Intelligent water meter cluster meter reading method and system

Abstract

The invention relates to the technical field of smart water meters, in particular to a smart water meter cluster meter reading method and system.The method comprises the steps that to-be-processed data is received, and the to-be-processed data is subjected to type division according to data content to obtain a data classification set; performing data classification extraction on the data classification set in sequence to obtain corresponding processing types, and obtaining a storage address based on the processing types, the storage address corresponding to the data classification set; and storing the data in the data classification set according to the storage address, performing grade division on the data classification set to obtain corresponding processing grades, and generating a processing scheduling scheme according to the processing grades. A processing scheduling scheme of the to-be-processed data is realized, and the data management efficiency of the smart water meter is improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent water meters, and particularly to a method and system for cluster meter reading of intelligent water meters. Background Art

[0002] An intelligent water meter is an intelligent water metering device based on Internet of Things technology. By real-time monitoring, remote data transmission, and intelligent analysis, it improves the efficiency of water resource management, reduces waste, and optimizes water supply services.

[0003] Currently, with the development of water meter technology, intelligent water meters have grown rapidly. When it is necessary to read the meters, the intelligent water meters are remotely connected to the meter reading platform through the pre-set sending end address and grounding end address, so that the water meter readings are sent to the meter reading platform to complete the meter reading of the intelligent water meters.

[0004] To achieve the management efficiency of intelligent water meters, it is necessary to receive multi-faceted data, process and store the data, but the types of received data are not unique. Among them, the received data involves rule data, meter reading data, and carry-over data corresponding to the meter reading data, etc. If all the data is uploaded and stored together, it will lead to a series of problems such as too high a load on the meter reading platform for data analysis, slower parsing and storing of data, and more connection timeouts, resulting in the inability of the intelligent water meters to upload data in a timely manner. Summary of the Invention

[0005] To improve the data management efficiency of intelligent water meters, this application provides a method and system for cluster meter reading of intelligent water meters.

[0006] In a first aspect, this application provides a method for cluster meter reading of intelligent water meters, adopting the following technical solution: A method for cluster meter reading of intelligent water meters includes the following steps: Receive data to be processed, and classify the data to be processed according to the data content to obtain a data classification set; Successively perform data classification extraction on the data classification set to obtain corresponding processing types, and obtain storage addresses based on the processing types, where the storage addresses correspond to the data classification set; Store the data in the data classification set according to the storage addresses, and perform a level division on the data classification set to obtain corresponding processing levels, and generate a processing scheduling plan based on the processing levels.

[0007] By adopting the above technical solution, the data to be processed is received, and type classification is carried out according to the data content of the data to be processed, obtaining different types of data classification sets, and obtaining corresponding processing types for different data classification sets according to their types, and constructing relevant processing levels according to the characteristics of the processing types, so as to implement the processing scheduling scheme for the data to be processed and improve the data management efficiency of the intelligent water meter.

[0008] In some of the embodiments, grading the data classification set to obtain the corresponding processing level includes the following steps: Obtain the data status according to the data classification set, and determine whether the data status is continuous data; If the data status is continuous data, obtain the corresponding processing resources according to the data content and the total amount of data in the data classification set, and generate the processing level according to the processing resources; If the data status is not continuous data, obtain the corresponding processing resources according to the corresponding historical processing resources and the corresponding historical processing duration in the data classification set, and generate the processing level according to the processing resources.

[0009] By adopting the above technical solution, corresponding sorting is carried out according to the data status in the data classification set, so as to judge whether the data classification set is continuous data. When the data classification set is continuous data, the processing level is generated according to the processing resources and the total amount of data. When the data classification set is not continuous data, the processing level is generated according to the processing resources obtained from the historical processing resources and the historical processing duration, so as to obtain the corresponding processing level according to the data classification sets with different data statuses, and thus the processing level can be obtained more accurately, which is convenient for accurately obtaining the processing scheduling scheme subsequently.

