CN117350456A - Environment-friendly monitoring method, system, device and medium for prefabricated part - Google Patents

Abstract

The embodiment of the specification provides a prefabricated part environment-friendly monitoring method, system, device and medium, wherein the method is realized based on the prefabricated part environment-friendly monitoring system and comprises the following steps: collecting collected data in the preparation process and/or the transfer process of the prefabricated part; determining acquisition change data of the acquisition data based on the acquisition data; determining the influence degree of the preparation environment based on the acquired change data; determining transfer parameters of the prefabricated part; determining a solid waste outward movement parameter based on the influence degree of the preparation environment; and sending the transfer parameters and the solid waste transfer parameters to the user terminal.

Description

Environment-friendly monitoring method, system, device and medium for prefabricated part

Technical Field

The specification relates to the field of environmental protection monitoring, in particular to a prefabricated part environmental protection monitoring method, system, device and medium.

Background

The prefabricated components have a plurality of convenience in the fields of assembly construction, production and transportation, but in the preparation and transfer processes, a large amount of solid wastes (building wastes such as concrete leftover wastes, sand and stone) and dust pollution and noise pollution are usually generated. The method and the corresponding system device related to the development of the environment-friendly monitoring aiming at the prefabricated part can reduce environmental pollution and meet the requirements of construction production and environment protection.

A low-carbon environment-friendly construction engineering energy-saving construction method with Chinese patent publication No. CN 115619073A. The dust emission, the waste water emission, the waste gas emission, the noise emission and the energy use of the construction site are monitored in real time through the cooperation among the modules, and meanwhile, the construction site management is evaluated to continuously and steadily improve the low-carbon environment-friendly construction. However, the technology mainly depends on constructors to monitor and manage corresponding data, and a great deal of labor cost is required. The technology is mainly aimed at monitoring construction links, and lacks a direct and effective means for environmental protection monitoring of prefabricated part transfer transportation.

Therefore, it is desirable to provide an environment-friendly monitoring system, method, device and medium for prefabricated parts, so as to realize an automatic environment-friendly monitoring function for the prefabricated parts in the production and transportation processes and realize full-period green construction production.

Disclosure of Invention

One or more embodiments of the present disclosure provide a method of environmental monitoring a prefabricated component. The prefabricated part environment-friendly monitoring method comprises the following steps: collecting data in the preparation process and/or the transfer process of the prefabricated part, wherein the collected data comprises at least one of solid waste amount, noise intensity and dust amount; based on the acquired data, determining acquisition variation data of the acquired data; determining the influence degree of the preparation environment based on the acquired change data; determining transfer parameters of the prefabricated part; the transfer parameters include a recommended transportation path; determining a solid waste outward movement parameter based on the influence degree of the preparation environment; the solid waste outward movement parameters comprise a solid waste transfer period; and sending the transfer parameters and the solid waste outward-moving parameters to a user terminal.

One or more embodiments of the present specification provide a prefabricated part environmental protection monitoring system, the prefabricated part environmental protection monitoring system comprising: the system comprises a data acquisition module, a monitoring module, an environment-friendly analysis module and a background control module, wherein the data acquisition module is deployed to acquire acquisition data in the preparation process and/or the transfer process of the prefabricated component, and the acquisition data comprises at least one of solid waste amount, noise intensity and dust amount; the monitoring module is deployed to determine acquisition variation data of the acquisition data based on the acquisition data; the environmental analysis module is deployed to determine a preparation environment influence degree based on the acquisition change data; the preparation environment influence degree is the influence degree on the environment in the preparation prefabrication process; the background control module is deployed to: determining transfer parameters of the prefabricated part; the transfer parameters include a recommended transportation path; determining a solid waste outward movement parameter based on the influence degree of the preparation environment; the solid waste outward movement parameters comprise a solid waste transfer period; and sending the transfer parameters and the solid waste outward-moving parameters to a user terminal.

One or more embodiments of the present specification provide a prefabricated component environmental monitoring device comprising at least one storage medium for storing computer instructions and at least one processor; the at least one processor is configured to execute the computer instructions to implement a prefabricated component environmental protection monitoring method.

One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform a method of monitoring environmental protection of a prefabricated part.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic diagram of a prefabricated component environmental monitoring system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow chart of a method of environmental monitoring of a prefabricated component according to some embodiments of the present description;

FIG. 3 is a schematic diagram of a path feature determination model shown in accordance with some embodiments of the present description;

FIG. 4 is a schematic diagram of a pre-estimated transfer environment influence determination model according to some embodiments of the present disclosure;

FIG. 5 is an exemplary flow chart for determining how much a preparation environment affects, according to some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

FIG. 1 is a schematic diagram of a prefabricated component environmental monitoring system according to some embodiments of the present disclosure.

