CN118249936A - Intelligent unidirectional shielding method and device for base station signals - Google Patents
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Abstract
The invention discloses an intelligent unidirectional shielding method and device for base station signals, and particularly relates to the technical field of signal transmission, comprising the steps of collecting transmission power training data of an nth remote machine in a shielding management area and collecting evaluation coefficient training data of a near-end machine connected with the nth remote machine; the transmission power training data comprise transmission power characteristic data collected in a test scene and corresponding transmission power; the evaluation coefficient training data comprise interference power characteristic data collected by a near-end machine in a test environment and corresponding loss evaluation coefficient values; according to the invention, through the real-time acquisition of the transmission power characteristic data of the remote terminal and the interference power characteristic data of the near-end terminal, the transmission power of the remote terminal is predicted in real time by combining the transmission power prediction model and the evaluation coefficient prediction model, and the self-adaptive adjustment is carried out to optimize the transmission power adjustment strategy of the shielding system.
Description
Technical Field
The invention relates to the technical field of signal transmission, in particular to an intelligent unidirectional shielding method and device for a base station signal.
Background
As the coverage of operators' networks continues to scale up, the impact of base station signals in various situations becomes more and more pronounced. In examination places, especially education examination places, ensuring fairness and fairness of examination becomes an important task. Therefore, it is important to deploy a shielding system facing to an operator network in an examination room, and most of the traditional examination room signal shielding systems adopt wall-mounted analog jammers, and the equipment transmits sweep signals in full frequency bands in a VCO (Voltage Controlled Oscillator) sweep mode and simultaneously transmits interference signals in uplink and downlink frequency bands and uplink and downlink time slots of a base station; the existing method can also ensure that the interference signal is synchronous with the signal of the wireless communication system by periodically re-carrying out signal interception, so that the synchronous tracking of the time and the frequency of the wireless communication system is realized, and the method is suitable for the situation of network configuration change of the wireless communication system in the shielding process; for example, chinese patent publication No. CN102868421a discloses a method, apparatus and system for shielding signals in a wireless communication system, where although the method can realize mobile signal shielding and reduce communication interference of a mobile terminal without shielding, studies and applications of the method and the prior art by the inventor find that the method and the prior art have at least the following partial defects:
(1) The traditional jammer produces strong interference to the uplink communication of the base station when working, which can cause the peripheral unable normal communication, influence the normal operation of the mobile communication network, and even can cause communication interruption or signal quality degradation;
(2) The transmission power of the interference signal of the remote terminal is not automatically adjusted according to the strength change of the signal of the near terminal, so that the output of the interference signal is unstable, and the shielding failure is caused.
Therefore, the invention provides an intelligent unidirectional shielding method and device for base station signals.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an intelligent unidirectional shielding method and device for a base station signal, which are used for solving the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent unidirectional shielding method for a base station signal comprises the following steps:
Step 1: collecting transmission power training data of an nth remote machine in a shielding management area and collecting evaluation coefficient training data of a near-end machine connected with the nth remote machine; the transmission power training data comprise transmission power characteristic data collected in a test scene and corresponding transmission power; the evaluation coefficient training data comprise interference power characteristic data collected by a near-end machine in a test environment and corresponding loss evaluation coefficient values; the transmission power characteristic data comprise amplification factors, modulation depth, modulation frequency and loss evaluation coefficient values of interference signals;
step 2: training a transmission power prediction model for feedback predicting transmission power based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data;
step 3: acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time;
Step 4: obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value;
Step 5: obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model;
Step 6: and calculating a difference value between the predicted transmission power and the measurement correction value of the transmission power, taking the difference value between the predicted transmission power and the measurement correction value of the transmission power as a power adjustment value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjustment value.
Further, the amplification factor of the interference signal is obtained through real-time monitoring of a power amplifier integrated by the remote machine; the modulation depth refers to the amplitude transformation range of the interference signal, namely the degree of change of the signal from the lowest amplitude to the highest amplitude; the modulation frequency refers to the frequency variation range of the interference signal;
the interference power characteristic data comprise transmission distance, impedance evaluation coefficient and environment humidity;
the impedance evaluation coefficient refers to the influence degree of the characteristic impedance of the transmission line on the interference signal when the nth far-end machine receives the interference signal of the near-end machine;
The method for calculating the impedance evaluation coefficient comprises the following steps:
acquiring the resistance value and reactance value of the transmission line, respectively marked as And；
Resistance value is setAnd reactance valueAnd carrying out formulated calculation to obtain an impedance evaluation coefficient, wherein the calculation formula is as follows:
In the method, in the process of the invention, Representing an impedance evaluation coefficient of the transmission line;
The loss evaluation coefficient value refers to the loss power of the interference signal of the near-end machine, and is obtained by measuring the transmission power of the interference signal sent by the near-end machine and transmitted to the nth far-end machine through a power sensor, and then calculating the difference value between the transmission power and the actual transmission power of the near-end machine.