[0010] In some of the embodiments, obtaining the corresponding processing resources according to the data content and the total amount of data in the data classification set includes the following steps: Obtain the estimated execution duration according to the data content and the total amount of data, and generate the corresponding processing resources based on the estimated execution duration.

[0011] By adopting the above technical solution, the corresponding estimated execution duration is obtained for the specific data content and the total amount of data of different data classification sets, so that the corresponding processing resources can be allocated to the data classification sets according to the estimated execution duration, realizing the reasonable allocation of processing resources, improving the processing efficiency of the data classification sets, and further improving the accuracy of the grading of the processing level.

[0012] In some of the embodiments, obtaining the corresponding processing resources according to the corresponding historical processing resources and the corresponding historical processing duration in the data classification set includes the following steps: Obtain the estimated processing resources of the data classification set based on the historical processing resources and the historical processing duration, and determine whether the current resources corresponding to the estimated processing resources are in an occupied state; If the current resources are in an occupied state, obtain idle resources, and use the idle resources and the data content of the data classification set as the processing resources; If the current resources are not in an occupied state, use the estimated processing resources as the processing resources.

[0013] By adopting the above technical solution, the estimated processing resources are obtained based on the historical processing resources and the historical processing duration corresponding to the data classification set, and by judging whether the current resources corresponding to the estimated processing resources are occupied, the specific acquisition characteristics of obtaining the processing resources are obtained, further improving the acquisition accuracy of the processing resources.

[0014] In some of the embodiments, after determining whether the data status is continuous data, the following steps are further included: When it is determined that the data status is not continuous data, determine whether the data status is random data; If the data status is random data, generate corresponding processing resources according to the data content and the total amount of data corresponding to the data classification set; If the data status is not random data, obtain the estimated processing duration corresponding to the data classification set based on the historical processing data, and generate processing resources according to the estimated processing duration.

[0015] By adopting the above technical solution, the data status of the data classification set is judged, so as to obtain whether the data classification set is random data. If so, corresponding processing resources are generated according to the data content and the total amount of data, and then the processing resources can be accurately obtained, which is convenient for obtaining an accurate processing level subsequently. When the data status is not random data, the estimated processing duration corresponding to the data classification set is directly obtained based on the historical processing data, and the processing resources are obtained according to the estimated processing duration.

[0016] In some of the embodiments, after generating the processing scheduling plan according to the processing level, the following steps are further included: When it is determined that the data status is continuous data, generate a fixed processing mark, and mark the processing level based on the fixed processing mark to obtain a processing level with a fixed processing mark; When it is determined that the data status is random data, generate a random processing mark, and mark the processing level based on the random processing mark to obtain a processing level with a random processing mark; When it is determined that the data status is not random data, a regular processing flag is generated, and the processing level is marked based on the regular processing flag to obtain a processing level with a regular processing flag.

[0017] By adopting the above technical solution, the processing level is marked accordingly according to different data statuses, so that processing levels with different marks can be obtained. According to the marks corresponding to the processing levels, the reasonable allocation of processing resources can be ensured, and reasonable calculations can be performed according to the specific data status of the data classification set.

[0018] In some of the embodiments, a processing scheduling scheme is generated based on the processing level, including the following steps: Obtain a processing flag based on the processing level, and determine the type of the processing flag, where the processing flag includes a fixed processing flag, a random processing flag, and a regular processing flag; When it is determined that the processing level is a fixed processing flag, allocate the processing resources corresponding to the data classification set to generate the processing scheduling scheme; When it is determined that the processing level is a random processing flag, allocate the processing resources corresponding to the data content and the total amount of data in the data classification set to generate the processing scheduling scheme; When it is determined that the processing level is a regular processing flag, perform fixed-point and timed allocation on the data to be processed based on the estimated processing duration to generate the processing scheduling scheme.

[0019] By adopting the above technical solution, the processing level is marked accordingly according to different data statuses, so that processing levels with different marks can be obtained, which is convenient for obtaining the processing scheduling scheme. According to the marks corresponding to the processing levels, the reasonable allocation of processing resources can be ensured, and reasonable calculations can be performed according to the specific data status of the data classification set, thereby improving the accuracy of obtaining the processing scheduling scheme.

