Data processing method and device and electronic equipment

Abstract

The invention provides a data processing method and device and electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: receiving a data transmission request, and encrypting to-be-processed data to obtain encrypted data; obtaining service quality parameters of a plurality of second servers, wherein the service quality parameters comprise interface response time, channel encryption strength and server load rates; based on a trained weight model, obtaining weight values of a plurality of second servers according to the service quality parameters; selecting a target server according to the weight values of the plurality of second servers; and sending the encrypted data to the target server, so that the target server processes the encrypted data. The first server encrypts the to-be-processed data, so that the security of data transmission is improved, and the second server capable of improving the data processing speed is determined through the service quality parameter and the weight model, so that the data processing speed is improved on the premise of ensuring the security of data transmission.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and particularly relates to a data processing method and device and electronic equipment. BACKGROUND

[0002] With the rapid development of information society, data has become an indispensable key resource in various industries. Under this background, data security issues have gradually become an important issue that needs to be concerned in various fields. Especially in some specific application scenarios, such as vehicle networks in intelligent transportation systems, the security of data is particularly critical. Usually, a server is needed to collect data from different devices or sensors, and to securely transmit the collected information to another server for further processing and analysis.

[0003] However, how to further improve the speed of data processing on the basis of ensuring the security of data transmission has become a technical problem to be solved at present. SUMMARY

[0004] The present disclosure provides a data processing method, device and electronic equipment, which improves the speed of data processing at least on the premise of ensuring the security of data transmission.

[0005] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a data processing method is provided, applied to a first server, comprising: receiving a data transmission request, the data transmission request carrying to-be-processed data; performing encryption processing on the to-be-processed data to obtain encrypted data; obtaining service quality parameters of a plurality of second servers, the service quality parameters including interface response time, channel encryption strength and server load rate; obtaining weight values of the plurality of second servers based on a trained weight model according to the service quality parameters; selecting a target server according to the weight values of the plurality of second servers; and sending the encrypted data to the target server, so that the target server processes the encrypted data.

[0007] According to another aspect of the present disclosure, a data processing method applied to a second server is provided, comprising: receiving encrypted data sent by a first server, the encrypted data being obtained by the first server performing encryption processing on to-be-processed data after receiving a data transmission request, wherein the data transmission request carries the to-be-processed data; processing the encrypted data to obtain a processing result; and sending the processing result to the first server; wherein the second server is a target server determined by a weight value of the second server, and the weight value of the second server is obtained based on a trained weight model according to a quality of service parameter, the quality of service parameter including an interface response time, a channel encryption strength and a server load rate.

[0008] According to still another aspect of the present disclosure, a data processing apparatus applied to a first server is provided, comprising: a first receiving module configured to receive a data transmission request, the data transmission request carrying to-be-processed data; an encryption module configured to perform encryption processing on the to-be-processed data to obtain encrypted data; an obtaining module configured to obtain quality of service parameters of a plurality of second servers, the quality of service parameters including an interface response time, a channel encryption strength and a server load rate; a calculation module configured to obtain weight values of the plurality of second servers based on a trained weight model according to the quality of service parameters; a selection module configured to select a target server according to the weight values of the plurality of second servers; and a first sending module configured to send the encrypted data to the target server so that the target server processes the encrypted data.

[0009] According to still another aspect of the present disclosure, a data processing apparatus applied to a second server is provided, comprising: a second receiving module configured to receive encrypted data sent by a first server, the encrypted data being obtained by the first server performing encryption processing on to-be-processed data after receiving a data transmission request, wherein the data transmission request carries the to-be-processed data; a processing module configured to process the encrypted data to obtain a processing result; and a second sending module configured to send the processing result to the first server; wherein the second server is a target server determined by a weight value of the second server, and the weight value of the second server is obtained based on a trained weight model according to a quality of service parameter, the quality of service parameter including an interface response time, a channel encryption strength and a server load rate.

[0010] According to still another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute any of the above-mentioned data processing methods by executing the executable instructions.

[0011] According to still another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the data processing method of any one of the preceding aspects.

[0012] According to still another aspect of the present disclosure, there is provided a computer program product comprising a computer program or computer instructions which are loaded and executed by a processor to cause a computer to implement the data processing method of any one of the preceding aspects.

[0013] In the embodiments of the present disclosure, the first server receives a data transmission request, and performs encryption processing on the data to be processed to obtain encrypted data; obtains service quality parameters of a plurality of second servers, the service quality parameters including interface response time, channel encryption strength and server load rate; obtains weight values of the plurality of second servers based on the trained weight model according to the service quality parameters; selects a target server according to the weight values of the plurality of second servers; and sends the encrypted data to the target server to enable the target server to process the encrypted data. The first server of the present disclosure increases the security of data transmission by encrypting the data to be processed, and determines the second server capable of improving the data processing speed through the service quality parameters and the weight model, thereby improving the data processing speed under the premise of ensuring the security of data transmission.

[0014] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A schematic diagram of an exemplary system architecture to which the data encryption method or the data processing method of the embodiments of the present disclosure can be applied is shown.

[0016] Figure 2 A flowchart of the data processing method of an embodiment of the present disclosure is shown.

[0017] Figure 3 A flowchart of the data processing method of an embodiment of the present disclosure is shown.

[0018] Figure 4 A flowchart of the data processing method of another embodiment of the present disclosure is shown.

[0019] Figure 5 A schematic diagram of a data processing device in an embodiment of the present disclosure is shown.

[0020] Figure 6 A schematic diagram of a data processing device in an embodiment of the present disclosure is shown.

[0021] Figure 7A structural block diagram of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0022] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the gist of the example implementations to those skilled in the art. The features, structures, or characteristics described in connection with the examples can be combined in any suitable manner in one or more implementations.

[0023] In addition, some of the blocks in the diagrams can be functional blocks that represent functions implemented by a processor, software, or any combination thereof (e.g., firmware). In this regard, functional blocks of the diagrams can be implemented using, for example, by hardware components such as a processor of a mobile device, a microprocessor, a microcontroller, a hardware logic circuit such as a gate, or any combination thereof.

[0024] It should be understood that each of the steps in the method implementations of the present disclosure can be performed in a different order, and / or in parallel. In addition, the method implementations can include additional steps and / or omit performing the steps shown. The scope of the present disclosure is not limited in this regard.

[0025] It should be noted that the terms "first", "second", and the like in the present disclosure are used only to distinguish different devices, modules, or units, and do not imply the order or interdependence of the functions performed by these devices, modules, or units.

[0026] It should be noted that the terms "one", "multiple", and the like in the present disclosure are illustrative and not limiting, and those skilled in the art should understand that "one" or "multiple" should be understood as "one or more" unless the context clearly indicates otherwise.

[0027] Figure 1 A schematic diagram of an exemplary system architecture to which the data processing method of the embodiments of the present disclosure can be applied is shown.

[0028] As Figure 1 shown, the system architecture can include a terminal device 101, a first server 102, and a second server 103, and the terminal device 101, the first server 102, and the second server 103 are communicatively connected through a network, which can be a wired network or a wireless network.

[0029] Optionally, the wireless or wired networks described above use standard communications technologies and / or protocols. The network typically comprises the Internet, although it also can be any network, including, but not limited to, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network, or a virtual private network (VPN). In some embodiments, the data exchanged over the network is represented using technologies and / or formats including, but not limited to, hypertext markup language (HTML), extensible markup language (XML), etc. In addition, conventional encryption technologies such as the secure sockets layer (SSL), transport layer security (TLS), virtual private networks (VPNs), Internet Protocol security (IPsec), etc. can be used to encrypt all or some of the links. In other embodiments, custom and / or proprietary data communications technologies and / or protocols can be used in place of, or in addition to, the ones described above.

[0030] The terminal device 101 that performs data transmission with the first server 102 can include, but is not limited to, a mobile device such as a smartphone, a tablet computer, a notebook computer, etc., and a smart speaker, a digital assistant, an AR (Augmented Reality) device, a VR (Virtual Reality) device, a smart wearable device, a drone, a camera, etc. that has a specific function or form. Alternatively, the terminal device 101 can also be a personal computer such as a laptop computer and a desktop computer, etc.

[0031] The first server 102 can be an intermediary device or system for data exchange and protocol conversion between different networks in a computer network. For example, the first server can be a gateway. The first server 102 can also be a server with computing capability. For example, the first server 102 can be an edge server.

[0032] The second server 103 can be a standalone physical server, a server cluster composed of multiple physical servers, or a distributed system, and can also be a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms. In some actual applications, the second server 103 can also be a server of a network platform, for example, a transaction platform, a live broadcast platform, a social platform, or an audio platform, and the like, and the present disclosure is not limited thereto. The second server 103 can be one server or a cluster formed by multiple servers, and the present disclosure does not limit the specific architecture of the server.

