Auxiliary decision-making device and method for service electric power high-voltage customer electricity charge saving

Abstract

The invention relates to an auxiliary decision-making device and method for electric charge expenditure saving of a service electric power high-voltage customer, which is quick to operate, can display three basic electric charge calculation amounts to the customer only by inputting a user number and a user name, assists the customer in reasonably selecting a basic electric charge calculation scheme, improves the cabinet approaching efficiency by 60 times, and greatly reduces the waiting time of the customer. According to the invention, all data sources are from enterprise-level data medium stations, the data of the data medium stations are from business systems in different systems, and through the data medium stations, the data acquisition is fast, the transmission stability is high, and the security problem does not exist. The cloud technology is adopted to deploy calculation at the cloud, hosted operation is achieved, data calculation is fast, and programs run stably. A browser / server (B / S) architecture is adopted to design a Web browser display interface, man-machine interaction is friendly, maintenance is easy, and quality improvement, burden reduction and efficiency improvement are achieved for a first-line basic layer.

Description

Technical Field

[0001] The present invention belongs to the technical field of auxiliary decision-making for customer electricity cost saving, and in particular relates to an auxiliary decision-making device and method for serving high-voltage power customers for electricity cost saving. Background Art

[0002] Power supply companies must implement the decisions and deployments of State Grid Corporation of China and relevant governments, proceed from the actual needs of enterprises, and actively and reasonably select basic electricity fee calculation plans for industrial and commercial customers, effectively reduce enterprise electricity costs, and promote social and economic development.

[0003] like Figure 1 As shown in the figure, currently, tellers in business halls need to manually select the basic electricity fee receivable amount under the three scenarios of capacity and demand for industrial and commercial customers. With the implementation of the "three types and one standardization" transformation and upgrading strategy of the power supply business hall of State Grid Corporation, the number of C-level business hall counters in power supply stations has been reduced to one. Taking a five-star comprehensive teller (the highest star) as an example, the entire optimization process takes an average of 10 minutes. If there are more customers at the counter, the waiting time for customers will be longer. Simply improving manual efficiency can no longer meet the customer service needs of a single counter.

[0004] Through analysis, we believe that we can innovate service methods, reengineer existing business processes, and quickly select the most affordable basic electricity billing plan for customers, so that customers can enjoy a "zero-perception" electricity service experience. Therefore, the demand is determined as: "Quickly select the basic electricity billing plan".

[0005] A similar technical solution for solving the above problem to achieve rapid optimization of the basic electricity fee calculation scheme is as follows:

[0006] In the paper "Application of RPA Technology in Electricity Bill Management under the Background of Reducing Burdens at the Grassroots Level" published in the "Internet + Application" journal, the research on key technologies uses RPA robot process automation technology to realize automatic proofreading of electricity bill income detailed accounts, effectively solving the problems of traditional electricity bill accounting business processing large data volume, complex content, cumbersome data verification steps, and easy errors, and achieved 100% accuracy in electricity bill calculation.

[0007] RPA technology can simulate manual automation to complete tedious and repetitive tasks with high reliability and reduced error rate, forming a machine-automated interactive system, greatly saving manpower and improving work efficiency.

[0008] From a practical point of view, RPA technology can be used to automatically log in to multiple business system function interfaces such as marketing and procurement, automatically obtain relevant ledger data, and then use Excel's built-in formula compiler to quickly and automatically calculate three basic electricity bill amounts.

[0009] RPA technology has shown great potential in automating office work and optimizing business processes, but it also has some shortcomings and deficiencies. The following are the main shortcomings and deficiencies of RPA technology:

[0010] 1. Data security and privacy risks exist

[0011] Sensitive data access: RPA robots usually need to access and process sensitive data of the enterprise, such as customer personal information, financial information, etc. If the RPA system is not properly configured and protected, it may face the risk of data leakage and abuse.

