental data in S200 is calculated, and the formula is:

[0047]

[0048] In the above formula, Si represents the similarity between the environmental data of the receiving end of the signal abnormal warning and each type of environmental data in S200, α represents the number of types of elements in the environmental data of the receiving end of the signal abnormal warning and each type of environmental data, G t represents the number of elements of the same type in the environmental data of the receiving end of the signal abnormality warning, and k represents the total number of elements in each environmental data; similarity calculation is performed on p types of environmental data according to the formula;

[0049] S602, after calculation, p similarity values ​​are obtained, and the environmental data h_s corresponding to the maximum similarity value is selected. When the selected environmental data h_s is in set B, it is determined that the receiving end where the signal anomaly occurs is suitable for processing using the smart reflective surface. On the contrary, if h_s is not in set B, it is determined that it is not suitable for processing using the smart reflective surface.

[0050] S603: According to the above method, similarity calculation is performed on K receiving ends with signal anomalies, and it is determined that the receiving ends with signal anomalies suitable for processing by using the intelligent reflective surface are {J_S1, J_S2, J_S3...J_S u}, J_s1, J_s2, J_s3...J_s u Indicates the first, second, third, ..., u receiving ends with signal anomalies that are suitable for processing by the intelligent reflective surface, where u is a positive integer, 0≤u≤K;

[0051] S604, calculate the required signal strength H i Whether the area is suitable for placing smart reflective surfaces, the formula is:

[0052]

[0053] In the above formula, β represents the proportion of receiving ends with signal abnormalities suitable for intelligent reflective surface processing in the total number of receiving ends with signal abnormalities; K represents the number of receiving ends with signal abnormalities;

[0054] According to the above formula, when β≥50%, the required signal strength is determined to be H i The area is suitable for placing smart reflective surfaces, and the smart reflective surface uses signals for output; when β<50%, the required signal strength is determined to be H i Areas with high humidity are not suitable for placement of smart reflective surfaces.

[0055] Since the smart reflective surface requires certain usage conditions, not all signal anomalies at the receiving end can be processed using the smart reflective surface. Therefore, it is necessary to judge the real-time environment of the receiving end to determine whether the environmental data is suitable for the use of the smart reflective surface and whether the area is suitable for the use of the smart reflective surface. Only when it is judged to be suitable can the smart reflective surface be used to process the signal anomaly at the receiving end.

[0056] A wireless communication transmission system based on an intelligent reflective surface, the wireless communication transmission system comprising a data collection module, a correlation data search module, a data processing module, a signal strength classification module, an early warning module, a similarity calculation module, and an output module;

[0057] The data collection module is used to collect historical associated data and environmental data of the receiving end, and monitor and collect the associated data and environmental data of the receiving end in real time;

[0058] The associated data search module is used to analyze the collected receiving end historical data, search for data associated with the signal strength, and define it as associated data;

[0059] The data processing module is used to extract features from the collected environmental data of the receiving end and classify them according to the extracted features;

[0060] The signal strength classification module is used to calculate and analyze the density of receiving end devices in different regions, and classify the signal strength required in different regions according to the receiving end density;

[0061] The warning module is used to calculate the environmental data of the receiving end collected in real time, determine whether a signal abnormality may occur at the receiving end, and issue a warning;

[0062] The similarity calculation module is used to perform similarity calculation on the environmental data of the warning receiving end after making a real-time judgment and warning on the receiving end, judge whether it is suitable to use the intelligent reflective surface to process abnormal signals, and finally judge whether the monitoring area is suitable for placing the intelligent reflective surface, and generate the intelligent reflective surface use signal;

[0063] The output module outputs the intelligent emitting surface usage signal generated by the similarity calculation module.

[0064] The data processing module includes an environmental data classification unit and an intelligent reflective surface condition calculation unit;

[0065] The environmental data classification unit is used to extract features from the collected environmental data of the receiving end and classify the data according to the extracted features;

[0066] The smart reflective surface condition calculation unit is used to analyze the environmental data of the use of the smart reflective surface in history, generate condition elements for the use of the smart reflective surface, and search and classify the types of environmental data suitable for the use of the smart reflective surface according to the condition elements.

