CN110059762A

CN110059762A - Fire-fighting vehicle sends the screening technique and system, terminal device of scheme

Info

Publication number: CN110059762A
Application number: CN201910340948.4A
Authority: CN
Inventors: 彭明喜; 张胜; 雷霆; 邱祥平; 杜渂; 周赵云; 宋平超; 王宇文; 王文英; 王玉叶; 陈弢; 张昆鹏
Original assignee: Di'aisi Information Technology Ltd By Share Ltd
Current assignee: Di'aisi Information Technology Ltd By Share Ltd
Priority date: 2019-04-26
Filing date: 2019-04-26
Publication date: 2019-07-26
Anticipated expiration: 2039-04-26
Also published as: CN110059762B

Abstract

The invention discloses screening techniques and system, terminal device that a kind of fire-fighting vehicle sends scheme, comprising: collects the case sample that scheme is sent about fire-fighting case and its vehicle；The case sample of collection is clustered according to default characteristic attribute, obtains m class case sample database；N case sample is randomly selected from every class case sample database respectively；For each case sample randomly selected, k case samples recently are found out from the case sample database of its affiliated class, find k neighbour's case sample respectively from each case sample database of other classes；According to the n case sample randomly selected from every class case sample database respectively and its corresponding k nearest case samples and (m-1) k neighbour's case sample, calculate separately to obtain the weight of each characteristic attribute；In conjunction with the target case of acquisition and the weight for each characteristic attribute being calculated, the case sample most like with target case is filtered out from the case sample of collection.The selection result of the present invention is more acurrate.

Description

Fire-fighting vehicle sends the screening technique and system, terminal device of scheme

Technical field

The present invention relates to security against fire field more particularly to a kind of fire-fighting vehicle send scheme screening technique and system, Terminal device.

Background technique

The probability that Urban Fires occur constantly rises, and be easy to cause a large amount of casualties and property loss, is receiving After fire alert, artificial experience is generally relied on to determine to send specific fire fighting truck quantity and its type, but it is this by people The decision of work experience has certain randomness and blindness, and than relatively time-consuming.

Currently, in response to this problem, traditional solution is to weigh the inspection of method using kNN (k-NearestNeighbor) Rope strategy searches similar case, is provided according to similar case and sends strategy.But kNN weighs method and does not consider characteristic attribute to weight It influences, usually different characteristic attributes has different weights to the expression of each case, and kNN weighs each feature category of method The weight of property is identical, will affect the reliability of calculated result, the reasonability of suggested design also will receive certain influence.

Summary of the invention

The object of the present invention is to provide screening techniques and system, terminal device that a kind of fire-fighting vehicle sends scheme, improve The accuracy of the similar cases screened.

Technical solution provided by the invention is as follows:

A kind of fire-fighting vehicle sends the screening technique of scheme, comprising the following steps: collects about fire-fighting case and its vehicle Send the case sample of scheme；The case sample of collection is clustered according to default characteristic attribute, obtains m class case sample database；Point N case sample is not randomly selected from every class case sample database；For each case sample randomly selected, from its affiliated class Case sample database in find out k case samples recently, find k neighbour's case respectively from each case sample database of other classes Example sample；Wherein, nearest case sample be in the case sample database for the affiliated class of case sample randomly selected described in distance with The nearest case sample of case sample that machine extracts, neighbour's case sample be in the case sample database of other classes described in distance with The nearest case sample of the case sample that machine extracts；According to the n case sample randomly selected from every class case sample database respectively Sheet and its corresponding k nearest case samples and (m-1) k neighbour's case sample, calculate separately to obtain each characteristic attribute Weight；Wherein, m, n, k are greater than the integer equal to 1；Target case in conjunction with acquisition and each feature for being calculated The weight of attribute filters out the case sample most like with the target case from the case sample of collection.

In the above-mentioned technical solutions, the weight of characteristic attribute according to extracted from all kinds of case sample databases several Case sample and its nearest case sample, neighbour's case sample are averaging after calculating respective weight, reduce case sample Influence of the distributional difference to the weight of characteristic attribute, to obtain the weight of more effective characteristic attribute, subsequent calls are with this When the weight for the characteristic attribute that mode is calculated carries out Case Retrieval, search result (the most like case screened Sample) with more science, matching degree is higher.

Further, the weight of the combination obtains target case and each characteristic attribute being calculated, from The case sample most like with the target case is filtered out in the case sample collected specifically: considers each spy The weight of attribute is levied, the similarity of each of target case and collection the case sample is calculated；By the highest case of similarity Sample is as most like case sample.

In the above-mentioned technical solutions, the case sample of all collections and the similarity of target case are calculated, is not leaked through any One possibility, ensure that the accuracy of the selection result.

Further, it is described using the highest case sample of similarity as most like case sample include: when exist it is more When the highest case sample of a identical similarity, multiple identical highest case samples of similarity are all used as most like Case sample.

In the above-mentioned technical solutions, multiple identical highest case samples of similarity are all regard as most like case sample Originally it accounts for, there are more referentials.

Further, the weight of each characteristic attribute of consideration, it is described to calculate each of target case and collection The calculation formula of the similarity of goal-trail example and the case sample collected is calculated in the similarity of case sample are as follows:

Wherein, b is characterized the quantity of attribute, and T is target case, and S is sending about fire-fighting case and its vehicle for collection One of case sample in the case sample of scheme, T_aFor value of the target case on a-th of characteristic attribute, S_aFor institute State value of the case sample on a-th of characteristic attribute, W_aFor the weight of a-th of characteristic attribute, f (T_a,S_a) be target case and That collects sends one of case sample in the case sample of scheme in a-th of feature category about fire-fighting case and its vehicle Similar function in property.

