Cell energy saving method, apparatus, device, medium and program product

Abstract

The invention provides a cell energy-saving method, device and equipment, a medium and a program product, and the method comprises the steps: determining a plurality of global energy-saving combination states according to the energy-saving characteristic opening states of a plurality of capacity layer cells in a target region; the global energy-saving combination state comprises energy-saving characteristic opening states respectively corresponding to a plurality of capacity layer cells; for each global energy-saving combination state, respectively acquiring predicted electricity charge and predicted income corresponding to the target area in the first time period; calculating a ratio corresponding to each global energy-saving combination state, wherein the ratio is the ratio of the predicted income to the predicted electricity charge in the first time period; and determining that the energy-saving characteristic opening states of the plurality of capacity layer cells in the target area in the first time period are the energy-saving characteristic opening states of the capacity layer cells corresponding to the global energy-saving combination state with the maximum ratio. By adopting the method provided by the invention, the service requirements of the user can be met in the first time period, and the operation benefit maximization of the target area can be realized.

Description

Technical Field

[0001] This invention relates to the field of wireless technology, and in particular to a method, apparatus, equipment, medium, and program product for energy saving in a residential community. Background Technology

[0002] Currently, energy conservation in base station cells mainly includes: channel shutdown, shallow hibernation, and deep hibernation. The principle behind these methods is to reduce power consumption by disabling certain functions of the base station to varying degrees.

[0003] There are two main types of methods for controlling cell energy saving in existing technologies. One type is to manually set the start and end times of each energy-saving feature and trigger the base station cell to turn on or off the energy-saving feature according to the time dimension. The other type is to manually set the service threshold for each energy-saving feature. When one or more service indicator values ​​related to the base station cell enter the corresponding threshold range, the base station cell is triggered to turn on the energy-saving feature, and vice versa.

[0004] However, both existing methods require extensive manual monitoring and analysis of base station cell service conditions, followed by setting time periods or thresholds for each cell based on individual experience. But manual experience is often limited by personal knowledge, experience, and subjective judgment, making it difficult to comprehensively and accurately reflect the actual situation. As a result, either the set time range is too small or the threshold is too high, leading to insufficient energy saving in base station cells; or the set time range is too large or the threshold is too low, resulting in excessive energy saving in some cells, leaving the remaining base station cells with insufficient service capacity, thus impacting user experience. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, equipment, medium, and program product for energy saving in residential communities, which can solve the problem of insufficient energy-saving performance in residential communities leading to insufficient capacity to undertake services or inadequate energy saving, thereby affecting energy consumption and user experience.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for energy conservation in residential communities, comprising:

[0007] Based on the energy-saving feature activation status of multiple capacity-layer cells within the target area, various global energy-saving combination states are determined; wherein, one of the global energy-saving combination states includes: the energy-saving feature activation status corresponding to each of the multiple capacity-layer cells;

[0008] For each of the global energy-saving combination states, the predicted electricity cost and predicted revenue for the target area within the first time period are obtained respectively.

[0009] Calculate the ratio corresponding to each of the global energy-saving combination states, where the ratio is the ratio of the predicted revenue in the first time period to the predicted electricity cost in the first time period;

[0010] The energy-saving characteristic activation status of multiple capacity layer cells in the target area within the first time period is determined as: the energy-saving characteristic activation status of the capacity layer cell corresponding to the global energy-saving combination status with the largest ratio.

[0011] Optionally, based on the energy-saving characteristics activation status of multiple capacity-level cells within the target area, various global energy-saving combinations can be determined, including:

[0012] The cells are grouped according to the correspondence between capacity layer cells and coverage layer cells in the target area to obtain multiple cell groups; wherein each cell group includes at least one coverage layer cell and a capacity layer cell corresponding to the coverage layer cell.

[0013] Based on the combination of the various energy-saving feature activation states corresponding to the capacity layer cells in each cell group, multiple energy-saving combination states corresponding to each cell group are obtained. Among them, a target energy-saving combination state includes: the energy-saving feature activation state corresponding to each capacity layer cell in the cell group.

[0014] For each cell group, the energy-saving combination status of the cell group is filtered based on the historical data of the capacity layer cells and the historical data of the coverage layer cells within the cell group to obtain multiple target energy-saving combination statuses corresponding to each cell group;

[0015] By sequentially selecting one target energy-saving combination state for each of the aforementioned cell groups, multiple global energy-saving combination states can be obtained.

[0016] Optionally, for each cell group, based on historical data of the capacity layer cells and the coverage layer cells within the cell group, the energy-saving combination status of the cell group is filtered to obtain multiple target energy-saving combination statuses corresponding to each cell group, including:

[0017] For each cell group, predictions are made based on historical data of the capacity layer cells and the coverage layer cells within the cell group to obtain the minimum number of users and the minimum service traffic that each cell group should support during the first time period.

[0018] Based on the historical data of the capacity layer cells and the historical data of the coverage layer cells, predictions are made for each of the energy-saving combination states to obtain the predicted number of users and the predicted service traffic for each of the energy-saving combination states in the first time period.

[0019] For each cell group, the minimum number of users and minimum service traffic that the cell group should support, as well as the predicted number of users and predicted service traffic of the energy-saving combination state of the cell group, are used for filtering. The energy-saving combination state that meets the preset conditions is selected as the target energy-saving combination state of the corresponding cell group.

[0020] Optionally, the preset conditions include:

[0021] The predicted number of users in the energy-saving combination state is greater than or equal to the minimum number of users that the corresponding cell group should support, and the predicted service traffic in the energy-saving combination state is greater than or equal to the minimum service traffic that the corresponding cell group should support.

[0022] Optionally, the energy-saving feature activation state includes: energy-saving feature deactivation state, activation channel deactivation of energy-saving feature state, activation of shallow sleep feature state, and activation of deep sleep feature state.

