CN114266285A - Cell value prediction method, device, electronic equipment and storage medium - Google Patents
Cell value prediction method, device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN114266285A CN114266285A CN202111359940.6A CN202111359940A CN114266285A CN 114266285 A CN114266285 A CN 114266285A CN 202111359940 A CN202111359940 A CN 202111359940A CN 114266285 A CN114266285 A CN 114266285A
- Authority
- CN
- China
- Prior art keywords
- base station
- value
- station cell
- cell
- prediction model
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000004891 communication Methods 0.000 claims abstract description 89
- 238000010801 machine learning Methods 0.000 claims abstract description 33
- 238000012545 processing Methods 0.000 claims description 49
- 238000012360 testing method Methods 0.000 claims description 28
- 238000005259 measurement Methods 0.000 claims description 23
- 238000012549 training Methods 0.000 claims description 22
- 230000001186 cumulative effect Effects 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 15
- 238000012795 verification Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000005315 distribution function Methods 0.000 description 4
- 238000010295 mobile communication Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Abstract
The application provides a cell value prediction method, a device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring input characteristic indexes of a base station cell in any time period of the day, wherein the input characteristic indexes comprise communication state information, value labels and basic information of the base station cell and time characteristics of the time periods; using the input characteristic index of the base station cell as input data of a value prediction model to obtain a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell. Through the scheme, an accurate value prediction model can be established based on a large number of historical characteristic indexes through machine learning, so that accurate prediction of the cell value is achieved by using the value prediction model.
Description
Technical Field
The present application relates to the field of communication networks, and in particular, to a method and an apparatus for predicting a cell value, an electronic device, and a storage medium.
Background
At present, mobile communication network technology is rapidly developing, as the 5G era comes, multi-mode networks coexist, a cloud network architecture is gradually developed, various new services are continuously emerging, equipment connection is greatly quantized, and the explosive growth of mobile communication network data traffic is continuously promoted.
However, rapid development of mobile communication network technology is accompanied by rapid increase in the number of base stations and energy consumption. The traditional base station energy-saving scheme is mainly based on manual experience, and relevant historical data are respectively subjected to statistical analysis through service relevant indexes such as uplink and downlink flow, the number of online users and the like, and a relevant index threshold value of base station cell energy saving is given, so that energy saving is implemented. In the face of the development trend of complicated communication network and diversified service scenes, it is difficult to realize accurate energy saving on the premise of ensuring the service performance of the user and not influencing the perception of the user.
Disclosure of Invention
The application provides a cell value prediction method, a cell value prediction device, electronic equipment and a storage medium, which are used for realizing accurate prediction of cell values.
In a first aspect, the present application provides a method for predicting a cell value, including: acquiring input characteristic indexes of a base station cell in any time period of the day, wherein the input characteristic indexes comprise communication state information, value labels and basic information of the base station cell and time characteristics of the time periods; using the input characteristic index of the base station cell as input data of a value prediction model to obtain a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods.
In one possible implementation, the method further includes: acquiring historical characteristic indexes of a base station cell in a first time period of historical dates; selecting historical characteristic indexes with dates before a preset first threshold value from the historical characteristic indexes as a training set, selecting a preset number of historical characteristic indexes from the historical characteristic indexes except the training set as a verification set, and using the rest historical characteristic indexes as a test set; based on the historical characteristic indexes in the training set, an initial value prediction model is obtained by utilizing a machine learning algorithm; performing model adjustment on the initial value prediction model based on the historical characteristic indexes in the verification set; and testing the current value prediction model according to the historical characteristic indexes in the test set, and finishing the establishment of the value prediction model if the test is passed.
In one possible implementation, the method further includes: fitting each communication state information of the base station cell by using exponential distribution to obtain an exponential probability density function of each communication state information, and performing integral processing on the exponential probability density function to obtain cumulative probability distribution of each communication state information; calculating the value degree of the base station cell based on a value degree formula; wherein the value degree formula comprises:
where n is the number of first communication status information except for the measurement report coverage, Wi is the weight of the ith first communication status information, and Fi(x) The cumulative probability value W when the ith first communication state information takes the value of xmrIs the weight of the measurement report coverage, M is the measurement report coverage; if the base station cellIf the value degree is smaller than a preset second threshold value, adding a low-value cell label for the base station cell; and if the value degree of the base station cell is not less than the second threshold value, adding a high-value label to the base station cell.
In one possible implementation, the basic information includes a location, a type, and an identity of a base station cell; the communication state information includes at least one of: the method comprises the steps of uplink network resource utilization rate, downlink network resource utilization rate, uplink flow, downlink flow, user quantity, VIP user quantity, hour charge and measurement report coverage rate.
