408results about How to "Efficient screening" patented technology

The invention provides a textile defect type identification method, and relates to the technical field of textile defect identification, and the method specifically comprises the steps: collecting original images of a plurality of textile samples, and carrying out the detailed marking; adjusting the original image to 224 * 224 pixels, constructing a CNN model, extracting feature maps of different convolutional layers, and generating a fusion feature vector in combination with the weights of the feature layers; performing pooling processing on the fused feature map to generate a single-channel space attention map, and obtaining a weighted feature map in combination with a channel attention weight; constructing a segmentation model, outputting a mask after training, and identifying a normal region and a defect region; analyzing and identifying a defect area and extracting related features through a connected component to form a feature set; and a comprehensive scoring coefficient is constructed based on the features, so that the textile defect type can be accurately identified. According to the method, the recognition capability on fine defects can be effectively enhanced, and finer deviations can be captured, so that the recognition rate is remarkably improved.

The invention discloses an organ chip capable of simulating a physiological microenvironment in a joint and application of the organ chip. The organ chip comprises a cell culture layer 1, an elastic film 2, an air pressure control layer 3, a cell culture channel 4, a cell culture fluid perfusion channel 5, a cell suspension inlet 6, a cell suspension outlet 7, a culture fluid perfusion inlet 8, a culture fluid perfusion outlet 9, a through hole 10, a through hole 11, a first air pressure groove 12, a second air pressure groove 13, a pneumatic chamber 14, a pneumatic chamber 15, a cell bearing platform 16 and a matrix 17. The organ chip can simulate physical factors such as dynamic compression and relative sliding in joint movement in vitro, and the physical factors are combined with chemical stimulation factors.

The invention relates to a material screening method and device based on muon transmission imaging, electronic equipment and a storage medium. The method comprises the steps that actually-measured muon track flux data and a cosmic ray muon flux model are acquired; according to the actual measurement muon track flux data, the cosmic ray muon flux model and the voxel discrete grid, determining an actual measurement muon blocking capability matrix; simulating penetration processes of cosmic ray muons in different track directions for multiple times according to the cosmic ray muon flux model to obtain multiple groups of simulated muon track flux data; according to the simulation muon track flux data, the cosmic ray muon flux model and the voxel discrete grid, determining a simulation muon prevention capability matrix; determining the coincidence degree of each prior material combination according to the actually measured muon prevention capability matrix and the plurality of simulated muon prevention capability matrixes of each prior material combination; and determining a material screening result according to the coincidence degree of each prior material combination. Therefore, the actual material composition in the to-be-detected object can be effectively discriminated.

The invention provides an esophageal cancer marker screening method based on enzyme transfer method methylation sequencing, which comprises the following steps: S1, collecting esophageal early cancer and para-carcinoma tissue samples, and carrying out enzyme transfer method whole genome methylation sequencing; s2, integrating methylation data of esophageal cancer and para-carcinoma tissues in databases of TCGA, GEO, MethMarker DB and the like; s3, screening differential methylation sites, preferably sites which are hypermethylated in esophageal cancer but hypomethylated in samples such as leukocytes, so as to reduce plasma background interference; s4, obtaining a candidate marker combination based on an integrated methylation feature screening model of LASSO regression and a random forest algorithm; s5, constructing a classifier model based on the candidate marker combination, carrying out training and reasoning test on the model, and evaluating the prediction performance of the model; and S6, obtaining the esophageal cancer marker combination. According to the method, enzyme transfer sequencing and machine learning are combined, the marker prediction accuracy is remarkably improved, and a new reference is provided for clinical research of esophageal cancer.

The invention discloses a model determination method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a to-be-replaced model and an operator support library of a target platform, the target platform being a platform to be converted by a to-be-converted model; analyzing the characteristics of each operator to be replaced contained in the model to be replaced, and determining operator related information of each operator to be replaced; based on the relevant information of each operator, performing similarity matching in an operator support library to obtain a target operator matched with the operator to be replaced; and replacing the operator to be replaced based on the target operator to obtain a target model adaptive to the target platform. The characteristics of operators are analyzed to obtain operator related information, analysis and comparison are performed in an operator support library pre-constructed for a target platform based on the operator related information, and the similarity with the operator to be replaced is determined, so that the optimal target operator is found, the reasonability of operator replacement is enhanced, and the accuracy of operator replacement is improved. And the most suitable operator replacement scheme can be more efficiently screened out.

