Industrial identification data cache processing method and device, medium and electronic equipment

Abstract

The present disclosure provides an industrial identification data cache processing method and device, medium and electronic equipment, and relates to the field of computer technology. An industrial identification data cache processing method, characterized by comprising: obtaining a target industrial identification data request; generating a target industrial identification data request record according to the target industrial identification data request; obtaining an industrial identification data set; classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record; generating a cache strategy based on the classification result; and adjusting the industrial identification data cache according to the cache strategy. The present application optimizes cache management by actively collecting industrial identification data request records and analyzing them according to the request records to classify the industrial identification data set, and formulates a cache strategy for each type of data, thereby further improving the comprehensive protection capabilities and risk management efficiency of the industrial identification resolution system.

Description

Background Art

[0002] The Industrial Internet identification resolution system is the key hub for realizing information exchange among all industrial elements and links. By assigning an identification to each object and using the Industrial Internet identification resolution system, cross-regional, cross-industry, and cross-enterprise information query and sharing can be achieved.

[0003] The scale and growth rate of industrial identification far exceed the traditional domain name / Internet user registration volume. Faced with a large number of identification resolution requests, higher performance requirements are placed on cache in application construction. On another level, the access volume of industrial identification far exceeds that of traditional Internet. How to improve the comprehensive protection capability and risk management efficiency of the industrial identification resolution system is an urgent problem that technicians in this field need to solve.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide an industrial identification data cache processing method and device, medium and electronic equipment, which at least to a certain extent solve the problems of insufficient comprehensive protection capabilities and low risk management efficiency of the industrial identification resolution system.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of the present disclosure, there is provided an industrial identification data cache processing method, including: obtaining a target industrial identification data request; generating a target industrial identification data request record according to the target industrial identification data request; obtaining an industrial identification data set; classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record; generating a cache strategy based on the classification result; and adjusting the industrial identification data cache according to the cache strategy.

[0008] In one embodiment of the present disclosure, classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record also includes: classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record, expert experience data and business characteristic data, wherein the expert experience data includes the life cycle of the target field and the periodic usage rules of the target data; the business characteristic data includes the business characteristic data including the life cycle of the target data with business characteristics.

[0009] In one embodiment of the present disclosure, the time characteristic includes at least one of the following: a timeliness category of data, which includes general data and short-term data; a predictability category of data, which includes distributed data and periodic data.

[0010] In one embodiment of the present disclosure, generating a cache strategy based on the classification result includes: if the classification result is short-term data, deleting the cache related to the short-term data at a preset time.

[0011] In one embodiment of the present disclosure, generating a cache strategy based on the classification result further includes: if the classification result is periodic data, pre-loading data related to the periodic data into the cache.

[0012] In one embodiment of the present disclosure, the method further includes periodically performing security verification on the industrial identification data request record according to the industrial identification data set; and processing the industrial identification data request according to the security verification result.

[0013] In one embodiment of the present disclosure, processing an industrial identification data request based on a security verification result includes: if a data request in an industrial identification data request record fails the security verification, setting an access count threshold for the industrial identification data request; if the number of accesses within a preset time exceeds the access count threshold, rejecting the industrial identification data request.

[0014] According to another aspect of the present disclosure, an industrial identification data cache processing device is provided, including: a diversion module for obtaining a target industrial identification data request; an operation recording module for generating a target industrial identification data request record according to the target industrial identification data request; an intelligent analysis module for obtaining an industrial identification data set, classifying the industrial identification data set according to time characteristics based on the target industrial representation data request record, and generating a cache strategy based on the classification result; and an instruction dispatching module for adjusting the industrial identification data cache according to the cache strategy.

[0015] According to another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned industrial identification data cache processing method by executing the executable instructions.

[0016] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the industrial identification data cache processing method described above is implemented.

[0017] The industrial identification data cache processing method provided in the embodiments of the present disclosure actively collects industrial identification data request records and analyzes the request records to classify the industrial identification data set, and formulates a cache strategy for each type of data, which is beneficial to the management of cached data and further optimizes the memory, thereby effectively realizing the intelligent management of the industrial identification data cache and reducing the management difficulty.

[0018] Furthermore, by classifying and processing the cache data according to time characteristics to generate a cache strategy, the defects of the industrial identification node cache management strategy in the existing technology are solved, and the risk of memory overflow and cache breakdown and penetration is reduced in time, thereby improving the comprehensive protection capability and risk management efficiency of the industrial identification resolution system.

