46 results about "Abnormal distribution" patented technology

The present invention relates to pharmaceutical compositions suitable for the treatment of chronic diseases associated with the presence of abnormal or an abnormal distribution of microflora in the gastrointestinal tract of a mammalian host, which compositions comprise viable non-pathogenic or attenuated pathogenic Clostridia. The compositions further comprise one or more additional viable non-pathogenic or attenuated pathogenic microorganisms selected from the group consisting of Bacteroides, Eubacteria, Fusobacteria, Propionibacteria, Lactobacilli, anaerobic cocci, Ruminococcus, E.Coli, Gemmiger, Desullomonas, Peptostreptococcus, and fungi. The present invention also provides pharmaceutical compositions suitable for the treatment of the same chronic diseases comprising viable non-pathogenic or attenuated pathogenic Escherichia coli, at least one strain of viable non-pathogenic or attenuated pathoenic Bacteroides and at least one strain of viable non-pathogenic or attenuated pathogenic microorganism.

A method of treating chronic disorders associated with the presence of abnormal microflora or an abnormal distribution of microflora in the gastrointestinal tract involves removing the host's existing enteric microflora and substitution of feces from a disease screener donor or composition comprising microorganism selected from the group consisting of Bacteroides and E. coli.

The present invention relates to pharmaceutical compositions suitable for the treatment of chronic diseases associated with the presence of abnormal or an abnormal distribution of microflora in the gastrointestinal tract of a mammalian host, which compositions comprise viable non-pathogenic or attenuated pathogenic Clostridia. The compositions further comprise one or more additional viable non-pathogenic or attenuated pathogenic microorganisms selected from the group consisting of Bacteroides, Eubacteria, Fusobacteria, Propionibacteria, Lactobacilli, anaerobic cocci, Ruminococcus, E.Coli, Gemmiger, Desullomonas, Peptostreptococcus, and fungi. The present invention also provides pharmaceutical compositions suitable for the treatment of the same chronic diseases comprising viable non-pathogenic or attenuated pathogenic Escherichia coli, at least one strain of viable non-pathogenic or attenuated pathoenic Bacteroides and at least one strain of viable non-pathogenic or attenuated pathogenic microorganism.

The invention discloses a GIS space service based distribution network power supply capacity real-time analysis method. The method comprises the steps of real-time power failure data analysis, real-time extra low voltage circuit analysis and real-time heavy overload platform analysis; according to historical power failure data and real-time power failure data, frequent power failure analysis is conducted from regional distribution and time distribution, and pre-alarm is conducted on frequent power failure areas and time frames; power failure influence, breakdown repair, distribution network investment and consumer complaint information are associated, and the planned outage schedule rationality, distribution network fault repair efficiency, sensitivity of different areas on power failure and distribution network effectiveness are analyzed; by means of GIS space service, real-time states and analysis results of distribution transform and feeders are displayed on a map. Accordingly, locating and tracking of abnormal distribution transform information and image display of the regional abnormal degree are achieved, auxiliary correlation analysis is conducted on abnormal areas and all related factors, distribution network power supply capacity influence factors are deeply analyzed, and the decision basis is provided for distribution network transformation.

An express and logistics distribution anomaly detection method is used to quickly and effectively discover the abnormal distribution conditions of delivery time delay, process error (detour), end point error (non-delivery of the correct destination) and so on in the distribution process. The method comprises steps of analyzing the multi-attribute characteristic of the express and logistics distribution trajectory data, proposing a method combining ideals of the distance and angle-based anomaly detection algorithms to measure the abnormal degree of each trajectory sub-segment; converting detection of the delivery time delay into detection of the average speed of the delivery process; and detecting the distribution end point error by using the position coordinates, city number, city level and other attributes of the trajectory data. With the method, it is possible to quickly and accurately discover the abnormal distribution of the express and logistics, timely feedback to the logistics enterprise for processing, reduce the economic loss, and create the conditions for improving the competitiveness of the logistics enterprise.

