Energy technology service informationization platform based on enumeration induction

Abstract

The application provides an energy technology service informationization platform based on enumeration induction method, which comprises an energy-saving evaluation knowledge base, a data collection module, a data evaluation module and an energy-saving evaluation module, the energy-saving evaluation knowledge base stores energy consumption characteristic parameters of a unit to be evaluated, energy consumption evaluation standard items and energy-saving suggestion effects constructed based on the enumeration induction method, and the data collection module, the data evaluation module and the energy-saving evaluation module are jointly used to generate an energy technology service report of the unit to be evaluated according to the energy-saving evaluation knowledge base and to-be-evaluated data of the unit to be evaluated, so that the process of energy-saving evaluation is efficient and accurate.

Description

Technical Field

[0001] This application relates to the field of energy management technology, and in particular to an information platform for energy technology services based on enumeration and induction. Background Technology

[0002] Currently, the compilation of energy technology service reports relies on manual operation. In terms of data collection, manually collecting energy data from various units to be evaluated is time-consuming and labor-intensive, and is prone to data omissions or tampering. This not only results in low statistical efficiency but also makes it difficult to guarantee the accuracy of the data. Summary of the Invention

[0003] This application provides an information platform for energy technology services based on enumeration and induction, which is used to improve the efficiency and accuracy of energy technology service report preparation and to enhance the data security of energy technology service reports.

[0004] In a first aspect, embodiments of this application provide an energy technology service information platform based on enumeration and induction, the energy technology service information platform based on enumeration and induction comprising:

[0005] An energy-saving assessment knowledge base, which stores energy consumption characteristic parameters, energy consumption evaluation standard items, and energy-saving suggestion effects of the unit to be assessed, constructed based on the enumeration and induction method;

[0006] A data acquisition module, which is used to collect and save the data to be evaluated of the unit to be evaluated based on blockchain technology;

[0007] The data evaluation module is used to determine the comprehensive energy consumption data of the unit to be evaluated based on the data to be evaluated and the energy consumption characteristic parameters, and to determine the energy utilization evaluation of the unit to be evaluated based on the comprehensive energy consumption data of the unit to be evaluated and the energy consumption evaluation standard items.

[0008] An energy-saving assessment module is used to simulate the transformation based on the data to be assessed, the comprehensive energy consumption data of the unit to be assessed, the energy utilization evaluation, and the effect of the energy-saving suggestions, and generate an energy technology service report.

[0009] This application provides an energy technology service information platform based on enumeration and induction. The platform includes: an energy-saving assessment knowledge base, a data acquisition module, a data assessment module, and an energy-saving assessment module. The energy-saving assessment knowledge base stores energy consumption characteristic parameters, energy consumption evaluation standards, and energy-saving suggestion effects for the unit to be assessed, constructed based on enumeration and induction. The data acquisition module collects and saves the data to be assessed for the unit based on blockchain technology. The data assessment module determines the comprehensive energy consumption data of the unit to be assessed based on the data to be assessed and the energy consumption characteristic parameters, and determines the energy utilization evaluation of the unit to be assessed based on the comprehensive energy consumption data and the energy consumption evaluation standards. The energy-saving assessment module simulates modifications based on the data to be assessed, the comprehensive energy consumption data of the unit to be assessed, the energy utilization evaluation, and the energy-saving suggestion effects, generating an energy technology service report. In the aforementioned platform, the units to be evaluated are systematically and hierarchically analyzed through enumeration and induction. By summarizing data patterns, the energy consumption characteristic parameters, energy use evaluation standards, and energy-saving recommendations of the units to be evaluated are obtained. This constructs a comprehensive and highly accurate energy-saving assessment knowledge base. Then, the data acquisition module collects and saves the data to be evaluated from the units to be evaluated. Both the data assessment module and the energy-saving estimation module, following the principle of enumeration and induction, call the corresponding data from the energy-saving assessment knowledge base in an orderly manner to compile energy technology service reports based on the data to be evaluated. This improves the efficiency and accuracy of energy technology service report compilation and reduces the proportion of manual operation. In addition, the collection and storage of the data to be evaluated are completed through blockchain technology to ensure the accuracy of the basic data of the energy technology service reports. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic block diagram of an energy technology service information platform based on enumeration and induction provided for embodiments of this application;

[0012] Figure 2 This is a schematic block diagram illustrating a unit relationship provided for an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0015] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0016] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0017] Please see Figure 1 , Figure 1 This is a schematic block diagram of an energy technology service information platform based on enumeration and induction, provided in an embodiment of this application. Figure 1 As shown in the embodiment of this application, the energy technology service information platform 100 based on enumeration and induction includes: an energy-saving assessment knowledge base 11, a data acquisition module 12, a data assessment module 13, and an energy-saving assessment module 14. The data acquisition module 12 is connected to the unit to be assessed 200 through various data acquisition interfaces to obtain the assessment data of the unit to be assessed 200. The data acquisition module 12 is also connected to the data assessment module 13. The data assessment module 13 is connected to the energy-saving assessment module 14. All three modules—data acquisition module 12, data assessment module 13, and energy-saving assessment module 14—are connected to the energy-saving assessment knowledge base 11, and all three can access the energy-saving assessment knowledge base 11.

[0018] The energy conservation assessment knowledge base 11 stores the energy consumption characteristic parameters, energy consumption evaluation standard items, and energy conservation suggestion effects of 200 units to be assessed, constructed based on the enumeration and induction method.

[0019] The data acquisition module 12 is used to collect and save the data to be evaluated from the 200 units to be evaluated based on blockchain technology.

[0020] The data acquisition module 12 is equipped with multiple data acquisition interfaces, enabling it to interface with various data sources, including smart metering units, major energy-consuming equipment, and manually submitted data from the units to be evaluated (200). Through an automated data acquisition program, it can completely and accurately collect the data to be evaluated and store it in the blockchain network. The application of blockchain technology ensures the security, immutability, and traceability of the data during collection and storage. After collecting the data, the data acquisition module 12 organizes it using enumeration and induction to obtain the association information of each unit to be evaluated (200). Based on this association information, it connects the units to be evaluated (200) to the system and stores the data in a database constructed using blockchain technology.