As shown in fig. 1, some embodiments of the present description provide a prefabricated component environmental monitoring system 100. In some embodiments, the prefabricated component environmental monitoring system 100 may include a data acquisition module 110, a monitoring module 120, an environmental analysis module 130, and a background control module 140.

In some embodiments, the data acquisition module 110 is deployed to acquire acquisition data during the preparation process and/or the transfer process of the prefabricated component. In some embodiments, the collected data includes at least one of a solid waste amount, a noise intensity, and a dust amount.

In some embodiments, the monitoring module 120 is deployed to determine acquisition variation data of the acquisition data based on the acquisition data.

In some embodiments, the environmental analysis module 130 is configured to determine the preparation environment impact level based on the acquisition of the change data.

In some embodiments, the background control module 140 is deployed as: determining transfer parameters of the prefabricated components, wherein the transfer parameters comprise recommended transportation paths; determining solid waste transfer parameters based on the influence degree of the preparation environment, wherein the solid waste transfer parameters comprise a solid waste transfer period; further, the transfer parameter and the solid waste transfer parameter are sent to a user terminal.

For more details on the various modules, see fig. 2 and its associated description.

It should be understood that the system shown in fig. 1 and its modules may be implemented in a variety of ways.

It should be noted that the above description of the prefabricated component environmental protection monitoring system and the modules thereof is for convenience of description only, and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the data acquisition module 110, the monitoring module 120, the environmental analysis module 130, and the background control module 140 disclosed in fig. 1 may be different modules in one system, or may be one module to implement the functions of two or more modules described above. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

FIG. 2 is an exemplary flow chart of a method of environmental monitoring a prefabricated component according to some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. The process 200 is performed by the prefabricated component environmental monitoring system 100.

Step 210, collecting collected data during the preparation process and/or the transfer process of the prefabricated parts.

Prefabricated components refer to building components prefabricated at a prefabricated site according to design specifications. For example, the prefabricated components may include prefabricated beam slabs, rebar constructions, timber constructions, or concrete components of bridges, etc.

The acquired data refers to the influence data generated by the prefabricated part during the manufacturing process and/or the transferring process. The preparation process comprises the steps of binding reinforcing steel bars, casting concrete, removing templates, curing and the like in a prefabricated field which is built in a temporary building way. The transfer process comprises the steps of transporting the prefabricated part to a construction site after the prefabricated part is maintained, erecting the prefabricated part at a designated position and the like.

In some embodiments, the collected data may include at least one of a solid waste amount, a noise intensity, and a dust amount. The solid waste amount generally refers to the amount of building waste such as concrete leftover bits and pieces, sand and stone and the like generated in the preparation process. Noise intensity generally refers to the level of noise generated during transfer. The dust amount generally refers to the amount of dust generated during the transfer process.

In some embodiments, the data acquisition module 110 may acquire acquired data during the manufacturing process and/or the transfer process via noise sensors, dust monitoring instruments, and/or pressure sensors, among others. In some embodiments, the data acquisition module 110 may also acquire image data, including solid waste images, through an image acquisition device (e.g., an optical camera); further, the solid waste image in the collected image data is identified through an image identification algorithm, and the solid waste amount is determined based on the identification result. The solid waste image is an image containing construction waste such as concrete leftover bits and pieces, sand and stone. In some embodiments, the image recognition algorithm may be a neural network algorithm.

Step 220, based on the collected data, the collected change data of the collected data is determined.

The acquisition change data is data reflecting the change condition of the acquisition data. In some embodiments, the collection variation data includes at least one of solid waste variation data, noise intensity variation data, and dust variation data.

In some embodiments, the monitoring module 120 may compare the collected data corresponding to the plurality of historical time points, and take the difference value as the collected change data.

Step 230, determining the preparation environment influence on the basis of the acquired change data.

The degree of influence of the preparation environment refers to the degree of influence of the preparation process of the prefabricated part on the environment. For example, preparing the environmental impact may include generating solid waste and generating noise, dust, etc.

In some embodiments, the environmental analysis module 130 may determine the preparation environment impact level by a variety of methods based on the acquisition of the change data. For example, the environmental analysis module may perform trend analysis based on the collected change data, and evaluate the influence of the preparation environment according to the analysis result.

In some embodiments, the monitoring module may determine solid waste amount variation data based on the solid waste amount; further, the environmental analysis module may determine the preparation environment influence degree through the second vector database based on the solid waste amount change data. For more on determining the extent of influence of the preparation environment based on the amount of solid waste, see fig. 5 and the related description.

Step 240, determining transfer parameters of the preform.

The transfer parameters are parameters of the preform related to the transfer process. In some embodiments, the transfer parameters may include recommended transportation paths, and the like.

The recommended transportation path is a recommended path for transporting the prefabricated member.

In some embodiments, the background control module may use the historical path or the default path as the recommended transportation path. The history path is a path transported in the history time of the prefabricated member. The default path may include a path that is manually preset or a path that is recommended by the background control module based on a third party (e.g., navigation software).