A device for detecting a command and control channel includes: a session log collector for collecting log information of sessions generated between at least one communication device of the first network and at least one communication device of the second network; an analyzer for generating test data for respective sessions based on the log information, and calculating a test data distribution based on test data of the sessions; and a determiner for extracting a test data value corresponding to an abnormal distribution from the test data distribution based on an abnormal distribution determination standard, and estimating sessions relating to the extracted test data value as a command and control channel.

In alternative embodiments, the invention provides compositions and methods for treating various disorders and conditions in mammals, including chronic disorders in which there is a presence of an abnormal microbiota or an abnormal distribution of microflora in the gastrointestinal tract. In alternative embodiments, the invention provides liquid preparations or formulations derived from a human fecal material (e.g., a stool) processed, e.g., filtered and / or centrifuged, such that all bacteria, fungal spores and viruses are removed, but retaining the native biologically active molecules from the fecal material and bacteriophages. In alternative embodiments, the invention provides a "rough-", "incomplete-" or medium- filtered microbiota which still comprises native physiological components or nutritive agents for the bacteria, e.g., retains native biologically and nutritionally active components. In alternative embodiments, the invention provides a highly filtered or substantially purified microbiota in combination with, or having added back, a liquid preparation or formulation of the invention. In alternative embodiments, the invention provides compositions or formulations where the bacteria, or microbiota, component has been cultured, or cultured under anaerobic conditions, or harvested, stored and / or cultured under anaerobic conditions. In alternative embodiments, the invention provides various additives, compositions and donor restrictions for treating these disorders and conditions.

The invention provides an abnormal remote sensing information extraction method and device based on spatial data set analysis. The method comprises the following steps of: step 1, calculating a feature vector and a feature value of obtained remote sensing image data by adopting a spatial data set analysis mode according to the characteristics of electron abnormity and group abnormity, and carryingout data segmentation according to the feature value and the feature vector; step 2, conducting abnormal information extraction on the segmented data through a principal component analysis method, and conducting abnormal information cutting and verification; and step 3, in combination with a false color image which is formed by waveband selection and synthesis of remote sensing image data, carrying out superposition processing on grids and vectors by using coordinate layering, and then outputting a remote sensing abnormal image which is suitable for human eye observation. According to the method, the abnormal distribution range obtained by adopting a spatial data set analysis-based electronic abnormity and group abnormity extraction technology is small, the investigation pertinence on oredeposit in different landscape areas is high, ore finding target areas can be optimized more quickly and accurately, and the abnormal extraction efficiency is improved.

The invention relates to a severe infectious disease and mortality risk early warning system and method based on the virus detection rate. The severe infectious disease and mortality risk early warning system comprises a risk definition module, a risk element selection module, a risk network construction module, a risk evaluation prediction module and a graded early warning module. The severe infectious disease and mortality risk early warning system and method can effectively resolve the problems that an early warning mode based on infectious disease abnormal distribution judgment is inexact and not timely in judgment, prone to generating excessive early warning and the like and can be used for complementing and optimizing an infectious disease early-stage space-time early warning mode of a domestic infectious disease monitoring automatic early warning information platform.

The invention belongs to the field of underground pipeline detection, and particularly relates to a pipeline detection and positioning method based on buried iron pipeline magnetic field indirect detection. According to the specific realization method, three-axis magnetic field sensors are used for forming a linear detection array, abnormal distribution of magnetic induction intensity near the surface is detected in a detected area along a linear detection path, a detection magnetic field tri-component far away from the magnetic induction intensity abnormal peak point in the detection path and little influenced by the magnetic abnormal condition is used as a background magnetic field, the background magnetic field tri-component is subtracted from a detection magnetic field tri-component on each measurement point, vectors are solved as magnetic fields, and the peak connection of the obtained magnetic fields detected and calculated by each path of probe is used for positioning horizontal distribution of the underground pipeline. The pipeline is accurate to detect and position, the equipment cost and the use cost are low, and the instrument is easy to make.