[0021] It should be noted that before the data acquisition module 12 collects the data to be evaluated through the data acquisition interface, it needs to obtain an empty list from the energy-saving assessment knowledge base 11. The empty list contains the data items that the unit to be evaluated 200 needs to upload. The data acquisition module 12 provides the corresponding access permissions to the data acquisition interface according to the empty list.

[0022] The data evaluation module 13 is used to determine the comprehensive energy consumption data of the unit to be evaluated 200 based on the data to be evaluated and energy consumption characteristic parameters, and to determine the energy utilization evaluation of the unit to be evaluated 200 based on the comprehensive energy consumption data of the unit to be evaluated 200 and energy consumption evaluation standard items.

[0023] After receiving the data to be evaluated from the data acquisition module 12, the data evaluation module 13 retrieves the corresponding energy consumption characteristic parameters from the energy-saving evaluation database. The energy consumption characteristic parameters include: the energy consumption coefficient and efficiency curve of a single device or a subset of devices under various operating conditions; parameters on the impact of building insulation performance, lighting effect, and spatial layout on energy consumption; and parameters on the effect of personnel activity patterns and operating habits on energy consumption.

[0024] In processing the data to be evaluated, the data evaluation module 13 matches and analyzes the data to be evaluated and energy consumption characteristic parameters using the enumeration and induction method to obtain comprehensive energy consumption data. The comprehensive energy consumption data at least reflects the energy consumption overview of equipment, the energy consumption overview of the population, the energy consumption demand of building characteristics, the constraints of energy configuration on the overall energy consumption effect, and the synergistic effects and mutual constraints among various factors.

[0025] After obtaining the comprehensive energy consumption data, the data evaluation module 13 will conduct a multi-dimensional comparative analysis of the comprehensive energy consumption data and the energy consumption evaluation standard items to obtain the energy utilization evaluation of the unit to be evaluated 200 in terms of personnel management, building characteristics, equipment operation and maintenance, energy configuration, etc.

[0026] The energy-saving assessment module 14 is used to simulate the transformation based on the data to be assessed, the comprehensive energy consumption data of the unit to be assessed 200, the energy utilization evaluation, and the effect of energy-saving suggestions, and generate an energy technology service report.

[0027] After receiving the data to be evaluated, comprehensive energy consumption data, and energy utilization evaluation results from the data evaluation module 13, the energy-saving assessment module 14 will automatically call the energy-saving assessment knowledge base 11, which is organized based on the enumeration and induction method. It will place each first-level sub-unit related to personnel management, building characteristics, equipment operation and maintenance, and energy configuration in the unit to be evaluated 200 into the corresponding energy-saving renovation suggestion simulator provided by the energy-saving assessment knowledge base 11 for scientific evaluation.

[0028] During the simulation process, the energy-saving assessment module 14 fully utilizes the comprehensive energy consumption data reflecting personnel management, building structural characteristics, equipment operation and maintenance status, and energy supply and demand matching. By adjusting key variables such as the performance parameters and operating conditions of each piece of equipment, the combination and configuration of each subset, and the corresponding renovation costs, it deeply analyzes the performance of different energy-saving recommendations in the actual application of the unit under evaluation 200. The simulator combines the improvement potential identified in the energy utilization evaluation in terms of personnel management, building characteristics, equipment operation and maintenance, and energy configuration to calculate the expected renovation effect of each piece of equipment or subset under a specific energy-saving renovation recommendation, including considerations such as saving standard coal, reducing carbon emissions, saving energy costs, required renovation costs, and expected investment payback period.

[0029] The energy conservation assessment module 14 compares the calculated effects of various energy conservation recommendations with the investment payback period recommendations provided in the energy conservation assessment knowledge base 11, which is based on enumeration and induction. It retains energy conservation measures that demonstrate good economic feasibility and technical implementability. Through this comprehensive renovation simulation analysis, the energy conservation assessment module 14 can select the most valuable renovation solutions for the assessed unit 200 across various aspects, including personnel management, building characteristics, equipment operation and maintenance, and energy configuration. It provides detailed quantitative evidence for each measure and generates a complete energy technology service report containing current status analysis, potential assessment, renovation recommendations, and benefit predictions.

[0030] This application provides an energy technology service information platform 100 based on enumeration and induction. The energy technology service information platform 100 includes: an energy-saving assessment knowledge base 11, a data acquisition module 12, a data assessment module 13, and an energy-saving assessment module 14. The energy-saving assessment knowledge base 11 stores energy consumption characteristic parameters, energy consumption evaluation standard items, and energy-saving suggestion effects of a unit 200 to be assessed, constructed based on enumeration and induction. The data acquisition module 12 is used to collect and save the data to be assessed of the unit 200 based on blockchain technology. The data assessment module 13 is used to determine the comprehensive energy consumption data of the unit 200 to be assessed based on the data to be assessed and the energy consumption characteristic parameters, and to determine the energy utilization evaluation of the unit 200 to be assessed based on the comprehensive energy consumption data and the energy consumption evaluation standard items. The energy-saving assessment module 14 is used to perform transformation simulations based on the data to be assessed, the comprehensive energy consumption data of the unit 200 to be assessed, the energy utilization evaluation, and the energy-saving suggestion effects, generating an energy technology service report. In the aforementioned platform, the unit 200 to be evaluated is systematically and hierarchically analyzed through enumeration and induction. By summarizing data patterns, the energy consumption characteristic parameters, energy use evaluation standards, and energy-saving suggestions of the unit 200 to be evaluated are obtained. This constructs a comprehensive and highly accurate energy-saving assessment knowledge base 11. Then, the data acquisition module 12 collects and saves the data to be evaluated for the unit 200. Both the data assessment module 13 and the energy-saving estimation module, following the principle of enumeration and induction, call the corresponding data from the energy-saving assessment knowledge base 11 in an orderly manner to compile the energy technology service report based on the data to be evaluated. This improves the efficiency and accuracy of the compilation of the energy technology service report and reduces the proportion of manual operation. In addition, the collection and storage of the data to be evaluated are completed through blockchain technology to ensure the accuracy of the basic data of the energy technology service report.

[0031] In some embodiments, the unit to be evaluated 200 includes: a plurality of primary sub-units, and the energy-saving assessment knowledge base 11 is constructed through technical standards.