[0012] 2. Insufficient support for complex business processes

[0013] Unstructured data processing: RPA is mainly good at processing tasks with clear rules and repetitiveness, but it may not be able to handle tasks that require human judgment, decision-making or involve unstructured data. This limits the application of RPA in more complex business processes.

[0014] Limited flexibility: Once an RPA system is designed and deployed, it may be relatively difficult to modify or adjust it. This means that when the business processes of an enterprise change, it may take more time and effort to update and adjust the RPA system.

[0015] 3. Vulnerability of Dependence on External Systems

[0016] Impact of system changes: RPA is not a completely closed environment, but needs to simulate human users to operate external systems. Therefore, any changes to external systems may cause fluctuations or even failures in the RPA process. This requires companies to pay close attention to changes in external systems and make timely adjustments when using RPA.

[0017] Interface element positioning problem: RPA mainly relies on web page elements or Windows control handles for positioning, but these elements are likely to change, thus affecting the stability of the RPA process. Although alternative solutions such as image positioning can be used to solve this problem, these solutions also have challenges in stability and speed.

[0018] In summary, while RPA technology brings many conveniences and benefits, it also has some obvious shortcomings and deficiencies. Enterprises need to fully consider these factors when introducing RPA and formulate corresponding solutions and measures to deal with possible risks and challenges. Summary of the invention

[0019] In order to solve the defects existing in the prior art, the present invention proposes an auxiliary decision-making device and method for serving high-voltage power customers to save electricity costs. The present invention is quick to operate, and only needs to enter the account number and name to display three basic electricity billing amounts to the customer, assisting the customer to reasonably choose the basic electricity billing plan, and improving the efficiency of the counter by 60 times, greatly reducing the waiting time for customers. The data sources of the present invention all come from the enterprise-level data middle platform, and the data of the data middle platform all come from business systems in different systems. Through the data middle platform, data acquisition is fast, transmission stability is high, and there is no security problem. Cloud technology is used to deploy computing and managed operation in the cloud, with fast data calculation and stable program operation. The present invention adopts B / S architecture to design the Web browser display interface, which is friendly to human-computer interaction and easy to maintain, and can improve the quality, reduce the burden and increase efficiency for the front-line grassroots.

[0020] The present invention uses the following technical solutions.

[0021] An auxiliary decision-making method for serving high-voltage power customers to save electricity costs, comprising:

[0022] Centrally obtain marketing and production professional data sources from the power supply company's data center, obtain high-voltage customer basic file information, transformer operation, monthly receivable electricity charges, and demand value data from the marketing side; obtain line basic file ledgers, line loads, and maximum allowable current data from the production side to build an intelligent recommendation model for basic electricity price pricing plans for high-voltage customers.

[0023] Preferably, the auxiliary decision-making method for serving high-voltage power customers to save electricity costs specifically includes:

[0024] Step 1: Use the self-service application form to obtain source data;

[0025] Step 2: According to the successfully applied source data form, the real-time processing scheduling task is used to obtain the processed data;

[0026] Step 3: Provide RDS relational database services based on the processed data;

[0027] Step 4: Encapsulate the data stored in the RDS database using the RPC interface.

[0028] Preferably, step 1 specifically includes:

[0029] Step 1-1: Sort out the relevant data fields required to calculate the basic electricity fee calculation plan and determine the data requirements;

[0030] Step 1-2: Log in to the Digital Capability Open Platform and use the keyword search function to trace the source data form corresponding to the data requirement;

[0031] Step 1-3: Use the data platform embedded in the digital capability open platform to submit a data form application by yourself according to the power company's data application process.

[0032] Step 1-4: After approval, read the successfully applied source data form and test the single acquisition time of the data form.

[0033] Preferably, the relevant data fields in step 1-1 include basic files of industrial and commercial customers, electricity bill ledgers, and production load data fields.

