CN112990787B - Service strategy determination method, system, equipment and medium - Google Patents

Service strategy determination method, system, equipment and medium Download PDF

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CN112990787B
CN112990787B CN202110503174.XA CN202110503174A CN112990787B CN 112990787 B CN112990787 B CN 112990787B CN 202110503174 A CN202110503174 A CN 202110503174A CN 112990787 B CN112990787 B CN 112990787B
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姚娟娟
钟南山
樊代明
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Shanghai Mingping Medical Data Technology Co ltd
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Abstract

The invention provides a service strategy determination method, a system, equipment and a medium, wherein the method comprises the steps of obtaining evaluation information of a plurality of objects to be served, classifying the evaluation information according to a preset classification rule to obtain a plurality of sub evaluation information belonging to different categories, clustering the sub evaluation information to obtain a plurality of sub object groups to be served, obtaining the sub evaluation information of the preset objects to be served in the sub object groups to be served, using the sub evaluation information as matching characteristic information of the sub object groups to be served to obtain service demand information of a target object to be served, determining an optimal service strategy of the target object to be served according to the service demand information, paying attention to the individual demand of each old man, being simple and easy to operate, saving cost and making dedicated customized service for the old man simply, conveniently and economically.

Description

Service strategy determination method, system, equipment and medium
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a method, a system, a device, and a medium for determining a service policy.
Background
At present, the society is gradually stepping into the aging era, the old-age care problem of the old is more and more concerned and valued by the public, the community old care and the spontaneous group old care are gradually aroused, and a plurality of old people also choose to enter the home.
However, the physical conditions, emotional requirements, living habits and the like of each of the old people are different, if a corresponding service strategy is independently formulated according to the conditions of each of the old people, the execution level is complex, the cost is high, more energy of an organizer is required to be consumed, and the development of work is not facilitated. If a series of standardized services are directly carried out by 'one-time cutting', the diversified and personalized requirements of old people can be ignored, the experience degree of the old people is not high, and the contradiction of old care is further aggravated. Therefore, a service policy determination method which can not only focus on the personalized requirements of each old person, but also is simple and easy to operate and saves cost is urgently needed.
Disclosure of Invention
In view of the above disadvantages of the prior art, the present invention provides a method, a system, a device and a medium for determining a service policy, so as to solve the technical problem of an urgent need in the related art for a method for determining a service policy that is simple and easy to operate and can save cost while paying attention to the personalized needs of each elderly person.
The invention provides a service strategy determination method, which comprises the following steps:
obtaining evaluation information of a plurality of objects to be served, wherein the evaluation information comprises evaluation items and evaluation scores corresponding to the evaluation items;
classifying the evaluation information according to a preset classification rule to obtain a plurality of sub-evaluation information belonging to different classes;
clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served;
acquiring sub-evaluation information of a preset object to be served in the sub-object group to be served, and using the sub-evaluation information as matching characteristic information of the sub-object group to be served;
acquiring service demand information of a target object to be served, wherein the service demand information comprises the matching characteristic information of each sub-service object group where the target object to be served is located, and the target object to be served is one of a plurality of objects to be served;
and determining the optimal service strategy of the target object to be served according to the service demand information.
Optionally, the determining the optimal service policy of the target object to be served according to the service demand information includes:
the method comprises the steps of obtaining a plurality of preset service strategies, wherein the service strategies comprise service information, and the service information comprises evaluation items aimed at by the service strategies and evaluation score thresholds of the evaluation items;
and determining the optimal service strategy according to the service demand information and the service information.
Optionally, the determining the optimal service policy according to the service demand information and the service information includes:
if each service strategy comprises the evaluation item of one category, respectively acquiring each matching characteristic information corresponding to the target object to be served;
determining a target service strategy, wherein the category of the evaluation item in the target service strategy is the same as the category of the evaluation item in the matching characteristic information;
if the evaluation score of the matched characteristic information is within the evaluation score threshold value of the target service strategy, taking the target service strategy as a sub-optimal service strategy corresponding to the matched characteristic information;
and integrating the sub-optimal service strategies as the optimal service strategies of the target object to be served.
Optionally, clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served includes:
converting the sub-evaluation information into a coordinate information group of the object to be served according to the evaluation item and the evaluation score;
selecting a plurality of coordinate information groups of the objects to be served as initial clustering centers, and determining initial distances between the initial clustering centers and the coordinate information groups of the objects to be served except the initial clustering centers;
determining a plurality of initial subgroups according to the initial distances;
if the initial group meets the preset condition, taking the initial group as a sub object group to be served;
if the initial groups do not accord with the preset condition, performing iterative processing on the initial groups, wherein the iterative processing comprises determining an iterative clustering center from each initial group, determining iterative distances between the iterative clustering center and coordinate information groups of the objects to be served except the iterative clustering center, determining a plurality of iterative groups according to the iterative distances, and if the iterative groups accord with the preset condition, taking the iterative groups as the sub object groups to be served;
and if the iteration group does not meet the preset condition, repeating the iteration processing on the iteration group until the newly obtained iteration group meets the preset condition, and taking the newly obtained iteration group as a sub-object group to be served.