The invention discloses a fast polluted soil diagnostic method based on the electrical resistance geophysical prospecting technology. The method comprises the following steps that firstly, the electrical resistance of polluted soil in a testing area is tested through a high-density electrical method, a geophysical prospecting data abnormal distribution range is found out, and thus a key area for further detection is determined; secondly, in the key area, the electrical resistance of the polluted soil is tested through one or a combination of at least two of geophysical prospecting methods of electrical resistance static sounding, electrical resistance logging and electrical resistance CT; thirdly, the pollution degree of the key area is judged according to the change rate of the electrical resistance of the key area relative to the electrical resistance of a non-polluted area. The method has the advantages that site soil and water pollution can be quickly diagnosed based on the various electrical resistance geophysical prospecting testing methods including the high-density electrical method, the electrical resistance logging method, the electrical resistance static sounding method and the electrical resistance CT method and the like.

The invention provides ultrasound-based silicon wafer distribution state recognition method and device. At two stages, namely after silicon wafers are conveyed and before the silicon wafers are taken, a first ultrasonic wave sensor and a second ultrasonic wave sensor above a silicon wafer group firstly work in a self-receiving mode, carry out an abnormal state limit position pre-scanning instruction of a protruding silicon wafer, and then carry out a silicon wafer distribution state abnormity scanning instruction through an image sensing unit on the circumferential side of a silicon wafer loader. Therefore, according to the ultrasound-based silicon wafer distribution state recognition method and device, whether abnormal distribution states of the protruding silicon wafer, an inclined silicon wafer, a laminated silicon wafer and / or a null silicon wafer exist in the region in the loader or not can be rapidly and accurately diagnosed; and a plurality of scanning detection points are distributed around the loader, so that multi-angle detection is realized; the detection accuracy is further improved; and the damages to the silicon wafers and equipment caused by manipulator movement can be well avoided. An experiment proves that the ultrasound-based silicon wafer distribution state recognition method and device are simple to realize and good in effect.

The invention belongs to the computer technological field and provides a detection method and a system of non response during an interruption. The method includes reading data in abnormal entry addresses, obtaining entry addresses of an excNormVec function through a system symbol table; reading the data of the entry addresses till the end of the function; and comparing data read in the abnormal entry addresses with data read in an excNormVec entry address till data of the end of the function are identical, wherein if the data are identical, an abnormal distribution function is analyzed to be capable of working normally; and if the data are not identical, the abnormal distribution function is analyzed to be not accurately placed in an abnormal entry point, an error message is output, and an error point is reported. According to the detection method and the system of non response during the interruption, development efficiency and diagnosibility can be greatly improved.

The invention provides an urban rail transit steel rail potential measuring method. According to the method, a voltage-to-current converter, a current-to-voltage converter, a high-precision data acquisition card, a computer, and an alternating-current power supply are adopted, wherein the voltage-to-current converter is connected with a steel rail. With the measurement method adopted, the problemthat the field measurement of the potential-to-ground of the steel rail at the power supply interval of urban rail transit due to daytime vehicle operation can be solved; a potential between the steelrail and the ground can be measured, and measurement data can be recorded; and therefore, the method is of great significance for clarifying the potential abnormal distribution of the urban rail transit.

A device for detecting a command and control channel includes: a session log collector for collecting log information of sessions generated between at least one communication device of the first network and at least one communication device of the second network; an analyzer for generating test data for respective sessions based on the log information, and calculating a test data distribution based on test data of the sessions; and a determiner for extracting a test data value corresponding to an abnormal distribution from the test data distribution based on an abnormal distribution determination standard, and estimating sessions relating to the extracted test data value as a command and control channel.

The invention discloses a method for monitoring the falling off of a distributing chute of a blast furnace. The method comprises the steps of adopting scanning radar to determine the shape of a chargelevel, specific as follows: a charge level scanning radar unit, a database unit, construction of a chute state recognition knowledge base unit, top charge level shape pattern recognition, etc; if theshape of the charge level is similar to a normal case base, judging that the chute is normal, and enabling the blast furnace to normally work; and if the shape of the charge level is similar to an abnormal case base, judging that the chute works abnormally, and carrying out blast furnace maintenance. According to the technical scheme, whether the distributing chute of the blast furnace falls offor not can be found in the first time according to the abnormal distribution of the charge level; once the distributing chute falling off is recognized by the abnormal charge level, a computer gives an alarm in time, the blast furnace shuts down, the top of the blast furnace is opened for manhole maintenance, a new chute is mounted in time, and therefore the blast furnace loss is significantly reduced.