[0033] In some embodiments of the present disclosure, the first server 102 is configured to receive a data transmission request sent by the terminal device 101, the data transmission request carrying to-be-processed data; perform encryption processing on the to-be-processed data to obtain encrypted data; obtain quality of service parameters of the plurality of second servers 103, the quality of service parameters including interface response time, channel encryption strength, and server load rate; obtain weight values of the plurality of second servers 103 based on the trained weight model and the quality of service parameters; select a target server based on the weight values of the plurality of second servers 103; and send the encrypted data to the target server, so that the target server processes the encrypted data.

[0034] In some embodiments of the present disclosure, the second server 103 is configured to receive encrypted data sent by the first server, the encrypted data being obtained by the first server performing encryption processing on to-be-processed data after receiving a data transmission request, wherein the data transmission request carries the to-be-processed data; process the encrypted data to obtain a processing result; and send the processing result to the first server.

[0035] In addition, it should be noted that, Figure 1 The environment shown in the figure is only one application environment of the data processing method provided by the present disclosure. Figure 1 The number of terminal devices 101, first servers 102, and second servers 103 in the figure is only illustrative, and any number of terminal devices, first servers, and second servers can be provided according to actual needs.

[0036] Figure 2 The figure shows a flowchart of a data processing method according to an embodiment of the present disclosure, Figure 2 The execution subject of the method provided by the embodiment can be any electronic device, for example Figure 1 The first server 102 in the embodiment, but the present disclosure is not limited thereto. Referring to Figure 2The data processing method provided in the embodiments of the present disclosure includes S201 to S206.

[0037] S201, receiving a data transmission request, the data transmission request carrying to-be-processed data.

[0038] In the embodiments of the present disclosure, the data transmission request is a request sent by a terminal device to a first server, and is used to request the first server to select a second server with high processing speed and / or high success rate to process the to-be-processed data. The to-be-processed data can be data collected by the terminal device, or can be data stored on the terminal device.

[0039] For example, the terminal device is a drone that collects sensitive information (such as enemy troop movement information or geographic images for building planning), and the to-be-processed data can include data collected by the drone. For example, the enemy troop movement information, the geographic images or power images for building planning, and the like.

[0040] For example, the terminal device is a mobile phone, and the to-be-processed data can include data stored on the mobile phone. For example, member points stored on a member point management system or a fund amount stored on a fund management platform.

[0041] S202, performing encryption processing on the to-be-processed data to obtain encrypted data.

[0042] In the embodiments of the present disclosure, in order to improve the security of the to-be-processed data transmission, the to-be-processed data is encrypted. The embodiments of the present disclosure do not make specific limitations on the encryption algorithm used to encrypt the to-be-processed data.

[0043] For example, the to-be-processed data is encrypted by using a symmetric key encryption algorithm (Advanced Encryption Standard, AES) to obtain encrypted data.

[0044] For another example, in order to further increase security and confidentiality, the to-be-processed data is first encrypted, then the encrypted data is blinded, and then the blinded data is signed to obtain the encrypted data.

[0045] S203, obtaining service quality parameters of a plurality of second servers, the service quality parameters including interface response time, channel encryption strength and server load rate.

[0046] In the embodiments of the present disclosure, the service quality parameters are parameters used to reflect the service quality of the second server.

[0047] It should be noted that, purely from the perspective of time delay, the smaller the interface response time is, the better, the smaller the server load rate is, the better, and the smaller the channel encryption strength is, the better.

[0048] S204, obtaining weight values of the plurality of second servers according to the quality of service parameters based on the trained weight model.

[0049] The weight model can be any model as long as it can obtain the weight values of the plurality of second servers according to the quality of service parameters and select the second server with high processing speed and high reliability according to the weight values of the second servers.

[0050] S205, selecting the target server according to the weight values of the plurality of second servers.

[0051] In the embodiments of the present disclosure, the weight value represents the quality of service of the second server, and the higher the weight value, the better the quality of service.

[0052] For example, there are five second servers, and the weight values of the five second servers are 1, 0.9, 0.8, 0.65 and 0.7 respectively, and the second server corresponding to the weight value 1 is selected as the target server.

[0053] For example, there are five second servers, and the weight values of the five second servers are 1, 0.9, 0.8, 0.65 and 0.9 respectively, and the second server with the most usage frequency is selected as the target server from the first second server and the last second server with the maximum and same weight values, that is, when the weight values are the same, the commonly used second server is selected as the target server.

[0054] S206, sending the encrypted data to the target server to process the encrypted data.

[0055] In the embodiments of the present disclosure, in order to further increase the security of data transmission, a TLS 1.3 (Transport Layer Security 1.3, a protocol for protecting the security of communication on the Internet) encrypted channel can be selected to transmit the encrypted data.

[0056] In the embodiments of the present disclosure, the first server encrypts the data to be processed, thereby increasing the security of data transmission, and determines the second server that can improve the data processing speed through the quality of service parameters and the weight model, thereby improving the data processing speed under the premise of ensuring the security of data transmission.

[0057] The present disclosure will be described below through several exemplary embodiments.

[0058] In an exemplary embodiment, the data processing method provided in this disclosure may further include: training the initial weights of the weight model based on the ant colony algorithm to obtain the trained weight model.

[0059] In this embodiment of the disclosure, historical data can be obtained and used as a training set. The initial weights of the weight model are trained using the ant colony algorithm to obtain the target weights of the weight model. The weight model with the target weights is then configured as the trained weight model.

[0060] In this embodiment of the disclosure, no specific limitation is made regarding the type of weight model. For example, the weight model can be a deep learning model or a machine learning model.

[0061] This disclosure discloses an embodiment that trains an initial weight model based on an ant colony algorithm, which optimizes weight allocation, improves the model's convergence speed and accuracy, and enhances its generalization ability and robustness. By avoiding local optima through global search, it exhibits strong adaptability, particularly excelling in handling complex and high-dimensional problems, thus improving the model's stability and efficiency in various application scenarios.

[0062] To quickly and accurately determine the target weight, this disclosure provides the following pheromone update algorithm using ant colony optimization.

[0063] In one embodiment, the pheromone update method in the ant colony algorithm is as follows: The target pheromone increment is obtained based on the pheromone increments of the optimal and suboptimal paths under the current path; the pheromone weight coefficient is calculated, which is 1 minus the pheromone evaporation coefficient; the pheromone weight coefficient is multiplied by the pheromone intensity of the current path to obtain the pheromone intensity retention; the updated pheromone intensity is calculated, which is the sum of the pheromone intensity retention and the target pheromone increment.

[0064] In this embodiment of the disclosure, the pheromone update formula in the ant colony algorithm is shown in Formula 1 below.

[0065]

[0066] in, Let be the pheromone intensity of the path from node i to node j at time t+1. The pheromone volatility coefficient ( ), Let be the pheromone intensity of the path from node i to node j at time t. For the optimal path pheromone increment, For the pheromone increment of the suboptimal path, This is the adjustment coefficient for the pheromone increment in the suboptimal path. It is a value less than 1.

[0067] For example, It can be any value among 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9.

[0068] In this embodiment of the disclosure, increasing the pheromone increment of the suboptimal path helps to increase the diversity of solutions, discover potential effective paths, avoid the algorithm getting trapped in local optima in the early stages, and improve the global search capability and robustness of the system.

[0069] In this embodiment, the adjustment coefficient of the suboptimal path pheromone increment provides a more flexible adaptive capability, which can find the best balance between exploration and utilization according to the actual situation, and further optimize the efficiency and stability of the search process.

[0070] This embodiment of the disclosure combines the pheromone increments of the current optimal and second-best paths, enabling the system to dynamically adjust path priorities and thus optimize route selection. During the update process, the pheromone evaporation coefficient ensures the system is not excessively influenced by outdated path information, while the pheromone weighting coefficient controls the amount of pheromone retained, ensuring effective utilization of historically high-quality paths. Ultimately, by adding the retained path pheromone intensity to the new increment, the system achieves continuous optimization of path quality, adaptively selecting the optimal path in complex and changing environments, thereby improving the overall performance, stability, and robustness of the system.

[0071] In another embodiment, the weight model includes a first weight, a second weight, and a third weight, which correspond to the interface response time, the channel encryption strength, and the server load rate, respectively.

[0072] The expression for the weighted model is shown in Formula 2 below.

[0073]

[0074] in, For the first l The weight value of the second server, 、 and These represent the first weight, the second weight, and the third weight, respectively. The standardized value for the interface response time. The normalized value for the channel encryption strength. The standardized value for server load rate.

[0075] In this embodiment of the disclosure, in order to make the weight value as large as possible, the interface response time, channel encryption strength and server load rate are standardized, so as to accurately reflect the relationship between the weight value of the second server and the interface response time, channel encryption strength and server load rate.