[0067] The similarity calculation module is used to perform similarity calculation on the environmental data of the warning receiving end after making real-time judgment and warning on the receiving end, to determine whether it is suitable to use the intelligent reflective surface for abnormal signal processing, and then calculate whether the selected area is suitable for placing the intelligent reflective surface based on all the receiving ends suitable for using the intelligent reflective surface. When it is determined to be suitable, a signal for using the intelligent reflective surface is generated.

[0068] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0069] 1. The present invention collects and judges historical receiving end data, draws a signal power curve diagram to find the data types that are linear and change in the same direction as the signal power changes. The historical data can be used to accurately find the data types that can reflect the signal strength in the receiving end, so as to facilitate the monitoring of the signal strength of the receiving end.

[0070] 2. The present invention classifies the signal strength required for different regions. When real-time monitoring is performed on the signals of receiving ends in different regions, different associated data thresholds are calculated according to different signal strength levels, so that it can more accurately warn whether signal abnormalities occur at receiving ends in different regions.

[0071] 3. The present invention determines whether the receiving end and the area where the receiving end is located are suitable for using the smart reflective surface. When it is determined that the smart reflective surface is suitable for using, a smart reflective surface use signal is generated. The signal abnormality processing scheme can be intelligently selected according to different environmental conditions to avoid the problem of being unable to process abnormalities or inefficiency caused by using the smart reflective surface in an environment where the smart reflective surface cannot be used. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0073] In the attached picture:

[0074] Figure 1 It is a module flow chart of a wireless communication transmission system based on an intelligent reflective surface of the present invention;

[0075] Figure 2 It is a schematic diagram of the steps of a wireless communication transmission method based on a smart reflective surface of the present invention;

[0076] Figure 3 It is a signal power curve diagram of a wireless communication transmission method based on an intelligent reflecting surface of the present invention. DETAILED DESCRIPTION

[0077] 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.

[0078] See also Figure 1-Figure 3 , the present invention provides a technical solution:

[0079] A wireless communication transmission method based on a smart reflective surface, the method comprising the following steps:

[0080] S100, collecting receiving end data in historical wireless communication transmissions, analyzing the receiving end data in the history, and searching for associated data with signal anomalies;

[0081] The specific steps to find the associated data with signal anomalies are:

[0082] S101, assuming that there are n types of data collected from the receiving end of wireless communication transmission in history, specifically {q1, q2, q3...q n}, q1, q2, q3...q n The first, second, third, ... nth types of data collected from the receiving end of the wireless communication transmission in history, where n is a positive integer; the receiving end data of the wireless base station at m different signal transmission powers in history are collected;

[0083] S102, when the signal transmission power of the wireless base station changes in history, the collected data of n types of receiving ends are analyzed, the signal transmission power of the wireless base station is used as the x-axis independent variable, each data value of the receiving end is used as the y-axis variable, and the signal power curve of each type of receiving end data is drawn using the point drawing method;

[0084] S103, analyzing the signal power curves of each type of data drawn, selecting a linear curve in the curve graph, and a curve showing an upward trend as the independent variable signal transmission power increases as the signal-related data curve, and the corresponding receiving end data type q_x is signal abnormality related data.

[0085] Selecting a linear curve in the signal power curve diagram can select the data type that changes with the signal power of the wireless base station, and further selecting data with an upward trend can make the selected data curve have the same change pattern as the signal power of the wireless base station. According to the change pattern, it is determined that this data type can reflect the change pattern of the signal strength at the receiving end.

[0086] S200, collecting historical environmental data when signal anomalies occurred at the receiving end, extracting features from the environmental data, and classifying the data;

[0087] The specific steps for feature extraction and classification of environmental data are as follows:

[0088] S201, collect the environmental data when the signal abnormality occurs at the receiving end j times in the history, specifically {h1, h2, h3...h j}, h1, h2, h3...h j is the environmental data collected when the 1st, 2nd, 3rd, ...jth signal anomaly occurs at the receiving end, where j is a positive integer;

[0089] S202, suppose that the environmental data h collected each time a signal anomaly occurs at the receiving end has k elements, h = {g1, g2, g3...g k}, g1, g2, g3...g k are the 1st, 2nd, 3rd, ... kth elements in each collected environmental data, where k is a positive integer; calculate the number of identical elements in h, and take the element with the largest number as the feature of environmental data h;

[0090] S203. According to the above method, feature extraction is performed on the environmental data collected for j times of abnormal signals at the receiving end, and environmental data with the same features are classified into the same category, and finally the environmental data are classified into p categories.

