A multi-module inflatable cabinet assembly method and system

Abstract

The present invention relates to the technical field of machine learning, and is a method and system for combining an inflatable cabinet based on multiple modules, comprising: establishing an inflatable cabinet combination unit, performing load detection, obtaining a load data set, detecting ambient temperature and ambient humidity, obtaining an ambient temperature set and an ambient humidity set, collecting electricity prices, obtaining an electricity price data set, constructing a load mapping function, calculating a load prediction value, constructing a spacing matrix, a function matrix and an offset matrix, calculating a spatial matching degree based on the spacing matrix, the function matrix and the offset matrix, calculating a maintenance expected value, constructing a comprehensive objective function, calculating a comprehensive fitness set based on the comprehensive objective function and a combination scheme, extracting a maximum fitness based on the comprehensive fitness set, matching based on the maximum fitness, obtaining an optimal combination scheme, and completing the inflatable cabinet combination. The present invention can improve the stability and operation efficiency of an inflatable cabinet under multiple modules.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to a multi-module-based inflatable cabinet assembly method and system. Background Art

[0002] With the continuous development of intelligent power systems, gas cabinets, as important electrical equipment, play a vital role in power transmission and distribution systems. Gas cabinets not only need to have efficient electrical performance, but also need to be able to operate stably under various complex environmental conditions to ensure the safety and reliability of the power system. In actual applications, the operating efficiency and stability of gas cabinets are affected by many factors. If these influencing factors cannot be effectively predicted and adjusted, the gas cabinet may be overloaded or inefficiently operated at certain times, affecting the overall performance of the power system.

[0003] Traditional inflatable cabinet operation control methods mostly rely on fixed parameters and rules for adjustment. However, this method often lacks flexibility in complex dynamic environments and cannot adapt to changes under different conditions in real time, especially under the influence of external factors such as load fluctuations, ambient temperature and humidity changes. It is difficult to achieve precise control. Therefore, how to improve the stability and operation efficiency of multi-module inflatable cabinets is an important issue that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a method and system for assembling an inflatable cabinet based on multiple modules, the main purpose of which is to improve the stability and operation efficiency of the inflatable cabinet under the multiple modules.

[0005] To achieve the above object, the present invention provides a multi-module inflatable cabinet assembly method, comprising:

[0006] Establishing an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment collection unit and a data analysis unit;

[0007] Performing load detection based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set are sorted according to the timestamp;

[0008] Detecting the ambient temperature and ambient humidity according to the ambient collection unit and the timestamp to obtain an ambient temperature set and an ambient humidity set, and collecting electricity prices based on the data analysis unit and the timestamp to obtain an electricity price data set;

[0009] An external influence function is constructed based on the ambient temperature set, the ambient humidity set and the electricity price data set, and a load mapping function is constructed according to the load data set, the external influence function and a preset machine learning model, wherein the machine learning model refers to an XGBoost model;

[0010] Calculate a load prediction value according to the load mapping function, the external influence function and the load data set;

[0011] Constructing a spacing matrix, a function matrix and an offset matrix, and calculating the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix;

[0012] Calculating the maintenance expected value, and constructing a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value;

[0013] Calculating a comprehensive fitness set based on the comprehensive objective function and the preset combination scheme, and extracting a maximum fitness based on the comprehensive fitness set;

[0014] Matching is performed based on the maximum fitness to obtain the optimal combination solution and complete the inflatable cabinet combination.

[0015] Optionally, constructing an external influence function based on the ambient temperature set, the ambient humidity set and the electricity price data set includes:

[0016] Based on the ambient temperature set, ambient humidity set and electricity price data set, outliers are eliminated to obtain a normal temperature set, a normal humidity set and a normal electricity price set;

[0017] Performing mean calculation based on the conventional temperature set, conventional humidity set and conventional electricity price set to obtain a temperature mean, a humidity mean and an electricity price mean;

[0018] Set the time parameter, temperature weight coefficient, humidity weight coefficient and electricity price weight coefficient, and construct an external influence function based on the time parameter, temperature mean, humidity mean, electricity price mean, temperature weight coefficient, humidity weight coefficient and electricity price weight coefficient:

[0019] ;

[0020] in, The time parameter is The external influence function when refers to the temperature weight coefficient, refers to the mean temperature, refers to the humidity weight coefficient, Refers to the average humidity, Refers to the electricity price weight coefficient, Refers to the average electricity price.

[0021] Optionally, calculating the load prediction value according to the load mapping function, the external influence function and the load data set includes:

[0022] Calculate the time point interval based on the timestamp, set the historical interval and the integral variable based on the time point interval, calculate the historical load accumulation value based on the load data set and the historical interval, obtain the historical load value, and calculate the load prediction value based on the load mapping function, the external influence function, the load data set, the historical load accumulation value, the historical load value and the integral variable:

[0023] ;

[0024] in, The time parameter is The load forecast value at refers to the load mapping function, Refers to arrive The historical load accumulation value between is the integration interval, The integral variable is The historical load value at that time.

[0025] Optionally, constructing the spacing matrix, the function matrix and the offset matrix includes:

[0026] Get the module, set the module tag based on the module, establish the module coordinate system, and collect based on the module coordinate system Axis coordinates, Axis coordinates and Axis coordinates, set Axis weight, Axis weights and Axis weights, based on Axis coordinates, Axis coordinates, Axis coordinates, Axis weight, Axis weight, Axis weights, modules, and module labels calculate module spacing:

[0027] ;

[0028] in, Refers to the module marked as The modules with the module marked as The module spacing between modules, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates;

[0029] A spacing matrix is ​​constructed based on the module spacing, and a function association value and a baseline offset value are calculated. A function matrix and an offset matrix are constructed according to the function association value and the baseline offset value respectively. A spatial matching degree is calculated based on the spacing matrix, the function matrix and the offset matrix.