Further, the method for training the transmission power prediction model for feedback prediction of the transmission power comprises:
Combining each set of transmission power characteristic data into a first characteristic vector form, wherein the amplification factors, the modulation depth, the modulation frequency and the loss evaluation coefficient values in all the first characteristic vectors are used as inputs of a transmission power prediction model, the transmission power prediction model takes the transmission power predicted for each set of transmission power characteristic data as output, the actual transmission power corresponding to each set of transmission power characteristic data is used as a prediction target, and the sum of the first prediction accuracy of all the predicted transmission powers is minimized to be used as a training target; the calculation formula of the first prediction accuracy is as follows: Where e is the number of each group of transmission power characteristic data, For the first degree of accuracy of the prediction,For the predicted transmission power corresponding to the e-th group transmission power characteristic data,The actual transmission power corresponding to the e-th group transmission power characteristic data; training the transmission power prediction model until the sum of the first prediction accuracy reaches convergence, and stopping training; the transmission power prediction model is specifically any one of a deep neural network model and a deep belief network model.
Further, training an evaluation coefficient prediction model for feedback loss evaluation coefficient values, comprising:
Combining each set of interference power characteristic data into second characteristic vectors, wherein the transmission distance, the impedance evaluation coefficient and the environmental humidity in all the second characteristic vectors are used as inputs of an evaluation coefficient prediction model, the evaluation coefficient prediction model takes a loss evaluation coefficient value predicted for each set of interference power characteristic data as output, takes an actual loss evaluation coefficient value corresponding to each set of interference power characteristic data as a prediction target, and takes the sum of second prediction accuracy of the minimized all the predicted loss evaluation coefficient values as a training target; the calculation formula of the second prediction accuracy is as follows: wherein f is the number of each group of interference power characteristic data, For the second degree of prediction accuracy,The coefficient value is evaluated for the predicted loss corresponding to the f-th set of interference power characteristic data,Evaluating a coefficient value for the actual loss corresponding to the f-th group of interference power characteristic data; training the estimation coefficient prediction model until the sum of the second prediction accuracy reaches convergence, and stopping training; the evaluation coefficient prediction model is any one of a deep neural network model and a deep belief network model.
Further, the method for acquiring the measurement correction value of the transmission power of the nth remote machine includes:
Acquiring an initial measurement value of transmission power of an nth remote machine and acquiring running state data of the nth remote machine; combining the transmission power initial measurement value and the running state data into a form of a third eigenvector, and inputting the third eigenvector into a pre-constructed power correction model to obtain a measurement correction value of the transmission power of the nth remote machine;
The power correction model is obtained through training according to the running state training data of the nth remote machine, wherein the running state training data comprises running state characteristic data and a measurement correction value of transmission power corresponding to the running state characteristic data; the operation state characteristic data comprise transmission power initial measurement values and operation state data; the operating state data includes an operating temperature and an operating voltage of the nth remote machine.
Further, obtaining a measurement correction value of the transmission power in the running state training data includes:
acquiring an initial transmission power measured value of the nth remote machine at the operating temperature of adeg.C through a power sensor; taking an initial transmission power measured value of the nth remote machine at the operating temperature of adeg.C as a first transmission power measured value to obtain i first transmission power measured values, wherein i is an integer greater than zero;
under the same condition, extracting the transmission power value of the nth remote machine at the set standard operating temperature, and taking the transmission power value of the nth remote machine at the set operating temperature as a second transmission power measurement value;
The same condition means that the nth remote machine is in the same state at the running temperature and the set standard running temperature, including but not limited to the same remote machine model, the same power consumption and the same shielding network system;
And calculating a difference value of each first transmission power measured value and each second transmission power measured value, taking the difference value of each first transmission power measured value and each second transmission power measured value as a first correction value of the transmission power initial measured value, and correcting the transmission power initial measured value based on the first correction value to obtain a measurement correction value of the transmission power.
Further, acquiring a measurement correction value of the transmission power in the running state training data, further includes:
acquiring an initial measurement value of transmission power under the bV running voltage of an nth remote machine through a power sensor; taking the transmission power initial measurement value under the bV operation voltage of the nth remote machine as a third transmission power measurement value to obtain r third transmission power measurement values, wherein r is an integer larger than zero, b is a constant, and V is volts;
Under the same condition, extracting the transmission power value of the nth remote machine under the set standard operating voltage, and taking the transmission power value of the nth remote machine under the set standard humidity as a fourth transmission power measurement value;
The same condition means that the nth remote machine is in the same state under the operation voltage and the set standard operation voltage, including but not limited to the same remote machine model, the same power consumption and the same shielding network system;
Calculating a difference value of each third transmission power measurement value and the fourth transmission power measurement value, taking the difference value of each third transmission power measurement value and the fourth transmission power measurement value as a second correction value of the transmission power initial measurement value, and correcting the transmission power initial measurement value based on the second correction value to obtain a measurement correction value of the transmission power.
Further, the method for generating the pre-constructed power correction model comprises the following steps:
Combining the transmission power initial measurement value and the operation state data in each group of operation state characteristic data into third characteristic vectors, wherein elements of all the third characteristic vectors are used as inputs of a power correction model, the power correction model takes a measurement correction value predicted for each group of operation state characteristic data as output, takes an actual measurement correction value corresponding to each group of operation state characteristic data as a prediction target, and takes the sum of third prediction accuracy of the minimized all the predicted measurement correction values as a training target; the calculation formula of the third prediction accuracy is as follows: Wherein, the method comprises the steps of, wherein, For each set of numbers of operating condition characteristic data,For the third degree of accuracy of the prediction,Is the firstPredicted measurement correction values corresponding to the group operating state characteristic data,Is the firstActual measurement correction values corresponding to the group operation state characteristic data; training the power correction model until the sum of the third prediction accuracy reaches convergence, and stopping training; the power correction model is any one of a deep neural network model and a deep belief network model.
