A blockchain-based distributed energy transaction verification method and system

Abstract

The application relates to a kind of blockchain-based distributed energy transaction verification method and system, relate to the field of blockchain energy transaction, which includes the transaction parameters and identity parameters of the transaction collected;The collected transaction parameters and identity parameters are checked, and transaction code and identity code are determined;After verification, the matching hash value of the blockchain is determined based on the transaction code and the identity code;The matching hash value is converted into a paired string;When the paired string is added to 0, pairing is completed, transactions are carried out according to the transaction code, and data is uploaded to the blockchain platform, and the transaction is completed after the settlement is completed.The application has the effect of reducing transaction time and improving efficiency.

Description

Technical Field

[0001] This invention relates to the field of blockchain energy trading, and in particular to a blockchain-based distributed energy trading verification method and system. Background Technology

[0002] With the popularization of distributed energy, peer-to-peer transactions face challenges in trust and verification. Blockchain technology provides a new way to build a trustworthy and efficient energy trading system.

[0003] Currently, this field primarily relies on the characteristics of blockchain, combined with smart contracts and IoT devices, to achieve full-process verification of the identity of transaction entities, the authenticity of energy data, contract execution, and record storage. During the transaction process, it is necessary to verify transaction information, such as verifying whether the actual power generation recorded by the seller on the blockchain is consistent with the agreed-upon power sales volume, whether the energy type matches the transaction requirements, and whether the contract terms signed by both parties are consistent with the actual submitted transaction parameters.

[0004] In the existing transaction scheme, multiple verifications are required during the transaction. The verification process is cumbersome and time-consuming, the verification content is complex, and the processing speed is limited, resulting in long transaction times and high costs. Summary of the Invention

[0005] To reduce transaction time and improve efficiency, this invention provides a blockchain-based distributed energy transaction verification method and system.

[0006] In a first aspect, the present invention provides a blockchain-based method for verifying distributed energy transactions, employing the following technical solution:

[0007] A blockchain-based method for verifying distributed energy transactions includes:

[0008] Collect transaction parameters and identity parameters;

[0009] The collected transaction parameters and identity parameters are verified, and the transaction code and identity code are determined.

[0010] After successful verification, a blockchain matching hash value is determined based on the transaction code and the identity code;

[0011] Convert the matching hash value into a pairing string;

[0012] When the sum of the pairing strings is 0, the pairing is completed, the transaction is carried out according to the transaction code, the order is locked and uploaded to the blockchain platform, and the transaction is closed.

[0013] By adopting the above technical solution, transaction parameters and identity parameters are converted into matching strings and their algebraic sum is zero, thereby enabling instant order locking, reducing the matching process, achieving millisecond-level identity confirmation and transaction triggering, improving transaction speed, and ensuring that subsequent data cannot be tampered with once it is on the blockchain, reducing settlement disputes in distributed energy scenarios, reducing transaction time, and improving efficiency.

[0014] Optionally, methods for verifying and confirming the transaction code and identity code include:

[0015] The transaction information and transaction time are retrieved based on the aforementioned transaction parameters;

[0016] The information string is determined based on the transaction information and verified to confirm its authenticity;

[0017] Once the verification is successful, a timestamp is determined based on the transaction time.

[0018] Substitute the timestamp into a preset combination formula to determine the number of combinations;

[0019] The information string and the timestamp are combined using the specified combination number to obtain the transaction string;

[0020] The transaction account number and transaction device number are extracted based on the identity parameters, and the transaction device identifier is determined based on the transaction device number.

[0021] The account number string is determined based on the transaction account number, and the account owner device symbol is determined based on the account number string;

[0022] When the account holder device identifier and the transaction device identifier match, the account number string is uploaded to the blockchain for verification to determine whether the identities match.

[0023] The transaction string is converted into a transaction code, and the verified account number string is converted into an identity code.

[0024] By adopting the above technical solutions, and through multiple processes such as transaction information authenticity verification, timestamp combination, device consistency verification, and blockchain identity matching, the generation logic of transaction codes and identity codes is made rigorous and traceable, effectively preventing transaction information tampering and identity impersonation, and improving the accuracy and security of distributed energy transaction verification.

[0025] Optionally, determining the owner's device identifier based on the account number string includes:

[0026] Based on the account number string, retrieve the identity information bound to the account number string from the preset system database and collect external verification information;

[0027] The verification result is obtained by comparing the external verification information with the identity information;

[0028] When the household owner's identity is determined to be qualified based on the verification result, the preset household owner device identifier association library bound to the household number string is retrieved based on the identity information to obtain the corresponding household owner device table;

[0029] Based on the household owner device table, the device usage status is determined, and the corresponding household owner device symbol is matched with the device usage status and the preset household owner device symbol association library.

[0030] By adopting the above technical solution, the account holder's device identifier can be accurately determined through binding the account number string with identity information, external verification, and analysis of device usage. This ensures the authenticity and reliability of the correspondence between the account holder's identity and the device, prevents non-account holder devices from participating in transactions, and strengthens the effectiveness of the association between the transaction subject and the device.

[0031] Optionally, methods for determining the verification results include:

[0032] Collect the homeowner's optical fingerprint image and capacitive fingerprint signal;

[0033] When the capacitive fingerprint signal is greater than the preset wetting threshold signal, a fingerprint reflection image is obtained through adaptive processing based on the optical fingerprint image;

[0034] The fingerprint ridge map is obtained by filtering and grayscale processing the fingerprint reflection image.

[0035] The capacitive fingerprint signal is differentially corrected and normalized to obtain a local fingerprint signal;

[0036] A complete fingerprint image is obtained by determining the local fingerprint map based on the local fingerprint signal conversion;

[0037] The complete fingerprint image is compared with the preset homeowner fingerprint image to determine the verification result.

[0038] By adopting the above technical solution, combining optical fingerprint images and capacitive fingerprint signals for multimodal verification, and through refined operations such as adaptive processing, filtering grayscale conversion, and signal correction, the accuracy of fingerprint verification under different environments (such as differences in finger moisture) is improved, thereby enhancing the accuracy and anti-interference capability of homeowner identity verification.

[0039] Optionally, specific methods for obtaining a complete fingerprint image include:

[0040] Feature anchor points are extracted based on the fingerprint ridge map and the local fingerprint map, and a mapping relationship between the feature anchor points of the two is established.

[0041] The overlapping area of ​​the fingerprint ridge map and the local fingerprint map is determined according to the mapping relationship;

[0042] Based on the ridge line orientation of the overlapping region, a fingerprint coordinate system is constructed by splicing the fingerprint ridge map and the local fingerprint map;

[0043] Based on the fingerprint coordinate system, ridge smoothing is performed to determine the transition region;

[0044] Based on the transition region, the ridge details of the transition region are supplemented by linear interpolation, and the thickness of the ridges is adjusted to obtain a complete fingerprint image.