[0030] Optionally, the calculating the functional association value and the baseline shift value comprises:

[0031] Calculate the function relevance, obtain the access frequency and data traffic, set the relevance weight, frequency weight and traffic weight, and calculate the function relevance value based on the function relevance, access frequency, data traffic, relevance weight, frequency weight and traffic weight:

[0032] ;

[0033] in, Refers to the module marked as The modules with the module marked as The functional association value between modules, refers to the association weight, Refers to the module marked as The modules with the module marked as The functional correlation between modules, refers to the frequency weight, Refers to the module marked as The modules with the module marked as The access frequency between modules, Refers to the traffic weight, Refers to the module marked as The modules with the module marked as Data flow between modules;

[0034] Set the ideal position of the module, displacement deviation weight and area deviation weight, obtain the actual position of the module and the actual area of ​​the module, and calculate the baseline offset value based on the ideal position of the module, displacement deviation weight, area deviation weight, actual position of the module and actual area of ​​the module:

[0035] ;

[0036] in, Refers to the module marked as The modules with the module marked as The baseline offset value between modules, refers to the displacement bias weight, Refers to the absolute value symbol, Modules are marked as The ideal location of the module, Refers to the module marked as The actual location of the module. refers to the area deviation weight, Refers to the module marked as The actual area of ​​the module, Refers to the module marked as The actual area of ​​the module.

[0037] Optionally, the calculating function association degree includes:

[0038] Get the function feature vector and feature vector norm, and calculate the function similarity based on the function feature vector and feature vector norm:

[0039] ;

[0040] in, Refers to the module marked as The modules with the module marked as The functional similarity between modules, Refers to the module marked as The module's functional feature vector, Points to the dot product symbol, Refers to the module marked as The module's functional feature vector, Refers to the module marked as The eigenvector norm of the module, Refers to the module marked as The eigenvector norm of the module;

[0041] Set the maximum interaction frequency, the maximum function relevance and the maximum data transmission volume, and calculate the function relevance based on the function similarity, access frequency, data flow, the maximum interaction frequency, the maximum function relevance and the maximum data transmission volume:

[0042] ;

[0043] in, refers to the maximum interaction frequency, Refers to the maximum functional association, Refers to the maximum data transfer rate.

[0044] Optionally, the calculating the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix includes:

[0045] Get the total space area, remaining space area, The norm formula and spatial weight are based on the spacing matrix, function matrix, offset matrix, total space area, remaining space area, Norm formula and spatial weight calculation of spatial matching degree:

[0046] ;

[0047] in, Refers to the spatial matching degree, Refers to the spacing matrix, Refers to the functional matrix, refers to the offset matrix, refer to norm formula, refers to the spatial weight, Refers to the remaining space area, Refers to the total space area.

[0048] Optionally, the calculating and maintaining the expected value includes:

[0049] Obtain the maximum mechanical life and the used mechanical life, and calculate the remaining mechanical life based on the maximum mechanical life and the used mechanical life;

[0050] Get the equipment operation frequency, ambient humidity, and ambient dust concentration, set humidity sensitivity parameters and dust sensitivity parameters, and calculate the environmental maintenance factor based on humidity sensitivity parameters, dust sensitivity parameters, ambient humidity, and ambient dust concentration:

[0051] ;

[0052] in, Refers to environmental maintenance factors, Refers to humidity sensitive parameters, Refers to the ambient humidity, Refers to dust sensitivity parameters, Refers to the ambient dust concentration;

[0053] Calculate the maintenance expectation based on the remaining mechanical life, equipment operation frequency and environmental maintenance factors:

[0054] ;

[0055] in, Refers to maintaining expected value, Refers to the remaining mechanical life, Refers to the operating frequency of the device.

[0056] Optionally, constructing a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value includes:

[0057] Set the load weight and space weight, and build a comprehensive objective function based on the spatial matching degree, maintenance expected value, load prediction value, load weight and space weight:

[0058] ;

[0059] in, refers to the comprehensive objective function, Refers to the load weight, Refers to spatial weight.

[0060] To achieve the above object, the present invention also provides an inflatable cabinet combination system based on multiple modules, comprising:

[0061] A load data acquisition module is used to establish an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment acquisition unit and a data analysis unit; load detection is performed based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set is sorted according to the timestamp;

[0062] An external function construction module is used to detect the ambient temperature and ambient humidity according to the environmental collection unit and the timestamp to obtain an ambient temperature set and an ambient humidity set, collect electricity prices based on the data analysis unit and the timestamp to obtain an electricity price data set; construct an external influence function based on the ambient temperature set, the ambient humidity set and the electricity price data set, and construct a load mapping function according to the load data set, the external influence function and a preset machine learning model, wherein the machine learning model refers to an XGBoost model;

[0063] An objective function construction module is used to calculate the load prediction value according to the load mapping function, the external influence function and the load data set; construct a spacing matrix, a function matrix and an offset matrix, and calculate the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix; calculate the maintenance expected value, and construct a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value;

[0064] The optimal solution matching module is used to calculate the comprehensive fitness set based on the comprehensive objective function and the preset combination solution, extract the maximum fitness based on the comprehensive fitness set; match based on the maximum fitness to obtain the optimal combination solution and complete the inflatable cabinet combination.

[0065] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0066] A memory storing at least one instruction;

[0067] The processor executes the instructions stored in the memory to implement the above-mentioned inflatable cabinet assembly method based on multiple modules.

[0068] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned inflatable cabinet assembly method based on multiple modules.