[0023] Optionally, for each of the global energy-saving combination states, obtaining the predicted electricity cost and predicted revenue for the target area within the first time period includes:

[0024] Based on the historical data of the capacity layer cells and the coverage layer cells within the target area, predict the target number of users and target service traffic of each cell under each global energy-saving combination state, wherein the cells include the capacity layer cells and the coverage layer cells;

[0025] For each of the global energy-saving combination states, based on the number of target users, the target service traffic, and the correspondence between the base station and the cell for each cell, the predicted electricity cost for the target area within the first time period is obtained by using the power consumption change model of the base station.

[0026] For each of the global energy-saving combination states, based on the target service traffic of each cell, the predicted revenue for the target area within the first time period is obtained by using the traffic revenue function of each service.

[0027] Optionally, for each of the global energy-saving combination states, based on the number of target users, the target service traffic, and the correspondence between the base station and the cell for each cell, a prediction is made using the power consumption change model of the base station to obtain the predicted electricity cost for the target area within the first time period, including:

[0028] Based on the correspondence between base stations and cells, determine the set of cells corresponding to each base station.

[0029] For each of the global energy-saving combination states, predictions are made based on the number of target users and the target service traffic of each cell in each cell set to obtain the predicted number of base station users and the predicted service traffic of each base station in the target area under the global energy-saving combination state during the first time period.

[0030] For each of the global energy-saving combination states, the power consumption of each base station in the target area during the first time period is obtained by calculating the predicted number of users and the predicted service traffic of each base station through the power change model of the base station.

[0031] For each of the global energy-saving combination states, predictions are made based on the power consumption and electricity price of each base station to obtain the predicted electricity cost for the target area within the first time period.

[0032] Optionally, the method further includes:

[0033] Obtain historical power data for each base station and historical user count and service traffic for each cell;

[0034] Based on the correspondence between the base station and the cell, the historical number of users and the historical service traffic of the cells corresponding to the base station are summed to obtain the historical number of users and the historical service traffic of each base station.

[0035] The power variation model of the base station is obtained by calculating based on the historical power data of the base station, the historical number of users of the base station, and the historical service traffic of the base station; wherein, the power variation model of the base station is different for different models and / or different manufacturers.

[0036] Optionally, the historical data of the capacity layer cell includes one or more of the following:

[0037] Historical user count, historical service type, historical service traffic, and historical energy-saving feature activation status of the capacity layer cell;

[0038] The historical data of the coverage cell includes one or more of the following:

[0039] Historical user count, historical service types, and historical service traffic of the coverage cell.

[0040] This invention also provides a community energy-saving device, comprising:

[0041] The first determining module is used to determine multiple global energy-saving combination states based on the energy-saving characteristic activation status of multiple capacity-layer cells within the target area; wherein, a global energy-saving combination state includes: the energy-saving characteristic activation status corresponding to each of the multiple capacity-layer cells;

[0042] The first acquisition module is used to acquire the predicted electricity cost and predicted revenue for the target area within a first time period for each global energy-saving combination state.

[0043] The first calculation module is used to calculate the ratio corresponding to each global energy-saving combination state, wherein the ratio is the ratio of the predicted revenue in the first time period to the predicted electricity cost in the first time period.

[0044] The second determining module is used to determine the energy-saving characteristic activation status of multiple capacity layer cells in the target area during the first time period as: the energy-saving characteristic activation status of the capacity layer cell corresponding to the global energy-saving combination status with the largest ratio.

[0045] This invention also provides a network device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the cell energy-saving method as described in any of the preceding embodiments.

[0046] This invention also provides a readable storage medium, comprising: a program stored on the readable storage medium, wherein when the program is executed by a processor, it implements the steps of the community energy-saving method as described in any of the preceding claims.

[0047] This invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the community energy-saving method as described in any of the preceding embodiments.

[0048] At least one of the above technical solutions of the present invention has the following beneficial effects:

[0049] In the above scheme, each capacity layer cell corresponds to multiple selectable energy-saving feature activation states. Different energy-saving feature activation states correspond to different energy-saving effects. Therefore, based on the energy-saving feature activation states of multiple capacity layer cells in the target area, multiple global energy-saving combination states are determined. For each global energy-saving combination state, the predicted electricity cost and predicted revenue for the target area in the first time period are predicted. Then, the ratio of predicted revenue to predicted electricity cost for each global energy-saving combination state is calculated. The energy-saving feature activation state of the capacity layer cell corresponding to the global energy-saving combination state with the largest ratio of predicted revenue to predicted electricity cost is selected as the energy-saving feature activation state of the target area in the first time period. The cell energy-saving method provided by this invention determines the energy-saving feature activation state of the capacity layer cells in the target area in the first time period more accurately, which can not only meet the user's business needs, but also maximize the energy saving and operational efficiency of the target area. Attached Figure Description

[0050] Figure 1 This is a schematic flowchart of a community energy-saving method according to an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the structure of a community energy-saving device according to an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0055] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" in this application are often used interchangeably, and the described technologies can be used not only in the systems and radio technologies mentioned above, but also in other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and NR terminology is used in most of the following description; however, these technologies can also be applied to applications beyond NR systems, such as 6th Generation (6G) communication systems.

[0056] like Figure 1 As shown, an embodiment of the present invention provides a method for energy saving in a residential community, comprising:

[0057] Step S101: Determine multiple global energy-saving combination states based on the energy-saving characteristic activation status of multiple capacity layer cells within the target area; wherein, one of the global energy-saving combination states includes: the energy-saving characteristic activation status corresponding to each of the multiple capacity layer cells;

[0058] In step S101, the energy-saving feature activation states include, but are not limited to: energy-saving feature deactivation state, channel activation deactivation of energy-saving feature state, shallow sleep feature activation state, and deep sleep feature activation state. Different energy-saving feature activation states correspond to different energy-saving effects.