The present invention relates to the field of grain transport technology, and in particular to an agricultural grain transport device, comprising a base, a shell fixedly installed on the top of the base, a feed hopper and a discharge hopper respectively arranged at both ends of the shell, a cylindrical screen arranged inside the shell, a refrigeration mechanism arranged inside the shell, a mounting plate fixedly installed on the side of the discharge hopper away from the shell, a motor fixedly installed on the top of the mounting plate, a rotating shaft fixedly connected to the output end of the motor, and a spiral hollow disk and a solid disk fixedly connected to the outer wall of the rotating shaft. The advantages are: the inside of the shell maintains a low temperature, the low temperature helps to increase the hardness difference between grain and impurities, improve the efficiency of impurity screening, reduce the stickiness between grains, ensure the smooth flow of grain, the screen effectively screens out impurities in the grain, improves the purity of the grain, pushes the grain in layers, prevents excessive accumulation of grain, and reduces the damage of grain due to friction and extrusion.

The invention discloses a method for rapidly screening a Pennisetum americanum germplasm with freezing-resistant potential, and relates to the related fields of plant physiology and molecular biology. The method comprises the following steps: (1) preparing germplasm resource plantlets of Pennisetum americanum; (2) cold domestication at 2 DEG C; (3) freezing treatment at-7 DEG C; and (4) recovering at high temperature of 35-40 DEG C. The method has the following advantages and positive effects: firstly, the low-temperature resistance of the pennisetum americana is enhanced through cold domestication, and the difference of different resources to low-temperature response is amplified; freezing the Pennisetum americanum germplasm at-7 DEG C for 5 hours, and starting difference of response of different resources to low temperature; the high-temperature-resistant mechanism of the pennisetum alopecuroides is utilized to assist the pennisetum alopecuroides germplasm to quickly recover the regeneration capacity, and the difference of different resources on low-temperature response is further amplified, so that the pennisetum alopecuroides germplasm with freezing-resistant potential is quickly and efficiently screened.

The invention discloses an industrial park net load prediction method and system based on a multilayer perceptron, and the method comprises the following steps: dividing historical industrial park operation data according to an audio frequency collection region, and obtaining region historical operation data corresponding to the audio frequency collection region; performing clustering analysis on the historical operation data of each region based on the audio frequency fluctuation, and obtaining audio frequency-aging correlation according to a clustering result; training by using historical industrial park operation data, historical distributed energy operation data and audio frequency-aging correlation to obtain an industrial park net load prediction model; and obtaining industrial park net load prediction data according to the current industrial park operation data, the current distributed energy operation data, the industrial park load net prediction model and the audio frequency-aging correlation. The method has the beneficial effects that an aging model does not need to be constructed for each industrial device, the overall model structure is simplified, and meanwhile, the prediction efficiency and prediction accuracy of the load of the industrial park are improved.

The invention discloses a self-propelled cyperus esculentus combine harvester, which belongs to the technical field of agricultural machinery, and comprises a vehicle body, and the front end of the vehicle body is connected with an excavation table; a conveying plate chain is arranged on the excavating table, and the rear end of the conveying plate chain is in butt joint with a lifting chain mechanism fixedly connected to the vehicle body. The rear end of the lifting chain mechanism extends into the front part of the screen drum; a rotatable rotating shaft is arranged in the center of the screen drum; the rotating shaft is connected with a plurality of grass guide sheets and a plurality of throwing ropes; below the screen drum, the vehicle body is connected with a vibrating screen capable of swinging back and forth; the outlet end of the fruit and bean elevator is located above a storage bin fixedly connected to the vehicle body. And a plurality of small sieve holes are formed in the collecting plate, a chain plate belt of the conveying plate chain and a chain plate belt of the lifting chain mechanism. Under the condition that the space of the self-propelled vehicle body is limited, the mandulapalka harvester comprises five stages of screening mechanisms, has strong soil screening capacity, can effectively screen out soil blocks carried by mandulapalka root hairs, and improves the mandulapalka harvesting efficiency and quality.