[0045] By adopting the above technical solutions, through feature anchor point mapping, overlapping area analysis, coordinate system construction, and interpolation to supplement details, the problem of incomplete information from a single fingerprint acquisition is solved, generating a complete and detailed fingerprint image, improving the success rate of fingerprint comparison, and further ensuring the reliability of identity verification.

[0046] Optionally, the methods for determining the verification results also include:

[0047] Collect the action characteristics and password length of the current user when entering a password. The action characteristics include key interval, key pressure, and input duration.

[0048] Based on the account number string, a qualified action curve is obtained by matching it in a preset input feature library;

[0049] The motion curve is obtained by fitting the key interval and the key force.

[0050] The selection range is determined based on the input duration and the password length;

[0051] The motion curve is selected based on the defined selection area, and the motion curve is updated.

[0052] The degree of fit between the motion curve and the qualified motion curve is calculated by comparing the motion curve with the input duration.

[0053] If the degree of fit is not higher than the preset qualified threshold, a prompt will be made to re-enter the verification or switch the verification method;

[0054] When the degree of fit is higher than the preset qualified threshold, the verification result is determined based on the degree of fit.

[0055] By adopting the above technical solution, the action characteristics of password input are compared with the preset qualified curve, and the verification result is judged by the degree of matching. The uniqueness of the action characteristics is used to supplement the password verification, prevent the illegal use after the password is leaked, and improve the multi-dimensional security of identity verification.

[0056] Optionally, the matching corresponding owner device identifier includes:

[0057] Based on the household equipment table, extract the transaction volume, inventory, and power consumption rate of each device in the table;

[0058] Targeted filtering is performed based on preset equipment types, which include collection equipment and power-consuming equipment.

[0059] Based on the transaction volume of the collection device, the most recent collection time is collected, and the collection device with the largest transaction volume that is closest to the preset current time at the most recent collection time is selected and included in the candidate device list;

[0060] Calculate the power consumption time corresponding to the power-consuming equipment based on the inventory level and the power consumption rate;

[0061] Power-consuming devices whose power consumption time is less than a preset threshold time are selected and included in the candidate device list;

[0062] Based on the candidate device list, it is matched with the preset owner device symbol association library, and finally the corresponding owner device symbol is output.

[0063] By adopting the above technical solution, candidate devices are screened based on dynamic data such as device transaction volume, inventory, and power consumption rate. The device owner's device identifier is matched with a preset association database to ensure that the screened devices are currently active or critical devices, thereby improving the adaptability and timeliness of the device owner's device identifier with the actual usage scenario.

[0064] Optionally, determining the information string based on the transaction information includes:

[0065] The transaction quantity and transaction amount are determined based on the transaction information;

[0066] Determine the combination column of separate transaction quantities based on the transaction quantity, and obtain the current time;

[0067] If the current time is the preset peak power time, the remaining quantity is determined based on the combination of the transaction amount and the quantity.

[0068] If the current time is a preset off-peak electricity time, the transaction amount is adjusted based on the remaining quantity and the preset off-peak electricity price;

[0069] Match transactions based on the revised transaction amount, transaction quantity, and quantity combination column;

[0070] The information string is determined based on the matching transaction details.

[0071] By adopting the above technical solution, the calculation logic of transaction amount is adjusted according to peak and off-peak electricity time, and the transaction parameters are corrected by combining the transaction quantity combination and the remaining quantity. This enables the information string to accurately reflect the actual situation of energy trading in different time periods, adapt to the time-of-use energy trading rules, and ensure the authenticity of transaction information and scenario adaptability.

[0072] Optionally, methods for determining the information string include:

[0073] When the transaction amounts are the same, if the transaction quantity of one of the parties involved in the transaction is the same as the quantity in the quantity combination column, then the remaining quantity in the quantity combination column shall be used as the transaction quantity.

[0074] When the transaction amounts are inconsistent, the amount combination column is determined based on the quantity combination column and the transaction amount;

[0075] When the transaction amount of one of the parties involved in the transaction matches the amount in the amount combination column, update the quantity combination column and use the remaining amount in the amount combination column as the transaction amount;

[0076] After the initial transaction is completed, an information string is determined based on the transaction quantity and the transaction amount.

[0077] By adopting the above technical solution, when the amount or quantity is inconsistent, an amount combination column is generated in real time and the quantity combination column is updated in reverse, realizing asynchronous matching and automatic clearing of the remainder. Energy is settled separately through transactions with different amounts, thereby improving transaction efficiency.

[0078] Secondly, this application provides a blockchain-based distributed energy trading verification system, which adopts the following technical solution:

[0079] A blockchain-based distributed energy trading verification system includes:

[0080] The acquisition module is used to obtain transaction parameters and identity parameters;

[0081] A memory used to store programs that implement any blockchain-based distributed energy trading verification method;

[0082] The processor loads and executes programs from memory.

[0083] In summary, this application includes at least one of the following beneficial technical effects:

[0084] 1. By converting transaction parameters and identity parameters into matching strings and ensuring their algebraic sum is zero, orders can be locked instantly, reducing the matching process and achieving millisecond-level identity confirmation and transaction triggering, thus improving transaction speed. Once the data is uploaded to the blockchain, it cannot be tampered with, reducing settlement disputes in distributed energy scenarios, reducing transaction time, and improving efficiency.

[0085] 2. By combining optical fingerprint images and capacitive fingerprint signals for multimodal verification, and through refined operations such as adaptive processing, filtering grayscale conversion and signal correction, the accuracy of fingerprint verification under different environments (such as differences in finger moisture) is improved, thereby enhancing the accuracy and anti-interference ability of homeowner identity verification.

[0086] 3. When the amount or quantity is inconsistent, an amount combination column is generated in real time and the quantity combination column is updated in reverse to realize asynchronous matching and automatic clearing of the remainder. Energy is settled separately through transactions with different amounts, thereby improving transaction efficiency. Attached Figure Description

[0087] Figure 1 This is a flowchart of a blockchain-based distributed energy transaction verification method according to an embodiment of the present invention.

[0088] Figure 2 This is a flowchart illustrating a specific method for obtaining a complete fingerprint image according to an embodiment of the present invention.

[0089] Figure 3 This is a flowchart of the method for determining the information string according to an embodiment of the present invention. Detailed Implementation

[0090] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0091] This application discloses a blockchain-based method for verifying distributed energy transactions.

