CN110753366A - Prediction processing method and device for industry short message gateway capacity - Google Patents

Abstract

An embodiment of the present invention provides a method and device for predicting the capacity of an industry short message gateway. The method includes: acquiring historical operating parameter characteristics of an industrial short message gateway in a first time period, and according to the historical operating parameter characteristics and a preset prediction The neural network model obtains the capacity prediction sequence in the second time period, compares each capacity value in the capacity prediction sequence with the preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, a capacity expansion reminder message is issued, Obtain the maximum capacity value in the capacity prediction sequence, and obtain the capacity expansion value of the industry short message gateway according to the maximum capacity value and the preset capacity expansion formula, so as to achieve the purpose of predicting the capacity of the industry short message gateway in the future time period, and for the subsequent capacity expansion for reference.

Description

Prediction processing method and device for industry short message gateway capacity

technical field

The invention relates to the field of communication technologies, in particular to a method and device for predicting the capacity of an industrial short message gateway.

Background technique

With the explosive growth of mobile Internet applications, the demand for SMS from enterprises in all walks of life has not decreased but increased, such as bank verification code SMS, e-commerce payment SMS, various APP login reminder SMS, corporate promotion SMS, etc. Application SMS has accounted for the majority of the total amount of SMS issued by operators. Therefore, in the era of mobile Internet, ensuring the smooth operation of industry SMS gateways is of great significance to operators. In the prior art, there is no precise judgment and calculation method for when the industry SMS gateway needs to be expanded and how much capacity to expand. Often, the timing of expansion is determined by simply setting a threshold for the load, and the capacity to be expanded is determined by manual expert experience. Therefore, the expansion method is still in the stage of simple and extensive, and it is difficult to obtain high accuracy in capacity estimation. Facing the increasing demand, it is very urgent to accurately estimate the load capacity to support accurate expansion. Timely and accurate expansion is an improvement. The key to the stability of SMS gateways in the core network element industry of operators.

SUMMARY OF THE INVENTION

The present invention provides a method and a device for predicting the capacity of an industrial short message gateway, which are used to solve the problem that the time for capacity expansion cannot be accurately obtained in the prior art.

In a first aspect, an embodiment of the present invention provides a method for predicting the capacity of an industry short message gateway, including:

Obtain the historical operating parameter characteristics of the industry SMS gateway in the first time period;

The capacity prediction sequence in the second time period is obtained according to the historical operating parameter characteristics and the preset prediction neural network model.

Optionally, also include:

comparing each capacity value in the capacity prediction sequence with a preset load threshold respectively;

When it is determined that there is a capacity value exceeding the load threshold, a capacity expansion reminder message is issued.

Optionally, also include:

obtaining the maximum capacity value in the capacity prediction sequence;

The capacity expansion value of the industry short message gateway is obtained according to the maximum capacity value and the preset capacity expansion formula.

Optionally, the expansion formula includes:

R=Max×k-Z, where R is the capacity expansion value, Max is the maximum capacity value in the capacity prediction sequence, k is a preset coefficient, and Z is the total capacity of the industry short message gateway before capacity expansion.

In a second aspect, an embodiment of the present invention provides an apparatus for predicting the capacity of an industry short message gateway, including:

The acquisition module is used to acquire the historical operation parameter characteristics of the industry short message gateway in the first time period;

A prediction module, configured to obtain a capacity prediction sequence in a second time period according to the historical operating parameter characteristics and a preset prediction neural network model.

Optionally, a reminder module is further included, configured to: compare each capacity value in the capacity prediction sequence with a preset load threshold value, and issue a capacity expansion reminder message when it is determined that there is a capacity value exceeding the load threshold value.

Optionally, a capacity expansion module is further included, configured to: obtain the maximum capacity value in the capacity prediction sequence, and obtain the capacity expansion value of the industry short message gateway according to the maximum capacity value and a preset capacity expansion formula.

Optionally, the expansion formula includes:

R=Max×k-Z, where R is the capacity expansion value, Max is the maximum capacity value in the capacity prediction sequence, k is a preset coefficient, and Z is the total capacity of the industry short message gateway before capacity expansion.

