Private network planning method and device, electronic equipment and storage medium

Abstract

The application provides a private network planning method and device, electronic equipment and a storage medium, wherein the method comprises: obtaining site parameters and business parameters of a to-be-planned port, the business parameters being determined based on a selected business mode; inputting the site parameters and the business parameters into a device quantity prediction model to obtain a device quantity prediction result output by the device quantity prediction model; and determining a private network planning result of the to-be-planned port based on the device quantity prediction result. The application can predict the device quantity of the to-be-planned port based on the site parameters and the business parameters of the to-be-planned port through the device quantity prediction model, automatically obtain the device quantity prediction result, and then determine the private network planning result based on the device quantity prediction result, thereby improving the efficiency of private network planning and configuration; and compared with manually performing private network planning and configuration, the application can improve the accuracy of private network planning and configuration based on the device quantity prediction model.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a private network planning method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the rapid development of 5G (5th Generation Mobile Communication Technology), vertical industry projects are growing rapidly, especially in port operations. Therefore, there is a need to provide dedicated network planning solutions for port operations to meet the network requirements of port customers.

[0003] Currently, private network planning and configuration are done manually based on customers' business needs. However, relying on manual methods for private network planning and configuration is inefficient due to the need for manual analysis of business requirements; moreover, it depends too much on expert experience, resulting in low accuracy in private network planning and configuration. Summary of the Invention

[0004] This invention provides a private network planning method, apparatus, electronic device, and storage medium to address the shortcomings of low efficiency and low accuracy in private network planning and configuration in the prior art, and to achieve high-efficiency and high-accuracy private network planning and configuration.

[0005] This invention provides a private network planning method, comprising:

[0006] Obtain the site parameters and business parameters of the port to be planned. The business parameters are determined based on the selected business mode. The port to be planned is the port of the private network to be planned.

[0007] The site parameters and the business parameters are input into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model;

[0008] Based on the predicted number of devices, the private network planning results for the port to be planned are determined.

[0009] According to a private network planning method provided by the present invention, the step of inputting the site parameters and the service parameters into a device quantity prediction model to obtain the device quantity prediction result output by the device quantity prediction model includes:

[0010] The site parameters and the business parameters are input into the feature mapping layer of the equipment quantity prediction model to obtain the site features and business features output by the feature mapping layer.

[0011] The site features and business features are input into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer;

[0012] The classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample devices corresponding to the sample site parameters and sample service parameters.

[0013] According to a private network planning method provided by the present invention, the classification layer includes a communication equipment classification layer. The step of inputting the site characteristics and the service characteristics into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer includes:

[0014] The site features and the service features are input into the communication equipment classification layer to obtain the predicted number of communication equipment output by the communication equipment classification layer;

[0015] The communication equipment classification layer includes an active antenna unit (AAU) classification layer and / or a baseband board classification layer.

[0016] When the communication device classification layer includes an AAU classification layer, the communication device quantity prediction result includes the AAU quantity prediction result;

[0017] When the communication device classification layer includes a baseband board classification layer, the communication device quantity prediction result includes the baseband board quantity prediction result.

[0018] According to a private network planning method provided by the present invention, the classification layer includes a service equipment classification layer. The step of inputting the site characteristics and the service characteristics into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer includes:

[0019] The site features and the business features are input into the business equipment classification layer to obtain the business equipment quantity prediction result output by the business equipment classification layer;

[0020] The business equipment classification layer includes at least one of the following: a yard crane classification layer, an unmanned container truck classification layer, and an intelligent cargo sorting classification layer.

[0021] When the business equipment classification layer includes a yard crane classification layer, the business equipment quantity prediction result includes the yard crane quantity prediction result;

[0022] When the business equipment classification layer includes an unmanned truck classification layer, the business equipment quantity prediction result includes the unmanned truck quantity prediction result;

[0023] When the business equipment classification layer includes an intelligent cargo sorting classification layer, the business equipment quantity prediction result includes the intelligent cargo sorting quantity prediction result.

[0024] According to a private network planning method provided by the present invention, the private network planning results include at least one of the following: equipment quantity prediction results, equipment cost results, maintenance cost results, service fee results, network capacity results, and total quotation results;

[0025] The process of determining the private network planning results for the port to be planned based on the predicted number of devices includes:

[0026] If the private network planning results include equipment cost results, the equipment cost results are determined based on the predicted equipment quantity and the preset unit cost of the equipment.

[0027] If the private network planning results include maintenance cost results, the maintenance cost results are determined based on the predicted number of devices and the preset unit price of maintenance cost.

[0028] If the private network planning results include service fee results, the service fee results are determined based on the predicted number of devices and the preset service fee unit price.

[0029] If the private network planning results include network capacity results, the network capacity results are determined based on the predicted number of devices and the preset single device capacity.

[0030] If the private network planning result includes a total quotation result, the total quotation result is determined based on at least one of the equipment cost result, the maintenance cost result, and the service fee result, as well as the contract period indicated by the service parameters.

[0031] According to a private network planning method provided by the present invention, the site parameters include offshore depth and / or berth shoreline length.

[0032] According to a private network planning method provided by the present invention, the service parameters include at least one of contract period, package mode, and yard services;

[0033] The yard operations include at least one of the following: yard crane, unmanned truck, intelligent tallying, first combined operations, second combined operations, third combined operations, and fourth combined operations;

[0034] The first combined service includes yard cranes and unmanned trucks; the second combined service includes unmanned trucks and intelligent tallying; the third combined service includes yard cranes and intelligent tallying; and the fourth combined service includes yard cranes, unmanned trucks, and intelligent tallying.