[0092] Reference Figure 1 A blockchain-based method for verifying distributed energy transactions includes the following steps:

[0093] Step S100: Collect transaction parameters and identity parameters.

[0094] Transaction parameters refer to data such as quantity, price, electricity volume, and time involved in the transaction.

[0095] Identity parameters refer to the unique identification information of both parties to the transaction and the transaction equipment (such as account number, equipment number, etc.).

[0096] The transaction and identity parameters are obtained by retrieving relevant parameters from authorized nodes and corresponding terminal devices in the blockchain through the mobile device at the transaction end.

[0097] Step S101: Verify the collected transaction parameters and identity parameters, and determine the transaction code and identity code.

[0098] A transaction code is a string generated after the transaction parameters have been verified, used to uniquely identify this transaction. It serves as information for the conversion of transactions, and transactions can be carried out directly based on the transaction code.

[0099] An identity code is a string generated after identity parameter verification, used to uniquely identify the identity of the transaction entity.

[0100] The specific methods are described in steps S200 to S208, and will not be repeated here.

[0101] The method for verifying and confirming the transaction code and identity code includes the following steps:

[0102] Step S200: Obtain transaction information and transaction time according to transaction parameters.

[0103] Transaction information refers to details including electricity volume, electricity price, and meter readings.

[0104] Transaction time refers to the specific point in time when a transaction is initiated.

[0105] The transaction parameters include transaction information and transaction time, which can be called directly.

[0106] The transaction information and time are retrieved based on the transaction parameters, which are then used for subsequent verification and determination of the transaction code.

[0107] Step S201: Determine the information string based on the transaction information and verify it to confirm the authenticity of the information.

[0108] Information strings refer to structured strings formed by extracting core key elements from transaction information.

[0109] The transaction information is serialized according to a preset conversion format, and the resulting string is the information string. The conversion format is preset by technical personnel according to the actual situation, and will not be described in detail here.

[0110] The specific methods are described in steps S800 to S805, and will not be repeated here.

[0111] Step S202: After the verification is successful, determine the timestamp based on the transaction time.

[0112] A timestamp is a digital representation of a specific time.

[0113] The transaction time data is arranged in numerical form to obtain a timestamp.

[0114] Step S203: Substitute the timestamp into the preset combination formula to determine the number of combinations.

[0115] The combination formula refers to the formula that sorts changes based on timestamps, calculating time as subsequent content that changes over time and is arranged at intervals, for subsequent encryption. The number of combinations is (T×K1+Hid×K2+Ht×K3)*mod M.

[0116] K1, K2, K3: Preset weighting coefficients (e.g., K1=10^6, K2=10^3, K3=1, to ensure that the contributions of T, Hid, and Ht to the results are distinguishable).

[0117] M: Preset modulus (e.g., M=10^12, which controls the number of combinations to be a 12-bit integer for easy storage and recognition).

[0118] Hid / Ht: The identity code / transaction code is hashed using SHA-256, and the first 8 bits are converted to a decimal integer (e.g., Hid=12345678, Ht=87654321).

[0119] For example: Assume T=1716000000 (preprocessed second-level timestamp), K1=10^6, K2=10^3, K3=1, M=10^12, Hid=12345678, Ht=87654321:

[0120] Calculate the following items:

[0121] T×K1=1716000000×10^6=1716000000000000;

[0122] Hid×K2=12345678×10^3=12345678000;

[0123] Ht×K3=87654321×1=87654321;

[0124] Summation:

[0125] 1716000000000000+12345678000+87654321=1716012431111321;

[0126] Mold taking:

[0127] 1716012431111321 mod 10^12 = 012431111321 (combinations)

[0128] Step S204: Combine the information string and the timestamp using a combination number to obtain the transaction string.

[0129] A transaction string is the concatenation of an information string and a timestamp, containing the original string of core transaction information.

[0130] For example, if the combination number is 012431111321, divide the combination number into multiple groups of 01, 24, 31, 11, 13, and 21. In each group, take 0 numbers from the information string and 1 number from the timestamp. Then take 2 numbers from the information string and 4 numbers from the timestamp. The string obtained by combining the combination numbers in order is the transaction string.

[0131] Step S205: Extract the transaction account number and transaction device number based on the identity parameters, and determine the transaction device identifier based on the transaction device number.

[0132] The transaction account number refers to a user's unique account number in the system.

[0133] The transaction equipment number refers to the unique serial number of equipment such as meters / inverters.

[0134] The transaction device symbol refers to the short character encoding of the device number, which facilitates zero-knowledge comparison.

[0135] The identity parameters include the transaction account number and the transaction device number, which can be directly extracted and retrieved. The transaction device number corresponds to a matching transaction device symbol. The transaction device number is entered into the preset device symbol database to obtain the transaction device symbol. The device symbol database is a database that is preset by technical personnel according to the actual situation. The device symbol database contains a table of correspondence between the transaction device number and the transaction device symbol. The table of correspondence is preset by technical personnel according to the actual situation and will not be described in detail here.

[0136] Step S206: Determine the account number string based on the transaction account number, and determine the account owner device symbol based on the account number string.

[0137] The account number string refers to the hashable string generated from the account number according to the rules.

[0138] The homeowner's device identifier refers to the character identifier of the "main table" or "main device" under the homeowner's name.

[0139] The string obtained by serializing the transaction account number according to the preset conversion format is the account number string.

[0140] The specific determination method is described in steps S300 to S303, and will not be repeated here.

[0141] Determining the account holder's device identifier based on the account number string includes the following steps:

[0142] Step S300: Based on the account number string, retrieve the identity information bound to the account number string from the preset system database and collect external verification information.

[0143] The system database refers to the platform database that stores the account holder's identity information. The storage of account number, identity, device, contract address, and other information is pre-set by technical personnel according to the actual situation, and will not be elaborated here.

[0144] Identity information refers to the verification information used to confirm whether the account holder is the account holder, which is obtained by matching against the system database.

[0145] External verification information refers to off-chain factors such as fingerprints and passwords, which are collected through mobile devices on the transaction side, and will not be elaborated here.

[0146] Step S301: Obtain the verification result by comparing the external verification information with the identity information.

[0147] The verification result refers to whether the verification passes or fails, and is used to determine the homeowner's identity verification status.

[0148] If the comparison results match, the application is considered successful and the confirmed head of household is deemed qualified; otherwise, the application is considered unsuccessful and the confirmed head of household is deemed unqualified.

[0149] The specific determination method is described in steps S400 to S405 and steps S600 to S607, and will not be repeated here.

[0150] The method for determining the verification result includes the following steps:

[0151] Step S400: Collect the homeowner's optical fingerprint image and capacitive fingerprint signal.