The utility model discloses equipment for screening building materials in the technical field of building materials, which comprises a screening box, a screen mesh, a feed port, a first discharge port, a second discharge port, a supporting seat, a scraper mechanism and a vibrating mechanism, the feed port is arranged at the top of the screening box, the first discharge port is arranged on the side wall of the screening box, and the second discharge port is arranged on the side wall of the screening box. The second discharging port is formed in the bottom wall of the screening box, a cavity is formed in the screening box, the screen is arranged in the cavity, the feeding port, the first discharging port and the second discharging port are communicated through the cavity, the scraper mechanism comprises a scraper, a push rod and a power mechanism, and the power mechanism drives the scraper to move back and forth on the upper surface of the screen towards the first discharging port. Supporting legs are arranged at the bottom of the supporting seat, a cavity structure sleeved with the supporting legs is arranged at the top of the vibration mechanism body, the vibration mechanism is arranged at the bottom of the supporting seat, and a convex block of the vibration mechanism rotates to drive the screening box to vibrate up and down. The technical problems that a screen of an existing device is prone to being blocked, and the screening efficiency is low are solved.

The invention discloses a method for extracting macadamia nut green peel polyphenol by screening a deep eutectic solvent (DES) based on molecular simulation, and belongs to the field of molecular simulation and natural product extraction. The method comprises the following steps: firstly, quantitatively analyzing the composition of the macadamia nut green peel polyphenol, then based on the composition of the green peel polyphenol, researching the dissolving-out capacity of DES composed of different hydrogen bond donors / hydrogen bond receptors on the main polyphenol of the macadamia nut by adopting molecular simulation, screening DES with different hydrogen bond donor / hydrogen bond receptor compositions and molar ratios, and determining the content of the main polyphenol in the macadamia nut. Finally, the screened DES system is subjected to process optimization, and the optimal DES proportion is obtained. The DES system can be rapidly screened on the basis of the main composition of macadamia nut green peel polyphenol, the polyphenol yield is increased, and the problems of large use amount of an organic solvent and low extraction efficiency in traditional polyphenol extraction are solved.

The invention discloses a partial discharge identification method and device of power equipment, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring a partial discharge pulse signal of power equipment in a first preset time window; determining at least one statistical parameter of the partial discharge pulse signal based on the partial discharge pulse signal; determining a statistical significance index of the partial discharge pulse signal based on the at least one statistical parameter; on the basis of a comparison result of the statistical significance index and a statistical significance index threshold value, a target recognition model is called to detect the partial discharge pulse signal, a partial discharge recognition result of the power equipment is obtained, and the partial discharge recognition result at least comprises the discharge type of partial discharge of the power equipment. And the confidence coefficient of the partial discharge identification result is used for evaluating the accuracy degree of the partial discharge identification result. According to the invention, the technical problem of inaccurate partial discharge identification of power equipment is solved.

The invention relates to an electric power large language model construction method based on knowledge enhancement and self-adaptive fine tuning, which comprises the following steps: S1, collecting text data related to the electric power field, and preprocessing the text data; s2, using SeqGAN to generate diversified data samples, and adding a confrontation mechanism in the generation step; s3, performing text labeling and basic NLP processing by using SpaCy, and identifying and labeling electric power related entities; s4, using Neo4j to design and construct a knowledge graph of the power field; s5, designing a hybrid expert model architecture, processing different types of power data through different expert modules, and constructing a power big language model; and S6, combining the knowledge graph with the electric power large language model, and enhancing the reasoning ability of the model through a prompt and attention mechanism. Structured information of the knowledge graph can be effectively combined, and the reasoning ability of the model and the application ability of the model in complex electric power tasks can be enhanced.

The invention relates to the technical field of intelligent locks, and discloses an unlocking method and device of an intelligent door lock, computer equipment and a storage medium, and the method comprises the following steps: obtaining a working state of a dynamic tracking function in the intelligent door lock; when the working state of the dynamic tracking function is a trigger state, acquiring video information of an outdoor environment; determining a moving track of the target object in the outdoor environment according to the video information; whether the target object has a return intention or not is determined based on the moving track, and if yes, the intelligent door lock is controlled to execute unlocking operation. The intelligent door lock solves the problems that the existing intelligent door lock technology only judges the user going-out return condition through a detection module, errors are large, mistaken door opening is likely to occur, the real action intention of the user cannot be accurately recognized, and the tracking precision is single.

