CN112860768B - Electromagnetic spectrum available frequency recommendation method - Google Patents
Electromagnetic spectrum available frequency recommendation method Download PDFInfo
- Publication number
- CN112860768B CN112860768B CN202110186296.0A CN202110186296A CN112860768B CN 112860768 B CN112860768 B CN 112860768B CN 202110186296 A CN202110186296 A CN 202110186296A CN 112860768 B CN112860768 B CN 112860768B
- Authority
- CN
- China
- Prior art keywords
- frequency
- signal
- task
- monitoring
- available
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a method for recommending available frequency of an electromagnetic spectrum, which comprises the following steps: step 1, screening monitoring data to obtain an available frequency result table: and 2, evaluating the available frequency result obtained in the step 1 by adopting a fuzzy comprehensive evaluation method. The electromagnetic spectrum available frequency recommendation method disclosed by the invention selects massive multi-station spectrum data from multiple dimensions such as frequency, field intensity, step length and occupancy rate by adopting a big data storage and query technology, evaluates the available degree of available frequency by adopting a fuzzy comprehensive evaluation method, evaluates the available state of frequency by utilizing the strong processing uncertainty and randomness capacity of the available frequency, converts a qualitative evaluation process into a quantitative evaluation process, obtains a reliable frequency recommendation result, improves the accuracy and applicability of available frequency recommendation, and reduces the cost of manpower and material resources consumed by verifying the availability of frequency.
Description
Technical Field
The invention belongs to the field of electromagnetic spectrum, and particularly relates to a method for recommending available frequency of electromagnetic spectrum in the field.
Background
As the number and density of frequency-using devices increases, the available frequency resources in a limited area become very scarce. In order to improve the utilization rate of the existing frequency and ensure the safety and stability of the frequency utilization, the frequency recommendation problem is more and more emphasized, and the frequency utilization effect is directly influenced by the frequency recommendation result. Therefore, frequency recommendation becomes an important content in frequency allocation assignment in the electromagnetic spectrum application field.
For a large-scale mixed application scene of various frequency utilization devices, the frequency utilization devices are various, the communication network is complex, the frequency range is wide, and the frequency consumption amount is huge. In this scenario, the deficiencies and drawbacks of the existing electromagnetic spectrum available frequency recommendation technology mainly include:
(1) the amount of screening data is limited. The conventional selection mode mainly selects the frequency spectrum data obtained by a single frequency spectrum monitoring device, the processed frequency spectrum data amount is limited, and the accuracy and the applicability of a selection result are poor. The technical defect of the selection mode is that a relational database is mainly adopted to store and query the frequency spectrum data obtained by a single frequency spectrum monitoring device, and the data storage capacity and the data processing capacity are weak;
(2) the recommendations for available frequencies are determined primarily based on daily experience, without a quantitative assessment model. This subjective randomness can lead to a bias in the available frequency estimates.
Disclosure of Invention
The invention aims to provide a method for recommending available frequencies of an electromagnetic spectrum.
The invention adopts the following technical scheme:
in a method for recommending available frequencies in the electromagnetic spectrum, the improvement comprising the steps of:
step 1, screening monitoring data to obtain an available frequency result table:
step 11, acquiring and storing spectrum monitoring data:
executing one or more frequency spectrum monitoring tasks to obtain mass historical monitoring data, establishing a mass frequency monitoring data storage method based on a data warehouse, storing the frequency spectrum monitoring data in the data warehouse, and storing the monitoring data and the monitoring tasks in a correlation manner to form a monitoring task table;
step 12, acquiring and storing signal information:
processing the frequency information in the step 1 by adopting a signal identification algorithm to obtain signal information, and storing the signal information in a data warehouse; when signal information is stored in a data warehouse, establishing double partitions in the data warehouse based on task numbers and task execution time;
and 13, selecting available frequencies, and acquiring available frequency information from the signal information:
step 131, inputting a spectrum monitoring task query condition, and querying a monitoring task table in the data warehouse in step 11 to obtain one or more spectrum monitoring task information; the query strategy set in the task query is as follows: the starting time in the query condition is not less than the task starting time in the monitoring task table, the ending time in the query condition is not more than the task ending time in the monitoring task table, the starting frequency in the query condition is not less than the starting frequency in the monitoring task table, the ending frequency in the query condition is not more than the ending frequency in the monitoring task table, the step length in the query condition is equal to the step length in the monitoring task table, the duration time in the query condition is not more than the task execution time in the monitoring task table, and a plurality of equipment numbers can be input;
