Method and device for reducing server load and charging system

Abstract

A method, device and charging system for reducing the load of a server, the server has a plurality of data resources, the server provides corresponding data resources for a terminal according to the request of the terminal, the method comprises the following steps: determining at least one load optimization resource according to the request condition of the data resources and the attribute of the data resources; updating the load optimization resource of the server to the latest; and the server provides a load optimization resource query service for the terminal, wherein the load optimization resource query service is used for the terminal to query whether the local load optimization resource is the latest or not according to the at least one beacon of the load optimization resource. According to the method for reducing the load of the server, the terminal judges whether the local load optimization resource of the terminal is equal to the latest load optimization resource of the server or not through the load optimization resource query service provided by the server so as to determine whether the terminal requests the load optimization resource from the server or not. The invention further provides a server load reducing and charging system.

Description

Technical Field

[0001] The present invention relates to the field of new energy charging, and in particular to a method, device and charging system for reducing server load. Background Art

[0002] As the number of active terminal users in the charging system increases, the frequency of use of the core pages of the terminal application also increases. The server receives a large number of data resource requests from the terminal application, which increases the server burden and causes server performance to deteriorate, resulting in problems such as slow loading of terminal application pages and delayed information updates. Summary of the invention

[0003] In order to solve the above problem, the present invention proposes a method for reducing server load, wherein the server has multiple data resources, and the terminal requests data resources from the server during use, and the server provides corresponding data resources to the terminal according to the request of the terminal. The method comprises the following steps:

[0004] Determine at least one load optimization resource according to the request status of the data resource and the attribute of the data resource, wherein the load optimization resource is a data resource that can reduce the server load when the terminal requests the data resource from the server;

[0005] Updating the load optimization resources of the server to the latest;

[0006] The server provides a load optimization resource query service to the terminal, wherein the load optimization resource query service allows the terminal to query whether the local load optimization resource is the latest according to at least one beacon of the load optimization resource.

[0007] In one embodiment of the method for reducing server load of the present invention, the step of determining at least one load optimization resource according to the request status of the data resource and the attribute of the data resource further comprises:

[0008] A load optimization resource is determined according to a request frequency of the data resource and an update frequency of the data resource.

[0009] In one embodiment of the method for reducing server load of the present invention, the step of determining at least one load optimization resource according to the request status of the data resource and the attribute of the data resource further comprises:

[0010] At least one load optimization resource is predicted by a first neural network model according to the request status of the data resource and the attribute of the data resource.

[0011] In one embodiment of the method for reducing server load of the present invention, the data resource providing service stores the latest data resource to the server, and the step of updating the load optimization resource of the server to the latest further includes:

[0012] When the load optimization resource of the server changes, the server updates the beacon of the changed load optimization resource.

[0013] In one embodiment of the method for reducing server load of the present invention, when the load optimization resource of the server changes, the step of the server updating the beacon of the changed load optimization resource further includes:

[0014] The server updates the beacon of the load optimization resource according to the scope dimension and / or object dimension of the service it provides.

[0015] In one embodiment of the method for reducing server load of the present invention, the step of providing a load optimization resource query service to the terminal by the server further comprises:

[0016] The server provides a load optimization resource query interface for the terminal to determine whether the local load optimization resources of the terminal are equal to the latest load optimization resources of the server according to the beacon within the range dimension and / or object dimension.

[0017] In one embodiment of the method for reducing server load of the present invention, the step of the server providing corresponding data resources to the terminal according to the request of the terminal further includes:

[0018] The timing at which the server provides the load optimization resource to the terminal is adjusted according to the user usage history data of the terminal.

[0019] The present invention also provides a device for reducing server load, which is used to implement any of the above methods, including:

[0020] A load optimization resource determination module, used to determine at least one load optimization resource according to the request status of the data resource and the attribute of the data resource, wherein the load optimization resource is a data resource that can reduce the server load when the terminal requests the data resource from the server;

[0021] A load optimization resource updating module, used for updating the load optimization resources of the server to the latest;

[0022] A load optimization resource query module, wherein the server provides a load optimization resource query service to the terminal, wherein the load optimization resource query service allows the terminal to query whether the local load optimization resource is the latest based on at least one beacon of the load optimization resource.

[0023] The present invention also provides a method for reducing server load, wherein a terminal requests data resources from a server during use, and the method comprises the steps of:

[0024] The terminal determines whether the local load optimization resource of the terminal is equal to the latest load optimization resource of the server through the load optimization resource query service provided by the server, so as to determine whether the terminal requests the load optimization resource from the server.

[0025] The load optimization resource query service allows the terminal to query whether the local load optimization resource is the latest based on at least one beacon of the load optimization resource. The load optimization resource is a data resource that can reduce the server load when the terminal requests the data resource from the server.

[0026] In one embodiment of the method for reducing server load of the present invention, the step of the terminal requesting the load optimization resource from the server further comprises:

[0027] The timing at which the terminal requests the load optimization resource from the server is adjusted according to the user usage history data of the terminal.