[0059] Based on the various energy-saving feature activation states available for each capacity-layer cell within the target area, multiple global energy-saving combination states are determined. Among these, the activation states of energy-saving features for capacity-layer cells differ for each global energy-saving combination state.

[0060] Step S102: For each of the global energy-saving combination states, obtain the predicted electricity cost and predicted revenue for the target area within the first time period;

[0061] In step S102, for each of the global energy-saving combination states, the predicted electricity cost and predicted revenue for the target area in the first time period in the future are predicted respectively.

[0062] Step S103: Calculate the ratio corresponding to each of the global energy-saving combination states, where the ratio is the ratio of the predicted revenue in the first time period to the predicted electricity cost in the first time period.

[0063] Step S104: Determine the energy-saving characteristic activation status of multiple capacity layer cells in the target area within the first time period as: the energy-saving characteristic activation status of the capacity layer cell corresponding to the global energy-saving combination status with the largest ratio.

[0064] In steps S103 and S104, the ratio of predicted revenue to predicted electricity cost for each global energy-saving combination state is calculated within the first time period. The final determination of the energy-saving feature activation status of multiple capacity-layer cells in the target area during the first time period is as follows: the energy-saving feature activation status of the capacity-layer cell corresponding to the global energy-saving combination status with the largest ratio. This ensures that the target area can both meet user business needs and achieve maximum energy saving and operational efficiency.

[0065] In this embodiment of the invention, multiple global energy-saving combination states are determined based on the energy-saving feature activation status of multiple capacity-layer cells within the target area. For each global energy-saving combination state, the predicted electricity cost and predicted revenue for the target area within a first time period are predicted. The ratio of predicted revenue to predicted electricity cost for each global energy-saving combination state is then calculated. The energy-saving feature activation status of the capacity-layer cells corresponding to the global energy-saving combination state with the largest ratio of predicted revenue to predicted electricity cost is selected as the energy-saving feature activation status of the target area within the first time period. The energy-saving feature activation status of capacity-layer cells within the target area within the first time period determined by the cell energy-saving method provided by this invention is more accurate, meeting both user service needs and maximizing energy saving and operational efficiency in the target area.

[0066] Optionally, based on the energy-saving characteristics activation status of multiple capacity-level cells within the target area, various global energy-saving combinations can be determined, including:

[0067] The cells are grouped according to the correspondence between capacity layer cells and coverage layer cells in the target area to obtain multiple cell groups; wherein each cell group includes at least one coverage layer cell and a capacity layer cell corresponding to the coverage layer cell.

[0068] Based on the combination of the various energy-saving feature activation states corresponding to the capacity layer cells in each cell group, multiple energy-saving combination states corresponding to each cell group are obtained. Among them, a target energy-saving combination state includes: the energy-saving feature activation state corresponding to each capacity layer cell in the cell group.

[0069] For each cell group, the energy-saving combination status of the cell group is filtered based on the historical data of the capacity layer cells and the historical data of the coverage layer cells within the cell group to obtain multiple target energy-saving combination statuses corresponding to each cell group;

[0070] By sequentially selecting one target energy-saving combination state for each of the aforementioned cell groups, multiple global energy-saving combination states can be obtained.

[0071] In this embodiment of the invention, the method for determining multiple global energy-saving combination states in step S101 is further explained. To ensure that the cells in the target area can meet user service needs and avoid excessive energy saving that would prevent service provision, a preliminary screening is performed during the determination of multiple global energy-saving combination states, as detailed below:

[0072] The first step is to group the cells in the target area for easier calculation. The cells include capacity layer cells and coverage layer cells, and there is a certain correspondence between capacity layer cells and coverage layer cells. Capacity layer cells have multiple energy-saving feature activation states, and different energy-saving feature activation states produce different energy-saving effects.

[0073] Assume the target area comprises i capacity-layer cells and j coverage-layer cells, with a specific correspondence between them. Based on this correspondence, all cells are grouped by coverage-layer cells, resulting in y cell groups. It is ensured that each cell group contains all coverage-layer cells corresponding to all capacity-layer cells, where i, j, and y are all positive integers. The cell groups are represented by G. m (m is an integer greater than 0 and less than or equal to y) indicates that each G m There will be mi (mi is a positive integer) capacity layer cells and mj (mj is a positive integer) coverage layer cells. The following example illustrates this:

[0074] Assume the following correspondence between capacity layer cells and coverage layer cells within the target area:

[0075] Capacity tier (cell) Coverage layer (cell) C1 C2 C1 C3 C4 C2 C5 C3 C6 C7 C8 C7 C8 C9

[0076] Table 1;

[0077] According to the above grouping rules, the communities in Table 1 can be divided into two community groups: Group G1 includes five communities: C1, C2, C3, C4, and C5; Group G2 includes four communities: C6, C7, C8, and C9.

[0078] Taking G1 as an example, the specific method of dividing the cell group is as follows:

[0079] First, select a coverage layer cell, such as C2; then, obtain the capacity layer cells corresponding to C2: C1 and C4; next, obtain the other coverage layer cells corresponding to C1 and C4, such as C3, which also corresponds to C1; finally, obtain the other capacity layer cell C5, which also corresponds to C3, but C5 has no other corresponding coverage layer cells, thus ending the process. Therefore, the five cells C1, C2, C3, C4, and C5 are assigned to a single cell group G1.

[0080] The second step involves combining the various energy-saving feature activation states corresponding to the capacity-layer cells in each cell group to obtain multiple energy-saving combination states for each cell group. Each capacity-layer cell may have four energy-saving feature activation states at any given time: energy-saving feature deactivation, channel activation with energy-saving feature deactivation, shallow sleep feature activation, and deep sleep feature activation. Therefore, for cell group G... mIt includes mi capacity tier cells, which means it has 4 mi A variety of energy-saving combination states.

