CN113706000A

CN113706000A - Environment monitoring resource allocation method and system based on Internet of things

Info

Publication number: CN113706000A
Application number: CN202110957115.XA
Authority: CN
Inventors: 熊枝光
Original assignee: Hainan Lvneng Environmental Engineering Co ltd
Current assignee: Hainan Lvneng Environmental Engineering Co ltd
Priority date: 2021-08-19
Filing date: 2021-08-19
Publication date: 2021-11-26

Abstract

The application relates to an environmental monitoring resource allocation method and system based on the Internet of things, which comprises the steps of triggering environmental resource monitoring triggering instructions of Internet of things monitoring modules which are arranged in each environmental area to be monitored in advance by obtaining, and extracting the current monitoring time point when the environmental resource monitoring triggering instructions are obtained; respectively acquiring current monitoring demand data at each environmental area to be monitored based on an Internet of things monitoring module; and generating a dynamic model of the current regional environment monitoring demand, traversing data in a preset environment monitoring execution main person database according to each dynamic model of the current regional environment monitoring demand, calling an environment monitoring standard execution group matched with each dynamic model of the current regional environment monitoring demand from the environment monitoring execution main person database respectively, and recording the environment monitoring standard execution group as a matched demand environment monitoring group. The invention realizes the accurate, efficient and high-matching-degree environmental monitoring resource distribution.

Description

Environment monitoring resource allocation method and system based on Internet of things

Technical Field

The application relates to the technical field of environmental monitoring, in particular to an environmental monitoring resource allocation method and system based on the Internet of things.

Background

The internet of things is characterized in that any object or process needing monitoring, connection and interaction is collected in real time through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors, laser scanners and the like, various needed information such as sound, light, heat, electricity, mechanics, chemistry, biology, positions and the like is collected, ubiquitous connection of objects and objects, and ubiquitous connection of objects and people are achieved through various possible network accesses, and intelligent sensing, identification and management of the objects and the processes are achieved. The internet of things is an information bearer based on the internet, a traditional telecommunication network and the like, and all common physical objects which can be independently addressed form an interconnected network.

At present, the technology of internet of things is applied to environmental monitoring, for example, an evaluation method system disclosed in the invention patent with the publication number of CN102831320A, and particularly relates to an evaluation method system for quality control indexes in the whole process of watershed water environment organic pollutant monitoring. The following modules: a watershed water environment organic pollutant monitoring whole-process quality control index system module; a watershed water environment organic pollutant monitoring whole-process quality control index weight analysis module; a watershed water environment organic pollutant monitoring overall process quality control index evaluation method module.

Although, the above evaluation system can track the progress of the quality control of the whole environmental monitoring process and perform predictive analysis on the quality control data of the whole environmental monitoring process. However, when the environment monitoring resources are allocated, problems of inaccurate allocation, low allocation efficiency and low allocation matching degree still exist at present.

Disclosure of Invention

Therefore, it is necessary to provide an environment monitoring resource allocation method and system based on the internet of things, which can improve data processing efficiency, in order to solve the above technical problems.

The technical scheme of the invention is as follows:

an environmental monitoring resource allocation method based on the Internet of things, the method comprising:

acquiring an environmental resource monitoring trigger instruction for triggering the Internet of things monitoring modules which are preset at each environmental area to be monitored, and extracting a current monitoring time point when the environmental resource monitoring trigger instruction is acquired, wherein at least one Internet of things monitoring module is arranged in each environmental area to be monitored; respectively acquiring current monitoring demand data at each environmental area to be monitored based on an Internet of things monitoring module, wherein each current monitoring demand data comprises a monitoring starting demand time point and a current monitoring expected effect; respectively generating current region environment monitoring demand dynamic models according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expected effect in each current monitoring demand data correspondence, traversing data in a preset environment monitoring execution main body personnel database according to each current region environment monitoring demand dynamic model, respectively calling environment monitoring standard execution groups matched with each current region environment monitoring demand dynamic model from the environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current region environment monitoring demand dynamic model one to one.