[0019] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0021] Figure 1 A schematic diagram showing an exemplary system architecture of an industrial identification data cache processing method in an embodiment of the present disclosure;

[0022] Figure 2 A flow chart showing an industrial identification data cache processing method according to an embodiment of the present disclosure is shown;

[0023] Figure 3 A flowchart showing a method for generating a cache strategy based on a target industrial identification data request record, expert experience data, and business characteristic data in an industrial identification data cache processing method in an embodiment of the present disclosure;

[0024] Figure 4 A flowchart of an industrial identification data cache processing method based on security verification in an industrial identification data cache processing method in an embodiment of the present disclosure is shown;

[0025] Figure 5 A schematic diagram of an industrial identification data cache processing device according to an embodiment of the present disclosure is shown;

[0026] Figure 6 A business processing flow chart of an industrial identification data cache processing device according to an embodiment of the present disclosure is shown; and

[0027] Figure 7 A structural block diagram of an industrial identification data cache processing computer device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0028] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0029] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0030] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present disclosure, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0031] In view of the technical problems existing in the above-mentioned related technologies, the embodiments of the present disclosure provide a message display method to solve at least one or all of the above-mentioned technical problems.

[0032] Figure 1 A schematic diagram of an exemplary system architecture of an industrial identification data cache processing method in an embodiment of the present disclosure is shown. The system architecture includes: a plurality of terminals 120 and a server cluster 140 .

[0033] The terminal 120 may be a mobile terminal such as a mobile phone, a game console, a tablet computer, an e-book reader, smart glasses, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a smart home device, an AR (Augmented Reality) device, a VR (Virtual Reality) device, or the terminal 120 may also be a personal computer (PC), such as a laptop computer and a desktop computer.

[0034] The terminal 120 may be installed with an application for providing industrial identification data cache processing.

[0035] The terminal 120 is connected to the server cluster 140 via a communication network. Optionally, the communication network is a wired network or a wireless network.

[0036] The server cluster 140 is a server, or is composed of several servers, or is a virtualization platform, or is a cloud computing service center. The server cluster 140 is used to provide background services for the application program that provides industrial identification data cache processing.

[0037] In some optional embodiments, the server cluster 140 is used to store industrial identification data request information.

[0038] In the present application, the server cluster 140 is also connected to the blockchain system 160, and the server cluster 140 stores the industrial identification data in the blockchain system 160. In some optional embodiments, the server cluster 140 itself can also operate and store data as a node in the blockchain system.

[0039] Optionally, in the embodiment of the present application, the server cluster 140 includes a proxy server 142 and a cache server 144. The proxy server 142 is used to implement the logic control of the industrial identification data cache processing application, such as industrial identification data cache management, cache data arrangement management, etc. The cache server 144, as part of the blockchain system 160, is used to implement the storage of part of the industrial identification data to reduce resource consumption and improve the data request response speed.

[0040] It should be noted that the proxy server 142 and the cache server 144 may belong to the same computer device, or the proxy server 142 and the cache server 144 may belong to different computer devices.

[0041] Optionally, the client of the application installed in different terminals 120 is the same, or the client of the application installed on the two terminals 120 is the client of the same type of application of different control system platforms. Based on the different terminal platforms, the specific form of the client of the application may also be different, for example, the application client may be a mobile client, a PC client or a World Wide Web (Web) client.

[0042] Those skilled in the art will appreciate that the number of the terminals 120 may be more or less. For example, there may be only one terminal, or there may be dozens or hundreds of terminals, or a greater number. The embodiment of the present application does not limit the number and device type of the terminals.

[0043] Optionally, the system may also include a management device ( Figure 1 (not shown), the management device is connected to the server cluster 140 via a communication network. Optionally, the communication network is a wired network or a wireless network.

[0044] Optionally, the above-mentioned wireless network or wired network uses standard communication technology and / or protocol. The network is usually the Internet, but it can also 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 dedicated network or any combination of a virtual private network). In some embodiments, the data exchanged through the network is represented by technologies and / or formats including Hyper Text Mark-up Language (HTML), Extensible Markup Language (XML), etc. In addition, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet Protocol Security (IPsec) can also be used to encrypt all or some links. In other embodiments, customized and / or dedicated data communication technologies can also be used to replace or supplement the above-mentioned data communication technologies.

[0045] Below, each step of the industrial identification data request method in this example implementation will be described in more detail with reference to the accompanying drawings and embodiments.