[0152] Optical fingerprint images refer to fingerprint patterns captured by visible or infrared light.

[0153] Capacitive fingerprint signals refer to the sequence of minute electric field changes in a fingerprint collected by a capacitive sensor.

[0154] Optical fingerprint images are acquired by an optical fingerprint sensor on the mobile device at the transaction end, and capacitive fingerprint signals are acquired by a capacitive fingerprint sensor.

[0155] Step S401: When the capacitive fingerprint signal is greater than the preset wet threshold signal, the fingerprint reflection image is obtained by adaptive processing based on the optical fingerprint image.

[0156] The wetness threshold signal refers to the parameter value that technicians pre-set according to the actual situation to determine whether the fingers are sweaty or wet, which will not be elaborated here.

[0157] When the capacitive fingerprint signal exceeds the preset wetness threshold, it indicates that the finger is wet. In the case of wet hands, to address issues such as specular reflection and reduced contrast due to scattering caused by water film in optical fingerprint images, a dynamic threshold is first generated by real-time calculation of the global reflection mean to locate abnormally bright areas and weakly reflective blurred areas. Then, the bright areas are replaced with the neighborhood reflection mean to eliminate strong reflection interference, while the weakly reflective areas undergo local dynamic range stretching to amplify ridge-valley differences. Simultaneously, adaptive bilateral filtering is used to smooth stray watermark reflections while preserving ridge-valley reflection edges. Finally, contrast-limited adaptive histogram equalization (CLAHE) dynamically adjusts grayscale according to the local sub-block reflection distribution to further enhance ridge-valley reflection contrast. The final result is a fingerprint reflection image without significant water film interference, with significant ridge-valley reflection differences, and usable for subsequent feature extraction and comparison.

[0158] Step S402: Filter and grayscale processing is performed on the fingerprint reflection image to obtain the fingerprint ridge map.

[0159] A fingerprint ridge map is a binary map that retains only the fingerprint ridge skeleton, which facilitates subsequent feature extraction.

[0160] To address issues such as watermark noise, reflective impurities, and blurred grayscale differences in ridge and valley reflections caused by residual water film in fingerprint reflection images, an adaptive bilateral filtering process is first employed. This process dynamically adjusts the filter window size based on the local noise density of the reflection image, smoothing out high-frequency interference impurities like watermarks and reflections while accurately preserving the edge reflection features of the fingerprint ridges. Subsequently, grayscale preprocessing is performed, eliminating grayscale fluctuations caused by uneven pressure from wet hands through grayscale normalization. Then, the Sauvola adaptive threshold segmentation algorithm is used to dynamically generate segmentation thresholds based on the mean and variance of the reflection grayscale of each local sub-block, transforming the blurred grayscale differences in ridge and valley reflections into clear binary distinctions. Finally, a morphological "dilation-erosion" combination operation is used to fill in the tiny breaks in the ridges caused by wet hands and eliminate residual fine noise nodules, ultimately resulting in a fingerprint ridge map with continuous and complete ridges, clearly separated valleys, and ready for direct feature extraction.

[0161] Step S403: Differential correction and normalization are performed on the capacitive fingerprint signal to obtain the local fingerprint signal.

[0162] First, differential correction processing is performed on the original capacitive fingerprint signal: using the global capacitance average of the signal as a benchmark, the deviation of each capacitance unit from the benchmark value is calculated in real time. For capacitance values ​​exceeding the upper limit (more than 20% higher than the benchmark) caused by moisture micro-short circuits and capacitance values ​​exceeding the lower limit (less than 20% lower than the benchmark) caused by poor contact, they are replaced with reasonable range values ​​within ±5% of the benchmark value to eliminate extreme interference and restore the ridge and valley capacitance differences. Then, normalization processing is performed, dividing the signal into local sub-blocks (such as 8×8 pixel units), and dynamically mapping the capacitance values ​​in each sub-block to the standard grayscale range of [0,255] to eliminate signal intensity fluctuations caused by uneven pressing with wet hands and differences in local sensor sensitivity. Finally, a local fingerprint signal with clear ridge and valley capacitance differences, uniform signal amplitude, and usable for subsequent local feature extraction is obtained.

[0163] Step S404: Determine the local fingerprint image based on the local fingerprint signal conversion to obtain the complete fingerprint image.

[0164] A local fingerprint image refers to a small, high-resolution fingerprint image obtained by identifying a local area using capacitance signals.

[0165] A complete fingerprint image refers to a high-resolution fingerprint image after stitching together the images.

[0166] The specific determination method is described in steps S500 to S504, and will not be repeated here.

[0167] Reference Figure 2 The specific method for obtaining a complete fingerprint image includes the following steps:

[0168] Step S500: Extract feature anchor points based on fingerprint ridge map and local fingerprint map, and establish the mapping relationship between the feature anchor points of the two.

[0169] Feature anchors include fingerprint forks, endpoints, and other key points that are rotationally and scale invariant.

[0170] Based on the complete fingerprint ridge map and local fingerprint map, focusing on the ridge details of specific regions, we first extract feature anchor points that combine stability and recognizability from both—prioritizing ridge endpoints, bifurcation points, and other minutiae feature points, recording their position coordinates, ridge direction, and distance between neighboring ridges. Simultaneously, we use the core points or triangular areas (such as ridge transition points) of the fingerprint ridge map as global reference anchor points to eliminate false feature points in the local fingerprint map caused by residual noise from wet hands. Then, using the global reference anchor points of the ridge map as a benchmark, we rotate and translate the local fingerprint map to ensure both are in the same coordinate system. Next, by calculating the Euclidean distance between feature anchor points (with a threshold of within 5 pixels) and the ridge direction deviation (with a threshold of within 15°), we filter out anchor point pairs that meet the matching conditions. Combined with neighboring ridge texture consistency checks, we eliminate false matches, ultimately establishing a one-to-one feature anchor point mapping relationship between the fingerprint ridge map and the local fingerprint map, providing accurate feature association basis for subsequent fingerprint comparison.

[0171] Step S501: Determine the overlapping area of ​​the fingerprint ridge map and the local fingerprint map according to the mapping relationship.

[0172] Overlapping regions refer to areas that are covered by both optical and capacitance images and have the same characteristics.

[0173] The overlapping area is the region that is consistent with the ridge direction in the fingerprint ridge map and the local fingerprint map. This is common knowledge to those skilled in the art and will not be elaborated here.

[0174] Step S502: Based on the ridge direction of the overlapping region, construct a fingerprint coordinate system by splicing the fingerprint ridge map and the local fingerprint map.

