Platform architecture construction method and system based on digital model

Abstract

The invention discloses a platform architecture construction method and system based on a digital model, and relates to the technical field of digital models, and the method comprises the steps: constructing a data monitoring sub-platform, receiving a device list generation program interface script of a power grid, receiving the real-time data of the power grid through an interface, selecting one power grid from the data monitoring sub-platform, and carrying out the construction of the data monitoring sub-platform. The method comprises the following steps: receiving an electric energy type and a charging standard, generating a target cost in combination with an input electric energy and time curve of a power grid, receiving an actual cost, comparing the target cost with the actual cost, calculating a gap cost, constructing an early warning curve by utilizing the obtained input electric energy and time curve of the power grid and gap cost data, setting a threshold value, and training and optimizing the early warning curve. And feeding back a power grid risk level, and matching a correlation algorithm adjustment strategy. According to the invention, by incorporating the actual cost data, the risk level of the power grid can be effectively fed back, and the problem of total billing price error caused by distinguishing electric leakage, electricity stealing and electric energy billing unit price difference is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital models, and in particular to a method and system for constructing a platform architecture based on a digital model. Background Art

[0002] With the improvement of power grid equipment and related supporting facilities, the amount of electric energy transmitted in the power grid is increasing, and the control platform of the power grid has become more difficult to bill for electric energy. The main reason for the difficulty in electric energy billing is that there are many secondary power grids with many types; there are also cases of leakage and theft of electricity. At the same time, there are different billing unit prices for electric energy in different time periods; the control platform of the traditional power grid performs the billing task of electric energy based on digital models and adopts a hierarchical model. This type of digital model can adapt to the working conditions of a large number and variety of secondary power grids; however, this type of digital model has weak linkage and it is difficult to distinguish between the working conditions of total billing price errors caused by leakage and theft of electricity and the working conditions of total billing price errors caused by different billing unit prices of electric energy; for this reason, it is necessary to propose a platform architecture construction method and system based on digital models to address the defect of the difficulty of electric energy billing in the traditional control platform digital model. Summary of the invention

[0003] In view of the above existing problems, the present invention provides a method and system for constructing a platform architecture based on a digital model, so as to solve the problem of difficulty in electricity billing of the digital model of the control platform in the prior art.

[0004] In order to solve the above technical problems, a platform architecture construction method based on digital model is proposed, including:

[0005] Construct a data monitoring sub-platform, receive the equipment list of the power grid to generate a program interface script, and use the interface to receive real-time data from the power grid; select a power grid in the data monitoring sub-platform, receive the type of electricity and billing standards, generate target costs based on the input power energy and time curve of the power grid, receive actual costs, compare the target costs with the actual costs, and calculate the gap costs; use the obtained power grid input power energy and time curve and gap cost data to construct an early warning curve, set thresholds, train and optimize the early warning curve, feedback the power grid risk level, and match the correlation algorithm to adjust the strategy.

[0006] As a preferred solution of the method for constructing a platform architecture based on a digital model described in the present invention, wherein: constructing a data monitoring sub-platform includes determining monitoring requirements, designing a data monitoring sub-platform architecture according to the monitoring requirements, integrating a list of power grid equipment, writing a program interface script according to the device interface specification, and integrating the program interface script into the data monitoring sub-platform, configuring monitoring parameters, receiving real-time data of the power grid, and designing an architecture of a real-time management sub-platform based on the received data.

[0007] The data monitoring sub-platform architecture includes data processing, data storage, data communication and user interface; the integrated power grid equipment list includes collecting equipment information, establishing equipment files and updating equipment information; the architecture of the real-time management sub-platform design includes scheduled operations and automated data processing.

[0008] As a preferred solution of the method for constructing a platform architecture based on a digital model described in the present invention, the receiving of real-time data of the power grid includes using an interface to receive in real time power grid operation status data, equipment performance indicator data, power quality data, environmental data, security data and user electricity consumption data.