The invention relates to the field of methylation sequencing, in particular to a screening method of methylation sequencing fragments. The method comprises: 1) using a healthy sample, establishing a signal baseline by constructing an n-order Markov chain model, the healthy sample containing a methylation sequencing result, and a sequencing fragment of the healthy sample originating from an rDNA region; and 2) filtering a to-be-detected sample by using the established signal baseline, the to-be-detected sample containing a methylation sequencing result, and a sequencing fragment of the to-be-detected sample being derived from an rDNA region. According to the method disclosed by the invention, background noise from normal tissues is effectively filtered, and methylation signals (especially rDNA methylation signals) related to diseases are effectively screened out.

The invention discloses a commercial subject risk assessment method and system based on machine learning, and belongs to the technical field of knowledge maps. Basic features of an enterprise are obtained, a knowledge graph is constructed, and risk features are identified through graph mining; carrying out qualitative analysis and quantitative analysis on the enterprise to obtain a feature tag description; constructing a sample set, setting a misjudgment cost and a class weight based on the sample set, performing parameter tuning and verification set feedback, adjusting a decision threshold, performing binning on each feature, and obtaining a final risk score; and threshold selection and probability calibration are carried out to obtain risk grade division and decision suggestions of the enterprise, and risk label interpretation and large model auxiliary interpretation are carried out. The problems that manual collection and analysis of supervision policy documents consumes time and labor, careless omissions easily occur, and final risk judgment is prone to misalignment, misjudgment and frequent missed judgment are solved. According to the method, key rules can be accurately extracted, and hidden risk conduction links and potential risk points can be mined.

The invention relates to the field of image detection, in particular to a multi-dimensional data shrimp group health automatic evaluation method, which comprises the following steps of: arranging a main monitor and an auxiliary monitoring cluster above a shrimp group to be monitored, shooting the shrimp group to be monitored, forming a corresponding health evaluation image, numbering according to a shooting time sequence, and pre-learning to obtain a health evaluation result. The health learning model learns the main assessment pre-data, generates a bubble number change diagram, compares the bubble number change diagram with a number threshold value, generates a corresponding health assessment result according to a comparison result, and learns the auxiliary assessment pre-data to generate an auxiliary judgment diagram when the health assessment result is unqualified; and performing weighted average calculation on the bubble number change diagram and the auxiliary judgment diagram according to a preset proportion, comparing an evaluation average value with a number threshold value, generating a final health evaluation result, and automatically evaluating the health condition of the shrimp group by utilizing machine learning, thereby improving the evaluation efficiency and accuracy.

The invention discloses a functional protein mining and screening method and device, which combines multi-dimensional bioinformatics analysis such as protein structure prediction, protein function annotation, protein cluster mining and the like with an artificial intelligence method based on a protein big language to construct a process for mining and screening a functional protein. Protein structure analysis is introduced, and proteins with similar functions are explored by utilizing association of protein structures and functions and constructing a structure similarity map. The protein large language model can contain characteristic parameters related to protein functions, such as a protein structure, post-translational modification and biophysical characteristics, and reveals the inherent functional relevance of the protein in a vector matrix form. By introducing the two analysis means and combining with the traditional sequence-based protein function annotation method, the sequence similarity and affinity are lower, but proteins with corresponding functions are excavated and screened out, so that biomacromolecules can be utilized more efficiently and accurately.

The invention discloses a large-area photovoltaic land screening method, which comprises the following operation steps: S1, collecting and storing regional land resource space data, and collecting and storing large-scale DEM (Digital Elevation Model) data of a specified regional terrain; s2, inputting a specified photovoltaic land use policy, a selection tendency and sensitive area data, screening out land type data meeting specified photovoltaic land use policy conditions and the selection tendency, and rechecking and removing land parcels related to the sensitive area to obtain a preliminary range; s3, inputting topographic factors influencing photovoltaic land selection, carrying out topographic data processing, extracting corresponding data and carrying out vectorization processing; s4, mountain shadow overlay analysis and slope overlay analysis are sequentially carried out, and analyzed land pattern spots are obtained; and S5, carrying out concentration degree analysis and screening on the land pattern spots to obtain a large-area photovoltaic land preferential selection area. The photovoltaic land can be automatically and efficiently screened in a large area range, and the efficiency and accuracy of photovoltaic power station site selection are remarkably improved.