[0091] S300, analyzing the environmental data when the smart reflective surface is used to enhance the receiving end signal in the history, defining the characteristics of the environmental data when the smart reflective surface is used as conditional elements, and judging whether each type of environmental data after classification is suitable for using the smart reflective surface according to the conditional elements;

[0092] The specific steps to determine whether each type of environmental data after classification is suitable for using the smart reflective surface are:

[0093] S301, collecting historical environmental data when an abnormal signal occurs at the receiving end and using the intelligent reflective surface to handle the abnormal signal at the receiving end, extracting features of the environmental data of the intelligent reflective surface according to the method of S202, and defining the extracted features as conditional elements for using the intelligent reflective surface;

[0094] S302, after extracting the conditional elements for using the smart reflective surface, the conditional elements are compared with each type of classified environmental data, and the proportion of the conditional elements in each type of environmental data is calculated, and the formula is:

[0095]

[0096] In the above formula, d is the proportion of conditional elements in each environmental data, s_t is the number of conditional elements in each environmental data, and k is the total number of elements in each environmental data;

[0097] S303, after calculating the proportion of conditional elements in each environmental data according to the above formula, when it is determined that d ≥ 50%, the corresponding environmental data is suitable for using the smart reflective surface for signal anomaly processing; conversely, when d < 50%, the corresponding environmental data is not suitable for using the smart reflective surface for signal anomaly processing;

[0098] S304, after p calculations, where p represents the total number of types of classified environmental data, and after determining the proportion of conditional elements in the environmental data, the types of environmental data suitable for using the intelligent reflective surface for signal anomaly processing are obtained among all types of environmental data, and a set B is constructed, where B = {a1, a2, a3 ... a b}, a1, a2, a3...a b Indicates the 1st, 2nd, 3rd, ...bth environmental data types suitable for using smart reflective surfaces, where b is a positive integer and b <p。

[0099] By collecting and analyzing the environmental data when the smart reflector was used to enhance the signal at the receiving end in the past, the characteristics of the environmental data are extracted. This characteristic can represent the environmental condition element for using the smart reflector. By using the environmental condition element to compare and analyze the classified data, it can be determined which environmental data can use the smart reflector.

[0100] S400, analyzing the historical associated data and calculating the threshold of the associated data;

[0101] The specific steps for calculating the threshold of associated data are:

[0102] S401, collect the values ​​of the associated data when the receiving end signal is abnormal in the history {z1, z2, z3...z c}, z1, z2, z3...z c is the 1st, 2nd, 3rd, ... cth data of the receiving end in the historical wireless communication transmission collected, where c is a positive integer;

[0103] S402, collecting the number w of receiving-end devices in different regions, and calculating the density of receiving-end devices in different regions, the formula is:

[0104]

[0105] In the formula, ρ represents the receiving device density in different regions, w represents the number of receiving devices in different regions, and ar represents the area of ​​different regions. After calculating the receiving devices in each region, the required signal strength of each region is graded according to the different receiving device densities, and the standard deviation of the receiving device density is calculated. The region with the lowest receiving device density is selected as the first level of signal strength. After that, when the receiving device density increases by one standard deviation, the corresponding signal strength increases by one level, and then it is analogized to {1, 2, 3...H}, where 1, 2, 3...H means that different regions are divided into H levels according to the receiving device density, and H is a positive integer.

[0106] S403, calculating the average value and standard deviation of all the collected associated data values, and calculating the threshold of the associated data according to the calculated average value and standard deviation, the formula is:

[0107] q_y=z_p-H i ×z_s

[0108] In the above formula, q_y represents the threshold of the associated data, z_p represents the average value of the associated data, and z_s

[0109] Expressed as the standard deviation of the correlation data, H i Indicates the signal strength level of the receiving end in the area where the associated data threshold is calculated, 1≤H i ≤H.

[0110] Different regions require different signal strengths. The higher the density of receiving-end devices in a region, the higher the signal strength required to ensure normal wireless communication. By calculating the density of receiving-end devices in a region, the required signal strength in the corresponding region can be reflected.