[0076] It should be noted that the higher the value of the interface response time, the better. The smaller the value, the stronger the channel encryption. The smaller the value, the larger the server load rate value. The smaller the value.

[0077] In this embodiment of the disclosure, the values ​​of the first weight, the second weight, and the third weight can be obtained by training with the ant colony algorithm, and can also be adjusted according to a preset strategy. Furthermore, they can be adjusted according to the preset strategy after the ant colony algorithm has been trained.

[0078] For example, in order to ensure the smooth operation of terminal device users, Increase the value. To further enhance security, Increasing the value ensures that sensitive data is always transmitted through the most secure channel.

[0079] This embodiment of the disclosure achieves fine-grained control of system performance by mapping the first, second, and third weights to interface response time, channel encryption strength, and server load rate. The weight model ensures that different performance indicators receive targeted attention during the decision-making process, guaranteeing that the system comprehensively considers response speed, security, and load conditions when selecting a path, thereby improving overall performance optimization capabilities. Specifically, the system can dynamically adjust the weights of various indicators based on the real-time network environment and task requirements, optimizing resource allocation.

[0080] In another embodiment, the service quality parameters also include data processing failure rate and evaluation value; wherein, the weight model includes a first weight, a second weight, a third weight, a fourth weight and a fifth weight, the first weight, the second weight, the third weight, the fourth weight and the fifth weight corresponding to the interface response time, the channel encryption strength, the server load rate, the data processing failure rate and the evaluation value, respectively.

[0081] In this embodiment, the interface response time is an actual measured value, which can refer to the time interval from when the user initiates a data transmission request to when the second server begins processing and returns a response. The channel encryption strength is related to the encryption algorithm used by the channel. The server load rate refers to the server's workload within a specific time period and can be obtained from the second server. The data processing failure rate is the probability that the second server will fail to process data within a certain period. The evaluation value can be a rating of the terminal device user's satisfaction with the second server's data processing.

[0082] The expression of the weight model is shown in the following formula 3.

[0083]

[0084] wherein, is a weight value of the i-th second server, l 、 、 、 and respectively represent a first weight, a second weight, a third weight, a fourth weight and a fifth weight, is a value normalized by an interface response time, is a value normalized by a channel encryption strength, is a value normalized by a server load rate, is a value normalized by a data processing failure rate, is a value normalized by an evaluation value, wherein, l represents the i-th second processor. l

[0085] It should be noted that the greater the value of the interface response time, the smaller the value of the channel encryption strength, the greater the value of the server load rate, the greater the data processing failure rate, the smaller the value of the evaluation value, the greater the value.

[0086] Exemplarily, the scenario of integral exchange is taken as an example for illustration.

[0087] The user uses the integral in the terminal device to exchange a gift card, and the user sends a data transmission request (such as “immediate exchange”) to the first server through the terminal device, and the first server can be a routing gateway. The first server checks all second servers (suppliers) that can provide gift cards, for example, there are two suppliers channels, which are channel A and channel B, the first server pulls or reads from the cache in real time of each channel, and obtains the latest

[0088] Suppose the current data is as follows: ​​Channel A: The interface response time is 80ms. Channel A uses SM4 (a national cryptographic algorithm) encryption. The channel encryption strength is 1. The server load rate is 30%. The data processing failure rate is 2%. The evaluation value is 0.9.

[0089] Channel B: The interface response time is 200ms. Channel B uses AES-256 (an advanced encryption standard) encryption. The channel encryption strength is 1. The server load rate is 80%. The data processing failure rate is 1%. The evaluation value is 1.0.

[0090] The above data is standardized, with 1 representing the best and 0 the worst.

[0091] The standardized values ​​for channel A are as follows: =0.9， =1.0， =0.7， =0.98， =0.9。

[0092] The standardized values ​​for channel B are as follows: =0.4， =1.0， =0.2， =0.99， =1.0。

[0093] 、 、 、 and The values ​​are 0.4, 0.3, 0.2, 0.1 and 0.1 respectively.

[0094] =0.4 0.9+0.3 1.0+0.2 0.7+0.1 0.98+0.1 0.9=0.36+0.3+0.14+0.098+ 0.09=0.988 .

[0095] =0.4 0.4+0.3 1.0+0.2 0.2+0.1 0.99+0.1 1.0=0.16+0.3+0.04+0.099+0.1 =0.699 .

[0096] It should be noted that the embodiments of the present disclosure do not make specific limitations on how to perform the standardization processing. For example, the interface response time is standardized by the following formula 4.

[0097]

[0098] wherein, is the interface response time; is the value of the interface response time after standardization.

[0099] Routing decision: the weight value of channel A is 0.988, and the weight value of channel B is 0.699. The weight value of channel A is much higher than that of channel B. The first server routes the current exchange request to the second server with channel A.

[0100] Feedback learning: after the call is completed, the first server records the actual interface response time of the current call channel A and whether the data processing is successful (used to calculate the data processing failure rate), and then updates the historical data of and to affect the next decision.

[0101] The embodiments of the present disclosure can also adjust the first weight, the second weight, the third weight, the fourth weight and the fifth weight according to the preset strategy.

[0102] For example, in order to improve user experience, the first weight corresponding to the interface response time is configured as a high weight to ensure the ultimate smoothness of user operation.

[0103] For another example, in order to ensure system stability, the third weight and the fourth weight corresponding to the server load rate and the data processing failure rate are dynamically adjusted to automatically avoid fault and high load nodes, and to realize system self-healing and high availability.

[0104] For another example, in order to strengthen security, the second weight corresponding to the channel encryption strength is configured as a high weight to ensure that sensitive data is always transmitted through the safest channel.

[0105] For another example, in order to optimize resource cost, intelligent distribution (such as determining the first weight, the second weight, the third weight, the fourth weight and the fifth weight by ant colony algorithm) can indirectly promote the service quality of the partner to obtain more traffic, forming a virtuous circle.

[0106] The present disclosure can flexibly optimize various system performance indicators by introducing data-driven decisions based on weight models, significantly improving user experience and system efficiency. The present disclosure significantly improves system operation efficiency and decision-making intelligence through data-driven dynamic adjustment.

[0107] For example, during a data processing peak period (such as an integral exchange peak period), the weights of the weight model can be adjusted by using an ant colony algorithm. The adjustment process is as follows: Initial stage: the concentration of all channel pheromones is similar, and the distribution is based on the probability of five-dimensional weights 、 、 、 and Start exploring.

[0108] Positive feedback formation: a certain channel (such as channel A) has high request success rate and fast response due to good server performance and low load. Therefore, the pheromone released by the request selecting it is more, leading to rapidly rising, the information intensity of channel A.

[0109] It should be noted that the state transition formula of the ant colony algorithm is shown in the following formula 5.

[0110]

[0111] wherein, is the probability of ant k moving from the current node i to the next accessible node j; is the information concentration on the edge (i, j), that is, the information intensity of the path from node i to node j, reflecting the historical attractiveness of the path; is the heuristic factor; is the information importance factor, which controls the weight of pheromone on path selection, and the greater the value, the more the ant relies on historical experience; is the heuristic factor importance factor, which controls the weight of heuristic information on path selection, and the greater the value, the more the ant tends to choose a short path; is the set of nodes not yet visited by ant k, ensuring that the path is not repeated.

[0112] Intelligent convergence: as the iteration proceeds, higher and higher, even if its instantaneous state occasionally fluctuates (such as a slightly slower response time), according to the state transition formula, its probability of being selected is still high (because The item is large). This forms a kind of "credit accumulation", the system will trust the stable historical performance of high-quality channels.

[0113] Dynamic adaptation: the second server load interface response time corresponding to channel A Rise ( Rapid drop), resulting in the failure of several consecutive requests. These failed requests do not release pheromones, combined with the continuous evaporation of pheromones, Start to decline. At the same time, the Rise of another channel B, requests begin to be allocated exploratory to channel B. If channel B performs well, its pheromone Gradually increases, and eventually the system completes the traffic switch from channel A to channel B.

[0114] Global optimization: the ant colony algorithm will find a balance between "using" known high-quality paths and "exploring" possibly better paths, thus achieving the global performance optimization of the entire system under long-term operation.

[0115] The embodiments of the present disclosure no longer simply respond to real-time indicators, but rather act like an experienced dispatcher, both believing in the "credit" (pheromones) established by long-term cooperation and closely monitoring the "real-time situation" (five-dimensional weight), thus making more intelligent and stable routing decisions. This method can significantly improve the overall robustness, throughput and resource utilization of the system in a high-concurrency and unstable network environment. It should be noted that by using the integral exchange method of the present disclosure, the success rate of integral exchange can be improved to 99.99%, and millisecond-level integral exchange processing can also be achieved. The use of the embodiments of the present disclosure can achieve the following effects, and the specific data is shown in Table 1.