In the above-mentioned technical solutions, the weight that the characteristic attribute of calculating is introduced when calculating similarity, ensure that similar Spend the accuracy of calculated result.

Further, further comprising the steps of: according to the default expert side for sending a car deduction of points table and expert provides target case Case scores to the vehicle scheme of sending of most like case sample；When the scoring is less than preset threshold, then it is assumed that screening Most like case sample out is reasonable.

In the above-mentioned technical solutions, the case sample further progress screened is evaluated by scoring, guarantees to obtain Case reasonability.

Further, further comprising the steps of: when the scoring is not less than preset threshold, then it is assumed that is filtered out is most like Case sample is unreasonable, after the most like case sample modification, obtains modified case sample；Again according to default It sends a car deduction of points table and expert solution that expert provides target case, scheme is sent to the vehicle of modified case sample and is carried out Scoring.

In the above-mentioned technical solutions, it if the case sample being retrieved is unreasonable, is reappraised after can modifying, in many ways Position guarantees the reasonability of case.

Further, further comprising the steps of: when modified case sample is reasonable, to add it to the case of corresponding classification In example sample database.

In the above-mentioned technical solutions, constantly add using new problem and its revised solution as new case sample It adds and, promote the precision of retrieval, not only increase the accuracy that fire rescues scheme of sending a car, and for ensureing the people The security of the lives and property is of great significance.

The present invention also provides the screening systems that a kind of fire-fighting vehicle sends scheme, applied to any of the above-described fire fighting truck Send the screening technique of scheme, comprising: collection module, for collecting the case for sending scheme about fire-fighting case and its vehicle Sample；Cluster module clusters according to default characteristic attribute for the case sample to collection, obtains m class case sample database；It extracts Module, for randomly selecting n case sample from every class case sample database respectively；Searching module is randomly selected for being directed to Each case sample, k case samples recently are found out from the case sample database of its affiliated class, from each case of other classes K neighbour's case sample is found in sample database respectively；Wherein, nearest case sample is in the affiliated class of case sample randomly selected Case sample database in the nearest case sample of the case sample randomly selected described in distance, neighbour's case sample is in other classes Case sample database in the nearest case sample of the case sample randomly selected described in distance；Computing module, for according to respectively The n case sample randomly selected from every class case sample database and its corresponding k nearest case samples and (m-1) k are a close Adjacent case sample calculates separately to obtain the weight of each characteristic attribute；Wherein, m, n, k are greater than the integer equal to 1；Screening Module, for the weight of the target case in conjunction with acquisition and each characteristic attribute being calculated, from the case of collection The case sample most like with the target case is filtered out in example sample.

The present invention also provides a kind of terminal device, including memory, processor and storage are in the memory and can The computer program run on the processor, the processor are realized when running the computer program such as any of the above-described institute State the step of fire-fighting vehicle sends the screening technique of scheme.

The present invention also provides a kind of computer readable storage medium, the computer-readable recording medium storage has computer Program realizes that the fire-fighting vehicle as described in any of the above-described sends the screening technique of scheme when the computer program is executed by processor The step of.

Compared with prior art, the beneficial effects of the present invention are:

The weight of feature of present invention attribute according to several case samples extracted from all kinds of case sample databases and Its nearest case sample, neighbour's case sample are averaging after calculating respective weight, reduce case sample distribution difference pair The influence of the weight of characteristic attribute, to obtain the weight of more effective characteristic attribute, subsequent calls calculate in this way When the weight of the characteristic attribute come carries out Case Retrieval, search result (the most like case sample screened) has more Science, matching degree are higher.

Detailed description of the invention

Below by clearly understandable mode, preferred embodiment is described with reference to the drawings, to a kind of fire-fighting vehicle side of sending The screening technique and system of case, above-mentioned characteristic, technical characteristic, advantage and its implementation of terminal device give furtherly It is bright.

Fig. 1 is the flow chart of screening technique one embodiment that fire-fighting vehicle of the present invention sends scheme；

Fig. 2 is the flow chart that fire-fighting vehicle of the present invention sends another embodiment of the screening technique of scheme；

Fig. 3 is the structural schematic diagram of screening system one embodiment that fire-fighting vehicle of the present invention sends scheme；

Fig. 4 is the structural schematic diagram that fire-fighting vehicle of the present invention sends another embodiment of the screening system of scheme；

Fig. 5 is the structural schematic diagram of terminal device one embodiment of the present invention.

Drawing reference numeral explanation:

3. fire-fighting vehicle sends the screening system of scheme, 31. collection modules, 32. cluster modules, 33. abstraction modules, and 34. Searching module, 35. computing modules, 36. screening modules, 37. grading modules, 38. modified modules, 39. adding modules, 5. terminals are set It is standby, 51. memories, 52. computer programs, 53. processors.

Specific embodiment

In being described below, for illustration and not for limitation, the tool of such as particular system structure, technology etc is proposed Body details, so as to provide a thorough understanding of the present application embodiment.However, it will be clear to one skilled in the art that there is no these specific The application also may be implemented in the other embodiments of details.In other cases, it omits to well-known system, device, electricity The detailed description of road and method, so as not to obscure the description of the present application with unnecessary details.

It should be appreciated that ought use in this specification and in the appended claims, term " includes " indicates the description Feature, entirety, step, operation, the presence of element and/or component, but one or more other features, entirety, step are not precluded Suddenly, the presence or addition of operation, element, component and/or set.

To make simplified form, part related to the present invention is only schematically shown in each figure, they are not represented Its practical structures as product.In addition, there is identical structure or function in some figures so that simplified form is easy to understand Component only symbolically depicts one of those, or has only marked one of those.Herein, "one" is not only indicated " only this ", can also indicate the situation of " more than one ".