[0020] In a second aspect, the present application provides an intelligent water meter cluster meter reading system, adopting the following technical solution: An intelligent water meter cluster meter reading system that executes an intelligent water meter cluster meter reading method described in the first aspect, includes: A data partitioning module, which is used to receive data to be processed and partition the data to be processed according to the data content to obtain a data classification set; A storage processing module, which is used to sequentially perform data classification extraction on the data classification set to obtain corresponding processing types, and obtain storage addresses based on the processing types, where the storage addresses correspond to the data classification set; A solution generation module, which is used to store the data in the data classification set according to the storage address, classify the data classification set to obtain corresponding processing levels, and generate a processing scheduling solution based on the processing levels.

[0021] In some embodiments, the data in the data classification set is stored according to the storage address. Among them, corresponding microservice processing modules are set for different storage addresses, and the microservice processing modules are used to synchronously process the corresponding data classification sets.

[0022] In some embodiments, a service monitoring module is further included. The service monitoring module is used to monitor the processing status of multiple microservice processing modules in real time and determine whether the processing status is good. If the processing status is good, the generation module adds a signal, and updates the number of microservice processing modules based on the module addition signal.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: Receiving data to be processed, classifying the data according to the data content of the data to be processed to obtain different types of data classification sets, obtaining corresponding processing types for different data classification sets according to their types, and constructing relevant processing levels according to the characteristics of the processing types, so as to implement a processing scheduling solution for the data to be processed and improve the data management efficiency of smart water meters; Performing corresponding sorting according to the data status in the data classification set, thereby judging whether the data classification set is continuous data. When the data classification set is continuous data, generating a processing level according to the processing resources and the total amount of data. When the data classification set is not continuous data, generating a processing level according to the processing resources obtained from the historical processing resources and the historical processing duration, so as to obtain corresponding processing levels for the data classification sets in different data states, and thus being able to more accurately obtain the processing levels, which is convenient for accurately obtaining the processing scheduling solution subsequently; Obtaining the corresponding estimated execution duration for the specific data content and the total amount of different data classification sets, so that the corresponding processing resources can be allocated to the data classification sets according to the estimated execution duration, realizing the reasonable allocation of processing resources, improving the processing efficiency of the data classification sets, and further being able to improve the accuracy of the division of the processing levels. Description of the Drawings

[0024] Figure 1 is a schematic diagram of the data processing process provided by the present application; Figure 2 is a block diagram of the smart water meter cluster meter reading method provided by the embodiment of the present application; Figure 3 is a block diagram of the method for obtaining the processing level provided by the embodiment of the present application; Figure 4 It is a block diagram of the processing resource acquisition method provided by the embodiments of the present application; Figure 5 It is a block diagram of the method for obtaining the processing level of the mark provided by the embodiments of the present application; Figure 6 It is a schematic structural diagram of the intelligent water meter cluster meter reading system provided by the embodiments of the present application.

[0025] Explanation of reference numerals: 11, front-end module; 111, PC side; 112, mobile app; 12, first back-end module; 13, second back-end module; 20, data partitioning module; 30, storage processing module; 40, solution generation module. Detailed implementation manners

[0026] To more clearly understand the purpose, technical solution and advantages of the present application, the present application will be described and illustrated below with reference to the drawings and embodiments. However, those of ordinary skill in the art should understand that the present application can be implemented without these details. In some cases, well-known methods, processes, systems, components and / or circuits that have been described at a higher level will not be described in detail to avoid unnecessary description from obscuring various aspects of the present application. For those of ordinary skill in the art, it is obvious that various changes can be made to the disclosed embodiments of the present application, and the general principles defined in the present application can be applied to other embodiments and application scenarios without departing from the principles and scope of the present application. Therefore, the present application is not limited to the illustrated embodiments, but conforms to the broadest scope consistent with the scope claimed in the present application.