[0116] Table 1 Index Data

[0117] As can be seen from Table 1 above, the latency, accuracy and convergence speed of the embodiments of the present disclosure are better than those of the prior art.

[0118] Exemplarily, an image acquisition scenario is taken as an example for illustration.

[0119] The terminal device is a UAV, the UAV collects pictures of power supply lines, and the user sends a data transmission request (such as "line fault judgment") to the first server through the terminal device. The first server can be an edge server. The first server checks all second servers (central servers) that can provide fault judgment capabilities, for example, there are two central servers, which are the first central server and the second central server, the channel between the first central server and the first server is channel A, and the channel between the second central server and the first server is channel B. The first server pulls or reads from the cache in real time and get the latest and .

[0120] Assume the current data as follows: Channel A: interface response time is 80ms, channel A uses SM4 encryption, channel encryption strength is 1, server load rate is 30% load, data processing failure rate is 2%, evaluation value is 1.0 .

[0121] Channel B: interface response time is 200ms, channel B uses AES-256 encryption, channel encryption strength is 1, server load rate is 80% load, data processing failure rate is 1%, evaluation value is 1.0.

[0122] Standardize the above data, 1 is the best, 0 is the worst.

[0123] The normalized value of channel A is as follows: =0.9， =1.0， =0.7， =0.98， =1.0。

[0124] The normalized value of channel B is as follows: =0.4， =1.0， =0.2， =0.99， =1.0。

[0125] 、 、 、 and The values of 0.4, 0.3, 0.2, 0.1 and 0.1 respectively.

[0126] =0.4 0.9+0.3 1.0+0.2 0.7+0.1 0.98+0.1 0.9=0.36+0.3+0.14+0.098+0.1 =0.998 .

[0127] =0.4 0.4+0.3 1.0+0.2 0.2+0.1 0.99+0.1 1.0=0.16+0.3+0.04+0.099+0.1 =0.699 .

[0128] Routing decision: the weight value of channel A is 0.998, and the weight value of channel B is 0.699. The weight value of channel A is much higher than that of channel B. The first server routes the image processing request to the second server with channel A.

[0129] Feedback learning: after the call is completed, the first server records the actual interface response time of this call channel A and whether the data processing is successful (used to calculate the data processing failure rate), and then updates the historical data of and to affect the next decision.

[0130] The embodiments of the present disclosure optimize the multi-dimensional quality of service parameters by introducing a comprehensive weight model, to improve the overall performance and user experience of the system. Specifically, the weight model includes five key indicators: interface response time, channel encryption strength, server load rate, data processing failure rate, and evaluation value. By dynamically adjusting the proportion of each weight through the ant colony algorithm, the interface response time, channel encryption strength and other key parameters are given priority protection, ensuring that the system can adaptively optimize load distribution and fault handling on the basis of efficiency and security, thereby improving the overall service quality and enhancing the stability and reliability of the system.

[0131] In another exemplary embodiment, the data processing method provided by the present disclosure can include the following steps A1 to A4.

[0132] Step A1, before the transmission of encrypted data, the elliptic curve algorithm is used for public key exchange between the first server and the target server to ensure the forward secrecy of the session.

[0133] Exemplarily, when performing the TLS 1.3 protocol handshake, the first server and the target server perform a public key exchange using an elliptic curve (Diffie-Hellman, ECDHE) algorithm. In this way, the generated session key is only valid in the current session. Even if the private key is disclosed in the future, the past communication content cannot be decrypted, thereby ensuring forward secrecy.

[0134] It should be noted that the forward secrecy is achieved through a temporary key exchange, which can ensure the security of the session data.

[0135] Step A2, based on the elliptic curve algorithm, a shared key is obtained according to the public key of the target server.

[0136] Exemplarily, the first server and the target server randomly generate a first private key and a second private key respectively, the first server calculates a first public key based on the elliptic curve algorithm and the first private key, the target server calculates a second public key based on the elliptic curve algorithm and the second private key, the first server sends the first public key to the target server, and the target server sends the second public key to the first server. The first public key and the second public key are the public keys exchanged in step A1.

[0137] The first server uses the first private key and the second public key to calculate a shared key through the elliptic curve algorithm, and the target server uses the second private key and the first public key to calculate a shared key through the elliptic curve algorithm.

[0138] Step A3, generating a verification tag according to the shared key and the encrypted data.

[0139] Step A4, sending the verification tag and the encrypted data to the target server.

[0140] In the embodiment of the present disclosure, the target server recalculates the MAC value of the encrypted data using the same key (shared key) and algorithm, and compares it with the received MAC value (verification tag). If they are consistent, it means that the encrypted message has not been tampered with in the transmission process.

[0141] Exemplarily, the first server generates a MAC value using the algorithm MAC(M, K), where M is the encrypted data and K is the shared key. After receiving the encrypted data (M) and the verification tag (MAC) value, the target server calculates a new MAC value using the same algorithm. If the calculated MAC value is consistent with the received MAC value, the message is complete and has not been tampered with.

[0142] The embodiments of the present disclosure ensure the forward secrecy of the session by using an elliptic curve algorithm to realize the public key exchange between the first server and the target server before the encrypted data transmission. Based on the public key exchange, a shared key is generated using the public key of the target server, and a verification tag is further generated by the shared key and the encrypted data to ensure the integrity and authentication of the data. Finally, the verification tag and the encrypted data are sent to the target server, thereby realizing efficient and reliable data transmission while ensuring data security and confidentiality.

[0143] It should be noted that the above verification method of data by the MAC value can be applied between two APPs (applications) on a terminal device, can also be applied between the terminal device and the first server, and can also be applied between the first server and the second server. The following will be described by taking the scene of redeeming a gift card with points as an example.

[0144] For example, the terminal device is a mobile phone, and the above verification method is applied between the client of the terminal device and the point system. For example, it is applied in the scene of querying points, redeeming gifts, participating in activities, etc. by the user through the APP, applet, webpage. The data to be processed can include user login credentials, personal identity information, point balance, transaction records, etc. sensitive information, and the above verification method can ensure that the data is not stolen or tampered with during transmission.

[0145] For example, the first server is a smart routing gateway, and the above verification method is applied between the point system and the smart routing gateway. For example, the terminal device sends a data transmission request to the gateway for decision-making. The data to be processed includes sensitive information, and if the data to be processed is eavesdropped, the business details will be disclosed.

[0146] For example, the first server is a smart routing gateway, and the second server is a server of a supplier. For example, after the gateway selects the optimal channel according to the algorithm, it initiates an actual request to the gift supplier, etc. The data to be processed includes account information, point quantity, user identification, etc. highest sensitive level data. The security here is directly related to the security of the user's point assets.

[0147] For example, it is applied between various applications. For example, it is applied to internal API calls between user services, point services, order services, and risk control services. It should be noted that the internal network is not absolutely secure. According to the zero trust principle, all internal communications should also be encrypted to prevent risks caused by horizontal movement.

[0148] In still another example embodiment, the data to be processed can include a request timestamp, a user identification, and a processing instruction; the request timestamp can be a timestamp when the data transmission request is established, or a timestamp a period of time before the data transmission request is established, and the embodiments of the present disclosure are not limited specifically.

[0149] In some embodiments, the data processing method further comprises: storing the request timestamp, the processing instruction and the evaluation value in the blockchain; or performing a hash operation on the processing instruction to obtain an operation result, and storing the operation result, the request timestamp and the evaluation value in the blockchain. It should be noted that performing a hash operation on the processing instruction and storing the operation result in the blockchain can avoid exposing the operation instruction.

[0150] In some embodiments, the processing instruction is an instruction indicating how to process the data (e.g., data information described below). For example, in the points exchange scenario, the processing instruction can be an instruction related to points, such as increasing points or deducting points.

[0151] For example, the blockchain storage system adopts a Hyperledger Fabric architecture (an open-source enterprise-level blockchain framework), and whenever data changes, a new Merkle tree hash is generated by recalculating the hash value of the data and updating the Merkle tree.

[0152] In some embodiments, the request timestamp can be a Unix timestamp, and can also be a Unix timestamp + GPS week number. The data to be processed can also include a transaction serial number, and the hash value of the transaction serial number can be stored on the blockchain to protect user privacy and avoid exposing the serial number in plaintext.

[0153] It should be noted that the evaluation value needs to be updated every interval, for example, the evaluation value is updated every 5 minutes.

[0154] In some embodiments, the request timestamp, the processing instruction and the evaluation value are stored in the blockchain, or the processing instruction is hashed and the operation result, the request timestamp and the evaluation value are stored in the blockchain, to ensure the data is tamper-proof and transparent. Not only can improve the security and reliability of the data, but also can realize the traceability and audit of each request, thereby enhancing the credibility and tamper-proofing ability of the system.