The application discloses a distribution network operation monitoring method based on a holographic image technology, which includes the following steps: acquiring the equipment ledger of a medium- / low-voltage distribution network by taking 10kV feeders of the distribution network as units, and constructing a holographic medium- / low-voltage distribution network operation monitoring topology base map according to the equipment ledger and the topological relationship; acquiring the operation information of the 10kV feeders from a production management system; acquiring the geographic informationof the 10kV feeders from a distribution network GIS (Geographic Information System); acquiring the operation meteorological information of the 10kV feeders from a meteorological system; displaying all the information in the holographic medium- / low-voltage distribution network operation monitoring topology base map to obtain a holographic medium- / low-voltage distribution network operation monitoring map; and issuing an alarm for abnormal distribution network equipment according to the information displayed in the holographic medium- / low-voltage distribution network operation monitoring map. The method provided in the embodiment of the application, together with a background monitoring system, can improve the operation and maintenance level of 10kV medium-voltage lines, quickly locate equipment faults, prevent heavy load and overload of equipment and improve the power supply reliability of the system.

A method for predicting the response of a structural dynamic system considering parameter uncertainty, which is characterized by: including: describing the uncertainty of the system through random variables as parameters, and giving the distribution law of each random variable; Multivariate random variables are transformed into mutually statistically independent multivariate normal distribution variables; based on multivariate Fourier‑Hermite polynomial expansion, the response of the structural dynamic system is expressed as a mutually statistically independent multivariate normal distribution variable polynomial function form; using Gauss‑Hermite values Integrate and calculate the undetermined coefficients of polynomial expansion and calculate the mean response; Substitute the obtained polynomial coefficients and mean responses into the Fourier-Hermite polynomial to obtain the displayed polynomial function of the system response; Based on the system response polynomial function, embed local MonteCarlo simulation to obtain structural dynamics Statistical characteristics of the system response.

The invention belongs to the information processing technology field and especially relates to the statistical classification method of dysarthria pronunciation movement abnormal distribution. An articulator pronunciation movement statistics problem is solved. The method comprises the following steps of S100, drawing the distribution maps of a tongue tip, the center portion of a tongue and the root of the tongue in the pronunciation movement intervals of front and back, left and right, and up and down directions; S200, taking 25 mm as a threshold value and calculating the proportion of the pronunciation movement interval which is greater than the value; S300, fitting pronunciation movement interval distribution through lognormal distribution; and S400, fitting through normal distribution,using the 2.58 standard deviations of the normal distribution as the threshold value, and calculating the proportions of the abnormal pronunciation data of patients with dysarthria and normal people.In the invention, from the front and back, left and right, and up and down directions of tongue portion space movement, the pronunciation movement interval distribution of a speaker in three directions is researched, and then the patients with dysarthria and the normal people are counted according to the proportion of the abnormal pronunciation movement interval.

The invention relates to an experimental device for researching micro-leaking of gas in underground oil and gas reservoir through stratums. The experimental device comprises a simulation chamber, a gas cylinder and at least two layers of simulated stratums superposed up and down, and a soil layer positioned uppermost, wherein each simulated stratum comprises a direct covering layer with small hole penetration, and an overlying stratum with large hole penetration and positioned above the direct covering layer, and each direct covering layer is provided with a gas outlet. The experimental device can be used for simulating the micro-leaking rule and near-surface abnormal distribution of gas in a plurality of oil and gas reservoirs underground, so as to obtain theoretic knowledge for guiding the oil and gas earth chemical exploration practice; the experimental device can conduct oil-gas secondary migration simulation and mechanism study thereof; the experimental device can conduct the closure study of the oil and gas reservoir covering layer; the experimental device can conduct oil and gas consumption quantity simulation study, thus providing parameters for the calculation of resource quantity.