step 132, querying and acquiring signal information of the data warehouse double partitions in step 12 according to the task number, the task start time and the task end time of the monitoring task information in step 131;
step 133, querying and obtaining the total number of frames or total packets of data of the corresponding task in the data warehouse monitoring task table according to the task number, the task starting time and the task ending time of the monitoring task information queried and obtained in step 131 and by combining the device number and the frequency input in step 131, calculating the signal occupancy rate, and obtaining the result signal information by combining the signal information queried and obtained in step 132; the signal occupancy rate is the number of signal occurrences/total data frame number or total packet number;
step 134, inputting available frequency selection conditions including field intensity threshold, frequency point occupancy threshold, signal occupancy threshold and service time of available frequency, and selecting the result signal information obtained in step 133 to obtain target signal information and a target frequency band;
step 135, calculating occupied frequency bands in the target signal information, and obtaining all target signal occupied frequency bands:
the calculation formula of the frequency band occupied by the target signal is as follows: occupied frequency band [ target signal center frequency point- (1/2 + span of signal bandwidth) and target signal center frequency point + (1/2 + span of signal bandwidth) ]
Step 136, calculating the target frequency band in step 134 and all the target signal occupied frequency bands in step 135 by adopting logical operation negation to obtain a target available frequency;
step 14, calculating the target available frequency of all monitoring devices by adopting logical operation and to obtain an available frequency result table;
step 2, evaluating the available frequency result obtained in the step 1 by adopting a fuzzy comprehensive evaluation method:
step 21, analyzing the factors influencing the frequency availability degree and forming a set U ═ U1，u2，…，uKIn which ukA k-th factor representing the degree of frequency availability;
step 22, establishing a frequency availability degree comment set V ═ V1，v2，…，vJIn which v isjShowing the j evaluation result;
step 23, determining the weight vector W ═ (W) of the factors affecting the availability of frequencies1，w2，…，wK) Wherein w iskRepresents the weight of the kth factor that affects the degree of frequency availability, and w1+w2+…+wK＝1；
Step 24, establishing a fuzzy evaluation matrix by adopting an expert evaluation method:
by M bits of expert z1，z2，…，zMFor each factor ukTo evaluate, the available frequency to be evaluated is determined as o1，o2，…，oNO innBy an expert zmThe given evaluation matrix is:
wherein the content of the first and second substances,
presentation expert zmFor evaluation object onThe jth comment of the kth evaluation factor is made, and
step 25, calculating expert zmFor evaluation object onEvaluation membership vector of (2):
wherein
Presentation expert zmFor evaluation object onAt vjThe evaluation result of the aspect;
step 26, calculating the evaluation results of all experts by adopting a weighted average method to obtain the evaluation object o of all expertsnEvaluation of fuzzy vector (v):
step 27, calculating the evaluation results of all available frequencies to form an available frequency recommendation table:
further, the data warehouse is a Hive database.
Further, in step 11, the spectrum monitoring data includes frequency information and device information, and the frequency information includes: frequency point, field intensity, bandwidth, frequency duration and frequency occurrence time; the device information includes: device number, longitude, latitude;
the monitoring task table comprises a total data frame number or a total packet number uploaded by a task, a frequency point occupancy rate, a task number, a device number, a task starting time, a task ending time, a starting frequency, an ending frequency, a step length and a task execution time;
frequency point occupation rate is the frequency point field intensity exceeding the occurrence times/total data frame number or total packet number of the field intensity threshold;
and when monitoring data is stored based on the data warehouse, the equipment number, the task number and the data reporting time are used as indexes.
Further, in step 12, the stored signal information includes basic signal information, statistical signal information and device information, and the basic signal information includes: a central frequency point, a bandwidth, a field intensity and a signal occurrence time; the statistical signal information includes: average field strength, maximum field strength, minimum field strength and signal occurrence times; the device information includes: device number, longitude, latitude.
Further, in step 131, the task query condition includes: start time, end time, start frequency, end frequency, span, step size, device number, and duration.
Further, in step 133, the resulting signal information includes: the device comprises a central frequency point, a bandwidth, field intensity, signal occurrence time, average field intensity, maximum field intensity, minimum field intensity, signal occurrence times, equipment numbers, longitude, latitude, frequency point occupation degree and signal occupation degree.