Specifically, current area environment monitoring demand dynamic models are respectively generated according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect in each current monitoring demand data correspondence, data in a preset environment monitoring execution main body personnel database are traversed according to each current area environment monitoring demand dynamic model, environment monitoring standard execution groups matched with each current area environment monitoring demand dynamic model are respectively called from the environment monitoring execution main body personnel database and recorded as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group are stored in the environment monitoring execution main body personnel database in advance, each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one, and the method specifically comprises the following steps:

judging whether the actual time interval between the current monitoring time point and the monitoring starting demand time point is less than or equal to a preset emergency time interval or not according to the current monitoring time point and the monitoring starting demand time point in each current monitoring demand data correspondence; if the current monitoring demand data is judged to be yes, generating a current demand urgency degree value corresponding to the current monitoring demand data, and generating a continuous analysis instruction; extracting an environment monitoring feature weight corresponding to the current monitoring expectation effect according to the continuous analysis instruction; based on the current demand urgency degree value and the environment monitoring feature weight, adjusting the actual area of the environment area to be monitored corresponding to the current demand urgency degree value and the environment monitoring feature weight; acquiring a preset environment monitoring point corresponding to the environment area to be monitored in the actual area according to the actual area, marking the preset environment monitoring point in the actual area, and establishing a dynamic model of the current area environment monitoring requirement according to the actual area, the current requirement urgency degree value and the environment monitoring feature weight; and traversing data in a preset environment monitoring execution main person database according to each current area environment monitoring demand dynamic model, calling environment monitoring standard execution groups matched with each current area environment monitoring demand dynamic model from the environment monitoring execution main person database respectively, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main person database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one.

Specifically, preset environment monitoring points corresponding to the environment area to be monitored in the actual area are obtained according to the actual area, the preset environment monitoring points are marked in the actual area, and meanwhile, a dynamic model of the current area environment monitoring requirement is established according to the actual area, the current requirement urgency degree value and the environment monitoring feature weight; the method specifically comprises the following steps:

acquiring a preset environment monitoring point corresponding to the environment area to be monitored in the actual area according to the actual area, marking the preset environment monitoring point in the actual area, generating an initial area environment monitoring model according to the actual area, the current demand urgency degree value and the environment monitoring feature weight, and generating a real-time data updating instruction; acquiring the latest area of the environmental area to be monitored based on the real-time data updating instruction; after the latest region area is obtained, calling the latest environment monitoring point in the latest region area according to the latest region area; after the latest environment monitoring point is obtained, acquiring latest monitoring demand data when the latest environment monitoring point is obtained based on the Internet of things monitoring module; generating dynamic model adjustment parameters according to the latest region area, the latest environment monitoring point and the latest monitoring demand data; and updating the initial region environment monitoring model according to the dynamic model adjusting parameters, and generating a current region environment monitoring demand dynamic model.

Specifically, a matching degree nonlinear regression model is preset in the environment monitoring execution main person database, and a nonlinear matching regression line is arranged in the matching degree nonlinear regression model;

according to each current regional environment monitoring demand dynamic model, traversing data in a preset environment monitoring execution main person database, calling environment monitoring standard execution groups matched with each current regional environment monitoring demand dynamic model from the environment monitoring execution main person database respectively, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main person database in advance, and each matching demand environment monitoring group corresponds to each current regional environment monitoring demand dynamic model one to one, and the method specifically comprises the following steps:

according to the current regional environment monitoring demand dynamic models, importing the current regional environment monitoring demand dynamic models into a matching degree nonlinear regression model in an environment monitoring execution subject personnel database; generating a matching degree result according to each current region environment monitoring demand dynamic model and a matching degree nonlinear regression model; and calling environment monitoring standard execution groups matched with the current area environment monitoring demand dynamic models according to the matching degree result, and recording as matching demand environment monitoring groups, wherein data of each environment monitoring standard execution group is stored in an environment monitoring execution subject personnel database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one.

Specifically, a dynamic model of the current area environment monitoring demand is generated according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expected effect in correspondence of each current monitoring demand data, and traversing the data in the preset environment monitoring execution main personnel database according to each current area environment monitoring demand dynamic model, and respectively calling environment monitoring standard execution groups matched with the current region environment monitoring demand dynamic models from an environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein, the data of each environmental monitoring standard execution group is prestored in the environmental monitoring execution main personnel database, and each matching demand environmental monitoring group corresponds to each current regional environmental monitoring demand dynamic model one to one, and then still includes:

respectively acquiring actual environment monitoring data of each matched demand environment monitoring group to each corresponding environment area to be monitored; generating an actual satisfaction degree value of the demand body of each environment area to be monitored according to the actual environment monitoring data; judging a satisfaction matching degree actual difference value of the actual satisfaction degree value and the current monitoring expectation effect according to the actual satisfaction degree value; when the actual difference value of the satisfaction matching degree is larger than or equal to a preset qualified satisfaction matching degree value, generating a standard matching data set according to a corresponding matching demand environment monitoring group and an environment area to be monitored; and generating a standard matching modeling model according to each standard matching data set, wherein the standard matching modeling model is generated by training each standard matching data set.