[0028] In one embodiment of the method for reducing server load of the present invention, the step of adjusting the timing of the terminal requesting the load optimization resource from the server according to the user usage history data of the terminal further comprises:

[0029] According to the user usage history data of the terminal, the user's behavior is predicted through a second neural network model, and the timing of the terminal requesting the server for load optimization resources associated with the user's behavior is adjusted.

[0030] The present invention also provides a device for reducing server load, which is used to implement any of the above methods, including:

[0031] A load optimization resource acquisition module, wherein the terminal determines whether the local load optimization resources of the terminal are equal to the latest load optimization resources of the server through the load optimization resource query service; when the local load optimization resources of the terminal are the latest, the terminal does not request the load optimization resources from the server; when the local load optimization resources of the terminal are not the latest, the terminal requests the load optimization resources from the server.

[0032] The present invention also provides a charging system, comprising: at least one charging station, and a management cloud platform for managing the charging station, wherein a terminal is communicatively connected to the management cloud platform, and charging equipment of the charging station provides charging / discharging services for the new energy equipment corresponding to the terminal, and also includes the aforementioned device for reducing server load.

[0033] The present invention also discloses a storage medium for storing a computer control program, wherein the computer control program is used to execute the steps of any of the aforementioned methods.

[0034] The present invention also provides a computer program product, comprising a computer program, which implements the steps of any one of the above methods when executed by a processor.

[0035] Through the aforementioned method, device and charging system for reducing server load, load optimization resources are determined according to the request situation of data resources and the attributes of data resources, and the server provides a load optimization resource query service to the terminal. The query service uses the beacon of the load optimization resource to provide the terminal with whether the local load optimization resources of the terminal are the latest; the terminal determines whether the local load optimization resources of the terminal are equal to the latest load optimization resources of the server through the load optimization resource query service. When the local load optimization resources of the terminal are not the latest, the terminal requests the load optimization resources from the server, which effectively reduces a large amount of data resources transmitted between the terminal and the server, thereby improving the user experience.

[0036] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and description and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of a method for reducing server load according to an embodiment of the present invention is shown.

[0038] Figure 2 A schematic diagram illustrating determining load optimization resources through a neural network model according to an embodiment of the present invention.

[0039] Figure 3 A flow chart is shown of a server updating a beacon of a load optimization resource after a change in one embodiment of the present invention.

[0040] Figure 4 A flow chart of a server updating a beacon of a city-dimensional load optimization resource in one embodiment of the present invention is shown.

[0041] Figure 5 A flow chart of a server updating a beacon of load optimization resources at a power plant level according to an embodiment of the present invention is shown.

[0042] Figure 6 A flow chart of a server updating a beacon of a load optimization resource in an application dimension according to an embodiment of the present invention is shown.

[0043] Figure 7A flow chart is shown in which a terminal determines whether a local load optimization resource of the terminal is the latest through a load optimization resource query service in one embodiment of the present invention.

[0044] Figure 8 A flow chart is shown in which a terminal determines whether a local load optimization resource of the terminal is the latest through a load optimization resource query interface in one embodiment of the present invention.

[0045] Fig. 9 A flow chart of analyzing user usage habits through a neural network model in one embodiment of the present invention is shown.

[0046] Fig.10 A flow chart showing the timing of optimizing resources by adjusting the load associated with user behavior through a neural network model in one embodiment of the present invention.

[0047] Fig.11 A block diagram of a device for reducing server load in one embodiment of the present invention is shown.

[0048] Fig.12 A block diagram of a device for reducing server load in another embodiment of the present invention is shown.

[0049] Fig.13 A block diagram of a charging system according to an embodiment of the present invention is shown.

[0050] Wherein, the reference numerals are:

[0051] S1-S6…Steps

[0052] S31-S35...Steps

[0053] S201-S207…Steps

[0054] S211-S215…Steps

[0055] S221-S224…Steps

[0056] S41-S45…Steps

[0057] S411-S418…Steps

[0058] S601-S604...Steps

[0059] S611-S616…Steps

[0060] 1…Charging station

[0061] 2…Manage the cloud platform

[0062] 3…New energy equipment

[0063] 4…Charging equipment

[0064] 5…Terminal

[0065] 6…Resources provide services

[0066] 7…Content publishing management services

[0067] 8…Data analysis services

[0068] 10, 20…Reducing server load

[0069] 11…Load Optimization Resource Determination Module

[0070] 12…Load Optimization Resource Update Module

[0071] 13…Load Optimization Resource Query Module

[0072] 21…Load Optimization Resource Acquisition Module

[0073] 100…Charging system

[0074] Specific implementation issues

[0075] The following specific embodiments and accompanying drawings are used to illustrate the embodiments disclosed in the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. However, the contents disclosed below are not intended to limit the scope of protection of the present invention. Without departing from the spirit of the present invention, those skilled in the art can implement the present invention with other different embodiments based on different viewpoints and applications.