[0027] The embodiments of the present application disclose an intelligent water meter cluster meter reading method, which is applied to an intelligent water meter cluster meter reading system. This system is mainly used for the cluster meter reading of intelligent water meters, and improves the efficiency of water resource utilization, reduces waste, and optimizes water service management through remote monitoring, data analysis and automated management.

[0028] Refer to Figure 1As shown in the figure, the intelligent water meter cluster meter reading system adopts the separation of the front-end module 11 and the back-end module, parallel processing, and there is no need to wait for each other. The front-end is used to optimize the user interface and improve the user experience, and enhance the fast response and dynamic interaction experience. The front-end and the back-end can be independently deployed and updated, improving the release flexibility and solving the problems such as slow data response and unfriendly front-end display in system development. Specifically, the front-end module 11 includes the PC side 111 and the mobile app 112, etc., and the back-end module includes the first back-end module 12 and the second back-end module 13. The first back-end module 12 includes the API interface service, and the API interface service includes data interfaces, introduction of third-party interfaces, etc. The second back-end module 13 is mainly used to store various data, and the specific data includes structured data and unstructured data, such as pictures and documents, etc. The front-end mainly initiates HTTP requests through the browser, and the back-end responds to the customer requests, thereby improving the system response speed.

[0029] As Figure 2 shown, the intelligent water meter cluster meter reading method includes the following steps: S100, receive the data to be processed, and classify the data to be processed according to the data content to obtain a data classification set.

[0030] Among them, the data to be processed represents the data that needs to be processed. The data to be processed is specifically the data sent by each module, such as the meter reading data sent by the meter reading module, the data of equipment, personnel, and business rules input manually, and the data that needs to be carried forward, etc. The data that needs to be carried forward can be the required data input manually. The data to be processed also includes various pictures and data of certificate types. Specifically, through the processing of various data to be processed, the intelligent management of cluster meter reading can be realized based on the Internet of Things. The data content is to classify the specific meaning expressed by the data to be processed to obtain a data classification set, and the data stored in the data classification set belongs to the same type of data.

[0031] It should be noted here that classifying the data to be processed according to the data content is specifically to extract the text features of the data content of the data to be processed, and compare according to the text features to determine the data classification set.

[0032] S200, sequentially perform data classification extraction on the data classification set to obtain the corresponding processing type, and obtain the storage address based on the processing type. The storage address corresponds to the data classification set.

[0033] Among them, the processing type represents the data type corresponding to the data classification set, and the processing types include relational data, time-series data, analytical data, and file data. The storage address represents the corresponding address of the specific storage scheme corresponding to the processed data classification set. One data classification set corresponds to one storage address. Of course, when there is too much data content in the data classification set, one data classification set can also correspond to multiple storage addresses, as long as there is a corresponding relationship between the storage address and the data classification set.

[0034] It should be noted here that the storage address can be stored in different databases. The selection of the storage address can be correspondingly selected according to the data type of the data classification set. Specifically, for relational data with complex business logics, the storage scheme can use the MySQL database for corresponding storage. For time-series data with a large volume, the storage scheme uses Blockhouse, which can then receive time-series data at the level of hundreds of millions of rows. For analytical data, there may be thousands of query requests. Therefore, the Star Rocks storage scheme is used for corresponding storage, so as to be able to store the analytical data accordingly. For file data, such as pictures and certificate data, a storage scheme with fast read and write speeds is required. Therefore, the Mini storage scheme is used, which can then quickly identify file data.

[0035] It should be noted here that the MySQL database is mainly used to process relational databases, store structured data, and support functions such as indexes, foreign keys, and triggers. Blockhouse is mainly used for real-time query of massive data. StarRocks supports millisecond-level response and multi-table association optimization. Mini has high availability and is easy to expand, and is suitable for storing unstructured data such as pictures and videos. The MySQL database, Blockhouse, Star Rocks, and Mini all belong to existing technologies, and will not be elaborated here.

[0036] S300, store the data in the data classification set according to the storage address, and classify the data classification set to obtain the corresponding processing level, and generate a processing scheduling scheme based on the processing level.