Specifically, an environmental monitoring resource allocation system based on the internet of things comprises:

the system comprises an environment resource module, a monitoring module and a monitoring module management module, wherein the environment resource module is used for acquiring an environment resource monitoring triggering instruction for triggering the Internet of things monitoring modules which are arranged in each environment area to be monitored in advance, and extracting a current monitoring time point when the environment resource monitoring triggering instruction is acquired, wherein at least one Internet of things monitoring module is arranged in each environment area to be monitored;

the monitoring demand module is used for respectively acquiring current monitoring demand data at each environmental area to be monitored based on the Internet of things monitoring module, wherein each current monitoring demand data comprises a monitoring starting demand time point and a current monitoring expectation effect;

and the monitoring matching module is used for respectively generating a current regional environment monitoring demand dynamic model according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect in each current monitoring demand data correspondence, traversing data in a preset environment monitoring execution main body personnel database according to each current regional environment monitoring demand dynamic model, respectively calling environment monitoring standard execution groups matched with each current regional environment monitoring demand dynamic model from the environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current regional environment monitoring demand dynamic model one to one.

Specifically, the system further comprises:

the starting demand module is used for judging whether the actual time interval between the current monitoring time point and the monitoring starting demand time point is less than or equal to a preset emergency time interval or not according to the current monitoring time point and the monitoring starting demand time point corresponding to each current monitoring demand data;

a yes judgment module, configured to generate a current demand urgency degree value corresponding to the current monitoring demand data and generate a continuous analysis instruction if the yes judgment is made;

the continuous analysis module is used for extracting the environment monitoring feature weight corresponding to the current monitoring expectation effect according to the continuous analysis instruction;

the module is used for calling the actual area of the environment area to be monitored corresponding to the current demand urgency degree value and the environment monitoring feature weight based on the current demand urgency degree value and the environment monitoring feature weight;

the area module is used for acquiring preset environment monitoring points corresponding to the environment area to be monitored in the actual area according to the actual area, marking the preset environment monitoring points in the actual area, and establishing a dynamic model of the current area environment monitoring requirement according to the actual area, the current requirement urgency degree value and the environment monitoring feature weight;

and the monitoring execution module is used for traversing data in a preset environment monitoring execution main body personnel database according to each current area environment monitoring demand dynamic model, calling environment monitoring standard execution groups matched with each current area environment monitoring demand dynamic model from the environment monitoring execution main body personnel database respectively, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one.

Specifically, the system further comprises:

the area marking module is used for acquiring preset environment monitoring points corresponding to the environment area to be monitored in the actual area according to the actual area, marking the preset environment monitoring points in the actual area, generating an initial area environment monitoring model according to the actual area, the current demand urgency degree value and the environment monitoring feature weight, and generating a real-time data updating instruction;

the data updating module is used for acquiring the latest area of the environmental area to be monitored based on the real-time data updating instruction;

the latest region module is used for calling the latest environment monitoring point in the latest region area according to the latest region area after the latest region area is obtained;

the Internet of things monitoring module is used for acquiring the latest monitoring demand data when the latest environment monitoring point is acquired based on the Internet of things monitoring module after the latest environment monitoring point is acquired;

the model adjusting module is used for generating dynamic model adjusting parameters according to the latest region area, the latest environment monitoring point and the latest monitoring demand data;

the demand dynamic module is used for updating the initial region environment monitoring model according to the dynamic model adjusting parameters and generating a current region environment monitoring demand dynamic model;

the region importing module is used for importing each current region environment monitoring demand dynamic model into a matching degree nonlinear regression model in the environment monitoring execution main body personnel database according to each current region environment monitoring demand dynamic model;

the monitoring dynamic module is used for generating a matching degree result according to each current region environment monitoring demand dynamic model and the matching degree nonlinear regression model;

and the monitoring group module is used for calling an environment monitoring standard execution group matched with each current area environment monitoring demand dynamic model according to the matching degree result and recording the environment monitoring standard execution group as a matching demand environment monitoring group, wherein the data of each environment monitoring standard execution group is stored in an environment monitoring execution main personnel database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one.