Further, in step 134, the available frequency is used for a whole day of 0-24 hours, or for a specific period of time; the target signal information includes: the device comprises a central frequency point, a bandwidth, field intensity, signal occurrence time, average field intensity, maximum field intensity, minimum field intensity, signal occurrence times, equipment numbers, longitude, latitude, frequency point occupancy and signal occupancy; the target frequency band refers to a frequency band consisting of target signal center frequency points; the field intensity of the target signal is not greater than a field intensity threshold value; the frequency point occupancy rate of the target signal is not greater than the frequency point occupancy rate threshold; the signal occupancy of the target signal is not greater than the signal occupancy threshold.
The invention has the beneficial effects that:
the electromagnetic spectrum available frequency recommendation method disclosed by the invention selects massive multi-station spectrum data from multiple dimensions such as frequency, field intensity, step length and occupancy rate by adopting a big data storage and query technology, evaluates the available degree of available frequency by adopting a fuzzy comprehensive evaluation method, evaluates the available state of frequency by utilizing the strong processing uncertainty and randomness capacity of the available frequency, converts a qualitative evaluation process into a quantitative evaluation process, obtains a reliable frequency recommendation result, improves the accuracy and applicability of available frequency recommendation, and reduces the cost of manpower and material resources consumed by verifying the availability of frequency.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention;
FIG. 2 is a schematic flow chart of step 1 of the method disclosed in example 1 of the present invention;
FIG. 3 is a schematic flow chart of step 2 of the method disclosed in embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The invention is capable of other and different embodiments and its several details are capable of modifications and variations in various respects, all without departing from the spirit of the invention. It should be noted that the drawings provided in the following embodiments are only schematic illustrations for explaining the basic idea of the present invention, are schematic illustrations, and should not be construed as limiting the present invention; it will be understood by those skilled in the art that certain well-known structures shown in the drawings and descriptions thereof may be omitted.
The principle of the invention is shown in figure 1, the available frequency selection based on massive multi-station frequency spectrum monitoring data is carried out by adopting a big data storage and query technology, the selection frequency availability degree is evaluated by adopting a fuzzy comprehensive evaluation method, and a reliable frequency recommendation result is obtained, so that the accuracy and the applicability of available frequency recommendation are improved.
The method comprises the steps that firstly, screening is carried out on the basis of long-term monitoring data obtained by a plurality of monitoring stations, and the screening result is an available frequency result table; secondly, the usability degree of the selected frequency is evaluated by adopting a fuzzy comprehensive evaluation method, and a qualitative evaluation process is converted into a quantitative evaluation process to form a recommendation table of the usability frequency. The method specifically comprises the following steps:
step 1, as shown in fig. 2, screening the monitoring data to obtain a usable frequency result table:
step 11, acquiring and storing spectrum monitoring data:
executing one or more frequency spectrum monitoring tasks to obtain mass historical monitoring data, establishing a mass frequency monitoring data storage method based on a data warehouse, storing the frequency spectrum monitoring data in the data warehouse, and storing the monitoring data and the monitoring tasks in a correlation manner, wherein a Hive database is adopted to store the mass frequency spectrum monitoring data, and a Storm is adopted to perform correlation processing on the monitoring data and the monitoring tasks to form a monitoring task table;
the spectrum monitoring data comprises frequency information and equipment information, wherein the frequency information comprises: frequency point, field intensity, bandwidth, frequency duration and frequency occurrence time; the device information includes: device number, longitude, latitude;
the information stored in the monitoring task table comprises the total frame (packet) number of data uploaded by the task, frequency point occupancy rate, task number, equipment number, task starting time, task ending time, starting frequency, ending frequency, step length and task execution time;
frequency point occupation rate is the frequency point field intensity exceeding the occurrence times/total data frame (packet) number of the field intensity threshold; the total frame (packet) number of the data is determined by the prior monitoring equipment attribute;
when monitoring data are stored on the basis of a data warehouse, the equipment number, the task number and the data reporting time are used as important components of the Hive index, and therefore monitoring data can be conveniently searched in mass data when a frequency selection request is executed.
Step 12, acquiring and storing signal information:
processing the frequency information in the step 1 by adopting a signal identification algorithm to obtain signal information, and storing the signal information in a data warehouse; when signal information is stored in the data warehouse, double partitions are established in the data warehouse based on task numbers and task execution times, and the signal information is stored in the Hive database. The searching can be carried out according to one or more conditions of task numbers and task execution time.