[0009] The grid operation status data includes real-time voltage data, real-time current data, grid frequency data and the opening and closing status of the circuit breaker; the equipment performance index data includes the power generation, efficiency and temperature of the generator set, the load rate, oil temperature, insulation status of the transformer, and the current load and temperature of the transmission line; the power quality data includes harmonic content, voltage fluctuation and flicker, and three-phase imbalance; the environmental data includes temperature, humidity and smoke.

[0010] As a preferred solution of the method for constructing a platform architecture based on a digital model described in the present invention, the generation of the target cost includes, based on a program interface script of a data monitoring sub-platform, receiving real-time data of the power grid, calling a scheduled job program, executing a task of traversing the real-time data of each power grid, using an automated data processing program, extracting real-time data of the power grid, calling billing standards, selecting billing time periods, counting the costs of each time interval of the power grid, and generating the target cost.

[0011] The real-time data of the power grid includes output power, type of electric energy and billing standards; generating the target fee also includes retrieving the billing standard corresponding to the type of electric energy, selecting the billing time period, and determining whether the time period matches the time interval of the input electric energy and time curve. When the time period matches, the fee of the time interval is obtained, and the process returns to selecting the billing time period until all time periods are selected; when the time periods do not match, the process returns to selecting the billing time period until all time periods are selected, and the fee of each time zone of the power grid is counted to generate the target fee.

[0012] As a preferred solution of the method for constructing a platform architecture based on a digital model described in the present invention, wherein: the calculation of the gap cost includes receiving the actual cost, and comparing the target cost with the actual cost to calculate the gap cost;

[0013] The receiving of actual costs includes selecting a power grid, calling a real-time meter list of real-time data of the power grid, selecting a time interval, and counting the total billing amount of all real-time meters in the real-time meter list in the selected time interval to generate actual costs.

[0014] The calculation of the gap cost also includes selecting a grid input electric energy and time curve, and taking 24 hours as the cycle length, dividing the grid input electric energy and time curve according to the cycle length of the billing standard, selecting a billing cycle and time period, determining the total amount of input electric energy in the selected time period, returning to select the billing cycle and time period until all time periods and billing cycles are selected, counting the total amount of input electric energy in each billing cycle and time period, and obtaining the gap cost by subtracting the actual cost from the target cost.

[0015] As a preferred solution of the method for constructing a platform architecture based on a digital model described in the present invention, the construction of the early warning curve includes: selecting a billing cycle, obtaining the target cost, actual cost and gap cost of the billing cycle, using the total amount of input electrical energy in the time period, obtaining the influencing factor, associating the influencing factor with the gap cost, establishing an orthogonal two-dimensional coordinate system, and using a linear regression correlation algorithm to obtain a power grid early warning curve.

[0016] The linear regression correlation algorithm formula is:

[0017] y=mx+b+ε

[0018] Among them, x is the gap cost, y is the impact factor, m is the slope, b is the intercept, and ε is the error term.

[0019] As a preferred solution of the platform architecture construction method based on digital model described in the present invention, the matching parameter adjustment strategy includes: selecting the early warning curve, obtaining the predicted impact factor through the linear regression correlation algorithm, calling the input electric energy of the power grid corresponding to the real-time gap cost, calculating the actual impact factor of the input electric energy, judging whether the actual impact factor matches the predicted impact factor, counting the success rate of the early warning curve, and setting the success rate threshold, matching the correlation algorithm adjustment strategy.

[0020] The success rate of the statistical early warning curve includes judging whether the actual impact factor matches the predicted impact factor. When the actual impact factor matches the predicted impact factor, the number of successes is increased, and the selected early warning line is returned to obtain the predicted impact factor until the training early warning curve end instruction is received; when the actual impact factor does not match the predicted impact factor, the input early warning curve is returned to obtain the predicted impact factor until the training early warning curve end instruction is received, the success rate of the statistical early warning curve is set, and the success rate threshold S is set. th .