The invention discloses a BHET hydrolase designed based on an inverse folding algorithm and a preparation method of the BHET hydrolase, and relates to the technical field of biology. The preparation method comprises the following steps: selecting polyester hydrolase PHL7, learning a protein crystal structure by using a deep learning algorithm Protein MPNN, carrying out three-dimensional structure modeling by using Esmfold, carrying out molecular docking on candidate protein and BHET, carrying out molecular dynamics simulation, and screening out protein which is well combined with a substrate; performing expression and purification; the invention further discloses the application of the BHET hydrolase in hydrolysis of BHET. Through the screening process, the BHET hydrolase can be simply, rapidly and efficiently screened from the candidate sequences generated in a large scale, and the source of the BHET hydrolase is expanded.

The invention discloses a positioning method based on a weight factor and a WKNN, and relates to the technical field of Bluetooth positioning. The method comprises the following steps: dividing a preset site into a grid structure, and setting a positioning initial reference point at a node of each grid; setting a plurality of Bluetooth positioning beacons in a preset site, wherein the Bluetooth positioning beacons are used for sending Bluetooth signals to a plurality of positioning initial reference points and a to-be-positioned point; obtaining a first coordinate corresponding to each positioning initial reference point, and obtaining a plurality of first received signal strength indication values received by each positioning initial reference point; acquiring a plurality of second received signal strength indication values received by the to-be-positioned point; and obtaining an actual coordinate corresponding to the to-be-positioned point based on the plurality of first coordinates, the plurality of first received signal strength indication values and the plurality of second received signal strength indication values. By implementing the technical scheme of the invention, the accuracy of Bluetooth positioning measurement of the coordinates of the to-be-positioned point can be improved.

The invention discloses a large traffic model intelligent traffic event identification method based on cross-modal retrieval. The method comprises the following steps: inputting an image of a traffic event to be identified and text description thereof into a traffic event identification network model; a visual feature extraction module is used for extracting visual features in the image; the text feature extraction module is used for extracting text features in the text description; the visual selective filtering module is used for selectively filtering the irrelevant visual extraction features to obtain visual filtering features; the text selective filtering module is used for selectively filtering unnecessary text extraction features to obtain text filtering features; the cross-modal selective alignment module is used for performing cross-modal alignment on the visual and text extraction features to obtain visual alignment features and text alignment features; and the calculation module is used for fusing the visual filtering features and the visual alignment features as well as the text filtering features and the text alignment features to determine an identification result. According to the invention, the accuracy and efficiency of retrieval identification are improved.

The invention discloses a soybean fusarium oxysporum root rot resistant molecular marker DNKF765 and primers thereof, and belongs to the technical field of plant disease resistance detection. The invention provides a method for identifying soybean fusarium oxysporum root rot resistance. The invention provides a soybean fusarium oxysporum root rot related molecular marker, the chromosome of the molecular marker is on the No.7 chromosome of soybean, and the locus of the nucleotide at 19078765bp is G or T. The marker can be used for performing genotyping on soybean materials and accurately identifying a sample carrying a fusarium oxysporum root rot resistant gene. After the screened soybean material is inoculated with fusarium oxysporum in the seedling stage, the disease index is obviously lower than that of the soybean material without resistance. In actual production, the marker can be used for effectively screening soybean breeding materials with fusarium oxysporum root rot resistant genotypes.

The invention provides a device for screening noise stress resistant pseudosciaena crocea based on behavior response. The device comprises a culture system, a behavior monitoring system and a noise stimulation system, the culture system comprises a circulating water device arranged at the water tank, an oxygenation pump, a temperature sensing control switch and a heating rod; the behavior monitoring system comprises a monitor above the water tank and an underwater temperature-sensing depth-sensing monitor at the water tank; the noise stimulation system comprises a control module, a sound stimulation audio synthesis module, a sound stimulation signal release module and an underwater acoustic characteristic acquisition and analysis module. The underwater acoustic characteristic acquisition and analysis module comprises a hydrophone, a power amplifier and an AD converter; the screening device stimulates the large yellow croakers in the water tank through noise directionally played by the noise stimulation system, and the behavior monitoring system analyzes state changes of the large yellow croakers under noise stimulation so as to screen out the large yellow croakers resistant to noise stress. The method can be used for researching the behavior response of the pseudosciaena crocea under different noise stresses, and can be used for screening the pseudosciaena crocea individuals tolerant to noise stresses.