Optionally, the determining manner of the preset condition includes:
acquiring a coordinate information group of an object to be served, and determining a first average distance between the coordinate information group of the object to be served and other coordinate information groups of the object to be served in a sub object group to be served where the coordinate information group of the object to be served is located;
determining second average distances between the coordinate information group of the object to be served and other coordinate information groups of the object to be served except for the sub object group to be served where the coordinate information group of the object to be served is located, and selecting a minimum value in the second average distances as a minimum average distance;
determining cluster reliability according to the first average distance and the minimum average distance;
if the clustering reliability is greater than a preset clustering reliability threshold value, the preset condition is met;
and if the clustering reliability is less than or equal to a preset clustering reliability threshold value, the clustering reliability does not accord with the preset condition.
Optionally, the determining manner of the cluster reliability includes:
Figure 704246DEST_PATH_IMAGE001
wherein P (i) is the cluster confidence, Md (i) is the minimum average distance, and d (i) is the first average distance.
Optionally, the method further includes:
sequencing each sub object group to be served, wherein the sequencing is determined according to the number of the sub object groups to be served, which comprise the objects to be served;
acquiring the sub-object groups to be served which are sequenced at the top N as target sub-object groups to be served;
and arranging the objects to be served which simultaneously belong to the target sub-object group to be served in a target service area.
The invention also provides a service policy determination system, which comprises:
the system comprises an evaluation information acquisition module, a service management module and a service management module, wherein the evaluation information acquisition module is used for acquiring evaluation information of a plurality of objects to be served, and the evaluation information comprises evaluation items and evaluation scores corresponding to the evaluation items;
the classification module is used for classifying the evaluation information according to a preset classification rule to obtain a plurality of sub-evaluation information belonging to different classes;
the clustering processing module is used for clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served;
the matching characteristic information determining module is used for acquiring the sub evaluation information of the preset object to be served in the sub object group to be served and taking the sub evaluation information as the matching characteristic information of the sub object group to be served;
the service demand information acquisition module is used for acquiring service demand information of a target object to be served, wherein the service demand information comprises the matching characteristic information of each sub-service object group where the target object to be served is located, and the target object to be served is one of a plurality of objects to be served;
and the optimal service strategy determining module is used for determining the optimal service strategy of the target object to be served according to the service requirement information.
The invention also provides an electronic device, which comprises a processor, a memory and a communication bus;
the communication bus is used for connecting the processor and the memory;
the processor is configured to execute the computer program stored in the memory to implement the method according to any of the embodiments described above.
The invention also provides a computer-readable storage medium having stored thereon a computer program for causing a computer to perform the method according to any one of the embodiments described above.
The invention has the beneficial effects that: the method comprises the steps of obtaining evaluation information of a plurality of objects to be served, wherein the evaluation information comprises evaluation items and evaluation scores corresponding to the evaluation items, classifying the evaluation information according to a preset classification rule to obtain sub-evaluation information of the objects to be served, belonging to different classes, clustering the sub-evaluation information belonging to the same class to obtain a plurality of sub-object groups belonging to the same class, obtaining the sub-evaluation information of the preset objects to be served in the sub-object groups to be served as matching characteristic information of the sub-object groups to be served, obtaining service demand information of the target object to be served, wherein the service demand information comprises the matching characteristic information of the sub-object groups in which the target object to be served is located, and the target object to be served is one of the plurality of objects to be served, the optimal service strategy of the target object to be served is determined according to the service demand information, so that the individual demand of each old man can be concerned, the operation is simple and easy, the cost is saved, and the exclusive customized service is established for the old man simply, conveniently and economically.
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Fig. 1 is a flowchart illustrating a service policy determination method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a service policy determination system in an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
In the following description, numerous details are set forth to provide a more thorough explanation of embodiments of the present invention, however, it will be apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring embodiments of the present invention.
Example one
As shown in fig. 1, the present embodiment provides a service policy determining method, including:
s101: and acquiring evaluation information of a plurality of objects to be served.
The evaluation information comprises an evaluation item and an evaluation score corresponding to the evaluation item, and the determination of the evaluation score can be obtained by constructing a mapping relation between an evaluation item index and the evaluation score in advance or can be realized by the existing related technical means.
The evaluation items of each object to be served can be the same or different, and can be set by a person skilled in the art according to needs.
Optionally, a minimum evaluation information threshold may be set, that is, how long the evaluation content of how many evaluation items is acquired at the minimum, so as to form corresponding evaluation score, which may be calculated as qualified evaluation information. Otherwise, the single data is not representative enough for the object to be served.