[0081] The third step involves filtering the energy-saving combination states of each cell group based on the historical data of the capacity layer cells and the coverage layer cells within the cell group. The energy-saving combination state that meets the user's business needs is selected as the target energy-saving state to avoid excessive energy saving that would prevent the cell group from being able to handle the business.

[0082] The fourth step involves selecting one target energy-saving combination state from each cell group in sequence, based on the target energy-saving combination state of each cell group, and enumerating the multiple global energy-saving combination states formed by combining the target energy-saving combination states of all cell groups; for example, the m cell groups obtained in the third step correspond to S1, S2, S3...S... m-1 S m If there are several target energy-saving combination states, then the present invention has a total of S1×S2×S3×...×S m-1 ×S m A global energy-saving combination state.

[0083] Optionally, for each cell group, based on historical data of the capacity layer cells and the coverage layer cells within the cell group, the energy-saving combination status of the cell group is filtered to obtain multiple target energy-saving combination statuses corresponding to each cell group, including:

[0084] For each cell group, predictions are made based on historical data of the capacity layer cells and the coverage layer cells within the cell group to obtain the minimum number of users and the minimum service traffic that each cell group should support during the first time period.

[0085] Based on the historical data of the capacity layer cells and the historical data of the coverage layer cells, predictions are made for each of the energy-saving combination states to obtain the predicted number of users and the predicted service traffic for each of the energy-saving combination states in the first time period.

[0086] For each cell group, the minimum number of users and minimum service traffic that the cell group should support, as well as the predicted number of users and predicted service traffic of the energy-saving combination state of the cell group, are used for filtering. The energy-saving combination state that meets the preset conditions is selected as the target energy-saving combination state of the corresponding cell group.

[0087] In this embodiment of the invention, the historical data of the capacity layer cell includes, but is not limited to, one or more of the following: the historical number of users in the capacity layer cell, the historical service types in the capacity layer cell, the historical service traffic in the capacity layer cell, and the historical energy-saving feature activation status of the capacity layer cell. Specifically, the historical data of the capacity layer cell each day includes n sets of data corresponding to n time points. Each set of data includes the number of users in one cell, k service types, the service traffic corresponding to each service type, one subframe shutdown activation flag, one channel shutdown activation flag, one shallow hibernation activation flag, and one deep hibernation activation flag. n is an integer greater than 0, and k is an integer greater than 0.

[0088] Historical data for coverage cells includes, but is not limited to, one or more of the following: historical number of users in the coverage cell, historical service types in the coverage cell, and historical service traffic in the coverage cell. Specifically, the historical data for coverage cells each day includes n sets of data corresponding to n time points. Each set of data includes the number of users in one cell, k service types, and the service traffic corresponding to each service type.

[0089] The method for filtering the energy-saving combination states of the community group based on the above historical data to obtain multiple target energy-saving combination states of the community group is as follows:

[0090] The first step is to predict, based on the historical data mentioned above, the minimum number of users and minimum service traffic that each cell group should support during the first time period. Specifically:

[0091] Based on historical data from the capacity layer cells and the coverage layer cells, the number of users, service types, and corresponding traffic for each service type in each cell can be predicted at n time points within a future first time period. Generally, the first time period refers to a period within a future day.

[0092] Suppose that the predicted number of users for the a-th capacity layer cell (a is an integer greater than 0 and less than or equal to mi) at some point t (t is an integer greater than 0 and less than or equal to n) in the first time interval of the future is U′. at The predicted traffic volume for the p-th service (where p is an integer greater than 0 and less than or equal to k) at time t is D′. apt Let U″ be the predicted number of users in the b-th coverage cell (b is an integer greater than 0 and less than or equal to mj) at time t. bt The predicted traffic volume of the p-th service at time t in the first time period is D″. bpt Therefore, the minimum number of users that this group should support at time t is:

[0093]

[0094] The minimum service traffic that should be supported is:

[0095]

[0096] The second step involves making predictions based on the historical data mentioned above, obtaining the predicted number of users and the predicted service traffic for each service type within the first time period for each cell group under each of the energy-saving combination states. Specifically:

[0097] G m The group consists of mi capacity layer cells and mj coverage layer cells. The energy-saving combination states of these mi capacity layer cells at time t in the first time period are mq = 4. mi In the ms-th energy-saving combination state (ms is an integer greater than 0 and less than or equal to mq), assume that the predicted number of users in the a-th capacity layer cell at time t is... The predicted business volume of the p-th type of service at time t in the first future time period. Let the predicted number of users for the b-th coverage cell at time t be... The predicted business volume of the p-th type of service at time t in the first future time period. Then G m The predicted number of users at time t under the ms power-saving combination is:

[0098]

[0099] G m The predicted service traffic of the group at time t under the ms energy-saving combination is:

[0100]

[0101] The third step is to ensure that the cells in the target area can meet the user's business needs and avoid excessive energy saving that makes it unable to handle the business. For each cell group, the minimum number of users and minimum business traffic that the cell group should support, as well as the predicted number of users and predicted business traffic corresponding to each energy saving combination state of the cell group, are used to filter and select the energy saving combination state that meets the preset conditions as the target energy saving combination state of the corresponding cell group.

[0102] Optionally, the preset conditions include:

[0103] The predicted number of users in the energy-saving combination state is greater than or equal to the minimum number of users that the corresponding cell group should support, and the predicted service traffic in the energy-saving combination state is greater than or equal to the minimum service traffic that the corresponding cell group should support.

[0104] In this embodiment of the invention, the preset conditions mentioned above are explained as follows: In order to ensure that the cells in the target area can meet the user's service needs, the energy-saving combination state in which the predicted number of users is greater than or equal to the minimum number of users that the corresponding cell group should support, and the predicted service traffic is greater than or equal to the minimum service traffic that the corresponding cell group should support, is selected as the target energy-saving state; that is, the selected target energy-saving combination state needs to meet the following:

[0105]

[0106] Optionally, the energy-saving feature activation state includes: energy-saving feature deactivation state, activation channel deactivation of energy-saving feature state, activation of shallow sleep feature state, and activation of deep sleep feature state.