[0175] The fingerprint coordinate system refers to the coordinate system used to locate overlapping fingerprint ridge maps and local fingerprint maps.

[0176] Using the horizontal direction of the overlapping ridges as the X-axis and the vertical direction as the Y-axis, a unified fingerprint coordinate system is constructed by integrating the fingerprint ridge map with the corrected local fingerprint map.

[0177] Step S503: Perform ridge smoothing based on the fingerprint coordinate system to determine the transition region.

[0178] The transition area refers to the edge band where optical and capacitive images are stitched together, and the seam needs to be smoothed out.

[0179] Based on a fingerprint coordinate system established with the fingerprint core point as the origin and the triangular area direction as the reference axis, an adaptive Gaussian filter is first used to smooth the ridges, taking the position coordinates and direction angles of each ridge in the coordinate system as a reference. To address issues such as jagged edges and slight local distortions caused by wet hands, the filter parameters are dynamically adjusted according to the ridge extension direction. In specific local areas of the coordinate system (such as ridge turning points), the filter range is narrowed to preserve details, while the range is expanded in straight ridge segments to enhance the smoothing effect and avoid overall blurring. Subsequently, combined with coordinate system positioning, the transition region is determined by analyzing the grayscale continuity and direction consistency of the smoothed ridges. Starting from the endpoints of adjacent clear ridges in the coordinate system, areas with abrupt changes in grayscale gradients, deviations from the normal trend, and interruptions in pixel continuity are tracked. These areas, which are between clear ridges and background noise, and where ridge details are missing or blurred, are defined as transition regions, providing accurate spatial positioning basis for subsequent ridge detail supplementation.

[0180] Step S504: Based on the transition area, supplement the ridge details of the transition area through linear interpolation, adjust the thickness of the ridges, and obtain a complete fingerprint image.

[0181] Linear interpolation refers to the interpolation of missing ridge pixels within the transition zone.

[0182] The thickness of the ridge line is adjusted to maintain visual consistency with the single-modal image after stitching.

[0183] By identifying ridge transition areas in fingerprint images caused by wet hands (such as blurred or broken ridges, or missing details), the range of these transition areas is first located through image analysis. Using the endpoint coordinates and direction angles of the clear ridges on both sides as reference benchmarks, a linear interpolation algorithm is employed—based on the grayscale gradient and texture direction of adjacent ridges—to gradually fill in the missing ridge pixels within the transition area, allowing the broken ridges to connect naturally. Subsequently, the average thickness of the ridges in the effectively clear areas of the image is statistically analyzed. Using this as a standard, the thickness of the interpolated ridges in the transition areas is calibrated. Through local pixel grayscale adjustment, the problem of uneven ridge thickness caused by wet hands is eliminated, ultimately resulting in a complete fingerprint image with continuous, intact ridges, uniform thickness, and no obvious transition marks.

[0184] Step S405: Compare the complete fingerprint image with the preset homeowner fingerprint image to determine the verification result.

[0185] The homeowner's fingerprint image refers to the fingerprint information image of the homeowner, which is pre-set by technicians according to the actual situation and then obtained by matching identity information. It will not be elaborated here.

[0186] The process of filtering and matching fingerprints based on their overall pattern, core points, and triangular areas, and determining the verification result based on the matching results, is common knowledge known to those skilled in the art and will not be elaborated upon here.

[0187] The method for determining the verification results also includes the following steps:

[0188] Step S600: Collect the action features and password length of the current user when entering the password. The action features include key press interval, key press force, and input duration.

[0189] Action characteristics refer to the behavioral data of users when entering passwords, including key press intervals, key press force, and input duration. These are preset by technicians according to actual conditions and will not be elaborated here.

[0190] Password length refers to the number of characters a user enters in their password.

[0191] By utilizing the hardware sensors and system event monitoring capabilities of the mobile device on the trading platform, combined with the permission access and data processing of the trading platform APP, the key press interval, key pressure, and input duration when the current user enters their password can be obtained. This is common knowledge known to those skilled in the art and will not be elaborated upon here.

[0192] Step S601: Based on the household number string, a qualified action curve is obtained by matching it in the preset input feature library.

[0193] The input feature database refers to the database that stores the action curves corresponding to the user's input behavior features. The database contains the correspondence between the user ID string and the qualified action curve. It is preset by technical personnel according to the actual situation and will not be elaborated here.

[0194] The qualified action curve refers to the preset standard input behavior curve, which is set in advance by technicians in the input feature library according to the actual situation, and will not be elaborated here.

[0195] A qualified action curve is obtained by inputting the user number string into the feature database for matching.

[0196] Step S602: Obtain the motion curve by fitting the key interval and key force.

[0197] An action curve is a fitted curve of user input behavior.

[0198] Using the key press interval (time series, such as the millisecond difference between adjacent keys) when a user enters their password as the horizontal axis and the corresponding key pressure value (pressure sensor data) as the vertical axis, the "time-pressure" data points of each key are arranged in the input order. These discrete data points are connected by a smooth interpolation algorithm (such as linear interpolation or polynomial interpolation) to fit and form a continuous action curve. This curve can intuitively reflect the rhythm and pressure change pattern of the user's action when entering their password, and can be used for subsequent identity feature comparison.

[0199] Step S603: Determine the selection range based on the input duration and password length.

[0200] The selection range refers to the range used to filter motion curves.

[0201] First, calculate the average input time per character by dividing the input time by the password length. This serves as the core benchmark for determining the selection range, reflecting the basic rhythm of the user's password input. Combined with historical normal input data or preset thresholds (such as average input time ± 20%), define the reasonable upper and lower limits of this average input time. This upper and lower limit range is the final selection range, used to filter normal input behavior.

[0202] Step S604: Select the motion curve according to the selection range and update the motion curve.

[0203] Extract the corresponding segment from the motion curve by selecting the area, and filter out redundant data outside the area;

[0204] The original action curve is replaced with the truncated valid curve to complete the update, resulting in a new curve that focuses on key action features, which is then used for subsequent identity verification.

[0205] Step S605: Compare the motion curve with the qualified motion curve, and calculate the degree of fit between the curves based on the input duration.

[0206] Fit refers to the degree of matching between the user's action curve and the qualified action curve.

[0207] The difference between the two curves is calculated point by point using algorithms (such as Euclidean distance and dynamic time warping).

[0208] For example, if the difference between the third point of the current key press interval curve and the corresponding point of the qualified curve is small, the overall difference value is low, indicating a high initial fit; if the difference is large, the initial fit is low.