In some embodiments, the background control module may determine the candidate transportation path based on preset conditions; determining path fault probability characteristics of the candidate transportation path based on future weather data, the candidate transportation path, road condition data of the candidate transportation path and current traffic flow; further, a recommended transportation path is determined from the candidate transportation paths based on the path failure probability characteristics.

The candidate transportation path refers to a transportation path to be confirmed as the recommended transportation path.

The preset conditions can be set according to actual conditions. For example, the preset condition may be set such that the path length is less than a preset threshold. The preset threshold is set according to the requirement, and is not limited herein.

In some embodiments, the background control module may take a path with a path length less than a preset threshold as a candidate transport path.

Future weather data is data relating to weather at a future point in time. Such as temperature, precipitation, wind, etc. In some embodiments, future weather data may be obtained through a third party platform, such as a weather forecast network or the like.

The road condition data is data reflecting the road condition of the candidate transportation path.

The current traffic flow is the traffic flow of the candidate transportation path at the current time.

The path failure probability feature refers to a probability feature of failure occurring on the candidate transportation path. For example, the path failure probability feature may include a transport failure probability, a preform failure probability, and the like. The transportation failure probability refers to the probability of occurrence of traffic accidents in the transfer process. The failure probability of the prefabricated part refers to the probability of fracture, deformation and the like of the prefabricated part caused by jolting and the like in the transfer process.

In some embodiments, the background control module may determine the path failure probability characteristics of the candidate transportation path through the path characteristics determination model based on future weather data, the candidate transportation path, road condition data of the candidate transportation path, and the current traffic volume. For more details on the path characteristics determination model, see fig. 3 and the associated description.

In some embodiments, the background control module may select a candidate transportation path with a smallest failure probability index in the path failure probability characteristics as the recommended transportation path. The fault probability indices are weighted with different types of fault probability characteristics. For example, the fault probability feature includes a transport fault probability and a prefabricated member fault probability, and the corresponding weights are a first weight and a second weight. The background control module can calculate based on the transportation fault probability, the prefabricated part fault probability, the first weight and the second weight to obtain a fault probability index. The weights may be set empirically or as desired.

In some embodiments, the background control module may select the candidate transportation path with the highest overall evaluation index as the recommended transportation path. In some embodiments, the composite evaluation index is related to a weighted combination of the failure probability index and the predicted environmental impact. The background control module can calculate based on the fault probability index, the estimated environment influence degree, the third weight and the fourth weight to obtain the comprehensive evaluation index. The third weight and the fourth weight are weights respectively corresponding to the fault probability index and the estimated environmental impact. The weights may be set empirically or as desired.

In some embodiments, the determination of the recommended transportation path is also related to accident volume and traffic volume.

The accident amount is an accident amount occurring on the candidate transportation path within a preset period of time. The traffic flow is the traffic flow generated by the candidate transportation path within the preset time period. The preset time period may be set according to actual conditions. For example, it may be an expected transportation period.

In some embodiments, the background control module may perform a preliminary screening of the candidate transportation paths based on the accident volume being below the accident volume threshold and/or the traffic volume being below the traffic volume threshold, and determine the recommended transportation path based on the preliminary screened candidate transportation paths by the foregoing method. The accident volume threshold and the vehicle flow volume threshold can be set according to the requirements.

The candidate transportation paths are primarily screened through the accident quantity and the traffic flow, so that the calculation load caused by the fact that the number of the candidate transportation paths is large in the follow-up process can be reduced. Therefore, the system operation efficiency can be improved, and the rationality of the recommended transportation path is improved.

In some embodiments, the transfer parameters may also include a transfer measure. The background control module may determine the transfer measure based on the estimated transfer environment impact, future weather data, and predicted impact parameters of the recommended transportation path.

The future weather data is weather data for a future time period. For example, future temperature, future precipitation, future wind force, etc. In some embodiments, the background control module may obtain future weather data via a third party platform. Such as weather forecast, etc. The future time period may be preset or determined based on actual demand.

The transfer measure refers to a measure related to transfer of the prefabricated parts. In some embodiments, the transferring means may include adding a noise control means and/or a dust control means embodiment. For example, whether to add a sound insulation board, add a fog gun machine, a sprinkler, etc. and set parameters. The setting parameters may include the number and location of the regulatory device additions, etc.

In some embodiments, the estimated transfer environment impact includes noise impact, dust impact, etc.; the predicted impact parameters may include predicted noise variation data, predicted dust variation data, and the like. For more on the predicted transfer environment impact and predicted impact parameters, see fig. 4 and the associated description.