It will be further appreciated that the term "and/or" used in present specification and the appended claims is Refer to any combination and all possible combinations of one or more of associated item listed, and including these combinations.

In the specific implementation, terminal device described in the embodiment of the present application is including but not limited to such as with the sensitive table of touch Mobile phone, laptop computer or the tablet computer in face (for example, touch-screen display and/or touch tablet) etc other Portable device.It is to be further understood that in certain embodiments, the terminal device is not portable communication device, but Desktop computer with touch sensitive surface (such as: touch-screen display and/or touch tablet).

In following discussion, the terminal device including display and touch sensitive surface is described.However, should manage Solution, terminal device may include that other one or more physical Users of such as physical keyboard, mouse and/or control-rod connect Jaws equipment.

Terminal device supports various application programs, such as one of the following or multiple: drawing application program, demonstration application Program, network creation application program, word-processing application, disk imprinting application program, spreadsheet applications, game are answered With program, telephony application, videoconference application, email application, instant messaging applications, forging Refining supports application program, photo management application program, digital camera application program, digital camera applications program, web browsing to answer With program, digital music player application and/or video frequency player application program.

At least one of such as touch sensitive surface can be used in the various application programs that can be executed on the terminal device Public physical user-interface device.It can be adjusted among applications and/or in corresponding application programs and/or change touch is quick Feel the corresponding information shown in the one or more functions and terminal on surface.In this way, terminal public physical structure (for example, Touch sensitive surface) it can support the various application programs with user interface intuitive and transparent for a user.

In addition, term " first ", " second " etc. are only used for distinguishing description, and should not be understood as in the description of the present application Indication or suggestion relative importance.

In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, Detailed description of the invention will be compareed below A specific embodiment of the invention.It should be evident that drawings in the following description are only some embodiments of the invention, for For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings other Attached drawing, and obtain other embodiments.

Fig. 1 shows the implementation flow chart that a kind of fire-fighting vehicle of the present invention sends the screening technique of scheme, the screening technique Can be applied to terminal device (such as: computer understands in the present embodiment, all explanations using computer as subject, but Those skilled in the art understands that the screening technique can also be applied to other terminal devices, as long as being able to achieve corresponding function i.e. Can), screening technique the following steps are included:

S101 collects the case sample that scheme is sent about fire-fighting case and its vehicle.

Specifically, the present embodiment is mainly applied to fire-fighting domain, when reception fire protection warning needs to send fire-fighting vehicle, Most like history vehicle can be retrieved and send situation for reference.Therefore, case sample can be received from fire-fighting alert database Collection.

During collecting case sample, the information that can describe the characteristic attribute of each case sample can be extracted, Such as: weather information, time of putting on record, alarm people's description information, combustible substance, burning floor, disposition object and fire size class etc., The information that the characteristic attribute of each case sample has its exclusive.

Preferably, after having collected case sample, data cleansing can be carried out to the case sample of collection, removes repeated and redundant And and indicate unrelated field with case sample and the characteristic attribute of case sample encoded, facilitate subsequent calling.

Such as: combustible substance can be encoded from low to high according to inflammable attribute.Specific coding mode is according to actually making It is determined, is not limited thereto with situation.

S102 clusters the case sample of collection according to default characteristic attribute, obtains m class case sample database.

Each case sample of collection is clustered specifically, existing clustering algorithm can be used, such as: K-Means is poly- Class algorithm, the speed of service is fast, the result that can be quickly needed.

Default characteristic attribute can self-setting according to actual needs, such as:, can will be fiery for the case sample about fire-fighting Calamity grade is as default characteristic attribute.Reason is that generally in the case of fire-fighting, fire size class shares 5 grades, by this The categorical measure obtained if cluster is proper, convenient for calculating；Other attributive character or classification are few (3 classes or less), Calculating or the classification that will affect weight are especially more, general 70 class of object are such as disposed, so using fire size class as default feature Hierarchical cluster attribute is proper.

S103 randomly selects n case sample from every class case sample database respectively.

Specifically, above-mentioned steps, which are equivalent to from the case sample database of every one kind, all extracts n case sample, this implementation The extraction mode of example can be a for 1) the disposable n that directly extracts from every a kind of case sample database, and 2) it can also be every time from every One is extracted in class case sample database, repeats n times.

About extraction mode 2), identical case sample may be extracted sometimes, but when data volume is larger, Too much influence is not had, but extraction mode 1) does not have this misgivings completely, preferentially selects extraction mode 1).

Such as: when by all case sample clusterings of collection being 5 class case sample databases according to fire size class, when n=5, point 5 case samples are not extracted from every class case sample database, extract 5*5=25 case sample in total.

The case sample size randomly selected and its affiliated class are defined in the present embodiment, avoid repeating to be extracted into same class In multiple case samples lead to the possibility that greater weight is endowed on such, i.e. the mode of randomly selecting of the present embodiment guarantees The case sample reasonability with higher randomly selecting out.

S104 is directed to each case sample randomly selected, and k nearest cases are found out from the case sample database of its affiliated class Example sample, finds k neighbour's case sample respectively from each case sample database of other classes；Wherein, nearest case sample is The nearest case sample of the case sample that distance is randomly selected in the case sample database for the affiliated class of case sample randomly selected, Neighbour's case sample is the nearest case sample of distance is randomly selected in the case sample database of other classes case sample.

Specifically, being illustrated for the case sample B randomly selected by one, belong to the case that fire size class is 3 In sample database.

Case sample refers in feature space k of case sample B recently, in the case sample database that fire size class is 3 The k case sample nearest apart from case sample B.