A computer device comprises a storage and a processor, wherein the storage stores a computer program, and the processor realizes the steps of the Internet of things-based environment monitoring resource allocation method when executing the computer program.

A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the above-mentioned method for allocating resources for monitoring an environment based on the internet of things.

The invention has the following technical effects:

the method and the system for allocating the environment monitoring resources based on the Internet of things sequentially acquire an environment resource monitoring trigger instruction for triggering the Internet of things monitoring modules which are arranged in each environment area to be monitored in advance, and extract the current monitoring time point when the environment resource monitoring trigger instruction is acquired, wherein at least one Internet of things monitoring module is arranged in each environment area to be monitored; respectively acquiring current monitoring demand data at each environmental area to be monitored based on an Internet of things monitoring module, wherein each current monitoring demand data comprises a monitoring starting demand time point and a current monitoring expected effect; respectively generating current area environment monitoring demand dynamic models according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expected effect in the current monitoring demand data correspondence, traversing data in a preset environment monitoring execution main body personnel database according to the current area environment monitoring demand dynamic models, respectively calling environment monitoring standard execution groups matched with the current area environment monitoring demand dynamic models from the environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one by one, namely, the invention firstly passes through an internet of things monitoring module at each environment area to be monitored, when each environmental area to be monitored needs to be subjected to environmental monitoring, the internet of things monitoring module is triggered, namely an environmental resource monitoring triggering instruction for triggering the internet of things monitoring module which is preset at each environmental area to be monitored is obtained, meanwhile, when the environmental resource monitoring triggering instruction is obtained, the current monitoring time point for obtaining the environmental resource monitoring triggering instruction is extracted, then, in order to more accurately distribute the environmental monitoring resources, the requirements at each environmental area to be monitored need to be known, specifically, the monitoring starting required time point and the current monitoring expectation effect are obtained, wherein the monitoring starting required time point is the time point of expected monitoring starting, the time represents the urgency of the monitoring requirements, the current monitoring expectation effect is the actual effect which is expected to be achieved through the environmental monitoring, and then, in order to better distribute the existing environmental monitoring resources, respectively generating a current area environment monitoring demand dynamic model according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect in the corresponding current monitoring demand data, wherein the current area environment monitoring demand dynamic model at least comprises a three-dimensional dynamic model which is generated by taking the actual area of each environment area to be monitored and can broadcast the expectation effect after combining the actual area with the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect, so that environment monitoring execution main body personnel can conveniently check the three-dimensional dynamic model, can traverse the data in a preset environment monitoring execution main body personnel database according to each current area environment monitoring demand dynamic model, and respectively take an environment monitoring standard execution group matched with each current area environment monitoring demand dynamic model from the environment monitoring execution main body personnel database And each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one, so when each environment area to be monitored needs to be monitored, by considering multiple factors such as the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect and generating the dynamic model, the distribution of the environment monitoring standard execution group after comprehensive consideration is further realized, and the distribution of environment monitoring resources with accuracy, high efficiency and high matching degree is further realized.

Drawings

Fig. 1 is a schematic flowchart of an environmental monitoring resource allocation method based on the internet of things in one embodiment;

FIG. 2 is a block diagram of an environmental monitoring resource allocation system based on the Internet of things in one embodiment;

FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In one embodiment, as shown in fig. 1, there is provided an internet of things-based environment monitoring resource allocation method, including:

step S100: acquiring an environmental resource monitoring trigger instruction for triggering the Internet of things monitoring modules which are preset at each environmental area to be monitored, and extracting a current monitoring time point when the environmental resource monitoring trigger instruction is acquired, wherein at least one Internet of things monitoring module is arranged in each environmental area to be monitored;

specifically, firstly, when each environment area to be monitored needs to be subjected to environment monitoring through the internet of things monitoring module at each environment area to be monitored, the internet of things monitoring module is triggered, namely, an environment resource monitoring trigger instruction which triggers the internet of things monitoring module which is arranged at each environment area to be monitored in advance is acquired, and meanwhile, when the environment resource monitoring trigger instruction is acquired, the current monitoring time point of the environment resource monitoring trigger instruction is extracted and acquired.

The Internet of things monitoring module is a module designed based on the Internet of things technology, and the Internet of things monitoring module realizes efficient acquisition and conduction of data and utilizes the high efficiency of the Internet of things.