[0111] S500. Collect the associated data of the receiving end in real time, and judge whether the receiving end has signal anomalies according to the threshold and give early warnings.

[0112] The specific steps for judging whether the receiving end has signal anomalies according to the threshold and giving early warnings are as follows:

[0113] S501. Collect in real time the associated data values of all receiving ends in the area where the required signal strength is H i as {q_x1, q_x2, q_x3...q_x L}, where q_x1, q_x2, q_x3...q_x L represent the associated data values of the 1st, 2nd, 3rd...Lth receiving ends collected, and L is a positive integer.

[0114] S502. Judge the associated data of each receiving end in the area where the signal strength is H i . When q_x r < q_y, it is judged that the corresponding receiving end has signal anomalies. On the contrary, when q_x r ≥ q_y, it is judged that the signal of the corresponding receiving end is normal, 1 ≤ r ≤ L.

[0115] S503. According to the above method, judge the associated data of the L receiving ends collected, and finally obtain the receiving ends with signal anomalies as {J1, J2, J3...J K}, where J1, J2, J3...J K represent the 1st, 2nd, 3rd...Kth receiving ends judged to have signal anomalies, and K is a positive integer.

[0116] S504. After judging that the receiving end has signal anomalies, give early warnings for signal anomalies to each receiving end respectively.

[0117] S600. After giving early warnings to the receiving end, analyze the real-time environmental data at the time of early warning, and calculate the similarity between the real-time environment and each type of environmental data; select the corresponding type of environmental data according to the similarity, calculate the proportion of each type of environmental data in the whole area, and then judge whether the monitoring area is suitable for placing intelligent reflecting surfaces according to the proportion, and send out a signal to use the intelligent reflecting surface.

[0118] The specific steps for calculating the similarity between the real-time environment and each type of environmental data are as follows:

[0119] S601. After giving early warnings for signal anomalies to the receiving end, collect the environmental data where the receiving end is located, and calculate the similarity between the environmental data of the receiving end for which signal anomaly early warning is given and each type of environmental data in S200. The formula is:

[0120]

[0121] In the above formula, Si represents the similarity between the environmental data of the receiving end of the signal abnormal warning and each type of environmental data in S200, α represents the number of types of elements in the environmental data of the receiving end of the signal abnormal warning and each type of environmental data, G t represents the number of elements of the same type in the environmental data of the receiving end of the signal abnormality warning, and k represents the total number of elements in each environmental data; similarity calculation is performed on p types of environmental data according to the formula;

[0122] S602, after calculation, p similarity values ​​are obtained, and the environmental data h_s corresponding to the maximum similarity value is selected. When the selected environmental data h_s is in set B, it is determined that the receiving end where the signal anomaly occurs is suitable for processing using the smart reflective surface. On the contrary, if h_s is not in set B, it is determined that it is not suitable for processing using the smart reflective surface.

[0123] S603: According to the above method, similarity calculation is performed on K receiving ends with signal anomalies, and it is determined that the receiving ends with signal anomalies suitable for processing by using the intelligent reflective surface are {J_s1, J_s2, J_s3...J_s u}, J_s1, J_s2, J_s3...J_s u Indicates the first, second, third, ..., u receiving ends with signal anomalies that are suitable for processing by the intelligent reflective surface, where u is a positive integer, 0≤u≤K;

[0124] S604, calculate the required signal strength H i Whether the area is suitable for placing smart reflective surfaces, the formula is:

[0125]

[0126] In the above formula, β represents the proportion of receiving ends with signal abnormalities suitable for intelligent reflective surface processing in the total number of receiving ends with signal abnormalities; K represents the number of receiving ends with signal abnormalities;

[0127] According to the above formula, when β≥50%, the required signal strength is determined to be H i The area is suitable for placing smart reflective surfaces, and the smart reflective surface uses signals for output; when β<50%, the required signal strength is determined to be H i Areas with high humidity are not suitable for placement of smart reflective surfaces.

[0128] Since the smart reflective surface requires certain usage conditions, not all signal anomalies at the receiving end can be processed using the smart reflective surface. Therefore, it is necessary to judge the real-time environment of the receiving end to determine whether the environmental data is suitable for the use of the smart reflective surface and whether the area is suitable for the use of the smart reflective surface. Only when it is judged to be suitable can the smart reflective surface be used to process the signal anomaly at the receiving end.