In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory, a bus, and a computer program stored in the memory and running on the processor;

Wherein, the processor and the memory communicate with each other through the bus;

The processor executes the computer program to implement the method as described above.

In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium, where a computer program is stored on the non-transitory computer-readable storage medium, and the computer program implements the foregoing method when executed by a processor.

It can be seen from the above technical solutions that the method for predicting and processing the capacity of an industry short message gateway provided by the embodiment of the present invention obtains the historical operation parameter characteristics of the industry short message gateway in the first time period, according to the historical operation parameter characteristics and preset parameters. The prediction neural network model obtains the capacity prediction sequence in the second time period, so as to achieve the purpose of predicting the capacity of the industry short message gateway in the future time period, and provide a reference for the subsequent capacity expansion.

Description of drawings

1 is a schematic flowchart of a method for predicting and processing an industry short message gateway capacity provided by an embodiment of the present invention;

2 is a schematic flowchart of a method for predicting and processing the capacity of an industry short message gateway provided by an embodiment of the present invention;

3 is a schematic flowchart of a method for predicting the capacity of an industry short message gateway provided by an embodiment of the present invention;

4 is a schematic structural diagram of an apparatus for predicting the capacity of an industry short message gateway according to an embodiment of the present invention;

5 is a schematic structural diagram of an apparatus for predicting the capacity of an industry short message gateway according to an embodiment of the present invention;

6 is a schematic structural diagram of an apparatus for predicting the capacity of an industry short message gateway according to an embodiment of the present invention;

FIG. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments. The following examples are intended to illustrate the present invention, but not to limit the scope of the present invention.

1 shows a method for predicting the capacity of an industry short message gateway provided by an embodiment of the present invention, including:

S11. Obtain historical operating parameter characteristics of the industry short message gateway in the first time period;

S12. Obtain a capacity prediction sequence in a second time period according to the historical operating parameter characteristics and a preset prediction neural network model.

For step S11 and step S12, it should be noted that, in the embodiment of the present invention, the industry short message gateway: the full name of the industry application short message gateway, is a short message delivery channel for the enterprise industry application by the operator.

The preset prediction neural network model in this embodiment is a model established by using a deep learning framework to build a long short-term memory neural network.

Deep Neural Network (DNN, deep neural network): A neural network consists of an input layer, a hidden layer and an output layer. The so-called "depth" means that there are many layers in the middle hidden layer. So deep learning is actually a neural network with many hidden layers. Neuron is the basic unit of neural network, also known as node (Node), it receives input (Input) from external or other nodes, and calculates output (Output) through an activation function (Activation Function); each The input corresponds to the weight (Weight), that is, the relative importance of each input received by this node; the bias (Bias) can be understood as a special input.

RNN (recurrent neural network): Recurrent neural network. It is a neural network with memory. The output of each hidden layer in the RNN will be stored in the cache. The next time the hidden layer has data input, the data stored in the cache can also be regarded as part of the input. At one point in time, the output of the neuron will be put into the cache, and at the next point in time, the value in the cache will be overwritten. Reuse of the same neural network is called a recurrent neural network. RNNs can be thought of as multiple copies of the same neural network, each neural network module passing a message to the next.

LSTM (long short-term memory): Long short-term memory, which is a special type of RNN, can learn long-term dependent information, and can remember long-term information by controlling the time when the value in the cache is stored, which is suitable for time series. Prediction, which solves the problem of RNN gradient vanishing. Each neuron has four inputs and one output, and each neuron has a Cell to store the memory value.

The process of building a predictive neural network model is as follows:

S1. Obtain the historical operation parameter characteristics of the industry short message gateway in the third time period;

S2, normalize the historical operating parameter features, and divide them into a training set and a test set after processing;

S3, the training set is input into the deep long-term and short-term memory neural network model for model training, the test set is input into the deep long-term and short-term memory neural network model for model testing, and the corresponding weight of each neuron is obtained;

S4. Use the deep long-term short-term memory neural network model and the corresponding weight of each neuron as the prediction neural network model.