[0155] In another example embodiment, the data processing method further comprises: in response to the current time being equal to or greater than a first invalidation threshold and less than a second invalidation threshold, sending a warning to the target server; in response to the current time being equal to or greater than the second invalidation threshold and less than a third invalidation threshold, sending a first indication to the target server, the first indication indicating the target server to stop processing the encrypted data; in response to the current time being equal to or greater than the third invalidation threshold, sending a second indication to the first server, the second indication indicating the first server to delete the data to be processed; wherein the third invalidation threshold is greater than the second invalidation threshold, and the second invalidation threshold is greater than the first invalidation threshold.

[0156] In the embodiments of the present disclosure, the early warning is used to indicate that the target server can not process the encrypted data. The first indication is used to instruct the target server to stop processing the encrypted data. The second indication is used to instruct the first server to delete the data to be processed.

[0157] It should be noted that the embodiments of the present disclosure have a time limit on the duration of data processing to ensure the efficiency and timeliness of the processing process.

[0158] For example, the blockchain node deploys a smart contract, and the blockchain node can be managed by the first server and the second server in cooperation. The first time limit threshold is the sum of the request timestamp and 364 days, the second time limit threshold is the sum of the request timestamp and 365 days, and 1 hour is subtracted. The third time limit threshold is the sum of the request timestamp and 365 days, and 2 days are added.

[0159] For example, the first time limit threshold is the sum of the credit generation time and 364 days, the second time limit threshold is the sum of the credit generation time and 365 days, and 1 hour is subtracted. The third time limit threshold is the sum of the credit generation time and 365 days, and 2 days are added, as shown in Figure 3 In the credit exchange scenario, the credit life cycle includes the following S301 to S305.

[0160] S301, credit generation. For example, the user gets credit by consumption or gets credit by recharging.

[0161] S302, validity period setting. For example, the set validity period is the sum of the credit generation time and 365 days, and 2 days are added. After expiration, the credit is cleared.

[0162] S303, early warning. If the current time is equal to or greater than the first invalid threshold and less than the second invalid threshold, send an early warning to the target server.

[0163] S304, automatic freezing. If the current time is equal to or greater than the second invalid threshold and less than the third invalid threshold, send a first indication to the target server, and the first indication is used to instruct the target server to freeze the credit.

[0164] S305, credit clearing. If the current time is equal to or greater than the third invalid threshold (validity period), send a second indication to the first server, and the second indication is used to instruct the first server to clear the credit.

[0165] For example, in the credit exchange scenario, the three-level invalid threshold is set as shown in Table 2.

[0166] Table 2 Three-level invalid threshold table

[0167] As can be seen from Table 2, by setting different invalidation thresholds, automatic invalidation of the smart contract can be realized, thereby ensuring timely processing of the data to be processed.

[0168] The embodiments of the present disclosure can effectively control the data processing flow by setting multiple invalidation thresholds and triggering corresponding warnings and instructions according to the current time, thereby ensuring that corresponding measures are taken at different invalidation stages, and thus improving data security and processing efficiency.

[0169] In yet another exemplary embodiment, the first server acts as a master clock, and the target server synchronizes time with the first server through a PTP clock synchronization system.

[0170] In the embodiments of the present disclosure, the PTP (Precision Time Protocol) clock synchronization system adopts the IEEE 1588v2 protocol, and can achieve ±10 μs level time synchronization.

[0171] For example, in the application scenario of image processing, the validity period of image processing is 1 year ± 2 days, and the accuracy is ± 200 ms. Among them, ± 200 ms is the time synchronization accuracy (non-check code).

[0172] For example, in the integral exchange scenario, the integral validity period formula is as follows: Tvalid=Tcreate+(365±2)×3600×  ms±200 ms Tvalid represents the integral validity period, Tcreate is the request timestamp, Tvalid is 1 year ± 2 days, and the accuracy is ± 200 ms.

[0173] The embodiments of the present disclosure can trigger the integral invalidation operation in the precise time window.

[0174] The embodiments of the present disclosure can realize high-precision time consistency between servers by taking the first server as a master clock and making the target server synchronize time with it through a PTP clock synchronization system. The present disclosure can effectively reduce the data processing delay or timing error caused by clock deviation, ensure that the operations of encrypted data processing, instruction triggering, and invalidation threshold judgment are performed under a unified time reference, thereby improving the reliability, real-time performance, and accuracy of data processing of the system.

[0175] In yet another exemplary embodiment, the present disclosure provides a data processing method for encrypting data to be processed to obtain encrypted data, which can include: encrypting the data to be processed to obtain intermediate data; and blinding the intermediate data to obtain the encrypted data.

[0176] In the embodiments of the present disclosure, the encryption processing of the data to be processed is not specifically limited.

[0177] For example, in order to reduce the transmission amount of data, the data to be processed can be subjected to a hash operation to obtain intermediate data, and the intermediate data can be subjected to a blinding process to obtain encrypted data.

[0178] For example, in order to reduce the storage amount of data on the target server, the data to be processed can be encrypted using a symmetric encryption algorithm to obtain intermediate data, and the intermediate data can be subjected to a blinding process to obtain encrypted data.

[0179] The embodiments of the present disclosure can effectively enhance the security and privacy protection of data by encrypting the data to be processed to obtain intermediate data and further blinding the intermediate data to generate encrypted data. The blinding process can hide the original content of the data, ensuring that even if the data is intercepted during processing, the sensitive information of the data cannot be leaked or utilized, thereby improving the protection capability of the system and ensuring the security of the data during transmission and storage.

[0180] Based on the same inventive concept, the present disclosure also provides a data processing method, as described in the following embodiments. Since the principles of the embodiments of the method solve the problem similar to the above-mentioned method embodiments, the implementation of the method embodiments can refer to the implementation of the above-mentioned method embodiments, and the repeated parts will not be described again.

[0181] Figure 4 A flowchart of a data processing method of another embodiment of the present disclosure is shown, Figure 4 The execution subject of the method provided by the embodiments can be any electronic device, for example Figure 1 The second server 103 in the embodiments, but the present disclosure is not limited thereto. Referring to Figure 4 The data processing method provided by the embodiments of the present disclosure includes S401 to S403.

[0182] S401, receiving encrypted data sent by a first server, the encrypted data being obtained by the first server from data to be processed after receiving a data transmission request, wherein the data transmission request carries the data to be processed.

[0183] S402, processing the encrypted data to obtain a processing result.

[0184] In the embodiments of the present disclosure, the processing result can be the processing result of the encrypted data, such as processing success or processing failure.

[0185] S403, sending the processing result to the first server. The second server is a target server determined by a weight value of the second server, and the weight value of the second server is obtained based on the trained weight model according to the service quality parameters including the interface response time, the channel encryption strength and the server load rate.

[0186] In the second server of the embodiment of the present disclosure, the encrypted data sent by the first server is received, and the encrypted data is processed, and the processing result is sent to the first server. In the embodiment of the present disclosure, the data to be processed is encrypted, so that the security of data transmission is increased, and the second server capable of improving the data processing speed is determined through the service quality parameters and the weight model, so that the data processing speed is improved on the premise of ensuring the security of data transmission.

[0187] The present disclosure will be described below through several exemplary embodiments.

[0188] In one exemplary embodiment, the data to be processed includes a request timestamp, a user identifier, data information and a processing instruction. In the data processing method provided by the present disclosure, the encrypted data is processed to obtain the processing result, which can include the following steps B1 to B5.

[0189] Step B1, blind processing of the encrypted data to obtain intermediate data.

[0190] In the embodiment of the present disclosure, the encrypted data is deblinded by a deblinding algorithm to obtain intermediate data. The deblinding algorithm is not specifically limited in the embodiment of the present disclosure. For example, the deblinding algorithm is the inverse operation of the blind processing, which aims to restore the original data (intermediate data) by removing the random factor or encryption layer introduced in the blind process.

[0191] Step B2, decrypting the intermediate data to obtain the request timestamp, the user identifier, the data information and the processing instruction.

[0192] In the embodiment of the present disclosure, the data information can be information collected by the terminal device (such as image information), and can also be information stored on the terminal device (such as points). The processing instruction is used to indicate how to process the data information. For example, target extraction and failure are performed on the image information. For another example, points are added or exchanged for gift cards.

[0193] Step B3, obtaining the request timestamp stored on the blockchain and the user identifier stored on the second server.

[0194] In the embodiments of the present disclosure, the user identification is used for identity verification. The specific type of the user identification is not limited in the embodiments of the present disclosure. For example, the user identification can be a biological feature of the user, such as a facial feature or a voiceprint feature. For another example, the user identification can also be an identification that can prove identity, such as an ID number.