[0129] A wireless communication transmission system based on an intelligent reflective surface, the wireless communication transmission system comprising a data collection module, a correlation data search module, a data processing module, a signal strength classification module, an early warning module, a similarity calculation module, and an output module;

[0130] The data collection module is used to collect historical associated data and environmental data of the receiving end, and monitor and collect the associated data and environmental data of the receiving end in real time;

[0131] The associated data search module is used to analyze the collected receiving end historical data, search for data associated with the signal strength, and define it as associated data;

[0132] The data processing module is used to extract features from the collected environmental data of the receiving end and classify them according to the extracted features;

[0133] The signal strength classification module is used to calculate and analyze the density of receiving end devices in different regions, and classify the signal strength required in different regions according to the receiving end density;

[0134] The warning module is used to calculate the environmental data of the receiving end collected in real time, determine whether a signal abnormality may occur at the receiving end, and issue a warning;

[0135] The similarity calculation module is used to perform similarity calculation on the environmental data of the warning receiving end after making a real-time judgment and warning on the receiving end, judge whether it is suitable to use the intelligent reflective surface to process abnormal signals, and finally judge whether the monitoring area is suitable for placing the intelligent reflective surface, and generate the intelligent reflective surface use signal;

[0136] The output module outputs the intelligent emitting surface usage signal generated by the similarity calculation module.

[0137] The data processing module includes an environmental data classification unit and an intelligent reflective surface condition calculation unit;

[0138] The environmental data classification unit is used to extract features from the collected environmental data of the receiving end and classify the data according to the extracted features;

[0139] The smart reflective surface condition calculation unit is used to analyze the environmental data of the use of the smart reflective surface in history, generate condition elements for the use of the smart reflective surface, and search and classify the types of environmental data suitable for the use of the smart reflective surface according to the condition elements.

[0140] The similarity calculation module is used to perform similarity calculation on the environmental data of the warning receiving end after making real-time judgment and warning on the receiving end, to determine whether it is suitable to use the intelligent reflective surface for abnormal signal processing, and then calculate whether the selected area is suitable for placing the intelligent reflective surface based on all the receiving ends suitable for using the intelligent reflective surface. When it is determined to be suitable, a signal for using the intelligent reflective surface is generated.

[0141] Example 1

[0142] The data collected from a certain receiving end in history include signal-to-noise ratio, brightness, and voltage. By drawing a signal power curve such as Figure 3 As shown, the signal-to-noise ratio is selected as the correlation data as the signal power increases;

[0143] After searching for related data, the environmental data in history is collected and features are extracted. The extracted features are obstacles, rainy weather, and forests. The environmental data is divided into three environmental categories based on the three features.

[0144] Collect historical environmental data of using smart reflective surfaces, extract features, and obtain the conditional element with a large spatial range. Calculate the proportion of conditional elements in each environmental data. The formula is:

[0145]

[0146] After comparative calculation, the proportion of conditional elements in the environmental data of the three characteristics is 60%, 80%, and 30% respectively. It is judged that the environmental data with more than 50% is the characteristic of obstacles and rain. Therefore, it is judged that the environmental data suitable for using the smart reflective surface is the environment corresponding to obstacles and rain.

[0147] According to the number of receiving devices in regions A, B, and C in the collection history, the density of receiving devices in different regions is calculated using the formula: After calculation, the receiving device densities of the three regions were 0.4, 0.33, and 0.7 respectively. Then the different regions were classified, and the calculated standard deviation was 0.16. Then the three regions A, B, and C were classified, with B and A in the first level and C in the second level.

[0148] Now the receiving end in area C is monitored in real time. Assuming that the real-time signal-to-noise ratios of the three receiving ends in area C are 56, 44, and 30, the signal-to-noise ratio threshold of the receiving end in area C is calculated. The average and standard deviation of the signal-to-noise ratio calculated in history are 60 and 12 respectively. The formula is:

[0149] q_y=z_p-2×z_s

[0150] After calculation, the threshold of the signal-to-noise ratio in area C is 48. After judging that the receiving ends corresponding to 44 and 30 may have signal anomalies, an early warning is issued and the two receiving ends are marked as 1 and 2 respectively.