[0035] The present invention also provides a private network planning device, comprising:

[0036] The acquisition module is used to acquire the site parameters and business parameters of the port to be planned. The business parameters are determined based on the selected business mode, and the port to be planned is the port of the private network to be planned.

[0037] The prediction module is used to input the site parameters and the business parameters into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model;

[0038] The determination module is used to determine the private network planning result of the port to be planned based on the predicted number of devices.

[0039] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the private network planning method described above.

[0040] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the private network planning method as described above.

[0041] The present invention provides a private network planning method, apparatus, electronic device, and storage medium that acquires site parameters and service parameters of a port to be planned; inputs the site parameters and service parameters into an equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model; and determines the private network planning result for the port to be planned based on the equipment quantity prediction result. Through this method, the present invention can predict the equipment quantity of a port to be planned using a device quantity prediction model based on the site parameters and service parameters, automatically obtain the equipment quantity prediction result, and then determine the private network planning result based on the equipment quantity prediction result, thus improving the efficiency of private network planning and configuration. Furthermore, compared to manual private network planning and configuration, the present invention can improve the accuracy of private network planning and configuration based on the equipment quantity prediction model. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0043] Figure 1 This is one of the flowcharts illustrating the private network planning method provided by the present invention;

[0044] Figure 2 The second flowchart illustrating the private network planning method provided by this invention;

[0045] Figure 3 A schematic diagram of the private network planning device provided by the present invention;

[0046] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0048] With the rapid development of 5G, vertical industry projects are growing rapidly, especially port operations. Therefore, to meet customers' real-time needs for dedicated network planning and configuration solutions for port operations, it is necessary to provide dedicated network planning solutions for port operations to satisfy their network requirements.

[0049] Currently, private network planning and configuration are done manually based on clients' business needs. However, relying on manual methods for private network planning and configuration is inefficient, taking many days from receiving client requirements to analyzing and outputting a private network planning solution. Furthermore, over-reliance on expert experience may result in incomplete private network planning solutions that fail to meet client needs, leading to low accuracy in private network planning and configuration. Additionally, excessive reliance on expert experience introduces instability into the configuration of private network planning solutions, thus failing to improve client satisfaction and ultimately resulting in low accuracy in private network planning and configuration.

[0050] To address the above problems, the present invention proposes the following embodiments. Figure 1 This is one of the flowcharts illustrating the private network planning method provided by the present invention, such as... Figure 1 As shown, the private network planning method includes:

[0051] Step 110: Obtain the site parameters and business parameters of the port to be planned. The business parameters are determined based on the selected business mode. The port to be planned is the port of the private network to be planned.

[0052] Here, the port to be planned refers to a port that requires dedicated network planning. The site parameters of this port to be planned may include, but are not limited to, at least one of the following: offshore depth, berth quay length, etc. For example, the offshore depth is 350 meters, and the berth quay length is 500 meters. The business parameters of this port to be planned may include, but are not limited to, one or more of the following: contract period, package model, yard operations, etc. For example, contract periods include 3 years, 5 years, etc.; package models include activation fee + monthly package, activation fee, etc.; yard operations include yard cranes, unmanned trucks, intelligent tallying, yard crane + unmanned trucks, unmanned trucks + intelligent tallying, yard crane + intelligent tallying, yard crane + unmanned trucks + intelligent tallying, etc.

[0053] Site parameters can be determined based on the user's business needs. Specifically, they can be determined based on the results of on-site inspections of the port to be planned, or directly obtained from the site parameters determined by the port client.

[0054] The business parameters are determined based on the business model selected by the user. Specifically, this selection can be made by the port customer or the operator. The business model can include, but is not limited to, one or more of the following: contract period, package model, yard business, etc.

[0055] Step 120: Input the site parameters and the business parameters into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model.

[0056] Here, the equipment quantity prediction model is used to predict the equipment quantity based on site parameters and business parameters, and obtain the equipment quantity prediction results.

[0057] Here, the equipment quantity prediction results may include, but are not limited to, at least one of the following: communication equipment quantity prediction results, service equipment quantity prediction results, etc. Communication equipment quantity prediction results may include, but are not limited to, at least one of the following: AAU (Active Antenna Unit) quantity prediction results, baseband board quantity prediction results, BBU (Baseband Unit) quantity prediction results, etc. Service equipment quantity prediction results may include, but are not limited to, at least one of the following: yard crane quantity prediction results, unmanned container truck quantity prediction results, intelligent cargo handling quantity prediction results, etc.

[0058] The types of communication equipment corresponding to the predicted number of communication equipment can be set according to actual needs, and this embodiment of the invention does not impose specific limitations on this. For example, if this embodiment of the invention is for private network planning of 5G network, then the communication equipment may include AAU, baseband board, BBU, etc.

[0059] The types of business equipment corresponding to the predicted number of business equipment can be set according to actual needs, and this embodiment of the invention does not impose specific limitations on this. For example, if the yard operations in the business parameters include yard quay cranes, then the predicted number of operational equipment includes the predicted number of yard quay cranes; if the yard operations in the business parameters include unmanned trucks, then the predicted number of operational equipment includes the predicted number of unmanned trucks; if the yard operations in the business parameters include intelligent tallying, then the predicted number of operational equipment includes the predicted number of intelligent tallying; if the yard operations in the business parameters include both yard quay cranes and unmanned trucks, then the predicted number of operational equipment includes both yard quay cranes and unmanned trucks; if the yard operations in the business parameters include both yard quay cranes and intelligent tallying, then the predicted number of operational equipment includes both yard quay cranes and intelligent tallying; if the yard operations in the business parameters include both unmanned trucks and intelligent tallying, then the predicted number of operational equipment includes both unmanned trucks and intelligent tallying; if the yard operations in the business parameters include both yard quay cranes, unmanned trucks, and intelligent tallying, then the predicted number of operational equipment includes the predicted number of yard quay cranes, unmanned trucks, and intelligent tallying.

