A port multi-energy fusion management system and method based on multi-source data analysis

Abstract

The present invention discloses a port multi-energy fusion management system and method based on multi-source data analysis, and relates to the technical field of port energy management. The present invention can more accurately capture the key information of various types of data through a series of data-driven methods such as data classification and separate analysis, and then comprehensive analysis results, thereby improving the accuracy of data analysis, thereby improving the accuracy of final result generation. At the same time, the multi-energy fusion management value under each type of data is integrated, and on the premise of being able to effectively ensure the scientificity and accuracy of the management strategy finally generated, it can further ensure that the final management strategy is optimal in the global scope, enhance the robustness and adaptability of the system, and promote the sustainable development of the port.

Description

Technical Field

[0001] The present invention relates to the technical field of port energy management, and in particular to a port multi-energy fusion management system and method based on multi-source data analysis. Background Art

[0002] The port multi-energy integration management method based on multi-source data analysis refers to the use of data from different sources such as ship dynamics, weather forecasts, historical energy consumption records, electricity market information, etc., through advanced data processing and analysis technology to optimize the use and distribution of various energy sources within the port, such as electricity, fuel, natural gas, renewable energy, etc., in order to achieve the purpose of energy conservation and emission reduction and improve energy efficiency. This method not only helps to reduce operating costs, but also promotes the development of environmentally friendly ports.

[0003] Although the existing methods have improved the efficiency of energy management to a certain extent, there are still some defects and shortcomings in practical applications:

[0004] Existing methods usually process all data in a mixed manner without classifying them according to their characteristics, resulting in insufficient refinement in data processing. This mixed processing method may ignore the differences between different types of data, making it difficult to accurately capture the key information in each type of data, and the mutual interference between different types of data may affect the final analysis results. At the same time, when analyzing data, existing methods usually process all data as a whole, ignoring the impact mechanism of different data types on the use of multi-energy systems, resulting in insufficient targeting of optimization strategies. In addition, existing methods often rely on simple statistical analysis or empirical judgment when determining the optimal usage ratio of each energy source, lack precise quantitative methods, and result in insufficient decision-making accuracy. Moreover, existing methods lack flexible adjustment mechanisms when facing changes in environmental conditions, operating conditions, and energy consumption patterns, resulting in poor adaptability and robustness of management strategies.

[0005] Therefore, the prior art urgently needs a technical solution for a port multi-energy fusion management system and method based on multi-source data analysis. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a port multi-energy fusion management method based on multi-source data analysis, which specifically includes the following steps:

[0007] Step S1, collecting various data in the port in real time through IoT devices and sensor networks respectively;

[0008] Step S2, dividing the collected data into three categories according to data characteristics, and preprocessing the three categories of data respectively;

[0009] The three types of data include environmental data, operational data and energy consumption data;

[0010] Step S3, analyzing the influence of the three types of preprocessed data on the optimal utilization ratio of each energy in the multi-energy, and obtaining the multi-energy fusion management value under each type of data;

[0011] Step S3a, analyzing the influence of environmental data on the optimal utilization ratio of each energy in multiple energy sources, and obtaining the multi-energy fusion management value under the environmental data;

[0012] Step S3a1, extracting key environmental factors from the environmental data collected in real time, and quantifying each key environmental factor to obtain the actual measured value of each key environmental factor;

[0013] Step S3a2: Analyze the correlation between each key environmental factor and the consumption of each energy source in the multiple energy sources according to the actual measured value of each key environmental factor, obtain the correlation coefficient between each key environmental factor and the consumption of each energy source in the multiple energy sources, and assign the influence weight of each key environmental factor on each energy source in the multiple energy sources according to the correlation coefficient;

[0014] Step S3a3, obtaining an environmental impact index according to the impact weight of each key environmental factor on each energy source in the multiple energy sources and the actual measured value of each key environmental factor;

[0015] Among them, the calculation formula for the environmental impact index is:

[0016] ;

[0017] Among them, EII stands for Environmental Impact Index; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the i-th key environmental factor; represents the number of key environmental factors; Represents the number of energy types;