[0151] Calculate the similarity between the two receiving end real-time environment data and each environment data. The formula is:

[0152]

[0153] The similarities between receiving end 1 and each type of environmental data are calculated to be 30%, 50%, and 20%, respectively, and the similarities between receiving end 2 and each type of environmental data are 60%, 50%, and 90%, respectively. The one with the largest similarity value is selected to judge that receiving end 1 meets the environmental data characterized by cloudy and rainy weather, and receiving end 2 meets the environmental data characterized by forests. It is judged that receiving end 1 is suitable for using the intelligent reflective surface for signal processing;

[0154] Finally, calculate whether area C is suitable for placing a smart reflective surface. The formula is:

[0155]

[0156] The calculated value of β=50% determines that region C is suitable for placement of the smart reflective surface, and a smart reflective surface use signal is generated.

[0157] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0158] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A wireless communication transmission method based on a smart reflective surface, characterized in that: The method comprises the following steps: S100, collecting receiving end data in historical wireless communication transmissions, analyzing the receiving end data in the history, and searching for associated data with signal anomalies; The specific steps to find the associated data with signal anomalies are: S101, assuming that there are n types of data collected from the receiving end of wireless communication transmission in history, specifically {q1, q2, q3...q n }, q1, q2, q3...q n The first, second, third, ... nth types of data collected from the receiving end of the wireless communication transmission in history, where n is a positive integer; the receiving end data of the wireless base station at m different signal transmission powers in history are collected; S102, when the signal transmission power of the wireless base station changes in history, the collected data of n types of receiving ends are analyzed, the signal transmission power of the wireless base station is used as the x-axis independent variable, each data value of the receiving end is used as the y-axis variable, and the signal power curve of each type of receiving end data is drawn using the point drawing method; S103, analyzing the signal power curves of each type of data drawn, selecting a linear curve in the curve graph, and a curve showing an upward trend as the independent variable signal transmission power increases as the signal correlation data curve, and the corresponding receiving end data type q_x is the signal abnormal correlation data; S200, collecting historical environmental data when signal anomalies occurred at the receiving end, extracting features from the environmental data, and classifying the data; S300, analyzing the environmental data when the smart reflective surface is used to enhance the receiving end signal in the history, defining the characteristics of the environmental data when the smart reflective surface is used as conditional elements, and judging whether each type of environmental data after classification is suitable for using the smart reflective surface according to the conditional elements; S400, analyzing the historical associated data and calculating the threshold of the associated data; The specific steps for calculating the threshold of associated data are: S401, collect the values ​​of the associated data when the receiving end signal is abnormal in the history {z1, z2, z3...z c }, z1, z2, z3...z c is the 1st, 2nd, 3rd, ... cth data of the receiving end in the historical wireless communication transmission collected, where c is a positive integer; S402, collecting the number w of receiving-end devices in different regions, and calculating the density of receiving-end devices in different regions, the formula is: In the formula, ρ represents the receiving device density in different regions, w represents the number of receiving devices in different regions, and ar represents the area of ​​different regions. After calculating the receiving devices in each region, the required signal strength of each region is graded according to the different receiving device densities, and the standard deviation of the receiving device density is calculated. The region with the lowest receiving device density is selected as the first level of signal strength. After that, when the receiving device density increases by one standard deviation, the corresponding signal strength increases by one level, and then it is analogized to {1, 2, 3...H}, where 1, 2, 3...H means that different regions are divided into H levels according to the receiving device density, and H is a positive integer. S403, calculating the average value and standard deviation of all the collected associated data values, and calculating the threshold of the associated data according to the calculated average value and standard deviation, the formula is: q_y=z_p-H i ×z_s In the above formula, q_y represents the threshold of the associated data, z_p represents the average value of the associated data, z_s represents the standard deviation of the associated data, and H i Indicates the signal strength level of the receiving end in the area where the associated data threshold is calculated, 1≤H i ≤H; S500, collecting relevant data of the receiving end in real time, judging whether a signal abnormality occurs at the receiving end according to a threshold value and issuing an early warning; S600. After the receiving end is warned, the real-time environmental data at the time of the warning is analyzed, and the similarity between the real-time environment and each type of environmental data is calculated; the corresponding environmental data type is selected according to the similarity, and the proportion of environmental data types in the entire area is calculated. Then, when it is determined that the monitoring area is suitable for placing an intelligent reflective surface based on the proportion, a signal for using the intelligent reflective surface is issued.