[0060] The equipment quantity prediction model may include a feature mapping layer and a classification layer, or it may include a feature extraction layer and a classification layer. The feature mapping layer is used to perform feature mapping based on site parameters and business parameters to obtain site features and business features; the classification layer is used to predict the equipment quantity based on site features and business features to obtain the predicted equipment quantity result; the feature extraction layer is used to extract features based on site parameters and business parameters to obtain site features and business features. The feature mapping layer or feature extraction layer can be a convolutional layer or a mapping layer based on preset rules; this embodiment of the invention does not specifically limit this. The classification layer can be constructed by a classifier, which may include, but is not limited to, classifiers corresponding to the random forest algorithm, Naive Bayes classifiers, support vector machine classifiers, etc.; this embodiment of the invention does not specifically limit this.

[0061] The equipment quantity prediction model is trained based on sample site parameters, sample business parameters, and the sample equipment quantity corresponding to the sample site parameters and sample business parameters.

[0062] Step 130: Based on the predicted number of devices, determine the private network planning result for the port to be planned.

[0063] Here, the results of private network planning may include, but are not limited to, at least one of the following: number of devices, equipment cost, maintenance cost, service fee, network capacity, and total quotation.

[0064] The equipment quantity results may include, but are not limited to, at least one of the following: AAU quantity results, baseband board quantity results, BBU quantity results, yard crane quantity results, unmanned container truck quantity results, intelligent cargo handling quantity results, etc.

[0065] In one embodiment, the predicted number of devices is determined as the number of devices.

[0066] In another embodiment, the equipment quantity result is determined based on the equipment quantity prediction result and the equipment quantity parameter. This equipment quantity parameter is determined based on the equipment quantity selected by the user. This equipment quantity parameter may include, but is not limited to, at least one of the following: AAU quantity result, baseband board quantity result, BBU quantity result, yard crane quantity result, unmanned container truck quantity result, intelligent cargo handling quantity result, etc.

[0067] More specifically, the predicted number of equipment and the equipment quantity parameters are aggregated to obtain the final number of equipment. This aggregation can be done using weighted averaging, average averaging, or other similar methods. For example, if the predicted number of equipment is 10 (AAU) and the equipment quantity parameter is 6 (AAU), then averaging yields a final number of 8. If the predicted number of equipment has a weight of 80% and the equipment quantity parameter has a weight of 20%, then weighted averaging yields a final number of 9.

[0068] In another embodiment, the equipment quantity is determined based on the predicted and analyzed equipment quantity results. The equipment quantity analysis results are determined based on the site parameters of the port to be planned. These results may include, but are not limited to, at least one of the following: AAU quantity results, baseboard quantity results, BBU quantity results, yard crane quantity results, unmanned container truck quantity results, and intelligent tallying quantity results.

[0069] More specifically, the predicted and analyzed equipment quantity results are aggregated to obtain the final equipment quantity result. This aggregation can be done using weighted averaging, average averaging, or other similar methods. For example, if the predicted equipment quantity result is 10 AAUs and the analyzed equipment quantity result is 6 AAUs, then averaging yields an equipment quantity result of 8; if the predicted equipment quantity result has a weight of 80% and the analyzed equipment quantity result has a weight of 20%, then weighted averaging yields an equipment quantity result of 9.

[0070] The equipment quantity analysis results can be the minimum order quantity, the maximum available quantity, or the average of the minimum order quantity and the maximum available quantity.

[0071] For example, the minimum sale quantity for a yard crane = rounded up (((offshore depth - 50) / 50) * 2); the maximum sale quantity for a yard crane = rounded up ((offshore depth - 50) / 100) + minimum sale quantity for a yard crane; the minimum sale quantity for an unmanned truck = rounded up (((offshore depth - 50) / 50) * 4); the maximum sale quantity for an unmanned truck = rounded up ((offshore depth - 50) / 50) + minimum sale quantity for an unmanned truck; the minimum sale quantity for a smart tally truck = rounded up (offshore depth / 100); the maximum sale quantity for a smart tally truck = rounded up (offshore depth / 80). If the offshore depth in the site parameters is 350 meters, then the minimum order quantity for the yard crane is 12, the maximum order quantity for the yard crane is 15, the minimum order quantity for the unmanned truck is 24, the maximum order quantity for the unmanned truck is 30, the minimum order quantity for the intelligent cargo handling equipment is 4, and the maximum order quantity for the intelligent cargo handling equipment is 5.

[0072] In one specific embodiment, the number of BBUs can be determined based on the number of AAUs, meaning there is a mapping relationship between the number of AAUs and the number of BBUs. For example, one AAU corresponds to one BBU, or two AAUs correspond to one BBU. The number of baseband boards can also be determined based on the number of AAUs, meaning there is a mapping relationship between the number of AAUs and the number of baseband boards. For example, one AAU corresponds to one baseband board, or two AAUs correspond to one baseband board.

