Equipment performance consumption prediction method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of Internet, and provides an equipment performance consumption prediction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining first object information, and obtaining equipment information and second object information; wherein the first object information represents an object of a network device for preparing to consume a device performance index in the network system; the equipment information represents the operation condition of the network equipment in the network system; the second object information represents an object that the network device is consuming the performance index in the network system; calling a performance model to generate a prediction result according to the first object information and the equipment information; wherein the prediction result represents the equipment performance index consumed by the network equipment when the network equipment in the network system burdens the objects corresponding to the first object information and the second object information. The accuracy of the prediction result is ensured, and the generation efficiency of the prediction result is improved.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a method, apparatus, computer equipment and storage medium for predicting device performance consumption. Background Art

[0002] In the modern network environment, security is becoming more and more important, and firewalls are basically present in every network. The functions integrated in firewalls are also increasing, such as intrusion detection (IPS, full name Intrusion Prevention System), anti-virus (AV, full name Anti Virus), etc. Generally speaking, different network scales and when different functions are enabled, the performance requirements for network devices are different.

[0003] However, the inventors have found that when the firewall function in a network device is newly enabled, it is usually manually judged based on experience and the device performance indicators of the network device consumed by the newly enabled function are predicted, resulting in low accuracy of the prediction results. Summary of the invention

[0004] The present application provides a device performance consumption prediction method, apparatus, computer equipment and storage medium, which are used to solve the problem that the current prediction results are inaccurate because the device performance indicators of the network devices consumed are predicted manually based on experience judgment of newly enabled functions.

[0005] In a first aspect, the present application provides a device performance consumption prediction method, which is applied to a network system, comprising:

[0006] Acquire first object information, and acquire device information and second object information; wherein the first object information represents an object of which a network device in a network system is about to consume a device performance indicator; the device information represents an operation status of a network device in a network system; and the second object information represents an object of which a network device in a network system is consuming a performance indicator;

[0007] Call a preset performance model to generate a prediction result based on the first object information, the device information and the second object information; wherein the prediction result characterizes the network device in the network system, and the device performance indicator consumed by the network device when it bears the objects corresponding to the first object information and the second object information.

[0008] In the above solution, the first object information includes first function information, and the second object information includes second function information;

[0009] Acquiring first object information, and acquiring device information and second object information, including:

[0010] Receive first function information sent by the terminal, and obtain device information and second function information; wherein, the first function information represents the function that the terminal is ready to enable; the device information includes terminal access configuration information and operation information; the terminal access configuration information represents the threshold value of the number of terminals that the network device can access, and the operation information includes the number of terminals connected to the network device; the second function information represents the enabled function of the network device in the network system.

[0011] In this solution, by receiving the first function information sent by the terminal, the function that the terminal needs to newly enable in the network system is determined, and by acquiring the device information, the configuration status of the network equipment in the network system, the performance indicators of the network equipment in the network system, and the enabled functions of the network equipment in the network system are obtained, thereby ensuring the comprehensiveness of information acquisition.

[0012] In the above solution, calling a preset performance model to generate a prediction result according to the first object information, the device information and the second object information includes:

[0013] Calling a preset performance model to generate a target function interval according to the first function information, the second function information and the operation information in the device information; wherein the target function interval refers to the upper limit and lower limit of the device performance indicator that can be consumed by a terminal connected to the network device when the network device in the network system turns on the function of the first function information and the function of the second function information;

[0014] A prediction result of the first function information is determined according to the terminal access configuration information in the device information and the target function interval.

[0015] In this solution, a target functional range is generated by a performance model according to the first functional information, the second functional information and the operating information in the device information, so as to achieve a technical effect of multi-angle analysis to determine the upper and lower limits of the device performance indicators that can be consumed by a terminal connected to the network device, thereby ensuring the accuracy of the target functional range; by determining the prediction result based on the terminal access configuration information and the target functional range, while ensuring the accuracy of the target functional range, the prediction result is determined in a targeted manner based on the device information of different network devices, thereby ensuring the accuracy of the prediction result.

[0016] In the above solution, the network system includes a cloud and at least one network device;

[0017] Calling a preset performance model to generate a target function interval according to the first function information, the second function information, and the operation information in the device information includes:

[0018] The cloud enters the first function information, the second function information and the operation information in the first device information into a preset performance analysis interface; wherein the first device information refers to the device information of the first network device; the first network device is a network device among at least one of the network devices that receives the first function information sent by the terminal;

[0019] The cloud calls the performance model through the performance analysis interface, so that the performance model generates a target function interval according to the first function information, and the operation information and the second function information in the first device information.

[0020] In this solution, the first function information, the second function information and the operating information in the first device information are entered into a preset performance analysis interface through the cloud, and the performance model is called through the performance analysis interface. The performance model is called by the cloud using an interface according to the first function information, thereby clarifying the functions provided by the network device, reducing the coupling degree between other functions in the cloud and the performance model, and facilitating frequent training and maintenance of the performance model.

[0021] In the above solution, the network system includes a cloud and at least one network device;

[0022] Determining a prediction result of the first function information according to the terminal access configuration information in the device information and the target function interval includes:

[0023] The cloud multiplies the terminal access configuration information in the first device information by the upper limit and the lower limit in the target function interval respectively to obtain a predicted upper limit value and a predicted lower limit value; wherein the first device information refers to the device information of the first network device;

[0024] The cloud constructs a numerical range according to the prediction upper limit value and the prediction lower limit value, and uses the numerical range as the prediction result.

[0025] In this solution, the cloud multiplies the terminal access configuration information in the first device information by the upper limit value and the lower limit value in the target function interval, respectively, to obtain a predicted upper limit value and a predicted lower limit value, and constructs a numerical interval based on the predicted upper limit value and the predicted lower limit value, and uses the numerical interval as the prediction result. This achieves the goal of determining the interval of performance indicators that may be consumed by the network device once all terminals turn on the function of the first function information based on the terminal access configuration information of the network device and the target function interval of the performance indicators that may be consumed by the terminal when the network device accesses the terminal, thereby greatly ensuring the reliability of the prediction result.

[0026] In the above solution, the first object information includes first capacity information, and the second object information includes second capacity information;

[0027] Acquiring first object information, and acquiring device information and second object information, including:

[0028] Receive the first capacity information sent by the terminal, and obtain the device information and the second capacity information; wherein, the first capacity information represents the number of terminals that the network device in the network system is ready to add; the device information includes activation information; the activation information represents the enabled functions of the network device in the network system; the second capacity information represents the number of terminals that have been connected to the network device in the network system.

[0029] In this solution, by receiving the first capacity information sent by the terminal, the number of terminals that need to be newly connected to the network system is determined, and by obtaining the device information, the configuration status of the network equipment in the network system, the performance indicators of the network equipment in the network system, and the number of terminals that have been connected to the network equipment in the network system are obtained, thereby ensuring the comprehensiveness of information acquisition.

[0030] In the above solution, calling a preset performance model to generate a prediction result according to the first object information, the device information and the second object information includes:

[0031] Call a preset performance model to generate a target capacity interval according to the first capacity information, the second capacity information and the activation information in the device information, and use the target capacity interval as a prediction result of the first capacity information; wherein the target capacity interval refers to the upper and lower limits of the device performance indicator consumed by the network device in the network system when the network device is connected to the sum of the first and second numbers of terminals; the first number is the number of terminals corresponding to the first capacity information; the second number is the number of terminals corresponding to the second capacity information.

[0032] In this solution, a target capacity interval is generated by a performance model according to the first capacity information, the operating information in the device information, and the second capacity information, so as to achieve a technical effect of multi-angle analysis to determine the upper and lower limits of the device performance indicators that can be consumed by a terminal connected to the network device, thereby ensuring the accuracy of the target capacity interval; by determining the prediction result based on the terminal access configuration information and the target capacity interval, while ensuring the accuracy of the target capacity interval, the prediction result is determined in a targeted manner based on the device information of different network devices, thereby ensuring the accuracy of the prediction result.

[0033] In the above solution, before obtaining the first object information and obtaining the device information and the second object information, the method further includes:

[0034] Collect historical performance data of each type of operation information in a functional scenario; wherein the functional scenario represents the enabled functions of the network device in the network system; the operation information represents the number of terminals connected to the network device in the network system; the historical performance data represents the performance indicators of the equipment being consumed in the network system under a type of operation information in the functional scenario;

[0035] According to at least one historical performance data corresponding to the functional scenario, a functional performance interval of the functional scenario is obtained; wherein the functional performance interval represents the upper limit and lower limit of the device performance indicator that a terminal can consume the network device in a functional scenario;

[0036] At least one of the functional scenarios is used as input data, and the functional performance intervals corresponding to at least one of the functional scenarios are used as output data, and a preset initial model is trained to obtain a performance model.

[0037] In this solution, historical performance data of various operating information under various functional scenarios are collected to train the initial model and obtain the performance model, thereby ensuring the comprehensiveness of the performance model training.

[0038] In the above solution, obtaining the functional performance range of the functional scenario according to at least one historical performance data corresponding to the functional scenario includes:

[0039] According to at least one historical performance data under the target function scenario, at least one unit performance data under the target function scenario is obtained; wherein the unit performance data represents a device performance indicator consumed by a terminal under the target function scenario;

[0040] A regression operation is performed on each of the unit performance data under the target functional scenario to obtain a regression curve of the target functional scenario; wherein the regression curve represents the probability of occurrence of each category of unit performance data in the target functional scenario; the category represents the numerical interval in which the value of the unit performance data is located; the target functional scenario is one of at least one functional scenario;

[0041] Enter a preset regression interval into the regression curve to obtain a functional performance interval for the target functional scenario; wherein the regression interval is used to determine a target range for extracting categories in the regression curve; the target range determines at least one target category in the regression curve; the functional performance interval is used to cover unit performance data in at least one of the target categories; the upper limit of the functional performance interval is the first value of the unit performance data in at least one target category; the lower limit of the functional performance interval is the second value of the unit performance data in at least one target category.