[0034] Preferably, step 2 specifically includes:

[0035] Step 2-1: Alibaba Cloud uses the DataWorks data integration tool to centrally extract all the source data forms received from the data center and convert and load them;

[0036] Step 2-2: Locate the data fields that need to be processed, synchronize these data fields to the Log table, split them into the Delta table, and merge them into the Base table;

[0037] Step 2-3: Use Alibaba Cloud's intelligent scheduling function to configure the data computing task with the highest priority, ensuring that the three basic electricity fee calculation schemes of capacity and demand are calculated first;

[0038] Step 2-4: Select typical loads of industrial and commercial customers with different transformer capacities, different numbers of power sources, and different voltage levels to test the accuracy of the output basic electricity fee calculation plan. At the same time, record the real-time scheduling task throughput test data and use it as processed data.

[0039] Preferably, the data fields to be processed in step 2-2 include the name and number of the industrial and commercial customer, transformer capacity, electricity consumption and electricity fee ledger, and actual demand.

[0040] Preferably, step 3 specifically includes:

[0041] Step 3-1: Log in to Alibaba Cloud to create an RDS instance and set basic information. After configuration is complete, receive the processed data transmitted by DataWorks in the previous stage.

[0042] Step 3-2: Use the RDS database to store the data cloud to the Alibaba Cloud SSD disk, execute SQL statements, query the data cloud, and manage storage operations;

[0043] Step 3-3: Perform stress test on the RDS database and concurrent query test on the RDS database, test and record the QPS performance index of the RDS database, that is, the number of queries per second of the RDS database.

[0044] Preferably, the basic information in step 3-1 includes an instance name and a password.

[0045] Preferably, step 4 specifically includes:

[0046] Step 4-1: The backend reads the stored data in the RDS database;

[0047] Step 4-2: Sort out and define the service interface types to be encapsulated according to the query, screening, and retrieval functions;

[0048] Preferably, the service interface type in step 4-2 includes a method name, parameters and a return value.

[0049] Step 4-3: Generate stub code to implement communication between the client and the server, and use the RPC method to encapsulate data into different types of service interfaces;

[0050] Step 4-4: Perform stress test on the encapsulated interface, call the encapsulated RPC interface through the stub code, test and record the TPS performance of the interface call, that is, the number of transactions processed per second.

[0051] Preferably, the auxiliary decision-making method for serving high-voltage power customers to save electricity costs further includes:

[0052] Step 5: Design human-computer interaction for text input.

[0053] Preferably, step 5 specifically includes:

[0054] Step 5-1: Design a visual web browser display interface according to the B / S architecture, including layout, style and interaction logic;

[0055] Step 5-2: Use the Element-UI framework to develop server-side logic and compile the relevant components of the web browser display interface;

[0056] Step 5-3: Input text-based human-computer interaction query logic functions, conduct human-computer interaction transaction success rate tests, and record the test results.

[0057] An auxiliary decision-making device for serving high-voltage power customers to save electricity costs, comprising:

[0058] The acquisition module is used to obtain the source data form using the self-service application;

[0059] A processing module, which is used to obtain processed data by real-time processing scheduling tasks according to the successfully applied source data form;

[0060] A service module, which is used to provide RDS relational database services based on the processed data;

[0061] A packaging module, which is used to encapsulate the storage data of the RDS database using the RPC interface;

[0062] Design module, which is used to design human-computer interaction of input text.

[0063] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0064] The cloud technology used in this invention has significant advantages over RPA technology in solving problems such as data security and privacy, insufficient support for complex business processes, and vulnerability to external system reliance:

[0065] 1. Data security and privacy protection

[0066] Multi-layer security protection: Cloud platforms usually provide multi-layer security protection mechanisms, including firewalls, intrusion detection systems, data encryption, etc., to ensure the security of data during transmission and storage. These security measures can effectively resist external attacks and data leakage risks.