Assuming that k=3, fire size class is for four case samples in 3 case sample database such as following table at a distance from case sample A Shown in one, then case sample 1,2,4 is the nearest case sample of case sample B.

Table one

Neighbour's case sample is every class of other classes from being not belonging to find in case sample database locating for case sample B K neighbour's case sample is all found out in case sample database.

Such as: one shares 5 class case sample databases, and respectively fire size class is 1 case sample database 1, and fire size class is 2 Case sample database 2, the case sample database 3 that fire size class is 3, the case sample database 4 that fire size class is 4, the case that fire size class is 5 Example sample database 5, case sample B belong to case sample database 3.

Neighbour's case sample of case sample B have the k that is found from case sample 1 away from nearest case sample, It is a away from nearest away from nearest case sample, the k found from case sample 4 from the k found in case sample 2 Case sample and the k found from case sample 5 away from nearest case sample, 4k neighbour's case sample altogether.

S105 is nearest according to the n case sample randomly selected from every class case sample database respectively and its corresponding k Case sample and (m-1) k neighbour's case sample, calculate separately to obtain the weight of each characteristic attribute；Wherein, m, n, k are Integer more than or equal to 1.

Specifically, the value of m, n, k are arranged according to actual case sample situation.The weight of characteristic attribute is taken out according to random The corresponding nearest case sample of mn case sample and each case sample and neighbour's case sample taken is calculated.

According to the n case sample randomly selected from every class case sample database respectively and its corresponding k nearest cases Sample and (m-1) k neighbour's case sample, are calculated the calculation formula of the weight of a characteristic attribute are as follows:

Wherein, W'(A) be characteristic attribute A weight, W (A) is the initialization weight of characteristic attribute A, R_tiIt is from the i-th class case T-th of the case sample randomly selected in example sample database, H_tijIt is the distance found out from the i-th class case sample database from the i-th class case J-th of nearest case sample of t-th of the case sample randomly selected in example sample database, M_tijIt (C) is from C class case sample database In j-th of neighbour's case sample of t-th of case sample for being randomly selected from the i-th class case sample database of the distance found out, C class Case sample database is not belonging to the i-th class case sample database, diff (A, R_ti,H_tij) indicate case sample R_tiWith case sample H_tij? Difference on characteristic attribute A, diff (A, R_ti,M_tij(C)) case sample R is indicated_tiWith case sample M_tij(C) on characteristic attribute A Difference.

Specifically, the general weight that initializes is set as 0 in the weight calculation of a characteristic attribute, initialization power is not considered The influence of weight, only calculates from each case sample screened in the case sample correctly classified (i.e. according to nearest case Sample and the case sample randomly selected) difference on this feature attribute and case sample (the i.e. basis in mistake classification Neighbour's case sample and the case sample randomly selected) difference, after the two is averaging respectively, COMPREHENSIVE CALCULATING is obtained.

The weight of each characteristic attribute is according to the case sample and its nearest case sample, neighbour's case for randomly selecting out Example sample is calculated.

Such as: the weight of three characteristic attributes is if desired calculated, then three times using above-mentioned formula, every time in different features It is calculated on attribute, calculates the weight of a characteristic attribute every time.

In the present embodiment, when if desired calculating the weight of multiple characteristic attributes, the case sample randomly selected be can be applied to In the weight calculation of each characteristic attribute, as long as computing repeatedly repeatedly, the weight of each characteristic attribute, calculating side can be calculated Just, fast, and the weight of each characteristic attribute calculated is representative preferably.

In one embodiment it is preferred that diff (A, R_ti,H_tij) calculation formula are as follows:

R_tiIt (A) is the value of t-th of case sample being randomly selected from the i-th class case sample database on characteristic attribute A, H_tijIt (A) is t-th of the case sample randomly selected from the i-th class case sample database from the distance found out in the i-th class case sample database Value of this j-th of the nearest case sample on characteristic attribute A, max (A) refer to the characteristic attribute in the case sample of collection The maximum value of A, min (A) refer to the minimum value of the characteristic attribute A in the case sample of collection.

Specifically, acquiring case sample R in above-mentioned formula_tiWith case sample H_tijAfter the difference on characteristic attribute A, also Divided by the maximum value of characteristic attribute A and the difference of minimum value, dimensional normalization processing is carried out to the weight of this characteristic attribute A, just In the calling of subsequent weight.

In one embodiment it is preferred that diff (A, R_ti,M_tij(C)) calculation formula are as follows:

R_tiIt (A) is the value of t-th of case sample being randomly selected from the i-th class case sample database on characteristic attribute A, M_tij(C) (A) is t-th of the case randomly selected from the i-th class case sample database from the distance found out in C class case sample database Value of j-th of the neighbour's case sample of sample on characteristic attribute A, C class case sample database are not belonging to the i-th class case sample Library, max (A) refer to the maximum value of the characteristic attribute A in the case sample of collection, and min (A) refers in the case sample of collection The minimum value of characteristic attribute A.

Similarly, case sample R is acquired in above-mentioned formula_tiWith case sample M_tij(C) after the difference on characteristic attribute A, also Divided by the maximum value of characteristic attribute A and the difference of minimum value, dimensional normalization processing is carried out to the weight of this characteristic attribute A, just In the calling of subsequent weight.

Such as: when calculating the weight of burning this characteristic attribute of floor, the number of plies for the floor that burns is exactly that each case sample exists Value on this characteristic attribute, by R_ti(A) and H_tij(A) difference between calculate separately out it is cumulative after be averaging, then by R_ti (A) and M_tij(C) difference between (A) calculate separately out it is cumulative after be averaging, after comprehensively considering, this feature category is calculated The weight of property.

