CN107844957A - A kind of Human Resources Management System based on interface - Google Patents

A kind of Human Resources Management System based on interface Download PDF

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CN107844957A
CN107844957A CN201711125892.8A CN201711125892A CN107844957A CN 107844957 A CN107844957 A CN 107844957A CN 201711125892 A CN201711125892 A CN 201711125892A CN 107844957 A CN107844957 A CN 107844957A
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mrow
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head portrait
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兰利莹
吴野
贾颜
孙京
谷佳林
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Jilin Medical College
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    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G06T2207/30201—Face

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Abstract

The present invention relates to a kind of Human Resources Management System based on interface, including：To input and the interface module of output display image, word and form, storage image, form, the database module of word, also include processor, it obtains corresponding information from the database module and be back to interface module and is shown by the input information of interface module.The present invention calculates the match condition of head portrait, keyword, form by each computational methods, and program resource is saved compared to the mode matched one by one, also, head portrait, keyword, each attribute of form match respectively it is qualified after, exact value can be exported respectively；Or export uniform data after synthesis.

Description

A kind of Human Resources Management System based on interface
Technical field
The present invention relates to Human Resources Management System technical field, more particularly to a kind of Human Resources Management System based on interface.
Background technology
Human Resources Management System, realized essentially by computer software technology, mainly giving business and government machine Pass and various tissues provide a application software of personnel management function.The computer technology that is used due to the system realized, Its information content that can be stored is more and will not lose, convenient search, can effectively reduce the manpower management operation cost of enterprise, therefore It is many significantly convenient to be brought to user, is widely applied rapidly.
However, existing Human Resources Management System is basically by ready-made software systems or management system, by system from Personnel management is realized in the computing of body, but existing management system be it is fixed mode, its in data processing, data are numerous It is trivial, using single selecting module, data are handled, data selection is not accurate enough.
The content of the invention
It is an object of the invention to provide a kind of Human Resources Management System based on interface, to overcome the technology of prior art Defect.
To achieve the above object, the present invention provides a kind of Human Resources Management System based on interface, including：To input and defeated Go out the interface module of display image, word and form, storage image, form, the database module of word, in addition to processor, It obtains corresponding information by the input information of interface module from the database module and is back to interface module progress Display；
The interface module includes head portrait input module, head portrait output module, keyword input module, resume output mould Block, form sequence number input module, Output of for ms module；The head portrait input module is believed to input the head portrait of personnel to be found Breath, after the processor is compared processing, the head portrait output module exports the head image information of corresponding personnel；The pass Key word input module, it is described to input the text information of personnel to be found, and after the processor is compared processing Resume output module exports the biographic information of related personnel；The form sequence number input module, to the correlation of entry personnel Form serial number information, and after the processor is compared processing, the Output of for ms module output related personnel's is each Kind form data；
The database module includes image storage module, form memory module and word memory module, wherein, the figure As memory module memory storage has the head image information of all personnel, the form memory module internal memory contains the sequence number of various forms And corresponding form data, the word memory module internal memory contain corresponding each personnel's sequence number and corresponding word resume letter Breath；
The processor obtains picture registration degree Probability p by calculating, and the image set is more than in picture registration degree Probability p During registration threshold value p0, then it is assumed that can be mutually matched；Keyword coincidence angle value q is obtained by calculating, in keyword registration Value q is more than keyword registration q0When, then it is assumed that selection matching；Form coincidence angle value f is obtained by calculating, in form registration Value f is more than form registration f0When, then it is assumed that selection matching；
The processor obtains head portrait, keyword, the table array (p, q, f) retrieved every time, described by result of calculation Processor contrasts the difference of three groups of Similarity values respectively, if exceeding specified similarity threshold X0, it is determined that the head portrait, keyword, Form similarity can not meet to require simultaneously, it is impossible to while export head picture, resume, list data information, then selection output is respective Similarity value highest information, as head portrait similarity p be 0.99, keyword similarity be 0.92, form similarity be 0.92, then Only export head image information；If respectively less than specified similarity threshold X0, then illustrate that each index can meet to be mutually matched, export simultaneously Head portrait, resume, list data information；If specified similarity threshold X can not be less than or greater than simultaneously0, then it is relative to export similarity Two groups of higher data messages, such as two kinds of export head picture, resume information.