[0073] The private network planning method provided in this invention obtains the site parameters and service parameters of the port to be planned; inputs the site parameters and service parameters into an equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model; and determines the private network planning result of the port to be planned based on the equipment quantity prediction result. Through the above method, this invention can use an equipment quantity prediction model to predict the equipment quantity of the port to be planned based on the site parameters and service parameters, automatically obtain the equipment quantity prediction result, and then determine the private network planning result based on the equipment quantity prediction result, thus improving the efficiency of private network planning and configuration; moreover, compared with manual private network planning and configuration, this invention can improve the accuracy of private network planning and configuration based on the equipment quantity prediction model.

[0074] Based on the above embodiments, Figure 2 This is the second flowchart illustrating the private network planning method provided by the present invention, as shown below. Figure 2 As shown, step 120 above includes:

[0075] Step 121: Input the site parameters and the business parameters into the feature mapping layer of the equipment quantity prediction model to obtain the site features and business features output by the feature mapping layer.

[0076] Here, the feature mapping layer is used to map site parameters and business parameters into site features and business features. In one specific embodiment, site parameters and business parameters can be mapped into site features and business features in digital format.

[0077] In one embodiment, the feature mapping layer includes a first mapping layer corresponding to the offshore depth. The offshore depth is input to the first mapping layer to obtain the first site feature corresponding to the offshore depth output by the first mapping layer. That is, the offshore depth of the site parameters is digitally mapped. For example, 300 meters of offshore depth is mapped to 1, 350 meters to 2, 400 meters to 3, 450 meters to 4, 500 meters to 5, 550 meters to 6, 600 meters to 7, 650 meters to 8, and 700 meters to 9.

[0078] In one embodiment, the feature mapping layer includes a second mapping layer corresponding to the berth shoreline length. The berth shoreline length is input into the second mapping layer to obtain the second site feature corresponding to the berth shoreline length output by the second mapping layer. That is, the berth shoreline length of the site parameters is digitally mapped. For example, a 300-meter berth shoreline length is mapped to 1, a 350-meter berth shoreline length is mapped to 2, a 400-meter berth shoreline length is mapped to 3, a 450-meter berth shoreline length is mapped to 4, a 500-meter berth shoreline length is mapped to 5, a 550-meter berth shoreline length is mapped to 6, a 600-meter berth shoreline length is mapped to 7, a 650-meter berth shoreline length is mapped to 8, and a 700-meter berth shoreline length is mapped to 9.

[0079] In one embodiment, the feature mapping layer includes a third mapping layer corresponding to the package mode. The package mode is input to the third mapping layer to obtain the first service feature corresponding to the package mode output by the third mapping layer. That is, the package mode of the service parameters is numerically mapped. For example, the package mode of the monthly package is mapped to 0, and the package mode of the monthly package + activation fee is mapped to 1.

[0080] In one embodiment, the feature mapping layer includes a fourth mapping layer corresponding to the contract period. The contract period is input to the fourth mapping layer to obtain the second business feature corresponding to the contract period output by the fourth mapping layer. That is, the contract period of the business parameter is numerically mapped. For example, a 3-year contract period is mapped to 0, and a 5-year contract period is mapped to 1.

[0081] In one embodiment, the feature mapping layer includes a fifth mapping layer corresponding to yard operations. Yard operations are input to the fifth mapping layer to obtain the third operation feature corresponding to the yard operations output by the fifth mapping layer. That is, the yard operations of the operation parameters are digitally mapped. For example, yard operations of quay cranes are mapped to 1, yard operations of unmanned trucks are mapped to 2, yard operations of intelligent tallying are mapped to 3, yard operations of quay cranes + unmanned trucks are mapped to 4, yard operations of unmanned trucks + intelligent tallying are mapped to 5, yard operations of quay cranes + intelligent tallying are mapped to 6, and yard operations of quay cranes + unmanned trucks + intelligent tallying are mapped to 7.

[0082] Step 122: Input the site features and the business features into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer.

[0083] Here, the classification layer includes the communication equipment classification layer and / or the service equipment classification layer; the equipment quantity prediction results include the communication equipment quantity prediction results and / or the service equipment quantity prediction results.

[0084] In one embodiment, site features and service features are input into the communication equipment classification layer to obtain the predicted number of communication equipment output by the communication equipment classification layer.

[0085] In one embodiment, site features and service features are input into the service equipment classification layer to obtain the service equipment quantity prediction result output by the service equipment classification layer.

[0086] The classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample devices corresponding to the sample site parameters and sample service parameters.

[0087] Here, the sample site parameters, sample business parameters, and sample equipment quantity are determined based on historical data. These parameters can be obtained by pre-cleaning the historical data. The sample equipment quantity is used to label the sample site parameters and sample business parameters.

[0088] The sample site parameters may include, but are not limited to, at least one of the following: offshore depth, berth shoreline length, etc. The sample business parameters may include, but are not limited to, at least one of the following: contract period, package model, yard operations, etc.

[0089] In one embodiment, the communication device classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample communication devices corresponding to the sample site parameters and sample service parameters.

[0090] In one embodiment, the service equipment classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample service equipment corresponding to the sample site parameters and sample service parameters.

[0091] In one specific embodiment, the classification layer is a random forest classification layer, that is, the classification layer is built using the random forest machine learning algorithm. For ease of understanding, the AAU classification layer of the communication device classification layer is used as an example for illustration.