[0107] Optionally, for each of the global energy-saving combination states, obtaining the predicted electricity cost and predicted revenue for the target area within the first time period includes:

[0108] Based on the historical data of the capacity layer cells and the coverage layer cells within the target area, predict the target number of users and target service traffic of each cell under each global energy-saving combination state, wherein the cells include the capacity layer cells and the coverage layer cells;

[0109] For each of the global energy-saving combination states, based on the number of target users, the target service traffic, and the correspondence between the base station and the cell for each cell, the predicted electricity cost for the target area within the first time period is obtained by using the power consumption change model of the base station.

[0110] For each of the global energy-saving combination states, based on the target service traffic of each cell, the predicted revenue for the target area within the first time period is obtained by using the traffic revenue function of each service.

[0111] In this embodiment of the invention, the calculation method for the predicted electricity cost and predicted revenue corresponding to the target area in step S102 is explained:

[0112] The first step is to predict the target number of users and the target service traffic of each service type for each cell at n time points in the first time period, based on the historical data of the capacity layer cells and the historical data of the coverage layer cells, for each global energy-saving combination state.

[0113] The second step is to calculate the predicted electricity cost for the target area within the first time period. Predicted electricity cost = power consumption x electricity unit price. Power consumption typically refers to the power consumption of base stations within the target area. There is a certain correspondence between base stations and cells; one base station corresponds to multiple cells. Therefore, for each global energy-saving combination state, based on the number of target users in the multiple cells corresponding to a particular base station and the target service traffic corresponding to each service type, the power consumption change model of that base station is used to predict the power consumption of that base station within the first time period. Combined with the electricity unit price, the predicted electricity cost for that base station within the first time period can be obtained. Similarly, the predicted electricity cost for each base station within the first time period can be obtained, thus yielding the predicted electricity cost for the target area within the first time period. It should be noted that multiple cells corresponding to one base station do not necessarily belong to the same cell group; the division of cell groups is unrelated to the base station.

[0114] The third step involves understanding that user data usage generates revenue for the community, with each service type corresponding to a specific revenue function. For each global energy-saving combination, based on the target service traffic for each community under each service type, the revenue is predicted using the revenue function for that service type, thus obtaining the predicted revenue for the target area within the first time period. Specifically:

[0115] Suppose the revenue function for the p-th business is E = f p (Community Category, D) p In the e-th global energy-saving combination, the community group G m The traffic revenue at time t within the first time period in the future is:

[0116]

[0117] According to the above formula, cell group G m The predicted payoffs at n times within the first time period in the future are:

[0118]

[0119] Then, under the e-th global energy-saving combination, the total predicted benefit for all cell groups in the target area during the first time period is:

[0120]

[0121] Similarly, we can obtain the predicted revenue for the target area in the first time period under each global energy-saving combination.

[0122] Optionally, for each of the global energy-saving combination states, based on the number of target users, the target service traffic, and the correspondence between the base station and the cell for each cell, a prediction is made using the power consumption change model of the base station to obtain the predicted electricity cost for the target area within the first time period, including:

[0123] Based on the correspondence between base stations and cells, determine the set of cells corresponding to each base station;

[0124] For each of the global energy-saving combination states, predictions are made based on the number of target users and the target service traffic of each cell in each cell set to obtain the predicted number of base station users and the predicted service traffic of each base station in the target area under the global energy-saving combination state during the first time period.

[0125] For each of the global energy-saving combination states, the power consumption of each base station in the target area during the first time period is obtained by calculating the predicted number of users and the predicted service traffic of each base station through the power change model of the base station.

[0126] For each of the global energy-saving combination states, predictions are made based on the power consumption and electricity price of each base station to obtain the predicted electricity cost for the target area within the first time period.

[0127] In this embodiment of the invention, the method for obtaining the predicted electricity cost corresponding to the target area within the first time period is described in detail:

[0128] The first step is to determine the cell set corresponding to each base station based on the correspondence between base stations and cells, where one base station corresponds to multiple cells; for example, the cell set of the d-th base station includes cells C1, C2, C3...C x-1 C x x is an integer greater than 0.

[0129] In the case of the e-th global energy-saving combination, the predicted target user number for each cell in the cell set at time t within the first time period of the d-th base station is U. e1t U e2t ...U ext It should be noted that, corresponding to the above formula, if the cell is a capacity-level cell, then the target number of users for that cell is... If the cell is a coverage layer cell, then the target number of users corresponding to that cell is: ms refers to the cell group G to which the cell belongs in the e-th global energy-saving combination scenario. mThe target energy-saving combination state.

[0130] The predicted service volume for the p-th service in each cell at time t is D. e1pt D e2pt ...D expt It should be noted that, corresponding to the above formula, if the cell is a capacity-level cell, then the target number of users for that cell is... If the cell is a coverage layer cell, then the target number of users corresponding to that cell is: ms refers to the cell group G to which the cell belongs under the e-th global energy-saving combination. m The target energy-saving combination state.

[0131] The second step involves, for each of the aforementioned global energy-saving combination states, predicting the target user count and target service traffic of each cell in each cell set to obtain the predicted user count and predicted service traffic of each base station within the target area during the first time period. Specifically:

[0132] Under the e-th global energy-saving combination, the predicted number of users for the d-th base station at time t in the first future time period is:

[0133]

[0134] Under the e-th global energy-saving combination, the predicted service traffic of the d-th base station at time t in the first future time period is:

[0135]

[0136] The third step is to find the formula corresponding to the power change model of the base station: W station =f(U station D station ); where W station U represents the power consumption of the base station. station D represents the number of base station users. station This represents the total base station service traffic. For each global energy-saving combination state, based on the predicted number of users and predicted service traffic of the base stations, the power consumption of each base station in the target area during the first time period is calculated using the base station power change model. Specifically:

[0137] Under the e-th global energy-saving combination, the power consumption of the d-th base station at time t in the first future time period is:

[0138] W edt =f(U edt D edt ).