[0046] Figure 2 The flowchart of an industrial identification data cache processing method in an embodiment of the present disclosure is shown. The method provided in the embodiment of the present disclosure can be executed by any electronic device with computing and processing capabilities, such as Figure 1 In the following example description, the server cluster 140 is used as the execution subject for example description.

[0047] like Figure 2 As shown, the industrial identification data cache processing method 200 provided in the embodiment of the present disclosure may include the following steps:

[0048] Step S210, obtaining a target industry identification data request.

[0049] In some embodiments of the present disclosure, the proxy server obtains a target industrial identification data request from a terminal. In some embodiments, the target industrial identification data request is an identification resolution request for the target industrial identification data.

[0050] Step S220: Generate a target industry identification data request record according to the target industry identification data request.

[0051] In some embodiments of the present disclosure, the proxy server receives the target industrial identification data request instead of the cache device to record the target industrial identification data request, and then forwards the industrial identification data request to the cache device in real time. In some embodiments, the proxy server performs asynchronous storage of the industrial identification data request. For example, if the number of target industrial identification data requests reaches a preset number, the batch target industrial identification data requests can be packaged and saved to generate a target industrial identification data request record. The advantage of this is that the target industrial identification data request is recorded under the premise of ensuring that access to the industrial identification data is not affected, thereby increasing the execution efficiency of the proxy server.

[0052] Step S230, obtaining an industrial identification data set.

[0053] In some embodiments of the present disclosure, the industrial identification data set comes from an identification resolution server. In some embodiments, the proxy server periodically obtains the industrial identification data set from the identification resolution server, and the acquisition time can be set according to the update speed of the industrial identification data set.

[0054] Step S240: classify the industrial identification data set according to time characteristics based on the target industrial identification data request record.

[0055] In some embodiments of the present disclosure, classification by time characteristics can determine the retention time of related cache data, which is conducive to timely processing of cache data.

[0056] In some embodiments of the present disclosure, the time characteristics can be determined by the timeliness of the data, and can also be determined by the predictability of the data. For example, some hot data has the timeliness of short-term characteristics, and some regular data has the predictability of periodic characteristics. This application does not limit how to classify based on time characteristics and which categories to classify into.

[0057] Step S250: Generate a cache strategy based on the classification result.

[0058] In some embodiments of the present disclosure, the proxy server generates different cache strategies based on the classification results with time characteristics. For example, for hot data with short-term characteristics, the time for cache deletion can be set. For another example, for regular data with periodic characteristics, such data can be preloaded into the cache.

[0059] Step S260: adjusting the industrial identification data cache according to the cache strategy.

[0060] By using the industrial identification data cache processing method disclosed in the present invention, industrial identification data request records are actively collected and analyzed based on the request records to classify the industrial identification data set, and a cache strategy is formulated for each type of data, which is beneficial to the management of cached data and further optimizes the memory, effectively realizing the intelligent management of the industrial identification data cache and reducing the management difficulty.

[0061] Furthermore, by classifying and processing the cache data according to time characteristics to generate a cache strategy, the defects of the industrial identification node cache management strategy in the existing technology are solved, and the risk of memory overflow and cache breakdown and penetration is reduced in time, thereby improving the comprehensive protection capability and risk management efficiency of the industrial identification resolution system.

[0062] Figure 3 A flowchart showing a method for generating a cache strategy based on target industrial identification data request records, expert experience data and business characteristic data in an industrial identification data cache processing method according to an embodiment of the present disclosure is shown. Figure 3 As shown, the method 300 includes:

[0063] Step S310 , classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record, expert experience data and business characteristic data.

[0064] As shown in 310a, in some embodiments of the present disclosure, the expert experience data includes the life cycle of the target field and the periodic usage pattern of the target data. For example, it may include adding expert experience data for a specific application scenario. For example, in the industrial identification data set of the power system, the expert experience data of the "electricity meter accounting" field includes a life cycle of one week and a usage pattern that is called in the last week of each month.

[0065] As shown in 310b, in some embodiments of the present disclosure, the business characteristic data includes the life cycle of target data with business characteristics, such as "industrial flash sales" and "industrial promotions". Such businesses have specific life cycles, such as the industrial flash sales business ends at a certain time, and the industrial promotion business ends at a certain time period.

[0066] Through the analysis of access records, expert experience, business characteristics, etc., this method can use multi-source information to classify industrial identification data. The input of multi-source data makes up for the limitation of using only single data, improves the operability of industrial identification data classification at different scales, and thus improves the accuracy of data prediction.