Step S200: respectively acquiring current monitoring demand data at each environmental area to be monitored based on an Internet of things monitoring module, wherein each current monitoring demand data comprises a monitoring starting demand time point and a current monitoring expected effect;

specifically, in order to more accurately allocate the environment monitoring resources, and further need to know the requirements of each environment region to be monitored, specifically, the time point of the monitoring start demand and the current monitoring expectation effect are determined, where the time point of the monitoring start demand is the time point of the expected monitoring start, and this time represents the urgency of the monitoring requirements.

Step S300: respectively generating current region environment monitoring demand dynamic models according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expected effect in each current monitoring demand data correspondence, traversing data in a preset environment monitoring execution main body personnel database according to each current region environment monitoring demand dynamic model, respectively calling environment monitoring standard execution groups matched with each current region environment monitoring demand dynamic model from the environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current region environment monitoring demand dynamic model one to one.

Specifically, the current monitoring expectation effect is an actual effect that is desired to be achieved through the environmental monitoring, and then, in order to better allocate existing environmental monitoring resources, current area environmental monitoring demand dynamic models are respectively generated according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect in correspondence of each current monitoring demand data, wherein the current area environmental monitoring demand dynamic models at least comprise three-dimensional dynamic models capable of broadcasting expectation effects and generated by combining actual areas with the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect, so that environmental monitoring execution subject personnel can conveniently view the results, and meanwhile, a preset environmental monitoring execution subject personnel database can be traversed according to each current area environmental monitoring demand dynamic model The data in the method are used for respectively calling environment monitoring standard execution groups matched with the current area environment monitoring demand dynamic models from an environment monitoring execution main body personnel database, and each matched demand environment monitoring group is in one-to-one correspondence with each current area environment monitoring demand dynamic model, so that when each environment area to be monitored needs to be monitored, the distribution of the environment monitoring standard execution groups after comprehensive consideration is further realized by considering multiple factors, such as the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect, and generating the dynamic models, and further the distribution of environment monitoring standard execution groups after comprehensive consideration is realized, and the distribution of environment monitoring resources with high accuracy, high efficiency and high matching degree is further realized.

In one embodiment, step S300: respectively generating current region environment monitoring demand dynamic models according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expected effect in each current monitoring demand data correspondence, traversing data in a preset environment monitoring execution main body personnel database according to each current region environment monitoring demand dynamic model, respectively calling environment monitoring standard execution groups matched with each current region environment monitoring demand dynamic model from the environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current region environment monitoring demand dynamic model one to one, and the method specifically comprises the following steps:

step S310: judging whether the actual time interval between the current monitoring time point and the monitoring starting demand time point is less than or equal to a preset emergency time interval or not according to the current monitoring time point and the monitoring starting demand time point in each current monitoring demand data correspondence;

step S320: if the current monitoring demand data is judged to be yes, generating a current demand urgency degree value corresponding to the current monitoring demand data, and generating a continuous analysis instruction;

specifically, in this step, the actual time interval between the current monitoring time point and the monitoring start demand time point is determined to be less than or equal to a preset emergency time interval, where the emergency time interval is a preset parameter representing the degree of emergency.

Step S330: extracting an environment monitoring feature weight corresponding to the current monitoring expectation effect according to the continuous analysis instruction;

specifically, the environment monitoring feature weights corresponding to different current monitoring expectation effects are different, and a corresponding relation is preset, so that the environment monitoring feature weight corresponding to the current monitoring expectation effect is quickly extracted.

Step S340: based on the current demand urgency degree value and the environment monitoring feature weight, adjusting the actual area of the environment area to be monitored corresponding to the current demand urgency degree value and the environment monitoring feature weight;

step S350: acquiring a preset environment monitoring point corresponding to the environment area to be monitored in the actual area according to the actual area, marking the preset environment monitoring point in the actual area, and establishing a dynamic model of the current area environment monitoring requirement according to the actual area, the current requirement urgency degree value and the environment monitoring feature weight;

step S360: and traversing data in a preset environment monitoring execution main person database according to each current area environment monitoring demand dynamic model, calling environment monitoring standard execution groups matched with each current area environment monitoring demand dynamic model from the environment monitoring execution main person database respectively, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main person database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one.