[0139] The fourth step involves predicting the electricity cost for the target area within the first time period based on the power consumption and electricity price of each base station for each global energy-saving combination state. Specifically:

[0140] Suppose that the predicted power consumption of the d-th base station at time t in the first future time period is W under the e-th global energy-saving combination. edt Let the time interval from time t to time t+1 be Δ. t Let the unit electricity price of the base station at time t be P. dt Then the predicted electricity cost for the base station from time t to time t+1 is:

[0141] F edt =W edt *P dt *Δ t ;

[0142] Then the predicted electricity cost for this base station at n times within the first future time period is:

[0143]

[0144] Then, under this global energy-saving combination, the predicted electricity cost at n times within the first future time period is:

[0145]

[0146] Similarly, for each global energy-saving combination, the predicted electricity cost for the target area in the first time period is obtained.

[0147] Optionally, the method further includes:

[0148] Obtain historical power data for each base station and historical user count and service traffic for each cell;

[0149] Based on the correspondence between the base station and the cell, the historical number of users and the historical service traffic of the cells corresponding to the base station are summed to obtain the historical number of users and the historical service traffic of each base station.

[0150] The power variation model of the base station is obtained by calculating based on the historical power data of the base station, the historical number of users of the base station, and the historical service traffic of the base station; wherein, the power variation model of the base station is different for different models and / or different manufacturers.

[0151] In this embodiment of the invention, the power variation model described above is explained. Each base station corresponds to multiple cells. The historical data of each base station is obtained by summing the historical data of the cells. Specifically:

[0152] Obtain the historical number of users and historical service traffic for each cell; for each base station, sum the historical number of users of the multiple cells corresponding to the base station to obtain the historical number of users for each base station; for each base station, sum the historical service traffic of the multiple cells corresponding to the base station to obtain the historical service traffic for each base station; establish a power change model W based on the historical number of users, historical service traffic, and historical power data of the base station. station =f(U station D station ).

[0153] It should be noted that the power variation models corresponding to base stations of different models and / or from different manufacturers are different. Therefore, generally, before establishing the power variation model, the base stations are grouped according to their model and / or manufacturer to obtain multiple base station groups. Base stations of the same model and / or manufacturer are grouped into the same base station group. The specific grouping method is not limited in this invention. Then, the power variation model is established according to the base station group, with one base station group corresponding to one power variation model.

[0154] Optionally, the historical data of the capacity layer cell includes one or more of the following:

[0155] Historical user count, historical service type, historical service traffic, and historical energy-saving feature activation status of the capacity layer cell;

[0156] The historical data of the coverage cell includes one or more of the following:

[0157] Historical user count, historical service types, and historical service traffic of the coverage cell.

[0158] In this embodiment of the invention, the historical data of the capacity layer cell includes, but is not limited to, one or more of the following: the historical number of users in the capacity layer cell, the historical service types in the capacity layer cell, the historical service traffic in the capacity layer cell, and the historical energy-saving feature activation status of the capacity layer cell. Specifically, the historical data of the capacity layer cell each day includes n sets of data corresponding to n time points. Each set of data includes the number of users in one cell, k service types, the service traffic corresponding to each service type, one subframe shutdown activation flag, one channel shutdown activation flag, one shallow hibernation activation flag, and one deep hibernation activation flag. n is an integer greater than 0, and k is an integer greater than 0.

[0159] Historical data for coverage cells includes, but is not limited to, one or more of the following: historical number of users in the coverage cell, historical service types in the coverage cell, and historical service traffic in the coverage cell. Specifically, the historical data for coverage cells each day includes n sets of data corresponding to n time points. Each set of data includes the number of users in one cell, k service types, and the service traffic corresponding to each service type.

[0160] like Figure 2 As shown, this embodiment of the invention also provides a community energy-saving device, comprising:

[0161] The first determining module 201 is used to determine multiple global energy-saving combination states based on the energy-saving characteristic activation status of multiple capacity-layer cells in the target area; wherein, a global energy-saving combination state includes: the energy-saving characteristic activation status corresponding to each of the multiple capacity-layer cells;

[0162] The first acquisition module 202 is used to acquire the predicted electricity cost and predicted revenue of the target area within the first time period for each global energy-saving combination state.

[0163] The first calculation module 203 is used to calculate the ratio corresponding to each global energy-saving combination state, wherein the ratio is the ratio of the predicted revenue in the first time period to the predicted electricity cost in the first time period.

[0164] The second determining module 204 is used to determine the energy-saving characteristic activation status of multiple capacity layer cells in the target area during the first time period as: the energy-saving characteristic activation status of the capacity layer cell corresponding to the global energy-saving combination status with the largest ratio.

[0165] Optionally, the first determining module 201 includes:

[0166] The first partitioning unit is used to group cells according to the correspondence between capacity layer cells and coverage layer cells in the target area to obtain multiple cell groups; wherein each cell group includes at least one coverage layer cell and a capacity layer cell corresponding to the coverage layer cell.

[0167] The first combination unit is used to combine the multiple energy-saving feature activation states corresponding to the capacity layer cells in each cell group to obtain multiple energy-saving combination states corresponding to each cell group. A target energy-saving combination state includes: the energy-saving feature activation state corresponding to each capacity layer cell in the cell group.

[0168] The first filtering unit is used to filter the energy-saving combination status of each cell group based on the historical data of the capacity layer cells and the historical data of the coverage layer cells within the cell group, so as to obtain multiple target energy-saving combination statuses corresponding to each cell group.