[0032] For example, a primary sub-unit includes at least: personnel management, building characteristics, equipment operation and maintenance, and energy configuration. Conducting an energy conservation assessment on the unit 200 to be assessed requires considering the relationships between multiple primary sub-units, i.e., obtaining information about their interrelationships, thereby achieving an accurate assessment. The energy conservation assessment knowledge base 11 is built based on technical standards, which include standards for equipment, primary sub-units, and the unit to be assessed.

[0033] Technical standards for equipment include standards (including national standards) that specify the energy efficiency rating and energy efficiency limit values ​​of the equipment, and standards (including national standards and industry standards) that describe the test methods, indicators and calculation methods of the equipment.

[0034] Technical standards for first-level sub-units include standards (including national standards) that specify the characteristic attributes of each first-level sub-unit and standards (including national standards and industry standards) that describe the test methods and calculation methods of each first-level sub-unit's characteristic attributes.

[0035] For the technical standards of the entities to be evaluated, the standards specify the indicators and limit values ​​for each entity (including national standards and policy documents such as announcements issued by relevant departments at all levels).

[0036] These technical standards fully consider factors such as personnel management, building characteristics, and energy configuration of the primary sub-units, forming a detailed and hierarchical standard system through enumeration and induction. By organically combining equipment-level technical standards with unit-level management standards, the energy-saving assessment knowledge base 11 can provide the assessed units 200 with a comprehensive evaluation benchmark from micro-equipment to macro-unit, ensuring the scientific and practical nature of the energy-saving assessment results.

[0037] In some embodiments, if a primary sub-unit is equipped with a preset smart metering unit, the primary sub-unit can directly upload its comprehensive energy consumption data to the data acquisition module 12 through the preset smart metering unit.

[0038] For example, the intelligent metering unit can monitor and record the consumption of various types of energy within a primary sub-unit in real time, including the usage, usage time, and load change characteristics of various energy types such as electricity, gas, and water.

[0039] This smart metering unit has built-in data processing and communication modules, which can perform preliminary processing and integration of the collected raw energy consumption data, and automatically generate comprehensive energy consumption data reflecting the overall energy use status of the first-level sub-unit. Through preset communication protocols and data transmission interfaces, the smart metering unit can directly upload this comprehensive energy consumption data to the data acquisition module 12 on a timed or real-time basis, without manual intervention or additional data conversion steps.

[0040] The automated data collection and analysis process described above greatly reduces human intervention, shortening the energy audit work that originally required a lot of manpower and time to complete to a shorter time, thus significantly improving the efficiency of the audit work.

[0041] In some embodiments, when the data evaluation module 13 is used to determine the comprehensive energy consumption data of the unit 200 to be evaluated based on the data to be evaluated and the energy consumption characteristic parameters, it is specifically used to perform: S101-S105.

[0042] S101. Determine the scale parameters and energy consumption sub-parameters for each primary sub-unit based on the data to be evaluated.

[0043] For example, when the unit to be evaluated 200 includes multiple primary sub-units, and these primary sub-units have functional associations, spatial adjacencies, or management hierarchies, the data evaluation module 13 will perform detailed analysis and classification processing on the collected data to be evaluated. By parsing information such as spatial identifiers, functional zoning, and personnel configuration in the data to be evaluated, corresponding scale parameters are extracted for each primary sub-unit. For example, key indicators that can reflect the operational scale of the primary sub-unit include building area, number of personnel, equipment power, and energy configuration. At the same time, the data evaluation module 13 will analyze the energy consumption details recorded in the data to be evaluated, and analyze the energy consumption of each primary sub-unit in different energy categories such as lighting, air conditioning, office equipment, and special energy-consuming equipment, forming the energy consumption sub-parameters of the primary sub-unit. These energy consumption sub-parameters include: usage duration, load characteristics, seasonal changes, and other in-depth energy consumption characteristic information.

[0044] S102. Based on the scale parameter, match the energy consumption characteristic parameter from the energy-saving assessment knowledge base 11 to obtain the target characteristic parameter corresponding to each first-level sub-unit.

[0045] For example, the data evaluation module 13 uses a multi-dimensional matching algorithm to find reference cases and standard data that are closest to the scale parameters of the first-level sub-unit, and extracts target feature parameters from the energy-saving assessment knowledge base 11, including: energy consumption coefficient per unit area, per capita energy consumption benchmark, equipment load rate, and energy conversion efficiency. The target feature parameters reflect the average energy consumption level of first-level sub-units of the same type.

[0046] S103. Determine the first energy consumption data for each first-level sub-unit based on the energy consumption sub-parameters and target characteristic parameters.

[0047] For example, the data evaluation module 13 standardizes and corrects outliers in the energy consumption sub-parameters by using correction coefficients and adjustment factors in the target feature parameters to obtain the first energy consumption data. The first energy consumption data includes: the overall energy consumption information of the first-level sub-unit, and also includes: sub-data for each energy consumption type, such as detailed information on multiple dimensions such as energy type, consumption intensity, time distribution, and efficiency level.

[0048] S104. Obtain the association information of multiple first-level sub-units.

[0049] For example, the data assessment module 13 analyzes information such as the organizational structure, spatial layout, and business processes of the unit 200 to deeply identify and extract the relationship information between multiple first-level sub-units. This relationship information includes multiple levels of relationship characteristics, such as the relative physical location of each first-level sub-unit, the collaborative relationship in business functions, the subordinate relationship in management hierarchy, and the complementary support relationship in resource sharing. The data assessment module 13 pays special attention to relationship information that may affect the overall energy efficiency, such as heat exchange between adjacent first-level sub-units, load sharing of shared energy equipment, service range of centralized energy supply facilities, and transmission paths of energy cascade utilization. Through a comprehensive review and systematic organization of this relationship information, the module can accurately count the role and mutual influence of first-level sub-units in the overall energy system of the unit 200 to be assessed, and identify the energy consumption synergy effect, resource allocation optimization space, and systemic energy-saving potential generated by the relationship.

[0050] S105. Determine the comprehensive energy consumption data of the unit to be evaluated, 200, based on the first energy consumption data and related information.

[0051] For example, the data evaluation module 13 uses the first energy consumption data as the basis for calculation, and combines it with the various relationships and interactions reflected in the related information to generate comprehensive energy consumption data of the unit 200 to be evaluated through systematic data integration and correction calculation.