[0195] In step B4, the decrypted request timestamp and the decrypted user identification are respectively verified by the request timestamp stored on the blockchain and the user identification stored on the second server.

[0196] In the embodiments of the present disclosure, if the request timestamp stored on the blockchain is the same as the decrypted request timestamp, and the user identification stored on the second server is the same as the decrypted user identification, the verification is passed, otherwise, the verification is not passed.

[0197] In step B5, the data information is processed according to the processing instruction to obtain a processing result, when the verification is passed.

[0198] In the embodiments of the present disclosure, the decrypted data is verified by the data stored on the blockchain and the data stored on the second server. The two parties verify the decrypted data, which can ensure the accuracy of the decrypted data.

[0199] In another exemplary embodiment, the data to be processed includes a request timestamp, a user identification, data information and a processing instruction. The method for processing encrypted data provided by the present disclosure to obtain a processing result can include the following steps C1 to C6.

[0200] In step C1, the encrypted data is de-blinded to obtain intermediate data.

[0201] In step C2, the intermediate data is decrypted to obtain a request timestamp, a user identification, data information and a processing instruction.

[0202] In step C3, a request timestamp, an operation result stored on the blockchain and a user identification stored on the second server are obtained. The operation result is a hash value of the processing instruction.

[0203] In step C4, the processing instruction is subjected to a hash operation to obtain a hash value to be verified.

[0204] In step C5, the hash value to be verified, the decrypted request timestamp and the decrypted user identification are respectively verified by the operation result, the request timestamp stored on the blockchain and the user identification stored on the second server.

[0205] In the embodiments of the present disclosure, if the operation result is the same as the to-be-verified hash value, the request timestamp stored on the blockchain is the same as the decrypted request timestamp, and the user identifier stored on the second server is the same as the decrypted user identifier, the verification is successful. Otherwise, the verification fails.

[0206] In the case of successful verification, the data information is processed according to the processing instruction to obtain a processing result.

[0207] The embodiments of the present disclosure ensure the security and accuracy of the data processing process through de-blinding, decryption, hash operation and multiple verification of encrypted data. Through the verification of the request timestamp, the user identifier and the processing instruction, combined with the information stored on the blockchain and the second server, data tampering and identity forgery can be effectively prevented, ensuring that only legitimate requests and instructions can pass the verification and finally complete the data processing, thereby improving the security and reliability of the system.

[0208] In another example embodiment, the data to be processed includes a request timestamp, a user identifier, data information and a processing instruction. The data processing method provided by the present disclosure can include the following steps D1 to D6.

[0209] Step D1: De-blinding the encrypted data to obtain intermediate data, the intermediate data including a hash value of the request timestamp, a hash value of the user identifier, a hash value of the data information and a hash value of the processing instruction.

[0210] It should be noted that the first server performs hash operation on the data to be processed to obtain intermediate data, and performs blind processing on the intermediate data to obtain encrypted data. The target server performs de-blinding processing on the encrypted data to obtain intermediate data.

[0211] For example, the generation formula of the intermediate data is shown in the following formula 5.

[0212]

[0213] wherein, is the intermediate data, is used to represent the blind processing algorithm, is used to represent the hash encryption algorithm.

[0214] For example, in the scene of integrating to exchange gifts, is a blind signature integral code, and The request is transmitted to the exchange platform (target server) for de-blinding processing. Four elements (request timestamp, user identifier, data information, and processing instruction) are compared. If the comparison is successful, the points are exchanged. If the exchange is successful, the processing result of the successful exchange is sent. If the comparison is not successful, the processing result of the failed exchange is sent. It should be noted that the disclosed embodiments obtain the hash value by de-blinding, and then separate the four elements for independent verification without knowing the original request timestamp.

[0215] It should be noted that the first server performs hash operation on the request timestamp, user identifier, data information, and processing instruction respectively to obtain the hash value of the request timestamp, the hash value of the user identifier, the hash value of the data information, and the hash value of the processing instruction.

[0216] Step D2: Obtain the request timestamp stored on the blockchain.

[0217] Step D3: Perform hash operation on the request timestamp stored on the blockchain to obtain the time verification hash value.

[0218] Step D4: Obtain the verification hash value stored on the second server, which includes the verification user identifier hash value, the verification data information hash value, and the verification processing instruction hash value.

[0219] Step D5: Verify the hash value of the request timestamp, the hash value of the user identifier, and the hash value of the data information by using the time verification hash value, the verification user identifier hash value, and the verification data information hash value respectively.

[0220] Step D6: If the verification is passed, obtain the processing instruction according to the hash value of the processing instruction, and process the data information according to the processing instruction to obtain the processing result.

[0221] It should be noted that the second server stores the hash value corresponding to the processing instruction, so that the hash value corresponding to the processing instruction is queried and matched with the hash value of the processing instruction. The processing instruction corresponding to the hash value is the processing instruction of the data information. The data information is processed by the processing instruction to obtain the processing result.

[0222] The disclosed embodiments obtain the hash value by de-blinding processing, and independently verify the four elements of the request timestamp, the user identifier, the data information, and the processing instruction without directly contacting the original request timestamp, thereby improving the privacy protection and security of the system. In this way, the data processing can still effectively verify the authenticity and integrity of the data without exposing sensitive information, reducing the dependence on the original data, enhancing the anti-tampering ability and privacy protection, and improving the credibility and security of the system.

[0223] In yet another example embodiment, the data to be processed includes a request timestamp, a user identifier, data information, and processing instructions; wherein the encrypted data is processed to obtain a processing result, including: receiving a verification tag; generating a verification tag to be verified according to the shared key and the encrypted data; verifying the verification tag according to the verification tag to be verified; and in the case of verification passing, processing the encrypted data to obtain a processing result.

[0224] In the case of verification failure, the encrypted data is not processed, and information of verification failure is returned to the first server.

[0225] The embodiments of the present disclosure generate and verify the verification tag using the shared key and the encrypted data, ensuring that the encrypted data is not tampered with or forged during processing. In this way, the system can process the encrypted data on the premise of verification passing, effectively preventing data leakage and tampering, while enhancing the security and integrity of data processing. This method improves the accuracy and trustworthiness of data verification, ensuring the security of encrypted data throughout the processing flow.

[0226] In yet another example embodiment, applied to the first server or the second server, the data processing method provided by the present disclosure can further include: In response to the abnormal state, an abnormal warning is sent to the terminal device.

[0227] In the embodiments of the present disclosure, the detection of the abnormal state is realized by an anomaly detection model. The specific model of the anomaly detection model is not limited in the embodiments of the present disclosure. For example, the anomaly detection model can be a decision tree model (such as using the CART algorithm).

[0228] For example, the input features of the decision tree model are shown in Table 3.

[0229] Table 3 Input features

[0230] The decision tree model outputs an anomaly probability score (0-1), and a threshold greater than 0.925224 is defined as abnormal, and an abnormal warning is issued when abnormal.

[0231] It should be noted that the CART (Classification and Regression Trees) algorithm is a decision tree algorithm for classification and regression problems

[0232] The integral exchange can be implemented by the embodiment of the present disclosure, and the following technical effects can be achieved: the integral distribution delay can be reduced from 5s to 120ms, the exchange success rate can be improved from 95% to 99.99%, the abnormal identification accuracy is 99.3%, and the integral validity period management accuracy is ±200ms.

[0233] The effects of the embodiment of the present disclosure in the integral exchange scene are compared with the effects of the conventional technology, as shown in Table 4.

[0234] Table 4: Comparison of technical effects

[0235] It should be noted that before the integral is exchanged, it can be necessary to verify whether the user's integral balance is sufficient, whether the verification request timestamp is within the valid period, and whether the processing instruction is out of the instruction range. In the case where the verification is passed, the integral exchange gift operation is performed.

[0236] Based on the same inventive concept, the present disclosure also provides a data processing device, as described in the following embodiments. Since the principle of solving the problem of the device embodiment is similar to that of the above-mentioned method embodiment, the implementation of the device embodiment can be referred to the implementation of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0237] Figure 5 A schematic diagram of a data processing device in the embodiment of the present disclosure is shown, as shown in Figure 5 The application is applied to a first server, which includes a first receiving module 51, an encryption module 52, an acquisition module 53, a calculation module 54, a selection module 55, and a first sending module 56. The first receiving module 51 can be used to receive a data transmission request, and the data transmission request carries data to be processed. The encryption module 52 can be used to encrypt the data to be processed to obtain encrypted data. The acquisition module 53 can be used to acquire service quality parameters of a plurality of second servers, and the service quality parameters include interface response time, channel encryption strength, and server load rate. The calculation module 54 can be used to obtain weight values of the plurality of second servers based on a trained weight model according to the service quality parameters. The selection module 55 can be used to select a target server according to the weight values of the plurality of second servers. The first sending module 56 can be used to send the encrypted data to the target server, so that the target server processes the encrypted data.