[0067] Compliance support: Cloud service providers usually follow strict data protection regulations, such as GDPR, HIPAA, etc., to ensure the legality and compliance of user data. Enterprises can reduce compliance risks and meet regulatory requirements by using cloud services.

[0068] Data backup and recovery: The cloud computing platform has automatic backup and restore functions to ensure data security and integrity. Even if data is lost or damaged, data can be quickly restored to reduce the risk of business interruption.

[0069] 2. Support for Complex Business Processes

[0070] Elastic expansion capability: The cloud computing platform has strong elastic expansion capability and can adjust resource allocation in real time according to business needs. When the business process becomes complex or needs to process a large amount of data, the cloud platform can quickly increase computing resources and storage resources to ensure the smooth operation of the business process.

[0071] High availability guarantee: Cloud computing platforms usually adopt distributed architecture and redundant design to ensure high availability of services. Even if some nodes fail, the continuity and stability of services can be guaranteed through mechanisms such as automatic switching and load balancing.

[0072] Integration capability: Cloud computing platforms can be seamlessly integrated with other systems and applications, supporting the automation and collaboration of complex business processes. Enterprises can integrate data and information from different systems through cloud platforms to achieve comprehensive optimization and upgrade of business processes.

[0073] 3. Reduced vulnerability to external system dependence

[0074] Independence and stability: As an independent computing environment, the cloud computing platform does not rely on specific external systems or hardware. This reduces the vulnerability of dependence on external systems and ensures that the cloud platform can still operate stably and provide services when external systems change or fail.

[0075] Compatibility support: Cloud computing platforms usually support a variety of operating systems, databases, and application software, and have good compatibility. This means that enterprises can more flexibly choose and use different external systems without worrying about service interruptions or performance degradation caused by compatibility issues.

[0076] Disaster recovery and recovery capabilities: Cloud computing platforms usually have complete disaster recovery and recovery capabilities, and can quickly restore services when a disaster occurs. This reduces the vulnerability of dependence on external systems and ensures that enterprises can quickly resume business operations when faced with emergencies.

[0077] In summary, cloud technology has obvious advantages in solving problems such as data security and privacy, insufficient support for complex business processes, and vulnerability to external system reliance. These advantages make cloud technology an important support force for enterprise digital transformation and business innovation. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a partial work flow chart of a power supply business hall in the prior art;

[0079] Figure 2 It is a flow chart of the auxiliary decision-making method for serving high-voltage power customers to save electricity costs described in the present invention;

[0080] Figure 3 It is a schematic diagram of the system structure of the auxiliary decision-making device for serving high-voltage power customers to save electricity costs described in the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in combination with the drawings in the embodiments of the present invention. The embodiments expressed in this application are only part of the embodiments of the present invention, not all of them. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0082] like Figure 2 As shown, the present invention provides an auxiliary decision-making method for serving high-voltage power customers to save electricity costs, comprising:

[0083] This product is built based on cloud technology. It centrally obtains marketing and production professional data sources from the provincial power supply company's data center, and obtains high-voltage customer basic file information, transformer operation, monthly receivable electricity charges, demand values ​​and other data from the marketing side; it obtains line basic file ledgers, line load, maximum allowable current and other data from the production side to build an intelligent recommendation model for basic electricity price pricing plans for high-voltage customers, solving problems such as the reliance on manual labor for the recommendation of basic electricity price pricing methods.

[0084] In a preferred but non-limiting embodiment of the present invention, the auxiliary decision-making method for serving high-voltage power customers for electricity cost saving specifically includes:

[0085] Step 1: Use the self-service application form to obtain source data;

[0086] In a preferred but non-limiting embodiment of the present invention, step 1 specifically comprises:

[0087] Step 1-1: Sort out the relevant data fields required to calculate the basic electricity fee calculation plan and determine the data requirements;

[0088] In a preferred but non-limiting embodiment of the present invention, the relevant data fields in step 1-1 include basic files of industrial and commercial customers, electricity bill ledgers, and production load data fields.