[0076] For the sake of clarity, the drawings of the present invention are simplified schematic diagrams to illustrate the basic structure of the present invention. Therefore, the structures shown in the drawings of the present invention are not drawn according to the shape and size ratio of the actual implementation. For example, the size of a specific component is exaggerated for the convenience of explanation.

[0077] In addition, it should be understood that when a component such as a layer, film, region, or substrate is referred to as being "on" or "connected to" another component, it can be directly on or connected to the other component, or intermediate components may also exist. In contrast, when a component is referred to as being "directly on" or "directly connected to" another component, there are no intermediate components. As used herein, "connected" can refer to physical and / or electrical connections. Furthermore, "electrically connected" or "coupled" can mean the presence of other components between two components.

[0078] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as understood by a person of ordinary skill in the art to which the invention belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology and the present invention, and will not be interpreted as an idealized or overly formal meaning unless explicitly defined as such herein.

[0079] In addition, it should be understood that although the terms "first", "second", "third", etc. may be used herein to describe various components, parts, regions, layers and / or parts, these components, parts, regions, and / or parts should not be limited by these terms. These terms are only used to distinguish one component, part, region, layer or part from another component, part, region, layer or part. Therefore, the "first component", "component", "region", "layer" or "part" discussed below may be referred to as a second component, part, region, layer or part without departing from the teachings of this article.

[0080] It should be understood that references in the specification to "one embodiment", "an embodiment", "an example embodiment", etc., mean that the embodiment being described may include certain features, structures, or characteristics, but does not necessarily include these certain features, structures, or characteristics. In addition, such expressions do not refer to the same embodiment. Further, when a certain feature, structure, or characteristic is described in conjunction with an embodiment, it is indicated that it is within the knowledge of those skilled in the art to combine such feature, structure, or characteristic into other embodiments, whether or not it is explicitly described.

[0081] Certain words are used in the specification and subsequent claims to refer to specific modules, components or parts. It should be understood by those of ordinary skill in the art that technical users or manufacturers may refer to the same module, component or part by different nouns or terms. This specification and subsequent claims do not use differences in names as a way to distinguish modules, components or parts, but use differences in the functions of modules, components or parts as the criteria for distinction. "Including" and "including" mentioned throughout the specification and subsequent claims are open-ended terms and should be interpreted as "including but not limited to". In addition, the word "connect" here includes any direct and indirect electrical connection means. Indirect electrical connection means include connection through other devices.

[0082] Furthermore, in the following specification and claims, reference will be made to a number of terms which shall be defined as having the following meanings. The singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. "Optional" or "optionally" means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event occurs and instances where it does not.

[0083] As the number of active users in the charging system grows, the server receives a large number of data resource requests from terminal applications, which increases the server burden and causes server performance degradation, resulting in slow terminal application page loading and information update delays. To solve the problem of increased server load and performance degradation, please refer to Figure 1 A method for reducing server load is proposed. The server has multiple data resources. The terminal 5 requests data resources from the server during use. The server provides the corresponding data resources to the terminal 5 according to the request of the terminal 5, including the following steps:

[0084] Step S1: Determine at least one load optimization resource according to the request situation of the data resource and the attribute of the data resource, and the load optimization resource is a data resource that can reduce the server load when the terminal 5 requests the data resource from the server; Step S2: Update the server's load optimization resource to the latest; Step S3: The server provides a load optimization resource query service to the terminal 5, wherein the load optimization resource query service provides the terminal 5 with a query of whether the local load optimization resource is the latest according to at least one beacon of the load optimization resource; Step S4: The terminal 5 determines whether the local load optimization resource of the terminal 5 is equal to the latest load optimization resource of the server through the load optimization resource query service; Step S5: If the local load optimization resource of the terminal 5 is the latest, the terminal 5 does not request the load optimization resource from the server; Step S6: If the local load optimization resource of the terminal 5 is not the latest, the terminal 5 requests the load optimization resource from the server. In one embodiment, the order of step S1 and step S2 can be reversed. In one embodiment, the server may be a management cloud platform 2 (hereinafter referred to as the management cloud platform 2), which includes at least content publishing management service 7 (CMS), power station management service (SMS) and other data resource content management services, responsible for publishing and managing various data resources, and the server may also be other backend architectures, not limited to this. In one embodiment, at least one beacon of the load optimization resource may be an update timestamp of the load optimization resource, that is, the update time corresponding to the load optimization resource, or the beacon may also be a version record corresponding to the load optimization resource, or the result obtained by hashing the load optimization resource, not limited to this.

[0085] In one embodiment, when determining at least one load optimization resource according to the request situation of the data resource and the attribute of the data resource in step S1: the load optimization resource is determined according to the request frequency of the data resource and the update frequency of the data resource. The beneficial effect of this technical solution is that the load optimization resource can be screened by the request frequency of the data resource and the update frequency of the data resource, which can effectively reduce the load of the server and has almost no impact on the user experience.