[0067] In some embodiments of the present disclosure, the above-mentioned at least one data source can be combined, and calculations can be performed through big data analysis and artificial intelligence analysis to classify and process different data according to time characteristics. For example, the artificial intelligence analysis model can use the extreme gradient promotion Xgboost algorithm model. In some embodiments, the algorithm is not particularly limited. When those skilled in the art put the technical solution of the present application into practice, they can refer to the records in the relevant technology; for example, in one implementation, the above-mentioned artificial intelligence analysis model can be a deep learning model trained based on a neural network combined with a large number of personalized display samples. In some embodiments of the present disclosure, taking the Xgboost model as an example, a model is established for the second kill business, and the request history of the second kill business data is used as the input feature of the model, including but not limited to the duration of the second kill, product type, transaction volume per minute, page refresh times, etc., according to expert experience and business characteristic data, the cache of these data that needs to be cleared immediately after the second kill ends is marked as a short-term class label, and the others are marked as ordinary class labels, and the cache should not be cleared immediately. Then run the xgboost model to learn the relationship between the time characteristics of the second kill product and whether to clear the cache immediately after the second kill ends.

[0068] In some embodiments of the present disclosure, classifying different data according to time characteristics may include 310c, classifying data according to timeliness categories, where the timeliness categories include: general data and short-term data. Short-term data refers to data that is requested in large quantities in a short period of time; general data refers to general data that does not have the characteristics of short-term data.

[0069] In other embodiments of the present disclosure, classifying different data according to time characteristics may also include 310d, classifying data according to predictive categories, where the predictive categories include: sporadic data and periodic data. Periodic data refers to the periodic regularity of the time when data is requested in large quantities; sporadic data refers to data requests that do not have a periodic regularity.

[0070] In some other embodiments, multiple classification methods and more category data may be included. The industrial identification data type reflected by the time characteristics of different dimensions effectively overcomes the limitation of using a single category, thereby improving the generalization ability of the cache management method of the present application.

[0071] Step S320: Generate a cache strategy based on the classification result.

[0072] In some embodiments of the present disclosure, step S320 may include step S320a. If the classification result is short-term data, the cache related to the short-term data will be deleted at a preset time. For example, data of a flash sale activity will be accessed in large quantities in a short period of time. The page cache, parameter cache, order cache, etc. involved in this short-term data occupy a large space. When the pre-sale ends, the cached data should be invalidated immediately and the space should be released. By classifying and processing short-term data and cleaning it up in time after the data expires, the purpose of saving memory resources is achieved.

[0073] In some other embodiments of the present disclosure, step S320 may also include step S320b, and if the classification result is periodic data, the data related to the periodic data is pre-loaded into the cache. For example, the power system will batch process accounting services at the end of the month, and the requests for periodic industrial identification data will burst out at the end of the month. However, the outbreak will cause great pressure on the downstream industrial identification resolution server, which may cause the server to stop responding or even paralyzed. Therefore, these data should be pre-loaded before they are requested on a large scale to relieve the pressure on downstream servers. By classifying and processing periodic data, on the one hand, data can be pre-loaded periodically through pre-judgment of data to increase the speed at which users obtain content; on the other hand, the risk of memory overflow, resulting in cache breakdown and penetration can be reduced, further improving the comprehensive protection capabilities and risk management efficiency of the industrial identification system.

[0074] Figure 4 FIG. 1 is a flow chart of an industrial identification data cache processing method based on security verification in an industrial identification data cache processing method in an embodiment of the present disclosure. Figure 4 As shown, the method 400 includes:

[0075] Step S410, obtaining a target industry identification data request.

[0076] In some embodiments of the present disclosure, a spring interceptor may be used to filter acquired industrial identification data requests.

[0077] Step S420: Generate a target industry identification data request record according to the target industry identification data request.

[0078] In some embodiments of the present disclosure, Kafka can be used to process the data stream of industrial identification data requests to perform asynchronous message processing, and then the data stream of industrial identification data requests is forwarded to the Redis cache in real time, while the data records are packaged and stored in batches in the database. In some embodiments, the database can use MongoDB.

[0079] Step S430, obtaining an industrial identification data set.

[0080] In some embodiments of the present disclosure, the industrial identification dataset may be updated periodically to ensure the accuracy of the acquired industrial identification dataset.

[0081] Step S440: perform security verification on the industrial identification data request records according to the industrial identification data set. By performing security verification, malicious attacks can be prevented from forging a large number of industrial identification data requests in a short period of time to cause the cache device to fail, such as cache breakdown.