[0021] The matching correlation algorithm adjustment strategy includes: when the success rate of the early warning curve is less than S th When all training data are combined, the correlation algorithm is adjusted, and the input warning curve is returned to obtain the predicted impact factor until the success rate of the warning curve reaches the success threshold; when the success rate of the warning curve is greater than or equal to Sth When the prediction impact factor is obtained, the current correlation algorithm is continued to be used until all warning curves are selected.

[0022] Another object of the present invention is to provide a platform architecture construction system based on a digital model, which improves the operating efficiency, economy and safety of the power grid, reduces operating costs, prevents risks, and improves the overall performance of the power grid; the system of the present invention realizes real-time monitoring, cost management, risk warning and strategy adjustment of the power grid through a data monitoring sub-platform, cost calculation, early warning analysis, and adopts corresponding parameter adjustment strategies to improve the operating efficiency, economy and safety of the power grid.

[0023] As a preferred solution of the platform architecture construction system based on the digital model described in the present invention, it is characterized by including a data monitoring sub-platform module, a cost calculation module, an early warning analysis module and a parameter adjustment strategy module.

[0024] The data monitoring sub-platform module is used to receive a list of power grid equipment, generate a program interface script, and connect to the real-time data interface of the power grid to receive power grid data in real time, perform preliminary processing, and configure monitoring parameters according to monitoring requirements.

[0025] The cost calculation module is used to calculate the cost of each time interval by traversing the real-time data of the power grid, combining the type of electricity and the billing standard, generating a target cost, receiving and processing the real-time billing data of the power grid, generating an actual cost, comparing the target cost with the actual cost, calculating the difference, and obtaining the shortfall cost.

[0026] The early warning analysis module is used to construct an early warning curve, evaluate the risk of the power grid, set the early warning threshold, and train and optimize the early warning curve by using the power grid input electric energy and time curve and the gap cost data.

[0027] The parameter adjustment strategy module is used to count the actual matching success rate of the early warning curve, judge the accuracy of the early warning model, adjust the correlation algorithm according to the success rate of the early warning curve, and apply the adjusted correlation algorithm to the early warning model.

[0028] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in the method for constructing a platform architecture based on a digital model are implemented.

[0029] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method described in the method for constructing a platform architecture based on a digital model are implemented.

[0030] The beneficial effects of the present invention are as follows: the present invention incorporates each power grid into the settlement platform through the settlement platform, thereby solving the defect of weak linkage of the digital model, and generates the target fee by using the input electric energy and time curve, electric energy type, and billing standard of the power grid; receives the actual fee of the corresponding time period of the power grid, and determines the gap fee by using the difference between the target fee and the actual fee; forms an early warning digital model based on the input electric energy and time curve and the gap fee of the power grid, incorporates the actual fee of the corresponding time period of the target power grid into the early warning digital model, and feeds back the risk level of the target power grid. The early warning digital model solves the disadvantages of the working condition that it is difficult to distinguish between the total billing price error caused by leakage and theft of electricity and the total billing price error caused by different billing unit prices of electric energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0032] Figure 1 An overall flow chart of a method for constructing a platform architecture based on a digital model provided for one embodiment of the present invention.

[0033] Figure 2 A schematic diagram of platform architecture connection for a method for constructing a platform architecture based on a digital model provided by an embodiment of the present invention.

[0034] Figure 3 A system solution flow chart of a system for building a platform architecture based on a digital model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0036] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0038] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0039] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0040] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0041] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for constructing a platform architecture based on a digital model, comprising:

[0042] S1: Build a data monitoring sub-platform, receive the equipment list of the power grid to generate a program interface script, and use the interface to receive real-time data of the power grid.

[0043] The construction of the data monitoring sub-platform includes determining monitoring requirements, designing a data monitoring sub-platform architecture according to the monitoring requirements, integrating a list of power grid equipment, writing a program interface script according to the equipment interface specification, and integrating the program interface script into the data monitoring sub-platform, configuring monitoring parameters, receiving real-time data of the power grid, and designing a real-time management sub-platform architecture based on the received data.