The stored signal information includes basic signal information, statistical signal information, and device information, the basic signal information including: a central frequency point, a bandwidth, a field intensity and a signal occurrence time; the statistical signal information includes: average field strength, maximum field strength, minimum field strength and signal occurrence times; the device information includes: device number, longitude, latitude.
The data warehouse stores the signal information appearing in each frame (packet) frequency information uploaded by the monitoring equipment, can realize the second-level query speed of TB-level data, solves the problem of difficult mass data query in the prior query technology, and obviously improves the data query efficiency.
And 13, selecting available frequencies, and acquiring available frequency information from the signal information:
step 131, inputting a spectrum monitoring task query condition, and querying a monitoring task table in the data warehouse (in this embodiment, a Hive database) in step 11 to obtain one or more items of spectrum monitoring task information; the query strategy set in the task query is as follows: the starting time in the query condition is not less than the task starting time in the monitoring task table, the ending time in the query condition is not more than the task ending time in the monitoring task table, the starting frequency in the query condition is not less than the starting frequency in the monitoring task table, the ending frequency in the query condition is not more than the ending frequency in the monitoring task table, the step length in the query condition is equal to the step length in the monitoring task table, the duration time in the query condition is not more than the task execution time in the monitoring task table, and a plurality of equipment numbers can be input;
the task query conditions comprise: start time, end time, start frequency, end frequency, span, step size, device number, and duration.
Step 132, querying a Hive database according to the task number, the task start time and the task end time of the monitoring task information in step 131, and acquiring signal information of the double partitions of the data warehouse in step 12;
step 133, querying a Hive database according to the task number, the task start time and the task end time of the monitoring task information queried in step 131, in combination with the device number and the frequency input in step 131, to obtain the total data frame (packet) number of the corresponding task in the monitoring task table of the data warehouse, calculating the signal occupancy rate, and in combination with the signal information queried in step 132, obtaining result signal information; the signal occupancy rate is the number of signal occurrences/total data frame number or total packet number;
the resulting signal information includes: the device comprises a central frequency point, a bandwidth, field intensity, signal occurrence time, average field intensity, maximum field intensity, minimum field intensity, signal occurrence times, equipment numbers, longitude, latitude, frequency point occupation degree and signal occupation degree.
Step 134, inputting available frequency selection conditions including field intensity threshold, frequency point occupancy threshold, signal occupancy threshold and service time of available frequency, and selecting the result signal information obtained in step 133 to obtain target signal information and a target frequency band;
the using time of the available frequency can be 0-24 hours for a whole day, and can also be a specific time period; the target signal information includes: the device comprises a central frequency point, a bandwidth, field intensity, signal occurrence time, average field intensity, maximum field intensity, minimum field intensity, signal occurrence times, equipment numbers, longitude, latitude, frequency point occupancy and signal occupancy; the target frequency band refers to a frequency band consisting of target signal center frequency points; when the selection is carried out, the field intensity of the target signal is not greater than a field intensity threshold value; the frequency point occupancy rate of the target signal is not greater than the frequency point occupancy rate threshold; the signal occupancy of the target signal is not greater than the signal occupancy threshold.
Step 135, calculating occupied frequency bands in the target signal information, and obtaining all target signal occupied frequency bands:
the calculation formula of the frequency band occupied by the target signal is as follows: occupied frequency band [ target signal center frequency point- (1/2 + span of signal bandwidth) and target signal center frequency point + (1/2 + span of signal bandwidth) ]
Step 136, calculating the target frequency band in the step 134 and all the frequency bands occupied by the target signals in the step 135 by adopting logical operation 'negation', calculating unoccupied frequency band information in the target frequency band, and obtaining a target available frequency;
step 14, calculating the target available frequency of all monitoring devices by adopting logical operation and to obtain an available frequency result table;
step 2, as shown in fig. 3, evaluating the available frequency result obtained in step 1 by using a fuzzy comprehensive evaluation method:
step 21, analyzing factors influencing the frequency availability degree, including field intensity, signal occupancy rate, frequency point occupancy rate and signal occurrence frequency, and forming a set U ═ U1，u2，u3，u4And (5) determining the field intensity, the signal occupancy rate, the frequency point occupancy rate and the signal occurrence times.