[0086] In this embodiment, when determining the load optimization resources according to the request frequency of the data resources and the update frequency of the data resources, a data resource list may be defined first, see Table 1.

[0087] Table 1

[0088] Field Tags name type Nullable Example Value RD_ID Primary Key ID varchar(36) no guid RD_APPID App ID varchar(36) no RD_RMID Resource Definition ID varchar(36) no RD_DimValue Latitude value varchar(100) no RD_DimContent Dimension name varchar(100) yes RD_ChangeTime Update time datetime2 no RD_Property Resource Priority Int no 1～100 RD_ChangeDesc describe nvarchar(200) yes

[0089] Then, according to the defined data resource list, various data resources in the server are sorted out to obtain the results shown in Table 2.

[0090] Table 2

[0091]

[0092]

[0093] According to the contents of Table 2, the calling frequency and updating frequency of data resources are counted, and then the load optimization resources can be determined according to the calling frequency and updating frequency of data resources by setting thresholds, clustering, scoring, etc. Taking the threshold method as an example, thresholds are set for the calling frequency and updating frequency. For example, the calling frequency threshold is set to 45QPS (queries per second), and the updating frequency threshold is set to 5 times / day, thereby filtering out data resources with high calling frequency and low updating frequency, and determining that the filtered data resources are load optimization resources.

[0094] In one embodiment, when determining at least one load optimization resource based on the request status of the data resource and the attribute of the data resource in step S1: predicting at least one load optimization resource through a first neural network model based on the request status of the data resource and the attribute of the data resource.

[0095] In this embodiment, if Figure 2As shown, the process of determining load optimization resources through the first neural network model may include registration, collection, evaluation decision, output and reminder warning stages. In the registration stage, a new API interface is added to the current management cloud platform 2 technology stack framework, and the new API interface is used to collect data resource requests and data resource attributes (described in detail later). In the collection stage, during the operation of the management cloud platform 2 technology stack framework, the interface access monitoring and management are performed, and the new API interface and the existing API interface will automatically report the key parameters of the collection (including the access frequency of data resources, open level, resource update frequency, resource priority, isolation dimension, data persistence, unit granularity, text / multimedia, data privacy, security level, resource size, access priority and other information). The management method includes periodic active reporting, buried sampling, plug-in collection and other means, but is not limited to this, and the data of terminal 5 users including mini programs, html5 pages, APPs, etc. are collected. In the evaluation and decision-making stage, by collecting a large number of data resource request conditions and key parameters of the attributes of the data resources, and using decision parameters in the key parameters (decision parameters may include access frequency, open level, resource update frequency, resource priority, unit granularity, text / multimedia, data privacy, security level, resource size, access priority of data resources, and decision parameters can be adjusted according to actual conditions) to input the first neural network model, the first neural network model predicts the load optimization resources. Before the evaluation decision, the collected key parameters are used to train the model, the key parameters used to participate in the training, data privacy, such as sensitive identity information is highly private and not suitable for training, open level, representing the identity relevance of data resources, such as member avatars, nicknames, etc. The open level is higher, while the age and certification photos have a lower open level. After cleaning and processing the key parameters, the first neural network model is trained. In addition, the first neural network model can be any one or more of a feedforward neural network, a BP network, a long short-term memory neural network, a graph neural network, a convolutional neural network, and a recurrent neural network model, but is not limited thereto. In the output stage, the first neural network model outputs the prediction results of the load optimization resources, and then monitors the effect of reducing the server burden after using the load optimization resources through indicators such as estimated bandwidth savings, average traffic / page savings, and estimated QPS savings, and updates the decision parameters and / or key parameters. In the reminder and warning stage, when the first neural network model predicts the emergence of new suitable load optimization resources, it reminds you to use the newly added load optimization resources to reduce the server load. For example, when a new data resource is added, such as an advertising display, the data resource is not initially identified as a load optimization resource to reduce the server load. After the function is launched and runs for a period of time, the first neural network model determines that the data resource is predicted to be a load optimization resource, and sends notifications through the system, email, work group and other media to remind it to connect to the load optimization resource to reduce the server load.The beneficial effect of the above implementation scheme is that the load optimization resources can be determined dynamically, avoiding the lag of manually formulating the load optimization resources.

[0096] In one embodiment, if Figure 3 As shown, in step S2, when the load optimization resources of the server are updated to the latest, the data resource providing service 6 stores the latest data resources to the server and updates the load optimization resources of the server to the latest: when the load optimization resources of the server are changed, the server updates the beacon of the changed load optimization resources.