2. The wireless communication transmission method based on the smart reflective surface according to claim 1, characterized in that: The specific steps of extracting features and classifying the environmental data in S200 are as follows: S201, collect the environmental data when the signal abnormality occurs at the receiving end j times in the history, specifically {h1, h2, h3...h j }, h1, h2, h3...h j is the environmental data collected when the 1st, 2nd, 3rd, ...jth signal anomaly occurs at the receiving end, where j is a positive integer; S202, suppose that the environmental data h collected each time a signal anomaly occurs at the receiving end has k elements, h = {g1, g2, g3...g k }, g1, g2, g3...g k are the 1st, 2nd, 3rd, ... kth elements in each collected environmental data, where k is a positive integer; calculate the number of identical elements in h, and take the element with the largest number as the feature of environmental data h; S203. According to the above method, feature extraction is performed on the environmental data collected for j times of abnormal signals at the receiving end, and environmental data with the same features are classified into the same category, and finally the environmental data are classified into p categories.

3. The wireless communication transmission method based on the smart reflective surface according to claim 2, characterized in that: The specific steps of determining whether each type of classified environmental data is suitable for using the smart reflective surface in S300 are: S301, collecting historical environmental data when an abnormal signal occurs at the receiving end and using the intelligent reflective surface to handle the abnormal signal at the receiving end, extracting features of the environmental data of the intelligent reflective surface according to the method of S202, and defining the extracted features as conditional elements for using the intelligent reflective surface; S302, after extracting the conditional elements for using the smart reflective surface, the conditional elements are compared with each type of classified environmental data, and the proportion of the conditional elements in each type of environmental data is calculated, and the formula is: In the above formula, d is the proportion of conditional elements in each environmental data, s_t is the number of conditional elements in each environmental data, and k is the total number of elements in each environmental data; S303, after calculating the proportion of conditional elements in each environmental data according to the above formula, when it is determined that d ≥ 50%, the corresponding environmental data is suitable for using the smart reflective surface for signal anomaly processing; conversely, when d < 50%, the corresponding environmental data is not suitable for using the smart reflective surface for signal anomaly processing; S304, after p calculations, where p represents the total number of types of classified environmental data, and after determining the proportion of conditional elements in the environmental data, the types of environmental data suitable for using the intelligent reflective surface for signal anomaly processing are obtained among all types of environmental data, and a set B is constructed, where B = {a1, a2, a3 ... a b }, a1, a2, a3...a b Indicates the 1st, 2nd, 3rd, ...bth environmental data types suitable for using smart reflective surfaces, where b is a positive integer and b <p。 4. The wireless communication transmission method based on the smart reflective surface according to claim 1, characterized in that: The specific steps of determining whether a signal abnormality occurs at the receiving end and issuing an early warning according to the threshold value in S500 are: S501, real-time collection of required signal strength H i The associated data values ​​of all receivers in the region are {q_x1, q_x2, q_x3...q_x L }, q_x1, q_x2, q_x3...q_x L Indicates the associated data values ​​collected from the 1st, 2nd, 3rd, ...Lth receiving ends, where L is a positive integer; S502. Judge the associated data of each receiver in the area with a signal strength of H i When q_x r < q_y, it is judged that the corresponding receiver has a signal anomaly. On the contrary, when q_x r ≥ q_y, it is judged that the signal of the corresponding receiver is normal, where 1 ≤ r ≤ L; S503: According to the above method, the collected associated data of the L receiving ends are judged, and finally the receiving ends with signal abnormality are {J1, J2, J3...J K }, J1, J2, J3...J K Indicates the 1st, 2nd, 3rd ... Kth receiving end where signal abnormality occurs, where K is a positive integer; S504: After determining that a signal abnormality occurs at the receiving end, a signal abnormality warning is issued to each receiving end.