In a possible implementation manner, after the obtaining the input characteristic index of the base station cell at any time period of the day, the method further includes: if the base station cell belongs to a preset specific cell set, taking the input characteristic index of the base station cell as input data of a first price value prediction model corresponding to the base station cell to obtain a value prediction result of the base station cell output by the first price value prediction model; the first value prediction model is obtained through machine learning based on the historical characteristic index of the base station cell and a known result corresponding to the historical characteristic index; and each base station cell in the specific cell set corresponds to a pre-established value prediction model.
In one possible implementation, the method further includes: if the value prediction result of the base station cell in any future time interval is a low-value cell, executing a base station closing instruction on the base station cell in the later predetermined time interval; and if the value prediction result of the base station cell in any future time period is a high-value cell, not performing processing on the base station cell.
In a second aspect, the present application provides a cell value prediction apparatus, including: the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring input characteristic indexes of a base station cell in any time period of the day, and the input characteristic indexes comprise communication state information, value labels and basic information of the base station cell and time characteristics of the time periods; the processing module is used for taking the input characteristic index of the base station cell as input data of a value prediction model and obtaining a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods.
In one possible implementation, the apparatus further includes a modeling module: the acquisition module is further used for acquiring historical characteristic indexes of the base station cell in a first time period of historical dates; the acquisition module is further used for selecting historical characteristic indexes with dates before a preset first threshold value from the historical characteristic indexes as a training set, selecting a preset number of historical characteristic indexes from the historical characteristic indexes except the training set as a verification set, and using the rest historical characteristic indexes as a test set; the modeling module is used for obtaining an initial value prediction model by utilizing a machine learning algorithm based on the historical characteristic indexes in the training set; performing model adjustment on the initial value prediction model based on the historical characteristic indexes in the verification set; and the modeling module is also used for testing the current value prediction model according to the historical characteristic indexes in the test set, and completing the establishment of the value prediction model if the test is passed.
In one possible implementation, the apparatus further includes: the calculation module is used for fitting each communication state information of the base station cell by using exponential distribution to obtain an exponential probability density function of each communication state information, and performing integral processing on the exponential probability density function to obtain cumulative probability distribution of each communication state information; the calculation module is also used for calculating the value degree of the base station cell based on a value degree formula; wherein the value degree formula comprises:
wherein n is the first communication except the coverage of the measurement reportNumber of communication status information, Wi is weight of ith first communication status information, Fi(x) The cumulative probability value W when the ith first communication state information takes the value of xmrIs the weight of the measurement report coverage, M is the measurement report coverage; the calculation module is further used for adding a low-value cell label to the base station cell if the value degree of the base station cell is smaller than a preset second threshold; and if the value degree of the base station cell is not less than the second threshold value, adding a high-value label to the base station cell.
In one possible implementation, the basic information includes a location, a type, and an identity of a base station cell; the communication state information includes at least one of: the method comprises the steps of uplink network resource utilization rate, downlink network resource utilization rate, uplink flow, downlink flow, user quantity, VIP user quantity, hour charge and measurement report coverage rate.
In a possible implementation manner, the processing module is further configured to, if the base station cell belongs to a predetermined specific cell set, use an input feature index of the base station cell as input data of a first value prediction model corresponding to the base station cell, and obtain a value prediction result of the base station cell output by the first value prediction model; the first value prediction model is obtained through machine learning based on the historical characteristic index of the base station cell and a known result corresponding to the historical characteristic index; and each base station cell in the specific cell set corresponds to a pre-established value prediction model.
In a possible implementation manner, the processing module is further configured to execute a base station shutdown instruction on the base station cell in the predetermined period of time later if the value prediction result of the base station cell in any future period of time is a low-value cell; the processing module is further configured to not perform processing on the base station cell if the value prediction result of the base station cell at any future time interval is a high-value cell.
In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored by the memory to implement the method of any of the first aspects.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for execution by a processor to perform the method of any of the first aspects.
The cell value prediction method, the device, the electronic equipment and the storage medium provided by the application are used for acquiring input characteristic indexes of a base station cell in any time period of the day, wherein the input characteristic indexes comprise communication state information, a value label and basic information of the base station cell and time characteristics of the time period; using the input characteristic index of the base station cell as input data of a value prediction model to obtain a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods. Through the scheme, an accurate value prediction model can be established based on a large number of historical characteristic indexes through machine learning, so that accurate prediction of the cell value is achieved by using the value prediction model.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario of a cell value prediction method provided in the present application;
fig. 2 is a schematic flowchart of a method for predicting a cell value according to an embodiment of the present disclosure;
FIG. 3 is an example of data set acquisition provided by an embodiment of the present application
Fig. 4 is a schematic flowchart of a method for predicting a cell value according to a second embodiment of the present application;
fig. 5 is a schematic structural diagram of a device for predicting a cell value according to a third embodiment of the present application;
fig. 6 is a block diagram of a device for predicting a cell value according to a fifth embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present application;
with the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with aspects of the present application.
