Smart park energy consumption equipment operation management system based on block chain

Abstract

The invention relates to the technical field of energy management, in particular to a smart park energy consumption equipment operation management system based on a block chain, and the system comprises a data authentication module, a smart contract module, a credit scoring module, a collaborative management module, a renewable energy access module and a performance evaluation module. According to the invention, through the block chain technology, it is ensured that the data of the energy-consuming equipment is not tampered in the transmission and storage process, the credit score of the equipment is adjusted through real-time monitoring, the actual operation condition of the equipment can be continuously reflected, potential faults of the equipment can be timely identified, the maintenance and replacement efficiency can be improved, and cross-park and cross-park management can be realized. The energy consumption difference between the devices is optimized and coordinated through data analysis, energy sharing and efficient utilization between the parks are further promoted, when renewable energy sources are accessed, supply data of wind power and solar energy can be obtained in real time, the requirements of the renewable energy sources and traditional energy sources are balanced, and energy efficiency management of the parks is more accurate and continuous.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a blockchain-based smart park energy-consuming equipment operation management system. Background Art

[0002] The field of energy management technology focuses on the efficient, economical and sustainable use of energy in various environments, including the optimization of energy collection, storage, distribution and use. Modern energy management systems use advanced information technology and software solutions to monitor and control energy consumption, with the goal of reducing waste, lowering costs and improving energy efficiency. This technology covers energy management from smart buildings, industrial parks to entire cities, emphasizing the use of automation systems, artificial intelligence and data analysis to optimize energy use.

[0003] Among them, the blockchain smart park energy-consuming equipment operation management system refers to a system that uses blockchain technology to manage various energy-consuming equipment in the smart park. The purpose of this system is to improve the transparency, security and reliability of data through the decentralization of blockchain and the immutability of data. This can not only more effectively monitor and control energy consumption in the park, but also reduce management costs, improve the efficiency of equipment operation and overall management capabilities. It can achieve efficient collaboration between equipment and management parties to cope with the rapidly growing amount of data and increasingly complex management needs.

[0004] The existing technology has data opacity and centralization problems in park energy management. The equipment status and energy efficiency data rely on traditional centralized data storage and processing systems, which are easily affected by human errors or data tampering, resulting in low data reliability and difficulty in achieving effective real-time monitoring and dynamic adjustment. Equipment operation efficiency and energy consumption optimization still rely on manual scheduling, lacking an automated adaptive mechanism, resulting in limited energy efficiency improvement. Energy allocation is mostly based on fixed optimization rules, lacks flexible response to real-time changes, and cannot make timely adjustments to energy configuration according to actual conditions, which is prone to energy waste or imbalance between supply and demand. The maintenance and management cycle of equipment is also mostly preset, and cannot be dynamically adjusted based on the actual operation of the equipment, resulting in lagging management processes. There is a lack of effective coordination mechanism across parks, and the equipment operation efficiency of different parks is difficult to compare and optimize, and it is impossible to achieve global energy consumption coordination and resource sharing. The traditional system is relatively static in the management of renewable energy access, and fails to effectively integrate real-time data of unstable energy sources such as wind and solar energy, resulting in a lack of flexible adjustment capabilities for energy supply, which in turn affects the stability and security of energy supply. Summary of the invention

[0005] In order to solve the technical problems of data opacity and centralization in the prior art, the embodiment of the present invention provides a blockchain-based smart park energy-consuming equipment operation management system. The technical solution is as follows:

[0006] On the one hand, a blockchain-based smart park energy-consuming equipment operation management system is provided, which includes:

[0007] The data authentication module uses blockchain technology to verify the integrity of data uploaded by energy-consuming devices, verify that there is no tampering record, check the consistency of information, and generate data integrity verification results;

[0008] The smart contract module activates the preset smart contract based on the data integrity verification result, automatically adjusts the maintenance cycle and energy consumption configuration according to the equipment maintenance record, and obtains the equipment scheduling optimization result;

[0009] The credit scoring module uses the equipment scheduling optimization results to evaluate the operating efficiency and failure frequency of the equipment, applies statistical models, updates the equipment status, and obtains the equipment credit score;

[0010] The collaborative management module uses the equipment credit score to coordinate the energy consumption data of cross-park equipment, and obtains the cross-park energy consumption coordination result by comparing the operating efficiency of differentiated park equipment;

[0011] Based on the cross-park energy consumption coordination result, the renewable energy access module accesses the renewable energy supply interface in the region, analyzes the real-time supply capacity and demand forecast information of wind and solar energy, adjusts the energy supply ratio, and obtains the energy structure configuration;

[0012] The performance evaluation module adopts the energy structure configuration to evaluate the operating effect of the combination of new energy and traditional energy, monitor the efficiency performance and stability of differentiated types of energy, identify efficiency deviations and potential problems, and obtain an operating efficiency evaluation log.

[0013] As a further solution of the present invention, the data integrity verification result includes data integrity check, timestamp verification and audit tracking of uploaded records; the equipment scheduling optimization result includes adjustment of equipment operation priority, deployment of fault prediction and prevention measures, and energy efficiency optimization strategy; the equipment credit score includes equipment efficiency score, fault frequency score, and maintenance record score; the cross-park energy consumption coordination result includes energy efficiency comparison analysis of cross-park equipment, energy consumption differentiation optimization strategy, and energy distribution adjustment measures; the energy structure configuration includes adjustment of the proportion of renewable energy supply, optimal configuration of traditional energy, and response strategy for energy supply fluctuations; the operation efficiency evaluation log includes efficiency comparison of the combination of new energy and traditional energy, energy stability analysis, and identification and early warning of potential fault points.