Regarding steps S1-S4, it should be noted that, in this embodiment of the present invention, the historical operating parameter features include both multi-dimensional motion parameters obtained from the industry short message gateway and parameter features formed by external factors. For example, the hourly multi-dimensional operation parameters of the industry SMS gateway from 0:00 on September 12, 2017 to 24:00 on December 20, 2017 obtained from an operator's industry SMS gateway, totaling 100 days and 2400 pieces of data. In this 100-day span, some external factors are included, such as Double Eleven, Double Twelve, Mid-Autumn Festival and National Day. Therefore, it is necessary to introduce a new feature "date type" related to external factors, and divide the dates into four categories: 0 (non-holidays and activity days), 1 (legal holidays), 2 (e-commerce activity days, such as Double Eleven, Double 12), 3 (major events), in addition to adding key operating parameters at the same time in the previous quarter and the previous year to improve the forecast accuracy. The final industry SMS gateway operating parameters include 14 features, as follows:

X ₁ : The total amount of downlink SMS sent by MT;

X ₂ : MT downlink SMS sending success amount;

X ₃ : MT downlink SMS sending success rate (the success rate of industry SMS gateway submitting messages to SMS center);

X ₄ : The total amount of MO uplink short messages sent;

X ₅ : MO uplink SMS sending success amount;

X ₆ : The success rate of MO uplink SMS sending (the success rate of the SMS center submitting the message to the industry SMS gateway);

X ₇ : Peak load (bars/second), representing the overall highest load of the industry SMS gateway, which is the value to be predicted by this solution;

X ₈ : Date type (newly introduced external factor feature);

X ₉ : Peak load at the same time in the previous quarter;

X ₁₀ : The total amount of downlink SMS sent by MT at the same time in the previous quarter;

X ₁₁ : The total amount of MO uplink SMS sent at the same time in the previous quarter;

X ₁₂ : Peak load at the same time in the previous year;

X ₁₃ : The total amount of downlink SMS sent by MT at the same time in the previous year;

X ₁₄ : The total amount of MO uplink SMS sent at the same time in the previous year.

So the dataset can be represented as: S _{= [} X1, X2, _X3 , _X4 , _X5 , _X6 , _X7 , _X8 , _X9 , _X10 , _X11 , _X12 , _X13 , X ₁₄ ].

After the data is acquired, first normalize the data: Normalization refers to scaling the data so that it falls into a small specific interval. Since LSTM is sensitive to the size of the input data, it is necessary to uniformly map the data to the range of [0,1], and use the function MinMaxScaler(feature_range=(0,1)) in sklearn to scale the data to the given minimum value Between the value and the maximum value, usually between 0 and 1. After normalization, the convergence speed of the model will be improved and the accuracy of the model will be improved.

Divide the data set into training set and test set: the total length of the entire data set is 100 days, the training data is the data of the first 70 days, and the remaining 30 days of data is used as the test data. The total length of the dataset dataset_length: 2400 (that is, the time span is 2400 hours), the length of the training data is train_data_size: 1680, and the length of the test data is test_data_size: 720 (that is, the time span is 720 hours).

Shape transformation of data: Since the LSTM neural network requires a 3-dimensional array for the shape of the input data, it is necessary to convert the data from a 2-dimensional array [samples, features] to a 3-dimensional array [samples, timesteps, features], according to the recent n The 14 characteristic historical operating parameters of the hour are used to predict the peak load situation in the next m hours. Taking timesteps=n=3, m=1 as an example, timesteps means that LSTM thinks that each input data is related to the previous input data, that is, according to the operating parameters X ₁ -X ₁₄ of the last three hours to predict the next One-hour peak load X ₇ situation, that is, using the current time point (t) and the values of the previous two time points (t-1) and (t-2) to predict the value of the next time point (t+1) ( Input t-2, t-1, t, output is t+1). Therefore, the original data set shape is a two-dimensional array of 2400*14, and the converted data set shape is a three-dimensional array of 2400*3*14, that is, the shape of the training data X is train_X.shape= (1680,3,14), the training data The shape of y is train_y.shape=(1680,), the shape of test data X is test_X.shape=(717,3,14), and the shape of test data y is test_y.shape=(717,). Use the training data to train the model, and use the test data to test the performance of the model. After offline training and testing are completed, the calculated neural network weights are exported. The next time you need to make predictions, you don't need to train the neural network again, you can directly use the previously trained weights, saving valuable time and making predictions faster and more efficient.