In some embodiments, the background control module 140 may determine the transfer measure in a variety of ways. For example, the background control module may construct a transfer feature vector based on the predicted transfer environment impact, future weather data, and predicted impact parameters, and determine the transfer measure based on the retrieval result of the transfer feature vector in the first vector database. The first vector database comprises a plurality of first reference vectors and a transfer measure corresponding to each first reference vector. The first reference vector is constructed based on the transfer environment influence degree, weather data and influence parameters in the historical data. The background control module can select a transfer measure corresponding to a first reference vector with the minimum vector distance as the transfer measure by calculating the vector distance between the transfer feature vector and the first reference vector.

In some embodiments, the background control module 140 may determine the actual transfer environment impact level from the actual noise variation data and the actual dust variation data during use of the recommended transportation path; in response to the difference between the actual transfer environment impact and the predicted transfer environment impact being greater than the difference threshold, the background control module 140 may reselect the recommended transportation path.

The actual noise variation data characterizes the variation of the noise intensity at different points in time during actual transportation. In some embodiments, the noise intensity refers to the somatosensory intensity of the noise.

In some embodiments, the monitoring module 120 may determine the somatosensory intensity based on the data collected by the data collection module 110 to determine the actual noise variation data. Specifically, the monitoring module can obtain the body feeling intensity through a noise transformation algorithm based on the temperature, the precipitation amount and the absolute noise intensity, and the noise transformation algorithm calculates according to a formula (1):

t＝a×x+b×y+c×z (1)

wherein t is the noise somatosensory intensity, x is the temperature, y is the precipitation, and z is the noise absolute intensity. a. b and c are coefficients corresponding to the temperature, the precipitation and the absolute noise intensity respectively, and are determined through a pre-experiment. Specifically, the temperature, the precipitation amount, the absolute noise intensity and the body feeling intensity of the noise are obtained through pre-experiments, and the corresponding relation of the formula (1) is determined based on a data fitting algorithm.

The absolute intensity of noise is based on the intensity measured by the noise tester. The somatosensory intensity is the intensity of noise that a human body or animal can actually feel in an actual environment.

The actual noise change data is determined based on the body temperature sensing degree, so that the environment influence degree is determined, and the actual influence of noise on surrounding organisms in the transportation process can be accurately determined. And then select the road section that actually influences less, reduce the noise pollution in the transportation.

The actual dust change data characterizes the change of the solid dust amount at different time points in the actual transportation process. In some embodiments, the monitoring module 120 may determine actual dust variation data based on actual amounts of solid dust at a plurality of time points acquired by the data acquisition module 110.

The actual transfer environment influence represents the extent to which the prefabricated component has an influence on the environment during actual transportation.

In some embodiments, the background control module transfer may determine the actual transfer environmental impact based on an environmental impact determination model. For example, the influence degree determination layer based on the environment influence degree determination model determines the actual transfer environment influence degree. The predicted influence parameters input by the influence degree determining layer need to be replaced by actual influence parameters, namely actual noise change data and actual dust change data. For the environmental impact determination model and impact determination layer, see fig. 4 and the related description.

In some embodiments, the difference value between the two values can be obtained by directly subtracting the estimated transfer environment influence value from the actual transfer environment influence value. Further, the recommended transportation path is reselected in response to the variance value being greater than the variance threshold. In some embodiments, the variance threshold may be set as desired.

In some embodiments, the actual road conditions on the recommended transportation path may change due to road condition changes (e.g., road maintenance) and the like. The background control module can re-weight and determine the comprehensive evaluation index of each path based on the actual path fault probability characteristics corresponding to the actual road conditions and the actual transfer environment influence, and select the candidate path with the highest comprehensive evaluation index except the current road as the re-selected recommended transportation path.

And the recommended path is estimated again according to the actual collected change data, so that the path is optimized according to the actual road conditions, and the transportation efficiency of the prefabricated parts is ensured. Meanwhile, the transfer transportation process can be monitored in an environment-friendly way, and the ecological problems of dust pollution, noise pollution and the like in the transportation process are reduced.

And step 250, determining the solid waste outward movement parameters based on the influence degree of the preparation environment.

The solid waste outward movement parameter is a parameter related to the solid waste outward movement. In some embodiments, the solid waste transfer parameter comprises a solid waste transfer period.

The solid waste transfer cycle is a cycle in which solid waste is transferred outward from the prefabricated part.

In some embodiments, the background control module 140 may determine the solid waste migration parameters in a variety of ways based on the degree of influence of the manufacturing environment. For example, the background control module may determine the solid waste migration parameter by querying a preset table based on the preparation environment influence and the current time point. The preset table is set according to actual conditions, wherein the larger the environmental influence is, the shorter the solid waste transfer period is.

And step 260, transmitting the transfer parameters and the solid waste transfer parameters to the user terminal.

In some embodiments, the user terminal may be configured as a device with display functionality, such as a mobile handset, personal computer, or the like.

In some embodiments, the background control module 140 may send the transfer parameter and the solid waste transfer parameter to the user terminal according to a preset rule. The preset rules can be set manually according to requirements. For example, the background control module may send the transfer parameters and the solid waste move-out parameters to the user terminal at regular time. For another example, the background control module may periodically send the transfer parameter and the solid waste transfer parameter to the user terminal.