[0169] The second combination unit is used to sequentially select a target energy-saving combination state for each of the cell groups and combine them to obtain multiple global energy-saving combination states.

[0170] Optionally, the first filtering unit includes:

[0171] The first prediction unit is used to predict, for each cell group, the minimum number of users and the minimum service traffic that each cell group should support during the first time period, based on the historical data of the capacity layer cells and the historical data of the coverage layer cells in the cell group.

[0172] The second prediction unit is used to predict each of the energy-saving combination states based on the historical data of the capacity layer cell and the historical data of the coverage layer cell, and to obtain the predicted number of users and the predicted service traffic for each of the energy-saving combination states in the first time period.

[0173] The second filtering unit is used to filter each cell group based on the minimum number of users and the minimum service traffic that the cell group should support, as well as the predicted number of users and the predicted service traffic of the energy-saving combination state of the cell group, and select the energy-saving combination state that meets the preset conditions as the target energy-saving combination state of the corresponding cell group.

[0174] Optionally, the preset conditions in the second filtering unit include:

[0175] The predicted number of users in the energy-saving combination state is greater than or equal to the minimum number of users that the corresponding cell group should support, and the predicted service traffic in the energy-saving combination state is greater than or equal to the minimum service traffic that the corresponding cell group should support.

[0176] Optionally, the energy-saving feature activation state in the first determining module 201 includes one or more of the following:

[0177] Energy-saving feature off state, channel on state: energy-saving feature off state, shallow sleep state on state, and deep sleep state on state.

[0178] Optionally, the first acquisition module 202 includes:

[0179] The third prediction unit is used to predict the target number of users and target service traffic of each cell under each global energy-saving combination state based on the historical data of the capacity layer cell and the coverage layer cell in the target area. The cell includes the capacity layer cell and the coverage layer cell.

[0180] The fourth prediction unit is used to predict the predicted electricity cost of the target area within the first time period for each of the global energy-saving combination states, based on the number of target users, the target service traffic, and the correspondence between the base station and the cell for each cell, by using the power consumption change model of the base station.

[0181] The fifth prediction unit is used to predict the predicted revenue of the target area within the first time period by using the traffic revenue function of each service based on the target service traffic of each cell for each of the global energy-saving combination states.

[0182] Optionally, the fourth prediction unit includes:

[0183] The first determining unit is used to determine the set of cells corresponding to each base station based on the correspondence between the base station and the cells.

[0184] The sixth prediction unit is used to predict the number of target users and the target service traffic of each cell in each cell set for each global energy-saving combination state, and obtain the predicted number of base station users and the predicted service traffic of each base station in the target area in the first time period under the global energy-saving combination state.

[0185] The seventh prediction unit is used to calculate, based on the predicted number of users and the predicted service traffic of each base station in the target area during the first time period under each of the global energy-saving combination states, by using the power change model of the base station.

[0186] The eighth prediction unit is used to predict the predicted electricity cost of the target area within the first time period based on the power consumption and electricity price of each base station for each of the global energy-saving combination states.

[0187] Optionally, the device further includes:

[0188] The second acquisition module is used to acquire historical power data of each base station and historical user count and service traffic of each cell.

[0189] The second calculation module is used to sum up the historical user count and historical service traffic of the multiple cells corresponding to the base station according to the correspondence between the base station and the cell, so as to obtain the historical user count and historical service traffic of each base station.

[0190] The third calculation module is used to calculate the power change model of the base station based on the historical power data of the base station, the historical number of users of the base station, and the historical service traffic of the base station; wherein the power change model is different for base stations of different models and / or from different manufacturers.

[0191] Optionally, the historical data of the capacity layer cells in the first filtering unit or the third prediction unit includes one or more of the following:

[0192] Historical user count, historical service type, historical service traffic, and historical energy-saving feature activation status of the capacity layer cell;

[0193] The historical data of the coverage cell includes one or more of the following:

[0194] Historical user count, historical service types, and historical service traffic of the coverage cell.

[0195] It should be noted that the embodiments of this device are devices corresponding to the embodiments of the above methods. All implementations in the embodiments of the above methods are applicable to the embodiments of this device and can achieve the same technical effect.

[0196] This invention also provides a network device, including: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the cell energy-saving method described in any of the preceding claims and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0197] This invention also provides a readable storage medium, comprising: a program stored on the readable storage medium, wherein when the program is executed by a processor, it implements the steps of the community energy-saving method described in any of the preceding claims, and achieves the same technical effect; to avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0198] This invention also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement the steps of the community energy-saving method described in any of the preceding claims and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0199] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0200] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for energy conservation in residential communities, characterized in that, include: Based on the energy-saving feature activation status of multiple capacity-layer cells within the target area, various global energy-saving combination states are determined; wherein, the global energy-saving combination state includes: the energy-saving feature activation status corresponding to each of the multiple capacity-layer cells; For each of the global energy-saving combination states, the predicted electricity cost and predicted revenue for the target area within the first time period are obtained respectively. Calculate the ratio corresponding to each of the global energy-saving combination states, where the ratio is the ratio of the predicted revenue in the first time period to the predicted electricity cost in the first time period; The energy-saving characteristic activation status of multiple capacity layer cells in the target area within the first time period is determined as: the energy-saving characteristic activation status of the capacity layer cell corresponding to the global energy-saving combination status with the largest ratio.