[0042] In this scheme, a regression operation is performed on each unit performance data to obtain a regression curve, and the functional performance interval in the regression curve is determined based on the regression interval, wherein the regression interval defines the difference degree interval, that is, the upper and lower limits of the unit performance data in the N% range in the regression curve, for example: what are the upper and lower limits of the unit performance data in the regression curve with an exposure probability of 90% (that is, in most of the range of the curve), and the functional performance interval is constructed based on the upper and lower limits.

[0043] In the above solution, before obtaining the first object information and obtaining the device information and the second object information, the method further includes:

[0044] Collecting historical performance data of each type of enabling information under a capacity scenario; wherein the capacity scenario represents the number of terminals connected to the network device in the network system; the enabling information represents the enabled functions of the network device in the network system; the historical performance data represents the device performance indicators being consumed by the network device in a type of enabling information under the capacity scenario;

[0045] According to at least one of the historical performance data, a capacity performance interval of the capacity scenario is obtained; wherein the capacity performance interval represents an upper limit and a lower limit of a device performance indicator consumed by a network device in the capacity scenario;

[0046] At least one of the capacity scenarios is used as input data, and the capacity performance intervals corresponding to at least one of the capacity scenarios are used as output data, and a preset initial model is trained to obtain a performance model.

[0047] In this solution, historical performance data of various operating information under various capacity scenarios are collected to train the initial model and obtain the performance model, thereby ensuring the comprehensiveness of the performance model training.

[0048] In the above solution, after calling the preset performance model to generate a prediction result according to the first object information, the device information and the second object information, the method further includes:

[0049] Comparing the performance configuration information in the device information with the prediction result; wherein the performance configuration information represents the device performance indicator threshold that the network device can consume;

[0050] If it is determined that the prediction result exceeds the performance configuration information, functional warning information is generated.

[0051] In this solution, by comparing the performance configuration information with the prediction results and generating function warning information, the technical effect of timely feedback of function activation alarm is achieved.

[0052] In a second aspect, the present application provides a device performance consumption prediction apparatus, which is installed in a network system and includes:

[0053] An input module is used to obtain first object information, and to obtain device information and second object information; wherein the first object information represents an object of which a network device in a network system is about to consume a device performance indicator; the device information represents an operation status of a network device in a network system; and the second object information represents an object of which a network device in a network system is consuming a performance indicator;

[0054] A processing module is used to call a preset performance model to generate a prediction result based on the first object information, the device information and the second object information; wherein the prediction result represents the device performance index consumed by the network device in the network system when the network device bears the objects corresponding to the first object information and the second object information.

[0055] In the above solution, the first object information includes first function information, and the second object information includes second function information;

[0056] The input module is also used to receive the first function information sent by the terminal, and obtain the device information and the second function information; wherein, the first function information represents the function that the terminal is ready to enable; the device information includes the terminal access configuration information and operation information; the terminal access configuration information represents the threshold value of the number of terminals that the network device can access, and the operation information includes the number of terminals that the network device has been connected to; the second function information represents the enabled function of the network device in the network system.

[0057] In the above scheme, the processing module is further used to call a preset performance model to generate a target function interval according to the first function information, the second function information and the operation information in the device information; wherein the target function interval refers to the upper limit and lower limit of the device performance indicator that can be consumed by a terminal connected to the network device when the network device in the network system turns on the function of the first function information and the function of the second function information;

[0058] The processing module is further used to determine the prediction result of the first function information according to the terminal access configuration information in the device information and the target function interval.

[0059] In the above solution, the network system includes a cloud and at least one network device; the processing module is arranged in the cloud;

[0060] The processing module in the cloud is used to enter the first function information, the second function information and the operation information in the first device information into a preset performance analysis interface; wherein the first device information refers to the device information of the first network device; the first network device is a network device among at least one of the network devices that receives the first function information sent by the terminal;

[0061] The processing module in the cloud is used to call the performance model through the performance analysis interface, so that the performance model generates a target function interval according to the first function information, and the operation information and the second function information in the first device information.

[0062] In the above solution, determining the prediction result of the first function information according to the terminal access configuration information in the device information and the target function interval includes:

[0063] The processing module in the cloud is used to multiply the terminal access configuration information in the first device information by the upper limit value and the lower limit value in the target function interval respectively to obtain a predicted upper limit value and a predicted lower limit value;

[0064] The processing module in the cloud is used to construct a numerical range according to the prediction upper limit value and the prediction lower limit value, and use the numerical range as the prediction result.

[0065] In the above solution, the first object information includes first capacity information, and the second object information includes second capacity information;

[0066] The input module is also used to receive the first capacity information sent by the terminal, and obtain device information and second capacity information; wherein, the first capacity information represents the number of terminals that the network device in the network system is ready to add; the device information includes activation information; the activation information represents the enabled function of the network device in the network system; the second capacity information represents the number of terminals connected to the network device in the network system.

[0067] In the above scheme, the processing module is also used to call a preset performance model to generate a target capacity interval according to the first capacity information, the second capacity information and the activation information in the device information, and use the target capacity interval as the prediction result of the first capacity information; wherein, the target capacity interval refers to the upper and lower limits of the device performance indicators consumed by the network device in the network system when the network device is connected to the sum of the first and second numbers of terminals; the first number is the number of terminals corresponding to the first capacity information; the second number is the number of terminals corresponding to the second capacity information.

[0068] The above scheme also includes: a function collection module, which is used to collect historical performance data of each type of operation information under a function scenario; wherein the function scenario represents the enabled function of the network device in the network system; the operation information represents the number of terminals connected to the network device in the network system; the historical performance data represents the performance index of the device being consumed under a type of operation information in the network system in the function scenario;

[0069] A functional performance interval module, configured to obtain a functional performance interval of the functional scenario according to at least one historical performance data corresponding to the functional scenario; wherein the functional performance interval represents an upper limit and a lower limit of a device performance indicator that a terminal can consume a network device in a functional scenario;

[0070] The function model training module is used to take at least one of the function scenarios as input data and the function performance intervals corresponding to at least one of the function scenarios as output data, train a preset initial model, and obtain a performance model.

[0071] In the above solution, the function performance interval module is further used to obtain at least one unit performance data under the target function scenario according to at least one historical performance data under the target function scenario; wherein the unit performance data represents the device performance index consumed by a terminal under the target function scenario;

[0072] The functional performance interval module is further used to perform regression operation on each of the unit performance data under the target functional scenario to obtain a regression curve of the target functional scenario; wherein the regression curve represents the probability of occurrence of each category of unit performance data in the target functional scenario; the category represents the numerical interval in which the value of the unit performance data is located; the target functional scenario is one of at least one functional scenario;

[0073] The functional performance interval module is also used to enter the preset regression interval into the regression curve to obtain the functional performance interval of the target functional scenario; wherein the regression interval is used to determine the target range for extracting the category in the regression curve; the target range determines at least one target category in the regression curve; the functional performance interval is used to cover the unit performance data in at least one of the target categories; the upper limit of the functional performance interval is the first value of the unit performance data in at least one target category; the lower limit of the functional performance interval is the second value of the unit performance data in at least one target category.

[0074] The above solution also includes: a capacity acquisition module for

[0075] Collecting historical performance data of each type of enabling information under a capacity scenario; wherein the capacity scenario represents the number of terminals connected to the network device in the network system; the enabling information represents the enabled functions of the network device in the network system; the historical performance data represents the device performance indicators being consumed by the network device in a type of enabling information under the capacity scenario;

[0076] A capacity performance interval module, configured to obtain a capacity performance interval of the capacity scenario according to at least one of the historical performance data; wherein the capacity performance interval represents an upper limit and a lower limit of a device performance indicator consumed by a network device in the capacity scenario;

[0077] The capacity model training module is used to take at least one of the capacity scenarios as input data and the capacity performance intervals corresponding to at least one of the capacity scenarios as output data, train a preset initial model, and obtain a performance model.

[0078] The above scheme also includes:

[0079] A comparison module, used to compare the performance configuration information in the device information with the prediction result; wherein the performance configuration information represents the device performance indicator threshold that can be consumed by the network device;

[0080] The warning module is used to generate functional warning information if it is determined that the prediction result exceeds the performance configuration information.

[0081] In a third aspect, the present application provides a computer device, comprising: a processor and a memory communicatively connected to the processor;

[0082] The memory stores computer-executable instructions;

[0083] The processor executes the computer-executable instructions stored in the memory to implement the device performance consumption prediction method as claimed in claim 1.

[0084] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned device performance consumption prediction method.

[0085] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the above-mentioned device performance consumption prediction method when executed by a processor.

[0086] The present application provides a device performance consumption prediction method, apparatus, computer equipment and storage medium, which determines the object for which a network device is about to consume device performance indicators by receiving first object information sent by a terminal, and obtains device information to obtain the operating status of the network device and the object for which the network device in the network system is currently consuming performance indicators.