Further, the processor obtains the head portrait profile information of input, in the sampling time to each head portrait collection of illustrative plates Head portrait profile information is obtained in interval respectively, gray scale linear stretch, the head portrait collection of illustrative plates after being stretched are carried out to head portrait collection of illustrative plates；Obtain The first pixel and the second pixel of the head portrait collection of illustrative plates after the stretching are taken, wherein, the first pixel A is object pixel, the first pixel Gray value be more than or equal to initial segmentation threshold value T0, sum of all pixels N；Second pixel B is background pixel, the ash of the second pixel Angle value is less than initial segmentation threshold value T0, sum of all pixels M；Head portrait collection of illustrative plates f (i, j) maximum is Vmax, minimum value Vmin；
Wherein,
Calculate the global threshold T of the gray average of the first pixel and the second pixel；
Calculate the variances sigma of the first pixel and the second pixel2
σ2=(PA+PB)(T-T0)2(3)；
Wherein, the probability of the first pixel is：
The probability of second pixel is：
If variance is within a preset range, the head portrait collection of illustrative plates is split using T as global threshold.
Further, when the processor carries out selection matching by keyword, the input number of keyword is N1, every time The keyword of input is M comprising byte number1It is secondary, the instantaneous registration i of a byte is taken in retrieving, according to the following equation Be calculated the keyword registration q of whole process,
In formula, i represents to take the instantaneous value registration of a byte, I in retrievingm0kRepresent in retrieving, every time input The average degree of polymerization value of keyword, q represent to calculate the keyword registration of gained whole process, N1Represent the input time of keyword Number, M1Keyword includes byte number, and wt represents signal transmission angular frequency, is preset value.
Further, the processor, comprehensive similarity is judged according to following formula,
In formula, X1Represent first group of Similarity value, p1, q1, f1The head portrait of retrieval, keyword, form for the first time are represented respectively Matrix；∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation.Above-mentioned formula is transported using mean square deviation and integration Calculate the comprehensive similarity for counting each；
In formula, X2Represent second group of Similarity value, p2, q2, f2The head portrait, keyword, form of second of retrieval are represented respectively Matrix；∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation；
In formula, X3Represent the 3rd group of Similarity value, p3, q3, f3The head portrait, keyword, form of third time retrieval are represented respectively Matrix；∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation.
The beneficial effects of the present invention are the present invention calculates head portrait by each computational methods, closed compared with prior art The match condition of key word, form, program resource is saved compared to the mode matched one by one, also, in head portrait, keyword, form Each attribute match respectively it is qualified after, exact value can be exported respectively；Or export uniform data after synthesis.The processor obtains Head portrait, keyword, the table array (p, q, f) retrieved every time, and calculate and judged.By above-mentioned result of calculation, the place Reason device contrasts the difference of three groups of Similarity values respectively, if exceeding specified similarity threshold X0, it is determined that the head portrait, keyword, table Lattice similarity can not meet to require simultaneously, it is impossible to while export head picture, resume, list data information, then selection export respective phase Like angle value highest information；If respectively less than specified similarity threshold X0, then illustrate that each index can meet to be mutually matched, at the same it is defeated Lift one's head picture, resume, list data information；If specified similarity threshold X can not be less than or greater than simultaneously0, then similarity phase is exported To two groups of higher data messages, such as two kinds of export head picture, resume information.
Brief description of the drawings
Fig. 1 is the functional block diagram of the Human Resources Management System of the invention based on interface.
Embodiment
Below in conjunction with accompanying drawing, the forgoing and additional technical features and advantages are described in more detail.
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this A little embodiments are used only for explaining the technical principle of the present invention, it is not intended that limit the scope of the invention.