[0044] The data monitoring sub-platform architecture includes data processing, data storage, data communication and user interface; the integrated power grid equipment list includes collecting equipment information, establishing equipment files and updating equipment information; the architecture of the real-time management sub-platform design includes scheduled operations and automated data processing.

[0045] It should be noted that the data monitoring sub-platform can realize the call of real-time data of each power grid; the real-time management sub-platform relies on the scheduled operation program and the automatic data processing program to execute the real-time data task of traversing each power grid, and extract the output power information, power type and billing standard in the real-time data of the power grid; and then realize the calculation of the target cost and the data retrieval of the actual cost of the power grid in the corresponding time period.

[0046] The receiving of real-time data of the power grid includes using an interface to receive in real time power grid operation status data, equipment performance index data, power quality data, environmental data, security data and user power consumption data.

[0047] It should also be noted that when each power grid is included in the settlement platform, the sources of electricity transmitted in each power grid are different, such as electricity converted from wind power, thermal power, hydropower, photovoltaic power, and geothermal energy; each different source of electricity will result in different electricity costs, and the unit price of electricity charges will also be different.

[0048] The grid operation status data includes real-time voltage data, real-time current data, grid frequency data and the opening and closing status of the circuit breaker; the equipment performance index data includes the power generation, efficiency and temperature of the generator set, the load rate, oil temperature, insulation status of the transformer, and the current load and temperature of the transmission line; the power quality data includes harmonic content, voltage fluctuation and flicker, and three-phase imbalance; the environmental data includes temperature, humidity and smoke.

[0049] S2: Select a power grid in the data monitoring sub-platform, receive the type of electricity and the billing standard, generate the target cost based on the input electricity and time curve of the power grid, receive the actual cost, compare the target cost with the actual cost, and calculate the gap cost.

[0050] Furthermore, the generation of target costs includes, based on a program interface script of the data monitoring sub-platform, receiving real-time data of the power grid, calling a scheduled job program, executing a task of traversing the real-time data of each power grid, using an automated data processing program to extract real-time data of the power grid, calling billing standards, selecting billing time periods, counting the costs of each time interval of the power grid, and generating target costs.

[0051] The real-time data of the power grid includes output power, type of electric energy and billing standards; generating the target fee also includes retrieving the billing standard corresponding to the type of electric energy, selecting the billing time period, and determining whether the time period matches the time interval of the input electric energy and time curve. When the time period matches, the fee of the time interval is obtained, and the process returns to selecting the billing time period until all time periods are selected; when the time periods do not match, the process returns to selecting the billing time period until all time periods are selected, and the fee of each time zone of the power grid is counted to generate the target fee.

[0052] The calculation of the gap cost includes receiving the actual cost, comparing the target cost with the actual cost, and calculating the gap cost.

[0053] The receiving of actual costs includes selecting a power grid, calling a real-time meter list of real-time data of the power grid, selecting a time interval, and counting the total billing amount of all real-time meters in the real-time meter list in the selected time interval to generate actual costs.

[0054] Furthermore, the types of electricity and billing standards of the receiving power grid include confirming the types of electricity and billing standards of each power source, so as to calculate the theoretical electricity charges of the power grid. Since there is a difference between the actual electricity charges of the power grid and the theoretical electricity charges, the difference between the actual electricity charges and the theoretical electricity charges is used for prediction, so as to distinguish between the errors in the total billing price caused by leakage and theft of electricity and the errors in the total billing price caused by different billing unit prices of electricity.

[0055] The calculation of the gap cost also includes selecting a grid input electric energy and time curve, and taking 24 hours as the cycle length, dividing the grid input electric energy and time curve according to the cycle length of the billing standard, selecting a billing cycle and time period, determining the total amount of input electric energy in the selected time period, returning to select the billing cycle and time period until all time periods and billing cycles are selected, counting the total amount of input electric energy in each billing cycle and time period, and obtaining the gap cost by subtracting the actual cost from the target cost.