[0087] By calling a preset performance model to generate a prediction result based on the first object information, the device information and the second object information, it is possible to predict the device performance indicators that will be consumed by the network device when the network device adds a new object for consuming device performance indicators, thereby ensuring the accuracy of the prediction results and improving the efficiency of generating prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0089] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0090] Figure 2 A flowchart of an embodiment of a method for predicting device performance consumption provided in an embodiment of the present application;

[0091] Figure 3 A flowchart of another embodiment of a method for predicting device performance consumption provided in an embodiment of the present application;

[0092] Figure 4 A graph of a normal distribution curve in another embodiment of a method for predicting device performance consumption provided in an embodiment of the present application;

[0093] Figure 5 A flowchart of another embodiment of a method for predicting device performance consumption provided in an embodiment of the present application;

[0094] Figure 6 A schematic diagram of a program module of a device performance consumption prediction apparatus provided by the present invention;

[0095] Figure 7 The figure is a schematic diagram of the hardware structure of the computer device in the computer device of the present invention.

[0096] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0097] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0098] The specific application scenarios of this application are: in modern network environments, security is becoming more and more important, and firewalls are basically present in every network. The functions integrated in firewalls are also increasing, such as intrusion detection (IPS, full name Intrusion Prevention System), antivirus (AV, full name Anti Virus), etc. Usually, different network scales and different functions have different performance requirements for network devices. A simple example:

[0099] 1. Assume that the performance base of a firewall is 1G;

[0100] 2. When IPS is turned on, the remaining performance of the firewall is 300M;

[0101] 3. When the AV function is turned on, the remaining performance of the firewall is 200M;

[0102] 4. When the IPS+AV function is turned on, the remaining performance of the firewall may be only 100M;

[0103] 5. In the actual network environment, the situations we encounter are far more difficult than these. In the actual environment, there may be complex conditions such as different types of terminals and applications;

[0104] 6. Based on 5, when users want to adjust the configuration and enable new functions in their own network environment, the newly enabled functions may cause the device performance to degrade, and the user's on-site network may fluctuate or even disconnect.

[0105] However, there is no software solution available, and it is usually done manually. For example, when a device manufacturer releases a product, it will indicate the typical functions and the throughput it can run; or an engineer will estimate whether enabling a function will affect performance based on their own network conditions, and then try to enable the function.

[0106] In response to the problem that it is impossible to judge in advance whether the performance degradation after the function is turned on will cause network disconnection. This application hopes to propose a method to inform the user of sufficient information before the user turns on the function so that the user can perceive the risk in advance. The traffic sample information of this device is uploaded to the cloud through a large number of device terminals. The cloud performs big data analysis to generate traffic and function combination algorithms. When a new function needs to be turned on, it can give a performance loss prompt caused by the new function based on the current traffic and function conditions.

[0107] See also Figure 1 , the device performance consumption prediction method is applied in a network system 1, which includes a cloud 11 and at least one network device 12. The cloud 11 can refer to a cloud server. The cloud server (ECS, full name Elastic Compute Service) is a simple, efficient, safe, reliable, and elastically scalable computing service. Its management method is simpler and more efficient than that of a physical server; the network device 13 is a physical entity connected to the network, and the network device is equipped with network security protection devices such as firewalls and security gateways. Among them, the firewall is a network device that can monitor data packets entering and leaving the network, and determine whether the data packets are legal according to preset rules. If they are illegal, the transmission of the data packets will be rejected. The security gateway is a multifunctional device that also has network firewall functions, intrusion detection functions, anti-virus functions, etc. A cloud 12 is connected to at least one network device 13.

[0108] Specifically, the network device 12 receives the first object information sent by the terminal 13, and obtains the device information from the network device 12;

[0109] The performance model preset in the cloud 11 generates a prediction result based on the first object information and the device information; wherein the prediction result represents the device performance index consumed by the network device when the object corresponding to the first object information and the second object information is turned on;

[0110] The cloud 12 sends the prediction result to the network device 12 , and the network device 12 sends the prediction result to the terminal 14 .

[0111] The impact of the object on network devices after it is enabled, and / or the impact of the number of newly connected terminals on network devices, gives the expected impact. When customers actually use it, it is easier to use and has a stronger control over the overall network stability.

[0112] The technical solution of the present application and how the technical solution of the present application solves the current problem are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0113] In an exemplary embodiment, the first object information is obtained, and the device information and the second object information are obtained; wherein the first object information represents the object of the network device in the network system that is ready to consume the device performance indicator; the device information represents the operation status of the network device in the network system; the second object information represents the object of the network device in the network system that is consuming the performance indicator; if the first object information is the function that the network device is ready to enable, and the second object information is the function that the network device has enabled, the device information includes terminal access configuration information and operation information; the terminal access configuration information represents the threshold value of the number of terminals that the network device can access, and the operation information includes the number of terminals that the network device has connected; for example: the currently enabled function is function A, the function to be enabled is function B, and the terminal access configuration information indicates that the maximum number of terminals that the device can access is 10,000. Then, the cloud 11 will predict the device performance indicators that need to be consumed when the network device 12 enables functions A and B and the network device 12 accesses 10,000 terminals.

[0114] The performance model is called to generate a prediction result based on the first object information, the device information and the second object information, for example: predicting the device performance indicators that need to be consumed when the network device 12 enables function A and function B and the network device 12 is connected to 10,000 terminals.

[0115] In another exemplary embodiment, the first object information is obtained, and the device information and the second object information are obtained; wherein the first object information represents the object of the network device in the network system that is ready to consume the device performance indicator; the device information represents the operation of the network device in the network system; the second object information represents the object of the network device in the network system that is consuming the performance indicator; if the first object information is the number of terminals that the network device is ready to access, for example: 500. The second object information is the number of terminals that the network device has already accessed, for example: 1000. The device information includes: the device information includes enabling information, and the enabling information represents the enabled functions of the network device in the network system, for example: when the network device enables function A and function B and accesses 1000 terminals, the device performance indicator consumed by the network device. If on this basis, the network device is ready to access 500 more terminals, the cloud 11 will predict the device performance indicator that needs to be consumed when the network device 12 enables function A and function B and accesses 1500 terminals.

[0116] The performance model is called to generate a prediction result according to the first object information, the device information and the second object information, for example: predicting the device performance index that needs to be consumed by the network device 12 if 1500 terminals are connected under the premise of enabling function A and function B. In another exemplary embodiment, the first object information is obtained, and the device information and the second object information are obtained; wherein the first object information represents the object of the network device in the network system that is ready to consume the device performance index; the device information represents the operation status of the network device in the network system; the second object information represents the object of the network device in the network system that is consuming the performance index; if the first object information is the function that the network device is ready to enable, and the second object information is the function that the network device has enabled, the device information includes terminal access configuration information and operation information; the terminal access configuration information represents the number threshold of the terminals that the network device can access, and the operation information includes the number of terminals that the network device has connected, and the traffic of the network device in the network system; for example: the currently enabled function is function A, and the function to be enabled is function B, the terminal access configuration information indicates that the maximum number of terminals that the device can access is 10,000, and the operation information indicates that the current traffic of the network device is 3000M. Then, the cloud 11 will predict the device performance indicators that the network device 12 needs to consume when the network device 12 enables function A and function B.

[0117] The performance model is called to generate a prediction result based on the first object information, the device information, and the second object information, for example, predicting the device performance indicators that need to be consumed when the network device 12 enables function A and function B, the traffic is 3000M, and the network device 12 accesses 10,000 terminals. In another exemplary embodiment, if the first object information is the number of terminals that the network device is ready to access, for example, 500. The second object information is the number of terminals that the network device has already accessed, for example, 1000. The device information includes:

[0118] The device information includes activation information, which represents the enabled functions of the network device in the network system and the traffic of the network device. For example, when the network device enables functions A and B and connects to 1,000 terminals, and the current traffic of the network device is 3,000M, the device performance indicators consumed by the network device. If, on this basis, the network device is ready to connect to 500 more terminals, the cloud 11 will predict the device performance indicators that need to be consumed when the network device 12 enables functions A and B and connects to 1,500 terminals.

[0119] The performance model is called to generate a prediction result based on the first object information, device information and second object information. For example, the device performance indicators that need to be consumed when the network device 12 is connected to 1,500 terminals under the premise of enabling function A and function B and the traffic is 3,000M are predicted.

[0120] See also Figure 2 The present application provides a device performance consumption prediction method, which is applied to a network system and includes:

[0121] S201: Obtain first object information, and obtain device information and second object information; wherein the first object information represents an object of which a network device in a network system is ready to consume device performance indicators; the device information represents the operating status of the network device in the network system; and the second object information represents an object of which a network device in the network system is consuming performance indicators.

[0122] In this step, the object for which the network device is ready to consume the device performance indicator is determined by receiving the first object information sent by the terminal and / or receiving the first object information input by the user in the network device of the network system, and the operation status of the network device and the object for which the network device in the network system is currently consuming the performance indicator are obtained by obtaining the device information.

[0123] In one embodiment, the first object information includes first function information, and the second object information includes second function information;

[0124] Acquiring first object information, and acquiring device information and second object information, including:

[0125] Receive the first function information sent by the terminal, and obtain the device information and the second function information; wherein the first function information represents the function that the terminal is ready to enable; the device information includes terminal access configuration information and operation information; the terminal access configuration information represents the terminal access configuration information represents the number threshold of terminals that the network device can access, and the operation information includes the number of terminals connected to the network device; the second function information represents the enabled function of the network device in the network system.

[0126] In an optional embodiment, the first object information includes first function information, and the second object information includes second function information;

[0127] Acquiring first object information, and acquiring device information and second object information, including:

[0128] Receive the first function information sent by the terminal, and obtain the device information and the second function information; wherein the first function information represents the function that the terminal is ready to enable; the device information includes terminal access configuration information and operation information; the terminal access configuration information represents the threshold value of the number of terminals that the network device can access, and the operation information includes the number of terminals connected to the network device, and the traffic of the network device in the network system; the second function information represents the enabled function of the network device in the network system.