[0014] As a further solution of the present invention, the data authentication module includes:

[0015] The data verification submodule uses blockchain technology to collect the original data uploaded by the energy-consuming equipment in the smart park, check whether the data format meets the requirements, check the timestamp and data information of the uploaded data, verify that the data has been fully transmitted to the blockchain node, and compare the uploaded data with the internal records of the equipment to obtain data upload verification information;

[0016] The hash value comparison submodule uses the data uploaded to verify the information, extracts and calculates the hash value of the data according to the original data records stored on the blockchain, and compares the hash values ​​of the real-time data with the original data. If the data is identified to be deviated, the time point and data segment of the deviation are located to obtain the deviation detection result;

[0017] The consistency check submodule checks the time consistency and content consistency of the data uploaded by the energy-consuming equipment and nodes in the smart park based on the deviation detection results, compares the data between nodes item by item, and obtains the data consistency verification results.

[0018] As a further solution of the present invention, the smart contract module includes:

[0019] The contract execution submodule checks whether the data meets the preset integrity standard based on the data integrity verification result. If the verification passes, the execution of the smart contract is triggered, the preset contract rules are activated, and the smart contract activation result is obtained;

[0020] The status monitoring submodule uses the activation result of the smart contract to monitor the operating parameters of the energy-consuming equipment, including temperature, humidity and pressure, and identifies the real-time operating status of the energy-consuming equipment by comparing it with the preset working threshold value, and obtains the real-time status data of the equipment;

[0021] The maintenance cycle adjustment submodule uses the real-time status data of the equipment, combined with the maintenance records and workload of the energy-consuming equipment, to analyze the maintenance cycle that needs to be adjusted for the energy-consuming equipment, automatically generates the optimized maintenance cycle and pushes it to the smart contract to obtain the equipment scheduling optimization result.

[0022] As a further solution of the present invention, the formula for checking whether the data meets the preset integrity standard is:

[0023]

[0024] Among them, H represents the hash value calculation result, w1, w2 and w3 represent data weight coefficients, p1, p2 and p3 represent the real-time readings of temperature, pressure and flow rate data respectively, and δ represents the absolute value of data deviation.

[0025] As a further solution of the present invention, the credit scoring module includes:

[0026] The workload comparison submodule uses the equipment scheduling optimization results to extract the operating efficiency data of the energy-consuming equipment, analyzes the real-time performance of the energy-consuming equipment after scheduling, and identifies the operating efficiency index by comparing the energy consumption and workload of the energy-consuming equipment to obtain the equipment operating efficiency evaluation results;

[0027] The fault record analysis submodule collects the fault records and fault frequencies of energy-consuming equipment based on the equipment operation efficiency evaluation results, identifies energy-consuming equipment with abnormal fault frequencies in combination with the equipment maintenance records and operation data, and obtains equipment fault frequency analysis results;

[0028] The device status update submodule obtains the device credit score by combining the operating efficiency and failure frequency of the energy-consuming device with the device failure frequency analysis result and quantifying the operating status of the energy-consuming device.

[0029] As a further solution of the present invention, the collaborative management module includes:

[0030] The energy-consuming equipment classification submodule screens the operating status and energy efficiency performance of energy-consuming equipment in the smart park based on the equipment credit score, classifies and coordinates the energy-consuming equipment according to the credit score, prioritizes the energy consumption of equipment across the park, and obtains a priority ranking list;

[0031] The data correction submodule uses the priority sorting list to integrate the energy consumption data of equipment in multiple parks, extract the energy consumption of differentiated equipment in differentiated parks, compare the data, and perform corrections through unified energy consumption standards to obtain the park energy consumption data integration results;

[0032] The resource allocation optimization submodule utilizes the park energy consumption data integration results to analyze the energy efficiency differences of multi-park equipment, optimize the resource allocation strategy, adjust the operation priority and energy consumption configuration of energy-consuming equipment, and obtain cross-park energy consumption coordination results.

[0033] As a further solution of the present invention, the renewable energy access module comprises:

[0034] The transmission efficiency verification submodule identifies the renewable energy supply interface in the region based on the cross-park energy consumption coordination result, performs interface docking and configuration, verifies the transmission efficiency of wind and solar energy supply data, and obtains the energy supply interface access result;

[0035] The supply change identification submodule accesses the result through the energy supply interface, analyzes the impact of light intensity and wind speed on renewable energy supply capacity according to real-time meteorological data, calculates the supply capacity of renewable energy, predicts the change of renewable energy supply in the future time period, and obtains the supply capacity analysis result;

[0036] The energy supply ratio adjustment submodule adjusts the supply ratio of renewable energy and traditional energy according to the supply capacity analysis results and combines the energy demand and supply capacity across the park, optimizes the energy configuration, verifies the balance and stability of the energy supply, and obtains the energy structure configuration.

[0037] As a further solution of the present invention, the formula for calculating the supply capacity of renewable energy is as follows:

[0038]

[0039] Among them, S is the supply capacity of renewable energy, α a Represents the conversion efficiency of light intensity to renewable energy supply capacity, β a represents the conversion efficiency of wind speed to renewable energy supply capacity, γ a Represents the conversion efficiency from meteorological conditions to renewable energy supply capacity, I a Represents the real-time light intensity, V a Represents the real-time wind speed, Z a represents the influence of meteorological parameters, and t represents the square root of the forecast time span.