[0018] Step S3a4, according to the actual measured value of each key environmental factor, the impact weight between each key environmental factor and each energy in the multiple energy sources, and the environmental impact index, obtain the efficiency correction value of each energy in the multiple energy sources under each key environmental factor;

[0019] The calculation formula for obtaining the efficiency correction value of each energy source in multiple energy sources under each key environmental factor is:

[0020] ;

[0021] in, represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the ith key environmental factor; EII represents the environmental impact index; represents the number of key environmental factors; Represents the number of energy types; Represents the specific impact value of the i-th key environmental factor on the j-th energy in multiple energy sources; Represents the comprehensive impact value of the i-th key environmental factor on all energy sources in the multi-energy source;

[0022] Step S3a5, obtaining a benchmark usage ratio of each energy source in the multiple energy sources, and adjusting the benchmark usage ratio of each energy source in the multiple energy sources by using the efficiency correction value of each energy source in the multiple energy sources under each key environmental factor, to obtain an optimal usage ratio of each energy source in the multiple energy sources;

[0023] Among them, the calculation formula for obtaining the optimal usage ratio of each energy in multiple energy sources is:

[0024] ;

[0025] in, Represents the optimal usage ratio of the jth energy among multiple energy sources; Represents the benchmark usage ratio of the jth energy in multiple energy sources; represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; represents the number of key environmental factors;

[0026] Step S3a6, obtaining the multi-energy fusion management value under the environmental data according to the optimal use ratio of each energy in the multi-energy, the influence weight between each key environmental factor and each energy in the multi-energy, the actual measured value of each key environmental factor, the environmental impact index, and the efficiency correction value of each energy in the multi-energy under each key environmental factor;

[0027] Among them, the calculation formula of the multi-energy fusion management value under environmental data is:

[0028] ;

[0029] in, Represents the multi-energy fusion management value under environmental data; Represents the optimal usage ratio of the jth energy among multiple energy sources; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the ith key environmental factor; EII represents the environmental impact index; represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; represents the number of key environmental factors; Represents the number of energy types; represents the weighted sum of the impact of all key environmental factors on the jth energy source;

[0030] Step S3b, analyzing the influence of the operation data on the optimal utilization ratio of each energy in the multiple energy sources, and obtaining the multi-energy fusion management value under the operation data;

[0031] Step S3c, analyzing the influence of energy consumption data on the optimal usage ratio of each energy in multiple energy sources, and obtaining the multi-energy fusion management value under the energy consumption data;

[0032] Step S4, integrating the multi-energy fusion management value under each type of data to obtain a multi-energy fusion comprehensive management value;

[0033] Among them, the calculation formula for the comprehensive management value of multi-energy fusion is:

[0034] ;

[0035] in Represents the comprehensive management value of multi-energy integration; represents the weighting factor of environmental data; A weighting factor representing operational data; Represents the weight factor of energy consumption data; Represents the multi-energy fusion management value under environmental data; Represents the multi-energy fusion management value under operation data; Represents the multi-energy fusion management value under energy consumption data;

[0036] Step S5: formulate a multi-energy fusion management strategy based on the multi-energy fusion comprehensive management value.

[0037] A port multi-energy fusion management system based on multi-source data analysis, which implements the above-mentioned port multi-energy fusion management method based on multi-source data analysis, includes the following modules:

[0038] Data collection module: used to collect various data in the port in real time through IoT devices and sensor networks;

[0039] Data division and preprocessing module: connected to the data acquisition module, used to divide the collected data into three categories according to data characteristics, and preprocess the divided three categories of data respectively;

[0040] Analysis module: connected to the data division and preprocessing module, used to analyze the influence of the three types of preprocessed data on the optimal utilization ratio of each energy in the multi-energy, and obtain the multi-energy fusion management value under each type of data;

[0041] Comprehensive module: connected to the analysis module, used to integrate the multi-energy fusion management value under each type of data to obtain the multi-energy fusion comprehensive management value;

[0042] Strategy formulation module: connected to the comprehensive module, used to formulate multi-energy fusion management strategy based on the multi-energy fusion comprehensive management value.