[0092] During the training of the classification layer, firstly, sample data is established based on historical data. This sample data includes sample site parameters and sample business parameters, as well as the number of sample devices corresponding to these parameters. For example, it may include 100 sample data points, each containing offshore depth, berth shoreline length, contract period, package mode, yard business, and the corresponding number of labeled AAUs. Next, feature mapping is performed on the aforementioned sample site parameters and sample business parameters to obtain sample site features and sample business features. Then, a predetermined number of training samples (e.g., 60 training samples) are randomly selected with replacement from the 100 sample data points, and classification and regression trees (sub-decision trees) are constructed in parallel. This process is repeated a predetermined number of times (e.g., 80 times), with the number of sub-decision trees corresponding to the predetermined number of repetitions (e.g., 80 sub-decision trees). Subsequently, the training samples included in each sub-decision tree can include five feature subsets: offshore depth, berth shoreline length, contract period, package mode, and yard operations. Then, a preset number (e.g., four) of feature subsets can be arbitrarily selected from these five feature subsets for subsequent classification training based on these preset feature subsets. The resulting preset number of sub-decision trees are then used to form a random forest, ensuring that each sub-decision tree grows to its maximum potential without pruning, thereby completing the training of the classification layer. The splitting method for the random forest can include, but is not limited to, one of the following: the CART algorithm (using the Gini index minimization criterion for feature selection), the ID3 algorithm (using the feature with the highest information gain), or the C4.5 algorithm (using the information gain ratio for feature selection).

[0093] In addition, the specific execution processes of the gantry crane classification layer, unmanned container truck classification layer, intelligent cargo sorting classification layer, BBU classification layer, and baseband board classification layer are basically similar to the AAU classification layer mentioned above, and will not be described in detail here.

[0094] The private network planning method provided in this invention uses a feature mapping layer to map site parameters and service parameters into site features and service features. Then, a classification layer predicts the number of devices based on these site features and service features, automatically obtaining the predicted number of devices. Based on this prediction, the private network planning result is determined, improving the efficiency of private network planning and configuration. Furthermore, the classification layer, which predicts the number of devices based on sample site parameters and sample service parameters, as well as the corresponding number of sample devices, improves the accuracy of private network planning and configuration compared to manual private network planning and configuration.

[0095] Based on any of the above embodiments, in this method, the classification layer includes a communication device classification layer, and step 122 includes:

[0096] The site features and the service features are input into the communication equipment classification layer to obtain the predicted number of communication equipment output by the communication equipment classification layer;

[0097] The communication equipment classification layer includes an active antenna unit (AAU) classification layer and / or a baseband board classification layer.

[0098] When the communication device classification layer includes an AAU classification layer, the communication device quantity prediction result includes the AAU quantity prediction result;

[0099] When the communication device classification layer includes a baseband board classification layer, the communication device quantity prediction result includes the baseband board quantity prediction result.

[0100] In one embodiment, site features and business features are input into the AAU classification layer to obtain the AAU number prediction result output by the AAU classification layer.

[0101] The AAU classification layer is trained based on sample site parameters, sample business parameters, and the number of sample AAUs corresponding to the sample site parameters and sample business parameters.

[0102] In one embodiment, site features and service features are input into the baseband board classification layer to obtain the baseband board number prediction result output by the baseband board classification layer.

[0103] The baseband board classification layer is trained based on sample site parameters, sample service parameters, and the number of sample baseband boards corresponding to the sample site parameters and sample service parameters.

[0104] The private network planning method provided in this invention uses an AAU classification layer and a baseband board classification layer to predict the number of AAUs and baseband boards based on site characteristics and service characteristics. It automatically obtains the AAU and baseband board prediction results and then determines the private network planning result based on the device number prediction results, thereby improving the efficiency of private network planning and configuration. Moreover, device number prediction based on the AAU and baseband board classification layers can improve the accuracy of private network planning and configuration compared to manual private network planning and configuration.

[0105] Based on any of the above embodiments, in this method, the classification layer includes a service device classification layer, and step 122 includes:

[0106] The site features and the business features are input into the business equipment classification layer to obtain the business equipment quantity prediction result output by the business equipment classification layer;

[0107] The business equipment classification layer includes at least one of the following: a yard crane classification layer, an unmanned container truck classification layer, and an intelligent cargo sorting classification layer.

[0108] When the business equipment classification layer includes a yard crane classification layer, the business equipment quantity prediction result includes the yard crane quantity prediction result;

[0109] When the business equipment classification layer includes an unmanned truck classification layer, the business equipment quantity prediction result includes the unmanned truck quantity prediction result;

[0110] When the business equipment classification layer includes an intelligent cargo sorting classification layer, the business equipment quantity prediction result includes the intelligent cargo sorting quantity prediction result.

[0111] In one embodiment, site features and business features are input into the crane classification layer to obtain the crane quantity prediction result output by the crane classification layer.

[0112] The field crane classification layer is trained based on sample site parameters, sample business parameters, and the number of sample field cranes corresponding to the sample site parameters and sample business parameters.

[0113] In one embodiment, site features and business features are input into the unmanned truck classification layer to obtain the unmanned truck quantity prediction result output by the unmanned truck classification layer.

[0114] The unmanned truck classification layer is trained based on sample site parameters, sample business parameters, and the number of sample unmanned trucks corresponding to the sample site parameters and sample business parameters.

[0115] In one embodiment, site characteristics and business characteristics are input into the intelligent sorting and classification layer to obtain the intelligent sorting quantity prediction result output by the intelligent sorting and classification layer.

[0116] The intelligent cargo sorting and classification layer is trained based on sample site parameters, sample business parameters, and the number of intelligent cargo sorting samples corresponding to the sample site parameters and sample business parameters.