[0069] The present invention solves the problems described in the background technology. First, an inflatable cabinet combination unit is established. By integrating a load detection unit, an environment collection unit and a data analysis unit into one combination unit, centralized monitoring and management of the system can be realized, the accuracy and timeliness of data collection can be improved, the coordinated work of each unit can be ensured, and the stability and responsiveness of the overall system can be enhanced. The modular design is conducive to the later system expansion and function optimization, and the overall stability of the inflatable cabinet system is improved, and the influence of a single module failure on the overall operation is avoided; secondly, the load data is detected, the load is monitored in real time and sorted according to the timestamp, so that the load data is more standardized, the time consistency of the load data is ensured, and a reliable data source is provided for subsequent load prediction and adjustment, and the accuracy of the prediction is improved; thereafter, the ambient temperature, humidity, electricity price data and demand data are collected, and an external influence function is constructed according to the ambient temperature, humidity and electricity price data, and a load mapping function is constructed. The temperature, humidity and electricity price data are comprehensively considered, and the operating environment of the inflatable cabinet can be comprehensively evaluated. These data provide multi-dimensional inputs for constructing the external influence function, which can accurately capture the influence of external environmental changes on the operation of the inflatable cabinet, improve the adaptability of the system under different external environments, and can dynamically adjust according to real-time changes. The load-saving operation strategy is adopted to avoid the decline of efficiency or failure caused by external factors. At the same time, the load mapping function is constructed through the machine learning model, which can dynamically combine the load prediction with the external environmental factors, improve the accuracy and real-time performance of the load prediction, enable the system to adapt to the changes in complex environment in real time, and reduce the risk of system load overload and imbalance; then, the spacing matrix, function matrix and offset matrix are constructed, and the spatial matching degree is calculated based on the spacing matrix, function matrix and offset matrix. The spacing matrix, function matrix and offset matrix are the quantitative expressions of the relationship between different modules. The spacing matrix reflects the physical distance or influence range between the modules, the function matrix describes the functional association of each module, and the offset matrix helps to adjust the deviation and difference between the modules. The construction of these matrices can clearly identify the interaction relationship between the modules, ensure that the modules of the inflatable cabinet can work together efficiently, and reduce the system instability and inefficiency caused by the lack of coordination between the modules. At the same time, the spatial matching degree calculated by the spacing matrix, function matrix and offset matrix can reflect the matching degree between the modules of the inflatable cabinet. High matching degree means that the modules can work together, reduce energy consumption and equipment loss, improve the coordination between the modules, and improve the operation efficiency and economy;Finally, the maintenance expected value is calculated, and a comprehensive objective function is constructed to obtain the optimal combination scheme. Through the maintenance expected value, the maintenance needs of the gas cabinet can be predicted. This expected value provides early warning for maintenance personnel, helps to reasonably plan the maintenance cycle and overhaul time of the equipment, reduces the sudden downtime caused by equipment failure, and improves the reliability and stability of the equipment, thereby reducing the maintenance cost and the probability of sudden failure. The load prediction value, space matching degree, and maintenance expected value are integrated to obtain a comprehensive objective function, so that the combination scheme of the gas cabinet can be optimized in multiple dimensions. This function provides a clear optimization goal for the subsequent decision-making process, optimizes the balance of various factors, and improves the stability, reliability and economy of the gas cabinet system by comprehensively considering the performance indicators of multiple dimensions. Based on the comprehensive objective function, it can be ensured that the final selected gas cabinet combination scheme has the highest stability and operating efficiency among all possible schemes, ensuring the long-term stable operation of the gas cabinet. Therefore, the present invention can improve the stability and operating efficiency of the gas cabinet under multiple modules. ; BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a flow chart of a multi-module inflatable cabinet assembly method provided in one embodiment of the present invention;

[0071] Figure 2 A functional module diagram of an inflatable cabinet assembly system based on multiple modules provided in one embodiment of the present invention;

[0072] Figure 3 A schematic structural diagram of an electronic device for implementing the multi-module-based inflatable cabinet assembly method provided in one embodiment of the present invention.

[0073] Description of reference numerals:

[0074] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0075] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0076] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0077] The embodiment of the present application provides an inflatable cabinet assembly method based on multiple modules. The execution subject of the inflatable cabinet assembly method based on multiple modules includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the inflatable cabinet assembly method based on multiple modules can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0078] Reference Figure 1 FIG. 1 is a flow chart of a method for assembling an inflatable cabinet based on multiple modules according to an embodiment of the present invention. In this embodiment, the method for assembling an inflatable cabinet based on multiple modules includes:

[0079] S1. Establish an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment collection unit and a data analysis unit.

[0080] It can be understood that the gas-filled cabinet is a medium-voltage or high-voltage power switch cabinet that uses gas insulation technology. It is widely used in power systems for distribution, control and protection of equipment. The gas-filled cabinet consists of a shell and a module. The shell refers to the physical external structure of the gas-filled cabinet. The physical external structure refers to the outer shell of the gas-filled cabinet. The shell is the basic frame of the gas-filled cabinet, which is used to protect the internal equipment and provide necessary support space and installation space. The support space refers to the space provided by the shell to support the module, and the installation space refers to the space provided by the shell for installing the module. The module refers to the internal equipment protected by the shell, and the internal equipment refers to circuit breakers, disconnectors, voltage transformers, protective relays and other equipment.

[0081] It can be explained that the inflatable cabinet combination unit refers to a unit that detects, collects and analyzes the data related to the inflatable cabinet. The inflatable cabinet combination unit includes: a load detection unit, an environment collection unit and a data analysis unit. The relevant data refer to load power, ambient temperature, ambient humidity and electricity price data. Load power refers to the power consumed when the inflatable cabinet is in operation. The ambient temperature refers to the temperature of the environment around the inflatable cabinet. The ambient humidity refers to the humidity of the environment around the inflatable cabinet. The electricity price data refers to the real-time electricity price data. The load detection unit refers to a unit used to detect load power. The environment collection unit refers to a unit used to detect ambient temperature and ambient humidity. The data analysis unit refers to a unit used to analyze electricity price data.

[0082] S2. Perform load detection based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set are sorted according to the timestamp.

[0083] It can be explained that timestamp refers to continuous time points, the time intervals between time points are the same, load detection is performed based on the load detection unit and the preset timestamp, and obtaining the load data set means performing load detection using the load detection unit according to the timestamp, sorting and marking the obtained load power according to the timestamp, and obtaining the load data set. The load data set refers to the set obtained after sorting and marking the load power according to the timestamp.

[0084] Exemplarily, assuming that load detection is performed using a load detection unit and a timestamp, a set of data sorted and marked according to the timestamp is obtained, such as 2024-10-01T10:00:00Z, load power: q1, 2024-10-01T10:00:01Z, load power: q2, 2024-10-01 refers to the date part, and the date part specifically refers to 2024, October, 1st, T refers to a separator used to separate the date part and the time part, 10:00:00 refers to the time part, 10:00:00 refers to 10 o'clock, 00 minutes, and 00 seconds in the morning, and Z refers to the time zone part. The time zone part refers to a standard time reference, which is not affected by daylight saving time and other regional time zones, indicating that the time is in the UTC time zone and has no offset.

[0085] S3. Detect the ambient temperature and ambient humidity according to the environment acquisition unit and the timestamp to obtain an ambient temperature set and an ambient humidity set, and collect electricity prices based on the data analysis unit and the timestamp to obtain an electricity price data set.

[0086] Explainably, the ambient temperature set refers to the set consisting of ambient temperatures, the ambient humidity set refers to the set consisting of ambient humidity, and the electricity price data set refers to the set consisting of electricity price data.

[0087] Exemplarily, it is assumed that the ambient temperature and ambient humidity are collected using the environment collection unit and timestamp, and the electricity price data is collected using the data analysis unit. For example, 2024-10-01T11:00:00Z, ambient temperature: 23°C, ambient humidity: 58%, 2024-10-01T11:00:00Z, electricity price data: 0.3 yuan / kWh.

[0088] S4. Construct an external influence function based on the ambient temperature set, ambient humidity set and electricity price data set, and construct a load mapping function according to the load data set, the external influence function and a preset machine learning model, wherein the machine learning model refers to an XGBoost model.