Further, the logic process for obtaining the real-time loss evaluation coefficient value is as follows:
And taking the interference power characteristic data of the near-end machine as a second characteristic vector, and inputting the second characteristic vector into a trained evaluation coefficient prediction model to obtain a real-time loss evaluation coefficient value predicted by the evaluation coefficient prediction model.
Further, the method for obtaining the predicted transmission power is as follows:
And taking the real-time loss evaluation coefficient value predicted by the evaluation coefficient prediction model as the loss evaluation coefficient value in the transmission power characteristic data of the nth remote machine, converting the transmission power characteristic data into a first characteristic vector form, and inputting the first characteristic vector form into the transmission power prediction model to obtain the transmission power predicted by the transmission power prediction model.
In a second aspect, the present invention provides an intelligent unidirectional shielding device for a base station signal, configured to implement the above-mentioned intelligent unidirectional shielding method for a base station signal, including:
The training data acquisition module is used for collecting transmission power training data of the nth remote machine in the shielding management area and collecting evaluation coefficient training data of the near-end machine connected with the nth remote machine; the transmission power training data comprise transmission power characteristic data collected in a test scene and corresponding transmission power; the evaluation coefficient training data comprise interference power characteristic data collected by a near-end machine in a test environment and corresponding loss evaluation coefficient values;
The model training module is used for training a transmission power prediction model for predicting the transmission power in a feedback manner based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data;
The correction module is used for acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time;
The power loss prediction module is used for obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value;
The transmission power prediction module is used for obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model;
The self-adaptive adjusting module is used for calculating the difference value of the predicted transmission power and the measurement correction value of the transmission power, taking the difference value of the predicted transmission power and the measurement correction value of the transmission power as a power adjusting value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjusting value.
In a third aspect, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the intelligent unidirectional shielding method for the base station signals by calling the computer program stored in the memory.
In a fourth aspect, the present invention provides a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for intelligent unidirectional masking of a base station signal as described above.
The invention has the technical effects and advantages that:
1. The invention collects the transmission power training data of the nth remote machine in the shielding management area and the evaluation coefficient training data of the near-end machine connected with the nth remote machine; training a transmission power prediction model for feedback predicting transmission power based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data; acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time; obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value; obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model; calculating a difference value of the predicted transmission power and a measurement correction value of the transmission power, taking the difference value of the predicted transmission power and the measurement correction value of the transmission power as a power adjustment value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjustment value; according to the invention, through the real-time acquisition of the transmission power characteristic data of the remote machine and the interference power characteristic data of the near-end machine and the combination of the transmission power prediction model and the evaluation coefficient prediction model, the transmission power of the remote machine is predicted in real time, and the self-adaptive adjustment is carried out to optimize the transmission power adjustment strategy of the shielding system, so that the transmission power of the remote machine can be intelligently identified and adjusted, the shielding stability in a shielding management area is ensured, and the shielding effect is improved.
2. According to the invention, the transmission power of the remote terminal is automatically and accurately adjusted according to the change of the loss power of the near terminal, so that the accurate adjustment strategy of the transmission power of the shielding system is optimized, the stability of interference signals in the shielding management area is ensured, and the shielding failure is avoided.
Drawings
Fig. 1 is a flow chart of a method for intelligent unidirectional shielding of a base station signal in embodiment 1;
FIG. 2 is a schematic diagram of a mask management area according to embodiment 1;
Fig. 3 is a parameter diagram of a screening network system in embodiment 1;
fig. 4 is a schematic diagram of a base station signal intelligent unidirectional shielding device in embodiment 2;
Fig. 5 is a schematic diagram of an electronic device in embodiment 3;
fig. 6 is a schematic diagram of a computer-readable storage medium according to embodiment 4.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and a similar second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Example 1
Referring to fig. 1, the disclosure of the present embodiment provides an intelligent unidirectional shielding method for a base station signal, which is applied to a remote machine, and includes:
Step 1: collecting transmission power training data of an nth remote machine in a shielding management area and collecting evaluation coefficient training data of a near-end machine connected with the nth remote machine;
Referring to fig. 2, it should be appreciated that: the shielding management area is internally provided with n far-end machines connected with the near-end machines; the shielding management area comprises, but is not limited to, examination sites, power dispatching rooms, caregivers, hospitals, civil aviation air traffic control and the like; the near-end machine is equipment near the base station and is responsible for receiving and processing signals sent by the base station through a digital signaling level intelligent shielding technology so as to obtain interference signals needing shielding; the remote terminal refers to equipment connected with the near-end machine, and is used for receiving and amplifying an interference signal from the near-end machine and covering the interference signal to the shielding management area.