FIG. 3 is a schematic diagram of a path characterization model shown in accordance with some embodiments of the present description.

As shown in fig. 3, in some embodiments, the background control module may be configured to: the path failure probability feature 380 of the candidate transportation path is determined based on the future weather data 310-3, the candidate transportation path 340-2, the road condition data 310-4 of the candidate transportation path, and the current traffic volume 310-5 of the candidate transportation path.

In some embodiments, determining the path failure probability feature 380 for the candidate transportation path may include: determining future traffic flow 340-1 of the candidate transportation path through the traffic flow prediction layer 330 of the path feature determination model 320 based on the current traffic flow 310-5, the future weather data 310-3, the current weather data 310-2, the historical traffic flow 310-1 of the transportation path, and the road condition data 310-4 of the candidate transportation path; determining an estimated driving feature sequence 360-1 of the candidate transportation path through a driving feature determination layer 8/0 of the path feature determination model 320 based on the candidate transportation path 340-2, road condition data 310-4 of the candidate transportation path, future weather data 310-3, current weather data 310-2, current traffic flow 310-5 of the candidate transportation path, and future traffic flow 340-1 of the candidate transportation path; the path failure probability feature 380 of the candidate transportation path is determined by the failure probability prediction layer 370 of the path feature determination model 320 based on the road condition data 310-4 of the candidate transportation path, the future weather data 310-3, the current weather data 310-2, the current traffic flow 310-5 of the candidate transportation path, the future traffic flow 340-1 of the candidate transportation path, the estimated driving feature sequence 360-1 of the candidate transportation path, and the structural data 360-2 of the prefabricated part.

In some embodiments, the path feature determination model 320 may be any one or combination of a deep neural network model (DeepNeural Networks, DNN), a convolutional neural network model (Convolutional Neural Network, CNN), or the like, or other custom model structure, or the like.

In some embodiments, the path characteristics determination model 320 may include a traffic flow prediction layer 330, a driving characteristics determination layer 350, and a failure probability prediction layer 370.

In some embodiments, the traffic prediction layer 330 may be a deep neural network model (DeepNeural Networks, DNN).

The historical traffic 310-1 of the candidate transportation path refers to the traffic of the candidate transportation path at a historical point in time or a historical period of time. The historical time point or the historical time period may be preset. For example, the historical traffic may be the average traffic of the candidate transportation path over the past month, over the past year, etc.

In some embodiments, historical traffic flow data for candidate transportation paths may be obtained based on statistical websites regarding traffic data.

The current weather data 310-2 refers to the weather conditions of the candidate transportation path at the current point in time. For example, the current weather data may include a wind level of the day, a rainfall of the day, and the like.

In some embodiments, the current weather data may be obtained based on a third party platform, such as a weather forecast network, or the like.

For a description of the current traffic volume of the candidate transportation path, future weather data, road condition data of the candidate transportation path, etc., refer to the corresponding contents of fig. 2.

The future traffic flow 340-1 of the candidate transportation path refers to the traffic flow of the candidate transportation path at a future point in time or a future period of time. For example, the future traffic volume of the candidate transportation path may be a predicted value of the traffic volume of the candidate transportation path on a day in the future.

In some embodiments, traffic prediction layer 330 may be trained based on a number of first training samples with first labels. Constructing a loss function through the prediction results of the first tag and the initial traffic flow prediction layer, updating the initial traffic flow prediction layer based on the loss function iteration, and training when the loss function of the initial traffic flow prediction layer meets the preset condition, wherein the preset condition can be that the loss function converges, the iteration times reach a threshold value, and the like.

In some embodiments, the first sample may include a traffic flow of the sample transport path at a first historical time, a traffic flow at a second historical time, weather data at the first historical time, weather data at the second historical time, road condition data for the sample transport path; the first tag may be the actual traffic flow of the sample transportation path at the second historical time. Wherein the first historical time is before the second historical time.

In some embodiments, the driving characteristics determination layer 350 may be a convolutional neural network model (Convolutional Neural Network, CNN).

In the input of the driving characteristics determination layer 350, the candidate transportation path 340-2 may be represented based on a variety of forms. For example, each transport path may be numbered in advance, and the candidate transport path 340-2 may be input to the driving characteristics determination layer in the form of a number. For another example, each preset path point location may be numbered, and one transportation path may include one or more preset point locations, so that different candidate transportation paths may correspond to different preset point location sequences, and the candidate transportation path 340-2 may be input to the driving feature determination layer in the form of the preset point location sequences.

The estimated driving characteristic sequence output by the driving characteristic determination layer 350 refers to a sequence of driving characteristics of the vehicle on the candidate transportation path. The driving characteristics may include speed profile, time of arrival, etc.