[0043] The embodiments of the present invention have the following technical effects:

[0044] The present invention classifies data according to characteristics, and the classification processing can more accurately capture the key information in each type of data, avoid mutual interference between different types of data, and enhance the accuracy of data analysis; at the same time, by analyzing each type of data separately, the specific impact of each type of data on the use of the multi-energy system can be identified, and more targeted optimization can be achieved. Based on the separate analysis, it can help to more accurately determine the optimal use ratio of each energy, thereby improving the overall energy utilization efficiency, and then it can be more detailed to understand how various factors affect the use efficiency of the multi-energy system, and provide support for the formulation of refined management strategies. In addition, the separate analysis helps to adjust the management strategy in time to adapt to changes in environmental conditions, operating conditions and energy consumption patterns, and improve the flexibility of the strategy; not only that, the three aspects of environment, operation and energy consumption are comprehensively considered to ensure that the multi-energy fusion management strategy can optimize the energy use of the port as a whole. At the same time, the comprehensive management value can better reflect the performance of the system under various conditions, making the management strategy more robust; through the multi-energy fusion management strategy generated by the multi-energy fusion comprehensive management value, comprehensive management can be carried out, while ensuring operational efficiency, energy consumption and environmental pollution can be reduced, and the port can be promoted to develop in a more sustainable direction;

[0045] To sum up, the present invention can more accurately capture the key information of various types of data through a series of data-driven methods such as separate analysis of data classification and comprehensive analysis of the results, thereby improving the accuracy of data analysis and thus improving the accuracy of the final result generation. At the same time, the multi-energy fusion management value under each type of data is integrated to further ensure that the final management strategy is optimal on a global scale, while being able to effectively ensure the scientificity and accuracy of the management strategy finally generated, thereby enhancing the robustness and adaptability of the system and promoting the sustainable development of the port. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a flow chart of a port multi-energy integration management method based on multi-source data analysis provided by an embodiment of the present invention;

[0048] Figure 2 It is a framework diagram of a port multi-energy fusion management system based on multi-source data analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0050] Embodiment 1: Figure 1 As shown, the present invention provides a port multi-energy fusion management method based on multi-source data analysis, comprising the following steps:

[0051] Step S1, collecting various data in the port in real time through IoT devices and sensor networks respectively;

[0052] Various data collected in the port in real time through IoT devices and sensor networks may include but are not limited to: environmental monitoring data, such as temperature and humidity, wind speed and direction, and light intensity; energy consumption data, such as electricity usage, fuel consumption and natural gas flow, and renewable energy output; ship dynamic information, such as location information, port entry and exit time, cargo capacity, etc.; hydrological and meteorological data, such as water level, wave height, weather forecast, etc.; security monitoring data, such as video streaming, intrusion detection, fire alarm signals, etc.; logistics operation data, such as container movement trajectory, inventory status, loading and unloading efficiency, etc.

[0053] Step S2, dividing the collected data into three categories according to data characteristics, and preprocessing the three categories of data respectively;

[0054] This embodiment mainly illustrates that the various data collected above can be divided into three types of data according to data characteristics, including environmental data, operational data and energy consumption data; the environmental data includes: temperature, humidity, wind speed, wind direction, light intensity, water level, wave height and weather forecast, etc.; the operational data includes: the position, port entry and exit time, cargo volume, video stream, intrusion detection signal, fire alarm signal, container movement trajectory, inventory status and loading and unloading efficiency, etc. in the ship dynamic information; the energy consumption data includes: electricity usage, fuel consumption, natural gas flow, renewable energy such as solar energy, wind power generation, etc.;