[0089] Explainable, the external influence function refers to a function that integrates ambient temperature, ambient humidity and electricity price data, the load mapping function refers to a function trained by a machine learning model that combines the external influence function, historical load accumulation and historical load power to predict the load power, and the XGBoost model refers to a gradient boosting decision tree model.

[0090] In detail, the external influence function is constructed based on the ambient temperature set, the ambient humidity set and the electricity price data set, including:

[0091] Based on the ambient temperature set, ambient humidity set and electricity price data set, outliers are eliminated to obtain a normal temperature set, a normal humidity set and a normal electricity price set;

[0092] Performing mean calculation based on the conventional temperature set, conventional humidity set and conventional electricity price set to obtain a temperature mean, a humidity mean and an electricity price mean;

[0093] Set the time parameter, temperature weight coefficient, humidity weight coefficient and electricity price weight coefficient, and construct an external influence function based on the time parameter, temperature mean, humidity mean, electricity price mean, temperature weight coefficient, humidity weight coefficient and electricity price weight coefficient:

[0094] ;

[0095] in, The time parameter is The external influence function when refers to the temperature weight coefficient, refers to the mean temperature, refers to the humidity weight coefficient, Refers to the average humidity, Refers to the electricity price weight coefficient, Refers to the average electricity price.

[0096] Explainable, the conventional temperature set refers to the set obtained after removing outliers from the ambient temperature set, outliers refer to values ​​that deviate far from the normal numerical range, the conventional humidity set refers to the set obtained after removing outliers from the ambient humidity set, the conventional electricity price set refers to the set obtained after removing outliers from the electricity price data set, the temperature mean refers to the mean of all conventional temperatures in the conventional temperature set, the humidity mean refers to the mean of all conventional humidity in the conventional humidity set, the electricity price mean refers to the mean of all conventional electricity prices in the conventional electricity price set, the time parameter refers to the parameter representing the time, the temperature weight coefficient refers to the parameter that adjusts the degree of influence of the temperature mean on the external influence function, the humidity weight coefficient refers to the parameter that adjusts the degree of influence of the humidity mean on the external influence function, and the electricity price weight coefficient refers to the parameter that adjusts the degree of influence of the electricity price mean on the external influence function.

[0097] S5. Calculate a load prediction value according to the load mapping function, the external influence function and the load data set.

[0098] It can be explained that the load prediction value refers to the value obtained by predicting the load power.

[0099] In detail, the calculating the load prediction value according to the load mapping function, the external influence function and the load data set includes:

[0100] Calculate the time point interval based on the timestamp, set the historical interval and the integral variable based on the time point interval, calculate the historical load accumulation value based on the load data set and the historical interval, obtain the historical load value, and calculate the load prediction value based on the load mapping function, the external influence function, the load data set, the historical load accumulation value, the historical load value and the integral variable:

[0101] ;

[0102] in, The time parameter is The load forecast value at refers to the load mapping function, Refers to arrive The historical load accumulation value between is the integration interval, The integral variable is The historical load value at that time.

[0103] Explainable, the time point interval refers to the interval between every two time points of the timestamp, the historical load cumulative value refers to the cumulative value of the load power within the historical interval, the historical interval refers to an interval in the load data set, the interval is based on the integration interval and is obtained from the latest load data. For example, if the load data set is (1,2,3,4,5,6,7,8), the integration interval is 3, and the latest load data is 8, then the historical interval is (5,6,7,8), and the integral variable refers to The variable used to traverse the time parameter in the integration. The historical load value refers to the value of the load power corresponding to the time parameter in the load data set.

[0104] S6. Construct a spacing matrix, a function matrix and an offset matrix, and calculate the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix.

[0105] It can be explained that the spacing matrix refers to the matrix that describes the distance between each module in the inflatable cabinet, and the functional matrix refers to the matrix that describes the degree of dependence between each module in the inflatable cabinet. The degree of dependence is a specific value ranging from 0 to 1. The closer the degree of dependence is to 1, the stronger the dependence of one module on another module. The offset matrix refers to the matrix that describes the offset between the actual module position of each module in the inflatable cabinet and its corresponding ideal module position. The actual module position refers to the actual position of each module in the inflatable cabinet, and the ideal module position refers to the pre-set position of each module in the inflatable cabinet. For example, there is a module A in the inflatable cabinet, the ideal position of module A is (2,3), and the initial position of module A is (2.01,3.02).

[0106] In detail, the construction of the spacing matrix, the function matrix and the offset matrix includes:

[0107] Get the module, set the module tag based on the module, establish the module coordinate system, and collect based on the module coordinate system Axis coordinates, Axis coordinates and Axis coordinates, set Axis weight, Axis weights and Axis weights, based on Axis coordinates, Axis coordinates, Axis coordinates, Axis weight, Axis weight, Axis weights, modules, and module labels calculate module spacing:

[0108] ;

[0109] in, Refers to the module marked as Modules with modules marked as The module spacing between modules, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates;

[0110] A spacing matrix is ​​constructed based on the module spacing, and a function association value and a baseline offset value are calculated. A function matrix and an offset matrix are constructed according to the function association value and the baseline offset value respectively. A spatial matching degree is calculated based on the spacing matrix, the function matrix and the offset matrix.

[0111] Explainable, module mark refers to the parameters of the traversal module, the module coordinate system refers to the center of the inflatable cabinet as the origin, the east and west directions of the origin are The north-south direction of the axis and origin is Axis, passing through the origin, perpendicular to axis, Axis, direction upward The three-dimensional coordinate system established by the axis, the east-west direction refers to the direction from west to east, and the north-south direction refers to the direction from south to north. Axis coordinates refer to the module's coordinate system. Axis coordinates, Axis coordinates refer to the module's coordinate system. Axis coordinates, Axis coordinates refer to the module's coordinate system. Axis coordinates, Axis weight refers to The influence of axis coordinates on module spacing, Axis weight refers to The influence of axis coordinates on module spacing, Axis weight refers to The influence degree of axis coordinates on module spacing, the functional association value refers to the numerical value describing the functional association degree, access frequency and data flow between modules, the functional association degree refers to the functional coupling degree between modules, the functional coupling degree reflects the degree of interaction between modules, the access frequency refers to the frequency of access between modules, the data flow refers to the flow of data interaction between modules, the baseline offset value refers to the value after combining the displacement deviation and area deviation, the displacement deviation refers to the difference between the actual module position of the module and the corresponding ideal module position, the area deviation refers to the ratio of the actual module areas of different modules, the actual module area refers to the area of ​​the module, the space matching degree refers to an indicator to measure the effect of the configuration of the module in a given space, which is used to optimize the layout of the inflatable cabinet and improve the overall efficiency and stability of the inflatable cabinet, and the given space refers to the space of the inflatable cabinet.