2. The energy-saving method for residential communities according to claim 1, characterized in that, Based on the energy-saving characteristics activation status of multiple capacity-level cells within the target area, various global energy-saving combinations are determined, including: The cells are grouped according to the correspondence between capacity layer cells and coverage layer cells in the target area to obtain multiple cell groups; wherein each cell group includes at least one coverage layer cell and a capacity layer cell corresponding to the coverage layer cell. Based on the combination of the various energy-saving feature activation states corresponding to the capacity layer cells in each cell group, multiple energy-saving combination states corresponding to each cell group are obtained. Among them, a target energy-saving combination state includes: the energy-saving feature activation state corresponding to each capacity layer cell in the cell group. For each cell group, the energy-saving combination status of the cell group is filtered based on the historical data of the capacity layer cells and the historical data of the coverage layer cells within the cell group to obtain multiple target energy-saving combination statuses corresponding to each cell group; By sequentially selecting one target energy-saving combination state for each of the aforementioned cell groups, multiple global energy-saving combination states can be obtained.

3. The energy-saving method for residential communities according to claim 2, characterized in that, For each cell group, based on historical data of the capacity layer cells and the coverage layer cells within the cell group, the energy-saving combination status of the cell group is filtered to obtain multiple target energy-saving combination statuses corresponding to each cell group, including: For each cell group, predictions are made based on historical data of the capacity layer cells and the coverage layer cells within the cell group to obtain the minimum number of users and the minimum service traffic that each cell group should support during the first time period. Based on the historical data of the capacity layer cells and the historical data of the coverage layer cells, predictions are made for each of the energy-saving combination states to obtain the predicted number of users and the predicted service traffic for each of the energy-saving combination states in the first time period. For each cell group, the minimum number of users and minimum service traffic that the cell group should support, as well as the predicted number of users and predicted service traffic of the energy-saving combination state of the cell group, are used for filtering. The energy-saving combination state that meets the preset conditions is selected as the target energy-saving combination state of the corresponding cell group.

4. The energy-saving method for residential communities according to claim 3, characterized in that, The preset conditions include: The predicted number of users in the energy-saving combination state is greater than or equal to the minimum number of users that the corresponding cell group should support, and the predicted service traffic in the energy-saving combination state is greater than or equal to the minimum service traffic that the corresponding cell group should support.

5. The community energy-saving method according to claim 1 or 2, characterized in that, The energy-saving feature activation states include: energy-saving feature deactivation state, activation channel deactivation of energy-saving feature state, activation of shallow sleep feature state, and activation of deep sleep feature state.

6. The energy-saving method for residential communities according to claim 1, characterized in that, For each of the global energy-saving combination states, the predicted electricity cost and predicted revenue for the target area within the first time period are obtained, including: Based on the historical data of the capacity layer cells and the coverage layer cells within the target area, predict the target number of users and target service traffic of each cell under each global energy-saving combination state, wherein the cells include the capacity layer cells and the coverage layer cells; For each of the global energy-saving combination states, based on the number of target users, the target service traffic, and the correspondence between the base station and the cell for each cell, the predicted electricity cost for the target area within the first time period is obtained by using the power consumption change model of the base station. For each of the global energy-saving combination states, based on the target service traffic of each cell, the predicted revenue for the target area within the first time period is obtained by using the traffic revenue function of each service.

7. The energy-saving method for residential communities according to claim 6, characterized in that, For each of the global energy-saving combination states, based on the number of target users, the target service traffic, and the correspondence between the base station and the cell for each cell, a prediction is made using the power consumption change model of the base station to obtain the predicted electricity cost for the target area within the first time period, including: Based on the correspondence between base stations and cells, determine the set of cells corresponding to each base station; For each of the global energy-saving combination states, predictions are made based on the number of target users and the target service traffic of each cell in each cell set to obtain the predicted number of base station users and the predicted service traffic of each base station in the target area under the global energy-saving combination state during the first time period. For each of the global energy-saving combination states, the power consumption of each base station in the target area during the first time period is obtained by calculating the predicted number of users and the predicted service traffic of each base station through the power change model of the base station. For each of the global energy-saving combination states, predictions are made based on the power consumption and electricity price of each base station to obtain the predicted electricity cost for the target area within the first time period.

8. The community energy-saving method according to claim 6 or 7, characterized in that, The method further includes: Obtain historical power data for each base station and historical user count and service traffic for each cell; Based on the correspondence between the base station and the cell, the historical number of users and the historical service traffic of the cells corresponding to the base station are summed to obtain the historical number of users and the historical service traffic of each base station. The power variation model of the base station is obtained by calculating based on the historical power data of the base station, the historical number of users of the base station, and the historical service traffic of the base station; wherein, the power variation model of the base station is different for different models and / or different manufacturers.

9. The community energy-saving method according to claim 2, 3, or 6, characterized in that, The historical data of the capacity tier cells includes one or more of the following: Historical user count, historical service type, historical service traffic, and historical energy-saving feature activation status of the capacity layer cell; The historical data of the coverage cell includes one or more of the following: Historical user count, historical service types, and historical service traffic of the coverage cell.

10. A community energy-saving device, characterized in that, include: The first determining module is used to determine multiple global energy-saving combination states based on the energy-saving characteristic activation status of multiple capacity-layer cells within the target area; wherein, a global energy-saving combination state includes: the energy-saving characteristic activation status corresponding to each of the multiple capacity-layer cells; The first acquisition module is used to acquire the predicted electricity cost and predicted revenue for the target area within a first time period for each global energy-saving combination state. The first calculation module is used to calculate the ratio corresponding to each global energy-saving combination state, wherein the ratio is the ratio of the predicted revenue in the first time period to the predicted electricity cost in the first time period. The second determining module is used to determine the energy-saving characteristic activation status of multiple capacity layer cells in the target area during the first time period as: the energy-saving characteristic activation status of the capacity layer cell corresponding to the global energy-saving combination status with the largest ratio.

11. A network device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the community energy-saving method as described in any one of claims 1 to 9.

12. A readable storage medium, characterized in that, include: The readable storage medium stores a program that, when executed by a processor, implements the steps of the community energy-saving method as described in any one of claims 1 to 9.

13. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the community energy-saving method as described in any one of claims 1 to 9.

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