[0055] Next, the three types of data mentioned above need to be preprocessed separately. The preprocessing process for environmental data includes but is not limited to: data cleaning, that is, removing invalid values ​​or outliers, such as temperature readings that are obviously beyond the reasonable range; missing value processing, that is, using interpolation methods to fill in missing data in a short period of time, and long-term missing data may need to be estimated through historical data of the same period; standardization / normalization, that is, converting data obtained by different sensors to a unified scale for easy comparison and analysis; feature engineering, that is, creating new features, such as calculating the temperature and humidity index, to better reflect the actual environmental conditions;

[0056] The pre-processing process of operational data includes but is not limited to: data cleaning, that is, checking and correcting inconsistent information, such as the situation where the timestamp of the ship's position does not match the actual situation; format unification, that is, ensuring that all time-related data uses the same time zone and format; event tagging, that is, adding tags to specific events, such as the start / end of cargo loading and unloading, fire alarm triggering, etc., to facilitate event-driven analysis; image / video processing, that is, for video streams, computer vision technology can be used to extract key frames or perform target detection; for static images, compression or enhancement processing can be performed;

[0057] The preprocessing process of energy consumption data includes but is not limited to: data cleaning, that is, identifying and correcting erroneous records, such as negative energy consumption values; smoothing, that is, applying moving average or other filtering techniques to reduce noise interference and improve data stability; unit conversion, that is, if the data comes from different measurement systems such as metric / imperial, it is necessary to unify them into the same standard unit; periodic analysis, that is, identifying periodic laws in energy consumption patterns, such as daily cycles, weekly cycles, etc., to predict future energy consumption trends.

[0058] Step S3, analyzing the influence of the three types of preprocessed data on the optimal utilization ratio of each energy in the multi-energy, and obtaining the multi-energy fusion management value under each type of data;

[0059] Step S3a, analyzing the influence of environmental data on the optimal utilization ratio of each energy in multiple energy sources, and obtaining the multi-energy fusion management value under the environmental data;

[0060] Step S3a1, extracting key environmental factors from the environmental data collected in real time, and quantifying each key environmental factor to obtain the actual measured value of each key environmental factor;

[0061] In this embodiment, environmental data is taken as an example, and the key environmental factors extracted include: temperature, humidity, wind speed, light intensity, water level, and wave height; then quantification processing is performed, for temperature: the unit is Celsius (℃) or Fahrenheit (℉), and the average value per hour is recorded; for humidity: the unit is percentage (%), and the relative humidity per hour is recorded; for wind speed: the unit is meter / second (m / s) or kilometer / hour (km / h), and the maximum wind speed per hour is recorded; for light intensity: the unit is lux (lux), and the average light intensity per hour is recorded; for water level: the unit is meter (m), and the highest water level per hour is recorded; for wave height: the unit is meter (m), and the maximum wave height per hour is recorded; finally, the above recorded data is used as the actual measurement value of each key environmental factor.

[0062] Step S3a2: Analyze the correlation between each key environmental factor and the consumption of each energy source in the multiple energy sources according to the actual measured value of each key environmental factor, obtain the correlation coefficient between each key environmental factor and the consumption of each energy source in the multiple energy sources, and assign the influence weight of each key environmental factor on each energy source in the multiple energy sources according to the correlation coefficient;

[0063] This embodiment mainly illustrates the use of statistical methods such as the Pearson correlation coefficient or the Spearman rank correlation coefficient to calculate the correlation between key environmental factors and each energy consumption; then, based on the absolute value of the correlation coefficient, each key environmental factor is assigned an impact weight on each energy source. It is worth noting that this impact weight should be a normalized value so that the sum of the impact weights of all key environmental factors on each energy source is 1. Furthermore, the absolute value of the correlation coefficient is close to 1, indicating that the key influencing factor has a high correlation with energy consumption, and the absolute value of the correlation coefficient is close to 0, indicating that the key influencing factor has a low correlation with energy consumption. This embodiment assigns an impact weight to each key parameter on each energy source based on the level of correlation. The higher the correlation, the greater the impact weight, and vice versa.