[0112] In detail, the calculation of the functional association value and the baseline shift value includes:

[0113] Calculate the function relevance, obtain the access frequency and data traffic, set the relevance weight, frequency weight and traffic weight, and calculate the function relevance value based on the function relevance, access frequency, data traffic, relevance weight, frequency weight and traffic weight:

[0114] ;

[0115] in, Refers to the module marked as Modules with modules marked as The functional association value between modules, refers to the association weight, Refers to the module marked as Modules with modules marked as The functional correlation between modules, refers to the frequency weight, Refers to the module marked as Modules with modules marked as The access frequency between modules, Refers to the traffic weight, Refers to the module marked as Modules with modules marked as Data flow between modules;

[0116] Set the ideal position of the module, displacement deviation weight and area deviation weight, obtain the actual position of the module and the actual area of ​​the module, and calculate the baseline offset value based on the ideal position of the module, displacement deviation weight, area deviation weight, actual position of the module and actual area of ​​the module:

[0117] ;

[0118] in, Refers to the module marked as Modules with modules marked as The baseline offset value between modules, refers to the displacement bias weight, Refers to the absolute value symbol, Modules are marked as The ideal location of the module, Refers to the module marked as The actual location of the module. refers to the area deviation weight, Refers to the module marked as The actual area of ​​the module, Refers to the module marked as The actual area of ​​the module.

[0119] Explainably, association weight refers to the parameter that adjusts the influence of function association degree on function association value, frequency weight refers to the parameter that adjusts the influence of access frequency on function association value, traffic weight refers to the parameter that adjusts the influence of data traffic on function association value, displacement deviation weight refers to the parameter that adjusts the influence of displacement deviation on baseline offset value, and area deviation weight refers to the parameter that adjusts the influence of area deviation on baseline offset value.

[0120] In detail, the calculation of functional association includes:

[0121] Get the function feature vector and feature vector norm, and calculate the function similarity based on the function feature vector and feature vector norm:

[0122] ;

[0123] in, Refers to the module marked as Modules with modules marked as The functional similarity between modules, Refers to the module marked as The module's functional feature vector, Points to the dot product symbol, Refers to the module marked as The module's functional feature vector, Refers to the module marked as The eigenvector norm of the module, Refers to the module marked as The eigenvector norm of the module;

[0124] Set the maximum interaction frequency, the maximum function relevance and the maximum data transmission volume, and calculate the function relevance based on the function similarity, access frequency, data flow, the maximum interaction frequency, the maximum function relevance and the maximum data transmission volume:

[0125] ;

[0126] in, refers to the maximum interaction frequency, Refers to the maximum functional association, Refers to the maximum data transfer rate.

[0127] Interpretable, functional feature vector refers to the vector that describes the function of the module, feature vector norm refers to the norm of the functional feature vector, functional similarity refers to the degree of similarity of the functions between two modules, maximum interaction frequency refers to the pre-set maximum access frequency, maximum functional association refers to the pre-set maximum functional association that can be achieved between two modules, and maximum data transmission volume refers to the pre-set maximum data flow.

[0128] In detail, the calculating of the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix includes:

[0129] Get the total space area, remaining space area, The norm formula and spatial weight are based on the spacing matrix, function matrix, offset matrix, total space area, remaining space area, Norm formula and spatial weight calculation of spatial matching degree:

[0130] ;

[0131] in, Refers to the spatial matching degree, Refers to the spacing matrix, Refers to the functional matrix, refers to the offset matrix, refer to norm formula, refers to the spatial weight, Refers to the remaining space area, Refers to the total space area.

[0132] Explainable, the total space area refers to the total capacity inside the inflatable cabinet, and the remaining space area refers to the remaining capacity inside the inflatable cabinet. The norm formula refers to the Frobenius norm, the spatial weight refers to the parameter that adjusts the influence of the area ratio on the spatial matching degree, and the area ratio refers to the ratio of the remaining space area to the total space area.

[0133] S7. Calculate the expected maintenance value, and construct a comprehensive objective function based on the load prediction value, the spatial matching degree and the expected maintenance value.

[0134] Explainably, the expected maintenance value refers to the maintenance cycle predicted for the gas cabinet, the maintenance cycle refers to the time for maintaining the gas cabinet and the module, and the comprehensive objective function refers to a function that integrates the spatial matching degree, the expected maintenance value and the load prediction value, which is used to evaluate the current gas cabinet and module.

[0135] In detail, the calculation and maintenance of expected values ​​include:

[0136] Obtain the maximum mechanical life and the used mechanical life, and calculate the remaining mechanical life based on the maximum mechanical life and the used mechanical life;

[0137] Get the equipment operation frequency, ambient humidity, and ambient dust concentration, set humidity sensitivity parameters and dust sensitivity parameters, and calculate the environmental maintenance factor based on humidity sensitivity parameters, dust sensitivity parameters, ambient humidity, and ambient dust concentration:

[0138] ;

[0139] in, Refers to environmental maintenance factors, Refers to humidity sensitive parameters, Refers to the ambient humidity, Refers to dust sensitivity parameters, Refers to the ambient dust concentration;

[0140] Calculate the maintenance expectation based on the remaining mechanical life, equipment operation frequency and environmental maintenance factors:

[0141] ;

[0142] in, Refers to maintaining expected value, Refers to the remaining mechanical life, Refers to the operating frequency of the device.

[0143] It can be explained that the maximum mechanical life refers to the maximum mechanical life of the air cabinet and the module, the service life refers to the mechanical life that has been used, the remaining mechanical life refers to the remaining mechanical life, which is obtained by subtracting the service life from the maximum mechanical life, the equipment operation frequency refers to the usage frequency of each module in the air cabinet, the ambient humidity refers to the humidity in the environment around the air cabinet, the ambient dust concentration refers to the dust concentration in the environment around the air cabinet, the humidity sensitive parameter refers to the parameter for adjusting the influence of ambient humidity on the environmental maintenance factor, the dust sensitive parameter refers to the parameter for adjusting the influence of ambient dust concentration on the environmental maintenance factor, the environmental maintenance factor refers to the parameter that indicates the influence of environmental parameters on the mechanical life, and the environmental parameters refer to the ambient humidity and ambient dust concentration.