[0129] In this step, the network device is used to perform functional processing on the data packet through the enabled functions and the traffic of the network device, and send the processed data packet to each terminal. Therefore, by receiving the first function information sent by the terminal, it is determined that the terminal needs to newly enable the function of the network system, and by obtaining the device information, the configuration of the network device in the network system, the performance indicators of the network device in the network system, and the enabled functions of the network device in the network system are obtained to ensure the comprehensiveness of information acquisition. Among them, the traffic of the network device is the sum of the amount of data sent and received by the user when surfing the Internet. The larger the traffic, the more data the terminal connected to the network device sends and receives, resulting in higher consumption of performance indicators.

[0130] In one embodiment, the first object information includes first function information, and the second object information includes second function information;

[0131] Acquiring first object information, and acquiring device information and second object information, including:

[0132] The first network device receives the first function information sent by the terminal; wherein the first network device is one of the at least one network device; the first network device is used to provide a network environment for the terminal;

[0133] The first network device extracts the first device information, and sends the first device information and the first function information to the cloud; wherein the first device information refers to the device information of the first network device.

[0134] Exemplarily, the user opens the configuration management unit in the network device through the terminal, and sends the first function information to the configuration management unit through the terminal.

[0135] The information collection unit of the network device collects the terminal access configuration information, performance information and second function information of the network device; obtains the terminal access configuration information from the configuration management of the network device through the information collection unit, and collects the number of terminals currently connected to the network device, the traffic of the network device, the second function information of the network device, and the device performance indicators of the network device (for example: one or more of CPU usage, RAM usage, and network bandwidth utilization) from the data plane service of the network device through the information collection unit. The information collection unit sends the terminal access configuration information, operation information and second function information to the configuration management unit.

[0136] In another embodiment, the first object information includes first capacity information, and the second object information includes second capacity information;

[0137] Acquiring first object information, and acquiring device information and second object information, including:

[0138] Receive the first capacity information sent by the terminal, and obtain the device information and the second capacity information; wherein the first capacity information represents the number of terminals that the network device in the network system is ready to add; the device information includes activation information; the activation information represents the enabled function of the network device in the network system; the second capacity information represents the number of terminals connected to the network device in the network system.

[0139] In another optional embodiment, the first object information includes first capacity information, and the second object information includes second capacity information;

[0140] Acquiring first object information, and acquiring device information and second object information, including:

[0141] Receive the first capacity information sent by the terminal, and obtain the device information and the second capacity information; wherein the first capacity information represents the number of terminals that the network device in the network system is ready to add; the device information includes activation information; the activation information represents the enabled functions of the network device in the network system and the traffic of the network device; the second capacity information represents the number of terminals connected to the network device in the network system.

[0142] In this embodiment, by receiving the first capacity information sent by the terminal, the number of terminals that need to be added to the network device of the network system is determined; by obtaining the enabling information in the device information, the enabled functions of the network device and the traffic of the network device are obtained; by obtaining the second capacity information, the number of terminals currently connected to the network device is obtained.

[0143] Therefore, by receiving the first capacity information sent by the terminal, the number of terminals that need to be newly connected to the network system is determined, and by obtaining the device information, the configuration of the network equipment in the network system, the performance indicators of the network equipment in the network system, and the number of terminals that have been connected to the network equipment in the network system are obtained, thereby ensuring the comprehensiveness of information acquisition.

[0144] S202: Calling a preset performance model to generate a prediction result based on the first object information, the device information, and the second object information; wherein the prediction result represents the device performance index consumed by the network device in the network system when the network device bears the objects corresponding to the first object information and the second object information.

[0145] In this step, a prediction result is generated according to the first object information, the device information, and the second object information by calling a preset performance model, so as to achieve, based on the device information of the network device, a prediction of the device performance indicator that the network device will consume when a new object for consuming device performance indicators is added to the network device, thereby ensuring the accuracy of the prediction result and improving the efficiency of generating the prediction result. Among them, the prediction result represents the device performance indicator consumed by the network device when the functions corresponding to the first function information and the second function information are turned on. The device performance indicators consumed by the network device include: one or more of CPU usage, RAM usage, and network bandwidth utilization. A prediction result is generated according to the first object information, the device information, and the second object information by calling a preset performance model, so as to achieve, based on the device information of the network device, a prediction of the device performance indicator consumed by the network device when the network device turns on the function corresponding to the first function information, thereby ensuring the accuracy of the prediction result and improving the efficiency of generating the prediction result.

[0146] In one embodiment, calling a preset performance model to generate a prediction result according to the first object information, the device information, and the second object information includes:

[0147] Calling a preset performance model to generate a target function interval according to the first function information, the second function information and the operation information in the device information; wherein the target function interval refers to the upper limit and lower limit of the device performance indicator that can be consumed by a terminal connected to the network device when the network device in the network system starts the function of the first function information and the function of the second function information;

[0148] The prediction result of the first function information is determined based on the terminal access configuration information and the target function interval in the device information; the prediction result of the first function information represents the network device in the network system, and the device performance indicators consumed by the network device when the functions of the first function information and the second function information are enabled.

[0149] Therefore, by using the performance model to generate a target functional range based on the first functional information, the second functional information and the operating information in the device information, a multi-angle analysis is achieved to determine the technical effect of the upper and lower limits of the device performance indicators that can be consumed by a terminal connected to the network device, thereby ensuring the accuracy of the target functional range; by determining the prediction results based on the terminal access configuration information and the target functional range, while ensuring the accuracy of the target functional range, the prediction results are determined in a targeted manner based on the device information of different network devices, thereby ensuring the accuracy of the prediction results.

[0150] Further, the network system includes a cloud and at least one network device;

[0151] The preset performance model is called to generate a target function range according to the first function information, the operation information in the device information, and the second function information, including:

[0152] The cloud enters the first function information, the operation information in the first device information, and the second function information into a preset performance analysis interface; wherein the first device information refers to the device information of the first network device; the first network device is a network device among at least one network device that receives the first function information sent by the terminal;

[0153] The cloud calls the performance model through the performance analysis interface, so that the performance model generates a target function range according to the first function information, the second function information and the operation information in the device information.

[0154] In this step, the first function information, the second function information and the operating information in the first device information are entered into a preset performance analysis interface through the cloud, and the performance model is called through the performance analysis interface. The performance model is called by the cloud using an interface according to the first function information, thereby clarifying the functions provided by the network device, reducing the coupling degree between other functions in the cloud and the performance model, and facilitating frequent training and maintenance of the performance model.

[0155] Further, determining a prediction result of the first function information according to the terminal access configuration information and the target function interval in the device information includes:

[0156] The cloud multiplies the terminal access configuration information in the first device information by the upper limit and the lower limit in the target function interval respectively to obtain a predicted upper limit value and a predicted lower limit value; wherein the first device information refers to the device information of the first network device;

[0157] The cloud constructs a numerical range based on the prediction upper limit and the prediction lower limit, and uses the numerical range as the prediction result.

[0158] In this step, the cloud multiplies the terminal access configuration information in the first device information by the upper limit value and the lower limit value in the target function interval, respectively, to obtain the predicted upper limit value and the predicted lower limit value, and constructs a numerical interval based on the predicted upper limit value and the predicted lower limit value, and uses the numerical interval as a way to predict the result, thereby realizing the target function interval of the performance indicators that may be consumed by the terminal when the network device is connected to the terminal based on the terminal access configuration information of the network device, and the target function interval of the performance indicators that may be consumed by the terminal when the network device is connected to the terminal, and specifically judging the interval of the performance indicators that may be consumed by the network device once all terminals turn on the function of the first function information, thereby greatly ensuring the reliability of the prediction result.

[0159] Exemplarily, the configuration management unit sends the first function information, terminal access configuration information, operation information and the second function information to the performance analysis interface of the cloud. The cloud calls the performance model in the model library by calling the performance analysis interface to calculate the target function range based on the first function information, the second function information and the operation information in the device information.

[0160] The target function interval represents the device performance indicators that will be consumed when the network device turns on the first function information based on the function corresponding to the second function information; for example: when a user is preparing to change the function, it must be a change from Fa->Fb. At this time, by obtaining the maximum number of terminals K on this network device, Fa[Pn,Pm]*K->Fb[Pn,Pm]*K can be calculated, and Fb[Pn,Pm]*K is the device performance consumption prediction range we need. Among them, Fa represents function scenario a (function combination a), and Fb represents function scenario b (function combination b). It is assumed that the function scenario a corresponding to Fa is to turn on function A, and the function scenario b corresponding to Fb is to turn on function A and function B. Fa[Pn,Pm] is the functional performance range when the network device enables function A, and Fb[Pn,Pm] is the target functional range when the network device enables functions A and B. Fa[Pn,Pm]*K is the current performance consumption range of the network device, and Fb[Pn,Pm]*K is the predicted range of device performance consumption we need. Therefore, Fb[Pn,Pm]*K is the predicted result.

[0161] In another embodiment, calling a preset performance model to generate a prediction result according to the first object information, the device information, and the second object information includes:

[0162] Call a preset performance model to generate a target capacity interval according to the first capacity information, the second capacity information and the activation information in the device information, and use the target capacity interval as the prediction result of the first capacity information; wherein the target capacity interval refers to the upper and lower limits of the device performance indicators consumed by the network device in the network system when the network device is connected to the sum of the first and second numbers of terminals; the first number is the number of terminals corresponding to the first capacity information; the second number is the number of terminals corresponding to the second capacity information.

[0163] In order to achieve prediction of the device performance index consumed by the network device when the number of terminals corresponding to the first capacity information newly connected to the network device is realized based on the device information of the network device, the accuracy of the prediction result is ensured and the generation efficiency of the prediction result is improved.