It should be noted that in the description of the invention, the instruction such as term " on ", " under ", "left", "right", " interior ", " outer " Direction or the term of position relationship be to be based on direction shown in the drawings or position relationship, this is intended merely to facilitate description, and It is not instruction or implies that described device or element there must be specific orientation, with specific azimuth configuration and operation, therefore not It is understood that as limitation of the present invention.
In addition it is also necessary to explanation, in the description of the invention, unless otherwise clearly defined and limited, term " peace Dress ", " connected ", " connection " should be interpreted broadly, for example, it may be fixedly connected or be detachably connected, or integratedly Connection；Can be mechanical connection or electrical connection；Can be joined directly together, can also be indirectly connected by intermediary, It can be the connection of two element internals.To those skilled in the art, it can understand that above-mentioned term exists as the case may be Concrete meaning in the present invention.
As shown in fig.1, it is respectively the structural representation of the Human Resources Management System of the invention based on interface, the system bag Include to input and the interface module of output display image, word and form, storage image, form, the database module of word, Also include processor, it obtains corresponding information by the input information of interface module and is back to from the database module Interface module is shown.
Specifically, the interface module includes head portrait input module, head portrait output module, keyword input module, letter Go through output module, form sequence number input module, Output of for ms module；The head portrait input module is inputting personnel's to be found Head image information, after the processor is compared processing, the head portrait output module exports the head image information of corresponding personnel； The keyword input module, to input the text information of personnel to be found, and processing is compared by the processor Afterwards, the resume output module exports the biographic information of related personnel；The form sequence number input module, to entry personnel Related form serial number information, and after the processor is compared processing, the Output of for ms module exports relevant people The various form datas of member.
The present invention by the way that personal information is grouped into head portrait, resume and various form datas, respectively by modules from Obtained in the database module, can both obtain single information, as head portrait, resume, form data it is any, can also Much information is obtained, as described above several combination of several information.The present invention is selected by single piece of information selection or much information Select, program resource can be simplified.
Specifically, the database module includes image storage module, form memory module and word memory module, its In, described image memory module memory storage has the head image information of all personnel, and the form memory module internal memory contains various tables The sequence number of lattice and corresponding form data, the word memory module internal memory contain corresponding each personnel's sequence number and corresponding Word biographic information.
Specifically, processor of the present invention is when handling head image information, in order to save program resource as far as possible, Carried out by the way of collection of illustrative plates segmentation, the processor obtains the head portrait profile information of input, is adopted to each head portrait collection of illustrative plates Head portrait profile information is obtained in sample time interval respectively, gray scale linear stretch, the head portrait after being stretched are carried out to head portrait collection of illustrative plates Collection of illustrative plates；The first pixel and the second pixel of the head portrait collection of illustrative plates after the stretching are obtained, wherein, the first pixel A is object pixel, the The gray value of one pixel is more than or equal to initial segmentation threshold value T0, sum of all pixels N；Second pixel B is background pixel, the second picture The gray value of element is less than initial segmentation threshold value T0, sum of all pixels M；Head portrait collection of illustrative plates f (i, j) maximum is Vmax, minimum value For Vmin；
Wherein,
Calculate the global threshold T of the gray average of the first pixel and the second pixel；
Calculate the variances sigma of the first pixel and the second pixel2
σ2=(PA+PB)(T-T0)2(3)；
Wherein, the probability of the first pixel is：
The probability of second pixel is：
If variance is within a preset range, the head portrait collection of illustrative plates is split using T as global threshold.
The present invention determines the number that head portrait collection of illustrative plates is split by variance computingAnd by each segmentation after segmentation Image information presses series arrangement, is carried out one by one to the corresponding head portrait profile information in the image storage module in the data module Contrast, the picture registration degree probability P after segmentation is obtained, the picture registration degree threshold value P set is more than in picture registration degree probability P0 When, then it is assumed that it can be mutually matched.
Specifically, when the processor carries out selection matching by keyword, the input number of keyword is N1, every time The keyword of input is M comprising byte number1It is secondary, the instantaneous registration i of a byte is taken in retrieving, according to the following equation Be calculated the keyword registration q of whole process,
In formula, i represents to take the instantaneous value registration of a byte, I in retrievingm0kRepresent in retrieving, every time input The average degree of polymerization value of keyword, q represent to calculate the keyword registration of gained whole process, N1Represent the input time of keyword Number, M1Keyword includes byte number, and wt represents signal transmission angular frequency, is preset value.