[0082] In some embodiments of the present disclosure, security verification includes determining whether the data corresponding to the industrial identification data request is in the industrial identification data set. If the data corresponding to the industrial identification data request is not in the industrial identification data set, the industrial identification data request fails the security verification.

[0083] Step S450: Process the industrial identification data request according to the security verification result. In some embodiments of the present disclosure, if the industrial identification data request fails the security verification, a processing strategy for the industrial identification data request is generated, such as rejecting the industrial identification request.

[0084] Through the above method, while realizing the intelligent management of industrial identification data cache, the security of the industrial identification resolution system is taken into account, preventing memory breakdown caused by malicious requests, and maintaining the stability of the industrial identification resolution system.

[0085] In some embodiments, step S452 may also be included, if the data request in the industrial identification data request record fails to pass the security verification, then an access count threshold is set for the industrial identification data request, such as a threshold of 50 accesses within one hour. If the access count threshold is exceeded, step S454 is executed, if the access count within a preset time exceeds the access count threshold, the industrial identification data request is rejected. For example, if the number of industrial identification data requests that have not passed the security verification exceeds 50 times within one hour, the industrial identification data request is rejected.

[0086] By comparing with the access count threshold, the security of the access is determined. While ensuring that normal access is not hindered, the risk of malicious attacks is effectively reduced, thereby improving security.

[0087] It should be noted that the above figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0088] Figure 5 FIG. 2 is a schematic diagram of an industrial identification data cache processing device according to an embodiment of the present disclosure. Figure 5 As shown, the device 500 includes:

[0089] The traffic distribution module 510 is used to obtain a target industrial identification data request.

[0090] The operation record module 520 is used to generate a target industry identification data request record according to the target industry identification data request.

[0091] The intelligent analysis module 530 is used to obtain an industrial identification data set, classify the industrial identification data set according to time characteristics based on the target industry representation data request record, and generate a cache strategy based on the classification result.

[0092] The instruction dispatching module 540 is used to adjust the industrial identification data cache according to the cache strategy.

[0093] In some embodiments of the present disclosure, the intelligent analysis module 530 is also used to classify the industrial identification data set according to time characteristics based on the target industrial identification data request record and at least one of the following data: expert experience data, the expert experience data includes the life cycle of the target field and the periodic usage rules of the target data; business characteristic data, the business characteristic data includes the life cycle of the target data with business characteristics.

[0094] In some embodiments of the present disclosure, the time characteristic includes at least one of the following: a timeliness category of data, the timeliness category includes: general data and short-term data; a predictability category of data, the predictability category includes: scattered data and periodic data.

[0095] In some embodiments of the present disclosure, the intelligent analysis module 530 is further configured to execute a scheduled deletion of the cache associated with the short-term data at a preset time if the classification result is short-term data.

[0096] In some embodiments of the present disclosure, the intelligent analysis module 530 is further configured to preload data related to the periodic data into a cache if the classification result is periodic data.

[0097] In some embodiments of the present disclosure, the device 500 further includes a security verification module, which is used to perform security verification on industrial identification data request records at regular intervals according to the industrial identification data set; and process the industrial identification data request according to the security verification result.

[0098] In some embodiments of the present disclosure, the security verification module is also used to set an access count threshold for the industrial identification data request if the data request in the industrial identification data request record fails the security verification; if the number of accesses within a preset time exceeds the access count threshold, the industrial identification data request is rejected.

[0099] Figure 6 FIG. 6 is a flow chart showing a business process of an industrial identification data cache processing device 600 in an embodiment of the present disclosure. Figure 6 As shown, it includes: an industrial identification data cache processing device 600, a client 610, a cache 660, and a database 670. The industrial identification data cache processing device 600 includes: a diversion module 620, an operation recording module 630, an intelligent analysis module 640, and an instruction dispatching module 650.

[0100] In some embodiments of the present disclosure, the client 610 is used to input an industrial identification data request to the diversion module 620. The diversion module 620 is used to forward the industrial identification data request to the cache 660 in real time, and asynchronously store the industrial identification data request to the operation recording module 630. The operation recording module 630 is used to store data for the industrial identification data request. The intelligent analysis module 640 is used to retrieve the stored industrial identification data request record from the operation recording module 630, and obtain the industrial identification data set from the industrial identification database 670. The intelligent analysis module 640 is also used to classify the industrial identification data set according to the time characteristics according to the industrial identification data request record to generate a cache strategy. The intelligent analysis module 640 is also used to perform security verification on the industrial identification data request record according to the industrial identification data set, and generate a processing strategy for the industrial identification data request for the industrial identification data request that fails the security verification. The instruction dispatching module 650 is used to dispatch the cache strategy generated by the intelligent analysis module 640 and the processing strategy for the industrial identification data request to the cache 660 and the diversion module 620 respectively. The cache 660 is used to adjust the cache data according to the cache strategy, which may include deleting some data at a fixed time, and may also include preloading some data from the database 670. The diversion module 620 may process the industrial identification data request according to the processing strategy for the industrial identification data request.