[0209] The core of calculating the fit is the "consistency of eigenvalues ​​at corresponding points"—if the key press intervals of two curves are almost equal at a certain input step, that point is considered "coincident"; the more coincident points and the smaller the difference in eigenvalues ​​between points, the higher the overall fit. During calculation, first calculate the difference in eigenvalues ​​(such as absolute value or squared difference) of all corresponding points, then use a formula (such as "1 - total difference / maximum possible difference") to convert the difference into a fit of 0-100%. A high fit indicates a high degree of overlap, and vice versa.

[0210] Step S606: When the fit is not higher than the preset qualified threshold, prompt the user to re-enter the verification or switch the verification method.

[0211] The qualified threshold refers to the preset minimum matching standard, which is set in advance by technicians according to the actual situation, and will not be elaborated here.

[0212] If the match is not higher than the qualified threshold, it means that the person may not be the homeowner. In this case, the user will be prompted to re-enter the verification information or switch the verification method to ensure the identity is accurate.

[0213] Step S607: When the fit is higher than the preset pass threshold, the verification result is determined based on the fit.

[0214] The verification result refers to the final result of user authentication.

[0215] When the fit is higher than the acceptable threshold, it indicates that the person is the homeowner, and the verification result is passed.

[0216] Step S302: When the householder's identity is determined to be qualified based on the verification result, the preset householder device association library bound to the household number string is retrieved based on the identity information to obtain the corresponding householder device table.

[0217] The homeowner device association database refers to the database of all devices associated with the homeowner's name. It is obtained by recording the identity information of all legal devices under the homeowner's name and their character codes. It is pre-set by technicians according to the actual situation and will not be elaborated here.

[0218] Once the homeowner's identity is verified, it means that the homeowner is indeed the one in question. Based on the identity information, the system retrieves the associated database of the homeowner's devices and lists all the devices in the database in a certain order to obtain the homeowner's device table.

[0219] Step S303: Determine the device usage based on the owner device table, and match the corresponding owner device symbol with the preset owner device symbol association library based on the device usage.

[0220] Equipment usage status refers to the most recent usage status, such as online / offline, fault, or under maintenance, and is used to filter out non-trading equipment.

[0221] The device usage information can be directly retrieved from the device table of the account owner. When the device usage information shows that the power consumption or collection volume is high recently, the device is selected as the device for the transaction, and the corresponding account owner device symbol is matched in the account owner device symbol association library.

[0222] The specific determination method is described in steps S700 to S705, and will not be repeated here.

[0223] Matching the corresponding owner device identifier includes the following steps:

[0224] Step S700: Based on the household equipment table, extract the transaction volume, inventory, and power consumption rate of each device in the table.

[0225] Trading volume refers to the actual amount of energy that can be traded in the equipment.

[0226] Inventory refers to the amount of energy that equipment still has available for use.

[0227] The rate at which a device consumes energy refers to the rate at which that energy is used.

[0228] The household equipment table pre-records dynamic energy-related data for each device, and transaction volume, inventory, and power consumption rate can be directly read from the device.

[0229] Step S701: Targeted filtering is performed according to the preset device types, which include collection devices and power-consuming devices.

[0230] Equipment type refers to the functional classification of equipment, which is preset by technicians according to the actual situation, and will not be elaborated here.

[0231] Energy collection equipment refers to equipment that can collect energy.

[0232] Electrically powered equipment refers to equipment that requires a power supply.

[0233] Equipment is categorized so that transactions can be conducted based on the specific characteristics of the equipment.

[0234] Step S702: Based on the transaction volume of the collection device, collect the most recent collection time, filter out the collection device with the largest transaction volume that is closest to the preset current time in the most recent collection time, and include it in the candidate device list.

[0235] The most recent collection time refers to the time when the device last collected energy.

[0236] The candidate device list refers to the shortlist of devices selected for the transaction.

[0237] The most recent collection time is obtained by reading the last recorded time on the device. Then, the collection devices with the most recent collection time that are closest to the current time and the largest transaction volume are selected and included in the candidate device list.

[0238] Step S703: Calculate the power consumption time corresponding to the power-consuming equipment based on the inventory and power consumption rate.

[0239] Power consumption time refers to the time required for equipment to deplete its stored energy.

[0240] Power consumption time = inventory quantity ÷ energy consumed per unit time (i.e., power consumption rate).

[0241] For example, if the inventory is 10kWh and the power consumption rate of the equipment is 2kW / h, the theoretical power consumption time is 10÷2=5h.

[0242] Step S704: Select power-consuming devices whose power consumption time is less than a preset threshold time and add them to the candidate device list.

[0243] The threshold time refers to the preset time standard, which is set in advance by technicians according to the actual situation, and will not be elaborated here.

[0244] Based on the power consumption time, power-consuming devices with a time less than the threshold are selected, and then arranged in ascending order. The device with the shortest power consumption time is selected as the final power-consuming device and included in the candidate device list.

[0245] Step S705: Based on the candidate device column, match it with the preset owner device symbol association library, and finally output the corresponding owner device symbol.

[0246] Based on the final identified power-consuming and collection devices in the candidate device list, the corresponding homeowner device symbols are then matched in the homeowner device symbol association library.

[0247] Step S207: When the account holder device identifier and the transaction device identifier match, upload the account number string to the blockchain for verification to determine whether the identities match.

[0248] When the account holder's device identifier and the transaction device identifier match, the account holder is successfully matched with the device to be transacted, thus verifying the account holder's identity. If the identities match, the identity verification passes; otherwise, it fails.

[0249] Step S208: Convert the transaction string into a transaction code, and convert the verified account number string into an identity code.

[0250] The transaction string and account holder string are formatted according to a preset format (removing spaces, standardizing delimiters, and ensuring a fixed field order) to avoid generating different transaction codes and identity codes for the same transaction due to format differences. The strings are then simply anonymized (e.g., by adding a random salt value at the end to enhance security) to avoid directly exposing the real ID. The anonymized string is then concatenated with the user's on-chain public key (the core identifier in the identity parameters), and a fixed-length identity code and transaction code are generated using a preset encryption algorithm. The encryption algorithm is preset by technical personnel according to the actual situation and will not be elaborated here.

[0251] Step S102: After verification, determine the blockchain matching hash value based on the transaction code and identity code.

[0252] Blockchain matching hash value refers to the hash result generated by combining the transaction code and identity code according to hash rules, which is used for subsequent matching.

[0253] The input transaction code and identity code are processed using the SHA-256 hash algorithm, a common blockchain algorithm. The input string of "identity code + transaction code + timestamp" is encrypted using this algorithm, outputting a 256-bit binary value, known as the "blockchain matching hash value." This hash value has a unique correspondence: any slight change in the transaction code, identity code, or timestamp will result in a completely different hash value; conversely, if the input data remains unchanged, the hash value will remain constant.