[0064] Step S3a3, obtaining an environmental impact index according to the impact weight of each key environmental factor on each energy source in the multiple energy sources and the actual measured value of each key environmental factor;

[0065] Among them, the calculation formula for the environmental impact index is:

[0066] ;

[0067] Among them, EII stands for Environmental Impact Index; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the i-th key environmental factor; represents the number of key environmental factors; Represents the number of energy types;

[0068] Step S3a4, according to the actual measured value of each key environmental factor, the impact weight between each key environmental factor and each energy in the multiple energy sources, and the environmental impact index, obtain the efficiency correction value of each energy in the multiple energy sources under each key environmental factor;

[0069] The calculation formula for obtaining the efficiency correction value of each energy source in multiple energy sources under each key environmental factor is:

[0070] ;

[0071] in, represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the ith key environmental factor; EII represents the environmental impact index; represents the number of key environmental factors; Represents the number of energy types; Represents the specific impact value of the i-th key environmental factor on the j-th energy in multiple energy sources; Represents the comprehensive impact value of the i-th key environmental factor on all energy sources in the multi-energy source;

[0072] Step S3a5, obtaining a benchmark usage ratio of each energy source in the multiple energy sources, and adjusting the benchmark usage ratio of each energy source in the multiple energy sources by using the efficiency correction value of each energy source in the multiple energy sources under each key environmental factor, to obtain an optimal usage ratio of each energy source in the multiple energy sources;

[0073] Among them, the calculation formula for obtaining the optimal usage ratio of each energy in multiple energy sources is:

[0074] ;

[0075] in, Represents the optimal usage ratio of the jth energy among multiple energy sources; Represents the benchmark usage ratio of the jth energy in multiple energy sources; represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; represents the number of key environmental factors;

[0076] This embodiment mainly explains how to obtain the benchmark usage ratio of each energy source among multiple energy sources. First, the actual usage of various energy sources in the port in the past period of time, such as the past year, is analyzed. This can be done through historical energy consumption records. Then, based on historical data, the average proportion of each energy source in the total energy consumption is calculated. This proportion can be used as the benchmark usage ratio.

[0077] Step S3a6, obtaining the multi-energy fusion management value under the environmental data according to the optimal use ratio of each energy in the multi-energy, the influence weight between each key environmental factor and each energy in the multi-energy, the actual measured value of each key environmental factor, the environmental impact index, and the efficiency correction value of each energy in the multi-energy under each key environmental factor;

[0078] Among them, the calculation formula of the multi-energy fusion management value under environmental data is:

[0079] ;

[0080] in, Represents the multi-energy fusion management value under environmental data; Represents the optimal usage ratio of the jth energy among multiple energy sources; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the ith key environmental factor; EII represents the environmental impact index; represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; represents the number of key environmental factors; Represents the number of energy types; represents the weighted sum of the impact of all key environmental factors on the jth energy source;

[0081] Step S3b, analyzing the influence of the operation data on the optimal utilization ratio of each energy in the multiple energy sources, and obtaining the multi-energy fusion management value under the operation data;

[0082] Step S3c, analyzing the influence of energy consumption data on the optimal usage ratio of each energy in multiple energy sources, and obtaining the multi-energy fusion management value under the energy consumption data;

[0083] This embodiment mainly illustrates that the analysis and calculation process of step S3b and step S3c is consistent with step S3a, and the main difference lies in the different types of data considered; specifically: step S3a focuses on environmental data, such as temperature, humidity, wind speed, etc.; step S3b focuses on operational data, such as ship dynamics, safety monitoring, etc.; step S3c focuses on energy consumption data, such as electricity use, fuel consumption, etc.; the specific implementation methods of the above steps are the same, including obtaining the impact index, efficiency correction value, benchmark usage ratio, optimal usage ratio, etc. Therefore, for the sake of brevity, the detailed calculation process will not be repeated, but the key differences of each step are emphasized.