[0097] In this embodiment, when the data resource providing service 6 updates the data resource, the load optimization resource changes in step S31, the content publishing service receives the data resource change request in step S32, and the content publishing service checks whether the data resource has been registered in step S33. If not, the content publishing service performs step S34 to record the log and trigger an early warning. If it has been registered, the content publishing service performs step S35 to update the beacon in the database and update the cache. The beneficial effect of this embodiment is that the data resources stored in the server are recorded through the beacon of the data resource, and an early warning will be issued if an unregistered data resource is stored, which can effectively prevent the server from storing irrelevant data resources and avoid the waste of server storage resources.

[0098] In one embodiment, when the load optimization resources of a server change, when the server updates the beacon of the changed load optimization resources, the server updates the beacon of the load optimization resources according to the scope dimension and / or object dimension of the service it provides.

[0099] In this embodiment, the server may include national dimension, city dimension, power station dimension, etc. according to the dimension of the scope of the service it provides. The server may include application APP dimension, mini-program, html5 page, specific user dimension, etc. according to the dimension of the object of the service it provides. The beneficial effect of this embodiment is that the server provides load optimization resources of multiple dimensions, updates the load optimization resources, and when the terminal 5 obtains the load optimization resources of the corresponding dimension, it does not need to obtain the data resources it does not need, and at the same time reduces the burden on the server.

[0100] In one embodiment, if Figure 4As shown, the server updates the load optimization resource of the city dimension in the following steps: Step S201 Data resource providing service 6 updates the data resource, Step S202 Data resource providing service 6 obtains the application identifier (APPID), resource code (RMID), dimension list (DimValueList, corresponding to DimValue in the data resource detailed table), and timestamp (Timestamp) of the updated data resource, and calls the update load optimization resource service. In step S203, the content publishing management service 7 obtains the application identifier, resource code, city list (parsed and obtained through DimValueList), and timestamp. In step S204, the city list is looped until the loop is completed. In step S205, it is determined whether the city is "ALL". If so, step S206 is executed to update the timestamp of the load optimization resources of all cities. If not, step S207 is executed to update the timestamp of the load optimization resources of the specified city.

[0101] In one embodiment, if Figure 5 As shown, the server updates the load optimization resources of the power station dimension in the following steps: Step S211 Data Resource Provide Service 6 updates the data resources, Step S212 Data Resource Provide Service 6 obtains the application identifier (APPID), resource code (RMID), dimension list (DimValueList, corresponding to DimValue in the data resource detailed table), timestamp (Timestamp) of the updated data resources, and calls the update load optimization resource service. In step S213, the content publishing management service 7 obtains the application identifier, resource code, power station list (parsed and obtained through DimValueList), and timestamp. In step S214, the power station list is looped until the loop is completed, and in step S215, the timestamp of the load optimization resources of the specified power station is updated.

[0102] In one embodiment, if Figure 6 As shown, the server updates the load optimization resource of the specified application dimension in the following steps: Step S221 Data resource providing service 6 updates the data resource, Step S222 Data resource providing service 6 obtains the application identifier (APPID), resource code (RMID), and timestamp (Timestamp) of the updated data resource, and calls the update load optimization resource service. In step S223, the content publishing management service 7 obtains the application identifier, resource code, and timestamp. In step S224, the timestamp of the load optimization resource of the specified application is updated.

[0103] In one embodiment, the server can also update the load optimization resources of the user dimension, that is, update the load optimization resources through the application identifier, resource code, dimension list (specify the user identifier in the dimension list), and timestamp, thereby providing customized services for users.

[0104] In one embodiment, in step S3, the server provides a load optimization resource query service to the terminal 5. The server provides a load optimization resource query interface for the terminal 5 to determine whether the local load optimization resources of the terminal 5 are equal to the latest load optimization resources of the server according to the beacon.

[0105] In one embodiment, the server provides a load optimization resource query interface for the terminal 5 to determine whether the local load optimization resource of the terminal 5 is equal to the latest load optimization resource of the server according to the beacon. The load optimization resource query interface can be used by the terminal 5 to determine whether the local load optimization resource of the terminal 5 is equal to the latest load optimization resource of the server according to the beacon in the scope dimension and / or object dimension. The beneficial effect of the above implementation scheme is that the terminal 5 requests the data resources required in the current scenario according to the required dimension, avoiding increasing the load of the server.

[0106] In one embodiment, if Figure 7 As shown, when the terminal 5 calls the load optimization resource query interface provided by the server and determines whether the local load optimization resource is the latest, the terminal 5 calls the load optimization resource query interface in step S41, and the terminal 5 compares the timestamp of the load optimization resource in the cache with the timestamp of the latest load optimization resource in the system in step S42. In step S43, it is determined whether the load optimization resource needs to be updated. If the timestamp of the load optimization resource in the cache compared by the terminal 5 is not equal to the timestamp of the latest load optimization resource in the system, it needs to be updated. If it does not need to be updated, execute step S44 to construct an empty return value. If it needs to be updated, construct a return value of the load optimization resource corresponding to the timestamp of the latest load optimization resource in the system.