The utility model discloses a grouting device for road and bridge engineering construction, which belongs to the technical field of road and bridge engineering construction and comprises a storage barrel and a mounting seat. The universal wheels are all arranged at the bottom end of the mounting base, the storage barrel is arranged at the upper end of the mounting base, and a discharging pipe is arranged at the bottom end of the storage barrel; the filter plate is arranged in the material storage barrel, and the filter plate is matched with the inner wall of the material storage barrel and located at the upper end of the material storage barrel; according to the utility model, the filter plate can be pushed to move up and down orderly through the driving shaft and the cam which are arranged in the storage barrel, so that not only is the flowability of slurry enhanced, but also impurities or particulate matters in the slurry are effectively screened and filtered, the potential blocking risk of the impurities or particulate matters in the slurry to the grouting device is reduced, the grouting efficiency is improved, and the service life of the grouting device is prolonged. The grouting device for road and bridge engineering construction is better in practicability and convenience in use.

The invention discloses an embedded equipment broad bean screening method based on random forest, and the method specifically comprises the following steps: S1, collecting and preprocessing an original image, and obtaining a data set; s2, performing feature extraction on the data set, calculating importance of features, and removing redundant features to obtain features highly contributing to screening accuracy; s3, constructing a random forest model, calculating a feature gain according to the image features, determining a decision order of the features, and determining the structure and parameters of the random forest model through training adjustment; s4, performing parameter quantization on the random forest model, and performing embedded device parallel deployment on the quantized model; and S5, screening the input image data by using a random forest screening model, and rejecting by using a pneumatic mode according to a target category output after decision making to complete screening. The technical problems that a traditional screening method is low in sorting efficiency and high in manual design feature calculation complexity are solved.

The invention discloses a drought cause index screening method, and relates to the technical field of drought cause index screening, and the method comprises the steps: obtaining multi-scale drought data in time and space, and extracting the drought features of the multi-scale drought data; constructing a comprehensive drought index based on the drought characteristics; obtaining a data set based on the comprehensive drought index and the multi-scale drought data; analyzing the data set based on a drought evaluation model to obtain a loss value, inputting the loss value into an SHAP model to obtain an SHAP value, and screening drought cause indexes based on the SHAP value to obtain a screening result; the problems that in the process of obtaining the causal relationship between observation data and drought, an existing method neglects the influence of underlying surface factors on drought events, the relationship between agricultural and hydrological drought conditions and observation data cannot be mined, spatial features are neglected, and the spatial evolution features of the drought process are difficult to reflect can be solved.

The invention discloses a campus psychological health risk assessment platform and method based on artificial intelligence, and belongs to the technical field of psychological health information. Comprising a psychological health data preprocessing module, a psychological health data analysis module, a psychological health data transmission module and a risk assessment execution attribution allocation module. The campus psychological health data is screened to obtain the effective sub-psychological health data, the effective sub-psychological health data are distributed to the appropriate processing attribution, the operation condition of each available cloud processor is evaluated, and the effective sub-psychological health data in the automatic processing attribution are distributed to the appropriate available cloud processors. The high-quality data is accurately and effectively acquired and processed, and the problem that psychological data is difficult to accurately and effectively acquire and process in the prior art is solved.

The invention relates to an image segmentation technology, and provides an invoice reconciliation method and device based on image segmentation and a storage medium, and the method comprises the steps: obtaining an invoice image of a target invoice, and extracting a to-be-reconciliated region in the invoice image; according to the to-be-reconciliated area, performing area segmentation on the invoice image to obtain an area image corresponding to the to-be-reconciliated area; extracting to-be-reconciliated information in the regional image, and calculating the consistency between the to-be-reconciliated information and a preset enterprise bill; when the to-be-reconciliated information is inconsistent with a preset enterprise bill, marking the target invoice as a suspected invoice; and obtaining a transaction record corresponding to the doubted invoice, performing misjudgment verification on the doubted invoice according to the transaction record, and when the abnormal invoice is misjudged, cancelling the abnormal mark of the target invoice. According to the invention, the efficiency of invoice reconciliation can be improved.