[0084] Step S4, integrating the multi-energy fusion management value under each type of data to obtain a multi-energy fusion comprehensive management value;

[0085] Among them, the calculation formula for the comprehensive management value of multi-energy fusion is:

[0086] ;

[0087] in Represents the comprehensive management value of multi-energy integration; represents the weighting factor of environmental data; A weighting factor representing operational data; Represents the weight factor of energy consumption data; Represents the multi-energy fusion management value under environmental data; Represents the multi-energy fusion management value under operation data; Represents the multi-energy fusion management value under energy consumption data;

[0088] Step S5: formulate a multi-energy fusion management strategy based on the multi-energy fusion comprehensive management value.

[0089] This embodiment mainly explains the process of formulating a multi-energy fusion management strategy based on the multi-energy fusion comprehensive management value, including first determining the multi-energy fusion management value under the above-mentioned environmental data, the multi-energy fusion management value under the operation data, and the multi-energy fusion management value under the energy consumption data, and then determining the key factors under each type of data. For example, the key environmental factors under the environmental data are wind speed, temperature, humidity, etc. Then, the management strategy formulated based on the multi-energy fusion management value under the environmental data in the multi-energy fusion comprehensive management value includes but is not limited to: if the environmental data has a greater contribution to the multi-energy fusion management value, priority is given to increasing the proportion of renewable energy such as wind energy and solar energy, and if the wind speed is high, increase the proportion of wind power generation; if there is sufficient sunshine, increase the proportion of photovoltaic power generation; then the management strategy formulated based on the multi-energy fusion management value under the operation data includes but is not limited to: improving loading and unloading efficiency, reducing no-load operation time, reducing unnecessary energy consumption, and regularly performing equipment maintenance to ensure that the equipment is in the best working condition. working status; gradually eliminate high-energy-consuming equipment and introduce high-efficiency energy-saving equipment; the management strategies formulated based on the multi-energy fusion management values ​​under energy consumption data include but are not limited to: regularly conduct energy audits, identify high-energy-consuming links and take measures to improve them, and promote the use of energy-saving technologies and equipment, such as LED lighting, high-efficiency motors, etc.; based on the above analysis results, formulate a comprehensive multi-energy fusion management strategy, including but not limited to: when environmental conditions permit, give priority to the use of renewable energy such as wind and solar energy to reduce dependence on traditional fossil energy; regularly perform equipment maintenance to ensure that the equipment is in the best working condition, and gradually eliminate high-energy-consuming equipment and introduce high-efficiency energy-saving equipment; regularly conduct energy audits, identify high-energy-consuming links and take measures to improve them, and promote the use of energy-saving technologies and equipment; establish an energy recovery system to improve energy utilization efficiency and reduce energy waste; through the multi-energy fusion management strategy formulated above, the port can achieve energy conservation and emission reduction, improve energy efficiency, while reducing operating costs and promoting the development of environmentally friendly ports.

[0090] Embodiment 2: Figure 2 As shown, the present invention also proposes a port multi-energy fusion management system based on multi-source data analysis, which implements the above-mentioned port multi-energy fusion management method based on multi-source data analysis, including the following modules:

[0091] Data collection module: used to collect various data in the port in real time through IoT devices and sensor networks;

[0092] Data division and preprocessing module: connected to the data acquisition module, used to divide the collected data into three categories according to data characteristics, and preprocess the divided three categories of data respectively;

[0093] Analysis module: connected to the data division and preprocessing module, used to analyze the influence of the three types of preprocessed data on the optimal utilization ratio of each energy in the multi-energy, and obtain the multi-energy fusion management value under each type of data;

[0094] Comprehensive module: connected to the analysis module, used to integrate the multi-energy fusion management value under each type of data to obtain the multi-energy fusion comprehensive management value;

[0095] Strategy formulation module: connected to the comprehensive module, used to formulate multi-energy fusion management strategy based on the multi-energy fusion comprehensive management value.