[0052] In some embodiments, the energy consumption evaluation criteria include: equipment energy efficiency parameters, multi-condition energy consumption parameters, and unit index parameters calibrated according to the type of the first-level sub-unit. When the data evaluation module 13 is used to perform the energy utilization evaluation of the unit to be evaluated 200 based on the comprehensive energy consumption data and energy consumption evaluation criteria of the unit to be evaluated 200, it is specifically used to perform: S201-S204.

[0053] S201. The first-level sub-unit, which includes the second-level and third-level sub-units, is set as a further refined sub-unit. The energy efficiency level data of each third-level sub-unit is determined based on the first energy consumption data and equipment energy efficiency parameters of the further refined sub-unit.

[0054] For example, such as Figure 2 As shown, the further refined sub-unit 22 is the first-level sub-unit 21 corresponding to equipment operation and maintenance. Since equipment operation and maintenance is the key statistical object in the energy-saving assessment process, the further refined sub-unit 22 is analyzed by enumeration and induction.

[0055] The unit 200 to be evaluated includes multiple first-level sub-units 21 such as equipment operation and maintenance, personnel management, building characteristics and energy configuration. Among them, the subordinate units of the first-level sub-unit 21 corresponding to equipment operation and maintenance include second-level sub-units 221 (equipment subsets) and third-level sub-units 2211 (single equipment). The other first-level sub-units 21 have no subordinate units. Therefore, the first-level sub-unit 21 corresponding to equipment operation and maintenance is set as a further refined sub-unit 22, and then multi-level evaluation is carried out according to the enumeration and induction method.

[0056] It should be noted that the further refined sub-unit 22 is not only the first-level sub-unit 21 corresponding to equipment operation and maintenance. If other first-level sub-units 21 also have subordinate units, then other first-level sub-units 21 can also be set as further refined sub-units 22.

[0057] When evaluating the further refined sub-unit 22 using the enumeration and induction method, the evaluation proceeds from the unit level to the highest level. The first step involves evaluating the energy efficiency level of the individual device based on the energy consumption data and equipment energy efficiency parameters corresponding to the third-level sub-unit 2211 (a single device) in the first energy consumption data, and then summarizing this into energy efficiency level data.

[0058] S202. Determine the second energy consumption data of the second-level sub-unit 221 based on the first energy consumption data, energy efficiency level data, and multi-condition energy consumption parameters of the further refined sub-unit 22.

[0059] For example, the data evaluation module 13 logically groups functionally related or spatially adjacent third-level sub-units 2211 (individual devices) into corresponding second-level sub-units 221 (equipment subsets). During the calculation of the second energy consumption data for the second-level sub-units 221 (equipment subsets), the data evaluation module 13 comprehensively utilizes the first energy consumption data of the further refined sub-units 22 as an overall reference framework, combined with the individual performance differences reflected by the energy efficiency level data of the third-level sub-units 2211 (individual devices), and the energy consumption characteristics recorded in the multi-condition energy consumption parameters under different operating states. Based on enumeration and induction, the data evaluation module 13 performs collaborative analysis on third-level sub-units 2211 (individual devices) belonging to the same second-level sub-unit 221 (equipment subset), considering their mutual influence, load sharing, and operational sequence relationships in actual operation. Through multi-condition energy consumption parameters, the energy consumption performance of the third-level sub-units 2211 (individual devices) under different operating states, load levels, and environmental conditions is corrected and optimized. Based on this, the data evaluation module 13 weights and integrates the energy consumption data of the third-level sub-unit 2211 (individual equipment) after operating condition correction, generating second energy consumption data that reflects the overall energy consumption level and operating characteristics of the second-level sub-unit 221 (equipment subset). The second energy consumption data not only reflects the individual contribution of the third-level sub-unit 2211 (individual equipment), but also fully reflects the synergistic effect and system optimization effect generated by their combined operation.

[0060] S203. Calculate the third energy consumption data of the further refined sub-unit 22 based on all the second energy consumption data.

[0061] For example, after collecting the second energy consumption data corresponding to all secondary sub-units 221 (equipment subsets) within the further refined sub-unit 22, the data evaluation module 13 performs comprehensive data integration and systematic calculation analysis. During the calculation process, the data evaluation module 13, based on enumeration and induction, analyzes the interrelationships and coordination of each secondary sub-unit 221 (equipment subset) within the overall operation system of the further refined sub-unit 22, including their interdependence and influence in energy supply, load allocation, and operation scheduling. By analyzing the energy consumption characteristics and operational patterns reflected in the second energy consumption data of each secondary sub-unit 221 (equipment subset), the data evaluation module 13 identifies their complementary relationships in time series, coupling effects in spatial layout, and synergistic effects in functional configuration. Based on these in-depth correlation analyses, the data evaluation module 13 systematically corrects and optimizes the second energy consumption data of each secondary sub-unit 221 (equipment subset), eliminates duplicate calculations, supplements missing interactive effects, and generates third energy consumption data that can accurately reflect the real energy consumption status of the further refined sub-unit 22 as a whole unit.

[0062] S204. Based on the third energy consumption data of the further refined sub-unit 22, the first energy consumption data of other first-level sub-units, and the unit indicator parameters, determine the energy utilization evaluation of the unit 200 to be evaluated.

[0063] For example, the data evaluation module 13 comprehensively summarizes and compares the third energy consumption data of the further refined sub-unit 22 with the first energy consumption data of other first-level sub-units 21, such as personnel management, building characteristics, and energy configuration. During the analysis, the data evaluation module 13 uses unit indicator parameters as evaluation benchmarks. These unit indicator parameters include key evaluation standards such as energy intensity indicators, efficiency level requirements, and energy-saving target values, calibrated according to the type of first-level sub-unit 21. By comparing the third energy consumption data of the further refined sub-unit 22 with the corresponding unit indicator parameters, the data evaluation module 13 assesses the energy efficiency performance and improvement potential of equipment operation and maintenance. Simultaneously, it matches and evaluates the first energy consumption data of other first-level sub-units 21 with the corresponding type of unit indicator parameters, comprehensively analyzing the performance of various aspects such as personnel management efficiency, building energy consumption level, and the rationality of energy configuration.

[0064] In some embodiments, the energy-saving assessment knowledge base 11 is also equipped with a preset retrofit simulator. When the energy-saving assessment module 14 is used to perform retrofit simulation based on the data to be assessed, the comprehensive energy consumption data of the unit to be assessed 200, energy utilization evaluation and energy-saving suggestion effect, and generate an energy technology service report, it is specifically used to perform: S301-S302.