Angle value q, which is overlapped, in keyword is more than keyword registration q0When, then it is assumed that selection matching.
The present invention overlaps angle value, and keyword bag by the way that each keyword retrieval process is split as into each keyword Containing byte number, by knowing the information of any byte, and then the registration of whole keyword retrieval is calculated, compared to by every The contrast one by one of individual byte, save program resource.
Specifically, when the processor carries out selection matching by form sequence number, compared according to form sequence number matrix Right, in the present embodiment, form sequence number matrix is (α, beta, gamma0), wherein, α represents form number, and β represents that form includes information Quantized value, γ0Form benchmark ratio is represented, its value is between 0.01-0.05, and its value linearly determines according to form number, in table Lattice number corresponds to 0.01 when being 1, and when form number is 2, its value is 0.011, is sequentially increased successively.
The processor determines form number registration I respectively according to following formula1, form data amount registration I2,
In formula, α represents form number, and β represents that form includes information quantization value, I3Benchmark form registration value is represented, its Preset；γ0Form benchmark ratio is represented, between 0.01-0.05, its value linearly determines its value according to form number, Form number corresponds to 0.01 when being 1, and when form number is 2, its value is 0.011, is sequentially increased successively.
Determine that form overlaps angle value after above-mentioned calculating
Angle value f, which is overlapped, in form is more than form registration f0When, then it is assumed that selection matching.
Match condition is calculated by above-mentioned each computational methods, program money is saved compared to the mode matched one by one Source, also, head portrait, keyword, each attribute of form match respectively it is qualified after, exact value can be exported respectively.As more preferably Embodiment, the processor is by comparing to determine output situation.The processor obtains the head portrait retrieved every time, key Word, table array (p, q, f), and judged according to following formula.
Described processor, comprehensive similarity is judged according to following formula.
In formula, X1Represent first group of Similarity value, p1, q1, f1The head portrait of retrieval, keyword, form for the first time are represented respectively Matrix；∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation.Above-mentioned formula is transported using mean square deviation and integration Calculate the comprehensive similarity for counting each.
Wherein I represents any integral operation based on quadratic function, and above-mentioned formula is to obtain the ratio information of integration, following Two formula are identical, are such as based on function y=ax2, it is that a ＜ b are any number in (a, b) in x values.
In formula, X2Represent second group of Similarity value, p2, q2, f2The head portrait, keyword, form of second of retrieval are represented respectively Matrix；∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation.
In formula, X3Represent the 3rd group of Similarity value, p3, q3, f3The head portrait, keyword, form of third time retrieval are represented respectively Matrix；∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation.On
By above-mentioned result of calculation, the processor contrasts the difference of three groups of Similarity values respectively, if more than specified similar Spend threshold X0, it is determined that the head portrait, keyword, form similarity can not meet to require simultaneously, it is impossible to while export head picture, letter To go through, list data information, then selection exports respective Similarity value highest information, if head portrait similarity p is 0.99, keyword phase It is 0.92 like degree, form similarity is 0.92, then only exports head image information；
If respectively less than specified similarity threshold X0, then illustrate that each index can meet to be mutually matched, while export head picture, letter Go through, list data information；
If specified similarity threshold X can not be less than or greater than simultaneously0, then the of a relatively high two groups of data letter of similarity is exported Breath, such as two kinds of export head picture, resume information.
So far, combined preferred embodiment shown in the drawings describes technical scheme, still, this area Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these embodiments.Without departing from this On the premise of the principle of invention, those skilled in the art can make equivalent change or replacement to correlation technique feature, these Technical scheme after changing or replacing it is fallen within protection scope of the present invention.