[0040] As a further solution of the present invention, the performance evaluation module includes:

[0041] The supply and demand combination submodule analyzes the overall operation effect of the combination of renewable energy and traditional energy based on the energy structure configuration, and obtains the energy operation effect analysis results by combining the real-time supply and demand of multiple types of energy;

[0042] The fluctuation analysis submodule monitors the efficiency performance of renewable energy and traditional energy in real time through the energy operation effect analysis results, identifies the energy consumption and output data during operation, analyzes the efficiency fluctuation and stability of differentiated energy, and obtains energy efficiency monitoring results;

[0043] The stability identification submodule uses the energy efficiency monitoring results to identify efficiency deviations and potential problems in energy configuration, verifies the efficiency and stability of energy supply by adjusting energy ratios and optimizing operation strategies, and obtains an operation efficiency evaluation log.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] Through blockchain technology, the data uploaded by energy-consuming devices can be encrypted and verified to ensure that the data has not been tampered with during transmission and storage, enhance data transparency, and enable energy management decisions to be made based on reliable real-time data. Through smart contracts, the maintenance cycle and energy consumption configuration of the equipment can be automatically adjusted, and accurate judgments can be made based on real-time data and maintenance records, reducing manual intervention and improving the automation of equipment scheduling. The ability to dynamically optimize the equipment operation path and energy efficiency configuration effectively avoids energy efficiency waste and excessive equipment consumption caused by human factors or unreasonable periodic scheduling during equipment management. The credit score of the equipment is adjusted through real-time monitoring and data feedback, which can continuously reflect the actual operating status of the equipment, help identify potential equipment failures in a timely manner, and improve the efficiency of maintenance and replacement. Across parks, the energy consumption differences between equipment are optimized and coordinated through data analysis, further promoting energy sharing and efficient utilization between parks. When accessing renewable energy, it can obtain wind and solar power supply data in real time, and adjust energy configuration according to the prediction model to balance the demand for renewable energy and traditional energy, ensure the stability and flexibility of energy supply, and achieve comprehensive allocation of energy resources, making the energy efficiency management of the park more accurate and continuous. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 It is a flow chart of the data authentication module of the present invention;

[0049] Figure 4 This is a flow chart of the smart contract module of the present invention;

[0050] Figure 5 This is a flow chart of the credit scoring module of the present invention;

[0051] Figure 6 is a flow chart of the collaborative management module of the present invention;

[0052] Figure 7 This is a flow chart of the renewable energy access module of the present invention;

[0053] Figure 8 It is a flow chart of the performance evaluation module of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0056] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0057] See also Figure 1 , an embodiment of the present invention provides a blockchain-based smart park energy-consuming equipment operation and management system, the system comprising:

[0058] The data authentication module uses blockchain technology to verify the integrity of data uploaded by energy-consuming devices, verify that there is no tampering record, check the consistency of information, and generate data integrity verification results;

[0059] The smart contract module activates the preset smart contract based on the data integrity verification results, automatically adjusts the maintenance cycle and energy consumption configuration according to the real-time status and maintenance records of the equipment, optimizes the operating efficiency, and obtains the equipment scheduling optimization results;

[0060] The credit scoring module uses the equipment scheduling optimization results to evaluate the equipment's operating efficiency and failure frequency, and uses statistical models to update the equipment status to obtain the equipment credit score;

[0061] The collaborative management module uses equipment credit scores to coordinate the energy consumption data of cross-park equipment, optimizes resource allocation by comparing the operating efficiency of differentiated park equipment, and obtains cross-park energy consumption coordination results;

[0062] Based on the cross-park energy consumption coordination results, the renewable energy access module accesses the renewable energy supply interface in the region, analyzes the real-time supply capacity and demand forecast information of wind and solar energy, adjusts the supply ratio of renewable energy to traditional energy, and obtains the energy structure configuration;

[0063] The performance evaluation module adopts energy structure configuration to evaluate the operating effect of the combination of new energy and traditional energy, monitor the efficiency performance and stability of differentiated types of energy, identify efficiency deviations and potential problems in energy configuration, adjust energy ratios and operating strategies, and obtain operating efficiency evaluation logs.

[0064] The data integrity verification results include data integrity check, timestamp verification and audit tracking of uploaded records. The equipment scheduling optimization results include adjustment of equipment operation priority, deployment of fault prediction and prevention measures, and energy efficiency optimization strategy. The equipment credit score includes the equipment efficiency score, fault frequency score, and maintenance record score. The cross-park energy consumption coordination results include energy efficiency comparison and analysis of cross-park equipment, energy consumption differentiation optimization strategy, and energy distribution adjustment measures. The energy structure configuration includes adjustment of the proportion of renewable energy supply, optimal configuration of traditional energy, and response strategy for energy supply fluctuations. The operation efficiency evaluation log includes efficiency comparison of the combination of new energy and traditional energy, energy stability analysis, and identification and early warning of potential fault points.

[0065] See also Figure 2 , 3 , the data authentication module includes:

[0066] The data verification submodule uses blockchain technology to collect the original data uploaded by the energy-consuming equipment in the smart park, check whether the data format meets the requirements, check the timestamp and data information of the uploaded data, verify that the data has been fully transmitted to the blockchain node, and compare the uploaded data with the internal records of the device. The execution process of obtaining data upload verification information is as follows;

[0067] According to the application of blockchain technology, the original data uploaded by the energy-consuming equipment in the smart park needs to be verified in data format to ensure that the uploaded data conforms to the preset standard format. The timestamp and data information should be verified during the data upload process. The timestamp can ensure that the data is uploaded in order and verify the transmission timeliness of each data packet. The uploaded data and the internal records of the device need to be compared to ensure the integrity of the data transmission. In the case of data transmission to the blockchain node, the integrity and timestamp of each data block can be verified to ensure the correct transmission of data and reduce the probability of data loss. By comparing with the internal records of the device, the verification results of the data can be obtained to confirm whether the uploaded data is consistent with the device records, and obtain the data upload verification information.