[0065] S301. Input the data to be evaluated, the comprehensive energy consumption data of the unit to be evaluated 200, and the energy utilization evaluation into the preset renovation simulator to obtain the expected energy-saving effect of the unit to be evaluated 200.

[0066] For example, the energy-saving assessment module 14 takes the data to be assessed, comprehensive energy consumption data, and energy utilization evaluation as a complete set of input parameters and imports them into a preset retrofit simulator for in-depth analysis and simulation calculation. After receiving these input data, the retrofit simulator automatically parses the basic information contained in the data to be assessed, such as equipment configuration, operating status, and environmental conditions, based on enumeration and induction. Combined with the overall energy consumption level and sub-item energy consumption characteristics reflected by the comprehensive energy consumption data, as well as the weak links and improvement directions identified in the energy utilization evaluation, it constructs a virtual simulation environment reflecting the current energy consumption status of the unit to be assessed 200. The retrofit simulator uses built-in enumeration and induction methods and optimization strategies to systematically design retrofit schemes and predict the effects of various components of the unit to be assessed 200, including comprehensive improvement measures at multiple levels such as equipment upgrading, operating parameter adjustment, management system optimization, and building energy-saving retrofit. Through multiple rounds of iterative calculation and scheme optimization, the retrofit simulator can accurately predict the energy-saving and emission-reduction effects that different retrofit strategy combinations may produce in practical applications, generating expected energy-saving effect data including key indicators such as energy consumption reduction, efficiency improvement level, and cost-benefit analysis.

[0067] S302. Compare the expected energy-saving effect with the energy-saving recommendation effect to generate an energy technology service report.

[0068] For example, the energy-saving assessment module 14 uses enumeration and induction to comprehensively compare and evaluate the expected energy-saving effect calculated by the retrofit simulator with the energy-saving recommendations stored in the energy-saving assessment knowledge base 11. During the comparison, the energy-saving assessment module 14 matches and analyzes the differences between each indicator in the expected energy-saving effect and the corresponding energy-saving recommendations, identifying the degree and reasons for the differences between the retrofit simulator's prediction results and the standard recommendations in terms of technical feasibility, economic rationality, and implementation difficulty. By deeply analyzing these differences, the energy-saving assessment module 14 can accurately determine the reliability and probability of achieving the expected energy-saving effect, while also identifying key factors and potential risks that may affect the achievement of the energy-saving effect. Based on the comparison results, the energy-saving assessment module 14 will prioritize and recommend retrofit schemes whose expected energy-saving effects are significantly better than the energy-saving recommendations, weigh the cost-benefit of schemes with comparable effects, and propose adjustment suggestions or alternative schemes for schemes with unsatisfactory effects. By comprehensively compiling and comparing all the results, the energy-saving assessment module 14 generates a complete energy technology service report containing current status assessment, renovation suggestions, effect prediction, implementation suggestions, and risk warnings, providing the assessed unit 200 with a scientific and feasible energy-saving renovation guidance plan.

[0069] By comprehensively enumerating and scientifically summarizing and analyzing a large amount of energy data, the subjectivity and limitations of manual analysis are avoided, enabling more accurate identification of problems and energy-saving potential in energy utilization, and improving the accuracy and reliability of audit results.

[0070] In some embodiments, when the energy-saving assessment module 14 is used to compare the expected energy-saving effect with the energy-saving recommendation effect and generate an energy technology service report, it is specifically used to perform: S401-S403.

[0071] S401. Compare the expected energy-saving effect with the energy-saving recommendation effect to determine the energy-saving optimization rate, equipment replacement cost and technology implementation evaluation items.

[0072] For example, the energy-saving assessment module 14 extracts and calculates specific values ​​and evaluation conclusions for three core assessment dimensions by deeply comparing key indicators of the expected energy-saving effect and the effect of the energy-saving recommendations. In determining the energy-saving optimization rate, the energy-saving assessment module 14 quantitatively compares the energy consumption reduction and efficiency improvement levels reflected in the expected energy-saving effect with the standardized energy-saving potential in the energy-saving recommendations. Through difference analysis and ratio calculation, it determines the actual degree of energy-saving optimization that the assessed unit 200 may achieve after implementing the corresponding modification measures. Regarding the determination of equipment replacement costs, the energy-saving assessment module 14 comprehensively analyzes the investment needs related to equipment modification, technology upgrades, and infrastructure improvement involved in the expected energy-saving effect and the energy-saving recommendations. Combined with the actual equipment condition and modification difficulty of the assessed unit 200, it calculates the complete equipment replacement cost structure and overall investment scale. In determining the technical implementation assessment items, the energy-saving assessment module 14 compares factors such as the complexity of the technical solutions, implementation cycle requirements, personnel skill requirements, and supporting conditions reflected in the two effect data. This forms a comprehensive technical implementation assessment item covering multiple dimensions such as technical feasibility, implementation risk, and resource matching, providing crucial decision support information for subsequent solution selection and implementation planning.

[0073] S402. Perform content parsing on energy-saving optimization rate, equipment replacement cost and technology implementation assessment items, and convert them into pre-filled content in a specified expression format;

[0074] For example, the energy-saving assessment module 14 performs in-depth content analysis and formatting of the energy-saving optimization rate, equipment upgrade cost, and technology implementation assessment items determined in the previous step. When analyzing the energy-saving optimization rate, the module 14 converts the original numerical results into a structured description containing elements such as specific improvement range, comparison benchmarks, and implementation conditions, clearly reflecting the expected effectiveness and implementation path of the energy-saving renovation. For the analysis of equipment upgrade costs, the module 14 categorizes and organizes cost data according to dimensions such as equipment type, renovation stage, and investment nature, forming a detailed cost composition description and investment recommendations, facilitating decision-makers' understanding and assessment of the investment's rationality. For the content analysis of technology implementation assessment items, the module 14 converts each assessment conclusion into a systematic expression covering implementation difficulty level, key risk warnings, resource allocation recommendations, and timeline arrangements. After analysis, the module 14 standardizes this content according to preset report format requirements, generating pre-filled content that conforms to professional expression standards, has a clear logical structure, and is complete and accurate, ensuring good readability and professionalism when subsequently filled into the report template.