Alternatively, the evaluation information may be information within a preset time period, and the preset time period may be a time period set by a person skilled in the art as needed. It will be appreciated that if the predetermined time period is long enough, the health assessment information collected may be more reliable.
The service object may be an old person living in the same community, an old person who has a spontaneous gathering of a crowd-funding idea, an old person who is willing to enter a certain endowment institution, or a disabled person who needs external service.
For the old people participating in community care, the service strategy determination method provided by the embodiment can be used for dividing the old people into a plurality of sub-service object groups according to categories, and then determining the same sub-optimal service strategy according to each sub-service object group to provide corresponding services, so that the old people can obtain satisfied services, and on the other hand, the services can be provided in a large-scale intensive manner, the service providing cost is reduced, the energy for making the service strategy is reduced, and the method is simple and efficient.
Particularly, when the community street purchases related endowment services, the representativeness is limited in the past either by a sampling survey mode or a mass voting mode, and the standard of the endowment services is too single, so that the experience of the mass is not high. Through processing the evaluation information of the old people, a corresponding child object group to be served is obtained, and the community street can formulate corresponding endowment services according to the member number, the distribution condition and the like of the child object group to be served. There can also be specific data support to require specific service content for the endowment service.
Optionally, the obtaining source of the evaluation information includes but is not limited to: reporting information of the intelligent wearable device; information actively stated by the object to be served, native place, age, work experience, speciality and the like of the object to be served; historical medical information.
The wearable equipment of intelligence includes but not limited to intelligent bracelet, intelligent necklace, smart watch etc. gathers the speech information of waiting to serve the object, the image information of body side etc. through this equipment, and then can summarize the work and rest law, motion information, diet information etc. that obtain waiting to serve the object.
Medical information includes, but is not limited to, medical records, hospitalization records, drug purchase records, and the like.
Optionally, the plurality of objects to be served may be a collective object to be served, or may be a higher-level set formed by a plurality of collective objects to be served.
S102: classifying the evaluation information according to a preset classification rule to obtain a plurality of sub-evaluation information belonging to different categories.
Optionally, the evaluation information may include several categories of evaluation items, such as a mental health degree category, a dietary habit category, a chronic disease category, a life and rest category, an interest and hobby category, and the like, and the health condition, hobbies, a work and rest law, a dietary habit, a possibly required medical service, and the like of the object to be served may be determined through the evaluation information.
The preset classification rule may be preset by a person skilled in the art according to needs, and is not limited herein. The evaluation items may be set in advance by those skilled in the art.
Optionally, before classifying the evaluation information, data validation of the evaluation information is further required to ensure that the subsequently used evaluation information data are all data meeting requirements, for example, if the collected evaluation item quantity of a certain object to be served is less than a preset evaluation item quantity threshold, the evaluation information of the object to be served is screened out, and the object to be served is prompted to supplement the evaluation information.
Optionally, the evaluation dimensions of the plurality of objects to be served are at least two dimensions, that is, the evaluation items at least include two categories, if the objects to be served with the plurality of evaluation dimensions are matched with a proper service strategy, the service strategy needs to be formulated in a grading manner for the plurality of evaluation dimensions, the number of the service strategies is large, and thus the cost is relatively high. Although the method cannot achieve the purpose of 'thousands of people and faces', the method can also achieve the purpose of obtaining satisfactory service strategies as much as possible.
For example, if a plurality of objects to be served respectively acquire evaluation information of the type a, evaluation information of the type B, and evaluation information of the type C, and perform clustering processing on the evaluation information as a whole, firstly, the number of the objects to be served in the obtained sub-object group M to be served is small, and on the other hand, a large number of service policies need to be prepared when the service policies are preset or subsequently specified, which is high in cost and complex in process.
Assuming that the number of the obtained sub-object-to-be-serviced groups M is Q, at least a plurality of service policies corresponding to the evaluation information of the a category, the B category, and the C category need to be prepared, and the number of the policies is large. The evaluation information is classified firstly to obtain sub-evaluation information of 3 categories, then the sub-evaluation information of 3 categories is clustered respectively to obtain sub-service object groups of 3 groups of categories, and then sub-service strategies aiming at the sub-service object groups of the categories are matched one by one, so that the sub-service strategies aiming at the evaluation scores of different gradients are only required to be arranged in each evaluation category, and the arrangement and combination are not required according to the evaluation categories, thereby reducing the workload, simplifying the working flow, improving the efficiency, and finally, the sub-service strategies are aggregated to form the optimal service strategy aiming at the sub-service object groups.
It should be appreciated that the sub-optimal service policies corresponding to each sub-group of objects to be serviced are the same.
S103: and clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served.
The sub-evaluation information is necessarily in the same category, and the obtained sub-service object group is also the sub-evaluation information aiming at the same category.
In some embodiments, clustering the sub-evaluation information belonging to the same category to obtain a plurality of sub-object-to-be-served groups belonging to the same category includes:
converting the sub-evaluation information into a coordinate information group of the object to be served according to the evaluation item and the evaluation score;
selecting a plurality of coordinate information groups of the objects to be served as initial clustering centers, and determining initial distances between the initial clustering centers and other coordinate information groups of the objects to be served except the initial clustering centers;
determining a plurality of initial subgroups according to the initial distances;
if the initial group meets the preset conditions, taking the initial group as a sub object group to be served;
if the initial groups do not accord with the preset conditions, carrying out iterative processing on the initial groups, wherein the iterative processing comprises determining an iterative clustering center from each initial group, determining iterative distances between the iterative clustering centers and other coordinate information groups of the objects to be served except the iterative clustering centers, determining a plurality of iterative groups according to the iterative distances, and if the iterative groups accord with the preset conditions, taking the iterative groups as sub-object groups to be served;
and if the iteration group does not meet the preset condition, repeating iteration processing on the iteration group until the latest iteration group meets the preset condition, and taking the latest iteration group as a sub-object group to be served.
The conversion mode of the coordinate information group of the object to be served includes, but is not limited to, constructing a multi-dimensional coordinate system according to each evaluation item, obtaining a data point in the multi-dimensional coordinate system according to the evaluation score of the evaluation item, and forming the position of the object to be served in the multi-dimensional coordinate system. The sub-evaluation service information may include a plurality of data points if it includes a plurality of evaluation items.
The initial distance and the iterative distance include, but are not limited to, euclidean distance, etc., and are not limited thereto.
Optionally, the initial clustering center may be selected randomly, or may be selected according to a certain rule. For example, the coordinate information groups of the objects to be served are sorted first, and then the initial clustering centers are selected according to a certain amount of data at intervals. The iterative clustering center and the initial clustering center are not the same coordinate information group of the object to be served.
Optionally, the number of the initial cluster centers may also be determined according to the data dispersion degree of the coordinate information group of the object to be served. The higher the data dispersion degree, the more the number is selected. The data discrete degree can be realized by the existing technical means, and is not limited herein.
In some embodiments, the sub-groups of objects to be served may also be determined by comparing the initial group with the iterative group, or comparing the previous iterative group with the current iterative group. Specifically, if the initial group is the same as the iterative group, the initial group is used as a sub-object group to be served; if the previous iteration group is the same as the current iteration group, taking the previous iteration group as a sub object group to be served; if the similarity between the initial group and the iterative group exceeds a preset similarity threshold, taking the initial group as a sub-object group to be served; and if the similarity between the previous iteration group and the current iteration group exceeds a preset similarity threshold, taking the previous iteration group as a sub object group to be served, and the like.
The iteration group and the initial group both comprise the evaluation information and the information of the objects to be served corresponding to the evaluation information, so that the sub-groups of the objects to be served can be understood as dividing a plurality of the objects to be served into a plurality of sub-groups of the objects to be served according to the sub-evaluation information, and in the same sub-group of the objects to be served, the difference of each object to be served between each evaluation item of the current category is small. That is, a small group of "like-lanes" is found for the object to be served in a certain dimension.
For the newly obtained iteration group and the iteration group obtained by the previous iteration process, it can be understood that: after the coordinate information group of the object to be served is constructed, G initial clustering centers are determined; and respectively obtaining the initial distance between the coordinate information of each other object to be served except the initial clustering center and each initial clustering center, wherein if the distance between one object to be served coordinate information group R1 and a certain initial clustering center O1 is smaller than the distance between the object to be served coordinate information group R1 and the initial clustering center O1, the object to be served coordinate information group and the initial clustering center O1 are the same initial group. G initial subgroups C can be obtained according to the above rule0If the initial group does not meet the preset condition, the method is carried outInitial subgroup C0Executing the first iteration processing to obtain an iteration group C1If iteration group C1If the preset condition is not met, the iteration group C is judged1Executing the second iteration processing to obtain an iteration group C2If iteration group C1If the preset condition is not met, the iteration group C is judged2Executing the third iteration processing to obtain an iteration group C3… …, for iteration group CCExecuting the C iteration processing if the iteration group C CIf the preset condition is met, the iteration group C is CAs a sub-group of objects to be served. Wherein the iteration group CCI.e. the newly obtained iteration group, iteration group CC-1Namely the iteration group obtained by the previous iteration processing.
In some embodiments, the predetermined condition is determined by:
acquiring a coordinate information group t (i) of an object to be served, and determining a first average distance d (i) between the coordinate information group t (i) of the object to be served and other coordinate information groups t (m) of objects to be served in a sub object group G (i) of the coordinate information group of the object to be served;
determining second average distances between the coordinate information group t (i) of the object to be served and other coordinate information groups t (x) of the objects to be served except the sub object group G (i) of the object to be served where the coordinate information group t (i) of the object to be served is located, and selecting the minimum value in each second average distance as the minimum average distance Md (i);
determining the clustering credibility P (i) according to the first average distance d (i) and the minimum average distance Md (i);
if the clustering reliability P (i) is greater than a preset clustering reliability threshold, the preset condition is met;
if the clustering reliability P (i) is less than or equal to the preset clustering reliability threshold, the clustering reliability is not in accordance with the preset condition.
The coordinate information group t (i) of one object to be served may be any coordinate information group of one object to be served in any sub-object group to be served. The determination method may be random or designated, and is not limited herein.
The second average distance may be an average value of distances from the coordinate information set t (i) of the object to be serviced to each coordinate information set of the sub object to be serviced g (i) (the coordinate information set t (i) of the object not to be serviced is not located in the sub object to be serviced itself).
The second average distance may also be a distance calculated by respectively calculating the distance from the coordinate information set t (i) of the object to be served to each of the coordinate information sets of the sub object to be served g (i) (the sub object to be served group where the coordinate information set t (i) of the object not to be served is located).
Optionally, the preset rule may also be that a certain number of sets of coordinate information of the objects to be served are selected to respectively determine the cluster reliability, and then the relationship between the cluster reliability and the preset cluster reliability threshold is respectively determined to serve as the preset condition. Optionally, if the clustering reliability of one coordinate information group of the object to be served is smaller than the preset clustering reliability threshold, it is determined that the condition is not met.
Optionally, one way of determining the first average distance d (i) is as follows:
Figure 936513DEST_PATH_IMAGE002
formula (1);
alternatively, one way of determining the second average distance md (i) is as follows:
Figure 659619DEST_PATH_IMAGE003
formula (2);
optionally, the determining manner of the cluster confidence p (i) includes:
Figure 519996DEST_PATH_IMAGE001
formula (3);
wherein, p (i) is the cluster reliability, md (i) is the minimum average distance, d (i) is the first average distance, and k is the number of the sub-groups of objects to be served.
Optionally, the value of the cluster reliability should theoretically belong to the interval of [ 1,1 ], and if the cluster reliability p (i) =1, it is better to consider that the current packet is the best packet. The greater the clustering confidence, the better the grouping effect.
Optionally, the preset clustering reliability threshold also belongs to the interval of [ 1,1 ], and a person skilled in the art can select the threshold according to needs, which is not limited herein.
S104: and acquiring the sub evaluation information of the preset object to be served in the sub object group to be served, and taking the sub evaluation information as the matching characteristic information of the sub object group to be served.
Optionally, the preset object to be served may be determined in a random manner, or may be determined according to a certain selection rule, which is not limited herein.
In some embodiments, the matching feature information may be the sub-evaluation information of any one of the sub-objects to be served in the sub-object group, may also be a median or an average of the evaluation scores of the evaluation items of the sub-evaluation information in the sub-object group to be served, and may also be any value selected by those skilled in the art as needed.
It can be understood that the matching characteristic information of each object to be served belonging to the sub object group to be served is the same as the matching characteristic information, in other words, the matching characteristic information is used as a dimension of consideration of each object to be served belonging to the sub object group to be served when the subsequent service policy determination is performed. It can be known that the sub-optimal service policies of the respective objects to be served in the sub-group of objects to be served are all the same. The sub-evaluation information of itself is replaced by typical sub-evaluation information (sub-evaluation information of a preset object to be served).
S105: and acquiring service demand information of the target object to be served.
The service requirement information includes matching characteristic information of each sub-service object group where the target object to be served is located, the target object to be served is one of the plurality of objects to be served, that is, the service requirement information is a set of matching characteristic information of each sub-service object group where the target object to be served is located.
S106: and determining the optimal service strategy of the target object to be served according to the service demand information.
In some embodiments, determining the optimal service policy of the target object to be serviced according to the service demand information includes:
acquiring a plurality of preset service strategies, wherein the service strategies comprise service information, and the service information comprises evaluation items aimed at by the service strategies and evaluation score thresholds of the evaluation items;
and determining an optimal service strategy according to the service demand information and the service information.
Optionally, the evaluation score threshold included in the service information and the evaluation score in the matching feature information may both be a specific index value, or the evaluation score threshold may be an index range, and when the evaluation score threshold is an index range, if the evaluation score (specific value) in the matching feature information falls into the index range, that is, the service policy corresponding to the service information may be determined to be the sub-optimal service policy. If the evaluation score threshold is an index value, if the error between the evaluation score and the index value in the matching feature information is smaller than the error threshold, the service policy corresponding to the service information may also be determined to be the sub-optimal service policy.
Optionally, if a certain matching feature information is not matched with a proper service policy, a proper service policy may be formulated according to the matching feature information.
One possible way to develop an appropriate service policy includes: and acquiring a service strategy which is matched with the matched characteristic information, determining difference information between the matched characteristic information and the matched service strategy, and adjusting the matched service strategy according to the difference information.
That is, a more suitable service policy is selected from the existing service policies, and the service policy is adjusted, so that the adjusted service policy is finally matched with the matching feature information of the service policy which is not matched with the suitable service policy in the original service policy set.
Optionally, determining the optimal service policy according to the service demand information and the service information includes:
if each service strategy comprises a category evaluation item, respectively acquiring each matching characteristic information corresponding to the target object to be served;
determining a target service strategy, wherein the category of the evaluation item in the target service strategy is the same as the category of the evaluation item in the matched characteristic information;
if the evaluation score of the matched characteristic information is within the evaluation score threshold value of the target service strategy, taking the target service strategy as a sub-optimal service strategy corresponding to the matched characteristic information;
and integrating the sub-optimal service strategy as the optimal service strategy of the target object to be served.
That is, the sub-optimal service policies corresponding to the matching feature information are determined, and the sub-optimal service policies are integrated together to serve as the optimal service policy.
It can be known that the sub-optimal service policies of each object to be serviced in a certain sub-object group to be serviced are the same, but the final optimal service policies may be the same or different.
In some embodiments, the service policy determination method further comprises:
sequencing each sub object group to be served, wherein the sequencing is determined according to the number of the sub object groups to be served, which comprise the objects to be served;
acquiring the sub-object groups to be served which are sequenced at the top N as target sub-object groups to be served;
and arranging the objects to be served which simultaneously belong to the target sub-object group to be served in the target service area.
Wherein N is greater than or equal to 1.
Optionally, for the remaining objects to be served that are not arranged in the target service area, the remaining objects to be served may be arranged in other service areas in a centralized manner, or may be rearranged in the target service area according to a certain rule. E.g. arranged in the vicinity of objects to be served having at least one identical sub-group of objects to be served, etc.
That is, if the position of the object to be served waiting for receiving the service can be adjusted, the service unification in the region can be realized by adjusting the position of the object to be served, the developing radius of the service is reduced, the service cost is further reduced, and the service efficiency is improved. And if the service strategy is a food delivery service, the food delivery time can be ensured to be lower, and the freshness of food can be ensured.
Optionally, for the position distribution of the objects to be served in the target service area, the objects to be served in the group including the sub groups of the objects to be served ranked as the first name and the second name may be arranged in the first target sub-area, and then the objects to be served in the group including the sub groups of the objects to be served ranked as the first name and the third name may be arranged in the second target sub-area, where the first target sub-area is adjacent to the second target sub-area.
Alternatively, the target service area may be set in a cellular type shape.
In some embodiments, the service policy may be a service scheme set for a certain evaluation item, such as a meal delivery service (including a cuisine, a taste, a meal delivery frequency, a meal delivery time, etc.), a physical examination service policy (a physical examination time, a physical examination item, etc.), a company service policy (a trip, a family company, etc.), an interest service policy (an organization singing, a square dance activity, etc.).
The embodiment of the invention provides a service strategy determining method, which comprises the steps of obtaining evaluation information of a plurality of objects to be served, wherein the evaluation information comprises an evaluation item and an evaluation score corresponding to the evaluation item, classifying the evaluation information according to a preset classification rule, obtaining sub-evaluation information of each object to be served, which belongs to different classes, clustering the sub-evaluation information belonging to the same class to obtain a plurality of sub-object groups belonging to the same class, obtaining the sub-evaluation information of the preset objects to be served in the sub-object groups to be served as matching characteristic information of the sub-object groups to be served, obtaining service demand information of a target object to be served, wherein the service demand information comprises the matching characteristic information of each sub-object group in which the target object to be served is positioned, the target object to be served is one of the plurality of objects to be served, the optimal service strategy of the target object to be served is determined according to the service demand information, so that the individual demand of each old man can be concerned, the operation is simple and easy, and the cost is saved.
Example two
Referring to fig. 2, an embodiment of the present invention further provides a service policy determination system 200, including:
the evaluation information acquisition module 201 is configured to acquire evaluation information of a plurality of objects to be served, where the evaluation information includes evaluation items and evaluation scores corresponding to the evaluation items;
the classification module 202 is used for classifying the evaluation information according to a preset classification rule to obtain a plurality of sub-evaluation information belonging to different classes;
the clustering module 203 is used for clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served;
the matching characteristic information determining module 204 is configured to obtain sub evaluation information of a preset object to be served in the sub object group to be served, and use the sub evaluation information as matching characteristic information of the sub object group to be served;
the service demand information acquiring module 205 is configured to acquire service demand information of a target object to be served, where the service demand information includes matching feature information of each sub-service object group where the target object to be served is located, and the target object to be served is one of a plurality of objects to be served;
and an optimal service policy determining module 206, configured to determine an optimal service policy of the target object to be served according to the service requirement information.
In this embodiment, the service policy determining system executes the service policy determining method according to any of the embodiments, and specific functions and technical effects may refer to the embodiments described above, which are not described herein again.
Referring to fig. 3, an embodiment of the present application further provides an electronic device 1600, where the electronic device 1600 includes a processor 1601, a memory 1602 and a communication bus 1603;
the communication bus 1603 is used to connect the processor 1601 and the memory 1602;
the processor 1601 is configured to execute a computer program stored in the memory 1602 to implement the method according to any of the above embodiments.
Embodiments of the present application also provide a non-transitory readable storage medium, where one or more modules (programs) are stored in the storage medium, and when the one or more modules are applied to a device, the device may execute instructions (instructions) included in an embodiment of the present application.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, the computer program is used for causing the computer to execute the method according to the embodiment.
The foregoing embodiments are merely illustrative of the principles of the present invention and its efficacy, and are not to be construed as limiting the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
In the corresponding figures of the above embodiments, the connecting lines may represent the connection relationship between the various components to indicate that more constituent signal paths (consistent _ signal paths) and/or one or more ends of some lines have arrows to indicate the main information flow direction, the connecting lines being used as a kind of identification, not a limitation on the scheme itself, but rather to facilitate easier connection of circuits or logic units using these lines in conjunction with one or more example embodiments, and any represented signal (determined by design requirements or preferences) may actually comprise one or more signals that may be transmitted in any one direction and may be implemented in any suitable type of signal scheme.
In the above embodiments, unless otherwise specified, the description of common objects by using "first", "second", etc. ordinal numbers only indicate that they refer to different instances of the same object, rather than indicating that the objects being described must be in a given sequence, whether temporally, spatially, in ranking, or in any other manner.
In the above-described embodiments, reference in the specification to "the embodiment," "an embodiment," "another embodiment," or "other embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments. The various appearances of the phrase "the present embodiment," "one embodiment," or "another embodiment" are not necessarily all referring to the same embodiment. If the specification states a component, feature, structure, or characteristic "may", "might", or "could" be included, that particular component, feature, structure, or characteristic is not necessarily included. If the specification or claim refers to "a" or "an" element, that does not mean there is only one of the element. If the specification or claim refers to "a further" element, that does not preclude there being more than one of the further element.
In the embodiments described above, although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory structures (e.g., dynamic ram (dram)) may use the discussed embodiments. The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

Claims (7)

1. A method for service policy determination, the method comprising:
obtaining evaluation information of a plurality of objects to be served, wherein the evaluation information comprises evaluation items and evaluation scores corresponding to the evaluation items;
classifying the evaluation information according to a preset classification rule to obtain a plurality of sub-evaluation information belonging to different classes;
clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served;
acquiring sub-evaluation information of a preset object to be served in the sub-object group to be served, and using the sub-evaluation information as matching characteristic information of the sub-object group to be served;
acquiring service demand information of a target object to be served, wherein the service demand information comprises the matching characteristic information of each sub-service object group where the target object to be served is located, and the target object to be served is one of a plurality of objects to be served;
determining an optimal service strategy of the target object to be served according to the service demand information;
the determining the optimal service strategy of the target object to be served according to the service demand information comprises the steps of obtaining a plurality of preset service strategies, wherein the service strategies comprise service information, and the service information comprises evaluation items and evaluation score threshold values of the evaluation items, which are aimed at by the service strategies;
determining the optimal service strategy according to the service demand information and the service information;
the determining the optimal service policy according to the service demand information and the service information includes,
if each service strategy comprises the evaluation item of one category, respectively acquiring each matching characteristic information corresponding to the target object to be served;
determining a target service strategy, wherein the category of the evaluation item in the target service strategy is the same as the category of the evaluation item in the matching characteristic information;
if the evaluation score of the matched characteristic information is within the evaluation score threshold value of the target service strategy, taking the target service strategy as a sub-optimal service strategy corresponding to the matched characteristic information;
integrating the sub-optimal service strategy as the optimal service strategy of the target object to be served;
clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served,
converting the sub-evaluation information into a coordinate information group of the object to be served according to the evaluation item and the evaluation score;
selecting a plurality of coordinate information groups of the objects to be served as initial clustering centers, and determining initial distances between the initial clustering centers and the coordinate information groups of the objects to be served except the initial clustering centers;
determining a plurality of initial subgroups according to the initial distances;
if the initial group meets the preset condition, taking the initial group as a sub object group to be served;
if the initial groups do not accord with the preset condition, performing iterative processing on the initial groups, wherein the iterative processing comprises determining an iterative clustering center from each initial group, determining iterative distances between the iterative clustering center and coordinate information groups of the objects to be served except the iterative clustering center, determining a plurality of iterative groups according to the iterative distances, and if the iterative groups accord with the preset condition, taking the iterative groups as the sub object groups to be served;
and if the iteration group does not meet the preset condition, repeating the iteration processing on the iteration group until the newly obtained iteration group meets the preset condition, and taking the newly obtained iteration group as a sub-object group to be served.