The terms involved are explained first:
catboost: a machine learning algorithm based on a gradient boosting decision tree.
Fig. 1 is a schematic view of an application scenario of a cell value prediction method provided in an embodiment of the present application, as shown in fig. 1, the scenario includes: user terminal 1, base station cell 2, and cell value prediction apparatus 3.
Exemplified in connection with the illustrated scenario: the user terminal 1 sends a data request signal to the base station cell 2, the base station cell 2 sends a corresponding data signal according to the data request signal of the user terminal 1, and the user terminal 1 receives the data signal so as to realize mobile communication internet surfing. The cell value prediction device 3 acquires communication state information, a value tag, and basic information of the base station cell 2, and accurately predicts whether the base station cell 2 is a low-value cell or not by using a value prediction model established by machine learning.
In practical application, the prediction of the cell value of the base station can be used for saving energy of the base station. For example, if it is predicted that a certain base station cell is a low-value cell, it indicates that the actual usage amount of the user terminal to the base station cell is low, and the user terminal will not affect the usage even if only receiving data signals of other base station cells. At the moment, the closing instruction can be executed on the low-value cell to stop the operation of the low-value cell without influencing the use of the low-value cell by a user, so that accurate energy conservation is realized.
The following describes an example of the embodiments of the present application with reference to the following embodiments.
Example one
Fig. 2 is a schematic flowchart of a method for predicting a cell value according to an embodiment of the present application, where the method includes the following steps:
s101, obtaining input characteristic indexes of a base station cell in any time period of the day, wherein the input characteristic indexes comprise communication state information, value labels and basic information of the base station cell and time characteristics of the time periods;
s102, using the input characteristic index of the base station cell as input data of a value prediction model to obtain a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods.
In practical applications, the implementation subject of the embodiment may be a cell value prediction apparatus, and the cell value prediction apparatus may be implemented in various ways. For example, the program may be software, or a medium storing a related computer program, such as a usb disk; alternatively, the apparatus may also be a physical device integrated with or installed with an associated computer program, the physical device supporting USB interface connection, for example, a chip, an intelligent terminal, a computer, a server, and the like.
In one example, S101 specifically includes: acquiring historical characteristic indexes of a base station cell in a first time period of historical dates; selecting historical characteristic indexes with dates before a preset first threshold value from the historical characteristic indexes as a training set, selecting a preset number of historical characteristic indexes from the historical characteristic indexes except the training set as a verification set, and using the rest historical characteristic indexes as a test set; based on the historical characteristic indexes in the training set, an initial value prediction model is obtained by utilizing a machine learning algorithm; performing model adjustment on the initial value prediction model based on the historical characteristic indexes in the verification set; and testing the current value prediction model according to the historical characteristic indexes in the test set, and finishing the establishment of the value prediction model if the test is passed.
Alternatively, as shown in fig. 3, fig. 3 is a data set acquisition example. Acquiring historical characteristic indexes of a base station cell in historical 30 days, selecting data from the previous 12 th day to the previous 30 th day in the historical characteristic indexes as a training set, selecting data from the previous 6 th day to the previous 12 th day in the historical characteristic indexes as a verification set, and selecting the rest historical characteristic indexes as a test set. And acquiring an initial value prediction model based on the historical characteristic indexes in the training set by utilizing a Catboost machine learning algorithm. And substituting the historical characteristic indexes in the verification set into the initial value prediction model to carry out model adjustment, and adjusting and optimizing the performance of the initial value prediction model in the training process. And substituting the historical characteristic indexes in the test set into the currently optimized value prediction model, evaluating whether the value prediction model meets the target requirement through testing, and completing the establishment of the value prediction model if the value prediction model meets the target requirement.
Optionally, the historical characteristic indexes of the base station cell in the history of 1 month are obtained, 18 days of data are randomly selected from the historical characteristic indexes to serve as a training set, 6 days of data in the rest of the historical characteristic indexes are randomly selected to serve as a verification set, and the rest of the historical characteristic indexes are selected to serve as a test set.
Based on the above embodiment, a large number of historical characteristic indexes can be obtained, so that an accurate value prediction model is established through machine learning.
In one example, building a value prediction model includes: fitting each communication state information of the base station cell by using exponential distribution to obtain an exponential probability density function of each communication state information, and performing integral processing on the exponential probability density function to obtain cumulative probability distribution of each communication state information; calculating the value degree of the base station cell based on a value degree formula; wherein the value degree formula comprises:
where n is the number of first communication status information except for the measurement report coverage, Wi is the weight of the ith first communication status information, and Fi(x) The cumulative probability value W when the ith first communication state information takes the value of xmrIs the weight of the measurement report coverage, M is the measurement report coverage; if the value degree of the base station cell is smaller than a preset second threshold value, adding a low-value cell label to the base station cell; and if the value degree of the base station cell is not less than the second threshold value, adding a high-value label to the base station cell.