Specifically, when the actual time interval between the current monitoring time point and the monitoring starting demand time point is less than or equal to a preset emergency time interval according to the current monitoring time point and the monitoring starting demand time point in each current monitoring demand data correspondence; if the current monitoring demand data is judged to be yes, generating a current demand urgency degree value corresponding to the current monitoring demand data, and generating a continuous analysis instruction;

meanwhile, extracting an environment monitoring feature weight corresponding to the current monitoring expectation effect according to the continuous analysis instruction, and then, calling an actual area of an environment area to be monitored corresponding to the current demand urgency degree value and the environment monitoring feature weight based on the current demand urgency degree value and the environment monitoring feature weight; therefore, the preset environment monitoring points corresponding to the environment area to be monitored in the actual area are obtained according to the actual area, the preset environment monitoring points are marked in the actual area, and meanwhile, a dynamic model of the current area environment monitoring requirement is established according to the actual area, the current requirement urgency degree value and the environment monitoring feature weight.

In one embodiment, step S350: acquiring a preset environment monitoring point corresponding to the environment area to be monitored in the actual area according to the actual area, marking the preset environment monitoring point in the actual area, and establishing a dynamic model of the current area environment monitoring requirement according to the actual area, the current requirement urgency degree value and the environment monitoring feature weight; the method specifically comprises the following steps:

step S351: acquiring a preset environment monitoring point corresponding to the environment area to be monitored in the actual area according to the actual area, marking the preset environment monitoring point in the actual area, generating an initial area environment monitoring model according to the actual area, the current demand urgency degree value and the environment monitoring feature weight, and generating a real-time data updating instruction;

step S352: acquiring the latest area of the environmental area to be monitored based on the real-time data updating instruction;

step S353: after the latest region area is obtained, calling the latest environment monitoring point in the latest region area according to the latest region area;

step S354: after the latest environment monitoring point is obtained, acquiring latest monitoring demand data when the latest environment monitoring point is obtained based on the Internet of things monitoring module;

step S355: generating dynamic model adjustment parameters according to the latest region area, the latest environment monitoring point and the latest monitoring demand data;

step S356: and updating the initial region environment monitoring model according to the dynamic model adjusting parameters, and generating a current region environment monitoring demand dynamic model.

Specifically, in this step, in order to ensure the effectiveness, accuracy and real-time performance of the dynamic model for monitoring the current area environment demand when generating, a preset environment monitoring point corresponding to the environment area to be monitored in the actual area is obtained according to the actual area and is marked in the actual area, an initial area environment monitoring model is generated according to the actual area, the current demand urgency level value and the environment monitoring feature weight, a real-time data updating instruction is generated, a preset environment monitoring point corresponding to the environment area to be monitored in the actual area is obtained according to the actual area and is marked in the actual area, and an initial area environment monitoring model is generated according to the actual area, the current demand urgency level value and the environment monitoring feature weight, and generating a real-time data updating instruction, then, after the latest region area is obtained, calling the latest environment monitoring point in the latest region area according to the latest region area, next, generating a dynamic model adjusting parameter according to the latest region area, the latest environment monitoring point and the latest monitoring demand data, updating the initial region environment monitoring model according to the dynamic model adjusting parameter, and generating a current region environment monitoring demand dynamic model, thereby ensuring the accuracy and the real-time performance of the generated current region environment monitoring demand dynamic model.

In one embodiment, a matching degree nonlinear regression model is preset in the environment monitoring execution main person database, and a nonlinear matching regression line is arranged in the matching degree nonlinear regression model;

step S360: according to each current regional environment monitoring demand dynamic model, traversing data in a preset environment monitoring execution main person database, calling environment monitoring standard execution groups matched with each current regional environment monitoring demand dynamic model from the environment monitoring execution main person database respectively, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main person database in advance, and each matching demand environment monitoring group corresponds to each current regional environment monitoring demand dynamic model one to one, and the method specifically comprises the following steps:

step S361: according to the current regional environment monitoring demand dynamic models, importing the current regional environment monitoring demand dynamic models into a matching degree nonlinear regression model in an environment monitoring execution subject personnel database;

specifically, the matching degree nonlinear regression model is preset by a person skilled in the art, and is used for generating a matching degree result according to each current region environment monitoring demand dynamic model and the matching degree nonlinear regression model.

Step S362: generating a matching degree result according to each current region environment monitoring demand dynamic model and a matching degree nonlinear regression model;

step S363: and calling environment monitoring standard execution groups matched with the current area environment monitoring demand dynamic models according to the matching degree result, and recording as matching demand environment monitoring groups, wherein data of each environment monitoring standard execution group is stored in an environment monitoring execution subject personnel database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one.