[0037] Among them, when storing the data to be processed in the corresponding storage scheme, it is necessary to perform corresponding processing on the data in the data classification set. It is necessary to classify each data classification set separately to obtain the corresponding processing level, and generate a processing scheduling scheme according to the processing level, so as to be able to quickly process all the data to be processed. The processing level represents the priority processing degree corresponding to the data classification set, and the processing scheduling scheme represents the scheduling scheme formed by resource scheduling according to the data classification set formed by the data to be processed, so as to be able to automatically and quickly process the data to be processed.

[0038] Reference Figure 3 , in one of the embodiments, a hierarchical division is performed on the data classification set to obtain the corresponding processing level, including the following steps: S310, obtain the data status according to the data classification set, and determine whether the data status is continuous data.

[0039] S320, if the data status is continuous data, obtain the corresponding processing resources according to the data content and the total amount of data in the data classification set, and generate a processing level according to the processing resources.

[0040] S330, if the data status is not continuous data, obtain the corresponding processing resources according to the historical processing resources and the corresponding historical processing duration in the data classification set, and generate a processing level according to the processing resources.

[0041] Among them, the data status represents that the received data in the data classification set belongs to the received status. The data status includes continuous data and non - continuous data. Among them, continuous data is continuously sent data. For continuous data, corresponding processing can be performed through the corresponding data content in the data classification set to obtain corresponding processing resources. The processing resources are the resources allocated for processing the data in the data classification set, such as CPU, memory occupancy, etc.

[0042] It should be noted here that the processing resources can be allocated accordingly according to the data content and the total amount of data in the data classification set. For different amounts of data, corresponding allocation can be made to the data classification set according to the principle of fast processing, so as to obtain corresponding processing resources. After the data classification set is allocated corresponding processing resources, in order to obtain the corresponding processing level of the data classification set, it is necessary to analyze according to the corresponding processing resources of the data classification set and obtain the corresponding processing level based on the resources that need to be occupied currently.

[0043] In addition, when the data status of the data classification set is not continuous data, the corresponding processing resources are obtained according to the historical processing resources and the historical processing duration corresponding to the data in the data classification set, and the corresponding processing level is generated according to the amount of occupied resources.

[0044] It should be noted here that when the data status of the data classification set is not continuous data, it means that the data has randomness. Therefore, it is not necessary to set fixed processing resources for this type of data. Therefore, it is necessary to set resources according to the historical processing resources and the historical processing duration corresponding to this type of data.

[0045] In one of the embodiments, obtaining the corresponding processing resources according to the data content and the total amount of data in the data classification set includes the following steps: S321. Obtain the estimated execution duration based on the data content and the total amount of data, and generate corresponding processing resources based on the estimated execution duration.

[0046] Among them, the estimated execution time represents the estimated duration required to process the data classification set. Here, the estimated execution duration is calculated based on the data content and the total amount of data. The processing efficiency of the same type of data can be obtained by screening in the historical database according to the data content, and the product of the processing efficiency and the total amount of data is used as the estimated execution duration.

[0047] In one of the embodiments, obtaining the corresponding processing resources according to the historical processing resources and the corresponding historical processing duration corresponding to the data classification set includes the following steps: S331. Obtain the estimated processing resources of the data classification set according to the historical processing resources and the historical processing duration, and determine whether the current resources corresponding to the estimated processing resources are in an occupied state.

[0048] S332. If the current resources are in an occupied state, obtain idle resources, and use the idle resources and the data content of the data classification set as the processing resources.

[0049] S333. If the current resources are not in an occupied state, use the estimated processing resources as the processing resources.

[0050] Among them, the estimated processing resources represent the estimated resources obtained according to the historical processing resources and the historical processing duration corresponding to the data classification set. In order to ensure that the estimated resources can quickly and accurately process the data in the data classification set, it is necessary to determine whether the current resources corresponding to the estimated processing resources are in an occupied state. If the current resources are in an occupied state, obtain idle resources, and use the idle resources and the data content of the data classification set as the processing resources, then perform resource scheduling according to the idle resources, so as to be able to quickly process the data in the data classification set. If the current resources are not in an occupied state, use the estimated processing resources as the processing resources, reducing the process of resource scheduling and directly using the estimated processing resources as the processing resources.