[0068] The hash value comparison submodule uses data upload to verify information. According to the original data records stored on the blockchain, the hash value of the data is extracted and calculated. By comparing the hash values ​​of the real-time data and the original data, if the data is identified to be biased, the time point and data segment of the bias are located. The execution process of obtaining the bias detection result is as follows;

[0069] Extract the original data records from the blockchain, generate a hash value, calculate the real-time hash value of the uploaded data, and compare it with the hash value of the original data. If there is a deviation between the two hash values, it means that there is a problem in the data transmission process. The time point and data segment of the deviation data need to be accurately located through hash value comparison technology, which helps to determine the specific problems in the data transmission process and provide a basis for further processing of the data, ensuring the effectiveness and accuracy of subsequent data verification and obtaining deviation detection results.

[0070] The consistency check submodule checks the time consistency and content consistency of the data uploaded by the energy-consuming equipment and nodes in the smart park according to the deviation detection results, and compares the data between nodes item by item. The execution process of obtaining the data consistency verification results is as follows;

[0071] Obtain uploaded data from each device and node, including the device's operating status, energy consumption data, timestamp and other information. By comparing the timestamps of each device node, check whether there are inconsistencies in the time when the device and node upload data to ensure the timing and accuracy of data upload. Compare the data content between nodes item by item to check whether the energy consumption data and device status uploaded by each node are consistent, especially whether the data content at the same time point is consistent. Through comparison, identify potential data deviations or anomalies, ensure the consistency and reliability of the data of each node in the system, and obtain data consistency verification results.

[0072] See also Figure 2 , 4 , the smart contract module includes:

[0073] The contract execution submodule checks whether the data meets the preset integrity standards based on the data integrity verification results. If the verification passes, the execution of the smart contract is triggered and the preset contract rules are activated. The execution process of obtaining the smart contract activation result is as follows;

[0074] The formula for checking whether the data meets the preset integrity criteria is:

[0075]

[0076] Among them, H represents the hash value calculation result, w1, w2 and w3 represent data weight coefficients, p1, p2 and p3 represent the real-time readings of temperature, pressure and flow rate data respectively, and δ represents the absolute value of data deviation.

[0077] Parameter meaning and setting value:

[0078] w1 is the weight coefficient of temperature, which is set to 0.3, reflecting the importance of temperature data in equipment operation;

[0079] w2 is the weight coefficient of pressure, which is set to 0.5, indicating that pressure data has a more significant impact on equipment performance;

[0080] w3 is the weight coefficient of flow rate, which is set to 0.2, indicating that although flow rate is important, its influence is relatively small compared with temperature and pressure;

[0081] p1 represents the real-time reading of temperature, and the monitoring value is 50°C, which is the monitoring result of the temperature sensor at a specific time point;

[0082] p2 represents the real-time reading of pressure, and the monitoring value is 150kPa, which is also obtained through the pressure sensor;

[0083] p3 represents the real-time reading of flow velocity, the monitoring value is 3m / s, measured by a flow meter;

[0084] δ is the deviation from the preset threshold, which is set to 10, based on the actual measured difference between the current data and the set safe operation data threshold;

[0085] Substitute the parameters into the formula for calculation:

[0086]

[0087] The result 0.952 indicates that the triggering condition of the smart contract is met. If this value exceeds the set trigger threshold (set to 0.9), the smart contract will be activated and the preset rules will be executed. This result further verifies that the integrity of the data meets the preset integrity standards, and the smart contract can be activated based on the verification results.

[0088] The status monitoring submodule uses the smart contract activation results to monitor the operating parameters of the energy-consuming equipment, including temperature, humidity and pressure. By comparing with the preset working thresholds, the real-time operating status of the energy-consuming equipment is identified. The execution process of obtaining the real-time status data of the equipment is as follows;

[0089] The operating parameters of energy-consuming equipment are monitored, mainly including temperature, humidity and pressure. The monitoring data needs to be compared with the preset working threshold in real time to determine the operating status of the equipment. Temperature, humidity and pressure are key operating indicators of energy-consuming equipment and must be within the set range to ensure the normal operation of the equipment. By comparing the deviation between the parameters and the preset threshold in real time, it is possible to identify whether there is any abnormality in the equipment, ensure timely grasp of the equipment operation status, make corresponding adjustments and countermeasures, and obtain real-time status data of the equipment.

[0090] The maintenance cycle adjustment submodule uses the real-time status data of the equipment, combined with the maintenance records and workload of the energy-consuming equipment, to analyze the maintenance cycle that needs to be adjusted for the energy-consuming equipment, automatically generates the optimized maintenance cycle and pushes it to the smart contract. The execution process of obtaining the equipment scheduling optimization result is as follows;

[0091] By collecting the real-time status data of the equipment and combining it with the maintenance records of the equipment, we can understand the workload of the equipment and the changing trend of the operating status, analyze and calculate the optimal maintenance cycle of the equipment, and automatically generate the optimized maintenance cycle. This cycle is pushed to the smart contract to automatically adjust the maintenance plan of the equipment, and optimize the equipment scheduling and maintenance arrangements according to the real-time status of the equipment, to ensure the long-term stable operation of the equipment, to ensure that the maintenance work meets the actual needs of the equipment, and to obtain the equipment scheduling optimization results.

[0092] See also Figure 2 , 5 , the credit scoring module includes:

[0093] The workload comparison submodule uses the equipment scheduling optimization results to extract the operating efficiency data of energy-consuming equipment, analyzes the real-time performance of energy-consuming equipment after scheduling, and identifies the operating efficiency index by comparing the energy consumption and workload of energy-consuming equipment. The execution process of obtaining the equipment operating efficiency evaluation results is as follows;

[0094] Extract the operating efficiency data of energy-consuming equipment, analyze the relationship between the energy consumption and workload of the equipment after scheduling, analyze the matching degree between energy consumption and workload after the scheduling strategy is implemented, calculate by equipment type and workload classification, determine the changing trend of energy consumption, optimize through the feedback loop of equipment energy efficiency, load data and scheduling process, further obtain equipment energy efficiency comparison results and trends, and obtain equipment operating efficiency evaluation results.