[0107] In one embodiment, if Figure 8As shown, the terminal 5 calls the load optimization resource query interface provided by the server, and determines whether the local load optimization resources are the latest in the application dimension, so as to determine which load optimization resources need to be updated. In step S411, the terminal 5 checks the resource request list (i.e., whether it is the latest load optimization resource) according to the application identifier and calls the load optimization resource query interface. In step S412, the content publishing management service 7 obtains the application identifier and the resource check request list. In step S413, the resource check request list is looped. If the loop is not completed, step S414 is executed to determine whether the timestamp of the load optimization resource in the resource check request list is equal to the timestamp of the corresponding latest load optimization resource in the system. If they are equal, step S413 is executed. If they are not equal, step S415 is executed to add the load optimization resource in the resource check request list to the change load optimization resource list. When step S413 completes the resource check request list loop, step S416 is executed to return the change load optimization resource list to the terminal 5. The terminal 5 executes step S417 to loop the change load optimization resource list, and in step S418, requests the changed load optimization resource and updates the local cache. When step S417 completes the cycle of changing the load optimization resource list, the terminal 5 has completed calling the load optimization resource query interface provided by the server, judging whether the local load optimization resources are the latest in the application dimension, and requesting the server to obtain the load optimization resources that are not the latest and update the local cache. In one embodiment, the terminal 5 can also call the load optimization resource query interface provided by the server, judge whether the local load optimization resources are the latest in the city dimension, power station dimension, and user dimension, and request the changed load optimization resources from the server.

[0108] In one embodiment, if the local load optimization resource of the terminal 5 is not the latest, the step of the terminal 5 requesting the load optimization resource from the server further includes:

[0109] The timing of the terminal 5 requesting the server for load optimization resources is adjusted according to the user usage history data of the terminal 5. In this embodiment, the user usage history data, for example, records the behavior data of the user using the terminal 5 application to charge the new energy device 3, including the charging time, charging degree, charging location, recharge amount, charging duration and other data, and the daily usage data includes the frequency of paying attention to advertisements, viewing power station information, viewing the frequency of power station-related group chats, viewing the frequency of common problems, the user's voice when using the application and other data. The above data and the personal information and other data recorded by the user on the management cloud platform 2 are combined to predict the user's behavior, and then adjust the timing of the terminal 5 requesting the server for load optimization resources, which may have a positive impact on the user's next behavior. For example, according to the location data, the user is driving a new energy vehicle to the charging station 1, and the application obtains that the new energy vehicle is low on power and needs to be charged. There is currently a charging discount activity suitable for the current user. The local advertising data resources (load optimization resources) of the terminal 5 are not the latest. Normally, the user needs to open the application or click a page to refresh and display the activity. Because the local advertising data resources of the current terminal 5 are not the latest, the latest data resources need to be obtained from the server. During this acquisition process, the user may not see the charging discount activity advertisement in time due to data loading time or slow network speed. When the data analysis service 8 located at the terminal 5 or the server recognizes the scene, the terminal 5 can adjust the timing of obtaining the advertising data resources. When the terminal 5 is started or other times in advance of the original display of the advertising data resources, the terminal 5 calls the load optimization resource query interface provided by the server to update the advertising data resources, so that the user can directly see the latest, locally cached charging discount activity advertisement. The beneficial effect of this embodiment is that while reducing the server load as a whole through load optimization resources, personalized services are provided to users. In one embodiment, the second neural network model can be used to predict the user's behavior, and then the timing of the terminal 5 requesting the load optimization resources from the server is adjusted according to the predicted user behavior, and the corresponding load optimization resources that have a positive impact on the user behavior are obtained first. In addition, the second neural network model can be any one or more of a feedforward neural network, a BP network, a long short-term memory neural network, a graph neural network, a convolutional neural network, and a recurrent neural network model, but is not limited thereto.