The digital signaling level intelligent shielding technology is to collect base station signals through an outdoor signal collecting antenna of a near-end machine, demodulate and decode carrier network signals, synchronize pilot frequency and control channels through a digital technology, receive and analyze the characteristics of power, time slots, code channels and the like of the carrier network, reorganize and encode important channels of the carrier network, and promote mobile equipment in a shielding management area to be unable to establish normal synchronization and addressing with the carrier network, so that the connection between the mobile equipment and the carrier network is cut off.
Specifically, the transmission power training data includes transmission power characteristic data collected in a test scene and corresponding transmission power thereof;
Wherein the transmission power characteristic data comprises amplification factors, modulation depth, modulation frequency and loss evaluation coefficient values of interference signals;
it should be noted that: the amplification factor of the interference signal is obtained through real-time monitoring of a power amplifier integrated by a remote machine;
the modulation depth refers to the amplitude variation range of the interference signal, namely the variation degree of the signal from the lowest amplitude to the highest amplitude; the modulation frequency refers to the frequency variation range of the interference signal; it should be noted that: the magnitude of the modulation depth influences the intensity variation degree of the interference signal; the larger modulation depth indicates that the amplitude variation range of the interference signal is larger, and the interference effect is more obvious; the smaller modulation depth indicates that the amplitude variation range of the interference signal is smaller, and the interference effect is relatively weaker; the modulation depth and the modulation frequency are obtained through a signal generator connected with a remote machine;
Specifically, the evaluation coefficient training data comprise interference power characteristic data collected by a near-end machine in a test environment and a loss evaluation coefficient value corresponding to the interference power characteristic data;
The interference power characteristic data comprise transmission distance, impedance evaluation coefficient and environment humidity;
the impedance evaluation coefficient refers to the influence degree of the characteristic impedance of the transmission line on the interference signal when the nth far-end machine receives the interference signal of the near-end machine;
The method for calculating the impedance evaluation coefficient comprises the following steps:
acquiring the resistance value and reactance value of the transmission line, respectively marked as And；
Resistance value is setAnd reactance valueAnd carrying out formulated calculation to obtain an impedance evaluation coefficient, wherein the calculation formula is as follows:
In the method, in the process of the invention, Representing an impedance evaluation coefficient of the transmission line;
It should be noted that: the resistance value and the reactance value of the transmission line are obtained through an impedance measuring instrument; the closer the impedance evaluation coefficient is to 1, the better the impedance matching is, so that the loss of the transmission power of the interference signal in the transmission line is reduced;
The loss evaluation coefficient value refers to the loss power of an interference signal of the near-end machine, the loss evaluation coefficient value is obtained by measuring the transmission power of the interference signal transmitted by the near-end machine to the nth far-end machine through a power sensor, and then calculating the difference value between the transmission power and the actual transmission power of the near-end machine;
step 2: training a transmission power prediction model for feedback predicting transmission power based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data;
In an implementation, a method of training a transmission power prediction model for feedback predicting transmission power includes:
Combining each set of transmission power characteristic data into a first characteristic vector form, wherein the amplification factors, the modulation depth, the modulation frequency and the loss evaluation coefficient values in all the first characteristic vectors are used as inputs of a transmission power prediction model, the transmission power prediction model takes the transmission power predicted for each set of transmission power characteristic data as output, the actual transmission power corresponding to each set of transmission power characteristic data is used as a prediction target, and the sum of the first prediction accuracy of all the predicted transmission powers is minimized to be used as a training target; the calculation formula of the first prediction accuracy is as follows: Where e is the number of each group of transmission power characteristic data, For the first degree of accuracy of the prediction,For the predicted transmission power corresponding to the e-th group transmission power characteristic data,The actual transmission power corresponding to the e-th group transmission power characteristic data; training the transmission power prediction model until the sum of the first prediction accuracy reaches convergence, and stopping training;
it should be noted that: the transmission power prediction model is specifically any one of a deep neural network model and a deep belief network model;
in an implementation, an evaluation coefficient prediction model for feedback loss evaluation coefficient values is trained, comprising:
Combining each set of interference power characteristic data into second characteristic vectors, wherein the transmission distance, the impedance evaluation coefficient and the environmental humidity in all the second characteristic vectors are used as inputs of an evaluation coefficient prediction model, the evaluation coefficient prediction model takes a loss evaluation coefficient value predicted for each set of interference power characteristic data as output, takes an actual loss evaluation coefficient value corresponding to each set of interference power characteristic data as a prediction target, and takes the sum of second prediction accuracy of the minimized all the predicted loss evaluation coefficient values as a training target; the calculation formula of the second prediction accuracy is as follows: wherein f is the number of each group of interference power characteristic data, For the second degree of prediction accuracy,The coefficient value is evaluated for the predicted loss corresponding to the f-th set of interference power characteristic data,Evaluating a coefficient value for the actual loss corresponding to the f-th group of interference power characteristic data; training the estimation coefficient prediction model until the sum of the second prediction accuracy reaches convergence, and stopping training;
it should be noted that: the evaluation coefficient prediction model is any one of a deep neural network model and a deep belief network model;
step 3: acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time;
it should be appreciated that: the transmission power of the nth remote machine is acquired by the power sensor before regulation, however, as the power sensor may be affected by factors such as the running state of the remote machine, the power sensor may be interfered by different degrees, so as to cause deviation of transmission power measurement, and the deviation may affect the accuracy of the transmission power, so that the effect of interference signals emitted by the near-end machine connected with the nth remote machine in the shielding process is weakened. Therefore, in order to ensure the accuracy of the transmission power, measures need to be taken to eliminate the influence of the measurement deviation.