[0051] Refer to Figure 4 In one of the embodiments, after determining whether the data status is continuous data, the following steps are further included: S340. When it is determined that the data status is not continuous data, determine whether the data status is random data.

[0052] S350. If the data status is random data, generate corresponding processing resources based on the data content and the total amount of data corresponding to the data classification set.

[0053] S360. If the data status is not random data, obtain the estimated processing duration corresponding to the data classification set based on historical processed data, and generate processing resources based on the estimated processing duration.

[0054] Among them, random data indicates that the data in the data classification set is random and has no regularity. When the data status is random data, corresponding processing resources are generated according to the specific data content and the total amount of data in the data classification set. The specific steps for obtaining the processing resources are to obtain the processing efficiency corresponding to the data content in the historical database, then take the product of the processing efficiency and the total amount of data as the processing duration, and finally obtain the corresponding processing resources according to the processing duration. The data status being regular data or predictable data both indicates that the data status is not random data. When the data status is not random data, obtain the estimated processing duration corresponding to the data classification set based on historical processed data. The estimated processing duration indicates that corresponding estimation can be performed on the data classification set according to historical processed data. Finally, obtain the processing resources according to the specific value of the estimated processing duration.

[0055] Refer to Figure 5 , in one of the embodiments, after generating the processing scheduling plan according to the processing level, the following steps are further included: S410. When it is determined that the data status is continuous data, generate a fixed processing mark, and mark the processing level based on the fixed processing mark to obtain the processing level with the fixed processing mark.

[0056] S420. When it is determined that the data status is random data, generate a random processing mark, and mark the processing level based on the random processing mark to obtain the processing level with the random processing mark.

[0057] S430. When it is determined that the data status is not random data, generate a regular processing mark, and mark the processing level based on the regular processing mark to obtain the processing level with the regular processing mark.

[0058] Among them, the fixed processing mark indicates that the data corresponding to the data classification set needs to be processed fixedly. The random processing mark indicates that the data corresponding to the data classification set is randomly generated, so general processing is performed on such data. The regular processing mark indicates that the data in the data classification set is regular or predictable, so regular processing can be performed.

[0059] It should be noted here that marking the processing level can all be performed using characteristic signal marking and label marking on the processing level.

[0060] In one of the embodiments, generating the processing scheduling plan according to the processing level includes the following steps: S510, obtain a processing flag according to the processing level, and determine the type of the processing flag, where the processing flag includes a fixed processing flag, a random processing flag, and a regular processing flag.

[0061] S520, when it is determined that the processing level is a fixed processing flag, allocate according to the processing resources corresponding to the data classification set to generate a processing scheduling plan.

[0062] S530, when it is determined that the processing level is a random processing flag, allocate according to the data content of the data classification set and the processing resources corresponding to the total amount of data to generate a processing scheduling plan.

[0063] S540, when it is determined that the processing level is a regular processing flag, perform fixed-point and timed allocation on the data to be processed based on the estimated processing duration to generate a processing scheduling plan.

[0064] By judging the type of the processing flag, and then according to different types of processing levels, allocate resources for the data of the corresponding data classification set to obtain the corresponding processing scheduling plan. The processing scheduling file in step S520 can be specifically allocated according to the originally set processing resources, and the set processing resources are specifically obtained according to the process of step S320. In step S530, when the processing level is a random processing flag, allocate according to the data content of the data classification set and the processing resources corresponding to the total amount of data to ensure that a processing scheduling plan including the processing time, processing duration, and processing resources of the data in the data classification set is generated. In step S540, when the processing level is a regular processing flag, perform fixed-point and timed allocation on the data to be processed based on the estimated processing duration to generate a processing scheduling plan. The fixed-point and timed allocation is specifically to determine when to process the data of the data classification set, the resources used for processing, and the estimated processing duration based on the predicted estimated processing duration.

[0065] The embodiment of the present application also discloses an intelligent water meter cluster meter reading system.