Combining with a scene example, obtaining 1 month of historical communication state information, and showing that each piece of historical communication state information is approximate to exponential distribution through statistical analysis, so that performing exponential distribution fitting on the historical communication state information to obtain an exponential probability density function of the historical communication state information, and performing integration processing on the exponential probability density function to obtain an accumulated probability distribution function f (x) of each piece of communication state information:
F(X)＝P{X≤x}
wherein X is a random variable, X is any real number, and the cumulative probability distribution function F (X) represents the probability when the value of the random variable X is less than or equal to X. And carrying out weighted calculation on the cumulative probability distribution function F (X) of each piece of communication state information according to the preset corresponding weight to obtain the base station cell value degree.
Based on the above implementation mode, various types of data can be synthesized to respectively perform cumulative probability distribution function calculation, so that the base station cell worth degree closer to the real situation is obtained.
In one example, the basic information includes location, type, and identity of a base station cell; the communication state information includes at least one of: the method comprises the following steps of (1) utilizing uplink network resources, utilizing downlink network resources, utilizing uplink flow, utilizing downlink flow, utilizing the number of users, utilizing the number of visitors, carrying out hourly charge and measuring report coverage rate; the value tags comprise a low value cell tag and a high value cell tag; the temporal characteristics of the period include the hours, days of the period, whether the period is a day of the week, and whether it is a holiday.
The application of various input characteristic indicators will be described below with reference to a plurality of scenarios.
In combination with the scenario example, the basic information is an inherent feature of each base station, and is used for associating with a corresponding base station, so as to facilitate classification and search. For example, the location and type of the basic information may be used to add a screening field, and an administrator may screen the base station cell to be queried according to the field.
In combination with the scenario example, the usage amount of the base station cell by the user is reflected by the uplink network resource utilization rate, the downlink network resource utilization rate, the uplink traffic, the downlink traffic, the user number, the visitors user number, and the hour tariff in the communication state information. For example, if the uplink network resource utilization rate, the downlink network resource utilization rate, the uplink traffic and the downlink traffic are high, it indicates that the base station cell actually uses more resources and has less idle resources, and indicates that the user has high dependency on the base station cell and a large usage amount, and the value of the base station cell is higher. If the number of users is large, it means that the dependence of many users on the base station cell is high, the usage amount is large, and the value degree of the base station cell is higher.
In combination with the scenario example, the coverage of the measurement report in the communication status information represents the amount of resources provided by the base station cell. For example, if the coverage of the measurement report is high, it indicates that the signal strength of the base station cell is higher, and in the case that the usage amount of the user is not changed, the higher the signal strength of the base station cell is, the lower the corresponding resource utilization rate is, the higher the energy consumption is, and the lower the value of the base station cell is. Therefore, the coverage rate of the measurement report is calculated by taking the reciprocal of the value degree formula of the base station cell.
Optionally, the weight of the communication status information may be adjusted according to different service requirements. For example, if the user is concerned more about the download service of the user or the user in the coverage area of the base station cell has a greater demand for the download service, the downlink network resource utilization rate or the downlink traffic weight in the communication state information may be increased.
In connection with the scenario example, the value tag is used to provide information for machine learning to predict cell values. For example, if a cell is evaluated as a low value cell multiple times in a history, the probability that the cell will be predicted as a low value cell in the future is high.
In combination with the scene example, the time characteristics of the time period are used for providing time law information for predicting the cell value through machine learning. For example, if three to five pm on monday to friday in a cell history is evaluated as a low value cell many times, the probability that the cell is predicted as a low value cell at three to five pm on monday to friday in the future is high.
Based on the above embodiment, instead of analyzing each input characteristic index independently, various input characteristic indexes can be analyzed comprehensively, and accurate prediction can be realized in a communication network facing diversification of service scenes.
In one example, S101 is followed by: if the base station cell belongs to a preset specific cell set, taking the input characteristic index of the base station cell as input data of a first price value prediction model corresponding to the base station cell to obtain a value prediction result of the base station cell output by the first price value prediction model; the first value prediction model is obtained through machine learning based on the historical characteristic index of the base station cell and a known result corresponding to the historical characteristic index; and each base station cell in the specific cell set corresponds to a pre-established value prediction model.
In combination with a scene example, for a specific cell, such as a government office area, a hospital and other scenes, due to the particularity of the service, the specific cell is modeled separately, the input characteristic index of the base station cell is used as the input data of the first value prediction model corresponding to the base station cell, and the value prediction result of the base station cell output by the first value prediction model is obtained.
Based on the above embodiment, the specific cell is independently modeled, and only the historical characteristic indexes of the specific cell are obtained to perform machine learning modeling to obtain the value prediction model, so that the interference of the historical characteristic indexes of base station cells with different services on the value prediction model is eliminated, and the low-value base station cells are accurately identified in different scenes.