In yet another example, when needing normalized, R is calculated_ti(A) and H_tij(A) divided by combustion after the difference between The difference of floor maximum value and minimum value is burnt, then is added up, is similarly R_ti(A) and M_tij(C) (A), then the two is comprehensively considered Afterwards, the weight of this characteristic attribute is calculated.

S106 combines the weight of the target case obtained and each characteristic attribute being calculated, from the case sample of collection In filter out the case sample most like with target case.

Specifically, target case is intended to the case of retrieval, it can be by manually inputting the correlated characteristic category of target case Property information, the information of the correlated characteristic attribute of target case can also be transferred from database, according to actual use situation determine It is fixed.

Optionally, S106 combines the weight of the target case obtained and each characteristic attribute being calculated, from collection The case sample most like with target case is filtered out in case sample specifically: consider the weight of each characteristic attribute, calculate The similarity of target case and each case sample of collection；Using the highest case sample of similarity as most like case sample This.

Specifically, target case and the case sample of each collection are carried out COMPREHENSIVE CALCULATING by computer on characteristic attribute, it will The highest case sample of similarity is as most like case sample.

It in other embodiments, include: to work as to exist using the highest case sample of similarity as most like case sample When the highest case sample of multiple identical similarities, multiple identical highest case samples of similarity are all used as most like Case sample.

Such as: one shares 10 case samples, wherein the similarity of 3 case samples (e, f, g) is all 0.9, other 7 Case sample is both less than 0.9, then this 3 case samples (e, f, g) are all the case sample most like with target case.

Optionally, consider the weight of each characteristic attribute, calculate the similar of each case sample of target case and collection The calculation formula of the similarity of goal-trail example and the case sample collected is calculated in degree are as follows:

Wherein, b is characterized the quantity of attribute, and T is target case, and S is sending about fire-fighting case and its vehicle for collection One of case sample in the case sample of scheme, T_aFor value of the target case on a-th of characteristic attribute, S_aFor case Value of the example sample on a-th of characteristic attribute, W_aFor the weight of a-th of characteristic attribute, f (T_a,S_a) it is target case and collection Send one of case sample in the case sample of scheme on a-th of characteristic attribute about fire-fighting case and its vehicle Similar function.

Specifically, (being assumed to be 3 using the weight that weight equation calculates each characteristic attribute when there is target case It is a), the case sample of each collection and the similarity of target case then are calculated using similarity formula, to filter out phase Like the highest case sample of degree.

In the present embodiment, the weight of characteristic attribute is unrelated with its initial weight, according only to from all kinds of case sample databases Several case samples and its nearest case sample, the neighbour's case sample extracted asks flat after calculating respective weight , influence of the case sample distribution difference to the weight of characteristic attribute is reduced, to obtain the power of more effective characteristic attribute Weight, when the weight for the characteristic attribute that subsequent calls are calculated in this way carries out Case Retrieval, search result (is filtered out The most like case sample come) with more science, matching degree is higher.

Improvement based on the above embodiment, in another embodiment of the present invention, as shown in Fig. 2, a kind of fire-fighting vehicle Send the screening technique of scheme, comprising the following steps:

S201 collects the case sample that scheme is sent about fire-fighting case and its vehicle；

S202 clusters the case sample of collection according to default characteristic attribute, obtains m class case sample database；

S203 randomly selects n case sample from every class case sample database respectively；

S204 is directed to each case sample randomly selected, and k nearest cases are found out from the case sample database of its affiliated class Example sample, finds k neighbour's case sample respectively from each case sample database of other classes；Wherein, nearest case sample is The nearest case sample of the case sample that distance is randomly selected in the case sample database for the affiliated class of case sample randomly selected, Neighbour's case sample is the nearest case sample of distance is randomly selected in the case sample database of other classes case sample；

S205 is nearest according to the n case sample randomly selected from every class case sample database respectively and its corresponding k Case sample and (m-1) k neighbour's case sample, calculate separately to obtain the weight of each characteristic attribute；Wherein, m, n, k are Integer more than or equal to 1；

In conjunction with the target case of acquisition and the weight for each characteristic attribute being calculated, sieved from the case sample of collection Selecting the case sample most like with target case includes:

S206 considers the weight of each characteristic attribute, calculates the similarity of each case sample of target case and collection；

S207 is using the highest case sample of similarity as most like case sample.

Optionally, when case sample highest there are multiple identical similarities, by multiple identical similarity highests Case sample be all used as most like case sample.

S208 is according to the default expert solution for sending a car deduction of points table and expert provides target case, to most like case sample This vehicle scheme of sending scores；

S209 is less than preset threshold when scoring, then it is assumed that the most like case sample filtered out is reasonable.

Specifically, calculating for convenience, can be carried out according to the different types of fire fighting truck used in the case sample of collection Sequence, such as: the most first kind vehicle of quantity is foam truck (Z), is then fighting fires with compressed-air foam vehicle (B), speedily carries out rescue work Rescue fire vehicle (C), Appliance carrying fire vehicle (D), E, F, G ...

Scheme is sent according to the vehicle of most like case sample, is converted into one-dimension array, indexes corresponding fire fighting truck Type of vehicle, such as: the vehicle of most like case sample send scheme be emergency rescue fire vehicle 2, communication and command disappears 1, anti-vehicle, foam truck 3,2, fighting fires with compressed-air foam vehicle, fire-fighting truntable ladder 1, then corresponding matrix is Y= (3,2,2,0,1,0,0,1 ...), it is similarly expert solution Y ', the two is facilitated to carry out difference operation.