In the embodiment of the present invention, building a neural network model belongs to a relatively mature technology. After obtaining the required parameter features in a fixed field, the neural network model will be trained through the training set and the test set, so as to obtain a real-time A neural network model that makes predictions on data.

After the neural network model is established, when predicting the gateway capacity in the future time period, it is also necessary to obtain the historical operating parameter characteristics of the industry short message gateway in the first time period, and obtain the historical operating parameter characteristics and the preset prediction neural network model according to the historical operating parameter characteristics. A sequence of capacity forecasts for the second time period.

An embodiment of the present invention provides a method for predicting the capacity of an industry short message gateway, by acquiring historical operating parameter characteristics of an industrial short message gateway in a first time period, and obtaining the first The capacity prediction sequence in the second time period achieves the purpose of predicting the capacity of the industry SMS gateway in the future time period, and provides a reference for subsequent capacity expansion.

2 shows a method for predicting the capacity of an industry short message gateway provided by an embodiment of the present invention, including:

S21. Obtain historical operating parameter characteristics of the industry short message gateway in the first time period;

S22, obtaining the capacity prediction sequence in the second time period according to the historical operating parameter characteristics and the preset prediction neural network model;

S23. Compare each capacity value in the capacity prediction sequence with a preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, send out capacity expansion reminder information.

With regard to the above steps S21 to S22, it should be noted that these steps are the same in principle as the steps S11 to 12 described in the above embodiment, and are not repeated here.

Regarding step S23, it should be noted that, in the embodiment of the present invention, each capacity value in the capacity prediction sequence is compared with a preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, a capacity expansion reminder is issued information for the operation and maintenance personnel to plan and deal with it accordingly.

An embodiment of the present invention provides a method for predicting the capacity of an industry short message gateway, by acquiring historical operating parameter characteristics of an industrial short message gateway in a first time period, and obtaining the first For the capacity prediction sequence in the second time period, each capacity value in the capacity prediction sequence is compared with the preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, a capacity expansion reminder message is sent out, and the future time period is reached. The purpose of industry SMS gateway capacity prediction is to provide a reference for subsequent capacity expansion.

3 shows a method for predicting the capacity of an industry short message gateway provided by an embodiment of the present invention, including:

S31. Obtain historical operating parameter characteristics of the industry short message gateway in the first time period;

S32, obtaining the capacity prediction sequence in the second time period according to the historical operating parameter characteristics and the preset prediction neural network model;

S33. Compare each capacity value in the capacity prediction sequence with a preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, issue a capacity expansion reminder;

S34. Obtain the maximum capacity value in the capacity prediction sequence, and obtain the capacity expansion value of the industry short message gateway according to the maximum capacity value and a preset capacity expansion formula.

Regarding the above steps S31 to S33, it should be noted that these steps are the same in principle as the steps S21 to S23 described in the above embodiment, and are not repeated here.

For step S34, it should be noted that, in the embodiment of the present invention, the maximum capacity value in the capacity prediction sequence is obtained, and the capacity expansion value of the industry short message gateway is obtained according to the maximum capacity value and the preset capacity expansion formula.

In the embodiment of the present invention, the expansion formula includes:

R=Max×k-Z, where R is the capacity expansion value, Max is the maximum capacity value in the capacity prediction sequence, k is a preset coefficient, and Z is the total capacity of the industry short message gateway before capacity expansion.

But not limited to the above formula.

An embodiment of the present invention provides a method for predicting the capacity of an industry short message gateway, by acquiring historical operating parameter characteristics of an industrial short message gateway in a first time period, and obtaining the first For the capacity prediction sequence in two time periods, each capacity value in the capacity prediction sequence is compared with the preset load threshold, and when it is determined that there is a capacity value exceeding the load threshold, a capacity expansion reminder message is sent to obtain the capacity prediction For the maximum capacity value in the sequence, the capacity expansion value of the industry short message gateway is obtained according to the maximum capacity value and the preset capacity expansion formula, so as to achieve the purpose of predicting the capacity of the industry short message gateway in the future time period and provide a reference for the subsequent capacity expansion.