In implementation, the method for acquiring the measurement correction value of the transmission power of the nth remote machine includes:
Acquiring an initial measurement value of transmission power of an nth remote machine and acquiring running state data of the nth remote machine; combining the transmission power initial measurement value and the running state data into a form of a third eigenvector, and inputting the third eigenvector into a pre-constructed power correction model to obtain a measurement correction value of the transmission power of the nth remote machine;
the power correction model is obtained through training according to the running state training data of the nth remote machine, wherein the running state training data comprises running state characteristic data and a measurement correction value of transmission power corresponding to the running state characteristic data; the operation state characteristic data comprise transmission power initial measurement values and operation state data;
Specifically, the operation state data includes an operation temperature and an operation voltage of the nth remote machine; wherein the operating temperature is measured by a temperature sensor arranged on the remote machine;
In one embodiment, obtaining a measurement correction value of the transmission power in the running state training data includes:
acquiring an initial transmission power measured value of the nth remote machine at the operating temperature of adeg.C through a power sensor; taking an initial transmission power measured value of the nth remote machine at the operating temperature of adeg.C as a first transmission power measured value to obtain i first transmission power measured values, wherein i is an integer greater than zero;
under the same condition, extracting the transmission power value of the nth remote machine at the set standard operating temperature, and taking the transmission power value of the nth remote machine at the set operating temperature as a second transmission power measurement value;
It should be noted that: the same condition in the step means that the nth remote machine is in the same state at the running temperature and the set standard running temperature, including but not limited to the same remote machine model, the same power consumption, the same shielding network system and the like; the parameters of the shielding network system are shown in fig. 3;
Calculating a difference value of each first transmission power measured value and each second transmission power measured value, taking the difference value of each first transmission power measured value and each second transmission power measured value as a first correction value of a transmission power initial measured value, and correcting the transmission power initial measured value based on the first correction value to obtain a measurement correction value of the transmission power;
In another embodiment, obtaining a measurement correction value of the transmission power in the running state training data further includes:
acquiring an initial measurement value of transmission power under the bV running voltage of an nth remote machine through a power sensor; taking the transmission power initial measurement value under the bV operation voltage of the nth remote machine as a third transmission power measurement value to obtain r third transmission power measurement values, wherein r is an integer larger than zero, b is a constant, and V is volts;
Under the same condition, extracting the transmission power value of the nth remote machine under the set standard operating voltage, and taking the transmission power value of the nth remote machine under the set standard humidity as a fourth transmission power measurement value;
It should be noted that: the same condition in the step means that the nth remote machine is in the same state under the operation voltage and the set standard operation voltage, including but not limited to the same remote machine model, the same power consumption, the same shielding network system and the like;
Calculating a difference value of each third transmission power measurement value and the fourth transmission power measurement value, taking the difference value of each third transmission power measurement value and the fourth transmission power measurement value as a second correction value of the transmission power initial measurement value, and correcting the transmission power initial measurement value based on the second correction value to obtain a measurement correction value of the transmission power;
specifically, the method for generating the pre-constructed power correction model comprises the following steps:
Combining the transmission power initial measurement value and the operation state data in each group of operation state characteristic data into third characteristic vectors, wherein elements of all the third characteristic vectors are used as inputs of a power correction model, the power correction model takes a measurement correction value predicted for each group of operation state characteristic data as output, takes an actual measurement correction value corresponding to each group of operation state characteristic data as a prediction target, and takes the sum of third prediction accuracy of the minimized all the predicted measurement correction values as a training target; the calculation formula of the third prediction accuracy is as follows: Wherein, the method comprises the steps of, wherein, For each set of numbers of operating condition characteristic data,For the third degree of accuracy of the prediction,Is the firstPredicted measurement correction values corresponding to the group operating state characteristic data,Is the firstActual measurement correction values corresponding to the group operation state characteristic data; training the power correction model until the sum of the third prediction accuracy reaches convergence, and stopping training;
it should be noted that: the power correction model is any one of a deep neural network model and a deep belief network model;
the transmission power initial measured value of the nth remote machine is corrected, so that inaccurate adjustment of the transmission power caused by influence factors such as the running state of equipment is avoided, the performance and stability of the remote machine are effectively improved, and stable and normal operation of shielding signals in a shielding management area is further ensured.