In some embodiments, when the input form of the candidate transportation path 340-2 is different, the form of the predicted driving feature sequence may be different. Taking the input form of the candidate transportation path 340-2 as a preset point location sequence as an example, the estimated driving feature sequence may be estimated vehicle speed distribution, estimated arrival time distribution, etc. of the vehicle at different preset point locations of the candidate transportation path.

For example, the input candidate transport path 340-2 is candidate transport path A, which includes the preset point location a ₁ 、a ₂ ……a _n The outputted predicted driving feature sequence may include:preset point position a in candidate transportation path A ₁ Is a pre-estimated vehicle speed distribution, a pre-estimated arrival time distribution and a preset point position a ₂ Is determined by the estimated vehicle speed distribution and the estimated time of arrival distribution … … at the predetermined point location a _n Estimated vehicle speed distribution + estimated time of arrival distribution.

In some embodiments, a distribution refers to a collection of ordered pairs of some specific metrics and their corresponding probabilities. For example, the estimated vehicle speed distribution at a predetermined point may be expressed as: { (v) ₁ Probability 1), (v ₂ Probability 2.,. Wherein v ₁ 、v ₂ ... Similarly, according to the one-to-one correspondence between the estimated vehicle speed and the estimated arrival time, a corresponding estimated arrival time distribution can be obtained. For example, the estimated arrival time distribution corresponding to the estimated vehicle speed distribution described above may be expressed as: { (t) ₁ Probability 1), (t ₂ Probability 2., }. Wherein t is ₁ Is the velocity v ₁ Corresponding estimated arrival times, and so on.

In some embodiments, the driving characteristics determination layer 350 may be trained based on a plurality of second training samples with second tags. Specific training instructions can be found in the training of the traffic prediction layer described above.

In some embodiments, the second sample may include a sample transport path and traffic flow of the sample transport path at a first historical time, traffic flow at a second historical time, weather data at the first historical time, weather data at the second historical time, road condition data for the sample transport path; further description of the traffic flow of the sample transport path at the first historical time, the traffic flow of the second historical time, the weather data of the first historical time, the weather data of the second historical time, the road condition data of the sample transport path will be referred to the corresponding content of the first sample. The second tag may be a sequence of actual driving characteristics of the vehicle on the sample transportation path at a second historical time.

In some embodiments, the second label may be based on multiple tests performed on the same second sample. For example, 10 tests are performed, with the sample transportedPath a ₁ At the point of the velocity v ₁ The times of arrival are 5 times, and the time of use is t ₁ The method comprises the steps of carrying out a first treatment on the surface of the At a speed v ₂ The times of arrival are 3 times, and the time of use is t ₂ The method comprises the steps of carrying out a first treatment on the surface of the At a speed v ₃ The times of arrival are 2 times, and the time of arrival is t ₃ The method comprises the steps of carrying out a first treatment on the surface of the Then in the second label, a ₁ The vehicle speed distribution of the points is { (v) ₁ ，50％)，(v ₂ ，30％),(v ₃ 20%) whereby the time of arrival distribution is { (t) ₁ ，50％)，(t ₂ ，30％),(t ₃ 20%); according to the method, the vehicle speed distribution and the arrival time distribution of the rest points can be obtained respectively, and then the second label is obtained.

In some embodiments, the failure probability prediction layer 370 may be a convolutional neural network model (Convolutional Neural Network, CNN).

The structural data 360-2 of the prefabricated elements refers to data related to the internal and external structures of the prefabricated elements themselves. For example, the structural data of the prefabricated elements may include the outer dimensions (e.g., length, width, etc.), outer shape, etc. of the prefabricated elements. Wherein, the relevant description of the prefabricated parts is referred to the corresponding content of fig. 2.

For relevant description of future traffic flow 340-1 and predicted driving feature sequence 360-1, see correspondence above.

In some embodiments, the output of the failure probability prediction layer is the path failure probability feature 380.

For a relevant description of the path failure probability characteristics, see fig. 2 for correspondence.

In some embodiments, the failure probability prediction layer 370 may be trained based on a number of third training samples with third labels. Specific training instructions can be found in the training of the traffic prediction layer described above.

In some embodiments, the third sample may include a traffic flow of the sample transportation path at the first historical time, a traffic flow of the second historical time, weather data of the first historical time, weather data of the second historical time, road condition data of the sample transportation path, and an actual driving feature sequence of the vehicle on the sample transportation path at the second historical time, and structural data of the prefabricated part transported at the time; the third tag may be an actual path failure probability characteristic (e.g., a transport failure probability, a preform failure probability, etc.) of the vehicle on the sample transport path at the second historical time.

In some embodiments, the third tag may be obtained by performing multiple experiments under the same third sample. For example, under the condition corresponding to the same third sample, 10 prefabricated parts are transported, wherein the actual prefabricated part is damaged 3 times, and the failure probability is 30%.