4 shows an apparatus for predicting the capacity of an industry short message gateway provided by an embodiment of the present invention, including an acquisition module 41 and a prediction module 42, wherein:

The acquisition module 41 is used to acquire the historical operation parameter characteristics of the industry short message gateway in the first time period;

The prediction module 42 is configured to obtain the capacity prediction sequence in the second time period according to the historical operating parameter characteristics and the preset prediction neural network model.

Since the principle of the apparatus described in the embodiment of the present invention is the same as that of the method described in the foregoing embodiment, more detailed explanations are not repeated here.

It should be noted that, in the embodiments of the present invention, relevant functional modules may be implemented by a hardware processor (hardware processor).

The embodiment of the present invention provides an apparatus for predicting the capacity of an industry short message gateway. By acquiring the historical operation parameter characteristics of the industry short message gateway in a first time period, the first time period is obtained according to the historical operation parameter characteristics and a preset prediction neural network model. The capacity prediction sequence in the second time period achieves the purpose of predicting the capacity of the industry SMS gateway in the future time period, and provides a reference for subsequent capacity expansion.

5 shows an apparatus for predicting the capacity of an industry short message gateway provided by an embodiment of the present invention, including an acquisition module 51, a prediction module 52, and a reminder module 53, wherein:

Obtaining module 51 is used to obtain the historical operation parameter characteristics of the industry short message gateway in the first time period;

A prediction module 52, configured to obtain a capacity prediction sequence in the second time period according to the historical operating parameter characteristics and a preset prediction neural network model;

The reminding module 53 is configured to compare each capacity value in the capacity prediction sequence with a preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, a capacity expansion reminding message is issued.

Since the principle of the apparatus described in the embodiment of the present invention is the same as that of the method described in the foregoing embodiment, more detailed explanations are not repeated here.

It should be noted that, in the embodiments of the present invention, relevant functional modules may be implemented by a hardware processor (hardware processor).

The embodiment of the present invention provides an apparatus for predicting the capacity of an industry short message gateway. By acquiring the historical operation parameter characteristics of the industry short message gateway in a first time period, the first time period is obtained according to the historical operation parameter characteristics and a preset prediction neural network model. For the capacity prediction sequence in the second time period, each capacity value in the capacity prediction sequence is compared with the preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, a capacity expansion reminder message is sent out, and the future time period is reached. The purpose of industry SMS gateway capacity prediction is to provide a reference for subsequent capacity expansion.

Fig. 6 shows a kind of industry short message gateway capacity prediction processing device provided by an embodiment of the present invention, including acquisition module 61, prediction module 62, reminder module 63 and capacity expansion module 64, wherein:

an acquisition module 61, configured to acquire the historical operation parameter characteristics of the industry short message gateway in the first time period;

A prediction module 62, configured to obtain a capacity prediction sequence in the second time period according to the historical operating parameter characteristics and a preset prediction neural network model;

Reminder module 63, configured to compare each capacity value in the capacity prediction sequence with a preset load threshold value, and when it is determined that there is a capacity value exceeding the load threshold value, a capacity expansion reminder message is issued;

The capacity expansion module 64 obtains the maximum capacity value in the capacity prediction sequence, and obtains the capacity expansion value of the industry short message gateway according to the maximum capacity value and a preset capacity expansion formula.

Since the principle of the apparatus described in the embodiment of the present invention is the same as that of the method described in the foregoing embodiment, more detailed explanations are not repeated here.

It should be noted that, in the embodiments of the present invention, relevant functional modules may be implemented by a hardware processor (hardware processor).

The embodiment of the present invention provides an apparatus for predicting the capacity of an industry short message gateway. By acquiring the historical operation parameter characteristics of the industry short message gateway in a first time period, the first time period is obtained according to the historical operation parameter characteristics and a preset prediction neural network model. For the capacity prediction sequence in two time periods, each capacity value in the capacity prediction sequence is compared with the preset load threshold, and when it is determined that there is a capacity value exceeding the load threshold, a capacity expansion reminder message is sent to obtain the capacity prediction For the maximum capacity value in the sequence, the capacity expansion value of the industry short message gateway is obtained according to the maximum capacity value and the preset capacity expansion formula, so as to achieve the purpose of predicting the capacity of the industry short message gateway in the future time period and provide a reference for the subsequent capacity expansion.