[0254] Step S103: Convert the matching hash value into a matching string.

[0255] Pairing strings refer to the strings converted from hash values.

[0256] The blockchain matching hash value is converted into a basic binary string, which is the pairing string.

[0257] Step S104: When the sum of the paired strings is 0, the pairing is completed, the transaction is carried out according to the transaction code, the lock order data is uploaded to the blockchain platform, and the transaction is closed.

[0258] Locking an order means setting the status of the current transaction to lock to prevent duplicate matching.

[0259] The transaction is considered complete when both parties confirm that the electricity delivery has been completed and the on-chain status changes to "completed".

[0260] The pairing strings are binary strings, and the sum of the two strings is 0, meaning that the result of XORing the binary strings of both parties is all 0.

[0261] When the sum of the pairing strings of the two parties in a transaction is 0, the pairing is successful. Energy and monetary transactions are then conducted based on the transaction content on the transaction code. At the same time, the transaction order is locked, the transaction data is uploaded to the blockchain platform, and after the handover is completed, the status is changed to "settlement order," thus completing the transaction.

[0262] Reference Figure 3 Determining the information string based on transaction information includes the following steps:

[0263] Step S800: Determine the transaction quantity and transaction amount based on the transaction information.

[0264] The quantity of traded refers to the numerical value of the energy being traded.

[0265] Transaction amount refers to the monetary amount of the transaction.

[0266] The transaction information includes the number of transactions and the transaction amount, which can be retrieved.

[0267] Step S801: Determine the quantity combination column of separate transactions based on the transaction quantity, and obtain the current time.

[0268] The quantity combination column refers to the way transaction quantities are combined.

[0269] The current time refers to the time when the transaction occurred, which can be obtained by directly calling the real-time time on the blockchain.

[0270] The transaction quantity is divided into a series of quantity combinations for easy trading. For example, if the transaction quantity is 10, the quantity combination series are (1, 9), (2, 8), (3, 7), (4, 6), (5, 5).

[0271] Step S802: If the current time is the preset peak power time, determine the remaining quantity based on the combination column of transaction amount and quantity.

[0272] Peak electricity time refers to the preset peak electricity consumption period, which is set in advance by technicians according to the actual situation, and will not be elaborated here.

[0273] The remaining quantity refers to the number of transactions remaining after the transaction.

[0274] During peak electricity hours, after transactions are conducted based on the peak electricity hour price, the remaining amount is determined based on the transaction amount, and then the quantity of the traded portion is determined based on the quantity combination column. Finally, the untraded portion is taken as the remaining quantity.

[0275] Step S803: If the current time is the preset off-peak electricity time, adjust the transaction amount based on the remaining quantity and the preset off-peak electricity price.

[0276] Off-peak electricity time refers to the preset low electricity consumption period, which is set in advance by technicians according to the actual situation, and will not be elaborated here.

[0277] Off-peak electricity price refers to the preset electricity price during off-peak hours, which is set in advance by technicians based on actual conditions, and will not be elaborated on here.

[0278] The remaining quantity and off-peak electricity price are substituted into the preset amount correction calculation formula to obtain the result. The formula is preset by the technicians according to the actual situation and will not be described in detail here.

[0279] The adjusted transaction amount = (remaining quantity × off-peak electricity price) × dynamic adjustment value. The dynamic adjustment value is preset by technical personnel and will not be elaborated here.

[0280] During off-peak electricity hours, the original transaction amount is adjusted based on the remaining quantity and off-peak electricity price to obtain the adjusted transaction amount.

[0281] Step S804: Match transactions based on the corrected transaction amount, transaction quantity, and quantity combination column.

[0282] The transactions are matched on the blockchain platform based on the revised transaction amount, transaction quantity, and quantity combination.

[0283] Step S805: Determine the information string based on the matching transaction information.

[0284] The information string refers to the transaction information string used for blockchain verification.

[0285] The specific method for determining the information string is described in steps S900 to S903, and will not be repeated here.

[0286] The method for determining the information string includes the following steps:

[0287] Step S900: When the transaction amounts are the same, if the transaction quantity of one of the parties involved in the transaction is the same as the quantity in the quantity combination column, then the remaining quantity in the quantity combination column shall be used as the transaction quantity.

[0288] When the transaction amounts are the same, it means that the amounts match and a partial transaction can be carried out. If the transaction quantity of one of the parties involved in the transaction matches the quantity in the quantity combination column, it means that the transaction quantity is partially matched and a partial transaction can be carried out. Then, the remaining quantity in the quantity combination column will be used as the transaction quantity, and the remaining transaction quantity after the transaction will be used as the new transaction quantity.

[0289] Step S901: When the transaction amounts are inconsistent, determine the amount combination column based on the quantity combination column and the transaction amount.

[0290] The Amount Combination column refers to all permutations of the possible combinations of transaction amounts.

[0291] The total transaction amount is broken down into price categories, with some transactions at higher prices and others at lower prices. The resulting amount is then filtered based on the quantity combination column to obtain the total amount combination column.

[0292] For example: If the transaction amount is 10 yuan and the transaction quantity is 10, the quantity combination column can be 1, 9, 2, 8, etc. The total amount can be broken down to get the unit price of 1.5 and 8.5, 2.6 and 7.4, respectively, which are the same as the quantity combination column 1 and 9.

[0293] When the transaction amounts are inconsistent, it means that the amounts do not match. The transaction amounts are divided into a quantity combination column, and the amount combination column is determined based on the quantity combination column and the transaction amount.

[0294] Step S902: When the transaction amount of one of the parties involved in the transaction matches the amount in the amount combination column, update the quantity combination column and use the remaining amount in the amount combination column as the transaction amount.

[0295] When the transaction amount of one of the participants matches the amount in the amount combination column, it means that the amount after the price change matches after the separate transactions. The quantity combination column is updated and the remaining amount in the amount combination column is used as the transaction amount. After the partial transaction is completed, the remaining amount in the amount combination column is used as the new transaction amount.

[0296] Step S903: After the initial transaction is completed, determine the information string based on the transaction quantity and transaction amount.

[0297] After the initial transaction is completed, the remaining untraded transaction quantity and transaction amount are serialized according to a preset conversion format to obtain an information string.

[0298] Based on the same inventive concept, embodiments of the present invention provide a blockchain-based distributed energy trading verification system, comprising:

[0299] The acquisition module is used to acquire transaction parameters, identity parameters, external verification information, optical fingerprint images, capacitive fingerprint signals, action features, password length, most recent collection time, and current time.