[0075] S403. Fill the pre-filled content into the preset report template to obtain the energy technology service report.

[0076] For example, the energy-saving assessment module 14 uses pre-formatted content to perform precise filling operations according to the structure and content requirements of a preset report template. The preset report template includes standard elements such as the required chapter structure, table format, chart style, and text layout for energy technology service reports, providing a unified format foundation for generating professional energy technology service reports. During the filling process, the energy-saving assessment module 14 fills in the energy-saving optimization rate description from the pre-filled content into the energy-saving effect assessment chapter of the report template, the detailed analysis of equipment upgrade costs into the investment benefit analysis section, and the relevant content of the technology implementation assessment items into the implementation plan suggestion chapter. The energy-saving assessment module 14 automatically adapts the content and adjusts the format during the filling operation to ensure that the pre-filled content is consistent with the format requirements, font specifications, and paragraph structure of the report template, while also performing logical checks and completeness verification on the filled content. Through this automated filling process, the energy-saving assessment module 14 can generate energy technology service reports with standardized format, complete content, and clear logic, output scientific and accurate analysis conclusions and renovation suggestions, and also has good readability and practicality, providing effective guidance and reference for the energy-saving decisions of the unit to be assessed 200.

[0077] The energy technology service information platform 100, based on enumeration and induction, strictly adheres to unified energy audit standards and processes. From data collection and analysis to report preparation, each step has clear specifications and requirements, ensuring the standardization and consistency of energy audit work. Managers can quickly formulate reasonable energy management strategies and energy-saving measures based on the energy technology service reports provided by the platform, achieving the goals of energy conservation, emission reduction, and lower energy costs.

[0078] In some embodiments, the data acquisition module 12 includes a management node and multiple accounting nodes. When the data acquisition module 12 is used to collect and save the data to be evaluated of the unit 200 based on blockchain technology, it is specifically used to execute: S501-S508.

[0079] S501: Obtain the encrypted data packet, shared key set, and M-1 set of matching keys uploaded by the unit to be evaluated 200 through multiple data acquisition interfaces;

[0080] For example, the data acquisition module 12 simultaneously receives three types of key data components uploaded by the unit to be evaluated 200 through multiple deployed data acquisition interfaces. These interfaces include a Web service interface based on the HTTP protocol, a WebSocket connection supporting real-time transmission, an FTP interface for large file transfer, and a RESTful API optimized for mobile devices, among other diverse communication channels. During the upload process, the unit to be evaluated 200 processes the original energy-saving assessment-related data using a high-strength encryption algorithm and encapsulates it into an encrypted data packet. Simultaneously, it generates a shared key set for subsequent decryption verification. This shared key set contains the core key information capable of correctly decrypting the data packet. To enhance the security of data transmission and storage, the unit to be evaluated 200 also creates M-1 sets of matching keys as interference. These matching key sets are consistent with the shared key set in structure and format but cannot successfully decrypt the target data packet. By introducing multiple sets of matching key sets, this mechanism effectively prevents malicious attackers from obtaining the true decryption key through brute-force attacks or key enumeration, significantly improving the security level of the data transmission process. The parallel operation mode of multiple data acquisition interfaces not only ensures that 200 different types of units to be evaluated can find a suitable data upload method, but also improves the reliability and fault tolerance of data acquisition through a redundant transmission mechanism, ensuring that even if one interface fails or the network is interrupted, other interfaces can still complete the data acquisition task normally.

[0081] S502. At the management node, based on the shared key set and the M-1 sets of matching keys, a combination operation is performed on the shared key set and the matching key set to obtain a combined key sequence.

[0082] For example, after receiving the shared key set and M-1 sets of matching keys, the management node initiates a specialized key processing algorithm to perform deep combination operations on these key data. During the combination operation, the management node cross-combines each key element in the shared key set with the corresponding element in each set of matching keys according to preset mathematical operation rules. This includes performing XOR operations, modulo operations, bit shift operations, and other composite processing on the keys to generate a large number of candidate keys with different characteristic values. Through this combination operation mechanism, the originally relatively independent shared key set and matching key set are organically merged into a combined key sequence containing both correct decryption paths and numerous incorrect paths. This combined key sequence not only contains valid key combinations capable of successfully decrypting encrypted data packets but also a large number of seemingly reasonable but actually invalid interference key combinations. This design significantly increases the computational complexity and time cost for potential attackers to crack the keys. The technical effect of the combination operation is to deeply obfuscate and expand the original key information through mathematical transformations, making it impossible for attackers to easily deduce the correct decryption path even if they obtain a portion of the key set, thus laying a solid security foundation for the subsequent distributed decryption verification process.

[0083] S503. Randomly select and concatenate the elements in the combined key sequence to generate an M-tuple key. The M-tuple key includes: one correct key and M-1 incorrect keys.

[0084] For example, the management node uses a high-quality random number generation algorithm to randomly sample and select from the combined key sequence. Each selection operation extracts M key elements from different positions in the sequence and combines these elements into a complete M-tuple key structure using specific concatenation rules. In the construction of the M-tuple key, the management node ensures that each tuple contains exactly one correct key capable of successfully decrypting the encrypted data packet, while the remaining M-1 positions are filled with incorrect keys randomly selected from the results of the matching key set operation. This design makes each M-tuple key appear to have the same structural characteristics and data format, but in reality, only the correct key can effectively decrypt the data packet, while incorrect keys will produce obvious failure characteristics such as format errors, verification failures, or garbled content during decryption attempts. The technical effects of random selection and concatenation are reflected in two aspects: firstly, the randomness of the operation ensures that the composition of each M-tuple key is unpredictable, preventing attackers from finding the positional pattern of the correct key through pattern recognition or statistical analysis; secondly, the fixed tuple structure provides a unified processing framework for subsequent distributed decryption verification, enabling each computing node to process in parallel according to the same decryption process, thus ensuring both security and improving processing efficiency.

[0085] S504. Based on the number of accounting nodes and the preset node computing power weight coefficient, optimize the allocation of M-tuple keys to obtain key distribution subsets. The number of M-tuple keys contained in the key distribution subsets is different, and the number of key distribution subsets is the same as the number of nodes.