[0066] Such as Figure 6As shown in the figure, the intelligent water meter cluster meter reading system executes an intelligent water meter cluster meter reading method, including a data partitioning module 20, a storage and processing module 30, and a solution generation module 40. The data partitioning module 20 is used to receive the data to be processed and partition the data to be processed according to the data content to obtain a data classification set. The storage and processing module 30 is network-connected to the data partitioning module 20 to receive the data classification set, and is used to sequentially perform data classification extraction on the data classification set to obtain the corresponding processing types, and obtain the storage addresses based on the processing types. The storage addresses correspond to the data classification set. The solution generation module 40 is network-connected to the storage and processing module 30. The solution generation module 40 is used to store the data in the data classification set according to the storage addresses, and perform a level division on the data classification set to obtain the corresponding processing levels, and generate a processing scheduling solution based on the processing levels.

[0067] The other functions executed by the above data partitioning module 20, storage and processing module 30, and solution generation module 40, as well as the technical details of each function, are the same as or similar to the corresponding features in the intelligent water meter cluster meter reading method described above, so they will not be elaborated here.

[0068] In one of the embodiments, the data in the data classification set is stored according to the storage addresses. Among them, corresponding microservice processing modules are set for different storage addresses, and the microservice processing modules are used to synchronously process the corresponding data classification sets.

[0069] In one of the embodiments, it further includes a service monitoring module. The service monitoring module is used to monitor the processing status of multiple microservice processing modules in real time and determine whether the processing status is good. If the processing status is good, the generation module increases a signal, and updates the number of microservice processing modules based on the module increase signal.

[0070] It should be noted here that the processing status is specifically obtained by the service monitoring module monitoring multiple microservice processing modules, and is judged according to the processing data results of each microservice processing module, and the data processing efficiency and processing error rate of each microservice processing module are judged to determine the processing status. When the data processing efficiency is lower than the preset efficiency, the generation module increases a signal, adds corresponding servers or data monitoring nodes, and reduces the failure probability of data processing by all microservice processing modules.

[0071] In addition, setting redundancy using overall resources can dynamically increase resources according to the set redundancy, and can automatically perform health checks and periodically perform automatic restarts, improving the system stability of the entire intelligent water meter cluster meter reading system. When performing a health check, it can gradually verify and handle exceptions, gradually verify, verify exceptions, perform alarm processing, and then transfer to different processing work orders according to the type. Record the abnormal data and classify and organize it for easy troubleshooting and analysis, continuously improving the system health. The specific display can be presented through graphics and lists.

[0072] The implementation principle is as follows: The data division module 20 receives the data to be processed and divides the data to be processed into types according to the data content to obtain a data classification set. The storage processing module 30 is used to sequentially perform data classification extraction on the data classification set to obtain the corresponding processing types, and obtain the storage addresses based on the processing types. The storage addresses correspond to the data classification set. The solution generation module 40 is used to store the data in the data classification set according to the storage addresses, and perform level division on the data classification set to obtain the corresponding processing levels, and generate a processing scheduling solution based on the processing levels.

[0073] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit and can be executed in other orders.

[0074] The above are all the preferred embodiments of this application, and do not limit the protection scope of this application accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for collective meter reading of intelligent water meters, characterized in that, Including the following steps: Receiving the data to be processed, and classifying the data to be processed according to the data content to obtain a data classification set; Successively performing data classification extraction on the data classification set to obtain corresponding processing types, and obtaining storage addresses based on the processing types, where the storage addresses correspond to the data classification set; Storing the data in the data classification set according to the storage addresses, and classifying the data classification set to obtain corresponding processing levels, and generating a processing scheduling scheme based on the processing levels.

2. The intelligent water meter cluster meter reading method according to claim 1, characterized in that Classifying the data classification set to obtain corresponding processing levels, including the following steps: Obtaining the data status according to the data classification set, and judging whether the data status is continuous data; If the data status is continuous data, obtaining corresponding processing resources according to the data content and the total amount of data in the data classification set, and generating the processing level based on the processing resources; If the data status is not continuous data, obtaining corresponding processing resources according to the historical processing resources and the corresponding historical processing duration in the data classification set, and generating a processing level based on the processing resources.