Specifically, the non-linear regression model of the matching degree and the non-linear matching regression line set in the non-linear regression model of the matching degree are both set in advance by those skilled in the art.

Furthermore, when the matching degree result is on one side of the nonlinear matching regression line, the first result represents a first result, and when the matching degree result is on the other side of the nonlinear matching regression line, the second result represents a second result, wherein the two results are completely opposite, the first result is that the matching requirement is met, therefore, an environment monitoring standard execution group matched with each current regional environment monitoring requirement dynamic model is called and recorded as a matching requirement environment monitoring group, data of each environment monitoring standard execution group is stored in an environment monitoring execution subject personnel database in advance, and each matching requirement environment monitoring group corresponds to each current regional environment monitoring requirement dynamic model one to one.

The second result is that the matching requirement is not met, and further the next matching is not needed.

In one embodiment, step S300: respectively generating current region environment monitoring demand dynamic models according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expected effect in each current monitoring demand data correspondence, traversing data in a preset environment monitoring execution main body personnel database according to each current region environment monitoring demand dynamic model, respectively calling environment monitoring standard execution groups matched with each current region environment monitoring demand dynamic model from the environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current region environment monitoring demand dynamic model one to one, and then the method further comprises the following steps:

step S610: respectively acquiring actual environment monitoring data of each matched demand environment monitoring group to each corresponding environment area to be monitored;

step S620: generating an actual satisfaction degree value of the demand body of each environment area to be monitored according to the actual environment monitoring data;

the actual desirability measure value is a specific numerical value, as may be demonstrated by fractional values, including but not limited to 30, 40, 50, etc. equally divided.

Step S630: judging a satisfaction matching degree actual difference value of the actual satisfaction degree value and the current monitoring expectation effect according to the actual satisfaction degree value;

and the actual difference value of the satisfaction matching degree is the difference value between the actual satisfaction degree value and the current monitoring expectation effect, and the difference between the actual satisfaction degree value and the current monitoring expectation effect is represented.

Step S640: when the actual difference value of the satisfaction matching degree is larger than or equal to a preset qualified satisfaction matching degree value, generating a standard matching data set according to a corresponding matching demand environment monitoring group and an environment area to be monitored;

specifically, the qualified satisfactory matching degree value is preset and used for judging whether satisfaction exists, and the comparison accuracy is improved through digital measurement.

Step S650: and generating a standard matching modeling model according to each standard matching data set, wherein the standard matching modeling model is generated by training each standard matching data set.

Specifically, in order to perform matching training more quickly next time, a standard matching modeling model is generated by training each standard matching data set, and each standard matching data set is generated according to a corresponding matching demand environment monitoring group and an environment area to be monitored when the actual difference value of the satisfactory matching degree is greater than or equal to a preset qualified satisfactory matching degree value, so that the accuracy and the stability of the establishment of the standard matching modeling model are improved.

In summary, the method and system for allocating environmental monitoring resources based on the internet of things sequentially acquire an environmental resource monitoring trigger instruction for triggering internet of things monitoring modules pre-arranged in each environmental area to be monitored, and extract a current monitoring time point when the environmental resource monitoring trigger instruction is acquired, wherein at least one internet of things monitoring module is arranged in each environmental area to be monitored; respectively acquiring current monitoring demand data at each environmental area to be monitored based on an Internet of things monitoring module, wherein each current monitoring demand data comprises a monitoring starting demand time point and a current monitoring expected effect; respectively generating current area environment monitoring demand dynamic models according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expected effect in the current monitoring demand data correspondence, traversing data in a preset environment monitoring execution main body personnel database according to the current area environment monitoring demand dynamic models, respectively calling environment monitoring standard execution groups matched with the current area environment monitoring demand dynamic models from the environment monitoring execution main body personnel database, and recording the environment monitoring standard execution groups as matching demand environment monitoring groups, wherein the data of each environment monitoring standard execution group is stored in the environment monitoring execution main body personnel database in advance, and each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one by one, namely, the invention firstly passes through an internet of things monitoring module at each environment area to be monitored, when each environmental area to be monitored needs to be subjected to environmental monitoring, the internet of things monitoring module is triggered, namely an environmental resource monitoring triggering instruction for triggering the internet of things monitoring module which is preset at each environmental area to be monitored is obtained, meanwhile, when the environmental resource monitoring triggering instruction is obtained, the current monitoring time point for obtaining the environmental resource monitoring triggering instruction is extracted, then, in order to more accurately distribute the environmental monitoring resources, the requirements at each environmental area to be monitored need to be known, specifically, the monitoring starting required time point and the current monitoring expectation effect are obtained, wherein the monitoring starting required time point is the time point of expected monitoring starting, the time represents the urgency of the monitoring requirements, the current monitoring expectation effect is the actual effect which is expected to be achieved through the environmental monitoring, and then, in order to better distribute the existing environmental monitoring resources, respectively generating a current area environment monitoring demand dynamic model according to the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect in the corresponding current monitoring demand data, wherein the current area environment monitoring demand dynamic model at least comprises a three-dimensional dynamic model which is generated by taking the actual area of each environment area to be monitored and can broadcast the expectation effect after combining the actual area with the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect, so that environment monitoring execution main body personnel can conveniently check the three-dimensional dynamic model, can traverse the data in a preset environment monitoring execution main body personnel database according to each current area environment monitoring demand dynamic model, and respectively take an environment monitoring standard execution group matched with each current area environment monitoring demand dynamic model from the environment monitoring execution main body personnel database And each matching demand environment monitoring group corresponds to each current area environment monitoring demand dynamic model one to one, so when each environment area to be monitored needs to be monitored, by considering multiple factors such as the current monitoring time point, the monitoring starting demand time point and the current monitoring expectation effect and generating the dynamic model, the distribution of the environment monitoring standard execution group after comprehensive consideration is further realized, and the distribution of environment monitoring resources with accuracy, high efficiency and high matching degree is further realized.