Step 4: obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value;
In implementation, the logic for obtaining the real-time loss evaluation coefficient value is as follows:
The interference power characteristic data of the near-end machine is used as a second characteristic vector, and the second characteristic vector is input into a trained evaluation coefficient prediction model to obtain a real-time loss evaluation coefficient value predicted by the evaluation coefficient prediction model;
Step 5: obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model;
In implementation, the method for obtaining the predicted transmission power is as follows:
Taking the real-time loss evaluation coefficient value predicted by the evaluation coefficient prediction model as the loss evaluation coefficient value in the transmission power characteristic data of the nth remote machine, converting the transmission power characteristic data into a first characteristic vector form, and inputting the first characteristic vector form into the transmission power prediction model to obtain the transmission power predicted by the transmission power prediction model;
step 6: calculating a difference value of the predicted transmission power and a measurement correction value of the transmission power, taking the difference value of the predicted transmission power and the measurement correction value of the transmission power as a power adjustment value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjustment value;
An exemplary illustration is: if the measured correction value of the transmission power of the nth remote machine is 15 watts and the transmission power value in the predicted transmission power data is 20 watts, the difference between the predicted transmission power data and the measured correction value is +5 watts, so that +5 watts is used as a power adjustment value, and the transmission power of the nth remote machine is adaptively increased according to +5 watts; in contrast, if the measured correction value of the remote machine is 20 watts and the transmission power value in the predicted transmission power data is 15 watts, the difference between the predicted transmission power data and the measured correction value is-5 watts, so, -5 watts is used as the power adjustment value, and the transmission power of the nth remote machine is reduced in a self-adaptive manner according to-5 watts.
In the embodiment, the transmission power training data of the nth remote machine in the shielding management area is collected, and the evaluation coefficient training data of the near-end machine connected with the nth remote machine is collected; training a transmission power prediction model for feedback predicting transmission power based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data; acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time; obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value; obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model; calculating a difference value of the predicted transmission power and a measurement correction value of the transmission power, taking the difference value of the predicted transmission power and the measurement correction value of the transmission power as a power adjustment value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjustment value; according to the embodiment, through the real-time acquisition of the transmission power characteristic data of the remote machine and the interference power characteristic data of the near-end machine, the transmission power of the remote machine is predicted in real time by combining the transmission power prediction model and the evaluation coefficient prediction model, and the self-adaptive adjustment is carried out to optimize the transmission power adjustment strategy of the shielding system, the transmission power of the remote machine can be intelligently identified and adjusted, so that the shielding stability in a shielding management area is ensured, and the shielding effect is improved;
According to the embodiment, the transmission power of the remote terminal is automatically and accurately adjusted according to the change of the loss power of the near-end terminal, so that the accurate adjustment strategy of the transmission power of the shielding system is optimized, the stability of interference signals in the shielding management area is ensured, and shielding failure is avoided.
The formulas related in the above are all formulas with dimensions removed and numerical values calculated, and are a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and weight factors in the formulas and various preset thresholds in the analysis process are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data; the size of the weight factor is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the weight factor depends on the number of sample data and the corresponding processing coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected.
Example 2
Referring to fig. 4, this embodiment provides an intelligent unidirectional shielding device for a base station signal, which includes: the system comprises a training data acquisition module, a model training module, a correction module, a power loss prediction module, a transmission power prediction module and a self-adaptive adjustment module, wherein the modules are connected in a wired and/or wireless mode to realize data transmission between the modules;
The system comprises a training data acquisition module, a data acquisition module and a data processing module, wherein the training data acquisition module is used for collecting transmission power training data of an nth remote machine in a shielding management area and collecting evaluation coefficient training data of a near-end machine connected with the nth remote machine; the transmission power training data comprise transmission power characteristic data collected in a test scene and corresponding transmission power; the evaluation coefficient training data comprise interference power characteristic data collected by a near-end machine in a test environment and corresponding loss evaluation coefficient values;
The model training module is used for training a transmission power prediction model for predicting the transmission power in a feedback manner based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data;
The correction module is used for acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time;
The power loss prediction module is used for obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value;
The transmission power prediction module is used for obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model;
The self-adaptive adjusting module is used for calculating the difference value of the predicted transmission power and the measurement correction value of the transmission power, taking the difference value of the predicted transmission power and the measurement correction value of the transmission power as a power adjusting value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjusting value.
Example 3
Referring to fig. 5, the present embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes a base station signal intelligent unidirectional shielding method of embodiment 1 by calling a computer program stored in the memory.
Example 4
Referring to fig. 6, the present embodiment provides a computer readable storage medium storing instructions that when executed on a computer cause the computer to perform a method for intelligently and unidirectionally masking a base station signal according to embodiment 1.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (13)
1. An intelligent unidirectional shielding method for a base station signal is characterized by comprising the following steps:
Step 1: collecting transmission power training data of an nth remote machine in a shielding management area and collecting evaluation coefficient training data of a near-end machine connected with the nth remote machine; the transmission power training data comprise transmission power characteristic data collected in a test scene and corresponding transmission power; the evaluation coefficient training data comprise interference power characteristic data collected by a near-end machine in a test environment and corresponding loss evaluation coefficient values; the transmission power characteristic data comprise amplification factors, modulation depth, modulation frequency and loss evaluation coefficient values of interference signals;
step 2: training a transmission power prediction model for feedback predicting transmission power based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data;
step 3: acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time;
Step 4: obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value;
Step 5: obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model;
Step 6: and calculating a difference value between the predicted transmission power and the measurement correction value of the transmission power, taking the difference value between the predicted transmission power and the measurement correction value of the transmission power as a power adjustment value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjustment value.