The embodiment of the invention provides a prediction analysis method and device for a network cell hidden danger fault. The method comprises the following steps: acquiring values of multiple characteristic indexes of a target cell within a preset period of time; generating a characteristic distribution diagram according to the value of each characteristic index; comparing the characteristic distribution diagram of each characteristic index with a corresponding abnormal distribution diagram, and obtaining the number of the abnormal characteristic distribution diagrams; obtaining an abnormal specific value according to the number of the abnormal characteristic distribution diagrams and the number of the characteristic indexes; and determining the fault status of the target cell according tothe abnormal specific value. Therefore, the prediction analysis method for the network cell hidden danger fault provided by the invention can rapidly analyze and judge hidden danger faults of a wireless network cell, and improve the network quality.

A method for estimating a quantified dynamic threshold confidence interval of three strapdown inertial measurement units is a method for estimating a threshold confidence interval based on a quantile method. The method includes defining a quantile, taking trajectory data, with three strapdown inertial measurement units error model, generated through the Monte Carlo target shooting method, as sample data, ranking the sample data in an ascending way according to the numerical magnitude, merging same values, calculating the proportion the appearance frequency of each datum in original sample data to the total data to obtain the appearance probability of each sample datum, and finally estimating the threshold confidence interval. The method solves confidence interval of data that do not comply with abnormal distribution, and is effectively applicable to interval estimation of a quantified dynamic threshold. The method has the advantages of high confidence, simple and easy use, and easy realization on a trajectory.

The embodiment of the invention discloses a data processing method and device, a readable storage medium and electronic equipment. According to the embodiment of the invention, the method comprises the steps: obtaining a point set of a target distribution range, wherein the point set is used for representing a set of all points forming the target distribution range; determining a central point ofthe target distribution range according to the point set; according to the center point, determining a plurality of line segments with the set length with the center as the end point, wherein the included angle between every two adjacent line segments is the same, the set length is the radius of a circle, and the other end point of each line segment is located on the boundary of the circle with the center point as the circle center; determining the total intersection point number of the plurality of line segments with the set length and the boundary of the distribution range; and in response to the fact that the total intersection number is greater than or equal to a set threshold, determining that the target distribution range is an abnormal distribution range. Through the method, the abnormal distribution range can be automatically detected, the waste of human resources is reduced, and the accuracy and speed of detecting the abnormal distribution range can be improved.

The invention discloses a green ball distribution intelligent control system and a control method. The system comprises an industrial camera, a laser transmitter, an optical fiber transmission device,a control parameter and alarm signal output device, an industrial personal computer and a displayer; and linear laser is horizontally and directly shot on the upper surface of a grate bed through thelaser transmitter, the industrial camera collects green ball distribution images containing linear laser rays, by sequentially obtaining the quantity and positions of peaks of the laser rays in the collected images as well as the vertical distances between the peaks and the upper edges of the images, the collected images are compared with images at the normal distribution state, the conclusion whether the distribution state is abnormal or not is obtained, and accordingly, a parameter adjusting order is given so that the current abnormal distribution state can be adjusted. Through the green ball distribution intelligent control system and the control method, uneven distribution caused by the mechanical structure problem of equipment is effectively prevented, and the system and method haveimportant significance to improvement of the distribution uniformity and pellet production efficiency, and reduction of energy consumption and equipment damage rate.

The invention discloses ''an industrial enterprise power fault diagnosis system''. The system can automatically discover almost all faults generated in production and power consumption processes of an industrial enterprise, automatically determine a fault position, and automatically analyze and diagnose a fault reason. The faults comprise enterprise distribution network disconnection, short circuit and grounding faults, a whole network voltage abnormal distribution fault, a whole network load current abnormal distribution fault, a whole network load distribution imbalance fault, a low power factor fault of whole network distribution, a whole network distribution harmonic abnormal fault, a whole network distribution line loss supernormal fault, and a whole network electric equipment fault. In order to realize automatic information monitoring, automatic information processing and automatic data transmission in a whole network, a microprocessor chip is embedded in an enterprise distribution network and is organically combined.