[0056] It should be noted that the difference between actual electricity charges and theoretical electricity charges exists objectively. When the difference between actual electricity charges and theoretical electricity charges is too large, the difference between actual electricity charges and theoretical electricity charges is used to distinguish between the working conditions of total billing price errors caused by leakage and theft of electricity and the working conditions of total billing price errors caused by different billing unit prices of electricity.

[0057] S3: Use the obtained grid input power energy and time curve and gap cost data to build an early warning curve, set thresholds, train and optimize the early warning curve, feedback the grid risk level, and match the correlation algorithm to adjust the strategy.

[0058] Furthermore, the construction of the early warning curve includes selecting a billing cycle, obtaining the target cost, actual cost and gap cost of the billing cycle, using the total amount of input electrical energy in the time period to obtain the influencing factor, associating the influencing factor with the gap cost, establishing an orthogonal two-dimensional coordinate system, and using a linear regression correlation algorithm to obtain a power grid early warning curve.

[0059] An orthogonal two-dimensional coordinate system is established, and each group of interrelated influencing factors and gap costs are formed into coordinate parameters. Each coordinate point based on the coordinate parameters is marked in the two-dimensional coordinate system. Based on the correlation of the overall coordinate parameter set, abnormal coordinate parameters are determined to accurately distinguish between the working conditions of total billing price errors caused by leakage and theft of electricity and the working conditions of total billing price errors caused by different billing unit prices of electricity.

[0060] The linear regression correlation algorithm formula is:

[0061] y=mx+b+ε

[0062] Among them, x is the gap cost, y is the impact factor, m is the slope, b is the intercept, and ε is the error term.

[0063] The matching parameter adjustment strategy includes selecting an early warning curve, obtaining a predicted impact factor through a linear regression correlation algorithm, calling the input electric energy of the power grid corresponding to the real-time gap cost, calculating the actual impact factor of the input electric energy, judging whether the actual impact factor matches the predicted impact factor, calculating the success rate of the early warning curve, setting a success rate threshold, and matching the correlation algorithm adjustment strategy.

[0064] The success rate of the statistical early warning curve includes judging whether the actual impact factor matches the predicted impact factor. When the actual impact factor matches the predicted impact factor, the number of successes is increased, and the selected early warning line is returned to obtain the predicted impact factor until the training early warning curve end instruction is received; when the actual impact factor does not match the predicted impact factor, the input early warning curve is returned to obtain the predicted impact factor until the training early warning curve end instruction is received, the success rate of the statistical early warning curve is set, and the success rate threshold S is set. th .

[0065] The matching correlation algorithm adjustment strategy includes: when the success rate of the early warning curve is less than S th When all training data are combined, the correlation algorithm is adjusted, and the input warning curve is returned to obtain the predicted impact factor until the success rate of the warning curve reaches the success threshold; when the success rate of the warning curve is greater than or equal to S th When the prediction impact factor is obtained, the current correlation algorithm is continued to be used until all warning curves are selected.

[0066] like Figure 2The platform architecture of the digital model includes a server 100 and a power grid 200 . The server 100 is used to execute the platform architecture construction method based on the digital model, and multiple power grids 200 are all communicatively connected to the server 100 .

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0068] Example 2, reference Figure 3 , which is the second embodiment of the present invention, and this embodiment provides a platform architecture construction system based on a digital model, including a data monitoring sub-platform module M101, a cost calculation module M201, an early warning analysis module M301 and a parameter adjustment strategy module M401.

[0069] The data monitoring sub-platform module M101 is used to receive a list of power grid equipment, generate a program interface script, and connect to the real-time data interface of the power grid to receive power grid data in real time, perform preliminary processing, and configure monitoring parameters according to monitoring requirements.