[0300] The memory is used to store programs that implement any blockchain-based distributed energy trading verification method.

[0301] The processor loads and executes programs from memory.

[0302] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0303] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A blockchain-based method for verifying distributed energy transactions, characterized in that, include: Collect transaction parameters and identity parameters; The collected transaction parameters and identity parameters are verified, and the transaction code and identity code are determined. After successful verification, a blockchain matching hash value is determined based on the transaction code and the identity code; Convert the matching hash value into a pairing string; When the sum of the pairing strings is 0, the pairing is completed, the transaction is carried out according to the transaction code, the order is locked and the data is uploaded to the blockchain platform, the order is closed and the transaction is completed. The sum of the strings being 0 means that the result of the bitwise XOR of the binary strings of the two parties to the transaction is all 0. Methods for verifying and confirming transaction codes and identity codes include: The transaction information and transaction time are retrieved based on the aforementioned transaction parameters; The information string is determined based on the transaction information and verified to confirm its authenticity; Once the verification is successful, a timestamp is determined based on the transaction time. Substitute the timestamp into a preset combination formula to determine the number of combinations; The information string and the timestamp are combined using the specified combination number to obtain the transaction string; The transaction account number and transaction device number are extracted based on the identity parameters, and the transaction device identifier is determined based on the transaction device number. The account number string is determined based on the transaction account number, and the account owner device symbol is determined based on the account number string; When the account holder device identifier and the transaction device identifier match, the account number string is uploaded to the blockchain for verification to determine whether the identities match. The transaction string is converted into a transaction code, and the verified account number string is converted into an identity code. The household owner's device identifier is determined based on the household number string, including: Based on the account number string, retrieve the identity information bound to the account number string from the preset system database and collect external verification information; The verification result is obtained by comparing the external verification information with the identity information; When the household owner's identity is determined to be qualified based on the verification result, the preset household owner device identifier association library bound to the household number string is retrieved based on the identity information to obtain the corresponding household owner device table; Based on the household owner device table, the device usage status is determined, and the corresponding household owner device symbol is matched with the device usage status and the preset household owner device symbol association library.

2. The blockchain-based distributed energy transaction verification method according to claim 1, characterized in that, Methods for determining verification results include: Collect the homeowner's optical fingerprint image and capacitive fingerprint signal; When the capacitive fingerprint signal is greater than the preset wetting threshold signal, a fingerprint reflection image is obtained through adaptive processing based on the optical fingerprint image; The fingerprint ridge map is obtained by filtering and grayscale processing the fingerprint reflection image. The capacitive fingerprint signal is differentially corrected and normalized to obtain a local fingerprint signal; A complete fingerprint image is obtained by determining the local fingerprint map based on the local fingerprint signal conversion; The complete fingerprint image is compared with the preset homeowner fingerprint image to determine the verification result.

3. The blockchain-based distributed energy transaction verification method according to claim 2, characterized in that, Specific methods for obtaining a complete fingerprint image include: Feature anchor points are extracted based on the fingerprint ridge map and the local fingerprint map, and a mapping relationship between the feature anchor points of the two is established. The overlapping area of ​​the fingerprint ridge map and the local fingerprint map is determined according to the mapping relationship; Based on the ridge line orientation of the overlapping region, a fingerprint coordinate system is constructed by splicing the fingerprint ridge map and the local fingerprint map; Based on the fingerprint coordinate system, ridge smoothing is performed to determine the transition region; Based on the transition region, the ridge details of the transition region are supplemented by linear interpolation, and the thickness of the ridges is adjusted to obtain a complete fingerprint image.

4. The blockchain-based distributed energy transaction verification method according to claim 1, characterized in that, Methods for determining verification results also include: Collect the action characteristics and password length of the current user when entering a password. The action characteristics include key interval, key pressure, and input duration. Based on the account number string, a qualified action curve is obtained by matching it in a preset input feature library; The motion curve is obtained by fitting the key interval and the key force. The selection range is determined based on the input duration and the password length; The motion curve is selected based on the defined selection area, and the motion curve is updated. The degree of fit between the motion curve and the qualified motion curve is calculated by comparing the motion curve with the input duration. If the degree of fit is not higher than the preset qualified threshold, a prompt will be made to re-enter the verification or switch the verification method; When the degree of fit is higher than the preset qualified threshold, the verification result is determined based on the degree of fit.

5. The blockchain-based distributed energy transaction verification method according to claim 1, characterized in that, The matching corresponding owner device identifiers include: Based on the household equipment table, extract the transaction volume, inventory, and power consumption rate of each device in the table; Targeted filtering is performed based on preset equipment types, which include collection equipment and power-consuming equipment; Based on the transaction volume of the collection device, the most recent collection time is collected, and the collection device with the largest transaction volume that is closest to the preset current time at the most recent collection time is selected and included in the candidate device list; Calculate the power consumption time corresponding to the power-consuming equipment based on the inventory level and the power consumption rate; Power-consuming devices whose power consumption time is less than a preset threshold time are selected and included in the candidate device list; Based on the candidate device list, it is matched with the preset owner device symbol association library, and finally the corresponding owner device symbol is output.

6. The blockchain-based distributed energy transaction verification method according to claim 1, characterized in that, The information string determined based on the transaction information includes: The transaction quantity and transaction amount are determined based on the transaction information; Determine the combination column of separate transaction quantities based on the transaction quantity, and obtain the current time; If the current time is the preset peak power time, the remaining quantity is determined based on the combination of the transaction amount and the quantity. If the current time is a preset off-peak electricity time, the transaction amount is adjusted based on the remaining quantity and the preset off-peak electricity price; Match transactions based on the revised transaction amount, transaction quantity, and quantity combination column; The information string is determined based on the matching transaction details.

7. The blockchain-based distributed energy transaction verification method according to claim 6, characterized in that, Methods for determining the information string include: When the transaction amounts are the same, if the transaction quantity of one of the parties involved in the transaction is the same as the quantity in the quantity combination column, then the remaining quantity in the quantity combination column shall be used as the transaction quantity. When the transaction amounts are inconsistent, the amount combination column is determined based on the quantity combination column and the transaction amount; When the transaction amount of one of the parties involved in the transaction matches the amount in the amount combination column, update the quantity combination column and use the remaining amount in the amount combination column as the transaction amount; After the initial transaction is completed, an information string is determined based on the transaction quantity and the transaction amount.

8. A blockchain-based distributed energy trading verification system, characterized in that, include: The acquisition module is used to obtain transaction parameters and identity parameters; A memory for storing a program that implements any one of the blockchain-based distributed energy trading verification methods according to claims 1 to 7; The processor loads and executes programs from memory.

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