[0117] The private network planning method provided in this invention uses a gantry crane classification layer, an unmanned truck classification layer, and an intelligent tallying classification layer to predict the number of gantry cranes, unmanned trucks, and intelligent tallying equipment based on site and business characteristics. It automatically obtains these prediction results and then determines the private network planning result based on them, thus improving the efficiency of private network planning and configuration. Furthermore, predicting the number of equipment based on the gantry crane, unmanned truck, and intelligent tallying classification layers improves the accuracy of private network planning and configuration compared to manual methods.

[0118] Based on any of the above embodiments, in this method, the private network planning result includes at least one of the following: equipment quantity prediction result, equipment cost result, maintenance cost result, service fee result, network capacity result, and total quotation result; step 130 includes:

[0119] If the private network planning results include equipment cost results, the equipment cost results are determined based on the predicted equipment quantity and the preset unit cost of the equipment.

[0120] If the private network planning results include maintenance cost results, the maintenance cost results are determined based on the predicted number of devices and the preset unit price of maintenance cost.

[0121] If the private network planning results include service fee results, the service fee results are determined based on the predicted number of devices and the preset service fee unit price.

[0122] If the private network planning results include network capacity results, the network capacity results are determined based on the predicted number of devices and the preset single device capacity.

[0123] If the private network planning result includes a total quotation result, the total quotation result is determined based on at least one of the equipment cost result, the maintenance cost result, and the service fee result, as well as the contract period indicated by the service parameters.

[0124] Here, the preset unit cost price of the equipment is set according to the actual situation. For example, the unit cost price of the yard crane is 100,000 yuan / unit, or 2,700 yuan / unit per month, or 30,000 yuan / unit per year, etc.

[0125] In one embodiment, when the preset unit cost price of the equipment is the buyout price (e.g., 100,000 yuan / unit), the predicted equipment quantity is multiplied by the preset unit cost price of the equipment to obtain the equipment cost result.

[0126] In another embodiment, when the preset equipment cost unit price is the rental unit price (e.g., 2700 yuan / unit per month), the equipment quantity prediction result, the preset equipment cost unit price, and the contract period in the business parameters are multiplied to obtain the equipment cost result.

[0127] It is understandable that if multiple equipment cost results are included, the multiple equipment cost results will be added together to determine the final equipment cost result.

[0128] Here, the preset maintenance cost unit price is set according to the actual situation. For example, the maintenance unit price of the yard crane is 80,000 yuan / unit, or 2,000 yuan / unit per month, or 30,000 yuan / unit per year, etc.

[0129] In one embodiment, when the preset maintenance cost unit price is the buyout unit price (e.g., 80,000 RMB / unit), the predicted number of equipment is multiplied by the preset maintenance cost unit price to obtain the maintenance cost result.

[0130] In another embodiment, when the preset maintenance cost unit price is the rental unit price (e.g., 2,000 yuan / unit per month), the equipment quantity prediction result, the preset maintenance cost unit price, and the contract period in the business parameters are multiplied to obtain the maintenance cost result.

[0131] Understandably, if multiple maintenance cost results are included, the multiple maintenance cost results will be added together to determine the final maintenance cost result.

[0132] Here, the preset service fee unit price is set according to the actual situation. For example, the service fee unit price of AAU is 50,000 yuan / unit, or 1,500 yuan / unit per month, or 20,000 yuan / unit per year, etc.

[0133] In one embodiment, when the preset service fee unit price is the buyout unit price (e.g., 50,000 yuan / unit), the predicted number of devices is multiplied by the preset service fee unit price to obtain the service fee result.

[0134] In another embodiment, when the preset service fee unit price is the rental period unit price (e.g., RMB 1,500 per unit per month), the predicted number of devices, the preset service fee unit price, and the contract period in the business parameters are multiplied to obtain the service fee result.

[0135] It is understandable that if multiple service fee results are included, the multiple service fee results will be added together to determine the final service fee result.

[0136] Here, the preset single-device capacity is set according to the actual situation, such as single-cell capacity, single-device bandwidth requirements, etc. For example, the total network bandwidth in the network capacity result = single-cell capacity * total number of baseband boards.

[0137] Specifically, the total quote is determined based on at least one of the equipment cost results, maintenance cost results, and service fee results, as well as the contract period indicated by the preset profit margin and business parameters.

[0138] In one specific embodiment, the total equipment cost is determined based on the contract period indicated by business parameters, the equipment cost result, and the maintenance cost result. A cost quotation is determined based on the total equipment cost and a preset profit margin. Finally, a total quotation result is determined based on the cost quotation, the service fee result, and the contract period indicated by business parameters. For example, the total quotation result for a three-year contract period = (total equipment cost for a three-year contract period) / (1 - preset profit margin) + service fee result for a three-year contract period; the total quotation result for a five-year contract period = (total equipment cost for a five-year contract period) / (1 - preset profit margin) + service fee result for a five-year contract period.

[0139] In addition, the private network profit can be determined based on the total equipment cost and the preset profit margin. For example, private network profit = total equipment cost * (1 / (1-profit margin)-1)).

[0140] The private network planning method provided in this invention determines the private network planning result based on the equipment quantity prediction result, equipment cost result, maintenance cost result, service fee result, network capacity result, and total quotation result, thereby configuring the private network planning scheme more completely and further improving the accuracy of private network planning.

[0141] Based on any of the above embodiments, the site parameters include offshore depth and / or berth shoreline length.

[0142] Based on any of the above embodiments, the business parameters include at least one of contract period, package mode, and yard business; the yard business includes at least one of yard crane, unmanned truck, intelligent tallying, first combined business, second combined business, third combined business, and fourth combined business; the first combined business includes yard crane and unmanned truck, the second combined business includes unmanned truck and intelligent tallying, the third combined business includes yard crane and intelligent tallying, and the fourth combined business includes yard crane, unmanned truck, and intelligent tallying.