In some embodiments, the candidate transportation path with the smallest failure probability index may be selected as the recommended transportation path. Specifically, the failure probability index=coefficient 1×transportation failure probability+coefficient 2×failure probability of the prefabricated member.

For a description of the recommended transportation path, the transportation failure probability, and the failure probability of the prefabricated member, reference is made to the correspondence of fig. 2.

According to some embodiments of the present disclosure, the path feature determining model determines the path fault probability feature, so that the prediction result is closer to the actual situation, the reliability of the path fault probability feature is improved, and meanwhile, the path feature determining model can significantly improve the processing efficiency of data.

It should be noted that the above description of the path characteristics determination model 320 is for illustration and description only, and is not intended to limit the scope of applicability of the present description. Various modifications and variations of the path characteristics determination model 320 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description. For example, the path characteristics determination model 320 may also be determined based on any feasible combination of the different numbers of traffic flow prediction layers 330, driving characteristics determination layers 350, and failure probability prediction layers 370 above. Such variations are within the scope of the present description.

FIG. 4 is a schematic diagram of a predictive transfer environment impact determination model according to some embodiments of the present disclosure.

In some embodiments, the determination of the recommended transportation path is also related to estimating the transfer environment impact level.

In some embodiments, the environmental analysis module may also be deployed as: and determining the influence degree of the estimated transfer environment based on the estimated driving characteristic sequence, the future weather data and the road condition data.

In some embodiments, as shown in fig. 4, the background control module may be further configured to: based on the predicted driving feature sequence 360-1, future weather data 310-3, vehicle data 410-1, prefabricated component material 410-2, and road condition data 310-4, a predicted impact parameter 440-1 of the candidate transportation path is determined by an impact parameter prediction layer 430 of a transition environment impact determination model 420; and determining, by the influence degree determination layer 450 of the transfer environment influence degree determination model 420, an estimated transfer environment influence degree 460 of the candidate transportation path based on the predicted influence parameter 440-1, the environment parameter 440-2.

For a description of the predicted driving feature sequence 360-1, the future weather data 310-3, and the road condition data 310-4, see the corresponding descriptions above.

In some embodiments, the transfer environment influence determination model 420 may be one or any combination of a deep neural network model (DeepNeural Networks, DNN), a convolutional neural network model (Convolutional Neural Network, CNN), or the like, or other custom model structure, or the like.

In some embodiments, the transfer environment influence determination model 420 may include an influence parameter evaluation layer 430 and an influence determination layer 450.

In some embodiments, the impact parameter assessment layer 430 may be a deep neural network model (DeepNeural Networks, DNN).

The vehicle data 410-1 refers to data related to the vehicle itself. For example, the vehicle data may include a model number, a load amount, an age of the vehicle, and the like of the vehicle. In some embodiments, the vehicle data may be acquired based on an in-vehicle built-in information platform (e.g., a vehicle background information base, a vehicle recorder, etc.). In some embodiments, the vehicle data may also be determined based on obtaining manual input.

Preform material 410-2 refers to a material associated with preparing a preform. For example, the preform material may be an alloy, iron, wood, or the like. In some embodiments, the preform material may be determined based on taking a manual input.

In some embodiments, the output of the influence parameter evaluation layer 430 is the predicted influence parameter 440-1.

The predicted impact parameter 440-1 refers to a predicted parameter related to environmental impact during the transfer process. In some embodiments, the predicted impact parameters may include predicted noise variation data, predicted dust variation data, and the like. The estimated noise change data and the estimated dust change data refer to the change data of the noise intensity in the environment at different time points and the change data of the dust concentration in the environment at different time points respectively in the estimated transportation process.

In some embodiments, the impact parameter assessment layer 430 may be trained based on a number of fourth training samples with fourth tags. Specific training instructions can be found in the training of the traffic prediction layer described above.

In some embodiments, the fourth sample may include an actual driving feature sequence of the sample vehicle at the sample transportation path at the second historical time, weather data at the second historical time, vehicle data of the sample vehicle at the first historical time, sample preform material, sample road condition data of the sample transportation path; the fourth tag may be an actual environmental impact parameter corresponding to the sample vehicle on the sample transport path at the second historical time.

In some embodiments, the actual driving feature sequence of the fourth sample may be obtained by actual measurement multiple times during sample transportation, which refers to transportation of the prefabricated parts with the same sample load, sample vehicle data, and sample prefabricated part material, and the relevant description of obtaining the actual driving feature sequence is referred to in fig. 3 with respect to the corresponding content of the obtaining of the second tag.

It should be noted that the fourth label may be the average value of the multiple actual measurements.

In some embodiments, the influence determination layer 450 may be a convolutional neural network model (Convolutional Neural Network, CNN).

Environmental parameters 440-2 refer to parameters related to the environment surrounding the candidate transportation path. For example, the environmental parameters may include terrain data for the candidate transportation path, ambient environmental data (e.g., whether a residential or animal-plant protected area is present, the area coverage of such areas, etc.), vegetation cover, wildlife presence, etc.