[0101] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0102] Refer to the following Figure 7 An electronic device 700 according to this embodiment of the present invention will be described. Figure 7 The electronic device 700 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0103] like Figure 7 As shown, the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 may include but are not limited to: at least one processing unit 710, at least one storage unit 720, and a bus 730 connecting different system components (including the storage unit 720 and the processing unit 710).

[0104] The storage unit stores program codes, which can be executed by the processing unit 710, so that the processing unit 710 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 710 can perform the following steps: Figure 2 As shown in S210, a target industrial identification data request is obtained; S220, a target industrial identification data request record is generated according to the target industrial identification data request; S230, an industrial identification data set is obtained; S240, the industrial identification data set is classified according to time characteristics based on the target industrial identification data request record; S250, a cache strategy is generated based on the classification result; S260, the industrial identification data cache is adjusted according to the cache strategy.

[0105] The storage unit 720 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 7201 and / or a cache storage unit 7202 , and may further include a read-only storage unit (ROM) 7203 .

[0106] The storage unit 720 may also include a program / utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0107] Bus 730 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0108] The electronic device 700 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 700, and / or may communicate with any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 750. Furthermore, the electronic device 700 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 760. As shown, the network adapter 760 communicates with other modules of the electronic device 700 via a bus 730. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0109] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0110] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0111] The program product for implementing the above method according to an embodiment of the present invention may adopt a portable compact disk read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.

[0112] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, 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 suitable combination of the above.

[0113] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0115] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0116] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0117] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0118] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0119] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for caching industrial identification data, characterized in that: include: Obtain a target industrial identification data request, wherein the target industrial identification data request is an identification resolution request for target industrial identification data; generating a target industrial identification data request record according to the target industrial identification data request; Get the industrial identification dataset; Classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record, which includes: classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record, expert experience data and business characteristic data, wherein the expert experience data includes the life cycle of the target field and the periodic use law of the target data; the business characteristic data includes the life cycle of the target data with business characteristics; generating a cache strategy based on the classification result; and The industrial identification data cache is adjusted according to the cache strategy.

2. The industrial identification data cache processing method according to claim 1, wherein: The time characteristic includes at least one of the following: The timeliness category of the data, which includes: general data and short-term data; The predictability category of data includes: scattered data and periodic data.

3. The industrial identification data cache processing method according to claim 2, characterized in that: Generating a cache strategy based on the classification result includes: If the classification result is the short-term data, the cache related to the short-term data will be deleted regularly according to a preset time.

4. The industrial identification data cache processing method according to claim 2, characterized in that: Generating a cache strategy based on the classification result also includes: If the classification result is the periodic data, data related to the periodic data is preloaded into the cache.

5. The industrial identification data cache processing method according to claim 1, characterized in that: The method further comprises: Based on the industrial identification data set, periodically performing security verification on the industrial identification data request record; The industrial identification data request is processed according to the security verification result.

6. The industrial identification data cache processing method according to claim 5, characterized in that: Processing the industrial identification data request according to the security verification result includes: If the data request in the industrial identification data request record fails the security verification, setting an access count threshold for the industrial identification data request; If the number of accesses within a preset time exceeds the access number threshold, the industrial identification data request is rejected.

7. An industrial identification data cache processing device, characterized in that: include: A traffic distribution module, used for obtaining a target industrial identification data request, wherein the target industrial identification data request is an identification resolution request for the target industrial identification data; An operation record module, used for generating a target industrial identification data request record according to the target industrial identification data request; An intelligent analysis module, for acquiring an industrial identification data set, classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record, and generating a cache strategy based on the classification result, comprising: classifying the industrial identification data set according to time characteristics based on the target industrial identification data request record, expert experience data and business characteristic data, wherein the expert experience data includes the life cycle of the target field and the periodic use law of the target data; the business characteristic data includes the life cycle of the target data having business characteristics; and The instruction dispatching module is used to adjust the industrial identification data cache according to the cache strategy.

8. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the industrial identification data cache processing method according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the industrial identification data cache processing method according to any one of claims 1 to 6 is implemented.

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