[0143] In practical applications, by analyzing and summarizing the characteristics of port operations, deeply exploring the correlation between port business private network planning and configuration schemes and construction costs, a port business private network planning and configuration model is established. Through the random forest algorithm, the private network planning and configuration pattern is matched, and the private network planning and configuration scheme and cost assessment are automatically output. This solves the uncertainty of existing manual design, makes up for the shortcomings of existing manual analysis schemes, and optimizes the disadvantages of long processing time and low efficiency of manual operation.

[0144] The private network planning device provided by the present invention is described below. The private network planning device described below and the private network planning method described above can be referred to in correspondence.

[0145] Figure 3 This is a schematic diagram of the private network planning device provided by the present invention, as shown below. Figure 3 As shown, the private network planning device includes:

[0146] The acquisition module 310 is used to acquire the site parameters and business parameters of the port to be planned. The business parameters are determined based on the selected business mode. The port to be planned is the port of the private network to be planned.

[0147] Prediction module 320 is used to input the site parameters and the business parameters into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model;

[0148] The determination module 330 is used to determine the private network planning result of the port to be planned based on the predicted number of devices.

[0149] The private network planning device provided in this embodiment of the invention acquires the site parameters and service parameters of the port to be planned; inputs the site parameters and service parameters into an equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model; and determines the private network planning result of the port to be planned based on the equipment quantity prediction result. Through the above method, this invention can use an equipment quantity prediction model to predict the equipment quantity of the port to be planned based on the site parameters and service parameters, automatically obtain the equipment quantity prediction result, and then determine the private network planning result based on the equipment quantity prediction result, thus improving the efficiency of private network planning and configuration; moreover, compared with manual private network planning and configuration, this invention can improve the accuracy of private network planning and configuration based on the equipment quantity prediction model.

[0150] Based on any of the above embodiments, the prediction module 320 includes:

[0151] The feature mapping unit is used to input the site parameters and the business parameters into the feature mapping layer of the equipment quantity prediction model to obtain the site features and business features output by the feature mapping layer.

[0152] The quantity prediction unit is used to input the site features and the business features into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer;

[0153] The classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample devices corresponding to the sample site parameters and sample service parameters.

[0154] Based on any of the above embodiments, the classification layer includes a communication device classification layer, and the quantity prediction unit is further used for:

[0155] The site features and the service features are input into the communication equipment classification layer to obtain the predicted number of communication equipment output by the communication equipment classification layer;

[0156] The communication equipment classification layer includes an active antenna unit (AAU) classification layer and / or a baseband board classification layer.

[0157] When the communication device classification layer includes an AAU classification layer, the communication device quantity prediction result includes the AAU quantity prediction result;

[0158] When the communication device classification layer includes a baseband board classification layer, the communication device quantity prediction result includes the baseband board quantity prediction result.

[0159] Based on any of the above embodiments, the classification layer includes a business equipment classification layer, and the quantity prediction unit is further used for:

[0160] The site features and the business features are input into the business equipment classification layer to obtain the business equipment quantity prediction result output by the business equipment classification layer;

[0161] The business equipment classification layer includes at least one of the following: a yard crane classification layer, an unmanned container truck classification layer, and an intelligent cargo sorting classification layer.

[0162] When the business equipment classification layer includes a yard crane classification layer, the business equipment quantity prediction result includes the yard crane quantity prediction result;

[0163] When the business equipment classification layer includes an unmanned truck classification layer, the business equipment quantity prediction result includes the unmanned truck quantity prediction result;

[0164] When the business equipment classification layer includes an intelligent cargo sorting classification layer, the business equipment quantity prediction result includes the intelligent cargo sorting quantity prediction result.

[0165] Based on any of the above embodiments, the private network planning result includes at least one of the following: equipment quantity prediction result, equipment cost result, maintenance cost result, service fee result, network capacity result, and total quotation result; the determining module 330 is further used for:

[0166] If the private network planning results include equipment cost results, the equipment cost results are determined based on the predicted equipment quantity and the preset unit cost of the equipment.

[0167] If the private network planning results include maintenance cost results, the maintenance cost results are determined based on the predicted number of devices and the preset unit price of maintenance cost.

[0168] If the private network planning results include service fee results, the service fee results are determined based on the predicted number of devices and the preset service fee unit price.

[0169] If the private network planning results include network capacity results, the network capacity results are determined based on the predicted number of devices and the preset single device capacity.

[0170] If the private network planning result includes a total quotation result, the total quotation result is determined based on at least one of the equipment cost result, the maintenance cost result, and the service fee result, as well as the contract period indicated by the service parameters.

[0171] Based on any of the above embodiments, the site parameters include offshore depth and / or berth shoreline length.

[0172] Based on any of the above embodiments, the business parameters include at least one of contract period, package mode, and yard business;

[0173] The yard operations include at least one of the following: yard crane, unmanned truck, intelligent tallying, first combined operations, second combined operations, third combined operations, and fourth combined operations;

[0174] The first combined service includes yard cranes and unmanned trucks; the second combined service includes unmanned trucks and intelligent tallying; the third combined service includes yard cranes and intelligent tallying; and the fourth combined service includes yard cranes, unmanned trucks, and intelligent tallying.

[0175] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, communications interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a private network planning method. This method includes: obtaining site parameters and service parameters of the port to be planned, wherein the service parameters are determined based on a selected service mode, and the port to be planned is the port of the private network to be planned; inputting the site parameters and the service parameters into an equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model; and determining the private network planning result of the port to be planned based on the equipment quantity prediction result.