[0110] In one embodiment, if Fig. 9 and Fig.10As shown, when adjusting the timing of the terminal 5 requesting the load optimization resource from the server according to the user usage history data of the terminal 5, the user's behavior is predicted by the second neural network model according to the user usage history data of the terminal 5, and the timing of the server providing the load optimization resource associated with the user's behavior is adjusted. In this embodiment, when predicting the user's behavior by the second neural network model according to the user usage history data of the terminal 5, the user's historical behavior record is collected in step S601, and the collection method can use the API dotting method, and the user's historical behavior record is reported to the data analysis service 8 in step S602. The user's historical behavior record is stored in the database in step S603, and the data is cleaned in step S604, and the second neural network model is trained. The trained second neural network model is used to analyze the user's usage habits and predict the user's behavior. When the user uses the application, in step S611, the load optimization resource query interface can be called according to at least the application identifier, city code, current timestamp, user number, and location information. In step S612, when the content publishing management service 7 provides the load optimization resource query service, the data analysis service 8 is called to predict the user's behavior at least by the user number and location information. In step S613, the data analysis service 8 predicts the user behavior through the second neural network model at least according to the user number and location information and the aforementioned other decision information corresponding to the user number. In step S614, the load optimization resource list is obtained according to the timestamp query, and the load optimization resource list is adjusted according to the user behavior. In one embodiment, for example, during the application startup process, the load optimization resource list can be adjusted according to the priority method, such as specifying the priority of each load optimization resource in the load optimization resource list, and specifying the priority of the predicted user behavior, filtering out the priority of the load resources less than the user behavior priority, and only requesting resources greater than the user behavior priority, such as the above-mentioned user will let the user see the latest, locally cached advertisement of the charging promotion activity in advance before charging the new energy device 3. In addition to using the priority method, the load optimization resource list can also be adjusted in a user behavior correlation method. When adjusting the load optimization resource list, in addition to filtering certain load optimization resources, the order of the load optimization resources in the list can also be adjusted, and the order of the load optimization resources in the list can also be adjusted, and the method is not limited to this. In step S615, the load optimization resource list is changed and looped, and in step S616, the load optimization resources that are changed are requested and the local cache is updated. When step S615 completes the cycle of changing the load optimization resource list, terminal 5 completes calling the load optimization resource query interface provided by the server, determines whether the local load optimization resources are the latest in the application dimension, and requests the server to obtain load optimization resources that are not the latest and update the local cache.

[0111] In one embodiment, after the terminal requests the load optimization resource from the server, the step further includes: the server adjusts the timing of providing the load optimization resource to the corresponding terminal according to the load situation. When the server responds to the user's request for load optimization resources based on the priority of each load optimization resource in the load optimization resource list and the predicted priority of the user behavior, if the current server load pressure is large, the server will preferentially provide the load optimization resources corresponding to the load optimization resources in the load optimization resource list whose priority is greater than the user behavior priority, and delay the provision of the corresponding load optimization resources whose priority is less than the user behavior priority of the remaining load optimization resources, so as to reduce the server pressure without affecting the user's reception of important information.

[0112] In order to better reduce the server load increase and performance degradation caused by a large increase in resource requests, such as Fig.11 As shown, a device 10 for reducing server load is also disclosed, which is used to implement the aforementioned method for reducing server load, including: a load optimization resource determination module 11, a load optimization resource update module 12, a load optimization resource query module 13 and a load optimization resource acquisition module 21, the load optimization resource determination module 11 is used to determine at least one load optimization resource according to the request situation of the data resource and the attribute of the data resource, the load optimization resource is a data resource that can reduce the server load when the terminal 5 requests the data resource from the server; the load optimization resource update module 12 is used to update the server's load optimization resources to the latest; the load optimization resource query module 13 is used for the server to provide a load optimization resource query service to the terminal 5, wherein the load optimization resource query service is based on at least one beacon of the load optimization resource for the terminal 5 to query whether the local load optimization resource is the latest.

[0113] In order to better reduce the server load increase and performance degradation caused by a large increase in resource requests, such as Fig.12 As shown, a device 20 for reducing server load is also disclosed, and a load optimization resource acquisition module 21 is used for the terminal 5 to determine whether the local load optimization resources of the terminal 5 are equal to the latest load optimization resources of the server through the load optimization resource query service; when the local load optimization resources of the terminal 5 are the latest, the terminal 5 does not request the load optimization resources from the server; when the local load optimization resources of the terminal 5 are not the latest, the terminal 5 requests the load optimization resources from the server.

[0114] In order to better reduce the server load increase and performance degradation caused by a large increase in resource requests, such as Fig.13 As shown, a charging system 100 is also provided, including: at least one charging station 1, and a management cloud platform 2 for managing the charging station 1, a terminal 5 is communicatively connected with the management cloud platform 2, and a charging device 4 of the charging station 1 provides charging / discharging services for a new energy device 3 corresponding to the terminal 5, and also includes the aforementioned server load reduction device 10.

[0115] In order to better reduce the load increase and performance degradation of the server caused by a large number of resource requests, a storage medium is also provided for storing a computer control program, and the computer control program is used to perform the steps of any of the above methods. The above computer program can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0116] In order to better reduce the load increase and performance degradation of the server caused by a large increase in resource requests, a computer program product is also provided, including a computer program, which implements the steps of any of the above methods when executed by a processor. The above-mentioned processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components and other chips, or a combination of the above-mentioned chips.

[0117] The above-mentioned method, device and charging system for reducing server load determine the load optimization resources according to the request situation of data resources and the attributes of data resources. The server provides a load optimization resource query service to the terminal. The query service uses the beacon of the load optimization resource to provide the terminal with whether the local load optimization resources of the terminal are the latest. The terminal determines whether the local load optimization resources of the terminal are equal to the latest load optimization resources of the server through the load optimization resource query service. When the local load optimization resources of the terminal are not the latest, the terminal requests the load optimization resources from the server, which effectively reduces a large amount of data resources transmitted between the terminal and the server, thereby improving the user experience.