2. The intelligent unidirectional shielding method of a base station signal according to claim 1, wherein the amplification factor of the interference signal is obtained through real-time monitoring of a power amplifier integrated by a remote machine; the modulation depth refers to the amplitude transformation range of the interference signal, namely the degree of change of the signal from the lowest amplitude to the highest amplitude; the modulation frequency refers to the frequency variation range of the interference signal;
the interference power characteristic data comprise transmission distance, impedance evaluation coefficient and environment humidity;
the impedance evaluation coefficient refers to the influence degree of the characteristic impedance of the transmission line on the interference signal when the nth far-end machine receives the interference signal of the near-end machine;
The method for calculating the impedance evaluation coefficient comprises the following steps:
acquiring the resistance value and reactance value of the transmission line, respectively marked as And/>；
Resistance value is setAnd reactance value/>And carrying out formulated calculation to obtain an impedance evaluation coefficient, wherein the calculation formula is as follows:
；
In the method, in the process of the invention, Representing an impedance evaluation coefficient of the transmission line;
The loss evaluation coefficient value refers to the loss power of the interference signal of the near-end machine, and is obtained by measuring the transmission power of the interference signal sent by the near-end machine and transmitted to the nth far-end machine through a power sensor, and then calculating the difference value between the transmission power and the actual transmission power of the near-end machine.
3. The method for intelligently and unidirectionally masking a base station signal according to claim 2, wherein the method for training a transmission power prediction model for feedback predicting transmission power comprises:
Combining each set of transmission power characteristic data into a first characteristic vector form, wherein the amplification factors, the modulation depth, the modulation frequency and the loss evaluation coefficient values in all the first characteristic vectors are used as inputs of a transmission power prediction model, the transmission power prediction model takes the transmission power predicted for each set of transmission power characteristic data as output, the actual transmission power corresponding to each set of transmission power characteristic data is used as a prediction target, and the sum of the first prediction accuracy of all the predicted transmission powers is minimized to be used as a training target; the calculation formula of the first prediction accuracy is as follows: Wherein e is the number of each group of transmission power characteristic data,/> For the first prediction accuracy,/>Predicted transmit power for group e transmit power profile data,/>The actual transmission power corresponding to the e-th group transmission power characteristic data; training the transmission power prediction model until the sum of the first prediction accuracy reaches convergence, and stopping training; the transmission power prediction model is specifically any one of a deep neural network model or a deep belief network model.
4. A method of intelligent unidirectional masking of base station signals as claimed in claim 3, wherein training an evaluation coefficient prediction model for feedback loss evaluation coefficient values comprises:
Combining each set of interference power characteristic data into second characteristic vectors, wherein the transmission distance, the impedance evaluation coefficient and the environmental humidity in all the second characteristic vectors are used as inputs of an evaluation coefficient prediction model, the evaluation coefficient prediction model takes a loss evaluation coefficient value predicted for each set of interference power characteristic data as output, takes an actual loss evaluation coefficient value corresponding to each set of interference power characteristic data as a prediction target, and takes the sum of second prediction accuracy of the minimized all the predicted loss evaluation coefficient values as a training target; the calculation formula of the second prediction accuracy is as follows: Wherein f is the number of each group of interference power characteristic data,/> For the second prediction accuracy,/>Evaluating coefficient values for predicted losses corresponding to the f-th set of interference power characteristic data,/>Evaluating a coefficient value for the actual loss corresponding to the f-th group of interference power characteristic data; training the estimation coefficient prediction model until the sum of the second prediction accuracy reaches convergence, and stopping training; the evaluation coefficient prediction model is any one of a deep neural network model and a deep belief network model.
5. The method for intelligently and unidirectionally shielding a base station signal according to claim 4, wherein the method for acquiring the measurement correction value of the transmission power of the nth remote machine comprises:
Acquiring an initial measurement value of transmission power of an nth remote machine and acquiring running state data of the nth remote machine; combining the transmission power initial measurement value and the running state data into a form of a third eigenvector, and inputting the third eigenvector into a pre-constructed power correction model to obtain a measurement correction value of the transmission power of the nth remote machine;
The power correction model is obtained through training according to the running state training data of the nth remote machine, wherein the running state training data comprises running state characteristic data and a measurement correction value of transmission power corresponding to the running state characteristic data; the operation state characteristic data comprise transmission power initial measurement values and operation state data; the operating state data includes an operating temperature and an operating voltage of the nth remote machine.
6. The method of claim 5, wherein obtaining a measurement correction value of transmission power in the running state training data comprises:
acquiring an initial transmission power measured value of the nth remote machine at the operating temperature of adeg.C through a power sensor; taking an initial transmission power measured value of the nth remote machine at the operating temperature of adeg.C as a first transmission power measured value to obtain i first transmission power measured values, wherein i is an integer greater than zero;
under the same condition, extracting the transmission power value of the nth remote machine at the set standard operating temperature, and taking the transmission power value of the nth remote machine at the set operating temperature as a second transmission power measurement value;
The same condition means that the nth remote machine is in the same state at the running temperature and the set standard running temperature, including but not limited to the same remote machine model, the same power consumption and the same shielding network system;
And calculating a difference value of each first transmission power measured value and each second transmission power measured value, taking the difference value of each first transmission power measured value and each second transmission power measured value as a first correction value of the transmission power initial measured value, and correcting the transmission power initial measured value based on the first correction value to obtain a measurement correction value of the transmission power.