[0095] The fault record analysis submodule collects the fault records and fault frequencies of energy-consuming equipment based on the equipment operation efficiency evaluation results, and identifies energy-consuming equipment with abnormal fault frequencies in combination with the equipment maintenance records and operation data. The execution process of obtaining the equipment fault frequency analysis results is as follows;

[0096] Extract the operation data and fault records of the equipment. The operation data of the equipment includes information such as operation time, load conditions, energy efficiency level, etc. After eliminating outliers through data processing, a complete fault record is obtained. Based on the equipment maintenance record, the maintenance cycle and fault type of each equipment are determined. Combined with the timestamp and occurrence frequency in the fault record, the time interval and frequency of equipment faults are calculated. By classifying and counting the monthly and quarterly fault frequencies of the equipment, statistical methods are used to evaluate the frequency of occurrence of various types of faults and identify abnormal equipment. On this basis, by comparing the ratio of the equipment's fault frequency to the normal working time, it is determined whether there is a high-frequency fault situation, and the equipment is classified based on this result to generate the equipment fault frequency analysis results.

[0097] The equipment status update submodule uses the equipment failure frequency analysis results, combines the operating efficiency and failure frequency of the energy-consuming equipment, and quantifies the operating status of the energy-consuming equipment to obtain the equipment credit score. The execution process is as follows;

[0098] Through the equipment failure frequency analysis results, combined with the operating efficiency and failure frequency of energy-consuming equipment, and quantified the operating status of energy-consuming equipment, according to the formula:

[0099]

[0100] and

[0101]

[0102] Calculate the equipment credit score, where E score Represents the equipment credit score, C eff Represents the operating efficiency of the equipment, F freq Represents the equipment failure frequency, T avg Represents the average working time of the equipment, E actual is the actual energy consumption of the equipment, E standard is the expected energy consumption of the equipment under standard load;

[0103] Parameter meaning and calculation process:

[0104] E actual The actual energy consumption of the device is set to 100kWh;

[0105] E standard The expected energy consumption of the equipment under standard load is set to 120 kWh, and all energy consumption data is collected through real-time monitoring equipment;

[0106] Equipment failure frequency F freq Calculated from equipment failure records, set the equipment failure frequency to 0.02 times / hour, and record the number of equipment failures per unit time;

[0107] Average working time T avg Refers to the average value of the normal working time of the equipment. The average working time of the equipment is set to 150 hours and is calculated in real time by the equipment monitoring system;

[0108] Substituting into the formula we get:

[0109]

[0110] F freq =0.02;

[0111] T avg =150;

[0112] Substitute the values ​​into the calculated device credit score:

[0113]

[0114] The result shows that the credit score of the equipment is 0.000111, which means that the equipment is in good operating condition, but regular maintenance is required in the long term to keep it in good working condition.

[0115] See also Figure 2 , 6 , the collaborative management modules include:

[0116] The energy-consuming equipment classification submodule screens the operating status and energy efficiency performance of energy-consuming equipment in the smart park based on the equipment credit score, classifies and coordinates energy-consuming equipment according to the credit score, and prioritizes the energy consumption of equipment across the park. The execution process of obtaining the priority ranking list is as follows;

[0117] The specific scoring data of each device is extracted from the device credit score, and a comprehensive analysis is conducted on the device's workload, energy efficiency, and fault records. The devices are divided into different levels according to the set scoring criteria to ensure that the operating status of high-scoring devices is given priority attention. After the equipment classification is completed, the equipment in the park is coordinated according to the equipment's credit score, and the energy consumption priority of cross-park equipment is sorted considering the differences in equipment energy efficiency and operating conditions. During the sorting process, a comprehensive evaluation is conducted based on the equipment's energy consumption, operating efficiency, fault frequency and other dimensions, and equipment with higher energy efficiency and lower fault frequency is given priority to ensure the optimal use of park resources and obtain a priority sorting list.

[0118] The data correction submodule uses a priority sorting list to integrate the energy consumption data of equipment in multiple parks, extract the energy consumption of differentiated equipment in differentiated parks, compare the data, and correct it through a unified energy consumption standard. The execution process of obtaining the park energy consumption data integration result is as follows;

[0119] Through data exchange and integration between parks, the equipment operation data and energy consumption indicators in each park are collected. After obtaining the data, the energy consumption of the equipment in each park is extracted, and data comparison is performed to analyze the energy efficiency differences of equipment in different parks. Based on the unified energy consumption standard, the correction factor of the equipment is calculated. The correction process involves adjusting the energy consumption data of equipment in different parks according to the standardized energy consumption model of the park. Through data processing, the deviations caused by differences in equipment type and operating time are eliminated to ensure the uniformity and accuracy of the energy consumption data of all park equipment. After data correction, the integrated results of the park energy consumption data are generated.

[0120] The resource allocation optimization submodule uses the park energy consumption data integration results to analyze the energy efficiency differences of multi-park equipment, optimize the resource allocation strategy, adjust the operation priority and energy consumption configuration of energy-consuming equipment, and obtain the cross-park energy consumption coordination results. The execution process is as follows;

[0121]

[0122] Calculate the resource allocation optimization result, where R opt represents the optimized resource allocation result, E i represents the energy efficiency of the i-th park equipment, W i represents the weight of the i-th device in resource allocation, and n is the number of campus devices participating in the optimization;

[0123] There are three devices in the park, and the energy efficiency and weight data of the devices are as follows:

[0124] Device 1: E1=0.8, weight W1=0.5;

[0125] Device 2: E2 = 0.9, weight W2 = 0.3;

[0126] Device 3: E3 = 0.75, weight W3 = 0.2;

[0127] Substitute the formula to calculate the resource allocation optimization result:

[0128]

[0129] The result shows that the optimized resource allocation result is 0.82, which means that the energy efficiency of the park equipment is ideal after the resource allocation is optimized, and the energy use is effectively optimized.