[0144] In detail, the comprehensive objective function is constructed based on the load prediction value, the spatial matching degree and the maintenance expected value, including:

[0145] Set the load weight and space weight, and build a comprehensive objective function based on the spatial matching degree, maintenance expected value, load prediction value, load weight and space weight:

[0146] ;

[0147] in, refers to the comprehensive objective function, Refers to the load weight, Refers to spatial weight.

[0148] Explainably, load weight refers to the parameter that adjusts the influence of load prediction value on the comprehensive objective function, and spatial weight refers to the parameter that adjusts the influence of spatial matching degree on the comprehensive objective function.

[0149] S8. Calculate a comprehensive fitness set based on the comprehensive objective function and the preset combination scheme, and extract the maximum fitness based on the comprehensive fitness set.

[0150] Explainably, the combination scheme refers to the scheme for combining modules in the inflatable cabinet, the comprehensive fitness set refers to the set of comprehensive fitness obtained after calculating the combination scheme using the comprehensive objective function, and the maximum fitness refers to the maximum comprehensive fitness in the comprehensive fitness set.

[0151] S9. Matching is performed based on the maximum fitness to obtain an optimal combination solution and complete the inflatable cabinet combination.

[0152] It can be explained that the optimal combination scheme refers to the combination scheme corresponding to the maximum fitness. The present invention constructs a comprehensive objective function by calculating the load prediction value, space matching degree and maintenance expected value. According to the output of the comprehensive objective function, the advantages and disadvantages of the support space and installation space of the shell can be judged, so as to select the optimal scheme from multiple multi-module inflatable cabinet combination schemes. The output of the comprehensive objective function refers to the comprehensive fitness.

[0153] The present invention solves the problems described in the background technology. First, an inflatable cabinet combination unit is established. By integrating a load detection unit, an environment collection unit and a data analysis unit into one combination unit, centralized monitoring and management of the system can be realized, the accuracy and timeliness of data collection can be improved, the coordinated work of each unit can be ensured, and the stability and responsiveness of the overall system can be enhanced. The modular design is conducive to the later system expansion and function optimization, and the overall stability of the inflatable cabinet system is improved, and the influence of a single module failure on the overall operation is avoided; secondly, the load data is detected, the load is monitored in real time and sorted according to the timestamp, so that the load data is more standardized, the time consistency of the load data is ensured, and a reliable data source is provided for subsequent load prediction and adjustment, and the accuracy of the prediction is improved; thereafter, the ambient temperature, humidity, electricity price data and demand data are collected, and an external influence function is constructed according to the ambient temperature, humidity and electricity price data, and a load mapping function is constructed. The temperature, humidity and electricity price data are comprehensively considered, and the operating environment of the inflatable cabinet can be comprehensively evaluated. These data provide multi-dimensional inputs for constructing the external influence function, which can accurately capture the influence of external environmental changes on the operation of the inflatable cabinet, improve the adaptability of the system under different external environments, and can dynamically adjust according to real-time changes. The load-saving operation strategy is adopted to avoid the decline of efficiency or failure caused by external factors. At the same time, the load mapping function is constructed through the machine learning model, which can dynamically combine the load prediction with the external environmental factors, improve the accuracy and real-time performance of the load prediction, enable the system to adapt to the changes in complex environment in real time, and reduce the risk of system load overload and imbalance; then, the spacing matrix, function matrix and offset matrix are constructed, and the spatial matching degree is calculated based on the spacing matrix, function matrix and offset matrix. The spacing matrix, function matrix and offset matrix are the quantitative expressions of the relationship between different modules. The spacing matrix reflects the physical distance or influence range between the modules, the function matrix describes the functional association of each module, and the offset matrix helps to adjust the deviation and difference between the modules. The construction of these matrices can clearly identify the interaction relationship between the modules, ensure that the modules of the inflatable cabinet can work together efficiently, and reduce the system instability and inefficiency caused by the lack of coordination between the modules. At the same time, the spatial matching degree calculated by the spacing matrix, function matrix and offset matrix can reflect the matching degree between the modules of the inflatable cabinet. High matching degree means that the modules can work together, reduce energy consumption and equipment loss, improve the coordination between the modules, and improve the operation efficiency and economy;Finally, the maintenance expected value is calculated, and a comprehensive objective function is constructed to obtain the optimal combination scheme. Through the maintenance expected value, the maintenance needs of the gas cabinet can be predicted. This expected value provides early warning for maintenance personnel, helps to reasonably plan the maintenance cycle and overhaul time of the equipment, reduces the sudden downtime caused by equipment failure, and improves the reliability and stability of the equipment, thereby reducing the maintenance cost and the probability of sudden failure. The load prediction value, space matching degree, and maintenance expected value are integrated to obtain a comprehensive objective function, so that the combination scheme of the gas cabinet can be optimized in multiple dimensions. This function provides a clear optimization goal for the subsequent decision-making process, optimizes the balance of various factors, and improves the stability, reliability and economy of the gas cabinet system by comprehensively considering the performance indicators of multiple dimensions. Based on the comprehensive objective function, it can be ensured that the final selected gas cabinet combination scheme has the highest stability and operating efficiency among all possible schemes, ensuring the long-term stable operation of the gas cabinet. Therefore, the present invention can improve the stability and operating efficiency of the gas cabinet under multiple modules. ;

[0154] like Figure 2 1 is a functional module diagram of an inflatable cabinet combination system based on multiple modules provided in one embodiment of the present invention.

[0155] The multi-module inflatable cabinet combination system 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the multi-module inflatable cabinet combination system 100 can include a load data acquisition module 101, an external function construction module 102, an objective function construction module 103 and an optimal solution matching module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0156] The load data acquisition module 101 is used to establish an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment acquisition unit and a data analysis unit; load detection is performed based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set is sorted according to the timestamp;

[0157] The external function construction module 102 is used to detect the ambient temperature and ambient humidity according to the environmental collection unit and the timestamp to obtain the ambient temperature set and the ambient humidity set, collect the electricity price based on the data analysis unit and the timestamp to obtain the electricity price data set; construct an external influence function based on the ambient temperature set, the ambient humidity set and the electricity price data set, and construct a load mapping function according to the load data set, the external influence function and the preset machine learning model, wherein the machine learning model refers to the XGBoost model;

[0158] The objective function construction module 103 is used to calculate the load prediction value according to the load mapping function, the external influence function and the load data set; construct a spacing matrix, a function matrix and an offset matrix, and calculate the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix; calculate the maintenance expected value, and construct a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value;

[0159] The optimal solution matching module 104 is used to calculate the comprehensive fitness set based on the comprehensive objective function and the preset combination solution, extract the maximum fitness based on the comprehensive fitness set; match based on the maximum fitness to obtain the optimal combination solution and complete the inflatable cabinet combination.