Step 22, establishing a frequency availability degree comment set V ═ V1，v2，v3{ available, to-be-selected, unavailable };
step 23, determining the weight vector W ═ (W) of the factors affecting the availability of frequencies1，w2，w3，w4) And w is1+w2+w3+wK＝1；
Step 24, establishing a fuzzy evaluation matrix by adopting an expert evaluation method:
by M bits of expert z1，z2，…，zMFor each factor ukTo evaluate, the available frequency to be evaluated is determined as o1，o2，…，oNO innBy an expert zmThe given evaluation matrix is:
wherein the content of the first and second substances,
presentation expert zmFor evaluation object onThe jth comment of the kth evaluation factor is made, and
step 25, calculating expert zmFor evaluation object onEvaluation membership vector of (2):
wherein
Presentation expert zmFor evaluation object onAt vjThe evaluation result of the aspect;
step 26, calculating the evaluation results of all experts by adopting a weighted average method to obtain the evaluation object o of all expertsnEvaluation of fuzzy vector (v):
step 27, calculating the evaluation results of all available frequencies to form an available frequency recommendation table:
Claims (7)
1. a method for recommending available frequencies of an electromagnetic spectrum is characterized by comprising the following steps:
step 1, screening monitoring data to obtain an available frequency result table:
step 11, acquiring and storing spectrum monitoring data:
executing one or more frequency spectrum monitoring tasks to obtain mass historical monitoring data, establishing a mass frequency monitoring data storage method based on a data warehouse, storing the frequency spectrum monitoring data in the data warehouse, and storing the monitoring data and the monitoring tasks in a correlation manner to form a monitoring task table;
step 12, acquiring and storing signal information:
processing the frequency information in the step 1 by adopting a signal identification algorithm to obtain signal information, and storing the signal information in a data warehouse; when signal information is stored in a data warehouse, establishing double partitions in the data warehouse based on task numbers and task execution time;
and 13, selecting available frequencies, and acquiring available frequency information from the signal information:
step 131, inputting a spectrum monitoring task query condition, and querying a monitoring task table in the data warehouse in step 11 to obtain one or more spectrum monitoring task information; the query strategy set in the task query is as follows: the starting time in the query condition is not less than the task starting time in the monitoring task table, the ending time in the query condition is not more than the task ending time in the monitoring task table, the starting frequency in the query condition is not less than the starting frequency in the monitoring task table, the ending frequency in the query condition is not more than the ending frequency in the monitoring task table, the step length in the query condition is equal to the step length in the monitoring task table, the duration time in the query condition is not more than the task execution time in the monitoring task table, and a plurality of equipment numbers can be input;
step 132, querying and acquiring signal information of the data warehouse double partitions in step 12 according to the task number, the task start time and the task end time of the monitoring task information in step 131;
step 133, querying and obtaining the total number of frames or total packets of data of the corresponding task in the data warehouse monitoring task table according to the task number, the task starting time and the task ending time of the monitoring task information queried and obtained in step 131 and by combining the device number and the frequency input in step 131, calculating the signal occupancy rate, and obtaining the result signal information by combining the signal information queried and obtained in step 132; the signal occupancy rate is the number of signal occurrences/total data frame number or total packet number;
step 134, inputting available frequency selection conditions including field intensity threshold, frequency point occupancy threshold, signal occupancy threshold and service time of available frequency, and selecting the result signal information obtained in step 133 to obtain target signal information and a target frequency band;
step 135, calculating occupied frequency bands in the target signal information, and obtaining all target signal occupied frequency bands:
the calculation formula of the frequency band occupied by the target signal is as follows: occupied frequency band [ target signal center frequency point- (1/2 + span of signal bandwidth) and target signal center frequency point + (1/2 + span of signal bandwidth) ];
step 136, calculating the target frequency band in step 134 and all the target signal occupied frequency bands in step 135 by adopting logical operation negation to obtain a target available frequency;
step 14, calculating the target available frequency of all monitoring devices by adopting logical operation and to obtain an available frequency result table;
step 2, evaluating the available frequency result obtained in the step 1 by adopting a fuzzy comprehensive evaluation method:
step 21, analyzing the factors influencing the frequency availability degree and forming a set U ═ U1，u2，…，uKIn which ukA k-th factor representing the degree of frequency availability;
step 22, establishing a frequency availability degree comment set V ═ V1，v2，…，vJIn which v isjShowing the j evaluation result;
step 23, determining the influence frequencyWeight vector W using degree factor ═ W (W)1，w2，…，wK) Wherein w iskRepresents the weight of the kth factor that affects the degree of frequency availability, and w1+w2+…+wK＝1；
Step 24, establishing a fuzzy evaluation matrix by adopting an expert evaluation method:
by M bits of expert z1，z2，…，zMFor each factor ukTo evaluate, the available frequency to be evaluated is determined as o1，o2，…，oNO innBy an expert zmThe given evaluation matrix is:
wherein the content of the first and second substances,
presentation expert zmFor evaluation object onThe jth comment of the kth evaluation factor is made, and
step 25, calculating expert zmFor evaluation object onEvaluation membership vector of (2):
wherein
Presentation expert zmFor evaluation object onAt vjThe evaluation result of the aspect;
step (ii) of26, calculating the evaluation results of all experts by adopting a weighted average method to obtain the evaluation object o of all expertsnEvaluation of fuzzy vector (v):
step 27, calculating the evaluation results of all available frequencies to form an available frequency recommendation table:
2. the method of claim 1, wherein the method further comprises: the data warehouse is a Hive database.