[0130] See also Figure 2 , 7 , the renewable energy access module includes:

[0131] The transmission efficiency verification submodule identifies the renewable energy supply interface in the region based on the cross-park energy consumption coordination results, performs interface docking and configuration, verifies the transmission efficiency of wind and solar power supply data, and obtains the energy supply interface access results. The execution process is as follows;

[0132] Identifying the renewable energy supply interface in the region, docking and configuring the interface, and verifying the transmission efficiency of wind and solar power supply data are key processes. By actually docking the energy supply interface and evaluating the delay or loss in the data transmission process, the efficient transmission of wind and solar power supply data can be ensured. By monitoring and recording the real-time situation of data transmission, the access status of the supply interface and the stability of data transmission can be judged, and the access result of the energy supply interface can be obtained.

[0133] The supply change identification submodule accesses the results through the energy supply interface, analyzes the impact of light intensity and wind speed on renewable energy supply capacity based on real-time meteorological data, calculates the supply capacity of renewable energy, and predicts the changes in renewable energy supply in the future time period. The execution process of obtaining the supply capacity analysis results is as follows;

[0134] The formula for calculating the supply capacity of renewable energy is as follows:

[0135]

[0136] Among them, S is the supply capacity of renewable energy, α a Represents the conversion efficiency of light intensity to renewable energy supply capacity, β a represents the conversion efficiency of wind speed to renewable energy supply capacity, γ a Represents the conversion efficiency from meteorological conditions to renewable energy supply capacity, I a Represents the real-time light intensity, V a Represents the real-time wind speed, Z a represents the influence of meteorological parameters, and t represents the square root of the forecast time span.

[0137] Parameter meaning and setting value:

[0138] α a The conversion efficiency from light intensity to renewable energy supply capacity is set at 0.6, reflecting the contribution of each unit of light intensity to energy supply;

[0139] I a is the real-time light intensity (in watts per square meter, W / m 2 ), which is obtained in real time through the solar radiation monitoring system. The value can reach more than 800 on a sunny day and drop to around 200 on a cloudy or overcast day;

[0140] β a is the conversion efficiency from wind speed to renewable energy supply capacity, which is set to 0.8, indicating the contribution of each unit wind speed to energy supply capacity, V a The real-time wind speed (in meters per second, m / s) is obtained through a wind speed sensor. Under moderate wind speeds (for example, a wind speed of 6 m / s) it can provide a stable energy supply for wind power generation.

[0141] γ a It is the conversion efficiency of meteorological parameters (such as temperature and humidity) to renewable energy supply capacity. The setting value is 0.5, which means that the influence of the parameters on energy supply capacity is small, but not negligible.

[0142] Z aThe comprehensive impact of meteorological conditions. This value is obtained by combining temperature, humidity and other parameters in a weighted average manner. The current temperature is set to 25°C and the humidity is 60%, which results in Z a The value of is 0.75;

[0143] t is the square root of the forecast time span (in days). According to the forecast requirements, t=5 days is set, indicating that the forecast is being conducted for 5 days later.

[0144] Substitute the parameters into the formula for calculation:

[0145]

[0146] The results show that based on light intensity, wind speed and meteorological conditions, the renewable energy supply capacity in the next five days is 216.5 units, which indicates that in the future time period, according to the current meteorological conditions, the energy supply capacity will be significantly affected by light, wind speed and environmental factors, and this result can be further used for energy scheduling and optimization decisions.

[0147] The energy supply ratio adjustment submodule adjusts the supply ratio of renewable energy and traditional energy according to the supply capacity analysis results, combined with the energy demand and supply capacity across the park, optimizes the energy configuration, verifies the balance and stability of energy supply, and obtains the execution process of energy structure configuration as follows;

[0148] Extract the energy demand data and supply capacity data of each park from the energy supply system, including information such as the production capacity, consumption demand and seasonal fluctuations of renewable energy and traditional energy. By statistically analyzing the energy demand of the park and combining the energy production capacity of each park, evaluate the matching degree of the supply capacity of renewable energy and traditional energy. According to the energy demand and supply capacity of each park, adjust the energy supply ratio to ensure the balance and stability of energy supply in each park, and obtain the energy structure configuration that meets the needs of the park.

[0149] See also Figure 2 , 8 , the performance evaluation modules include:

[0150] The supply and demand combination submodule analyzes the overall operation effect of the combination of renewable energy and traditional energy based on the energy structure configuration, and combines the real-time supply and demand of multiple types of energy to obtain the energy operation effect analysis results. The execution process is as follows;

[0151] It is necessary to monitor the supply and demand relationship between renewable energy and traditional energy in real time, obtain the real-time supply and demand values, calculate the supply, demand and fluctuation of renewable energy and traditional energy through the collected real-time data, and use a variety of sensor equipment to track the output power, load demand and change trend of various energy sources in real time. After processing the real-time data, combined with the energy structure model, calculate the alternating supply capacity and load adaptability of various energy sources, evaluate the operating efficiency of the overall system after the combination of the two, monitor the supply and demand balance, identify oversupply or shortage problems, and generate energy operation effect analysis results through comprehensive analysis of the coordination of renewable energy and traditional energy and system stability.