[0096] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of more restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0097] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship 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 orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A port multi-energy integration management method based on multi-source data analysis, characterized in that: The following steps are involved: Step S1, collecting various data in the port in real time through IoT devices and sensor networks respectively; Step S2, the collected data are divided into three categories according to data characteristics, and the three categories of data are pre-processed respectively; the three categories of data include environmental data, operation data and energy consumption data; the operation data includes: the position, port entry and exit time, cargo capacity, video stream, intrusion detection signal, fire alarm signal, container movement trajectory, inventory status and loading and unloading efficiency in the ship dynamic information; Step S3, analyzing the influence of the three types of preprocessed data on the optimal utilization ratio of each energy in the multi-energy, and obtaining the multi-energy fusion management value under each type of data; Step S3a, analyzing the influence of environmental data on the optimal usage ratio of each energy in multiple energy sources, and obtaining the multi-energy fusion management value under the environmental data; including: Step S3a1, extracting key environmental factors from the environmental data collected in real time, and quantifying each key environmental factor to obtain the actual measured value of each key environmental factor; Step S3a2: Analyze the correlation between each key environmental factor and the consumption of each energy source in the multiple energy sources according to the actual measured value of each key environmental factor, obtain the correlation coefficient between each key environmental factor and the consumption of each energy source in the multiple energy sources, and assign the influence weight of each key environmental factor on each energy source in the multiple energy sources according to the correlation coefficient; Step S3a3, obtaining an environmental impact index according to the impact weight of each key environmental factor on each energy source in the multiple energy sources and the actual measured value of each key environmental factor; Step S3a4, according to the actual measured value of each key environmental factor, the impact weight between each key environmental factor and each energy in the multiple energy sources, and the environmental impact index, obtain the efficiency correction value of each energy in the multiple energy sources under each key environmental factor; Step S3a5, obtaining a benchmark usage ratio of each energy source in the multiple energy sources, and adjusting the benchmark usage ratio of each energy source in the multiple energy sources by using the efficiency correction value of each energy source in the multiple energy sources under each key environmental factor, to obtain an optimal usage ratio of each energy source in the multiple energy sources; Step S3a6, obtaining the multi-energy fusion management value under the environmental data according to the optimal use ratio of each energy in the multi-energy, the influence weight between each key environmental factor and each energy in the multi-energy, the actual measured value of each key environmental factor, the environmental impact index, and the efficiency correction value of each energy in the multi-energy under each key environmental factor; Step S3b, analyzing the influence of the operation data on the optimal utilization ratio of each energy in the multiple energy sources, and obtaining the multi-energy fusion management value under the operation data; Step S3c, analyzing the influence of energy consumption data on the optimal usage ratio of each energy in multiple energy sources, and obtaining the multi-energy fusion management value under the energy consumption data; Step S4: The multi-energy fusion management value under each type of data is integrated to obtain a multi-energy fusion comprehensive management value; the calculation formula of the multi-energy fusion comprehensive management value is: ； in Represents the comprehensive management value of multi-energy integration; represents the weighting factor of environmental data; A weight factor representing the operational data; Represents the weight factor of energy consumption data; Represents the multi-energy fusion management value under environmental data; Represents the multi-energy fusion management value under operation data; Represents the multi-energy fusion management value under energy consumption data; Step S5: formulate a multi-energy fusion management strategy based on the multi-energy fusion comprehensive management value.

2. According to claim 1, a port multi-energy integration management method based on multi-source data analysis is characterized in that: The calculation formula for obtaining the environmental impact index is: ； Among them, EII stands for Environmental Impact Index; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the i-th key environmental factor; represents the number of key environmental factors; Represents the number of energy types.

3. According to claim 1, a port multi-energy integration management method based on multi-source data analysis is characterized in that: The calculation formula for obtaining the efficiency correction value of each energy in the multiple energy sources under each key environmental factor is: ； in, represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the ith key environmental factor; EII represents the environmental impact index; represents the number of key environmental factors; Represents the number of energy types; Represents the specific impact value of the i-th key environmental factor on the j-th energy in multiple energy sources; Represents the comprehensive impact value of the i-th key environmental factor on all energy sources in multiple energy sources.