Claims (4)

  1. A kind of 1. Human Resources Management System based on interface, it is characterised in that including：To input and output display image, word With the interface module of form, storage image, form, the database module of word, in addition to processor, it passes through interface module Input information, which obtains corresponding information from the database module and is back to interface module, to be shown；
    The interface module includes head portrait input module, head portrait output module, keyword input module, resume output module, table Lattice sequence number input module, Output of for ms module；The head portrait input module passes through to input the head image information of personnel to be found After the processor is compared processing, the head portrait output module exports the head image information of corresponding personnel；The keyword is defeated Enter module, to input the text information of personnel to be found, and after the processor is compared processing, the resume is defeated Go out module to export the biographic information of related personnel；The form sequence number input module, to the related form sequence of entry personnel Number information, and after the processor is compared processing, the various forms of the Output of for ms module output related personnel Information；
    The database module includes image storage module, form memory module and word memory module, wherein, described image is deposited Storage module memory storage has the head image information of all personnel, and the form memory module internal memory contains the sequence number and phase of various forms The form data answered, the word memory module internal memory contain corresponding each personnel's sequence number and corresponding word biographic information；
    The processor obtains picture registration degree Probability p by calculating, and the picture registration set is more than in picture registration degree Probability p When spending threshold value p0, then it is assumed that can be mutually matched；Keyword coincidence angle value q is obtained by calculating, it is big to overlap angle value q in keyword In keyword registration q0When, then it is assumed that selection matching；Form coincidence angle value f is obtained by calculating, it is big to overlap angle value f in form In form registration f0When, then it is assumed that selection matching；
    The processor obtains head portrait, keyword, the table array (p, q, f) retrieved every time, by result of calculation, the processing Device contrasts the difference of three groups of Similarity values respectively, if exceeding specified similarity threshold X0, it is determined that the head portrait, keyword, form Similarity can not meet to require simultaneously, it is impossible to while export head picture, resume, list data information, then selection export each self similarity Angle value highest information；If respectively less than specified similarity threshold X0, then illustrate that each index can meet to be mutually matched, export simultaneously Head portrait, resume, list data information；If specified similarity threshold X can not be less than or greater than simultaneously0, then it is relative to export similarity Two groups of higher data messages, such as two kinds of export head picture, resume information.
  2. 2. the Human Resources Management System according to claim 1 based on interface, it is characterised in that the processor obtains input Head portrait profile information, head portrait profile information is obtained respectively in the sampling time interval to each head portrait collection of illustrative plates, to head portrait figure Spectrum carries out gray scale linear stretch, the head portrait collection of illustrative plates after being stretched；Obtain the head portrait collection of illustrative plates after the stretching the first pixel and Second pixel, wherein, the first pixel A is object pixel, and the gray value of the first pixel is more than or equal to initial segmentation threshold value T0, as Plain sum is N；Second pixel B is background pixel, and the gray value of the second pixel is less than initial segmentation threshold value T0, sum of all pixels M； Head portrait collection of illustrative plates f (i, j) maximum is Vmax, minimum value Vmin；
    Wherein,
    Calculate the global threshold T of the gray average of the first pixel and the second pixel；
    <mrow> <mi>T</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mrow> <mo>(</mo> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&amp;GreaterEqual;</mo> <msub> <mi>T</mi> <mn>0</mn> </msub> </mrow> </munder> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> <mi>N</mi> </mfrac> <mo>+</mo> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <msub> <mi>T</mi> <mn>0</mn> </msub> </mrow> </munder> <mi>f</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>,</mo> <mi>j</mi> <mo>)</mo> </mrow> </mrow> <mi>M</mi> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    Calculate the variances sigma of the first pixel and the second pixel2
    σ2=(PA+PB)(T-T0)2(3)；
    Wherein, the probability of the first pixel is：
    <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>M</mi> <mo>+</mo> <mi>N</mi> </mrow> </munderover> <mfrac> <mi>N</mi> <mrow> <mi>M</mi> <mo>+</mo> <mi>N</mi> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
    The probability of second pixel is：
    <mrow> <msub> <mi>P</mi> <mi>B</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>M</mi> <mo>+</mo> <mi>N</mi> </mrow> </munderover> <mfrac> <mi>M</mi> <mrow> <mi>M</mi> <mo>+</mo> <mi>N</mi> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
    If variance is within a preset range, the head portrait collection of illustrative plates is split using T as global threshold.