In one example. S102 specifically comprises the following steps: and taking the input characteristic index of the base station cell as input data of a value prediction model, wherein the value prediction model outputs the prediction probability that the base station cell is a low-value cell in a certain future time period, and if the prediction probability that the base station cell is the low-value cell in the certain future time period is greater than a preset third threshold value, the base station cell is determined to be the low-value cell in the certain future time period.
For example, if the predicted probability that a certain cell is a low-value cell at tomorrow time is 0.62 and is less than the third threshold 0.80, the certain cell is determined to be a high-value cell at tomorrow time.
Based on the above embodiment, the result output by the value prediction model can be judged by a method of presetting a third threshold, and the third threshold can be adjusted according to the actual scene, so that the accuracy of value prediction is effectively improved.
In the method for predicting a cell value provided in this embodiment, an input feature index of a base station cell at any time interval of the day is obtained, where the input feature index includes communication state information, a value tag, and basic information of the base station cell, and a time feature of the time interval. And taking the input characteristic index of the base station cell as input data of a value prediction model to obtain a value prediction result of the base station cell output by the value prediction model. The value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods. Through the scheme, an accurate value prediction model can be established based on a large number of historical characteristic indexes through machine learning, so that accurate prediction of the cell value is achieved by using the value prediction model.
Example two
Fig. 4 is a schematic flow chart of a method for predicting a cell value according to a second embodiment of the present application, where on the basis of the first embodiment, this embodiment illustrates a processing flow of a base station cell after obtaining a value prediction result, and as shown in fig. 4, on the basis of the first embodiment, the method further includes:
s103, if the value prediction result of the base station cell in any future time interval is a low-value cell, executing a base station closing instruction on the base station cell in the later predetermined time interval;
and S104, if the value prediction result of the base station cell in any future time period is a high-value cell, not performing processing on the base station cell.
In one example, S103 specifically includes: and automatically sending a closing instruction to the base station cell in the future time period, closing the power supply after the base station cell receives the closing instruction, and automatically opening the power supply after the base station cell finishes the future time period to recover to a normal operation state.
In one example, S103 further comprises: and monitoring the service load capacity of the base station cell adjacent to the base station cell with the power off in real time, and if the service load capacity of the adjacent base station cell exceeds a preset fourth threshold value, automatically sending a starting instruction to the base station cell with the power off to enable the base station cell to recover to a normal operation state.
Combining with the scenario example, a certain base station cell executes the close instruction to stop operating, and if it is detected that the load capacity of the adjacent base station cell exceeds the fourth threshold, it indicates that the adjacent base station cell shares the service of the base station cell executing the close instruction and exceeds the load capacity upper limit of the adjacent base station cell, and at this time, network fluctuation of the user may be caused. Therefore, the starting instruction is automatically sent to the base station cell with the power supply turned off, so that the base station cell is restored to a normal operation state, and normal use of a user is ensured.
Based on the above embodiment, the service load capacity of the base station cell adjacent to the base station cell with the power off is monitored in real time, so that the perception of a user is not influenced.
In the method for predicting a cell value provided in this embodiment, if the value prediction result of the base station cell in any future time period is a low-value cell, a base station shutdown instruction is executed on the base station cell in the predetermined time period. And if the value prediction result of the base station cell in any future time period is a high-value cell, not performing processing on the base station cell. Through the scheme, the corresponding processing mode can be executed according to different prediction results, so that accurate energy saving is realized on the premise of guaranteeing the service performance of the user.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a device for predicting a cell value according to a third embodiment of the present application, as shown in fig. 5, the device includes:
an obtaining module 61, configured to obtain an input feature index of a base station cell in any time period of the day, where the input feature index includes communication state information, a value tag, and basic information of the base station cell, and a time feature of the time period;
a processing module 62, configured to use the input feature index of the base station cell as input data of a value prediction model, and obtain a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods.
In practical applications, the cell value prediction apparatus is implemented in various ways. For example, the program may be software, or a medium storing a related computer program, such as a usb disk; alternatively, the apparatus may also be a physical device integrated with or installed with an associated computer program, the physical device supporting USB interface connection, for example, a chip, an intelligent terminal, a computer, a server, and the like.
In an example, the apparatus further includes a modeling module 63, and the obtaining module 61 is further configured to obtain a history characteristic index at a first time period of a history date of the base station cell; the obtaining module 61 is further configured to select, from the historical characteristic indexes, historical characteristic indexes whose dates are before a preset first threshold as a training set, select a predetermined number of historical characteristic indexes from the historical characteristic indexes other than the training set as a verification set, and select the remaining historical characteristic indexes as a test set; the modeling module 63 is further configured to obtain an initial value prediction model by using a machine learning algorithm based on the historical characteristic indexes in the training set; performing model adjustment on the initial value prediction model based on the historical characteristic indexes in the verification set; the modeling module 63 is further configured to test a current value prediction model according to the historical characteristic indexes in the test set, and if the test is passed, the establishment of the value prediction model is completed.