Default deduction of points table of sending a car is defined according to fire-fighting and rescue standard, the case where for sending, will cause the wasting of resources, phase more A part of score should be deducted；For the vehicle of few group, it is likely to result in undesirable fire fighting and rescue effect or even casualties and wealth Loss is produced, so deducting more scores, presets the case where deduction of points table of sending a car is a Che Duopai, sends less design, such as table Shown in two, wherein the value range of the α in table and β can flexible setting according to the actual situation, such as: 0 < α < 2,5 < β < 10.

Table two

Expert can send scheme according to the fire-fighting vehicle that the case where target case provides a set of expert solution Y ', will filter out Most like case sample Y vehicle send scheme and expert solution be difference DELTA Y=Y-Y '=(z, b, c, d, e, f, g, H ... ...), it gives a mark further according to default deduction of points table to most like case sample, to confirm whether it is reasonable.

Such as: Δ Y=Y-Y '=(1,0, -1,0, -3,0,0,0 ... ...), then its scoring situation is 1* α₁+1*β₃+3* β₅, the score calculated and preset threshold are compared, that is, can determine whether that the vehicle of most like case sample sends scheme Whether rationally.The size of preset threshold according to the required precision of evaluation confirm, such as: preset threshold is set as 3.

The screening technique that fire-fighting vehicle sends scheme is further comprising the steps of:

S210 is not less than preset threshold when scoring, then it is assumed that the most like case sample filtered out is unreasonable, to most phase As case sample modification after, obtain modified case sample；

S220 is again according to the default expert solution for sending a car deduction of points table and expert provides target case, to modified case The vehicle scheme of sending of example sample scores.

Specifically, when the scoring calculated is not less than preset threshold, then it is assumed that the most like case screened Sample is unreasonable, needs to modify it, that is, combines expert solution modification vehicle to send scheme, again to modified case sample It scores, if unreasonable, modifies again, until rationally.

When there are multiple most like case samples, scores respectively it, filter out reasonable case sample, when When all most like case samples are all unreasonable after scoring, it can modify respectively to it, score again, until rationally Until.

Preferably, further includes: S230 adds it to the case sample of corresponding classification when modified case sample is reasonable In this library.

Specifically, the qualification of the case sample new as one is had been provided with when modified case sample is reasonable, It is added to the case sample database of corresponding classification, to enrich the diversification of case sample, database is improved, further increases sieve Select the accuracy of result.

In the present embodiment, it can score the most like case sample screened, to judge its reasonability, into The scheme that one-step optimization fire-fighting vehicle is sent；The setting of preset threshold, can be in a manner of quantization to the case sample screened This is evaluated, clear, simple；Constantly it is added to using new problem and its revised solution as new case sample Come, promote the precision of retrieval, not only increases the accuracy that fire rescues scheme of sending a car, and for ensureing people's life Property safety is of great significance.

It should be understood that in the above-described embodiments, the size of each step number is not meant that the order of the execution order, each step Execution sequence should determine that the implementation process of the embodiments of the invention shall not be constituted with any limitation with function and internal logic.

Fig. 3 is the schematic diagram for the screening system 3 that fire-fighting vehicle provided by the present application sends scheme, for ease of description, only Show part relevant to the embodiment of the present application.

The screening system that the fire-fighting vehicle sends scheme can be the software unit being built in terminal device, hardware cell Or the unit of soft or hard combination, it can also be used as independent pendant and be integrated into terminal device.

The screening system that the fire-fighting vehicle sends scheme includes:

Collection module 31, for collecting the case sample for sending scheme about fire-fighting case and its vehicle.

Cluster module 32 clusters according to default characteristic attribute for the case sample to collection, obtains m class case sample Library.

Abstraction module 33, for randomly selecting n case sample from every class case sample database respectively.

Searching module 34, for being looked for from the case sample database of its affiliated class for each case sample randomly selected K nearest case samples out, find k neighbour's case sample respectively from each case sample database of other classes；Wherein, recently Case sample is that the case sample that distance is randomly selected in the case sample database for the affiliated class of case sample randomly selected is nearest Case sample, neighbour's case sample is the nearest case of distance is randomly selected in the case sample database of other classes case sample Example sample.

Specific example refers to corresponding embodiment of the method, and details are not described herein.

Computing module 35, for according to n case sample being randomly selected from every class case sample database respectively and its right The nearest case sample of k answered and (m-1) k neighbour's case sample, calculate separately to obtain the weight of each characteristic attribute；Its In, m, n, k are greater than the integer equal to 1.

Screening module 36, for combining the weight of the target case obtained and each characteristic attribute being calculated, from receipts The case sample most like with target case is filtered out in the case sample of collection.

Optionally, screening module 36, for combining the power of the target case obtained and each characteristic attribute being calculated Weight, filters out the case sample most like with target case from the case sample of collection specifically: screening module 36 considers each The weight of a characteristic attribute calculates the similarity of each case sample of target case and collection；By the highest case of similarity Sample is as most like case sample.

Specifically, target case and the case sample of each collection are carried out COMPREHENSIVE CALCULATING on characteristic attribute, by similarity Highest case sample is as most like case sample.