[0089] Step 1-2: Log in to the Digital Capability Open Platform and use the keyword search function to trace the source data form corresponding to the data requirement;

[0090] Step 1-3: Use the data middle platform embedded in the digital capability opening platform to submit a data form application by yourself according to the data application process of the State Grid's provincial power company.

[0091] Step 1-4: After approval, read the successfully applied source data form and test the single acquisition time of the data form.

[0092] Step 2: According to the successfully applied source data form, the real-time processing scheduling task is used to obtain the processed data;

[0093] In a preferred but non-limiting embodiment of the present invention, step 2 specifically comprises:

[0094] Step 2-1: Alibaba Cloud uses the DataWorks data integration tool to centrally extract all the source data forms received from the data center and convert and load them;

[0095] Step 2-2: Locate the data fields that need to be processed, synchronize these data fields to the Log table, split them into the Delta table, and merge them into the Base table to ensure the real-time and high efficiency of the data;

[0096] In a preferred but non-limiting embodiment of the present invention, the data fields to be processed in step 2-2 include the name and number of the industrial and commercial customer, transformer capacity, electricity consumption and electricity fee ledger, and actual demand.

[0097] Step 2-3: Use Alibaba Cloud's intelligent scheduling function to configure the data computing task with the highest priority, ensuring that the three basic electricity fee calculation schemes of capacity and demand are calculated first;

[0098] Step 2-4: Select typical loads of industrial and commercial customers with different transformer capacities, different numbers of power sources, and different voltage levels to test the accuracy of the output basic electricity fee calculation plan. At the same time, record the real-time scheduling task throughput test data and use it as processed data.

[0099] Step 3: Provide RDS relational database services based on the processed data;

[0100] In a preferred but non-limiting embodiment of the present invention, step 3 specifically comprises:

[0101] Step 3-1: Log in to Alibaba Cloud to create an RDS instance and set basic information. After configuration is complete, receive the processed data transmitted by DataWorks in the previous stage.

[0102] In a preferred but non-limiting embodiment of the present invention, the basic information in step 3-1 includes an instance name and a password.

[0103] Step 3-2: Use the RDS database to store the data cloud to the Alibaba Cloud SSD disk, execute SQL statements, and perform operations such as querying and storing the data cloud;

[0104] Step 3-3: Perform stress test on the RDS database and concurrent query test on the RDS database, test and record the QPS performance index of the RDS database, that is, the number of queries per second of the RDS database.

[0105] Step 4: Encapsulate the data stored in the RDS database using the RPC interface;

[0106] In a preferred but non-limiting embodiment of the present invention, step 4 specifically comprises:

[0107] Step 4-1: The backend reads the stored data in the RDS database;

[0108] Step 4-2: Sort out and define the service interface types to be encapsulated according to the functions such as query, screening, and retrieval;

[0109] In a preferred but non-limiting embodiment of the present invention, the service interface type in step 4-2 includes a method name, parameters, and a return value.

[0110] Step 4-3: Generate stub code to implement communication between the client and the server, and use the RPC method to encapsulate data into different types of service interfaces;

[0111] Step 4-4: Perform stress test on the encapsulated interface, call the encapsulated RPC interface through the stub code, test and record the TPS performance of the interface call, that is, the number of transactions processed per second.

[0112] In a preferred but non-limiting embodiment of the present invention, the auxiliary decision-making method for serving high-voltage power customers for electricity cost saving specifically includes:

[0113] Step 5: Design human-computer interaction for text input.

[0114] In a preferred but non-limiting embodiment of the present invention, step 5 specifically comprises:

[0115] Step 5-1: Design a visual web browser display interface according to the B / S architecture, including layout, style, and interaction logic;

[0116] Step 5-2: Use the Element-UI framework to develop server-side logic and compile the relevant components of the web browser display interface;

[0117] Step 5-3: Input text-based human-computer interaction query logic functions, conduct human-computer interaction transaction success rate tests, and record the test results.