The invention relates to the technical field of substation inspection robots, in particular to a robot measurement value identification algorithm for an insulating gas of a high-voltage circuit breaker. The invention provides the method for revealing a hidden danger of abnormal pressure change of a sulfur hexafluoride insulating gas of a high-voltage switch, successfully overcomes the defects thatan existing robot remote centralized control system can only find out dominant defects that the gas pressure measurement value is less than a low alarm and low blocking fixed value and the like, improves the success rate of finding out the latent defects of high-voltage switching equipment, and is conductive to reflecting features of abnormal distribution of gas data detected by the inspection robot.

A distributed equipment abnormality detection system is provided for monitoring physical amounts of equipments of identical type and detecting an abnormality of each equipment. The distributed equipment abnormality detection system includes equipment management apparatuses that manage the equipments; and a management server apparatus for communicating the equipment management apparatuses. Each equipment management apparatus includes a communication unit that communicates with the management server apparatus; a measurement unit that repeatedly measures the physical amount of the equipment; a distribution information generation unit that calculates distribution information from the measured physical amount; a distribution comparison unit that calculates a difference between the distribution information generated by the distribution information generation unit, and integrated distribution information on the entire equipments delivered from the management server apparatus; and an abnormality determination unit that determines whether or not the equipment is abnormal based on the calculated difference.

The invention relates to a feature-based preprocessing and recommending method. The feature-based preprocessing method comprises the steps of dividing feature data to be preprocessed into a pluralityof sub-buckets according to distribution of objects; obtaining a plurality of mean values of to-be-preprocessed feature data in the plurality of sub-buckets; obtaining a plurality of logarithms of theplurality of mean values; and performing normalization processing on the plurality of logarithms. According to the bucket mean logarithm standardization method, the model can be more stable, the reliability is higher, and the negative influence of abnormal distribution in the data is reduced.

The invention discloses abnormal distribution aluminum hydroxide powder. The abnormal distribution aluminum hydroxide powder is made by mixing of aluminum hydroxide powder in particle size of 2micronand aluminum hydroxide powder in particle size of 18micron, and a mixing mass ratio of the aluminum hydroxide powder in particle size of 2micron and the aluminum hydroxide powder in particle size of 18micron is 11:8-11:4, preferably 11:7-11:5 and most preferably 2:1. Application of the abnormal distribution aluminum hydroxide powder refers to adding of the abnormal distribution aluminum hydroxidepowder serving as filler in preparation of a component A of two-component epoxy resin electronic pouring sealant. The abnormal distribution aluminum hydroxide powder has advantages that problems of anti-settling mutual exclusion and low viscosity in an epoxy resin system can be effectively improved, and the fusion performance of the abnormal distribution aluminum hydroxide powder is effectively improved; by application of the abnormal distribution aluminum hydroxide powder serving as filler to the epoxy resin electronic pouring sealant, low-viscosity non-settling products can be obtained, service time and fluidity of the products can be improved remarkably, and high practicality is realized.

The invention provides ultrasound-based silicon wafer distribution state recognition method and device. At two stages, namely after silicon wafers are conveyed and before the silicon wafers are taken, a first ultrasonic wave sensor and a second ultrasonic wave sensor above a silicon wafer group firstly work in a self-receiving mode, carry out an abnormal state limit position pre-scanning instruction of a protruding silicon wafer, and then carry out a silicon wafer distribution state abnormity scanning instruction through an image sensing unit on the circumferential side of a silicon wafer loader. Therefore, according to the ultrasound-based silicon wafer distribution state recognition method and device, whether abnormal distribution states of the protruding silicon wafer, an inclined silicon wafer, a laminated silicon wafer and / or a null silicon wafer exist in the region in the loader or not can be rapidly and accurately diagnosed; and a plurality of scanning detection points are distributed around the loader, so that multi-angle detection is realized; the detection accuracy is further improved; and the damages to the silicon wafers and equipment caused by manipulator movement can be well avoided. An experiment proves that the ultrasound-based silicon wafer distribution state recognition method and device are simple to realize and good in effect.