In one example, the apparatus further comprises: the calculation module 64 is configured to fit each communication state information of the base station cell by using exponential distribution to obtain an exponential probability density function of each communication state information, and perform integration processing on the exponential probability density function to obtain cumulative probability distribution of each communication state information; a calculating module 64, configured to calculate a value degree of the base station cell based on a value degree formula; wherein the value degree formula comprises:
where n is the number of first communication status information except for the measurement report coverage, Wi is the weight of the ith first communication status information, and Fi(x) The cumulative probability value W when the ith first communication state information takes the value of xmrReporting coverage for measurementsM is the measurement report coverage; the calculating module 64 is further configured to add a low-value cell label to the base station cell if the value degree of the base station cell is smaller than a preset second threshold; and if the value degree of the base station cell is not less than the second threshold value, adding a high-value label to the base station cell.
In one example, the basic information includes location, type, and identity of a base station cell; the communication state information includes at least one of: the method comprises the following steps of (1) utilizing uplink network resources, utilizing downlink network resources, utilizing uplink flow, utilizing downlink flow, utilizing the number of users, utilizing the number of visitors, carrying out hourly charge and measuring report coverage rate; the value tags comprise a low value cell tag and a high value cell tag; the temporal characteristics of the period include the hours, days of the period, whether the period is a day of the week, and whether it is a holiday.
In an example, the obtaining module 61 is further configured to, if the base station cell belongs to a predetermined specific cell set, use the input characteristic index of the base station cell as input data of a first price prediction model corresponding to the base station cell, and obtain a value prediction result of the base station cell output by the first price prediction model; the first value prediction model is obtained through machine learning based on the historical characteristic index of the base station cell and a known result corresponding to the historical characteristic index; and each base station cell in the specific cell set corresponds to a pre-established value prediction model.
In one example. The processing module 62 is specifically configured to use the input characteristic index of the base station cell as input data of a value prediction model, where the value prediction model outputs a prediction probability that the base station cell is a low-value cell in a future certain time period, and if the prediction probability that the base station cell is a low-value cell in a future certain time period is greater than a preset third threshold, the base station cell is determined to be a low-value cell in a future certain time period.
In the device for predicting a cell value provided in this embodiment, the obtaining module is configured to obtain an input feature index of a base station cell in any time period of the day, where the input feature index includes communication state information, a value tag, and basic information of the base station cell, and a time feature of the time period. And the processing module is used for taking the input characteristic index of the base station cell as input data of a value prediction model and obtaining a value prediction result of the base station cell output by the value prediction model. The value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods. Through the scheme, an accurate value prediction model can be established based on a large number of historical characteristic indexes through machine learning, so that accurate prediction of the cell value is achieved by using the value prediction model.
Example four
An embodiment of the present application provides a multi-cluster container management apparatus, and on the basis of the third embodiment, the apparatus further includes:
the processing module is further configured to execute a base station shutdown instruction on the base station cell in the predetermined period of time later if the value prediction result of the base station cell in any future period of time is a low-value cell;
the processing module is further configured to not perform processing on the base station cell if the value prediction result of the base station cell at any future time interval is a high-value cell.
In an example, the processing module is specifically configured to automatically send a shutdown instruction to the base station cell in the future time period, the base station cell turns off the power supply after receiving the shutdown instruction, and the base station cell automatically turns on the power supply after the future time period is ended, and returns to a normal operation state.
In an example, the processing module is further configured to monitor a traffic load amount of a base station cell adjacent to the base station cell with the power off in real time, and if it is detected that the traffic load amount of the base station cell adjacent to the base station cell with the power off exceeds a preset fourth threshold, automatically send an on instruction to the base station cell with the power off to enable the base station cell with the power off to recover to a normal operation state.
In the device for predicting a cell value provided in this embodiment, the processing module is further configured to execute a base station shutdown instruction on the base station cell in the predetermined time period after the value prediction result of the base station cell in any future time period is a low-value cell. And the processing module is further used for not executing the processing on the base station cell if the value prediction result of the base station cell in any future time period is a high-value cell. Through the scheme, the corresponding processing mode can be executed according to different prediction results, so that accurate energy saving is realized on the premise of guaranteeing the service performance of the user.
EXAMPLE five
Fig. 6 is a block diagram illustrating an apparatus of a cell value prediction device, which may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, etc., according to an exemplary embodiment.
The apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
EXAMPLE six
Fig. 7 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, and as shown in fig. 7, the electronic device includes:
a processor (processor)291, the electronic device further including a memory (memory) 292; a Communication Interface 293 and bus 294 may also be included. The processor 291, the memory 292, and the communication interface 293 may communicate with each other via the bus 294. Communication interface 293 may be used for the transmission of information. Processor 291 may call logic instructions in memory 294 to perform the methods of the embodiments described above.