In one embodiment, as shown in fig. 2, an internet of things based environment monitoring resource allocation system includes:

In one embodiment, the system further comprises:

In one embodiment, the monitoring demand module is further configured to:

In one embodiment, as shown in fig. 3, a computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method for allocating resources for monitoring environment based on internet of things when executing the computer program.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. An environmental monitoring resource allocation method based on the Internet of things is characterized by comprising the following steps:

2. The method for allocating resources for environmental monitoring based on the internet of things according to claim 1, wherein dynamic models for environmental monitoring needs in current areas are generated according to the current monitoring time point, the monitoring start time point and the current monitoring expected effect in the data correspondence of each current monitoring need, and data in a preset database of the personnel in the environment monitoring executive subject are traversed according to each dynamic model for environmental monitoring needs in current areas, and an environmental monitoring standard executive group matched with each dynamic model for environmental monitoring needs in current areas is called from the database of the personnel in the environment monitoring executive subject and is recorded as a matching demand environmental monitoring group, wherein data of each environmental monitoring standard executive group is pre-stored in the database of the personnel in the environment monitoring executive subject, and each matching demand environmental monitoring group corresponds to each dynamic model for environmental monitoring needs in current areas one to one, the method specifically comprises the following steps:

3. The method for allocating resources for environmental monitoring based on the internet of things according to claim 2, wherein preset environmental monitoring points corresponding to the environmental area to be monitored in the actual area are obtained according to the actual area and are marked in the actual area, and a dynamic model of the current area environmental monitoring demand is established according to the actual area, the current demand urgency value and the environmental monitoring feature weight; the method specifically comprises the following steps:

4. The Internet of things-based environment monitoring resource allocation method according to claim 2, wherein a matching degree nonlinear regression model is preset in the environment monitoring execution subject personnel database, and a nonlinear matching regression line is arranged in the matching degree nonlinear regression model;

5. The Internet of things-based environment monitoring resource allocation method according to any one of claims 1 to 4, wherein current regional environment monitoring demand dynamic models are respectively generated according to the current monitoring time point, the monitoring start demand time point and the current monitoring expected effect in each current monitoring demand data correspondence, data in a preset environment monitoring execution subject personnel database are traversed according to each current regional environment monitoring demand dynamic model, environment monitoring standard execution groups matched with each current regional environment monitoring demand dynamic model are respectively retrieved from the environment monitoring execution subject personnel database and are recorded as matching demand environment monitoring groups, wherein data of each environment monitoring standard execution group are stored in the environment monitoring execution subject personnel database in advance, each matching demand environment monitoring group corresponds to each current regional environment monitoring demand dynamic model one by one, then also comprises the following steps:

6. An environmental monitoring resource allocation system based on the internet of things, the system comprising:

7. The IOT-based environmental monitoring resource allocation system of claim 6, further comprising:

8. The IOT-based environmental monitoring resource allocation system of claim 6, further comprising:

9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.