[0164] Therefore, by using the performance model to generate a target capacity interval based on the first capacity information, the operating information in the device information, and the second capacity information, a multi-angle analysis is achieved to determine the upper and lower limits of the device performance indicators that a terminal connected to the network device can consume, thereby ensuring the accuracy of the target capacity interval; by determining the prediction results based on the terminal access configuration information and the target capacity interval, while ensuring the accuracy of the target capacity interval, the prediction results are determined in a targeted manner based on the device information of different network devices, thereby ensuring the accuracy of the prediction results.

[0165] Specifically, after calling the preset performance model to generate a prediction result according to the first object information, the device information, and the second object information, the method further includes:

[0166] Send the prediction results to the terminal.

[0167] In this step, the prediction result is sent to the terminal so that the terminal can obtain the prediction result in time. Since the prediction result is generated based on the current device information of the network device, the content of the prediction result will greatly fit the current operating status of the network device, that is: according to the different configurations of each network device, the different device performance indicators, and the different functions that are currently enabled, determine what impact a new function or a new connection to a specific number of terminals will have on the network device; greatly improve the accuracy of the prediction. Therefore, this application is not only applicable to application scenarios of changing the functions enabled in network devices, but also applicable to application scenarios where the number of terminals connected to network devices is increased without expanding the bandwidth of network devices, which greatly improves the scope of application.

[0168] Exemplarily, the cloud sends the prediction result to the configuration management unit of the network device, and the configuration management unit sends the prediction result to the terminal.

[0169] See also Figure 3 The present application provides a device performance consumption prediction method, which is applied to a network system and includes:

[0170] S301: Collect historical performance data of each type of operation information under a functional scenario; wherein the functional scenario represents the enabled functions of the network equipment in the network system; the operation information represents the number of terminals connected to the network equipment in the network system; the historical performance data represents the performance indicators of the equipment being consumed in the network system under a type of operation information in the functional scenario.

[0171] Optionally, historical performance data of each type of operation information under a functional scenario is collected; wherein the functional scenario represents the enabled functions of the network equipment in the network system and the traffic of the network equipment; the operation information represents the number of terminals connected to the network equipment in the network system and the traffic of the network equipment in the network system; the historical performance data represents the performance indicators of the equipment being consumed in the network system under a type of operation information in the functional scenario.

[0172] Exemplarily, from the functional dimension, different functional combinations are obtained as different functional scenarios; for example: there are n (n is about 4 to 5) major functions that usually affect performance in network equipment. The total number is 2 to the power of n. Assuming n is 4 or 5, the total number of functional combinations is 15 to 31 (the case where the number of functional combinations is zero is not considered). Assuming n is 4, the functions of the network equipment include: A, B, C, D; then, the functional combinations include: A, B, C, D, AB, CD, AC, AD, BC, BD, ABC, BCD, ACD, ABD, ABCD; among them, the functional combination of none (that is, the network device does not enable any function) is not considered this time.

[0173] An operation information represents the network device of the network system, when the number of connected terminals is M and the flow of the network device is N, where M is a positive integer and N is a positive rational number;

[0174] Therefore, assuming that the functional scenario is AB, that is, function A and function B are turned on at the same time; a historical performance data representation of the operating information under the functional scenario, when the number of terminals connected to the network device is Ma and the traffic is Na, the device performance indicators consumed by the network device, such as: one or more of the CPU utilization rate Ca, RAM utilization rate Ra, and network bandwidth utilization rate Wa; where a∈[1, S], S is the total number of functional scenario types.

[0175] In an exemplary embodiment, the terminal access configuration information, activation information and functional scenarios of the network device are collected through the information collection unit; the terminal access configuration information is obtained from the configuration management of the network device through the information collection unit, and the number of terminals currently connected to the network device, the traffic of the network device, the second function information of the network device, and the device performance indicators of the network device (for example: one or more of CPU utilization, RAM utilization, and network bandwidth utilization) are collected from the data plane service of the network device through the information collection unit.

[0176] The information collection unit sends the historical performance data, functional scenarios and functional scenarios to the information collection unit in the cloud, and the information collection unit saves the historical performance data, functional scenarios and functional scenarios in the information database in the cloud.

[0177] S302: Obtain a functional performance range of the functional scenario according to at least one historical performance data corresponding to the functional scenario; wherein the functional performance range represents an upper limit and a lower limit of a device performance indicator that a terminal can consume a network device in a functional scenario.

[0178] In this step, the functional performance range includes the device performance indicators that a terminal can consume in multiple functional scenarios when one or more functions are enabled. Therefore, the functional performance range covers the device performance indicators that a terminal may consume under various operating information, ensuring the comprehensiveness of the device performance indicator prediction.

[0179] For example, assuming the functional scenario is AB, then multiple functional scenarios (S scenarios) under AB will be obtained, namely: when the number of terminals connected to the network device is M (1-S) and the traffic is N (1-S), the CPU utilization rate is C (from the 1st to the Sth), the RAM utilization rate is R (1-S), and the network bandwidth utilization rate is W (1-S).

[0180] In one embodiment, obtaining a functional performance interval of a functional scenario according to at least one historical performance data corresponding to the functional scenario includes:

[0181] According to at least one historical performance data under the target function scenario, at least one unit performance data under the target function scenario is obtained; wherein the unit performance data represents a device performance indicator consumed by a terminal under the target function scenario;

[0182] A regression operation is performed on each unit performance data under the target functional scenario to obtain a regression curve of the target functional scenario; wherein the regression curve represents the probability of occurrence of each category of unit performance data in the target functional scenario; the category represents the numerical interval in which the value of the unit performance data is located; the target functional scenario is one of at least one functional scenario;

[0183] Enter the preset regression interval into the regression curve to obtain the functional performance interval of the target functional scenario; wherein the regression interval is used to determine the target range of the category in the regression curve; the target range determines at least one target category in the regression curve; the functional performance interval is used to cover the unit performance data in at least one target category; the upper limit of the functional performance interval is the first value in the unit performance data in at least one target category; the lower limit of the functional performance interval is the second value of the unit performance data in at least one target category; in this embodiment, the first value is the maximum value in the unit performance data in at least one target category; the second value is the minimum value in the unit performance data in at least one target category.

[0184] Therefore, a regression curve is obtained by performing a regression operation on each unit performance data, and the functional performance interval in the regression curve is determined based on the regression interval, wherein the regression interval defines the difference degree interval, that is, the upper and lower limits of the unit performance data in the N% range in the regression curve, for example: what are the upper and lower limits of the unit performance data in the regression curve with an exposure probability of 90% (that is, in most of the range in the curve), and the functional performance interval is constructed based on the upper and lower limits.

[0185] Exemplarily, the regression curve is determined to be a normal distribution curve, and the regression interval (for example: 3σ range) of the performance loss of a single terminal under different functional combinations is calculated. The number of terminals during the peak period of the network is obtained and multiplied by the corresponding 3σ range according to the combination of functions. To predict the basically reliable performance data in the network. Among them, μ represents the average value of all unit performance data, σ represents the standard deviation of all unit performance data, and the normal distribution curve represents the probability of occurrence of each unit performance data. (μ-m×σ,μ+m×σ) is used to characterize the regression interval, where m is the adjustment coefficient. By adjusting m, the regression interval with different upper and lower thresholds will be obtained. The obtained regression interval will characterize the ratio of the area covered by the normal distribution curve to the total area under the normal distribution curve within m standard deviations, centered on the average value μ. For example: If the value of m is 1, we get (μ-σ,μ+σ), which represents the ratio of the area covered by the normal distribution curve within 1 standard deviation centered on the mean value μ to the total area under the normal distribution curve. Based on the standard normal distribution curve, we will get [15.9%, 84.1%], that is, 68.2% of the normal distribution curve range is obtained with the mean value μ as the center.

[0186] Assume that there are 6 unit performance data, namely data a: value 1, data 2: value 2; data 3: value 3; data 4: value 2; data 5: value 1; data 6: value 1. Then, the probability of value 1 is 50%; the probability of value 2 is 33%; the probability of value 3 is 17%. If the adjustment coefficient is 1, the regression interval range is 68.2%, which is expressed as [15.9%, 84.1%], the upper threshold is 15.9%, and the lower threshold is 84.1%; entering the regression interval into the regression curve will represent the value corresponding to the unit performance data with a probability of 68.2% in the unit performance data to be obtained, so the values ​​obtained are 1 and 2. Furthermore, the upper limit of the functional performance interval is 2, and the lower limit of the functional performance interval is 1. As for why the value 3 is not used, it is because the value 3 is a low-probability event and has no reference significance. Therefore, this low-probability value can be excluded through the above regression interval.

[0187] Specifically, calculate the device performance indicator consumption (i.e., unit performance data) of a terminal under a function combination. For example, based on the number of terminals connected to the network device, the CPU utilization of the device's current data plane (consumed device performance indicator), and the enabled function combination given to the cloud (for example, function A and function B, not only during peak hours, but also during off-peak hours), the data received by the cloud can roughly calculate the performance consumption of a single terminal in this network environment as P1. The cloud performs statistics based on a large amount of data (greater than 10,000). At this time, the cloud can obtain more than 10,000 data samples, P1, P2...Pn. These data samples must be discrete, but from a statistical point of view, they must conform to the normal distribution law. Therefore, the regression curve is a normal distribution curve, such as Figure 4 As shown, the normal distribution curve represents a functional scenario (functional combination, such as AB), the vertical axis of the normal distribution represents the value of the unit performance data; the horizontal axis represents the functional scenario corresponding to each unit performance data;

[0188] Exemplarily, according to the above distance, the adjustment coefficient m is set to 3, that is: the upper threshold is set to (μ+3σ), and the lower threshold is set to (μ-3σ). The probability that the value is distributed within +-3σ is 99.74%, and the regression interval is obtained. The threshold interval (μ-3σ, μ+3σ) corresponding to the regression interval is substituted into the above normal distribution curve, and the performance fluctuation interval [Pn, Pm] of the functional scenario (the functions enabled by the network device are function A and function B) is calculated.