7 shows an electronic device provided by an embodiment of the present invention, including: a processor 71, a memory 72, a bus 73, and a computer program stored in the memory and running on the processor;

Wherein, the processor and the memory communicate with each other through the bus;

When the processor executes the computer program, the above-mentioned method is implemented, for example, including: acquiring the historical operating parameter characteristics of the industry short message gateway in the first time period, and obtaining the historical operating parameter characteristics and the preset prediction neural network model according to the historical operating parameter characteristics and the preset prediction neural network model. A sequence of capacity forecasts for the second time period.

An embodiment of the present invention provides a non-transitory computer-readable storage medium, where a computer program is stored on the non-transitory computer-readable storage medium. When the computer program is executed by a processor, the above method is implemented, for example, including: obtaining The historical operation parameter characteristics of the industry short message gateway in the first time period, and the capacity prediction sequence in the second time period is obtained according to the historical operation parameter characteristics and the preset prediction neural network model.

Furthermore, those skilled in the art will appreciate that although some of the embodiments described herein include certain features, but not others, included in other embodiments, that combinations of features of different embodiments are intended to be within the scope of the invention within and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

It should be noted that the above-described embodiments illustrate rather than limit the invention, and that alternative embodiments may be devised by those skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. do not denote any order. These words can be interpreted as names.

Those of ordinary skill in the art can understand that: the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that : It is still possible to modify the technical solutions recorded in the foregoing embodiments, or to perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the claims of the present invention. range.

Claims (10)

1. A prediction processing method for industry short message gateway capacity is characterized by comprising the following steps:

acquiring historical operating parameter characteristics of an industry short message gateway in a first time period;

and obtaining a capacity prediction sequence in a second time period according to the historical operating parameter characteristics and a preset prediction neural network model.

2. The method of claim 1, further comprising:

comparing each capacity value in the capacity prediction sequence with a preset load threshold value respectively;

and when the existence capacity value is determined to exceed the load threshold value, sending out capacity expansion reminding information.

3. The method of claim 2, further comprising:

acquiring a maximum capacity value in the capacity prediction sequence;

and obtaining the capacity expansion capacity value of the industry short message gateway according to the maximum capacity value and a preset capacity expansion formula.

4. The method of claim 3, wherein the expansion formula comprises:

and R is Max multiplied by k-Z, wherein R is the capacity expansion capacity value, Max is the maximum capacity value in the capacity prediction sequence, k is a preset coefficient, and Z is the total capacity of the industry short message gateway before capacity expansion.

5. The utility model provides a prediction processing apparatus of trade SMS gateway capacity which characterized in that includes:

the acquisition module is used for acquiring the historical operating parameter characteristics of the industry short message gateway in a first time period;

and the prediction module is used for obtaining a capacity prediction sequence in a second time period according to the historical operating parameter characteristics and a preset prediction neural network model.

6. The apparatus of claim 5, further comprising a reminder module to: and comparing each capacity value in the capacity prediction sequence with a preset load threshold value respectively, and sending capacity expansion reminding information when determining that the capacity value exceeds the load threshold value.

7. The apparatus of claim 6, further comprising a capacity expansion module configured to: and acquiring a maximum capacity value in the capacity prediction sequence, and acquiring an expansion capacity value of the industry short message gateway according to the maximum capacity value and a preset expansion formula.

8. The apparatus of claim 7, wherein the expansion formula comprises:

and R is Max multiplied by k-Z, wherein R is the capacity expansion capacity value, Max is the maximum capacity value in the capacity prediction sequence, k is a preset coefficient, and Z is the total capacity of the industry short message gateway before capacity expansion.

9. An electronic device, comprising: a processor, a memory, a bus, and a computer program stored on the memory and executable on the processor;

the processor and the memory complete mutual communication through the bus;

the processor, when executing the computer program, implements the method of any of claims 1-4.

10. A non-transitory computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the method of any one of claims 1-4.

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