Further, the logic instructions in the memory 292 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product.
The memory 292 is a computer-readable storage medium for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present application. The processor 291 executes the functional application and data processing by executing the software program, instructions and modules stored in the memory 292, so as to implement the method in the above method embodiments.
The memory 292 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. Further, the memory 292 may include a high speed random access memory and may also include a non-volatile memory.
The present application provides a non-transitory computer-readable storage medium, in which computer-executable instructions are stored, and when executed by a processor, the computer-executable instructions are used to implement the method according to the foregoing embodiments.
The present application provides a computer program product, including a computer program, which when executed by a processor implements the method according to the foregoing embodiments.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (14)
1. A method for predicting a cell value, comprising:
acquiring input characteristic indexes of a base station cell in any time period of the day, wherein the input characteristic indexes comprise communication state information, value labels and basic information of the base station cell and time characteristics of the time periods;
using the input characteristic index of the base station cell as input data of a value prediction model to obtain a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods.
2. The method of claim 1, further comprising:
acquiring historical characteristic indexes of a base station cell in a first time period of historical dates;
selecting historical characteristic indexes with dates before a preset first threshold value from the historical characteristic indexes as a training set, selecting a preset number of historical characteristic indexes from the historical characteristic indexes except the training set as a verification set, and using the rest historical characteristic indexes as a test set;
based on the historical characteristic indexes in the training set, an initial value prediction model is obtained by utilizing a machine learning algorithm; performing model adjustment on the initial value prediction model based on the historical characteristic indexes in the verification set;
and testing the current value prediction model according to the historical characteristic indexes in the test set, and finishing the establishment of the value prediction model if the test is passed.
3. The method of claim 2, further comprising:
fitting each communication state information of the base station cell by using exponential distribution to obtain an exponential probability density function of each communication state information, and performing integral processing on the exponential probability density function to obtain cumulative probability distribution of each communication state information;
calculating the value degree of the base station cell based on a value degree formula; wherein the value degree formula comprises:
where n is the number of first communication status information except for the measurement report coverage, Wi is the weight of the ith first communication status information, and Fi(x) The cumulative probability value W when the ith first communication state information takes the value of xmrFor measuring newspapersReporting the weight of the coverage rate, wherein M is the coverage rate of the measurement report;
if the value degree of the base station cell is smaller than a preset second threshold value, adding a low-value cell label to the base station cell; and if the value degree of the base station cell is not less than the second threshold value, adding a high-value label to the base station cell.
4. The method of claim 1, wherein the basic information comprises a location, a type, and an identity of a base station cell; the communication state information includes at least one of: the method comprises the steps of uplink network resource utilization rate, downlink network resource utilization rate, uplink flow, downlink flow, user quantity, VIP user quantity, hour charge and measurement report coverage rate.
5. The method of claim 1, wherein after obtaining the input characteristic index of the base station cell at any time period of the day, the method further comprises:
if the base station cell belongs to a preset specific cell set, taking the input characteristic index of the base station cell as input data of a first price value prediction model corresponding to the base station cell to obtain a value prediction result of the base station cell output by the first price value prediction model; the first value prediction model is obtained through machine learning based on the historical characteristic index of the base station cell and a known result corresponding to the historical characteristic index;
and each base station cell in the specific cell set corresponds to a pre-established value prediction model.
6. The method according to any one of claims 1-5, further comprising:
if the value prediction result of the base station cell in any future time interval is a low-value cell, executing a base station closing instruction on the base station cell in the later predetermined time interval;
and if the value prediction result of the base station cell in any future time period is a high-value cell, not performing processing on the base station cell.
7. A cell value prediction apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring input characteristic indexes of a base station cell in any time period of the day, and the input characteristic indexes comprise communication state information, value labels and basic information of the base station cell and time characteristics of the time periods;
the processing module is used for taking the input characteristic index of the base station cell as input data of a value prediction model and obtaining a value prediction result of the base station cell output by the value prediction model; the value prediction model is established through machine learning based on historical characteristic indexes of each base station cell; the historical characteristic indexes comprise communication states, historical value labels and basic information of the base station cells in historical first time periods, and time characteristics of the preset time periods.
8. The apparatus of claim 7, further comprising a modeling module to:
the acquisition module is further used for acquiring historical characteristic indexes of the base station cell in a first time period of historical dates;
the acquisition module is further used for selecting historical characteristic indexes with dates before a preset first threshold value from the historical characteristic indexes as a training set, selecting a preset number of historical characteristic indexes from the historical characteristic indexes except the training set as a verification set, and using the rest historical characteristic indexes as a test set;
the modeling module is used for obtaining an initial value prediction model by utilizing a machine learning algorithm based on the historical characteristic indexes in the training set; performing model adjustment on the initial value prediction model based on the historical characteristic indexes in the verification set;
and the modeling module is also used for testing the current value prediction model according to the historical characteristic indexes in the test set, and completing the establishment of the value prediction model if the test is passed.