[0189] Each functional scenario has its corresponding Fx[Pn,Pm], where x is any one of the y functional scenarios and y is the total number of functional scenarios. In this way, we can actually build a similar terminal with a performance range (3σ) under a functional combination, and this value can cover most scenarios.

[0190] Function combination (functional scenario) A terminal consumes functional performance range F1 F1[Pn,Pm] F2 F2[Pn,Pm] F3 F3[Pn,Pm] F4 F4[Pn,Pm] … … F Fy[Pn,Pm]

[0191] When a user is preparing to make a function change, for example, switching from enabling function A (its corresponding function scenario Fa) to enabling functions A and B (its corresponding function scenario Fb), at this time, by obtaining the maximum number of terminals K on this network device, we can calculate Fa[Pn,Pm]*K->Fb[Pn,Pm]*K, where Fa[Pn,Pm]*K is the current performance consumption range of the network device, and Fb[Pn,Pm]*K is the predicted range of device performance consumption we need.

[0192] S303: Taking at least one functional scenario as input data and functional performance intervals corresponding to at least one functional scenario as output data, a preset initial model is trained to obtain a performance model.

[0193] In this step, a neural network or support vector machine is used as the initial model. Neural networks are a subset of machine learning and are also the core of deep learning algorithms. Their name and structure are inspired by the human brain and can imitate the way biological neurons transmit signals to each other. Artificial Neural Networks (ANN, full name Artificial Neural Network) consist of node layers, including an input layer, one or more hidden layers, and an output layer. Each node is also called an artificial neuron, which is connected to another node and has an associated weight and threshold. If the output of any single node is above a specified threshold, then the node is activated and the data is sent to the next layer of the network. Otherwise, the data is not passed to the next layer of the network. Neural networks rely on training data to learn and improve their accuracy over time.

[0194] Support Vector Machine (SVM) is a machine learning algorithm used to solve binary classification problems. It separates samples of different categories by finding a partitioning hyperplane in the sample space, while maximizing the minimum distance between two point sets and this plane and maximizing the distance between the edge points of the two point sets and this plane.

[0195] Therefore, by collecting historical performance data of various operating information in various functional scenarios, the initial model is trained and the performance model is obtained, thereby ensuring the comprehensiveness of the performance model training. Exemplarily, the data analysis unit on the cloud extracts historical performance data of each type of operating information in a functional scenario from the information database, and the data analysis unit calculates and processes at least one historical performance data of each type of operating information to obtain the performance model, and saves the performance model to the model library on the cloud.

[0196] S304: Obtain first object information, and obtain device information and second object information; wherein, the first object information represents the object of the network device in the network system that is ready to consume the device performance indicator; device information and second object information; the device information represents the operation status of the network device in the network system; the second object information represents the object of the network device in the network system that is consuming the performance indicator.

[0197] This step is consistent with S201 in Example 1, so it will not be described here in detail.

[0198] S305: Calling a preset performance model to generate a prediction result according to the first object information, the device information, and the second object information; wherein the prediction result represents the device performance index consumed by the network device in the network system when the network device bears the objects corresponding to the first object information and the second object information.

[0199] This step is consistent with S202 in Example 1, so it will not be described here in detail.

[0200] S306: Compare the performance configuration information in the device information with the prediction result; wherein the performance configuration information represents the maximum performance that can be lost by the network device.

[0201] Exemplarily, if the performance configuration information includes: a CPU usage limit (eg, 98%), a RAM usage limit (eg, 90%), and a network bandwidth usage limit (eg, 95%).

[0202] S307: If it is determined that the prediction result exceeds the performance configuration information, a function warning information is generated and sent to the terminal.

[0203] For example, if the prediction result indicates that if the new function B is enabled on the network device, the device performance indicator that the network device will consume is a CPU usage rate of 80%-99%. Then, it is determined that the prediction result exceeds the performance configuration information by 98%, and therefore an early warning message needs to be generated and sent to the terminal.

[0204] Therefore, by comparing the performance configuration information with the prediction results and generating functional warning information, the technical effect of timely feedback of function activation alarm is achieved.

[0205] Specifically, if it is determined that the prediction result exceeds the performance configuration information, a functional warning message is generated, including:

[0206] If it is determined that any device performance indicator in the prediction result exceeds the performance configuration information corresponding to the device performance indicator, a function warning information is generated. For example, assuming that the performance configuration information includes a CPU usage limit (e.g., 98%), a RAM usage limit (e.g., 90%), and a network bandwidth utilization limit (e.g., 95%). If the CPU usage in the prediction result reaches 80%-99%, then it is determined that the prediction result exceeds the CPU usage limit of 98% in the performance configuration information.

[0207] See also Figure 5 The present application provides a device performance consumption prediction method, which is applied to a network system and includes:

[0208] S501: Collect historical performance data of each type of activation information under a capacity scenario; wherein the capacity scenario represents the number of terminals connected to the network device in the network system; the activation information represents the functions that have been enabled by the network device in the network system; and the historical performance data represents the device performance indicators being consumed by the network device in a type of activation information under the capacity scenario;

[0209] Optionally, historical performance data of each type of enabling information under a capacity scenario is collected; wherein the capacity scenario represents the number of terminals connected to the network device in the network system; the enabling information represents the enabled functions of the network device in the network system and the traffic of the network device; the historical performance data represents the device performance indicators being consumed by the network device in a type of enabling information under the capacity scenario;

[0210] Exemplarily, from the aspect of the number of terminals connected to the network device, the capacity scenarios of different numbers of terminals are obtained; for example; the scenario when the number of terminals is 100, the scenario when the number of terminals is 200, etc. Therefore, assuming that the capacity scenario is 1000, that is: the number of connected terminals is 1000; the historical performance data representation of a functional scenario under the capacity scenario, when the network device is connected to 1000 terminals, the device performance indicators of the network device in a functional scenario (for example: turning on function A and function B). For example: each type of activation information under a capacity scenario is collected, assuming that the functions include function A and function B. Then the various activation information includes: function A, function B, function A+B; and then the historical performance data of turning on function A, the historical performance data of turning on function B, and the historical performance data of turning on function AB are obtained when the number of connected terminals is 1000.

[0211] S502: Obtain a capacity performance interval of a capacity scenario according to at least one historical performance data; wherein the capacity performance interval represents an upper limit and a lower limit of a device performance indicator consumed by a network device in a capacity scenario.

[0212] In this step, the capacity performance interval includes the device performance indicators consumed by the network device in different functional scenarios under a capacity scenario, that is, when the number of connection interruptions is 1000, the device performance indicators consumed when function A is turned on, the device performance indicators consumed when function A and function B are turned on, and so on. That is, according to the above historical performance data, the capacity performance interval of the network device is determined when the number of connected terminals is 1000. Assuming that the performance consumption of function A is the smallest, then the lower limit of the interval is the data of the minimum performance consumption when function A is turned on, and the upper limit is the data of the maximum performance consumption when function A+B is turned on. The purpose is that if a new terminal is added to make the number of network device connections reach 1000 terminals, no matter how the subsequent functions are turned on, the performance of the network device will not exceed the threshold. Perform regression operation on the historical performance data under each functional scenario to obtain the regression curve of the capacity scenario; enter the preset regression interval into the regression curve to obtain the capacity performance interval; wherein, the regression interval includes an upper threshold and a lower threshold; the upper threshold represents the upper limit of the degree of difference between at least one historical performance data; the lower threshold represents the lower limit of the degree of difference between at least one historical performance data.

[0213] For example: In a capacity scenario, such as 1,000 terminals, when different functions are turned on, different device performance indicators will appear. Each device performance indicator will conform to the normal distribution curve. For example, according to the 3σ principle, the upper threshold is set to (μ+3σ), and the lower threshold is set to (μ-3σ). The probability of the value distribution in +-3σ is 99.74%, and the regression interval is obtained, where μ is the average value of each unit performance data, and σ is the standard deviation of each performance data. The 3σ principle means that the probability of the value distribution in (μ-3σ, μ+3σ) is 0.9974, where σ represents the standard deviation in the normal distribution, and μ represents the mean x=μ, which is the symmetry axis of the image. The 3σ criterion is also called the Laida criterion. It first assumes that a set of detection data contains only random errors, calculates and processes them to obtain the standard deviation, and determines an interval according to a certain probability. It is believed that any error exceeding this interval is not a random error but a gross error, and the data containing this error should be eliminated. And 3σ is suitable when there are more sets of data.

[0214] Optionally, the historical performance data has a time period, which represents the time of the historical performance data. Assuming a functional scenario, that is, the enabled functions are function A and function B, based on at least one historical performance data, the capacity performance interval of the capacity scenario obtained includes at least one time capacity performance interval, for example: the time period includes: 0:00-12:00, 12:00-24:00; then, the time capacity performance interval of 0:00-12:00 will be obtained, which represents the device performance index consumed when the network device is connected to 1,000 terminals and function A and function B are turned on; and the time capacity performance interval of 12:00-24:00 represents the device performance index consumed when the network device is connected to 1,000 terminals and function A and function B are turned on.

[0215] S503: Taking at least one capacity scenario as input data and taking the capacity performance intervals respectively corresponding to the at least one capacity scenario as output data, training a preset initial model to obtain a performance model.