[0070] The cost calculation module M201 is used to calculate the cost of each time interval by traversing the real-time data of the power grid, combining the type of electricity and the billing standard, generate the target cost, receive and process the real-time billing data of the power grid, generate the actual cost, compare the target cost with the actual cost, calculate the difference, and obtain the shortfall cost.

[0071] The early warning analysis module M301 is used to construct an early warning curve, evaluate the risk of the power grid, set the early warning threshold, and train and optimize the early warning curve by using the power grid input electric energy and time curve and the gap cost data.

[0072] The parameter adjustment strategy module M401 is used to count the actual matching success rate of the early warning curve, determine the accuracy of the early warning model, adjust the correlation algorithm according to the success rate of the early warning curve, and apply the adjusted correlation algorithm to the early warning model.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0074] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0075] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0076] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0077] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0078] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A method for constructing a platform architecture based on a digital model, characterized in that: include, Build a data monitoring sub-platform, receive the equipment list of the power grid to generate a program interface script, and use the interface to receive real-time data of the power grid; Select a power grid in the data monitoring sub-platform, receive the type of electricity and billing standards, generate target costs based on the input electricity and time curves of the power grid, receive actual costs, compare the target costs with the actual costs, and calculate the gap costs; The obtained grid input power energy and time curve and gap cost data are used to build an early warning curve, set thresholds, train and optimize the early warning curve, feedback the grid risk level, and match the correlation algorithm to adjust the strategy.

2. A method for constructing a platform architecture based on a digital model as claimed in claim 1, characterized in that: The construction of the data monitoring sub-platform includes determining monitoring requirements, designing a data monitoring sub-platform architecture according to the monitoring requirements, integrating a list of power grid equipment, writing a program interface script according to the device interface specification, and integrating the program interface script into the data monitoring sub-platform, configuring monitoring parameters, receiving real-time data of the power grid, and designing an architecture of a real-time management sub-platform based on the received data; The data monitoring sub-platform architecture includes data processing, data storage, data communication and user interface; The integrated power grid equipment list includes collecting equipment information, establishing equipment archives and updating equipment information; the architecture of the designed real-time management sub-platform includes scheduled operations and automated data processing.

3. A method for constructing a platform architecture based on a digital model as claimed in claim 2, characterized in that: The receiving of real-time data of the power grid includes using an interface to receive in real time power grid operation status data, equipment performance index data, power quality data, environmental data, security data and user power consumption data; The grid operation status data includes real-time voltage data, real-time current data, grid frequency data and the opening and closing status of the circuit breaker; the equipment performance index data includes the power generation, efficiency and temperature of the generator set, the load rate, oil temperature, insulation status of the transformer, and the current load and temperature of the transmission line; the power quality data includes harmonic content, voltage fluctuation and flicker, and three-phase imbalance; the environmental data includes temperature, humidity and smoke.

4. A method for constructing a platform architecture based on a digital model as claimed in claim 3, characterized in that: The generating target cost includes, based on the program interface script of the data monitoring sub-platform, receiving the real-time data of the power grid, calling the timed operation program, executing the task of traversing the real-time data of each power grid, using the automatic data processing program, extracting the real-time data of the power grid, calling the billing standard, selecting the billing period, counting the cost of each time interval of the power grid, and generating the target cost; The real-time data of the power grid includes output power, type of electric energy and billing standards; generating the target fee also includes retrieving the billing standard corresponding to the type of electric energy, selecting the billing time period, and determining whether the time period matches the time interval of the input electric energy and time curve. When the time period matches, the fee of the time interval is obtained, and the process returns to selecting the billing time period until all time periods are selected; when the time periods do not match, the process returns to selecting the billing time period until all time periods are selected, and the fee of each time zone of the power grid is counted to generate the target fee.