[0086] For example, when performing key distribution optimization, the management node comprehensively considers the total number of currently available compute nodes and the preset computing power weight coefficient of each compute node. These weight coefficients reflect the differentiated characteristics of different nodes in terms of processing power, storage capacity, network bandwidth, etc. Based on the principles of load balancing and maximizing efficiency, the management node intelligently groups the generated multiple M-tuple keys according to the optimization algorithm. Compute nodes with stronger computing power will be allocated a key distribution subset containing more M-tuple keys, while nodes with relatively weaker computing power will receive a smaller but equally important subset. For example, in a network with 5 compute nodes, if the weight coefficient of node A is 0.3, node B is 0.25, node C is 0.2, node D is 0.15, and node E is 0.1, then the management node may distribute a subset containing 30 M-tuple keys to node A, 25 to node B, and so on. The technical effect of this differentiated allocation mechanism is that it makes full use of the heterogeneous computing resources of each computing node, avoiding the problem of idle resources of nodes with strong computing power and overload of nodes with weak capabilities. At the same time, through reasonable load distribution, it ensures that the entire decryption and verification process can be completed efficiently within the expected time, thereby improving the overall processing performance and resource utilization efficiency of the blockchain network.

[0087] S505. Obtain the connection relationship matrix between accounting nodes, and determine the decryption routing table based on the key distribution subset and the connection relationship matrix;

[0088] For example, the management node obtains the connection relationship matrix between the computing nodes in real time through network topology probing and node status query mechanisms. This matrix records in detail key network parameters such as network connectivity, communication latency, and bandwidth capacity between each computing node. The connection relationship matrix not only reflects the physical connection status between nodes but also includes network quality assessment indicators, such as a connection latency of 15 milliseconds, a bandwidth of 100 Mbps, and a connection stability score of 0.95 between node A and node B. Based on the allocation of the key distribution subset and the network topology information provided by the connection relationship matrix, the management node uses a path optimization algorithm to calculate the optimal encrypted data packet transmission path and decryption verification sequence, generating a decryption routing table containing detailed information such as transmission order, node hopping, and verification process. The decryption routing table prioritizes transmission paths with lower network latency, higher bandwidth, and more stable connections, while also considering load balancing and processing capacity matching among the computing nodes to ensure that encrypted data packets can be efficiently transmitted and distributed across the network along the optimal path. The technical effects of this dynamic routing optimization mechanism are reflected in significantly reducing the overall data transmission time, improving the utilization efficiency of network resources, and enhancing the reliability and fault tolerance of the entire decryption and verification process through multi-path redundancy design.

[0089] S506. Based on the decryption routing table, the encrypted data packet is transmitted between multiple accounting nodes, and a decryption attempt is performed at each accounting node using the corresponding key distribution subset to obtain the verification result.

[0090] For example, the management node sends encrypted data packets sequentially to designated compute nodes for distributed decryption processing according to the transmission sequence and path planning specified in the decryption routing table. Upon receiving the encrypted data packet, each compute node calls its locally stored key distribution subset and attempts to decrypt each M-tuple key contained within the subset. During the decryption attempt, the compute node uses each key element in the M-tuple key to perform decryption operations on the encrypted data packet and performs multi-level verification processing on the decryption results, including format verification, content verification, and data integrity checks. Since only one correct key in the M-tuple key can successfully decrypt the data packet, the remaining M-1 incorrect keys will generate obvious failure indicators such as decryption failure, format abnormality, and verification error. By analyzing the characteristics of these decryption attempt results, the compute nodes can accurately identify the successfully decrypted key and its corresponding decryption result. Each compute node summarizes the detailed process of the decryption attempt, success indicators, failure reasons, processing time, and other information to generate verification result data, which is then transmitted back to the management node via the network for unified aggregation and analysis. The technical effect of this distributed parallel decryption mechanism is that it greatly improves the speed and efficiency of decryption processing. Multiple encrypted data packets can be processed simultaneously through the collaborative work of multiple nodes, while the distributed verification mechanism enhances the credibility and security of the decryption results.

[0091] S507. If the test results meet the preset data format requirements, record the successfully decrypted accounting node, node status information and decryption path to obtain the decryption proof record and the data to be evaluated.

[0092] For example, the management node comprehensively analyzes and verifies the format of the verification results returned by each accounting node. It confirms the success of the decryption operation and the integrity of the data by matching and comparing them with preset data format requirements. These preset data format requirements include multiple verification dimensions such as data structure specifications, field integrity standards, numerical range limitations, and encoding format specifications. Only decryption results that fully comply with these requirements are considered valid data to be evaluated. Once the verification result passes format verification, the management node records detailed information about the accounting node that successfully decrypted the data, the node's operating status parameters at the time of decryption (such as CPU utilization, memory usage, and network status), and the complete transmission path and processing flow of the encrypted data packet from initial transmission to successful decryption. This recorded information is integrated into a decryption proof record that includes decryption process traceability, node performance, and verification result confirmation, providing detailed evidence for subsequent data traceability and security auditing. Simultaneously, the management node extracts the original data content after successful decryption, performs format conversion and structuring processing, and generates data to be evaluated that meets the requirements of the energy-saving assessment system. The mechanism for generating decryption proof records and data to be evaluated ensures the traceability and auditability of the data processing process, laying an important foundation for a blockchain-based data security storage and verification system.

[0093] S508. Perform block hash calculation on the decryption proof record and the data to be evaluated to obtain a hash value sequence. Concatenate the hash value sequence with the timestamp to obtain the proof string. Then, perform blockchain notarization on the node status information, the data to be evaluated, and the proof string to generate the accounting proof path.