[0152] The fluctuation analysis submodule uses the energy operation effect analysis results to monitor the efficiency performance of renewable energy and traditional energy in real time, identify the energy consumption and output data during operation, analyze the efficiency fluctuation and stability of differentiated energy, and obtain the execution process of energy efficiency monitoring results as follows;

[0153] Identify energy consumption and output data during operation, and analyze the efficiency fluctuation and stability of differentiated energy sources according to the formula:

[0154] ΔE g =E g,actual -E g,expected

[0155] Calculate the energy efficiency fluctuation and obtain the energy efficiency monitoring result, where ΔE g represents the efficiency fluctuation of the g-type energy, E g,actual is the actual energy consumption of the g-type energy, E g,expected is the standard expected energy consumption of energy category g;

[0156] Parameter meaning and calculation process:

[0157] E in the formula g,actual The actual consumption or output data of various energy sources collected by the real-time monitoring system is set to 100MWh for renewable energy and 150MWh for traditional energy.

[0158] E g,expected The expected consumption or output data is calculated based on the equipment performance model. For example, the expected output of renewable energy in the same time period is 120MWh, and the expected consumption of traditional energy is 140MWh.

[0159] Based on the above data, calculate the efficiency fluctuations of various energy sources:

[0160] For renewable energy:

[0161] ΔE renewable=100MWh-120MWh=-20MWh;

[0162] For traditional energy:

[0163] ΔE traditional =150MWh-140MWh=10MWh;

[0164] These efficiency fluctuations reflect the deviation between the actual performance and expectations of each energy type, and can be used to further analyze the efficiency fluctuations and stability of each energy type during operation;

[0165] The results show that the efficiency of renewable energy in this operating cycle is lower than expected, while the efficiency of traditional energy is slightly higher than expected, indicating that there is room for optimization in energy supply scheduling or equipment status.

[0166] The stability identification submodule uses the energy efficiency monitoring results to identify efficiency deviations and potential problems in energy configuration. By adjusting the energy ratio and optimizing the operation strategy, the efficiency and stability of energy supply are verified. The execution process of the operation efficiency evaluation log is as follows;

[0167] Identify efficiency deviations and potential problems in energy configuration, analyze and evaluate them, obtain performance indicators and operating efficiency data related to the deviations, and identify room for improvement by comparing them with standard models. The key parameters involved in the monitoring data include the deviation between the current energy configuration and the predetermined configuration, the difference between the equipment load and the load standard, and the balance between energy supply and demand. The quantitative processing of these parameters can be compared with the real-time collected energy data and historical data. Compare and analyze the deviation between the two, and use the data collected by the sensors to generate energy efficiency evaluation indicators. Based on the indicators, the difference between energy efficiency and supply stability can be calculated. By adjusting the energy ratio and optimizing the system's operating mode, the impact of energy configuration on the overall stability of the system can be evaluated. By recording the adjustment process, the effectiveness and stability of each energy configuration in actual operation can be reflected, and an operating efficiency evaluation log can be obtained.

[0168] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A blockchain-based smart park energy-consuming equipment operation and management system, characterized in that: The system comprises: The data authentication module uses blockchain technology to verify the integrity of data uploaded by energy-consuming devices, verify that there is no tampering record, check the consistency of information, and generate data integrity verification results; The smart contract module activates the preset smart contract based on the data integrity verification result, automatically adjusts the maintenance cycle and energy consumption configuration according to the equipment maintenance record, and obtains the equipment scheduling optimization result; The credit scoring module uses the equipment scheduling optimization results to evaluate the operating efficiency and failure frequency of the equipment, applies statistical models, updates the equipment status, and obtains the equipment credit score; The collaborative management module uses the equipment credit score to coordinate the energy consumption data of cross-park equipment, and obtains the cross-park energy consumption coordination result by comparing the operating efficiency of differentiated park equipment; Based on the cross-park energy consumption coordination result, the renewable energy access module accesses the renewable energy supply interface in the region, analyzes the real-time supply capacity and demand forecast information of wind and solar energy, adjusts the energy supply ratio, and obtains the energy structure configuration; The performance evaluation module adopts the energy structure configuration to evaluate the operating effect of the combination of new energy and traditional energy, monitor the efficiency performance and stability of differentiated types of energy, identify efficiency deviations and potential problems, and obtain an operating efficiency evaluation log.

2. According to the blockchain-based smart park energy-consuming equipment operation management system of claim 1, it is characterized in that: The data integrity verification results include data integrity check, timestamp verification and audit tracking of uploaded records; the equipment scheduling optimization results include adjustment of equipment operation priority, deployment of fault prediction and prevention measures, and energy efficiency optimization strategy; the equipment credit score includes equipment efficiency score, fault frequency score, and maintenance record score; the cross-park energy consumption coordination results include energy efficiency comparison analysis of cross-park equipment, energy consumption differentiation optimization strategy, and energy distribution adjustment measures; the energy structure configuration includes adjustment of the proportion of renewable energy supply, optimal configuration of traditional energy, and response strategy for energy supply fluctuations; the operation efficiency evaluation log includes efficiency comparison of the combination of new energy and traditional energy, energy stability analysis, and identification and early warning of potential fault points.

3. According to the blockchain-based smart park energy-consuming equipment operation and management system of claim 1, it is characterized in that: The data authentication module comprises: The data verification submodule uses blockchain technology to collect the original data uploaded by the energy-consuming equipment in the smart park, check whether the data format meets the requirements, check the timestamp and data information of the uploaded data, verify that the data has been fully transmitted to the blockchain node, and compare the uploaded data with the internal records of the equipment to obtain data upload verification information; The hash value comparison submodule uses the data uploaded to verify the information, extracts and calculates the hash value of the data according to the original data records stored on the blockchain, and compares the hash values ​​of the real-time data with the original data. If the data is identified to be deviated, the time point and data segment of the deviation are located to obtain the deviation detection result; The consistency check submodule checks the time consistency and content consistency of the data uploaded by the energy-consuming equipment and nodes in the smart park based on the deviation detection results, compares the data between nodes item by item, and obtains the data consistency verification results.