4. According to claim 1, a port multi-energy integration management method based on multi-source data analysis is characterized in that: The calculation formula for obtaining the optimal usage ratio of each energy source in the multi-energy source is: ； in, Represents the optimal usage ratio of the jth energy among multiple energy sources; Represents the benchmark usage ratio of the jth energy in multiple energy sources; represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; Represents the number of key environmental factors.

5. The port multi-energy integration management method based on multi-source data analysis according to claim 1 is characterized in that: The calculation formula of the multi-energy fusion management value under the environmental data is: ； in, Represents the multi-energy fusion management value under environmental data; Represents the optimal usage ratio of the jth energy among multiple energy sources; Represents the impact weight of the i-th key environmental factor on the j-th energy in multiple energy sources; represents the actual measured value of the ith key environmental factor; EII represents the environmental impact index; represents the efficiency correction value of the jth energy source among multiple energy sources under the i-th key environmental factor; represents the number of key environmental factors; Represents the number of energy types; Represents the weighted sum of the impact of all key environmental factors on the jth energy source.

6. A port multi-energy fusion management system based on multi-source data analysis, used to execute a port multi-energy fusion management method based on multi-source data analysis as claimed in any one of claims 1 to 5, characterized in that: The system comprises: Data collection module: used to collect various data in the port in real time through IoT devices and sensor networks; Data division and preprocessing module: connected to the data acquisition module, used to divide the collected data into three categories according to data characteristics, and preprocess the divided three categories of data respectively; the three categories of data include environmental data, operation data and energy consumption data; the operation data includes: the position, entry and exit time, cargo capacity, video stream, intrusion detection signal, fire alarm signal, container movement trajectory, inventory status and loading and unloading efficiency in the ship dynamic information; Analysis module: connected to the data division and preprocessing module, used to analyze the influence of the three types of preprocessed data on the optimal utilization ratio of each energy in the multi-energy, and obtain the multi-energy fusion management value under each type of data; including: Step SA1: extract key environmental factors from the environmental data collected in real time, and quantify each key environmental factor to obtain the actual measurement value of each key environmental factor; Step SA2: Analyze the correlation between each key environmental factor and the consumption of each energy source in the multiple energy sources according to the actual measured value of each key environmental factor, obtain the correlation coefficient between each key environmental factor and the consumption of each energy source in the multiple energy sources, and assign the influence weight of each key environmental factor on each energy source in the multiple energy sources according to the correlation coefficient; Step SA3, obtaining an environmental impact index according to the impact weight of each key environmental factor on each energy source in the multiple energy sources and the actual measured value of each key environmental factor; Step SA4, according to the actual measured value of each key environmental factor, the impact weight between each key environmental factor and each energy in the multiple energy sources, and the environmental impact index, obtain the efficiency correction value of each energy in the multiple energy sources under each key environmental factor; Step SA5: Obtain a benchmark usage ratio of each energy source in the multiple energy sources, and adjust the benchmark usage ratio of each energy source in the multiple energy sources by using the efficiency correction value of each energy source in the multiple energy sources under each key environmental factor to obtain an optimal usage ratio of each energy source in the multiple energy sources; Step SA6: Obtain the multi-energy fusion management value under environmental data according to the optimal usage ratio of each energy in the multi-energy, the influence weight between each key environmental factor and each energy in the multi-energy, the actual measured value of each key environmental factor, the environmental impact index, and the efficiency correction value of each energy in the multi-energy under each key environmental factor; Comprehensive module: connected to the analysis module, used to integrate the multi-energy fusion management value under each type of data to obtain the multi-energy fusion comprehensive management value; the calculation formula of the multi-energy fusion comprehensive management value is: ； in Represents the comprehensive management value of multi-energy integration; represents the weighting factor of environmental data; A weight factor representing the operational data; Represents the weight factor of energy consumption data; Represents the multi-energy fusion management value under environmental data; Represents the multi-energy fusion management value under operation data; Represents the multi-energy fusion management value under energy consumption data; Strategy formulation module: connected to the comprehensive module, used to formulate multi-energy fusion management strategy based on the multi-energy fusion comprehensive management value.

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