[0216] In this step, a neural network or support vector machine is used as the initial model. Neural networks are a subset of machine learning and are also the core of deep learning algorithms. Their name and structure are inspired by the human brain and can imitate the way biological neurons transmit signals to each other. Artificial neural networks (ANNs) are composed of layers of nodes, including an input layer, one or more hidden layers, and an output layer. Each node is also called an artificial neuron, which is connected to another node and has an associated weight and threshold. If the output of any single node is above a specified threshold, then the node is activated and the data is sent to the next layer of the network. Otherwise, the data is not passed to the next layer of the network. Neural networks rely on training data to learn and improve their accuracy over time.

[0217] Support Vector Machine (SVM) is a machine learning algorithm used to solve binary classification problems. It separates samples of different categories by finding a partitioning hyperplane in the sample space, while maximizing the minimum distance between two point sets and this plane and maximizing the distance between the edge points of the two point sets and this plane.

[0218] Therefore, by collecting historical performance data of various functional scenarios under various capacity scenarios, the initial model is trained to obtain a performance model, thereby ensuring the comprehensiveness of the performance model training.

[0219] S504: Obtain first object information, and obtain device information and second object information; wherein, the first object information represents the object of the network device in the network system that is ready to consume the device performance indicator; device information and second object information; the device information represents the operation status of the network device in the network system; the second object information represents the object of the network device in the network system that is consuming the performance indicator.

[0220] This step is consistent with S201 in Example 1, so it will not be described here in detail.

[0221] S505: Calling a preset performance model to generate a prediction result according to the first object information, the device information, and the second object information; wherein the prediction result represents the device performance index consumed by the network device in the network system when the network device bears the object corresponding to the first object information and the second object information.

[0222] This step is consistent with S202 in Example 1, so it will not be described here in detail.

[0223] S506: Compare the performance configuration information in the device information with the prediction result; wherein, compare the performance configuration information in the device information with the prediction result; wherein, the performance configuration information represents the device performance indicator threshold that can be consumed by the network device.

[0224] In this embodiment, the device performance indicator threshold may be a device performance indicator representing the maximum loss that the network device can sustain.

[0225] Exemplarily, if the performance configuration information includes: a CPU usage limit (eg, 98%), a RAM usage limit (eg, 90%), and a network bandwidth usage limit (eg, 95%).

[0226] S507: If it is determined that the prediction result exceeds the performance configuration information, a function warning information is generated and sent to the terminal.

[0227] For example, if the prediction result indicates that if the new function B is enabled on the network device, the device performance indicator that the network device will consume is a CPU usage rate of 80%-99%. Then, it is determined that the prediction result exceeds the performance configuration information by 98%, and therefore an early warning message needs to be generated and sent to the terminal.

[0228] Therefore, by comparing the performance configuration information with the prediction results and generating functional warning information, the technical effect of timely feedback of function activation alarm is achieved.

[0229] See also Figure 6 The present application provides a device performance consumption prediction device 6, which is installed in a network system and includes:

[0230] In a second aspect, the present application provides a device performance consumption prediction apparatus, which is installed in a network system and includes:

[0231] The input module 64 is used to obtain first object information, and obtain device information and second object information; wherein the first object information represents the object of the network device in the network system that is ready to consume the device performance indicator; the device information represents the operation status of the network device in the network system; the second object information represents the object of the network device in the network system that is consuming the performance indicator;

[0232] Processing module 65 is used to call a preset performance model to generate a prediction result based on the first object information, the device information and the second object information; wherein the prediction result represents the device performance index consumed by the network device in the network system when the network device bears the objects corresponding to the first object information and the second object information.

[0233] Optionally, the first object information includes first function information, and the second object information includes second function information;

[0234] The input module 64 is further used to receive the first function information sent by the terminal, and obtain the device information and the second function information; wherein the first function information represents the function that the terminal is ready to enable; the device information includes the terminal access configuration information and the operation information; the terminal access configuration information represents the number threshold of the terminals that the network device can access, and the operation information includes the number of terminals that the network device has connected; the second function information represents the function that the network device in the network system has enabled. In this embodiment, the number threshold is the maximum number of terminals that the network device can access.

[0235] The processing module 65 is further used to call a preset performance model to generate a target function interval according to the first function information, the second function information and the operation information in the device information; wherein the target function interval refers to the upper limit and lower limit of the device performance indicator that can be consumed by a terminal connected to the network device when the network device in the network system turns on the function of the first function information and the function of the second function information;

[0236] The processing module 65 is further configured to determine a prediction result of the first function information according to the terminal access configuration information and the target function interval in the device information.

[0237] Optionally, the network system includes a cloud and at least one network device; the processing module 65 is arranged in the cloud;

[0238] The processing module 65 in the cloud is used to input the first function information, the second function information and the operation information in the first device information into a preset performance analysis interface; wherein the first device information refers to the device information of the first network device; the first network device is a network device among at least one network device that receives the first function information sent by the terminal;

[0239] The processing module 65 in the cloud is used to call the performance model through the performance analysis interface, so that the performance model generates a target function interval according to the first function information, the operation information in the first device information, and the second function information.

[0240] Optionally, the processing module 65 in the cloud is used to multiply the terminal access configuration information in the first device information by the upper limit value and the lower limit value in the target function interval respectively to obtain a predicted upper limit value and a predicted lower limit value;

[0241] The processing module 65 in the cloud is used to construct a numerical range according to the prediction upper limit value and the prediction lower limit value, and use the numerical range as the prediction result.

[0242] Optionally, the first object information includes first capacity information, and the second object information includes second capacity information;

[0243] The input module 64 is also used to receive the first capacity information sent by the terminal, and obtain the device information and the second capacity information; wherein the first capacity information represents the number of terminals that the network device in the network system is ready to add; the device information includes activation information; the activation information represents the enabled function of the network device in the network system; the second capacity information represents the number of terminals that the network device in the network system has connected.

[0244] Optionally, the processing module 65 is also used to call a preset performance model to call the preset performance model according to the first capacity information, the second capacity information and the activation information in the device information, to generate a target capacity interval, and to use the target capacity interval as a prediction result of the first capacity information; wherein, the target capacity interval refers to the upper and lower limits of the device performance indicator consumed by the network device in the network system when the network device is connected to the sum of the first and second numbers of terminals; the first number is the number of terminals corresponding to the first capacity information; the second number is the number of terminals corresponding to the second capacity information.

[0245] Optionally, the device 6 further includes: a function collection module 611, which is used to collect historical performance data of each type of operation information under a function scenario; wherein the function scenario represents the enabled function of the network device in the network system; the operation information represents the number of terminals connected to the network device in the network system; the historical performance data represents the performance index of the device being consumed under a type of operation information in the network system in the function scenario;

[0246] The functional performance interval module 612 is used to obtain the functional performance interval of the functional scenario according to at least one historical performance data corresponding to the functional scenario; wherein the functional performance interval represents the upper limit and lower limit of the device performance indicator that a terminal can consume the network device in a functional scenario;

[0247] The function model training module 613 is used to take at least one function scenario as input data and the function performance intervals corresponding to at least one function scenario as output data, train a preset initial model, and obtain a performance model.

[0248] Optionally, the device 6 further comprises:

[0249] The function performance interval module 612 is further used to obtain at least one unit performance data under the target function scenario according to at least one historical performance data under the target function scenario; wherein the unit performance data represents the device performance index consumed by a terminal under the target function scenario;

[0250] The function performance interval module 612 is further used to perform regression operation on each unit performance data under the target function scenario to obtain a regression curve of the target function scenario; wherein the regression curve represents the probability of occurrence of each category of unit performance data in the target function scenario; the category represents the numerical interval in which the value of the unit performance data is located; the target function scenario is one of the at least one function scenario;

[0251] The functional performance interval module 612 is also used to enter the preset regression interval into the regression curve to obtain the functional performance interval of the target functional scenario; wherein the regression interval is used to determine the target range for extracting the category in the regression curve; the target range determines at least one target category in the regression curve; the functional performance interval is used to cover the unit performance data in at least one target category; the upper limit of the functional performance interval is the first value of the unit performance data in at least one target category; the lower limit of the functional performance interval is the second value of the unit performance data in at least one target category.

[0252] Optionally, the device 6 further includes: a capacity collection module 621, which is used to collect historical performance data of each type of enabling information under a capacity scenario; wherein the capacity scenario represents the number of terminals connected to the network device in the network system; the enabling information represents the functions enabled by the network device in the network system; and the historical performance data represents the device performance indicators being consumed by the network device in a type of enabling information under the capacity scenario;

[0253] The capacity performance interval module 622 is used to obtain the capacity performance interval of the capacity scenario according to at least one historical performance data; wherein the capacity performance interval represents the upper limit and lower limit of the device performance indicator consumed by the network device in the capacity scenario;

[0254] The capacity model training module 623 is used to take at least one capacity scenario as input data and capacity performance intervals corresponding to at least one capacity scenario as output data, train a preset initial model, and obtain a performance model.

[0255] Optionally, the device 6 further comprises:

[0256] A comparison module 66 is used to compare the performance configuration information in the device information with the prediction result; wherein the performance configuration information represents the device performance indicator threshold that the network device can consume;

[0257] The warning module 67 is used to generate functional warning information if it is determined that the prediction result exceeds the performance configuration information.