[0160] In detail, each module in the multi-module inflatable cabinet assembly system 100 according to the embodiment of the present invention is used in the same manner as described above. Figure 1 The same technical means as the multi-module inflatable cabinet combination method described in the text can produce the same technical effects, so they will not be repeated here.

[0161] like Figure 3 1 is a schematic diagram of the structure of an electronic device for implementing a multi-module inflatable cabinet assembly method provided by an embodiment of the present invention.

[0162] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a multi-module-based inflatable cabinet assembly method program.

[0163] The memory 11 includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the inflatable cabinet combination method program based on multiple modules, but also can be used to temporarily store data that has been output or is to be output.

[0164] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, a method program for combining an inflatable cabinet based on multiple modules, etc.), and calls data stored in the memory 11, so as to execute various functions of the electronic device 1 and process data.

[0165] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0166] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0167] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0168] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0169] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0170] The inflatable cabinet assembly method program based on multiple modules stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0171] Establishing an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment collection unit and a data analysis unit;

[0172] Performing load detection based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set are sorted according to the timestamp;

[0173] Detecting the ambient temperature and ambient humidity according to the ambient collection unit and the timestamp to obtain an ambient temperature set and an ambient humidity set, and collecting electricity prices based on the data analysis unit and the timestamp to obtain an electricity price data set;

[0174] An external influence function is constructed based on the ambient temperature set, the ambient humidity set and the electricity price data set, and a load mapping function is constructed according to the load data set, the external influence function and a preset machine learning model, wherein the machine learning model refers to an XGBoost model;

[0175] Calculate a load prediction value according to the load mapping function, the external influence function and the load data set;

[0176] Constructing a spacing matrix, a function matrix and an offset matrix, and calculating the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix;

[0177] Calculating the maintenance expected value, and constructing a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value;

[0178] Calculating a comprehensive fitness set based on the comprehensive objective function and the preset combination scheme, and extracting a maximum fitness based on the comprehensive fitness set;

[0179] Matching is performed based on the maximum fitness to obtain the optimal combination solution and complete the inflatable cabinet combination.

[0180] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0181] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0182] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0183] Establishing an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment collection unit and a data analysis unit;

[0184] Performing load detection based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set are sorted according to the timestamp;

[0185] Detecting the ambient temperature and ambient humidity according to the ambient collection unit and the timestamp to obtain an ambient temperature set and an ambient humidity set, and collecting electricity prices based on the data analysis unit and the timestamp to obtain an electricity price data set;

[0186] An external influence function is constructed based on the ambient temperature set, the ambient humidity set and the electricity price data set, and a load mapping function is constructed according to the load data set, the external influence function and a preset machine learning model, wherein the machine learning model refers to an XGBoost model;

[0187] Calculate a load prediction value according to the load mapping function, the external influence function and the load data set;

[0188] Constructing a spacing matrix, a function matrix and an offset matrix, and calculating the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix;

[0189] Calculating the maintenance expected value, and constructing a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value;

[0190] Calculating a comprehensive fitness set based on the comprehensive objective function and the preset combination scheme, and extracting a maximum fitness based on the comprehensive fitness set;

[0191] Matching is performed based on the maximum fitness to obtain the optimal combination solution and complete the inflatable cabinet combination.

[0192] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0193] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0195] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. 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 solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for assembling an inflatable cabinet based on multiple modules, characterized in that: The method comprises: Establishing an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment collection unit and a data analysis unit; Performing load detection based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set are sorted according to the timestamp; Detecting the ambient temperature and ambient humidity according to the ambient collection unit and the timestamp to obtain an ambient temperature set and an ambient humidity set, and collecting electricity prices based on the data analysis unit and the timestamp to obtain an electricity price data set; An external influence function is constructed based on the ambient temperature set, the ambient humidity set and the electricity price data set, and a load mapping function is constructed according to the load data set, the external influence function and a preset machine learning model, wherein the machine learning model refers to an XGBoost model; Calculate a load prediction value according to the load mapping function, the external influence function and the load data set; Constructing a spacing matrix, a function matrix and an offset matrix, and calculating the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix; Calculating the maintenance expected value, and constructing a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value; Calculate a comprehensive fitness set based on the comprehensive objective function and the preset combination scheme, and extract the maximum fitness based on the comprehensive fitness set; Matching is performed based on the maximum fitness to obtain the optimal combination solution and complete the inflatable cabinet combination.

2. The method for assembling an inflatable cabinet based on multiple modules according to claim 1, characterized in that: The constructing of the external influence function based on the ambient temperature set, the ambient humidity set and the electricity price data set includes: Based on the ambient temperature set, ambient humidity set and electricity price data set, outliers are eliminated to obtain a normal temperature set, a normal humidity set and a normal electricity price set; Performing mean calculation based on the conventional temperature set, conventional humidity set and conventional electricity price set to obtain a temperature mean, a humidity mean and an electricity price mean; Set the time parameter, temperature weight coefficient, humidity weight coefficient and electricity price weight coefficient, and construct an external influence function based on the time parameter, temperature mean, humidity mean, electricity price mean, temperature weight coefficient, humidity weight coefficient and electricity price weight coefficient: ； in, The time parameter is The external influence function when refers to the temperature weight coefficient, refers to the mean temperature, refers to the humidity weight coefficient, Refers to the average humidity, Refers to the electricity price weight coefficient, Refers to the average electricity price.

3. The method for assembling an inflatable cabinet based on multiple modules according to claim 2, characterized in that: The calculating the load prediction value according to the load mapping function, the external influence function and the load data set includes: Calculate the time point interval based on the timestamp, set the historical interval and the integral variable based on the time point interval, calculate the historical load accumulation value based on the load data set and the historical interval, obtain the historical load value, and calculate the load prediction value based on the load mapping function, the external influence function, the load data set, the historical load accumulation value, the historical load value and the integral variable: ； in, The time parameter is The load forecast value at refers to the load mapping function, Refers to arrive The historical load accumulation value between is the integration interval, The integral variable is The historical load value at that time.