[0118] The present invention is quick to operate. It only requires the input of the account number and name to display three basic electricity billing amounts to the customer, assist the customer in reasonably selecting the basic electricity billing plan, and improve the counter efficiency by 60 times, greatly reducing the waiting time for customers. The data sources of the present invention all come from the enterprise-level data middle platform. The data of the data middle platform all come from business systems in different systems. Through the data middle platform, data acquisition is fast, transmission stability is high, and there is no security problem. Cloud technology is used to deploy computing and managed operation in the cloud, with fast data calculation and stable program operation. The present invention adopts B / S architecture to design the Web browser display interface, which is friendly to human-computer interaction and easy to maintain, and can improve the quality, reduce the burden and increase efficiency for the front-line grassroots.

[0119] like Figure 3 As shown, the present invention provides an auxiliary decision-making device for serving high-voltage power customers to save electricity costs, comprising:

[0120] The acquisition module is used to obtain the source data form using the self-service application;

[0121] A processing module, which is used to obtain processed data by real-time processing scheduling tasks according to the successfully applied source data form;

[0122] A service module, which is used to provide RDS relational database services based on the processed data;

[0123] A packaging module, which is used to encapsulate the storage data of the RDS database using the RPC interface;

[0124] Design module, which is used to design human-computer interaction of input text.

[0125] The application of the present invention provides industrial and commercial customers with a fast basic electricity billing scheme optimization service, allowing customers to enjoy a new experience of "zero perception" electricity application, eliminating the phenomenon of customers queuing in long queues at the source, and consolidating a harmonious and stable power supply and use relationship. Figure 1 As shown, customer satisfaction rate continues to improve. Through the basic electricity billing scheme quick optimizer, customers are accurately provided with the most affordable basic electricity billing scheme, which changes the previous situation of low quality and efficiency of manual calculation schemes, avoids the social public opinion risks brought about by the increase in customer electricity costs, optimizes the electricity business environment, and promotes social and economic growth. The operating steps of counter staff in the business hall have been reduced from more than 10 steps to 1 step, that is, the optimization function can be executed with one click, which reduces the difficulty of operation and improves the work happiness index of tellers. The workload of counter staff that used to take 10 minutes can now be completed in less than 10 seconds, and the work efficiency has increased by 60 times, reducing the burden and increasing the efficiency of grassroots service personnel.

[0126] When using the present invention, when customers go to the business hall of the power supply company to inquire about the basic electricity fee calculation strategy, they can experience unprecedented convenience and efficiency. Customers only need to provide simple identity information such as account number and name to the comprehensive teller, and the company's independently developed results can be started in an instant, and the basic electricity fee amount under three different charging strategies: capacity, actual maximum demand, and contract maximum demand can be automatically calculated with one click. The system not only quickly displays these calculated values, but also intuitively estimates the electricity cost savings under each strategy for customers, so that customers can see at a glance which strategy is more economical. By automatically recommending the basic electricity fee calculation strategy that best suits customers in just 10 seconds, the waiting time of customers in the business hall is greatly shortened, and the efficiency of electricity handling at the counter is increased by nearly 60 times. This change effectively alleviated the phenomenon of customers queuing in the business hall and improved the customer's service experience.

[0127] Although there is an alternative solution, which is to apply RPA machine process automation technology, one is to automatically log in and operate multiple business system function interfaces such as marketing and procurement, and at the same time identify and capture relevant ledger data. The second is to use the formula compiler that comes with Excel to quickly and automatically calculate three basic electricity billing schemes in the background. However, RPA technology has the disadvantages of data security risks, insufficient business process support, and vulnerability to external system dependence.