3. The method of claim 1, wherein the method further comprises: in step 11, the spectrum monitoring data includes frequency information and device information, and the frequency information includes: frequency point, field intensity, bandwidth, frequency duration and frequency occurrence time; the device information includes: device number, longitude, latitude;
the monitoring task table comprises a total data frame number or a total packet number uploaded by a task, a frequency point occupancy rate, a task number, a device number, a task starting time, a task ending time, a starting frequency, an ending frequency, a step length and a task execution time;
frequency point occupation rate is the frequency point field intensity exceeding the occurrence times/total data frame number or total packet number of the field intensity threshold;
and when monitoring data is stored based on the data warehouse, the equipment number, the task number and the data reporting time are used as indexes.
4. The method of claim 1, wherein the method further comprises: in step 12, the stored signal information includes basic signal information, statistical signal information, and device information, the basic signal information including: a central frequency point, a bandwidth, a field intensity and a signal occurrence time; the statistical signal information includes: average field strength, maximum field strength, minimum field strength and signal occurrence times; the device information includes: device number, longitude, latitude.
5. The method of claim 1, wherein the method further comprises: in step 131, the task query condition includes: start time, end time, start frequency, end frequency, span, step size, device number, and duration.
6. The method of claim 1, wherein the method further comprises: in step 133, the resulting signal information includes: the device comprises a central frequency point, a bandwidth, field intensity, signal occurrence time, average field intensity, maximum field intensity, minimum field intensity, signal occurrence times, equipment numbers, longitude, latitude, frequency point occupation degree and signal occupation degree.
7. The method of claim 1, wherein the method further comprises: in step 134, the available frequency is used for a whole day of 0-24 hours, or for a specific period of time; the target signal information includes: the device comprises a central frequency point, a bandwidth, field intensity, signal occurrence time, average field intensity, maximum field intensity, minimum field intensity, signal occurrence times, equipment numbers, longitude, latitude, frequency point occupancy and signal occupancy; the target frequency band refers to a frequency band consisting of target signal center frequency points; the field intensity of the target signal is not greater than a field intensity threshold value; the frequency point occupancy rate of the target signal is not greater than the frequency point occupancy rate threshold; the signal occupancy of the target signal is not greater than the signal occupancy threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110186296.0A CN112860768B (en) | 2021-02-17 | 2021-02-17 | Electromagnetic spectrum available frequency recommendation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110186296.0A CN112860768B (en) | 2021-02-17 | 2021-02-17 | Electromagnetic spectrum available frequency recommendation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112860768A CN112860768A (en) | 2021-05-28 |
| CN112860768B true CN112860768B (en) | 2022-02-25 |
Family
ID=75989650
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110186296.0A Active CN112860768B (en) | 2021-02-17 | 2021-02-17 | Electromagnetic spectrum available frequency recommendation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112860768B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113271163B (en) * | 2021-07-20 | 2021-11-05 | 深圳市万联航通电子科技有限公司 | Unmanned aerial vehicle self-adaptive frequency selection method, device, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102130733A (en) * | 2011-04-19 | 2011-07-20 | 中国电子科技集团公司第五十四研究所 | Radio spectrum sensing realization method |
| CN103428533A (en) * | 2012-05-21 | 2013-12-04 | 索尼公司 | Devices and methods for dynamic broadcast |