3. The intelligent water meter cluster meter reading method according to claim 2, wherein Obtaining corresponding processing resources according to the data content and the total amount of data in the data classification set, including the following steps: Obtaining the estimated execution duration according to the data content and the total amount of data, and generating corresponding processing resources based on the estimated execution duration.

4. The smart water meter cluster reading method according to claim 2, characterized in that: Obtaining corresponding processing resources according to the historical processing resources and the corresponding historical processing duration in the data classification set, including the following steps: Obtaining the estimated processing resources of the data classification set according to the historical processing resources and the historical processing duration, and judging whether the current resources corresponding to the estimated processing resources are in an occupied state; If the current resources are in an occupied state, obtaining idle resources, and using the idle resources and the data content of the data classification set as the processing resources; If the current resources are not in an occupied state, using the estimated processing resources as the processing resources.

5. The intelligent water meter cluster meter reading method according to claim 2, characterized in that, After judging whether the data status is continuous data, the following steps are further included: When it is determined that the data status is not continuous data, judging whether the data status is random data; If the data status is random data, generating corresponding processing resources according to the data content and the total amount of data in the data classification set; If the data status is not random data, obtaining the estimated processing duration corresponding to the data classification set according to the historical processing data, and generating processing resources based on the estimated processing duration.

6. The intelligent water meter cluster reading method according to claim 5, wherein, After generating a processing scheduling scheme based on the processing level, the following steps are further included: When it is determined that the data status is continuous data, generating a fixed processing mark, and marking the processing level based on the fixed processing mark to obtain a processing level with a fixed processing mark; When it is determined that the data status is random data, a random processing flag is generated, and the processing level is marked based on the random processing flag to obtain a processing level with a random processing flag; When it is determined that the data status is not random data, a regular processing flag is generated, and the processing level is marked based on the regular processing flag to obtain a processing level with a regular processing flag.

7. The smart water meter cluster reading method according to claim 6, characterized in that: A processing scheduling scheme is generated according to the processing level, including the following steps: Obtain a processing flag according to the processing level, and judge the type of the processing flag, where the processing flag includes a fixed processing flag, a random processing flag, and a regular processing flag; When it is determined that the processing level is a fixed processing flag, allocate the processing resources corresponding to the data classification set to generate the processing scheduling scheme; When it is determined that the processing level is a random processing flag, allocate the processing resources corresponding to the data content and the total amount of data in the data classification set to generate the processing scheduling scheme; When it is determined that the processing level is a regular processing flag, perform fixed-point and timed allocation on the data to be processed based on the estimated processing duration to generate the processing scheduling scheme.

8. An intelligent water meter cluster reading system, characterized in that, Execute a smart water meter cluster meter reading method according to any one of claims 1-7, including: A data partitioning module (20), the data partitioning module (20) is configured to receive data to be processed, and partition the data to be processed according to the data content to obtain a data classification set; A storage processing module (30), the storage processing module (30) is configured to sequentially extract data classification from the data classification set to obtain corresponding processing types, and obtain storage addresses based on the processing types, and the storage addresses correspond to the data classification set; A scheme generation module (40), the scheme generation module (40) is configured to store the data in the data classification set according to the storage address, and perform level partitioning on the data classification set to obtain corresponding processing levels, and generate a processing scheduling scheme according to the processing levels.

9. The intelligent water meter cluster meter reading system according to claim 8, wherein, Store the data in the data classification set according to the storage address, where corresponding microservice processing modules are set at different storage addresses, and the microservice processing modules are used to synchronously process the corresponding data classification sets.

10. The intelligent water meter cluster meter reading system according to claim 9, characterized in that, It further includes a service monitoring module, the service monitoring module is configured to monitor the processing status of multiple microservice processing modules in real time, and judge whether the processing status is good. If the processing status is good, a module addition signal is generated, and the number of microservice processing modules is updated based on the module addition signal.

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