5. The wireless communication transmission method based on the smart reflective surface according to claim 1, characterized in that: The specific steps of calculating the similarity between the real-time environment and each type of environment data in S600 are: S601, after the signal abnormality warning is performed on the receiving end, the environmental data of the receiving end is collected, and the similarity between the environmental data of the receiving end for signal abnormality warning and each type of environmental data in S200 is calculated, and the formula is: In the above formula, Si represents the similarity between the environmental data of the receiving end of the signal abnormal warning and each type of environmental data in S200, α represents the number of types of elements in the environmental data of the receiving end of the signal abnormal warning and each type of environmental data, G t represents the number of elements of the same type in the environmental data of the receiving end of the signal abnormality warning, and k represents the total number of elements in each environmental data; similarity calculation is performed on p types of environmental data according to the formula; S602, after calculation, p similarity values ​​are obtained, and the environmental data h_s corresponding to the maximum similarity value is selected. When the selected environmental data h_s is in set B, it is determined that the receiving end where the signal anomaly occurs is suitable for processing using the smart reflective surface. On the contrary, if h_s is not in set B, it is determined that it is not suitable for processing using the smart reflective surface. S603: According to the above method, similarity calculation is performed on K receiving ends with signal anomalies, and it is determined that the receiving ends with signal anomalies suitable for processing by using the intelligent reflective surface are {J_s1, J_s2, J_s3...J_s u }, J_s1, J_s2, J_s3...J_s u Indicates the first, second, third, ..., u receiving ends with signal anomalies that are suitable for processing by the intelligent reflective surface, where u is a positive integer, 0≤u≤K; S604, calculate the required signal strength H i Whether the area is suitable for placing smart reflective surfaces, the formula is: In the above formula, β represents the proportion of receiving ends with signal abnormalities suitable for intelligent reflective surface processing in the total number of receiving ends with signal abnormalities; K represents the number of receiving ends with signal abnormalities; According to the above formula, when β≥50%, the required signal strength is determined to be H i The area is suitable for placing smart reflective surfaces, and the smart reflective surface uses signals for output; when β<50%, the required signal strength is determined to be H i Areas with high humidity are not suitable for placement of smart reflective surfaces.

6. A wireless communication transmission system based on a smart reflective surface, characterized in that: The wireless communication transmission system includes a data collection module, a correlation data search module, a data processing module, a signal strength classification module, an early warning module, a similarity calculation module, and an output module; The data collection module is used to collect historical associated data and environmental data of the receiving end, and monitor and collect the associated data and environmental data of the receiving end in real time; The associated data search module is used to analyze the collected receiving end historical data, search for data associated with the signal strength, and define it as associated data; The data processing module is used to extract features from the collected environmental data of the receiving end and classify them according to the extracted features; The signal strength classification module is used to calculate and analyze the density of receiving end devices in different regions, and classify the signal strength required in different regions according to the receiving end density; The warning module is used to calculate the environmental data of the receiving end collected in real time, determine whether a signal abnormality may occur at the receiving end, and issue a warning; The similarity calculation module is used to perform similarity calculation on the environmental data of the warning receiving end after making a real-time judgment and warning on the receiving end, judge whether it is suitable to use the intelligent reflective surface to process abnormal signals, and finally judge whether the monitoring area is suitable for placing the intelligent reflective surface, and generate the intelligent reflective surface use signal; The output module outputs the intelligent emitting surface usage signal generated by the similarity calculation module.

7. The wireless communication transmission system based on the smart reflective surface according to claim 6, characterized in that: The data processing module includes an environmental data classification unit and an intelligent reflective surface condition calculation unit; The environmental data classification unit is used to extract features from the collected environmental data of the receiving end and classify the data according to the extracted features; The smart reflective surface condition calculation unit is used to analyze the environmental data of the use of the smart reflective surface in history, generate condition elements for the use of the smart reflective surface, and search and classify the types of environmental data suitable for the use of the smart reflective surface according to the condition elements.

8. The wireless communication transmission system based on the smart reflective surface according to claim 6, characterized in that: The similarity calculation module is used to perform similarity calculation on the environmental data of the warning receiving end after making a real-time judgment and warning on the receiving end, to determine whether it is suitable to use the intelligent reflective surface to process abnormal signals, and then to calculate whether the selected area is suitable for placing the intelligent reflective surface based on all the receiving ends suitable for using the intelligent reflective surface, and to generate an intelligent reflective surface use signal when it is determined to be suitable.

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