5. A method for constructing a platform architecture based on a digital model as claimed in claim 4, characterized in that: The calculating of the gap cost includes receiving the actual cost, comparing the target cost with the actual cost, and calculating the gap cost; The receiving of the actual cost includes selecting a power grid, calling a real-time meter list of real-time data of the power grid, selecting a time interval, and calculating the total billing amount of all real-time meters in the real-time meter list in the selected time interval to generate the actual cost; The calculation of the gap cost also includes selecting a grid input electric energy and time curve, and taking 24 hours as the cycle length, dividing the grid input electric energy and time curve according to the cycle length of the billing standard, selecting a billing cycle and time period, determining the total amount of input electric energy in the selected time period, returning to select the billing cycle and time period until all time periods and billing cycles are selected, counting the total amount of input electric energy in each billing cycle and time period, and obtaining the gap cost by subtracting the actual cost from the target cost.

6. A method for constructing a platform architecture based on a digital model as claimed in claim 5, characterized in that: The construction of the early warning curve includes selecting a billing period, obtaining a target fee, an actual fee and a gap fee for the billing period, obtaining an impact factor using the total amount of input electric energy in the time period, associating the impact factor with the gap fee, establishing an orthogonal two-dimensional coordinate system, and obtaining a power grid early warning curve using a linear regression correlation algorithm; The linear regression correlation algorithm formula is: y=mx+b+ε Among them, x is the gap cost, y is the impact factor, m is the slope, b is the intercept, and ε is the error term.

7. A method for constructing a platform architecture based on a digital model as claimed in claim 6, characterized in that: The matching parameter adjustment strategy includes selecting an early warning curve, obtaining a predicted impact factor through a linear regression correlation algorithm, calling the input electric energy of the power grid corresponding to the real-time gap cost, calculating the actual impact factor of the input electric energy, judging whether the actual impact factor matches the predicted impact factor, calculating the success rate of the early warning curve, setting a success rate threshold, and matching the correlation algorithm adjustment strategy; The success rate of the statistical early warning curve includes judging whether the actual impact factor matches the predicted impact factor. When the actual impact factor matches the predicted impact factor, the number of successes is increased, and the selected early warning line is returned to obtain the predicted impact factor until the training early warning curve end instruction is received; when the actual impact factor does not match the predicted impact factor, the input early warning curve is returned to obtain the predicted impact factor until the training early warning curve end instruction is received, the success rate of the statistical early warning curve is set, and the success rate threshold S is set. th ; The matching correlation algorithm adjustment strategy includes: when the success rate of the early warning curve is less than S th When the prediction is completed, all training data are combined, the correlation algorithm is adjusted, and the input warning curve is returned to obtain the predicted impact factor until the success rate of the warning curve reaches the success threshold; When the success rate of the early warning curve is greater than or equal to S th When the prediction impact factor is obtained, the current correlation algorithm is continued to be used until all warning curves are selected.

8. A system using a platform architecture construction method based on a digital model as claimed in any one of claims 1 to 7, characterized in that: It includes data monitoring sub-platform module, cost calculation module, early warning analysis module and parameter adjustment strategy module; The data monitoring sub-platform module is used to receive a list of power grid equipment, generate a program interface script, and connect to the real-time data interface of the power grid to receive power grid data in real time and perform preliminary processing, and configure monitoring parameters according to monitoring requirements; The cost calculation module is used to calculate the cost of each time interval by traversing the real-time data of the power grid, combining the type of electric energy and the billing standard, generate the target cost, receive and process the real-time billing data of the power grid, generate the actual cost, compare the target cost with the actual cost, calculate the difference, and obtain the shortfall cost; The early warning analysis module is used to construct an early warning curve, evaluate the risk of the power grid, set the early warning threshold, and train and optimize the early warning curve by using the power grid input energy and time curve and the gap cost data; The parameter adjustment strategy module is used to count the actual matching success rate of the early warning curve, judge the accuracy of the early warning model, adjust the correlation algorithm according to the success rate of the early warning curve, and apply the adjusted correlation algorithm to the early warning model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for constructing a platform architecture based on a digital model according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for constructing a platform architecture based on a digital model according to any one of claims 1 to 7 are implemented.

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