9. The apparatus of claim 8, further comprising:
the calculation module is used for fitting each communication state information of the base station cell by using exponential distribution to obtain an exponential probability density function of each communication state information, and performing integral processing on the exponential probability density function to obtain cumulative probability distribution of each communication state information;
the calculation module is also used for calculating the value degree of the base station cell based on a value degree formula; wherein the value degree formula comprises:
where n is the number of first communication status information except for the measurement report coverage, Wi is the weight of the ith first communication status information, and Fi(x) The cumulative probability value W when the ith first communication state information takes the value of xmrIs the weight of the measurement report coverage, M is the measurement report coverage;
the calculation module is further used for adding a low-value cell label to the base station cell if the value degree of the base station cell is smaller than a preset second threshold; and if the value degree of the base station cell is not less than the second threshold value, adding a high-value label to the base station cell.
10. The apparatus of claim 7, wherein the basic information comprises a location, a type, and an identification of a base station cell; the communication state information includes at least one of: the method comprises the steps of uplink network resource utilization rate, downlink network resource utilization rate, uplink flow, downlink flow, user quantity, VIP user quantity, hour charge and measurement report coverage rate.
11. The apparatus of claim 7,
the processing module is further configured to, if the base station cell belongs to a predetermined specific cell set, use the input characteristic index of the base station cell as input data of a first price prediction model corresponding to the base station cell, and obtain a value prediction result of the base station cell output by the first price prediction model; the first value prediction model is obtained through machine learning based on the historical characteristic index of the base station cell and a known result corresponding to the historical characteristic index;
and each base station cell in the specific cell set corresponds to a pre-established value prediction model.
12. The apparatus according to any one of claims 7 to 11,
the processing module is further configured to execute a base station shutdown instruction on the base station cell in the predetermined period of time later if the value prediction result of the base station cell in any future period of time is a low-value cell;
the processing module is further configured to not perform processing on the base station cell if the value prediction result of the base station cell at any future time interval is a high-value cell.
13. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-6.
14. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111359940.6A CN114266285A (en) | 2021-11-17 | 2021-11-17 | Cell value prediction method, device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111359940.6A CN114266285A (en) | 2021-11-17 | 2021-11-17 | Cell value prediction method, device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114266285A true CN114266285A (en) | 2022-04-01 |
Family
ID=80825085
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111359940.6A Pending CN114266285A (en) | 2021-11-17 | 2021-11-17 | Cell value prediction method, device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114266285A (en) |
-
2021
- 2021-11-17 CN CN202111359940.6A patent/CN114266285A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111651263B (en) | Resource processing method and device of mobile terminal, computer equipment and storage medium | |
| US11403197B2 (en) | Method and device for controlling application, storage medium, and electronic device | |
| CN108337358B (en) | Application cleaning method and device, storage medium and electronic equipment | |
| CN108334651B (en) | Method, device and storage medium for collecting user side data to realize preset requirements | |
| CN109947497A (en) | Application program preloads method, apparatus, storage medium and mobile terminal | |
| CN108279764A (en) | A kind of application control method, device and mobile terminal | |
| CN110162384A (en) | Time-out time dynamic adjusting method and system based on Redis distributed lock | |
| CN114760339A (en) | Fault prediction method, apparatus, device, medium, and product | |
| CN113746118B (en) | Scheduling method, device, equipment and system based on energy storage power station | |
| CN107885307B (en) | Equipment control method and device | |
| CN109214175B (en) | Method, device and storage medium for training classifier based on sample characteristics | |
| CN107748697B (en) | Application closing method and device, storage medium and electronic equipment | |
| US20210342782A1 (en) | Method and apparatus for scheduling item, and computer-readable storage medium | |
| CN115544704B (en) | Charging pile topology determination method and device and storage medium | |
| CN114487840A (en) | Battery control method, device and storage medium | |
| CN108012258B (en) | Data traffic management method and device for virtual SIM card, terminal and server | |
| CN114266285A (en) | Cell value prediction method, device, electronic equipment and storage medium | |
| CN111722693B (en) | Power consumption adjusting method and device, storage medium, server and terminal | |
| CN111859097A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN115409094A (en) | Equipment fault prediction method, device and storage medium | |
| CN114186894A (en) | Project risk detection method and device, electronic equipment and storage medium | |
| CN111722695B (en) | Terminal equipment power consumption optimization method and device | |
| CN113346564A (en) | Power management chip control method and device and terminal equipment | |
| CN115983502B (en) | Data processing method, device and medium | |
| CN115022965B (en) | Cell positioning method, device, electronic equipment and storage medium |
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 |