| CN106056476A (en) * | 2016-06-06 | 2016-10-26 | 国家电网公司 | Recommendation method for power market multi-layer collaborative information service |
| CN106850104A (en) * | 2016-12-09 | 2017-06-13 | 中国人民解放军重庆通信学院 | The quick frequency-selective systems of NVIS and its method based on passive monitoring |
| CN109902076A (en) * | 2019-03-05 | 2019-06-18 | 上海创远仪器技术股份有限公司 | For the long system and method for exposing comparison spectrogram and carrying out big data processing |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102006124B (en) * | 2009-08-28 | 2014-05-07 | 华为技术有限公司 | Spectrum forecasting method, device and system |
-
2021
- 2021-02-17 CN CN202110186296.0A patent/CN112860768B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102130733A (en) * | 2011-04-19 | 2011-07-20 | 中国电子科技集团公司第五十四研究所 | Radio spectrum sensing realization method |
| CN103428533A (en) * | 2012-05-21 | 2013-12-04 | 索尼公司 | Devices and methods for dynamic broadcast |
| CN106056476A (en) * | 2016-06-06 | 2016-10-26 | 国家电网公司 | Recommendation method for power market multi-layer collaborative information service |
| CN106850104A (en) * | 2016-12-09 | 2017-06-13 | 中国人民解放军重庆通信学院 | The quick frequency-selective systems of NVIS and its method based on passive monitoring |
| CN109902076A (en) * | 2019-03-05 | 2019-06-18 | 上海创远仪器技术股份有限公司 | For the long system and method for exposing comparison spectrogram and carrying out big data processing |
Non-Patent Citations (2)
| Title |
|---|
| 复杂电磁环境下无线电频率筛选辅助系统研究;罗争等;《中国无线电》;20150726(第7期);全文 * |
| 认知无线电系统的可用频率研究;刘巍;《中国优秀硕博士学位论文全文数据库（硕士）信息科技辑》;20110915(第9期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112860768A (en) | 2021-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103678618B (en) | Web service recommendation method based on socializing network platform | |
| US20220179884A1 (en) | Label Determining Method, Apparatus, and System | |
| CN105227369B (en) | Based on the mobile Apps of the mass-rent pattern analysis method to the Wi-Fi utilization of resources | |
| CN105281925B (en) | The method and apparatus that network service groups of users divides | |
| CN109471847B (en) | I/O congestion control method and control system | |
| WO2017113774A1 (en) | Method and device for judging user priority in wireless communication system | |
| WO2022042152A1 (en) | Method and device for analyzing association rule of multi-dimensional network indexes, and storage medium | |
| CN106991577A (en) | A kind of method and device for determining targeted customer | |
| CN112860768B (en) | Electromagnetic spectrum available frequency recommendation method | |
| CN106296315A (en) | Context aware systems based on user power utilization data | |
| CN115600824A (en) | Early warning method and device for carbon emission, storage medium and electronic equipment | |
| CN102857938A (en) | Learning and predication of frequency spectrum environment in cognitive radio | |
| US20210099894A1 (en) | Forcasting time series data | |
| CN115422260A (en) | Base station activity analysis method and device, electronic equipment and readable storage medium | |
| CN113660687B (en) | Network difference cell processing method, device, equipment and storage medium | |
| CN110555619A (en) | Power supply capacity evaluation method based on intelligent power distribution network | |
| CN112508408B (en) | Mapping model construction method of radio resource management index under edge calculation | |
| CN110321930B (en) | Illegal broadcast signal classification method based on cyclic neural network | |
| CN111506813A (en) | Remote sensing information accurate recommendation method based on user portrait | |
| CN112200209A (en) | Poor user identification method based on day-to-day power consumption | |
| CN111092764A (en) | Real-time dynamic intimacy relationship analysis method and system | |
| Wu et al. | Identifying potential standard essential patents based on text mining and generative topographic mapping | |
| CN115660730A (en) | Loss user analysis method and system based on classification algorithm | |
| CN115510331A (en) | Shared resource matching method based on idle amount aggregation | |
| CN108388911A (en) | A kind of mobile subscriber's Dynamic Fuzzy Clustering Algorithm method towards mixed attributes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |