CN111563687A - Double random health supervision law enforcement method and terminal - Google Patents
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Abstract
The embodiment of the invention provides a double random health supervision law enforcement method, which comprises the following steps: determining a supervision object database according to the supervision object extraction characteristics; determining a supervised unit for the supervised object database according to a random sampling algorithm; determining a health supervisor database according to the alternative characteristics of the law enforcement officers; distributing the supervised unit to law enforcement personnel in the health supervisor database according to distribution characteristics and a random distribution algorithm to perform health supervision law enforcement work; determining a distribution proportion according to double random distribution law enforcement tasks, and determining the conformity of the law enforcement tasks by matching with a task requirement proportion; and when the conformity is judged to exceed the preset value, the calibration database has deviation characteristics and is used as a reference index for subsequently making an extraction plan. According to the method provided by the embodiment of the invention, the business model and the random algorithm can be combined, multiple weighting factors are integrated, the law enforcement capacity of the jurisdiction is comprehensively raised, the closed-loop check dynamic regulation law enforcement strategy is realized, and the high efficiency and the accuracy of the law enforcement process are ensured.
Description
Technical Field
The invention relates to the field of health supervision and law enforcement, in particular to a double-random health supervision and law enforcement method and a terminal.
Background
The health supervision work is an important component of national civilized city construction, and because the supervised units are wide in covered area, large in number, various in professional field and insufficient in manpower configuration of health supervision mechanisms, the public health level is difficult to guarantee by the health supervision law enforcement departments in a supervision full-coverage mode, so that the problem of pain which troubles the health supervision work is also caused, and needs to be solved urgently.
How to achieve key supervision and accurate supervision so as to improve supervision efficiency and success is an opportunity and challenge for health supervision work.
Disclosure of Invention
The invention aims to solve the problems that the existing health supervision work is difficult to realize key supervision, the supervision work efficiency is low and the like, and provides a double random health supervision law enforcement method with high supervision efficiency and key supervision accuracy.
In one aspect, an embodiment of the present invention provides a double random health surveillance law enforcement method, including:
determining a supervised object database of each district according to the supervised object extraction features and the extraction indexes;
determining a supervised unit for a supervised object database according to a random sampling algorithm;
determining a health supervisor database according to the alternative characteristics of the law enforcement officers;
distributing the supervised units to law enforcement personnel in a health supervisor database according to the distribution characteristics, the distribution indexes and a random distribution algorithm, performing health supervision and law enforcement work, and calculating to obtain each statistical index of task execution;
determining a distribution proportion according to the double random distribution law enforcement tasks, and determining the conformity of the distribution law enforcement tasks according to the distribution proportion and the requirement proportion of the tasks in each jurisdiction;
and when the conformity is judged to exceed the preset value, calibrating that the database in the district has deviation characteristics to serve as a reference index for subsequently making an extraction plan.
In some alternative embodiments, the supervised object extraction features include basic extraction features, professional extraction features, risk credit extraction features, and exclusion extraction features.
In some alternative embodiments, the risk credit extraction features increase the extraction rate of the key supervised objects by means of weighting and/or rating.
In some alternative embodiments, the assignment characteristics include a home characteristic, a job function characteristic, a task averageness characteristic, a geographic location characteristic, and a metropolitan characteristic.
In some optional embodiments, the attribution features comprise a double supervision principle, a non-transregional supervision principle and a non-transunit supervision principle;
the double supervision principle comprises that each supervision task is allocated with 2 law enforcement personnel, if the number of the law enforcement personnel is less than 2, the supervision task is adjusted to an upper-level unit for processing, and finally, the supervision task is adjusted to a provincial-level unit without acceptable personnel, and the supervision task is placed in an abnormal pool to wait for processing;
the task average characteristic sets a distribution mechanism according to a supervision capacity distribution principle and according to provincial level, city level, district level and direct prefecture city;
the geographical location characteristics comprise management of local and nearby handling principles and adjustment of tasks assigned by provincial units by weighting.
In some optional embodiments, the assignment feature further comprises a deduplication feature, the deduplication feature comprising: and if the combined repetition number of 2 law enforcement personnel in a certain specialty exceeds 50% of the total amount of the supervision tasks allocated by two persons, the specialty has a replaceable person, and the excessive part of the repeated tasks is randomly replaced by 1 person or 2 persons as optional persons without violating other allocation characteristics, the replacement operation is executed, otherwise, the processing is not carried out.
In some optional embodiments, extracting the indicator includes: the method comprises the following steps that the total number of effective units in the district where the task is extracted, the total extracted task amount, the total extracted task proportion, the provincial task requirement proportion of the direct district city, the city task requirement proportion of the direct district city, the provincial task requirement proportion of the non-direct district city, the city task requirement proportion of the non-direct district city, the county task requirement proportion of the non-direct district city and the like are extracted; the allocation index includes: the method comprises the following steps of (1) total number of supervisors in the district, total number of alternative supervisors, total number of drawn supervisors, per-capita task volume, provincial per-capita task volume of a city in direct jurisdictions, urban per-capita task volume of a city in non-direct jurisdictions, professional task occupation ratios of all districts, and the like;
the statistical indexes comprise: task total, task execution total/rate, supervision completion/rate, unit shutdown/rate, task non-completion/rate, task completion/rate, accountability justification/rate, case encounter/rate, penalty amount, etc.
In some optional embodiments, the determining the distribution proportion according to the double random distribution law enforcement task, and determining the conformity according to the distribution proportion and the task requirement proportion of each jurisdiction specifically includes:
calculating the conformity according to the following formula aiming at the provincial level and/or the direct-jurisdictional city level of the region where the supervised object is located;
θ＝(Q1÷Qs-Ky)÷Ky
calculating to obtain the conformity according to the following formula aiming at the fact that the region where the supervised object is located is in the city level of the non-direct district;
θ＝((Q1-Qw)÷(Qs-Qw)-Ky)÷Ky
calculating to obtain the conformity according to the following formula aiming at the condition that the region of the supervised object is in the city, district and county level of the non-direct district;
θ＝((Q1-Qz)÷(Qs-Qz)-Ky)÷Ky
wherein Q is1For the total amount of tasks in the jurisdiction, QsFor provincial/direct municipality's total amount of tasks, QwFor total number of tasks in district without district-county level supervising agency, QzFor the total amount of tasks in provincial, Vital and county, KyAnd theta is a conformity degree for the task requirement proportion of the district.
In some optional embodiments, when the conformity is judged to exceed the preset value, calibrating that the jurisdictional database has a deviation characteristic specifically includes:
the conformity degree exceeds or is lower than the task requirement proportion by 10 percent and within, the database of the jurisdiction is marked as a task proportion deviation characteristic, the conformity degree exceeds or is lower than the task requirement proportion range by more than 10 percent, and the database of the jurisdiction is marked as a task proportion abnormal characteristic.
In another aspect, an embodiment of the present invention further provides a dual random surveillance and hygiene enforcement terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the steps of the dual random surveillance and hygiene enforcement method in the above aspects.
The technical scheme has the following beneficial effects: according to the double-random health supervision and enforcement method provided by the invention, through the form of double random selection of the supervised unit and the supervising law enforcement personnel, the key supervision is performed in a random sampling and weighting mode, the full-coverage supervision is replaced, the manpower requirement of supervision and enforcement is reduced, multiple weightings are integrated on the premise of ensuring the randomness, the factors such as regional law enforcement capacity difference are considered, the supervision tasks are scientifically distributed, and the organic combination of two management thoughts of grid management and randomized supervision is realized. Meanwhile, a closed loop verification link is also arranged, so that the law enforcement strategy can be dynamically adjusted, and the high efficiency and the high accuracy of the health supervision law enforcement process are guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram illustrating a dual random surveillance of health enforcement method according to one embodiment of the present invention;
FIG. 2 is a flow chart illustrating a dual random surveillance of health enforcement method calculation according to one embodiment of the present invention;
FIG. 3 is a functional block diagram illustrating a dual random surveillance of health enforcement method according to one embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a dual random surveillance enforcement terminal according to one embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For better understanding of the present invention, the following describes in detail a dual random surveillance and health enforcement method and terminal provided by an embodiment of the present invention with reference to fig. 1 to 4. It should be noted that these examples are not intended to limit the scope of the present disclosure.
Fig. 1 and 2 show a schematic diagram and a computational flow diagram, respectively, of a dual random surveillance of health enforcement method according to one embodiment of the present invention. The double random surveillance health enforcement method includes steps S101 to S106.
And step S101, determining a supervised object database of each district according to the supervised object extraction characteristics and the extraction indexes.
Firstly, according to the total amount of the current supervised unit data of each specialty, the regional characteristic requirements and the supervised object extraction characteristics, the total number of the effective supervised units of each province in the country is calculated, and a random supervised object database is established. And calculating the total number of the units to be supervised extracted by each professional plan of each province in the country according to the specific requirements of the spot check plan, wherein the total number can be calculated respectively according to the proportion or number requirements in the spot check plan, and the factors of the last half year, the month, the quarter and the like are considered.
In one embodiment, the supervised object extraction features include basic extraction features, professional extraction features, risk credit extraction features, and exclusion extraction features. Wherein the basic extraction features include: 1) the effective supervised units all participate in the extraction; 2) if the supervised unit in the same profession is found to be extracted by the extraction features in the same profession, the extraction is not repeated (except for the extraction of the subdivision profession sequence from less to more and the whole extraction task). Professional extraction features include: based on the classification standard of the supervised unit (see the standard 2 part of the sanitation supervision basic data set: public data elements in detail), receiving the multi-view input parameters of extraction proportion, quantity, attribute range, random range and exclusion rule to form a spot check object. And the supervised units are extracted according to the spot check objects, so that the requirements of classification, classification differentiation and key supervision are met. See tables 1-12 for general professional extraction rules.
TABLE 1 public specialties
TABLE 2 Drinking Water (Water supply) specialty
TABLE 3 Drinking Water (wading) specialties
TABLE 4 school health specialty
TABLE 5 Disinfection of tableware
TABLE 6 prevention and treatment of infectious diseases specialty
TABLE 7 Disinfection product specialties
TABLE 8 medical and health specialties
TABLE 9 occupational hygiene specialty
Watch 10 radiological specialties
TABLE 11 blood safety specialty
TABLE 12 family planning specialty
In one embodiment, the risk credit extraction features may increase the extraction rate of the key supervised objects by means of weighting and/or rating. And weighting and extracting key supervised units according to the scale factor, the evaluation factor, the risk factor and the credit factor to complete the establishment of a random supervised unit library. Specifically, 1) wading products, disinfection products, medical and American advertisements and risk early warning signals are extracted by a supervision unit by 100%; 2) the disinfection product production enterprises extract the disinfection product with the credit rating of D according to 90 percent, extract C according to 50 percent and extract A or B according to 10 to 20 percent; 3) units penalized in the last year, 100% of this year.
In one embodiment, excluding features may include removing the last year and the year having been drawn past the supervised unit. The exclusion conditions may also be set according to specific actual requirements.
And step S102, determining a supervised unit aiming at the supervised object database according to a random sampling algorithm.
The total number of units to be supervised is extracted based on the professional plans of the nationwide provinces determined in step S101, and the units to be supervised are extracted from the already established database to be supervised by using a random sampling algorithm.
In one embodiment, the RANDOM sampling algorithm may employ an oracle BMS _ RANDOM computation library.
And step S103, determining a health supervisor database according to the alternative characteristics of the law enforcement officers.
In particular, law enforcement personnel alternative features include: 1) the health supervisors participating in the 'double random' spot check must establish information in the health and health supervision information platform system setting personnel (self-establishing service system provinces, establishing personnel information in respective systems and exchanging to national level in time), and establish cards in the law enforcement personnel information cards at the same time, wherein the identification numbers of the two types of information must be the same; 2) whether the personnel information card participating in the double random supervision is on duty or not must be yes, whether the personnel information card participates in the supervision or not must be yes, and whether the personnel information card participates in the supervision or not must be the practice range, wherein the personnel information card at least comprises one of the following professional ranges (which can be selected more, and the actual supervision is adopted as the standard): public place health specialty, drinking water health specialty, occupational health specialty, radiological health specialty, school health specialty, medical service supervision specialty, infectious disease management specialty, disinfection product specialty, tableware disinfection specialty, blood collection supervision specialty, and family planning specialty; 3) at least 2 persons are responsible for each professional scope of each supervision institution, and less than 2 persons are regarded as unqualified persons.
And step S104, distributing the supervised units to law enforcement personnel in a health supervisor database according to the distribution characteristics, the distribution indexes and the random distribution algorithm, performing health supervision and law enforcement work, and calculating to obtain each statistical index of task execution.
In one embodiment, the allocation characteristics may include a home characteristic, a functional characteristic, a mission averaging characteristic, a geographic location characteristic, and a metropolitan characteristic.
In one embodiment, the home features may include a double person supervision principle, a no cross-region supervision principle, and a no cross-unit supervision principle.
The double supervision principle comprises that each supervision task is allocated with 2 law enforcement personnel, if the number of the supervision tasks is less than 2 law enforcement personnel, the supervision tasks are adjusted to an upper-level unit for processing, and finally, the supervision tasks are adjusted to a provincial-level unit without acceptable personnel, and the supervision tasks are placed in an abnormal pool to wait for processing. Specifically, following the working mode of double-person law enforcement in the same health supervision unit, according to the execution units distributed by the supervised unit, 2 law enforcement persons are randomly extracted from corresponding professional candidates in the unit, if the county level is less than 2 persons set by the professional law enforcement persons, the city level is distributed, if the city level is less than 2 persons set by the professional law enforcement persons, the province level is distributed, and if the province level is still less than 2 persons set by the professional law enforcement persons, the supervision tasks are placed in an abnormal pool to wait for processing.
The non-cross-region supervision principle and the non-cross-unit supervision principle can be specifically a principle of following the health and health supervision home management, a transverse cross-region supervision task and a cross-unit execution supervision task.
In one embodiment, functional features may include dividing the demand following health care supervision responsibilities without being released to a district-level supervision agency for supervision by a supervised unit that emphasizes provincial and municipal level supervision.
In one embodiment, the task average characteristic may set the allocation mechanism according to provincial level, city level, prefecture level, and direct prefecture city according to the supervision capacity allocation principle.
Specifically, for the municipality: 1) calculating the total number of tasks required to be distributed according to the proportion requirements of provincial level and city level; 2) randomly distributing tasks from the extraction units according to the supervision units, if the city level supervision exists, distributing the tasks to the city level, and if the city level supervision exists, distributing the tasks to the province level; 3) and if the city level exceeds the total number of the current level, allocating to the provincial level.
Aiming at non-direct prefecture cities: 1) calculating the total number of tasks needing to be distributed to the supervised unit according to the provincial, city and county scale requirements; 2) distributing tasks according to a supervision unit in the determined random tasks of the supervised unit, if only provincial supervision exists, distributing the tasks to provinces; 3) otherwise, if only the city level supervision is adopted, the city level is assigned; 4) otherwise, distributing to the county level; 5) if the county level exceeds the distribution total number of the level, distributing the county level to the city level; 6) if the city level exceeds the total number of the local level, the provincial level is allocated; 7) in particular: no district-level units or less district-level units exist under the city, so that the proportion of the city-level tasks is higher, the city-level tasks are independently distributed by the provincial and direct administration tasks and do not float upwards (except that the setting of professional personnel is less than 2 persons).
In one embodiment, the geographic location characteristics may include governing home-proximity handling principles, adjusting assigned tasks by provincial units by weighting. In order to reduce the supervision cost and improve the supervision efficiency, units with urban areas as locations are distributed to provincial supervision institutions.
In one embodiment, the metropolitan feature may use a specific rule to assign each specialized task to an execution unit for a specific region according to the part region street grid management requirement. Specifically, the task allocation by street manner can be used for large-scale cities, wherein the large cities can include straight prefectures such as beijing, shanghai and the like, and can also include larger-scale cities such as guangzhou city, shenzhen city, hangzhou city and the like.
In one embodiment, the assignment feature may further include a deduplication feature, the deduplication feature comprising: and if the combined repetition number of 2 law enforcement personnel in a certain specialty exceeds 50% of the total amount of the supervision tasks allocated by two persons, the specialty has a replaceable person, and the excessive part of the repeated tasks is randomly replaced by 1 person or 2 persons as optional persons without violating other allocation characteristics, the replacement operation is executed, otherwise, the processing is not carried out.
In one embodiment, extracting the metrics may include: the method comprises the steps of task extraction of the total number of effective units in the district, the total extraction of tasks, the total extraction proportion of tasks, the provincial-level task requirement proportion of the direct district city, the city-level task requirement proportion of the direct district city, the provincial-level task requirement proportion of the non-direct district city, the city-level task requirement proportion of the non-direct district city, the county-level task requirement proportion of the non-direct district city, and the like.
The allocation index may include: the system comprises a total number of supervisors in the district, a total number of alternative supervisors, a total number of central supervisors, a per-capita task volume, a provincial per-capita task volume in the city of direct jurisdiction, a city per-capita task volume in the city of direct jurisdiction, a provincial per-capita task volume in the city of non-direct jurisdiction, a city per-capita task volume in the city of non-direct jurisdiction, a county per-capita task volume in the city of non-direct jurisdiction, a ratio of each professional task, a ratio of tasks in each district and the like. Specifically, the supervised object database of each jurisdiction is determined according to the matching of the extraction index and the extraction characteristic, each parameter of double random sampling is determined according to the distribution index and the distribution characteristic, and the specific values of each parameter of the extraction index and the distribution index can be determined according to the distribution plans of different law enforcement tasks and the actual conditions of the jurisdiction area where the distribution tasks are located.
The statistical indicators may include: task total, task execution total/rate, supervision completion/rate, unit shutdown/rate, task non-completion/rate, task completion/rate, accountability justification/rate, case encounter/rate, penalty amount, etc. In particular, the obtained statistical indexes can be used for overall evaluation of the current law enforcement task and play an important role in indicating the subsequent distribution plan of the law enforcement task.
And S105, determining a distribution proportion according to the double random distribution law enforcement tasks, and determining the conformity of the distribution law enforcement tasks according to the distribution proportion and the required proportion of the tasks in each jurisdiction. Specifically, the distribution proportion is a ratio of the distributed supervision tasks to the total supervision tasks determined according to the execution result of the double random distributed law enforcement tasks, and the task requirement proportion is a ratio of the supervision law enforcement tasks to the total supervision law enforcement tasks in the region set according to the actual supervision requirements in the jurisdiction.
In one embodiment, determining the distribution proportion according to the double random distribution law enforcement tasks, and determining the conformity according to the distribution proportion and the required proportion of the tasks in each jurisdiction may specifically include:
and calculating the conformity according to the following formula aiming at the provincial level and/or the direct-jurisdictional city level of the region where the supervised object is located.
θ＝(Q1÷Qs-Ky)÷Ky(1)
And calculating the conformity according to the following formula aiming at the condition that the region where the supervised object is located is in the city level of the non-direct district.
θ＝((Q1-Qw)÷(Qs-Qw)-Ky)÷Ky(2)
And calculating the conformity according to the following formula aiming at the condition that the region of the supervised object is in the city, district and county level of the non-direct district.
θ＝((Q1-Qz)÷(Qs-Qz)-Ky)÷Ky(3)
Wherein Q is1For the total amount of tasks in the jurisdiction, QsFor provincial/direct municipality's total amount of tasks, QwFor total number of tasks in district without district-county level supervising agency, QzFor the total amount of tasks in provincial, Vital and county, KyAnd theta is a conformity degree for the task requirement proportion of the district.
And step S106, when the conformity is judged to exceed the preset value, calibrating that the database in the jurisdiction has deviation characteristics, and using the deviation characteristics as a reference index for subsequently making an extraction plan. By verifying the conformity of the double random distribution law enforcement tasks, the accuracy and the coverage rate of the current strategy can be verified in a closed loop manner, and important reference indexes are provided for the subsequent formulation of an extraction plan.
In an embodiment, when the conformity is judged to exceed the preset value, calibrating that the jurisdiction database has a deviation characteristic may specifically include:
the conformity degree exceeds or is lower than the task requirement proportion by 10 percent and within, the database of the jurisdiction is marked as a task proportion deviation characteristic, the conformity degree exceeds or is lower than the task requirement proportion by more than 10 percent, and the database of the jurisdiction is marked as a task proportion abnormal characteristic. Specifically, when the calculated value of the conformity exceeds the task requirement proportion by within 10%, or the calculated value of the conformity is lower than the task requirement proportion by within 10%, the database in the current jurisdiction is marked as the task proportion deviation characteristic. And when the calculated value of the conformity exceeds the task requirement proportion by more than 10 percent or the calculated value of the conformity is lower than the task requirement proportion by more than 10 percent, marking the database of the current jurisdiction as the task proportion abnormal feature. The value of the task requirement proportion can be determined according to the actual situation.
In one embodiment, the RANDOM assignment algorithm may employ an oracle DBMS RANDOM computation library.
After the task is distributed, if no task is left, the task result accounting process is entered, and a functional structure schematic diagram of a specific and complete double-random health supervision law enforcement method is shown in fig. 3.
An embodiment of the present invention further provides a dual random surveillance enforcement terminal, and fig. 4 is a schematic diagram of a dual random surveillance enforcement terminal 100 according to yet another embodiment of the present invention. As shown in fig. 4, the double random health supervision enforcement terminal 100 of the present embodiment includes: a processor 110, a memory 120 and a computer program 130 stored in said memory 120 and executable on said processor 110, such as an executive of a dual random surveillance of health enforcement method. The processor 110, when executing the computer program 130, implements the steps in the various dual random surveillance enforcement method embodiments described above, such as steps S101-S104 shown in fig. 1.
Illustratively, the computer program 130 may be partitioned into one or more modules/units that are stored in the memory 120 and executed by the processor 110 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 130 in the dual random surveillance enforcement terminal 100.
The dual random health surveillance enforcement terminal 100 may be a desktop computer, a laptop, a palm top computer, a cloud server, or other computing device. The dual random surveillance enforcement terminal 100 may include, but is not limited to, a processor 110, a memory 120. It will be understood by those skilled in the art that fig. 4 is merely an example of a dual random surveillance enforcement terminal 100 and does not constitute a limitation of dual random surveillance enforcement terminal 100 and may include more or fewer components than shown, or some components in combination, or different components, e.g., dual random surveillance enforcement terminal 100 may also include input-output devices, network access devices, buses, etc.
The Processor 110 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 120 may be an internal storage unit of the dual random surveillance enforcement terminal 100, such as a hard disk or a memory of the dual random surveillance enforcement terminal 100. The memory 120 may also be an external storage device of the dual random health surveillance enforcement terminal 100, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard), and the like, which are provided on the dual random health surveillance enforcement terminal 100. Further, the memory 120 may also include both an internal storage unit and an external storage device of the dual random surveillance enforcement terminal 100. The memory 120 is used to store the computer program and other programs and data required by the dual random surveillance enforcement terminal 100. The memory 120 may also be used to temporarily store data that has been output or is to be output.
According to the double-random health supervision enforcement method and the terminal, the service model and the random algorithm can be combined, a double-random supervision object database and a health supervisor database are allowed to be established at any time, and supervision tasks are scientifically distributed. The organic combination of two management thoughts of gridding management and randomized supervision is realized. On the premise of ensuring randomness, task executable factors such as professions, geographic positions and manpower configuration factors are comprehensively considered, street affiliation and the like are considered in a large city, supervision resources are saved while a random effect is achieved, meanwhile, classification, grading supervision, risks and credit evaluation factors are comprehensively considered, the extraction proportion of key supervision objects is increased, a closed-loop verification link is increased, law enforcement strategies can be flexibly and dynamically adjusted, and high efficiency and high accuracy of a health supervision law enforcement process are achieved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium includes content that can be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunication signals according to legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method of dual stochastic surveillance health enforcement comprising:
determining a supervised object database of each district according to the supervised object extraction features and the extraction indexes;
determining a supervised unit for the supervised object database according to a random sampling algorithm;
determining a health supervisor database according to the alternative characteristics of the law enforcement officers;
distributing the supervised units to law enforcement personnel in the health supervisor database according to distribution characteristics, distribution indexes and a random distribution algorithm, performing health supervision and law enforcement work, and calculating to obtain each statistical index of task execution;
determining a distribution proportion according to the double random distribution law enforcement tasks, and determining the conformity of the distribution law enforcement tasks according to the distribution proportion and the required proportion of the tasks in each jurisdiction;
and when the conformity is judged to exceed a preset value, calibrating that the database in the district has deviation characteristics to serve as a reference index for making an extraction plan subsequently.
2. The dual stochastic surveillance law enforcement procedure of claim 1 wherein the surveillance object extraction features comprise basic extraction features, professional extraction features, risk credit extraction features and exclusion extraction features.
3. The dual stochastic health surveillance law enforcement method of claim 2, wherein the risk credit extraction feature increases the extraction rate of key surveillance objects by way of weighting and/or ranking.
4. The dual stochastic surveillance law enforcement method of claim 1 wherein the distribution features include a home feature, a functional feature, a mission average feature, a geographic location feature, and a metropolitan feature.
5. The dual stochastic health surveillance law enforcement method of claim 4 wherein the locality features include a double person surveillance principle, a no cross-region surveillance principle and a no cross-unit surveillance principle;
the double supervision principle comprises that each supervision task is allocated with 2 law enforcement personnel, if the number of the supervision tasks is less than 2 law enforcement personnel, the supervision tasks are adjusted to an upper-level unit for processing, and finally, the supervision tasks are adjusted to a provincial-level unit without acceptable personnel, and the supervision tasks are placed in an abnormal pool to wait for processing;
the task average characteristic sets a distribution mechanism according to a supervision capacity distribution principle and according to provincial level, city level, district level and direct prefecture city;
the geographical location characteristics comprise management of local handling principles and weighting adjustment of tasks distributed by provincial units.
6. The dual random surveillance law enforcement procedure of claim 4 wherein the distribution feature further comprises a deduplication feature comprising: and if the combined repetition number of 2 law enforcement personnel in a professional exceeds 50% of the total amount of the supervision tasks allocated by two persons, and the professional has a replaceable person, and simultaneously, the excessive part of the repeated tasks is randomly replaced by 1 person or 2 persons as optional persons without violating other allocation characteristics, performing replacement operation, otherwise, not processing.
7. The dual stochastic surveillance law enforcement method of claim 1 wherein the draw metrics comprise: the method comprises the following steps that the total number of effective units in the district where the task is extracted, the total extracted task amount, the total extracted task proportion, the provincial task requirement proportion of the direct district city, the city task requirement proportion of the direct district city, the provincial task requirement proportion of the non-direct district city, the city task requirement proportion of the non-direct district city, the county task requirement proportion of the non-direct district city and the like are extracted;
the allocation indicator includes: the method comprises the following steps of (1) total number of supervisors in the district, total number of alternative supervisors, total number of drawn supervisors, per-capita task volume, provincial per-capita task volume of a city in direct jurisdictions, urban per-capita task volume of a city in non-direct jurisdictions, professional task occupation ratios of all districts, and the like; the statistical indexes comprise: task total, task execution total/rate, supervision completion/rate, unit shutdown/rate, task non-completion/rate, task completion/rate, accountability justification/rate, case encounter/rate, penalty amount, etc.
8. The double random health supervision enforcement method according to claim 1, wherein the determining of the distribution proportion according to the double random distribution enforcement tasks and the determining of the conformity according to the distribution proportion and the required proportion of each jurisdiction task specifically comprises:
aiming at the fact that the region where the supervision object is located is provincial level and/or city level of the direct prefecture city, the conformity is calculated according to the following formula;
θ＝(Q1÷Qs-Ky)÷Ky
aiming at the fact that the region where the supervision object is located is in the city level of the non-direct district, the conformity degree is calculated according to the following formula;
θ＝((Q1-Qw)÷(Qs-Qw)-Ky)÷Ky
aiming at the condition that the region where the supervision object is located is at the city, district and county level of the non-direct district, calculating according to the following formula to obtain the conformity;
θ＝((Q1-Qz)÷(Qs-Qz)-Ky)÷Ky
wherein Q is1For the total amount of tasks in the jurisdiction, QsFor provincial/direct municipality's total amount of tasks, QwFor total number of tasks in district without district-county level supervising agency, QzFor the total amount of tasks in provincial, Vital and county, KyAnd theta is a conformity degree for the task requirement proportion of the district.
9. The method according to claim 1, wherein when the compliance is determined to exceed a predetermined value, the jurisdictional database is calibrated to have a deviation characteristic, and the method specifically comprises:
the conformity degree is more than or less than 10% of the task required proportion, the jurisdictional database is marked as a task proportion deviation characteristic, the conformity degree is more than or equal to 10% of the task required proportion range, and the jurisdictional database is marked as a task proportion abnormal characteristic.
10. A dual random surveillance law enforcement terminal comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, implements the steps of the visual control method of a video display system according to any one of claims 1 to 9.
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