  3. 3. the Human Resources Management System according to claim 2 based on interface, it is characterised in that the processor passes through key When word carries out selection matching, the input number of keyword is N1, the keyword that inputs every time is M comprising byte number1It is secondary, examining The instantaneous registration i of a byte is taken during rope, carries out being calculated the keyword registration of whole process according to the following equation Q,
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>T</mi> <mrow> <mi>m</mi> <mn>0</mn> <mi>k</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> </munderover> <msqrt> <mn>2</mn> </msqrt> <mo>&amp;times;</mo> <mi>i</mi> <mo>&amp;times;</mo> <mi>s</mi> <mi>i</mi> <mi>n</mi> <mrow> <mo>(</mo> <mi>w</mi> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <msub> <mi>N</mi> <mn>1</mn> </msub> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mi>q</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>M</mi> <mn>1</mn> </msub> </munderover> <msub> <mi>I</mi> <mrow> <mi>m</mi> <mn>0</mn> <mi>k</mi> </mrow> </msub> </mrow> <msub> <mi>M</mi> <mn>1</mn> </msub> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
    In formula, i represents to take the instantaneous value registration of a byte, I in retrievingm0kRepresent in retrieving, input is crucial every time The average degree of polymerization value of word, q represent to calculate the keyword registration of gained whole process, N1Represent the input number of keyword, M1 Keyword includes byte number, and wt represents signal transmission angular frequency, is preset value.
  4. 4. the Human Resources Management System according to claim 3 based on interface, it is characterised in that the processor, under Formula is stated to judge comprehensive similarity,
    <mrow> <msub> <mi>X</mi> <mn>1</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>T</mi> <mo>(</mo> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> </mrow> <mo>)</mo> <mo>&amp;times;</mo> <mi>I</mi> <mo>(</mo> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> </mrow> <mo>)</mo> <mo>)</mo> </mrow> </mrow> <msqrt> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> </mrow> </msub> <mi>T</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>&amp;times;</mo> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> </mrow> </msub> <mi>I</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>1</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
    In formula, X1Represent first group of Similarity value, p1, q1, f1The head portrait of retrieval, keyword, table array for the first time are represented respectively； ∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation.Above-mentioned formula is counted using mean square deviation and integral operation Each comprehensive similarity；
    <mrow> <msub> <mi>X</mi> <mn>2</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>T</mi> <mo>(</mo> <mrow> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> </mrow> <mo>)</mo> <mo>*</mo> <mi>I</mi> <mo>(</mo> <mrow> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> </mrow> <mo>)</mo> <mo>)</mo> </mrow> </mrow> <msqrt> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> </mrow> </msub> <mi>T</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>*</mo> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> </mrow> </msub> <mi>I</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>2</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
    In formula, X2Represent second group of Similarity value, p2, q2, f2The head portrait, keyword, table array of second of retrieval are represented respectively； ∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation；
    <mrow> <msub> <mi>X</mi> <mn>3</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> </mrow> </msub> <mrow> <mo>(</mo> <mi>T</mi> <mo>(</mo> <mrow> <msub> <mi>p</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> </mrow> <mo>)</mo> <mo>*</mo> <mi>I</mi> <mo>(</mo> <mrow> <msub> <mi>p</mi> <mn>3</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>3</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> </mrow> <mo>)</mo> <mo>)</mo> </mrow> </mrow> <msqrt> <mrow> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> </mrow> </msub> <mi>T</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>*</mo> <msub> <mi>&amp;Sigma;</mi> <mrow> <msub> <mi>p</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> </mrow> </msub> <mi>I</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mn>3</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> <mo>,</mo> <msub> <mi>q</mi> <mn>3</mn> </msub> <mo>+</mo> <msub> <mi>f</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
    In formula, X3Represent the 3rd group of Similarity value, p3, q3, f3The head portrait, keyword, table array of third time retrieval are represented respectively； ∑ represents summation operation, and T represents mean square deviation computing, and I represents integral operation.
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