[0176] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the private network planning method provided by the above methods. The method includes: obtaining site parameters and service parameters of a port to be planned, wherein the service parameters are determined based on a selected service mode, and the port to be planned is a port of the private network to be planned; inputting the site parameters and the service parameters into an equipment quantity prediction model to obtain an equipment quantity prediction result output by the equipment quantity prediction model; and determining the private network planning result of the port to be planned based on the equipment quantity prediction result.

[0178] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the private network planning method provided by the above methods. The method includes: obtaining site parameters and service parameters of a port to be planned, wherein the service parameters are determined based on a selected service mode, and the port to be planned is a port of the private network to be planned; inputting the site parameters and the service parameters into an equipment quantity prediction model to obtain an equipment quantity prediction result output by the equipment quantity prediction model; and determining the private network planning result of the port to be planned based on the equipment quantity prediction result.

[0179] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0180] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A private network planning method, characterized in that, include: Obtain the site parameters and business parameters of the port to be planned. The business parameters are determined based on the selected business mode. The port to be planned is the port of the private network to be planned. The site parameters and the business parameters are input into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model; Based on the predicted number of devices, the private network planning results for the port to be planned are determined. The step of inputting the site parameters and the business parameters into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model includes: The site parameters and the business parameters are input into the feature mapping layer of the equipment quantity prediction model to obtain the site features and business features output by the feature mapping layer. The site features and business features are input into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer; The classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample devices corresponding to the sample site parameters and sample service parameters.

2. The private network planning method according to claim 1, characterized in that, The classification layer includes a communication equipment classification layer. The process of inputting the site features and service features into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer includes: The site features and the service features are input into the communication equipment classification layer to obtain the predicted number of communication equipment output by the communication equipment classification layer; The communication equipment classification layer includes an active antenna unit (AAU) classification layer and / or a baseband board classification layer. When the communication device classification layer includes an AAU classification layer, the communication device quantity prediction result includes the AAU quantity prediction result; When the communication device classification layer includes a baseband board classification layer, the communication device quantity prediction result includes the baseband board quantity prediction result.

3. The private network planning method according to claim 1, characterized in that, The classification layer includes a business equipment classification layer. The process of inputting the site features and the business features into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer includes: The site features and the business features are input into the business equipment classification layer to obtain the business equipment quantity prediction result output by the business equipment classification layer; The business equipment classification layer includes at least one of the following: a yard crane classification layer, an unmanned container truck classification layer, and an intelligent cargo sorting classification layer. When the business equipment classification layer includes a yard crane classification layer, the business equipment quantity prediction result includes the yard crane quantity prediction result; When the business equipment classification layer includes an unmanned truck classification layer, the business equipment quantity prediction result includes the unmanned truck quantity prediction result; When the business equipment classification layer includes an intelligent cargo sorting classification layer, the business equipment quantity prediction result includes the intelligent cargo sorting quantity prediction result.

4. The private network planning method according to claim 1, characterized in that, The private network planning results include at least one of the following: equipment quantity prediction results, equipment cost results, maintenance cost results, service fee results, network capacity results, and total quotation results; The process of determining the private network planning results for the port to be planned based on the predicted number of devices includes: If the private network planning results include equipment cost results, the equipment cost results are determined based on the predicted equipment quantity and the preset unit cost of the equipment. If the private network planning results include maintenance cost results, the maintenance cost results are determined based on the predicted number of devices and the preset unit price of maintenance cost. If the private network planning results include service fee results, the service fee results are determined based on the predicted number of devices and the preset service fee unit price. If the private network planning results include network capacity results, the network capacity results are determined based on the predicted number of devices and the preset single device capacity. If the private network planning result includes a total quotation result, the total quotation result is determined based on at least one of the equipment cost result, the maintenance cost result, and the service fee result, as well as the contract period indicated by the service parameters.

5. The private network planning method according to any one of claims 1 to 4, characterized in that, The site parameters include offshore depth and / or berth shoreline length.

6. The private network planning method according to any one of claims 1 to 4, characterized in that, The business parameters include at least one of contract period, package mode, and yard business; The yard operations include at least one of the following: yard crane, unmanned truck, intelligent tallying, first combined operations, second combined operations, third combined operations, and fourth combined operations; The first combined service includes yard cranes and unmanned trucks; the second combined service includes unmanned trucks and intelligent tallying; the third combined service includes yard cranes and intelligent tallying; and the fourth combined service includes yard cranes, unmanned trucks, and intelligent tallying.

7. A private network planning device, characterized in that, include: The acquisition module is used to acquire the site parameters and business parameters of the port to be planned. The business parameters are determined based on the selected business mode, and the port to be planned is the port of the private network to be planned. The prediction module is used to input the site parameters and the business parameters into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model; The determination module is used to determine the private network planning result of the port to be planned based on the predicted number of devices; The step of inputting the site parameters and the business parameters into the equipment quantity prediction model to obtain the equipment quantity prediction result output by the equipment quantity prediction model includes: The site parameters and the business parameters are input into the feature mapping layer of the equipment quantity prediction model to obtain the site features and business features output by the feature mapping layer. The site features and business features are input into the classification layer of the equipment quantity prediction model to obtain the equipment quantity prediction result output by the classification layer; The classification layer is trained based on sample site parameters and sample service parameters, as well as the number of sample devices corresponding to the sample site parameters and sample service parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the private network planning method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the private network planning method as described in any one of claims 1 to 6.

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