7. The method of intelligent unidirectional masking of a base station signal of claim 6, wherein obtaining a measurement correction value for a transmission power in said operational state training data, further comprises:
acquiring an initial measurement value of transmission power under the bV running voltage of an nth remote machine through a power sensor; taking the transmission power initial measurement value under the bV operation voltage of the nth remote machine as a third transmission power measurement value to obtain r third transmission power measurement values, wherein r is an integer larger than zero, b is a constant, and V is volts;
Under the same condition, extracting the transmission power value of the nth remote machine under the set standard operating voltage, and taking the transmission power value of the nth remote machine under the set standard humidity as a fourth transmission power measurement value;
The same condition means that the nth remote machine is in the same state under the operation voltage and the set standard operation voltage, including but not limited to the same remote machine model, the same power consumption and the same shielding network system;
Calculating a difference value of each third transmission power measurement value and the fourth transmission power measurement value, taking the difference value of each third transmission power measurement value and the fourth transmission power measurement value as a second correction value of the transmission power initial measurement value, and correcting the transmission power initial measurement value based on the second correction value to obtain a measurement correction value of the transmission power.
8. The intelligent unidirectional shielding method of a base station signal according to claim 7, wherein the method for generating the pre-constructed power correction model comprises:
Combining the transmission power initial measurement value and the operation state data in each group of operation state characteristic data into third characteristic vectors, wherein elements of all the third characteristic vectors are used as inputs of a power correction model, the power correction model takes a measurement correction value predicted for each group of operation state characteristic data as output, takes an actual measurement correction value corresponding to each group of operation state characteristic data as a prediction target, and takes the sum of third prediction accuracy of the minimized all the predicted measurement correction values as a training target; the calculation formula of the third prediction accuracy is as follows: Wherein/> For each group of running state characteristic data numbering,/>For the third prediction accuracy,/>For/>Predicted measurement correction value corresponding to group operation state characteristic data,/>For/>Actual measurement correction values corresponding to the group operation state characteristic data; training the power correction model until the sum of the third prediction accuracy reaches convergence, and stopping training; the power correction model is any one of a deep neural network model and a deep belief network model.
9. The intelligent unidirectional shielding method of claim 8, wherein the acquiring logic of the real-time loss evaluation coefficient value comprises the following steps:
And taking the interference power characteristic data of the near-end machine as a second characteristic vector, and inputting the second characteristic vector into a trained evaluation coefficient prediction model to obtain a real-time loss evaluation coefficient value predicted by the evaluation coefficient prediction model.
10. The intelligent unidirectional shielding method of a base station signal according to claim 9, wherein the predicted transmission power obtaining method comprises the following steps:
And taking the real-time loss evaluation coefficient value predicted by the evaluation coefficient prediction model as the loss evaluation coefficient value in the transmission power characteristic data of the nth remote machine, converting the transmission power characteristic data into a first characteristic vector form, and inputting the first characteristic vector form into the transmission power prediction model to obtain the transmission power predicted by the transmission power prediction model.
11. A base station signal intelligent unidirectional shielding device for implementing the base station signal intelligent unidirectional shielding method as claimed in any one of claims 1 to 10, comprising:
The training data acquisition module is used for collecting transmission power training data of the nth remote machine in the shielding management area and collecting evaluation coefficient training data of the near-end machine connected with the nth remote machine; the transmission power training data comprise transmission power characteristic data collected in a test scene and corresponding transmission power; the evaluation coefficient training data comprise interference power characteristic data collected by a near-end machine in a test environment and corresponding loss evaluation coefficient values;
The model training module is used for training a transmission power prediction model for predicting the transmission power in a feedback manner based on the transmission power training data; training an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value based on the evaluation coefficient training data;
The correction module is used for acquiring a measurement correction value of the transmission power of the nth remote machine, and acquiring transmission power characteristic data of the nth remote machine and interference power characteristic data of a near-end machine connected with the nth remote machine in real time;
The power loss prediction module is used for obtaining a real-time loss evaluation coefficient value based on the interference power characteristic data and an evaluation coefficient prediction model for feeding back the loss evaluation coefficient value;
The transmission power prediction module is used for obtaining predicted transmission power based on the transmission power characteristic data of the nth remote machine, the real-time loss evaluation coefficient value and the transmission power prediction model;
The self-adaptive adjusting module is used for calculating the difference value of the predicted transmission power and the measurement correction value of the transmission power, taking the difference value of the predicted transmission power and the measurement correction value of the transmission power as a power adjusting value, and carrying out self-adaptive adjustment on the nth remote machine according to the power adjusting value.
12. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor performs a base station signal intelligent unidirectional shielding method as claimed in any one of claims 1 to 10 by invoking a computer program stored in the memory.
13. A computer readable storage medium storing instructions which, when run on a computer, cause the computer to perform a base station signal intelligent unidirectional screening method as claimed in any one of claims 1 to 10.
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