4. According to the blockchain-based smart park energy-consuming equipment operation and management system of claim 1, it is characterized in that: The smart contract module includes: The contract execution submodule checks whether the data meets the preset integrity standard based on the data integrity verification result. If the verification passes, the execution of the smart contract is triggered, the preset contract rules are activated, and the smart contract activation result is obtained; The status monitoring submodule uses the activation result of the smart contract to monitor the operating parameters of the energy-consuming equipment, including temperature, humidity and pressure, and identifies the real-time operating status of the energy-consuming equipment by comparing it with the preset working threshold value, and obtains the real-time status data of the equipment; The maintenance cycle adjustment submodule uses the real-time status data of the equipment, combined with the maintenance records and workload of the energy-consuming equipment, to analyze the maintenance cycle that needs to be adjusted for the energy-consuming equipment, automatically generates the optimized maintenance cycle and pushes it to the smart contract to obtain the equipment scheduling optimization result.

5. According to the blockchain-based smart park energy-consuming equipment operation management system of claim 4, in, H represents the hash value calculation result, w1, w2 and w3 represent data weight coefficients, p1, p2 and p3 represent the real-time readings of temperature, pressure and flow rate data respectively, and δ represents the absolute value of data deviation.

6. The blockchain-based smart park energy-consuming equipment operation and management system according to claim 1 is characterized in that: The credit scoring module includes: The workload comparison submodule uses the equipment scheduling optimization results to extract the operating efficiency data of the energy-consuming equipment, analyzes the real-time performance of the energy-consuming equipment after scheduling, and identifies the operating efficiency index by comparing the energy consumption and workload of the energy-consuming equipment to obtain the equipment operating efficiency evaluation results; The fault record analysis submodule collects the fault records and fault frequencies of energy-consuming equipment based on the equipment operation efficiency evaluation results, identifies energy-consuming equipment with abnormal fault frequencies in combination with the equipment maintenance records and operation data, and obtains equipment fault frequency analysis results; The device status update submodule obtains the device credit score by combining the operating efficiency and failure frequency of the energy-consuming device with the device failure frequency analysis result and quantifying the operating status of the energy-consuming device.

7. The blockchain-based smart park energy-consuming equipment operation and management system according to claim 1 is characterized in that: The collaborative management module includes: The energy-consuming equipment classification submodule screens the operating status and energy efficiency performance of energy-consuming equipment in the smart park based on the equipment credit score, classifies and coordinates the energy-consuming equipment according to the credit score, prioritizes the energy consumption of equipment across the park, and obtains a priority ranking list; The data correction submodule uses the priority sorting list to integrate the energy consumption data of equipment in multiple parks, extract the energy consumption of differentiated equipment in differentiated parks, compare the data, and correct it through a unified energy consumption standard to obtain the integrated results of the park energy consumption data; The resource allocation optimization submodule utilizes the park energy consumption data integration results to analyze the energy efficiency differences of multi-park equipment, optimize the resource allocation strategy, adjust the operation priority and energy consumption configuration of energy-consuming equipment, and obtain cross-park energy consumption coordination results.

8. The blockchain-based smart park energy-consuming equipment operation and management system according to claim 1 is characterized in that: The renewable energy access module comprises: The transmission efficiency verification submodule identifies the renewable energy supply interface in the region based on the cross-park energy consumption coordination result, performs interface docking and configuration, verifies the transmission efficiency of wind and solar energy supply data, and obtains the energy supply interface access result; The supply change identification submodule accesses the result through the energy supply interface, analyzes the impact of light intensity and wind speed on renewable energy supply capacity according to real-time meteorological data, calculates the supply capacity of renewable energy, predicts the change of renewable energy supply in the future time period, and obtains the supply capacity analysis result; The energy supply ratio adjustment submodule adjusts the supply ratio of renewable energy and traditional energy according to the supply capacity analysis results and combines the energy demand and supply capacity across the park, optimizes the energy configuration, verifies the balance and stability of the energy supply, and obtains the energy structure configuration.

9. The blockchain-based smart park energy-consuming equipment operation and management system according to claim 8 is characterized in that: The formula for calculating the supply capacity of renewable energy is as follows: Among them, S is the supply capacity of renewable energy, α a Represents the conversion efficiency of light intensity to renewable energy supply capacity, β a represents the conversion efficiency of wind speed to renewable energy supply capacity, γ a Represents the conversion efficiency from meteorological conditions to renewable energy supply capacity, I a Represents the real-time light intensity, V a Represents the real-time wind speed, Z a represents the influence of meteorological parameters, and t represents the square root of the forecast time span.

10. The blockchain-based smart park energy-consuming equipment operation and management system according to claim 1 is characterized in that: The performance evaluation module includes: The supply and demand combination submodule analyzes the overall operation effect of the combination of renewable energy and traditional energy based on the energy structure configuration, and obtains the energy operation effect analysis results by combining the real-time supply and demand of multiple types of energy; The fluctuation analysis submodule monitors the efficiency performance of renewable energy and traditional energy in real time through the energy operation effect analysis results, identifies the energy consumption and output data during operation, analyzes the efficiency fluctuation and stability of differentiated energy, and obtains energy efficiency monitoring results; The stability identification submodule uses the energy efficiency monitoring results to identify efficiency deviations and potential problems in energy configuration, verifies the efficiency and stability of energy supply by adjusting energy ratios and optimizing operation strategies, and obtains an operation efficiency evaluation log.

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