[0258] To achieve the above-mentioned purpose, the present application further provides a computer device 7, comprising: a processor 72 and a memory 71 in communication with the processor 72; the memory stores computer execution instructions;

[0259] The processor executes the computer execution instructions stored in the memory 71 to implement the above-mentioned device performance consumption prediction method, wherein the components of the device performance consumption prediction device can be dispersed in different computer devices, and the computer device 7 can be a smart phone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server or cabinet server (including independent servers, or server clusters composed of multiple application servers) that executes the program. The computer device of this embodiment includes at least but is not limited to: a memory 71 and a processor 72 that can be interconnected through a system bus, such as Figure 7 It should be pointed out that Figure 7 Only a computer device with component - is shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. In this embodiment, the memory 71 (i.e., readable storage medium) includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 71 can be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 71 can also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (SMC, full name Smart Media Card), a secure digital (SD, full name SecureDigital,) card, a flash card (Flash Card), etc. Of course, the memory 71 can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory 71 is generally used to store the operating system and various application software installed on the computer device, such as the program code of the device performance consumption prediction device of the third embodiment. In addition, the memory 71 can also be used to temporarily store various types of data that have been output or are ready to be output. The processor 72 can be a central processing unit (CPU, full name Central Processing Unit), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 72 is generally used to control the overall operation of the computer device. In this embodiment, the processor 72 is used to run the program code stored in the memory 71 or process data, such as running the device performance consumption prediction device, to implement the device performance consumption prediction method of the above embodiment.

[0260] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the methods of each embodiment of the present application. It should be understood that the above-mentioned processor can be a central processing unit (CPU, full name Central Processing Unit), or other general-purpose processors, digital signal processors (DSP, full name Digital Signal Processor), application-specific integrated circuits (ASIC, full name Application Specific Integrated Circuit), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as a hardware processor to be executed, or the hardware and software modules in the processor are combined and executed. The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and can also be a U disk, a mobile hard disk, a read-only memory, a disk or an optical disk, etc.

[0261] To achieve the above purpose, the present application also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, App application store, etc., which stores computer execution instructions, and the corresponding functions are realized when the program is executed by the processor 72. The computer-readable storage medium of this embodiment is used to store computer execution instructions for implementing the device performance consumption prediction method, and when executed by the processor 72, the device performance consumption prediction method of the above embodiment is realized.

[0262] The above storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0263] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC, full name Application Specific Integrated Circuits). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0264] The present application provides a computer program product, including a computer program, which implements the above-mentioned device performance consumption prediction method when executed by a processor.

[0265] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0266] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0267] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for predicting equipment performance consumption, characterized in that: Applied in network systems, including: Acquire first object information, and acquire device information and second object information; wherein the first object information represents an object of which a network device in a network system is about to consume a device performance indicator; the device information represents an operation status of a network device in a network system; and the second object information represents an object of which a network device in a network system is consuming a performance indicator; Call a preset performance model to generate a prediction result based on the first object information, the device information and the second object information; wherein the prediction result characterizes the network device in the network system, and the device performance indicator consumed by the network device when it bears the objects corresponding to the first object information and the second object information.

2. The device performance consumption prediction method according to claim 1, characterized in that: The first object information includes first function information, and the second object information includes second function information; Acquiring first object information, and acquiring device information and second object information, including: Receive first function information sent by the terminal, and obtain device information and second function information; wherein, the first function information represents the function that the terminal is ready to enable; the device information includes terminal access configuration information and operation information; the terminal access configuration information represents the threshold value of the number of terminals that the network device can access, and the operation information includes the number of terminals connected to the network device; the second function information represents the enabled function of the network device in the network system.

3. The device performance consumption prediction method according to claim 1 or 2, characterized in that: Calling a preset performance model to generate a prediction result according to the first object information, the device information, and the second object information includes: Calling a preset performance model to generate a target function interval according to the first function information, the second function information and the operation information in the device information; wherein the target function interval refers to the upper limit and lower limit of the device performance indicator that can be consumed by a terminal connected to the network device when the network device in the network system turns on the function of the first function information and the function of the second function information; A prediction result of the first function information is determined according to the terminal access configuration information in the device information and the target function interval.

4. The device performance consumption prediction method according to claim 3, characterized in that: The network system includes a cloud and at least one network device; Calling a preset performance model to generate a target function interval according to the first function information, the second function information, and the operation information in the device information includes: The cloud enters the first function information, the second function information and the operation information in the first device information into a preset performance analysis interface; wherein the first device information refers to the device information of the first network device; the first network device is a network device among at least one of the network devices that receives the first function information sent by the terminal; The cloud calls the performance model through the performance analysis interface, so that the performance model generates a target function interval according to the first function information, and the operation information and the second function information in the first device information.

5. The device performance consumption prediction method according to claim 3, characterized in that: The network system includes a cloud and at least one network device; Determining a prediction result of the first function information according to the terminal access configuration information in the device information and the target function interval includes: The cloud multiplies the terminal access configuration information in the first device information by the upper limit and the lower limit in the target function interval respectively to obtain a predicted upper limit value and a predicted lower limit value; wherein the first device information refers to the device information of the first network device; The cloud constructs a numerical range according to the prediction upper limit value and the prediction lower limit value, and uses the numerical range as the prediction result.

6. The device performance consumption prediction method according to claim 1, characterized in that: The first object information includes first capacity information, and the second object information includes second capacity information; Acquiring first object information, and acquiring device information and second object information, including: Receive the first capacity information sent by the terminal, and obtain the device information and the second capacity information; wherein, the first capacity information represents the number of terminals that the network device in the network system is ready to add; the device information includes activation information; the activation information represents the enabled functions of the network device in the network system; the second capacity information represents the number of terminals that have been connected to the network device in the network system.

7. The device performance consumption prediction method according to claim 1 or 6, characterized in that: Calling a preset performance model to generate a prediction result according to the first object information, the device information, and the second object information includes: Call a preset performance model to generate a target capacity interval according to the first capacity information, the second capacity information and the activation information in the device information, and use the target capacity interval as a prediction result of the first capacity information; wherein the target capacity interval refers to the upper and lower limits of the device performance indicator consumed by the network device in the network system when the network device is connected to the sum of the first and second numbers of terminals; the first number is the number of terminals corresponding to the first capacity information; the second number is the number of terminals corresponding to the second capacity information.

8. The device performance consumption prediction method according to claim 1, characterized in that: Before acquiring the first object information and acquiring the device information and the second object information, the method further includes: Collect historical performance data of each type of operation information in a functional scenario; wherein the functional scenario represents the enabled functions of the network device in the network system; the operation information represents the number of terminals connected to the network device in the network system; the historical performance data represents the performance indicators of the equipment being consumed in the network system under a type of operation information in the functional scenario; According to at least one historical performance data corresponding to the functional scenario, a functional performance interval of the functional scenario is obtained; wherein the functional performance interval represents the upper limit and lower limit of the device performance indicator that a terminal can consume the network device in a functional scenario; At least one of the functional scenarios is used as input data, and the functional performance intervals corresponding to at least one of the functional scenarios are used as output data, and a preset initial model is trained to obtain a performance model.

9. The device performance consumption prediction method according to claim 8, characterized in that: Obtaining a functional performance range of the functional scenario according to at least one historical performance data corresponding to the functional scenario includes: According to at least one historical performance data under the target function scenario, at least one unit performance data under the target function scenario is obtained; wherein the unit performance data represents a device performance indicator consumed by a terminal under the target function scenario; A regression operation is performed on each of the unit performance data under the target functional scenario to obtain a regression curve of the target functional scenario; wherein the regression curve represents the probability of occurrence of each category of unit performance data in the target functional scenario; the category represents the numerical interval in which the value of the unit performance data is located; the target functional scenario is one of at least one functional scenario; Enter a preset regression interval into the regression curve to obtain a functional performance interval for the target functional scenario; wherein the regression interval is used to determine a target range for extracting categories in the regression curve; the target range determines at least one target category in the regression curve; the functional performance interval is used to cover unit performance data in at least one of the target categories; the upper limit of the functional performance interval is the first value of the unit performance data in at least one target category; the lower limit of the functional performance interval is the second value of the unit performance data in at least one target category.

10. The device performance consumption prediction method according to claim 1, characterized in that: Before acquiring the first object information and acquiring the device information and the second object information, the method further includes: Collect historical performance data of each type of enabling information under a capacity scenario; wherein the capacity scenario represents the number of terminals connected to the network device in the network system; the enabling information represents the enabled functions of the network device in the network system; the historical performance data represents the device performance indicators being consumed by the network device in a type of enabling information under the capacity scenario; According to at least one of the historical performance data, a capacity performance interval of the capacity scenario is obtained; wherein the capacity performance interval represents an upper limit and a lower limit of a device performance indicator consumed by a network device in the capacity scenario; At least one of the capacity scenarios is used as input data, and the capacity performance intervals corresponding to at least one of the capacity scenarios are used as output data, and a preset initial model is trained to obtain a performance model.

11. The device performance consumption prediction method according to claim 1, characterized in that: After calling a preset performance model to generate a prediction result according to the first object information, the device information and the second object information, the method further includes: Comparing the performance configuration information in the device information with the prediction result; wherein the performance configuration information represents the device performance indicator threshold that the network device can consume; If it is determined that the prediction result exceeds the performance configuration information, functional warning information is generated.

12. A device for predicting equipment performance consumption, characterized in that: Installed in network systems, including: An input module is used to obtain first object information, and to obtain device information and second object information; wherein the first object information represents an object of which a network device in a network system is about to consume a device performance indicator; the device information represents an operation status of a network device in a network system; and the second object information represents an object of which a network device in a network system is consuming a performance indicator; A processing module is used to call a preset performance model to generate a prediction result based on the first object information, the device information and the second object information; wherein the prediction result represents the device performance index consumed by the network device in the network system when the network device bears the objects corresponding to the first object information and the second object information.

13. A computer device, characterized in that: include: A processor and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the device performance consumption prediction method as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the device performance consumption prediction method as described in any one of claims 1 to 11.

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