4. The method for assembling an inflatable cabinet based on multiple modules as claimed in claim 3, characterized in that: The step of constructing the spacing matrix, the function matrix and the offset matrix includes: Get the module, set the module tag based on the module, establish the module coordinate system, and collect based on the module coordinate system Axis coordinates, Axis coordinates and Axis coordinates, set Axis weight, Axis weights and Axis weights, based on Axis coordinates, Axis coordinates, Axis coordinates, Axis weight, Axis weight, Axis weights, modules, and module labels calculate module spacing: ； in, Refers to the module marked as Modules with modules marked as The module spacing between modules, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates, refer to Axis weights, Refers to the module marked as The module Axis coordinates, Refers to the module marked as The module Axis coordinates; A spacing matrix is ​​constructed based on the module spacing, and a function association value and a baseline offset value are calculated. A function matrix and an offset matrix are constructed according to the function association value and the baseline offset value respectively. A spatial matching degree is calculated based on the spacing matrix, the function matrix and the offset matrix.

5. The method for assembling an inflatable cabinet based on multiple modules according to claim 4, characterized in that: The calculating of the functional association value and the baseline shift value comprises: Calculate the function relevance, obtain the access frequency and data traffic, set the relevance weight, frequency weight and traffic weight, and calculate the function relevance value based on the function relevance, access frequency, data traffic, relevance weight, frequency weight and traffic weight: ； in, Refers to the module marked as Modules with modules marked as The functional association value between modules, refers to the association weight, Refers to the module marked as Modules with modules marked as The functional correlation between modules, refers to the frequency weight, Refers to the module marked as Modules with modules marked as The access frequency between modules, Refers to the traffic weight, Refers to the module marked as Modules with modules marked as Data flow between modules; Set the ideal position of the module, displacement deviation weight and area deviation weight, obtain the actual position of the module and the actual area of ​​the module, and calculate the baseline offset value based on the ideal position of the module, displacement deviation weight, area deviation weight, actual position of the module and actual area of ​​the module: ； in, Refers to the module marked as Modules with modules marked as The baseline offset value between modules, refers to the displacement bias weight, Refers to the absolute value symbol, Modules are marked as The ideal location of the module, Refers to the module marked as The actual location of the module. refers to the area deviation weight, Refers to the module marked as The actual area of ​​the module, Refers to the module marked as The actual area of ​​the module.

6. The method for assembling an inflatable cabinet based on multiple modules according to claim 5, characterized in that: The calculation of functional association comprises: Get the function feature vector and feature vector norm, and calculate the function similarity based on the function feature vector and feature vector norm: ； in, Refers to the module marked as Modules with modules marked as The functional similarity between modules, Refers to the module marked as The module's functional feature vector, Points to the dot product symbol, Refers to the module marked as The module's functional feature vector, Refers to the module marked as The eigenvector norm of the module, Refers to the module marked as The eigenvector norm of the module; Set the maximum interaction frequency, the maximum function relevance and the maximum data transmission volume, and calculate the function relevance based on the function similarity, access frequency, data flow, the maximum interaction frequency, the maximum function relevance and the maximum data transmission volume: ； in, refers to the maximum interaction frequency, Refers to the maximum functional association, Refers to the maximum data transfer rate.

7. The method for assembling an inflatable cabinet based on multiple modules according to claim 6, characterized in that: The calculating of the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix includes: Get the total space area, remaining space area, The norm formula and spatial weight are based on the spacing matrix, function matrix, offset matrix, total space area, remaining space area, Norm formula and spatial weight calculation of spatial matching degree: ； in, Refers to the spatial matching degree, Refers to the spacing matrix, Refers to the functional matrix, refers to the offset matrix, refer to norm formula, refers to the spatial weight, Refers to the remaining space area, Refers to the total space area.

8. The method for assembling an inflatable cabinet based on multiple modules according to claim 7, characterized in that: The calculation maintains the expected value, including: Obtain the maximum mechanical life and the used mechanical life, and calculate the remaining mechanical life based on the maximum mechanical life and the used mechanical life; Get the equipment operation frequency, ambient humidity, and ambient dust concentration, set humidity sensitivity parameters and dust sensitivity parameters, and calculate the environmental maintenance factor based on humidity sensitivity parameters, dust sensitivity parameters, ambient humidity, and ambient dust concentration: ； in, Refers to environmental maintenance factors, Refers to humidity sensitive parameters, Refers to the ambient humidity, Refers to dust sensitivity parameters, Refers to the ambient dust concentration; Calculate the maintenance expectation based on the remaining mechanical life, equipment operation frequency and environmental maintenance factors: ； in, Refers to maintaining expected value, Refers to the remaining mechanical life, Refers to the operating frequency of the device.

9. The method for assembling an inflatable cabinet based on multiple modules according to claim 8, characterized in that: The constructing of a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value includes: Set the load weight and space weight, and build a comprehensive objective function based on the spatial matching degree, maintenance expected value, load prediction value, load weight and space weight: ； in, refers to the comprehensive objective function, Refers to the load weight, Refers to spatial weight.

10. A multi-module inflatable cabinet combination system, characterized in that: The system comprises: A load data acquisition module is used to establish an inflatable cabinet assembly unit, wherein the inflatable cabinet assembly unit includes: a load detection unit, an environment acquisition unit and a data analysis unit; load detection is performed based on the load detection unit and a preset timestamp to obtain a load data set, wherein the load data in the load data set is sorted according to the timestamp; An external function construction module is used to detect the ambient temperature and ambient humidity according to the environmental collection unit and the timestamp to obtain an ambient temperature set and an ambient humidity set, collect electricity prices based on the data analysis unit and the timestamp to obtain an electricity price data set; construct an external influence function based on the ambient temperature set, the ambient humidity set and the electricity price data set, and construct a load mapping function according to the load data set, the external influence function and a preset machine learning model, wherein the machine learning model refers to an XGBoost model; An objective function construction module is used to calculate the load prediction value according to the load mapping function, the external influence function and the load data set; construct a spacing matrix, a function matrix and an offset matrix, and calculate the spatial matching degree based on the spacing matrix, the function matrix and the offset matrix; calculate the maintenance expected value, and construct a comprehensive objective function based on the load prediction value, the spatial matching degree and the maintenance expected value; The optimal solution matching module is used to calculate the comprehensive fitness set based on the comprehensive objective function and the preset combination solution, extract the maximum fitness based on the comprehensive fitness set; match based on the maximum fitness to obtain the optimal combination solution and complete the inflatable cabinet combination.

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