[0238] In one embodiment, the data processing device further includes a training module, which is configured to train the initial weight of the weight model based on an ant colony algorithm to obtain a trained weight model.

[0239] In one embodiment, the pheromone updating method in the ant colony algorithm is as follows: obtaining a target pheromone increment according to the optimal path pheromone increment and the suboptimal path pheromone increment in the current path; calculating a pheromone weight coefficient, which is 1 minus the pheromone evaporation coefficient; calculating the product of the pheromone weight coefficient and the current path pheromone intensity to obtain a pheromone intensity reservation; and calculating the updated pheromone intensity, which is the sum of the pheromone intensity reservation and the target pheromone increment.

[0240] In one embodiment, the first sending module 56 is further configured to perform a public key exchange between the first server and the target server using an elliptic curve algorithm before the encrypted data transmission, to ensure forward secrecy of the session; obtain a shared key based on the public key of the target server according to the elliptic curve algorithm; generate a verification tag according to the shared key and the encrypted data; and send the verification tag and the encrypted data to the target server.

[0241] In one embodiment, the data to be processed includes a request timestamp, a user identifier, and a processing instruction; and the data processing apparatus further includes a storage module configured to store the request timestamp, the processing instruction, and the evaluation value in a blockchain; or perform a hash operation on the processing instruction to obtain an operation result, and store the operation result, the request timestamp, and the evaluation value in the blockchain.

[0242] In one embodiment, the first sending module 56 is further configured to send a warning to the target server in response to the current time being equal to or greater than a first invalidation threshold and less than a second invalidation threshold; send a first indication to the target server in response to the current time being equal to or greater than the second invalidation threshold and less than a third invalidation threshold, the first indication being used to instruct the target server to stop processing the encrypted data; and send a second indication to the first server in response to the current time being equal to or greater than the third invalidation threshold, the second indication being used to instruct the first server to delete the data to be processed; wherein the third invalidation threshold is greater than the second invalidation threshold, and the second invalidation threshold is greater than the first invalidation threshold.

[0243] In one embodiment, the first server acts as a master clock, and the target server synchronizes time with the first server through a PTP clock synchronization system.

[0244] In one embodiment, the encryption module 52 is configured to encrypt the data to be processed to obtain intermediate data; and perform a blinding process on the intermediate data to obtain the encrypted data.

[0245] The data processing apparatus disclosed in the embodiments of the present disclosure increases the security of data transmission by encrypting the data to be processed, and determines the second server capable of improving the data processing speed through the quality of service parameter and the weight model, thereby improving the data processing speed under the premise of ensuring the security of data transmission.

[0246] Based on the same inventive concept, this disclosure also provides a data processing apparatus, as described in the following embodiments. Since the principle by which this apparatus solves the problem is similar to that of the method embodiments described above, the implementation of this apparatus embodiment can refer to the implementation of the method embodiments described above, and repeated details will not be repeated.

[0247] Figure 6 This diagram illustrates a data processing apparatus according to an embodiment of the present disclosure, such as... Figure 6 As shown, the device, applied to a second server, may include: a second receiving module 61, a processing module 62, and a second sending module 63. The second receiving module 61 can receive encrypted data sent by the first server. The encrypted data is obtained by encrypting data to be processed after the first server receives a data transmission request, wherein the data transmission request carries the data to be processed. The processing module 62 can process the encrypted data to obtain a processing result. The second sending module 63 can send the processing result to the first server. The second server is a target server determined by its weight value, which is obtained based on a trained weight model and service quality parameters, including interface response time, channel encryption strength, and server load rate.

[0248] In one embodiment, the data to be processed includes a request timestamp, a user identifier, data information, and processing instructions. The processing module 62 can also be used to deblind the encrypted data to obtain intermediate data; decrypt the intermediate data to obtain the request timestamp, user identifier, data information, and processing instructions; obtain the request timestamp stored on the blockchain and the user identifier stored on the second server; verify the decrypted request timestamp and user identifier using the request timestamp stored on the blockchain and the user identifier stored on the second server, respectively; if the verification is successful, process the data information according to the processing instructions to obtain the processing result.

[0249] In one embodiment, the data to be processed includes a request timestamp, a user identifier, data information, and a processing instruction. The processing module 62 can also be used to deblind the encrypted data to obtain intermediate data; decrypt the intermediate data to obtain the request timestamp, user identifier, data information, and processing instruction; obtain the request timestamp stored on the blockchain, the calculation result, and the user identifier stored on the second server, with the calculation result being the hash value of the processing instruction; perform a hash operation on the processing instruction to obtain a hash value to be verified; verify the hash value to be verified, the decrypted request timestamp, and the user identifier using the calculation result, the request timestamp stored on the blockchain, and the user identifier stored on the second server, respectively; if the verification passes, process the data information according to the processing instruction to obtain the processing result.

[0250] In one embodiment, the data to be processed includes a request timestamp, a user identifier, data information, and a processing instruction; the processing module 62 can also be configured to perform unblinding processing on the encrypted data to obtain intermediate data, the intermediate data including a hash value of the request timestamp, a hash value of the user identifier, a hash value of the data information, and a hash value of the processing instruction; obtain the request timestamp stored on the blockchain; perform a hash operation on the request timestamp stored on the blockchain to obtain a time verification hash value; obtain a verification hash value stored on the second server, the verification hash value including a verification user identifier hash value, a verification data information hash value, and a verification processing instruction hash value; verify the hash value of the request timestamp, the hash value of the user identifier, and the hash value of the data information by using the time verification hash value, the verification user identifier hash value, and the verification data information hash value respectively; in the case of passing the verification, obtain the processing instruction according to the hash value of the processing instruction, and process the data information according to the processing instruction to obtain a processing result.

[0251] In one embodiment, the data to be processed includes a request timestamp, a user identifier, data information, and a processing instruction; the processing module 62 can also be configured to receive a verification tag; generate a verification tag to be verified according to a shared key and the encrypted data; verify the verification tag according to the verification tag to be verified; in the case of passing the verification, process the encrypted data to obtain a processing result.

[0252] The data processing apparatus disclosed by the embodiments of the present disclosure increases the security of data transmission by encrypting the data to be processed, and determines the second server capable of improving the data processing speed through the quality of service parameter and the weight model, thereby improving the data processing speed under the premise of ensuring the security of data transmission.

[0253] The electronic device 700 according to this implementation of the present disclosure will be described below with reference to Figure 7 . Figure 7 The electronic device 700 shown is merely an example, and should not impose any limitation on the function and scope of use of the embodiments of the present disclosure.

[0254] As shown in Figure 7 , the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 can include, but are not limited to, the at least one processing unit 710 described above, the at least one storage unit 720 described above, and a bus 730 connecting different system components, including the storage unit 720 and the processing unit 710.

[0255] The storage unit stores program codes which can be executed by the processing unit 710, so that the processing unit 710 performs the steps described in the above "Exemplary Methods" section according to various exemplary embodiments of the present disclosure.

[0256] The storage unit 720 can include a readable medium in the form of volatile storage such as a random access memory (RAM) 7201 and / or cache memory 7202, and further can include a read-only memory (ROM) 7203.

[0257] The storage unit 720 can further include a program / utility 7204 having a set of program modules 7205 such as an operating system, one or more application programs, other program modules, and program data, each of which can give the electronic device 700 its functionality, at least in part. Each of the operating system, one or more application programs, other program modules, and program data can include implementations of the features of the present disclosure in combination with the processor 710.

[0258] The bus 730 can represent one or more of several types of bus structures, including a storage bus or bus controller, a peripheral bus, a graphics bus (e.g., an Accelerated Graphics Port (AGP)), a processor or local bus using any of a variety of bus structures, and so on.

[0259] The electronic device 700 can also communicate with one or more external devices 740 such as a keyboard or pointing device, a Bluetooth device, etc.; other devices such as printers, scanners, etc.; and / or various types of networks. In general, use of the term device herein is intended to encompass a personal computer, a server, a network device, or any other type of device. Communication over such devices can be achieved by input / output (I / O) interfaces 750. Specially, the electronic device 700 can communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), and / or a public network, such as the Internet, via a network adapter 760. As shown, the network adapter 760 communicates with the other components of the electronic device 700 via the bus 730. It should be appreciated that although the network adapter 760 is shown as a single component, the network adapter 760 can comprise two or more components that work together to facilitate communications between the electronic device 700 and one or more other devices. Further, it should be appreciated that the electronic device 700 can have more than one network adapter 760 to communicate with more than one network.

[0260] Those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by software in combination with necessary hardware, through the above description of the embodiments. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or a network, and includes a number of instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the methods according to the embodiments of the present disclosure.