Improvement based on the above system embodiment, in another embodiment of the present invention, as shown in figure 4, a kind of fire-fighting Vehicle sends the screening system 3 of scheme, comprising:

Collection module 31, for collecting the case sample for sending scheme about fire-fighting case and its vehicle；

Cluster module 32 clusters according to default characteristic attribute for the case sample to collection, obtains m class case sample Library；

Abstraction module 33, for randomly selecting n case sample from every class case sample database respectively；

Searching module 34, for being looked for from the case sample database of its affiliated class for each case sample randomly selected K nearest case samples out, find k neighbour's case sample respectively from each case sample database of other classes；Wherein, recently Case sample is that the case sample that distance is randomly selected in the case sample database for the affiliated class of case sample randomly selected is nearest Case sample, neighbour's case sample is the nearest case of distance is randomly selected in the case sample database of other classes case sample Example sample；

Computing module 35, for according to n case sample being randomly selected from every class case sample database respectively and its right The nearest case sample of k answered and (m-1) k neighbour's case sample, calculate separately to obtain the weight of each characteristic attribute；Its In, m, n, k are greater than the integer equal to 1；

Screening module 36, for combining the weight of the target case obtained and each characteristic attribute being calculated, from receipts It includes: screening module 36 that the case sample most like with target case is filtered out in the case sample of collection, considers each feature category Property weight, calculate the similarity of each case sample of target case and collection；And by the highest case sample of similarity As most like case sample.

Optionally, screening module 36 are further used for when case sample highest there are multiple identical similarities, will Multiple highest case samples of identical similarity are all used as most like case sample.

Grading module 37, for according to the default expert solution for sending a car deduction of points table and expert provides target case, to most The vehicle scheme of sending of similar case sample scores；When scoring is less than preset threshold, then it is assumed that is filtered out is most like Case sample it is reasonable.

Expert can send scheme according to the fire-fighting vehicle that the case where target case provides a set of expert solution Y ', will filter out Most like case sample Y vehicle send scheme and expert solution be difference DELTA Y=Y-Y '=(z, b, c, d, e, f, g, H ... ...), it gives a mark further according to default deduction of points table to most like case sample, to confirm whether it is reasonable.Preset threshold Size according to the required precision of evaluation confirm, such as: preset threshold is set as 3.

Fire-fighting vehicle sends the screening system of scheme further include:

Modified module 38, for being not less than preset threshold when scoring, then it is assumed that the most like case sample filtered out is not Rationally, after to most like case sample modification, modified case sample is obtained；

Grading module 37 is further used for again according to the default expert for sending a car deduction of points table and expert provides target case Scheme scores to the vehicle scheme of sending of modified case sample.

Preferably, fire-fighting vehicle sends the screening system of scheme further include: adding module 39, when modified case sample When reasonable, add it in the case sample database of corresponding classification.

It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each journey The division progress of sequence module can according to need and for example, in practical application by above-mentioned function distribution by different programs Module is completed, i.e., the internal structure of the system is divided into different program unit or module, described above complete to complete Portion or partial function.Each program module in embodiment can integrate in one processing unit, can also be each unit list It is solely physically present, can also be integrated in a processing unit with two or more units, above-mentioned integrated unit both can be with Using formal implementation of hardware, can also be realized in the form of software program unit.In addition, the specific name of each program module Also it is only for convenience of distinguishing each other, the protection scope being not intended to limit this application.

Fig. 5 is the structural schematic diagram of the terminal device 5 provided in one embodiment of the invention.As shown in figure 5, the present embodiment Terminal device 5 include: processor 53, memory 51 and be stored in the memory 51 and can be on the processor 53 The computer program 52 of operation, such as: fire-fighting vehicle sends the screening sequence of scheme.The processor 53 executes the computer Realize that above-mentioned each fire-fighting vehicle sends the step in the screening technique embodiment of scheme when program 52, alternatively, the processor Realize that above-mentioned each fire-fighting vehicle sends each module in the screening system embodiment of scheme when the 53 execution computer program 52 Function.

The terminal device 5 can set for desktop PC, notebook, palm PC, Tablet PC, mobile phone etc. It is standby.The terminal device 5 may include, but be not limited only to, processor 53, memory 51.It will be understood by those skilled in the art that figure 5 be only the example of terminal device, does not constitute the restriction to terminal device 5, may include than illustrating more or fewer portions Part perhaps combines certain components or different components, such as: terminal device can also be set including input-output equipment, display Standby, network access equipment, bus etc..

The processor 53 can be central processing unit (Central Processing Unit, CPU), can also be Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field- Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic, Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor Deng.

The memory 51 can be the internal storage unit of the terminal device 5, such as: the hard disk of terminal device is interior It deposits.The memory is also possible to the External memory equipment of the terminal device, such as: the grafting being equipped on the terminal device Formula hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card (Flash Card) etc..Further, the memory 51 can also both including the terminal device 5 internal storage unit or Including External memory equipment.The memory 51 is for storing required for the computer program 52 and the terminal device 5 Other programs and data.The memory can be also used for temporarily storing the data that has exported or will export.

In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in some embodiment Or the part recorded, reference can be made to the related descriptions of other embodiments.

Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually It is executed with hardware or software, specific application and design constraint depending on technical solution.Professional technician can be with Each specific application is used different methods to achieve the described function, but this realization is it is not considered that exceed this Shen Range please.

In embodiment provided herein, it should be understood that disclosed system/terminal device and method, it can be with It realizes in other way.For example, system described above/terminal device embodiment is only schematical, for example, institute The division of module or unit is stated, only a kind of logical function partition, there may be another division manner in actual implementation, example Such as, multiple units or components can be combined or can be integrated into another system, or some features can be ignored, or not hold Row.Another point, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, The INDIRECT COUPLING or communication connection of system or unit can be electrical, mechanical or other forms.

The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme 's.

It, can also be in addition, each functional unit in each embodiment of the application may be integrated in a processing unit It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list Member both can take the form of hardware realization, can also realize in the form of software functional units.