[0118] The above disclosed contents are only preferred feasible embodiments of the present invention, and do not limit the scope of the patent application of the present invention. Therefore, all equivalent technical changes made using the contents of the specification and drawings of the present invention fall within the scope of the patent application of the present invention.

Claims

1. A method for reducing server load, wherein the server has multiple data resources, and the server provides corresponding data resources to a terminal according to a request of the terminal, characterized in that: The method comprises the following steps: Determine at least one load optimization resource according to the request status of the data resource and the attribute of the data resource, wherein the load optimization resource is a data resource that can reduce the server load when the terminal requests the data resource from the server; Updating the load optimization resources of the server to the latest; The server provides a load optimization resource query service to the terminal, wherein the load optimization resource query service allows the terminal to query whether the local load optimization resource is the latest according to at least one beacon of the load optimization resource.

2. The method according to claim 1, characterized in that The step of determining at least one load optimization resource according to the request status of the data resource and the attribute of the data resource further includes: A load optimization resource is determined according to a request frequency of the data resource and an update frequency of the data resource.

3. The method according to claim 1 or 2, characterized in that: The step of determining at least one load optimization resource according to the request status of the data resource and the attribute of the data resource further includes: According to the request status of the data resource and the attribute of the data resource, the at least one load optimization resource is predicted by a first neural network model.

4. The method according to claim 1, wherein the data resource providing service stores the latest data resources to the server, characterized in that: The step of updating the load optimization resources of the server to the latest one further comprises: When the load optimization resource of the server changes, the server updates the beacon of the changed load optimization resource.

5. The method according to claim 4, characterized in that When the load optimization resource of the server changes, the step of updating the beacon of the changed load optimization resource by the server further includes: The server updates the beacon of the load optimization resource according to the scope dimension and / or object dimension of the service it provides.

6. The method according to claim 5, characterized in that The step of the server providing a load optimization resource query service to the terminal further includes: The server provides a load optimization resource query interface for the terminal to determine whether the local load optimization resources of the terminal are equal to the latest load optimization resources of the server according to the beacon within the range dimension and / or object dimension.

7. The method according to claim 1, characterized in that The step of the server providing corresponding data resources to the terminal according to the request of the terminal further includes: The timing at which the server provides the load optimization resource to the terminal is adjusted according to the user usage history data of the terminal.

8. A method for reducing server load, wherein a terminal requests data resources from a server during use, characterized in that: The method comprises the steps of: The terminal determines whether the local load optimization resource of the terminal is equal to the latest load optimization resource of the server through the load optimization resource query service provided by the server, so as to determine whether the terminal requests the load optimization resource from the server. The load optimization resource query service allows the terminal to query whether the local load optimization resource is the latest based on at least one beacon of the load optimization resource. The load optimization resource is a data resource that can reduce the server load when the terminal requests the data resource from the server.

9. The method according to claim 8, characterized in that The step of the terminal requesting the load optimization resource from the server further comprises: The timing at which the terminal requests the load optimization resource from the server is adjusted according to the user usage history data of the terminal.

10. The method according to claim 9, characterized in that The step of adjusting the timing of the terminal requesting the load optimization resource from the server according to the user usage history data of the terminal further comprises: According to the user usage history data of the terminal, the user's behavior is predicted through a second neural network model, and the timing of the terminal requesting the server for load optimization resources associated with the user's behavior is adjusted.

11. A device for reducing server load, used to implement the method according to any one of claims 1 to 7, characterized in that: include: A load optimization resource determination module, configured to determine at least one load optimization resource according to the request status of the data resource and the attribute of the data resource, wherein the load optimization resource is a data resource that can reduce the server load when the terminal requests the data resource from the server; A load optimization resource updating module, used for updating the load optimization resources of the server to the latest; A load optimization resource query module, wherein the server provides a load optimization resource query service to the terminal, wherein the load optimization resource query service allows the terminal to query whether the local load optimization resource is the latest based on at least one beacon of the load optimization resource.

12. A device for reducing server load, used to implement the method according to any one of claims 8 to 10, characterized in that: include: A load optimization resource acquisition module, wherein the terminal determines whether the local load optimization resource of the terminal is equal to the latest load optimization resource of the server through the load optimization resource query service; When the local load optimization resource of the terminal is the latest, the terminal does not request the load optimization resource from the server; when the local load optimization resource of the terminal is not the latest, the terminal requests the load optimization resource from the server.

13. A charging system comprising: At least one charging station and a management cloud platform for managing the charging station, the terminal is communicatively connected to the management cloud platform, and the charging equipment of the charging station provides charging / discharging services for the new energy equipment corresponding to the terminal, characterized in that it also includes a server load reduction device as described in claims 11 and 12.

14. A storage medium for storing a computer control program, characterized in that: The computer control program is used to execute the steps of the method according to any one of claims 1 to 10.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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