2. The service policy determination method according to claim 1, wherein the predetermined condition is determined in a manner that includes:
acquiring a coordinate information group of an object to be served, and determining a first average distance between the coordinate information group of the object to be served and other coordinate information groups of the object to be served in a sub object group to be served where the coordinate information group of the object to be served is located;
determining second average distances between the coordinate information group of the object to be served and other coordinate information groups of the object to be served except for the sub object group to be served where the coordinate information group of the object to be served is located, and selecting a minimum value in the second average distances as a minimum average distance;
determining cluster reliability according to the first average distance and the minimum average distance;
if the clustering reliability is greater than a preset clustering reliability threshold value, the preset condition is met;
if the clustering reliability is less than or equal to a preset clustering reliability threshold value, the clustering reliability does not accord with the preset condition;
the second average distance between the coordinate information group of the object to be served and the coordinate information groups of other objects to be served except the sub object group to be served where the coordinate information group of the object to be served is located is determined to include any one of an average value of distances from the coordinate information group of the object to be served to each coordinate information group of other sub object groups to be served, or a distance from the coordinate information group of the object to be served to each coordinate information group of other sub object groups to be served.
3. The service policy determination method according to claim 2, wherein the determination of the cluster reliability comprises:
Figure 902035DEST_PATH_IMAGE001
wherein P (i) is the cluster confidence, Md (i) is the minimum average distance, and d (i) is the first average distance.
4. A service policy determination method according to any one of claims 1-3, characterised in that the method further comprises:
sequencing each sub object group to be served, wherein the sequencing is determined according to the number of the sub object groups to be served, which comprise the objects to be served;
acquiring the sub-object groups to be served which are sequenced at the top N as target sub-object groups to be served;
and arranging the objects to be served belonging to the target sub-object group to be served in a target service area.
5. A service policy determination system, the system comprising:
the system comprises an evaluation information acquisition module, a service management module and a service management module, wherein the evaluation information acquisition module is used for acquiring evaluation information of a plurality of objects to be served, and the evaluation information comprises evaluation items and evaluation scores corresponding to the evaluation items;
the classification module is used for classifying the evaluation information according to a preset classification rule to obtain a plurality of sub-evaluation information belonging to different classes;
the clustering processing module is used for clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served;
the matching characteristic information determining module is used for acquiring the sub evaluation information of the preset object to be served in the sub object group to be served and taking the sub evaluation information as the matching characteristic information of the sub object group to be served;
the service demand information acquisition module is used for acquiring service demand information of a target object to be served, wherein the service demand information comprises the matching characteristic information of each sub-service object group where the target object to be served is located, and the target object to be served is one of a plurality of objects to be served;
the optimal service strategy determining module is used for determining the optimal service strategy of the target object to be served according to the service requirement information;
the determining the optimal service strategy of the target object to be served according to the service demand information comprises the steps of obtaining a plurality of preset service strategies, wherein the service strategies comprise service information, and the service information comprises evaluation items and evaluation score threshold values of the evaluation items, which are aimed at by the service strategies;
determining the optimal service strategy according to the service demand information and the service information;
the determining the optimal service policy according to the service demand information and the service information includes,
if each service strategy comprises the evaluation item of one category, respectively acquiring each matching characteristic information corresponding to the target object to be served;
determining a target service strategy, wherein the category of the evaluation item in the target service strategy is the same as the category of the evaluation item in the matching characteristic information;
if the evaluation score of the matched characteristic information is within the evaluation score threshold value of the target service strategy, taking the target service strategy as a sub-optimal service strategy corresponding to the matched characteristic information;
integrating the sub-optimal service strategy as the optimal service strategy of the target object to be served;
clustering the sub-evaluation information to obtain a plurality of sub-object groups to be served,
converting the sub-evaluation information into a coordinate information group of the object to be served according to the evaluation item and the evaluation score;
selecting a plurality of coordinate information groups of the objects to be served as initial clustering centers, and determining initial distances between the initial clustering centers and the coordinate information groups of the objects to be served except the initial clustering centers;
determining a plurality of initial subgroups according to the initial distances;
if the initial group meets the preset condition, taking the initial group as a sub object group to be served;
if the initial groups do not accord with the preset condition, performing iterative processing on the initial groups, wherein the iterative processing comprises determining an iterative clustering center from each initial group, determining iterative distances between the iterative clustering center and coordinate information groups of the objects to be served except the iterative clustering center, determining a plurality of iterative groups according to the iterative distances, and if the iterative groups accord with the preset condition, taking the iterative groups as the sub object groups to be served;
and if the iteration group does not meet the preset condition, repeating the iteration processing on the iteration group until the newly obtained iteration group meets the preset condition, and taking the newly obtained iteration group as a sub-object group to be served.
6. An electronic device comprising a processor, a memory, and a communication bus;
the communication bus is used for connecting the processor and the memory;
the processor is configured to execute a computer program stored in the memory to implement the method of any one of claims 1-4.
7. A computer-readable storage medium, having stored thereon a computer program for causing a computer to perform the method of any one of claims 1-4.
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