If the integrated module/unit be realized in the form of SFU software functional unit and as independent product sale or In use, can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-described embodiment All or part of the process in method can also send instructions to relevant hardware by computer program and complete, the meter Calculation machine program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that on The step of stating each embodiment of the method.Wherein, the computer program includes: computer program code, the computer program Code can be source code form, object identification code form, executable file or certain intermediate forms etc..It is described computer-readable to deposit Storage media may include: can carry the computer program code any entity or device or system, recording medium, USB flash disk, Mobile hard disk, magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory Device (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It needs to illustrate Be, the content that the computer readable storage medium includes can according in jurisdiction make laws and patent practice requirement into Row increase and decrease appropriate, such as: it does not include electricity according to legislation and patent practice, computer-readable medium in certain jurisdictions Carrier signal and telecommunication signal.

It should be noted that above-described embodiment can be freely combined as needed.The above is only of the invention preferred Embodiment, it is noted that for those skilled in the art, in the premise for not departing from the principle of the invention Under, several improvements and modifications can also be made, these modifications and embellishments should also be considered as the scope of protection of the present invention.

Claims

1. the screening technique that a kind of fire-fighting vehicle sends scheme, which comprises the following steps:

Collect the case sample that scheme is sent about fire-fighting case and its vehicle；

The case sample of collection is clustered according to default characteristic attribute, obtains m class case sample database；

N case sample is randomly selected from every class case sample database respectively；

For each case sample randomly selected, k case samples recently are found out from the case sample database of its affiliated class, from K neighbour's case sample is found in each case sample database of other classes respectively；

Wherein, nearest case sample is taken out at random described in distance in the case sample database for the affiliated class of case sample randomly selected The nearest case sample of the case sample taken, neighbour's case sample are taken out at random described in distance in the case sample database of other classes The nearest case sample of the case sample taken；

According to the n case sample randomly selected from every class case sample database respectively and its corresponding k nearest case samples (m-1) k neighbour's case sample, calculates separately to obtain the weight of each characteristic attribute；Wherein, m, n, k are greater than equal to 1 Integer；

In conjunction with the target case of acquisition and the weight for each characteristic attribute being calculated, from the case sample of collection In filter out the case sample most like with the target case.

2. the screening technique that fire-fighting vehicle as described in claim 1 sends scheme, which is characterized in that the combination obtained The weight of target case and each characteristic attribute being calculated, filtered out from the case sample of collection with it is described The most like case sample of target case specifically:

Consider the weight of each characteristic attribute, calculates the similarity of each of target case and collection the case sample；

Using the highest case sample of similarity as most like case sample.

3. the screening technique that fire-fighting vehicle as claimed in claim 2 sends scheme, which is characterized in that it is described by similarity most High case sample includes: as most like case sample

When case sample highest there are multiple identical similarities, all by multiple identical highest case samples of similarity As most like case sample.

4. the screening technique that fire-fighting vehicle as claimed in claim 2 sends scheme, which is characterized in that each institute of the consideration State the weight of characteristic attribute, calculate calculated in the similarity of each of target case and collection the case sample goal-trail example and The calculation formula of the similarity for the case sample collected are as follows:

Wherein, b is characterized the quantity of attribute, T is target case, and S be that collection sends scheme about fire-fighting case and its vehicle Case sample in one of case sample, T_aFor value of the target case on a-th of characteristic attribute, S_aFor the case Value of the example sample on a-th of characteristic attribute, W_aFor the weight of a-th of characteristic attribute, f (T_a,S_a) it is target case and collection Send one of case sample in the case sample of scheme on a-th of characteristic attribute about fire-fighting case and its vehicle Similar function.

5. the screening technique that fire-fighting vehicle as described in claim 1 sends scheme, which is characterized in that further comprising the steps of:

According to the default expert solution for sending a car deduction of points table and expert provides target case, to the vehicle of most like case sample The scheme of sending scores；

When the scoring is less than preset threshold, then it is assumed that the most like case sample filtered out is reasonable.

6. the screening technique that fire-fighting vehicle as claimed in claim 5 sends scheme, which is characterized in that further comprising the steps of:

When the scoring is not less than preset threshold, then it is assumed that the most like case sample filtered out is unreasonable, to the most phase As case sample modification after, obtain modified case sample；

Again according to the default expert solution for sending a car deduction of points table and expert provides target case, to modified case sample The vehicle scheme of sending scores.

7. the screening technique that fire-fighting vehicle as claimed in claim 6 sends scheme, which is characterized in that further comprising the steps of:

When modified case sample is reasonable, add it in the case sample database of corresponding classification.

8. the screening system that a kind of fire-fighting vehicle sends scheme, which is characterized in that disappear applied to as claimed in claim 1 to 7 Anti-vehicle sends the screening technique of scheme, comprising:

Collection module, for collecting the case sample for sending scheme about fire-fighting case and its vehicle；

Cluster module clusters according to default characteristic attribute for the case sample to collection, obtains m class case sample database；

Abstraction module, for randomly selecting n case sample from every class case sample database respectively；

Searching module, for finding out k from the case sample database of its affiliated class most for each case sample randomly selected Nearly case sample, finds k neighbour's case sample respectively from each case sample database of other classes；

Computing module, for according to the n case sample randomly selected from every class case sample database respectively and its corresponding k Nearest case sample and (m-1) k neighbour's case sample, calculate separately to obtain the weight of each characteristic attribute；Wherein, m, n, k All it is greater than the integer equal to 1；

Screening module, for the weight of the target case in conjunction with acquisition and each characteristic attribute being calculated, from collection The case sample in filter out the case sample most like with the target case.

9. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor The computer program of operation, which is characterized in that the processor is realized when running the computer program as in claim 1-7 The step of any one fire-fighting vehicle sends the screening technique of scheme.

10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists In realization fire-fighting vehicle as described in any one of claim 1-7 sends scheme when the computer program is executed by processor Screening technique the step of.