[0261] Based on the same inventive concept, the present embodiment further provides a computer readable storage medium, which can be a readable signal medium or a readable storage medium. The computer readable storage medium stores a program product capable of implementing the method of the present disclosure.

[0262] More specific examples of the computer readable storage medium in the present disclosure can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination of the above.

[0263] Based on the same inventive concept, the present embodiment further provides a computer program product, which includes a computer program product, including: a computer program or instructions, which, when executed by a processor, implements the method of the present disclosure. Since the principle of the computer program product embodiment solving the problem is similar to the above-mentioned method embodiment, the implementation of the computer program product embodiment can be referred to the implementation of the above-mentioned method embodiment, and the repeated parts will not be described here.

[0264] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure following the general principles thereof and including such departures from the present disclosure that come within known use or custom in the art. The specification and examples are to be regarded as illustrative only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A data processing method, characterized by, The application is applied to a first server, comprising: receiving a data transmission request, wherein the data transmission request carries to-be-processed data; encrypting the to-be-processed data to obtain encrypted data; obtaining service quality parameters of a plurality of second servers, wherein the service quality parameters include interface response time, channel encryption strength and server load rate; obtaining weight values of the plurality of second servers according to the service quality parameters based on a trained weight model; selecting a target server according to the weight values of the plurality of second servers; sending the encrypted data to the target server to enable the target server to process the encrypted data.

2. The method of claim 1, wherein, The method further comprises: training initial weights of the weight model based on an ant colony algorithm to obtain the trained weight model.

3. The method of claim 2, wherein, The pheromone updating mode in the ant colony algorithm is as follows: obtaining target pheromone increments according to optimal path pheromone increments and suboptimal path pheromone increments in a current path; calculating a pheromone weight coefficient, wherein the pheromone weight coefficient is 1 minus a pheromone evaporation coefficient; calculating a product of the pheromone weight coefficient and a current path pheromone intensity to obtain a pheromone intensity retention amount; calculating updated pheromone intensity, wherein the updated pheromone intensity is a sum of the pheromone intensity retention amount and the target pheromone increments.

4. The method according to claim 2 or 3, characterized in that, The weight model includes a first weight, a second weight and a third weight, wherein the first weight, the second weight and the third weight correspond to the interface response time, the channel encryption strength and the server load rate, respectively.

5. The method according to claim 2 or 3, characterized in that, The service quality parameters further include a data processing failure rate and an evaluation value. The weight model includes a first weight, a second weight, a third weight, a fourth weight and a fifth weight, wherein the first weight, the second weight, the third weight, the fourth weight and the fifth weight correspond to the interface response time, the channel encryption strength, the server load rate, the data processing failure rate and the evaluation value, respectively.

6. The method of claim 1, wherein, The sending of the encrypted data to the target server comprises: performing public key exchange between the first server and the target server using an elliptic curve algorithm before the encrypted data transmission to ensure forward secrecy of a session; obtaining a shared key based on the public key of the target server based on the elliptic curve algorithm; generating a verification tag based on the shared key and the encrypted data; sending the verification tag and the encrypted data to the target server.

7. The method of claim 5, wherein, The to-be-processed data includes a request timestamp, a user identifier and a processing instruction. The method further comprises: storing the request timestamp, the processing instruction and the evaluation value in a blockchain; or performing a hash operation on the processing instruction to obtain an operation result, and storing the operation result, the request timestamp and the evaluation value in a blockchain.

8. The method of claim 7, wherein, The method further comprises: in response to the current time being equal to or greater than a first invalidation threshold and less than a second invalidation threshold, sending a warning to the target server; in response to the current time being equal to or greater than the second invalidation threshold and less than a third invalidation threshold, sending a first indication to the target server, the first indication being used to instruct the target server to stop processing the encrypted data; in response to the current time being equal to or greater than the third invalidation threshold, sending a second indication to the first server, the second indication being used to instruct the first server to delete the data to be processed; wherein the third invalidation threshold is greater than the second invalidation threshold, and the second invalidation threshold is greater than a first invalidation threshold.

9. The method of claim 1, wherein, The first server acts as a master clock, and the target server synchronizes time with the first server through a PTP clock synchronization system.

10. The method of claim 1, wherein, The encrypted data obtained by the encrypted processing of the data to be processed includes: encrypting the data to be processed to obtain intermediate data; blind processing the intermediate data to obtain the encrypted data.

11. A data processing method, characterized by, Applied to a second server, comprising: receiving encrypted data sent by a first server, the encrypted data being obtained by the first server performing encrypted processing on data to be processed after receiving a data transmission request, wherein the data transmission request carries the data to be processed; processing the encrypted data to obtain a processing result; sending the processing result to the first server; wherein the second server is a target server determined by a weight value of the second server, and the weight value of the second server is obtained based on a trained weight model according to a quality of service parameter, the quality of service parameter including interface response time, channel encryption strength and server load rate.

12. The method of claim 11, wherein, The data to be processed includes a request timestamp, a user identifier, data information and a processing instruction; wherein the processing of the encrypted data to obtain a processing result includes: blind processing the encrypted data to obtain intermediate data; decrypting the intermediate data to obtain the request timestamp, the user identifier, the data information and the processing instruction; obtaining a request timestamp stored on a blockchain and a user identifier stored on the second server; verifying the decrypted request timestamp and the user identifier respectively through the request timestamp stored on the blockchain and the user identifier stored on the second server; if the verification is passed, processing the data information according to the processing instruction to obtain a processing result.

13. The method of claim 11, wherein, The data to be processed includes a request timestamp, a user identifier, data information and a processing instruction; wherein the processing of the encrypted data to obtain a processing result includes: blind processing the encrypted data to obtain intermediate data; decrypting the intermediate data to obtain the request timestamp, the user identifier, the data information and the processing instruction; obtaining a request timestamp stored on a blockchain, an operation result and a user identifier stored on the second server, the operation result being a hash value of the processing instruction; performing a hash operation on the processing instruction to obtain a hash value to be verified; The encrypted data is verified by the operation result, the request timestamp stored on the blockchain, and the user identifier stored on the second server. In the case of verification, the data information is processed according to the processing instruction to obtain a processing result.

14. The method of claim 11, wherein, The data to be processed includes a request timestamp, a user identifier, data information, and a processing instruction. The encrypted data is processed to obtain a processing result, including: The encrypted data is de-blinded to obtain intermediate data, including a hash value of the request timestamp, a hash value of the user identifier, a hash value of the data information, and a hash value of the processing instruction. Obtain the request timestamp stored on the blockchain. The request timestamp stored on the blockchain is hashed to obtain a time verification hash value. Obtain the verification hash value stored on the second server, including a verification user identifier hash value, a verification data information hash value, and a verification processing instruction hash value. The hash values of the request timestamp, the user identifier, and the data information are verified by the time verification hash value, the verification user identifier hash value, and the verification data information hash value. In the case of verification, the processing instruction is obtained according to the hash value of the processing instruction, and the data information is processed according to the processing instruction to obtain a processing result.

15. The method of claim 12, 13 or 14, characterized in that, The data to be processed includes a request timestamp, a user identifier, data information, and a processing instruction. The encrypted data is processed to obtain a processing result, including: Receive a verification tag; Generate a verification tag to be verified according to the shared key and the encrypted data; Verify the verification tag according to the verification tag to be verified; In the case of verification, the encrypted data is processed to obtain a processing result.

16. A data processing apparatus, characterized by Applied to a first server, including: A first receiving module for receiving a data transmission request, the data transmission request carrying data to be processed; An encryption module for encrypting the data to be processed to obtain encrypted data; An acquisition module for acquiring service quality parameters of a plurality of second servers, the service quality parameters including interface response time, channel encryption strength, and server load rate; A calculation module for obtaining weight values of a plurality of second servers based on a trained weight model according to the service quality parameters; A selection module for selecting a target server according to the weight values of a plurality of second servers; A first sending module for sending the encrypted data to the target server to enable the target server to process the encrypted data.

17. A data processing apparatus, characterized by Applied to a second server, including: A second receiving module for receiving encrypted data sent by a first server, the encrypted data being obtained by the first server encrypting data to be processed after receiving a data transmission request, wherein the data transmission request carries the data to be processed; A processing module for processing encrypted data to obtain a processing result; A second sending module is configured to send the processing result to the first server. The second server is a target server determined by a weight value of the second server, and the weight value of the second server is obtained based on a trained weight model according to a service quality parameter, the service quality parameter including an interface response time, a channel encryption strength, and a server load rate. 18.An electronic device comprising: a processor; and a memory storing executable instructions of the processor; wherein the processor is configured to perform the data processing method of any one of claims 1-15 via executing the executable instructions.

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