[0128] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0129] The cloud technology used in this invention has significant advantages over RPA technology in solving problems such as data security and privacy, insufficient support for complex business processes, and vulnerability to external system reliance:

[0130] 1. Data security and privacy protection

[0131] Multi-layer security protection: Cloud platforms usually provide multi-layer security protection mechanisms, including firewalls, intrusion detection systems, data encryption, etc., to ensure the security of data during transmission and storage. These security measures can effectively resist external attacks and data leakage risks.

[0132] Compliance support: Cloud service providers usually follow strict data protection regulations, such as GDPR, HIPAA, etc., to ensure the legality and compliance of user data. Enterprises can reduce compliance risks and meet regulatory requirements by using cloud services.

[0133] Data backup and recovery: The cloud computing platform has automatic backup and restore functions to ensure data security and integrity. Even if data is lost or damaged, data can be quickly restored to reduce the risk of business interruption.

[0134] 2. Support for complex business processes

[0135] Elastic expansion capability: The cloud computing platform has strong elastic expansion capability and can adjust resource allocation in real time according to business needs. When the business process becomes complex or needs to process a large amount of data, the cloud platform can quickly increase computing resources and storage resources to ensure the smooth operation of the business process.

[0136] High availability guarantee: Cloud computing platforms usually adopt distributed architecture and redundant design to ensure high availability of services. Even if some nodes fail, the continuity and stability of services can be guaranteed through mechanisms such as automatic switching and load balancing.

[0137] Integration capability: Cloud computing platforms can be seamlessly integrated with other systems and applications, supporting the automation and collaboration of complex business processes. Enterprises can integrate data and information from different systems through cloud platforms to achieve comprehensive optimization and upgrade of business processes.

[0138] 3. Reduced vulnerability to external system dependence

[0139] Independence and stability: As an independent computing environment, the cloud computing platform does not rely on specific external systems or hardware. This reduces the vulnerability of dependence on external systems and ensures that the cloud platform can still operate stably and provide services when external systems change or fail.

[0140] Compatibility support: Cloud computing platforms usually support a variety of operating systems, databases, and application software, and have good compatibility. This means that enterprises can more flexibly choose and use different external systems without worrying about service interruptions or performance degradation caused by compatibility issues.

[0141] Disaster recovery and recovery capabilities: Cloud computing platforms usually have complete disaster recovery and recovery capabilities, and can quickly restore services when a disaster occurs. This reduces the vulnerability of dependence on external systems and ensures that enterprises can quickly resume business operations when faced with emergencies.

[0142] In summary, cloud technology has obvious advantages in solving problems such as data security and privacy, insufficient support for complex business processes, and vulnerability to external system reliance. These advantages make cloud technology an important support force for enterprise digital transformation and business innovation.

[0143] The present disclosure can be a system, a method and / or a computer program product. The computer program product can include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement each aspect of the present disclosure.

[0144] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (such as a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0145] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0146] The computer program instructions for performing the operations of the present disclosure can be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer-readable program instructions can be executed entirely on the controller computer, partially on the controller computer, as a separate software package, partially on the controller computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the controller computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as connecting through the Internet using an Internet service provider). In some embodiments, each aspect of the present disclosure is implemented by utilizing state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), which can execute computer-readable program instructions.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assisting decision-making in saving electricity bills for high-voltage power customers, characterized in that: include: Centrally obtain marketing and production professional data sources from the power supply company's data center, obtain high-voltage customer basic file information, transformer operation, monthly receivable electricity charges, and demand value data from the marketing side; obtain line basic file ledgers, line loads, and maximum allowable current data from the production side to build an intelligent recommendation model for basic electricity price pricing plans for high-voltage customers.

2. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 1 is characterized in that: The auxiliary decision-making method for serving high-voltage power customers to save electricity costs includes: Step 1: Use the self-service application form to obtain source data; Step 2: According to the successfully applied source data form, the real-time processing scheduling task is used to obtain the processed data; Step 3: Provide RDS relational database services based on the processed data; Step 4: Encapsulate the data stored in the RDS database using the RPC interface.

3. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 2 is characterized in that: Step 1 specifically includes: Step 1-1: Sort out the relevant data fields required to calculate the basic electricity fee calculation plan and determine the data requirements; Step 1-2: Log in to the Digital Capability Open Platform and use the keyword search function to trace the source data form corresponding to the data requirement; Step 1-3: Use the data platform embedded in the digital capability open platform to submit a data form application by yourself according to the power company's data application process. Step 1-4: After approval, read the successfully applied source data form and test the single acquisition time of the data form.

4. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 3 is characterized in that: The relevant data fields in step 1-1 include basic files of industrial and commercial customers, electricity bill ledgers, and production load data fields.

5. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 4 is characterized in that: Step 2 specifically includes: Step 2-1: Alibaba Cloud uses the DataWorks data integration tool to centrally extract all the source data forms received from the data center and convert and load them; Step 2-2: Locate the data fields that need to be processed, synchronize these data fields to the Log table, split them into the Delta table, and merge them into the Base table; Step 2-3: Use Alibaba Cloud's intelligent scheduling function to configure the data computing task with the highest priority, ensuring that the three basic electricity fee calculation schemes of capacity and demand are calculated first; Step 2-4: Select typical loads of industrial and commercial customers with different transformer capacities, different numbers of power sources, and different voltage levels to test the accuracy of the output basic electricity fee calculation plan. At the same time, record the real-time scheduling task throughput test data and use it as processed data.

6. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 5 is characterized in that: The data fields that need to be processed in step 2-2 include the name and number of the industrial and commercial customer, transformer capacity, electricity consumption and electricity fee ledger, and actual demand.

7. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 6 is characterized in that: Step 3 specifically includes: Step 3-1: Log in to Alibaba Cloud to create an RDS instance and set basic information. After configuration is complete, receive the processed data transmitted by DataWorks in the previous stage. Step 3-2: Use the RDS database to store the data cloud to the Alibaba Cloud SSD disk, execute SQL statements, query the data cloud, and manage storage operations; Step 3-3: Perform stress test on the RDS database and concurrent query test on the RDS database, test and record the QPS performance index of the RDS database, that is, the number of queries per second of the RDS database.

8. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 7 is characterized in that: The basic information in step 3-1 includes the instance name and password.

9. The auxiliary decision-making method for saving electricity bills for high-voltage power customers according to claim 8 is characterized in that: Step 4 specifically includes: Step 4-1: The backend reads the stored data in the RDS database; Step 4-2: Sort out and define the service interface types to be encapsulated according to the query, screening, and retrieval functions; the service interface types include method names, parameters, and return values; Step 4-3: Generate stub code to implement communication between the client and the server, and use the RPC method to encapsulate data into different types of service interfaces; Step 4-4: Perform stress test on the encapsulated interface, call the encapsulated RPC interface through the stub code, test and record the TPS performance of the interface call, that is, the number of transactions processed per second; The auxiliary decision-making method for serving high-voltage power customers to save electricity costs also includes: Step 5: Design human-computer interaction for text input; Step 5 specifically includes: Step 5-1: Design a visual web browser display interface according to the B / S architecture, including layout, style and interaction logic; Step 5-2: Use the Element-UI framework to develop server-side logic and compile the relevant components of the web browser display interface; Step 5-3: Design the text-based human-computer interaction query logic function, conduct a human-computer interaction transaction success rate test, and record the test results.

10. An auxiliary decision-making device for serving high-voltage power customers to save electricity costs, characterized in that: include: The acquisition module is used to obtain the source data form using the self-service application; A processing module, which is used to obtain processed data by real-time processing scheduling tasks according to the successfully applied source data form; A service module, which is used to provide RDS relational database services based on the processed data; A packaging module, which is used to package storage data of the RDS database using an RPC interface; Design module, which is used to design human-computer interaction of input text.

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