[0094] For example, the management node uses high-strength hash algorithms such as SHA-256 to perform block processing and hash calculation operations on the decryption proof record and the data to be evaluated. This divides the large amount of data into fixed-size blocks, calculates the hash value for each block independently, and generates a hash value sequence that uniquely identifies the original data content and its integrity. After obtaining the hash value sequence, the management node acquires the current high-precision timestamp information and combines the hash value sequence with the timestamp through cascading operations to generate a proof string containing time attributes and data fingerprint characteristics. This string can simultaneously prove the authenticity of the data content and the accuracy of the generation time. The management node uses the status information of the accounting node, the verified data to be evaluated, and the generated proof string as a complete data package. Through the consensus mechanism of the blockchain network, it performs distributed notarization operations, establishing a permanent data record in the immutable ledger of the blockchain. The blockchain notarization process generates an accounting proof path containing information such as block hash, transaction hash, and notarization timestamp. This path not only provides precise location information of the data in the blockchain but also ensures the tamper-proof and verifiable nature of the notarized data through cryptographic proof. The technical effect of this blockchain-based evidence storage mechanism is that it establishes an unforgeable digital identity for energy-saving assessment data. Any modification or tampering with the data will result in a change in the hash value and failure of the evidence storage verification, thereby ensuring the long-term credibility and legal validity of the data.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy technology service information platform based on enumeration and induction, characterized in that, The energy technology service information platform based on enumeration and induction includes: An energy-saving assessment knowledge base stores energy consumption characteristic parameters, energy consumption evaluation standard items, and energy-saving suggestion effects of the units to be assessed, constructed based on enumeration and induction. The units to be assessed include multiple first-level sub-units. The energy-saving assessment knowledge base is constructed through technical standards. The energy consumption evaluation standard items include equipment energy efficiency parameters, multi-condition energy consumption parameters, and unit index parameters calibrated according to the type of the first-level sub-units. A data acquisition module, which is used to collect and save the data to be evaluated of the unit to be evaluated based on blockchain technology; The data evaluation module is used to determine the comprehensive energy consumption data of the unit to be evaluated based on the data to be evaluated and the energy consumption characteristic parameters; and to set the first-level sub-unit, which includes second-level and third-level sub-units, as a further refined sub-unit; to determine the energy efficiency level data of each third-level sub-unit based on the first energy consumption data of the further refined sub-unit and the equipment energy efficiency parameters; to determine the second energy consumption data of the second-level sub-unit based on the first energy consumption data of the further refined sub-unit, the energy efficiency level data, and the multi-condition energy consumption parameters; to calculate the third energy consumption data of the further refined sub-unit based on all the second energy consumption data; and to determine the energy utilization evaluation of the unit to be evaluated based on the third energy consumption data of the further refined sub-unit, the first energy consumption data of the other first-level sub-units, and the unit index parameters. An energy-saving assessment module is used to simulate the transformation based on the data to be assessed, the comprehensive energy consumption data of the unit to be assessed, the energy utilization evaluation, and the effect of the energy-saving suggestions, and generate an energy technology service report.

2. The energy technology service information platform based on enumeration and induction as described in claim 1, characterized in that, If the primary sub-unit is equipped with a preset smart metering unit, the primary sub-unit can directly upload its comprehensive energy consumption data to the data acquisition module through the preset smart metering unit.

3. The energy technology service information platform based on enumeration and induction as described in claim 1, characterized in that, When the data evaluation module is used to determine the comprehensive energy consumption data of the unit to be evaluated based on the data to be evaluated and the energy consumption characteristic parameters, it is specifically used to perform the following: The scale parameters and energy consumption parameters of each primary sub-unit are determined based on the data to be evaluated. Based on the scale parameter, the energy consumption characteristic parameter is matched with the energy-saving assessment knowledge base to obtain the target characteristic parameter corresponding to each first-level sub-unit; Based on the energy consumption sub-parameters and the target characteristic parameters, determine the first energy consumption data for each of the first-level sub-units; Obtain the association information of multiple first-level sub-units; The comprehensive energy consumption data of the unit to be evaluated is determined based on the first energy consumption data and the associated information.

4. The energy technology service information platform based on enumeration and induction as described in claim 1, characterized in that, The energy-saving assessment knowledge base is also equipped with a preset retrofit simulator. When the energy-saving assessment module performs a retrofit simulation based on the data to be assessed, the comprehensive energy consumption data of the unit to be assessed, the energy utilization evaluation, and the effectiveness of the energy-saving recommendations, and generates an energy technology service report, it specifically performs the following: The data to be evaluated, the comprehensive energy consumption data of the unit to be evaluated, and the energy utilization evaluation are input into the preset renovation simulator to obtain the expected energy-saving effect of the unit to be evaluated. The expected energy-saving effect and the energy-saving suggestion effect are compared to generate the energy technology service report.

5. The energy technology service information platform based on enumeration and induction as described in claim 4, characterized in that, When the energy-saving assessment module is used to compare the expected energy-saving effect with the energy-saving recommendation effect and generate the energy technology service report, it is specifically used to perform the following: The expected energy-saving effect and the energy-saving suggestion effect are compared to determine the energy-saving optimization rate, equipment replacement cost and technology implementation evaluation items; The energy-saving optimization rate, the equipment replacement cost, and the technology implementation evaluation items are parsed and converted into pre-filled content in a specified expression format; The pre-filled content is then filled into a preset report template to obtain the energy technology service report.

6. The energy technology service information platform based on enumeration and induction as described in claim 1, characterized in that, The data acquisition module includes a management node and multiple accounting nodes. When the data acquisition module is used to collect and save the data to be evaluated from the unit to be evaluated based on blockchain technology, it specifically performs the following: The encrypted data packets, shared key sets, and M-1 sets of matching keys uploaded by the entity to be evaluated are obtained through multiple data acquisition interfaces. At the management node, a combination operation is performed on the shared key set and the matching key set according to the shared key set and the M-1 sets of matching keys to obtain a combined key sequence; The elements in the combined key sequence are randomly selected and concatenated to generate an M-tuple key, which includes: one correct key and M-1 incorrect keys; Based on the number of nodes of the accounting nodes and the preset node computing power weight coefficient, the M-tuple key is allocated and optimized to obtain a key distribution subset. The number of M-tuple keys contained in the key distribution subset is different, and the number of key distribution subsets is the same as the number of nodes. Obtain the connection relationship matrix between the computing nodes, and determine the decryption routing table based on the key distribution subset and the connection relationship matrix; The encrypted data packet is transmitted among multiple computing nodes according to the decryption routing table, and a decryption attempt is performed at each computing node using the corresponding key distribution subset to obtain the verification result; If the test result meets the preset data format requirements, record the successfully decrypted accounting node, node status information and decryption path to obtain the decryption proof record and the data to be evaluated. The decryption proof record and the data to be evaluated are subjected to block hash calculation to obtain a hash value sequence. The hash value sequence is concatenated with the timestamp to obtain a proof string. The node status information, the data to be evaluated and the proof string are stored on the blockchain to generate a verification proof path.

Images