In some embodiments, the environmental parameters may be obtained based on an image monitoring device (e.g., camera, etc.). In some embodiments, the environmental parameters may also be obtained based on a third party geographic information platform (e.g., map software).

In some embodiments, the input to the influence determination layer 450 also includes seasonal and temporal data 440-3.

Season data and time data 440-3 refers to data related to time or season. For example, season data includes spring, summer, autumn, winter, time data may include day, night, etc.

For example, when the season is winter, the weather is drier, and the influence of dust generated in the transferring process is larger; for another example, when the time is daytime, pedestrians on the road are more, and dust and noise generated in the transferring process have larger influence than those at night.

In some embodiments, the seasonal data and the temporal data may be entered based on the acquisition of manual input. In some embodiments, the seasonal data and the temporal data may also be obtained based on reading from a third party platform.

According to some embodiments of the present disclosure, the seasonal data and the time data are included in the input of the influence degree determining layer in the transfer environment influence degree determining model, so that the input data is more comprehensive, and the output result considering the factors such as the season, the time and the like has higher accuracy and reliability.

In some embodiments, the output of the influence determination layer is an estimated transfer environment influence degree 460.

The estimated transfer environment influence 460 refers to the estimated influence of the vehicle and the carried prefabricated components on the environment during the transfer process. In some embodiments, the estimated transfer environment influence may be the influence of noise generated by the vehicle during transfer, dust generated by the vehicle or prefabricated part during jolt, and the like on the environment.

In some embodiments, the influence determination layer 450 may be trained based on a number of fifth training samples with fifth tags. Specific training instructions can be found in the training of the traffic prediction layer described above.

In some embodiments, the fifth sample may include a sample influencing parameter, sample environment data; the fifth tag may be the actual environmental impact of the vehicle on the sample transport path.

In some embodiments, the fifth tag may be determined by the noise impact and dust impact of the vehicle on the sample transport path. Specifically, the environmental impact=coefficient 1×noise impact+coefficient 2×dust impact. Wherein, in the sample environment data, the larger the range of the protection area is, the larger the numerical values of the coefficient 1 and the coefficient 2 are.

In some embodiments, the noise impact level may be determined based on the area of the protected area of the noise emission and the duration of the noise (i.e., the time the actual decibel value returns to less than a threshold value). Illustratively, the larger the area of the noise radiation protection area, the longer the duration, the greater the noise impact. The noise radiation protection area refers to a protection area radiated by noise, and the noise decibel value measured at the position is larger than the noise threshold value.

In some embodiments, the noise radiation protection area may be determined by actually measuring the radiation range of the noise. For example, an array of decibel monitors consisting of a plurality of decibel monitors is installed within the protected area to determine the range of noise emissions.

It is understood that the description of the determination of the dust influence and the dust radiation range is similar to the corresponding manner of the determination of the noise influence, and will not be repeated.

In some embodiments, the candidate transportation path with the smallest overall evaluation index may be selected as the recommended transportation path. Wherein, the comprehensive evaluation index=coefficient 3×fault probability index+coefficient 4×estimated transfer environment influence degree. The coefficients 3 and 4 may be determined based on a preset, and if environmental impact is more important, the value of the coefficient 4 may be greater than the coefficient 3.

According to some embodiments of the present disclosure, the estimated transfer environment influence degree is determined by the transfer environment influence degree determining model, so that the prediction result is closer to the actual situation, the reliability of the estimated transfer environment influence degree is improved, and meanwhile, the data processing efficiency can be significantly improved by the transfer environment influence degree determining model.

According to some embodiments of the present disclosure, by introducing the association between the predicted transfer environment influence degree and the recommended transportation path, factors affecting the result of the recommended transportation path are more comprehensively evaluated, and further introducing a corresponding transfer environment influence degree determination model to obtain the predicted transfer environment influence degree, the determination method and the process of the recommended transportation path are more reliable, and the final obtained result of the recommended transportation path is more accurate.

FIG. 5 is an exemplary flow chart for determining how much a preparation environment affects, according to some embodiments of the present description. The process 500 of determining the degree of influence of the manufacturing environment may be performed based on the prefabricated component environmental monitoring system 100.

Step 510, determining solid waste amount change data based on the solid waste amount.

The solid waste amount change data refers to data reflecting the degree of change in the solid waste amount. Such as production speed of solid wastes, change data of the type of solid wastes, etc. The solid waste production rate is the amount of solid waste generated per unit time. The types of solid waste may include concrete scraps, sand, wood scraps, and the like.

In some embodiments, the monitoring module 120 may determine the solid waste variation data based on the solid waste collected by the data collection module 110 at a plurality of time points.

Step 520, determining the preparation environment influence degree based on the solid waste amount change data.

In some embodiments, the environmental analysis module 130 may implement determining the preparation environment impact level based on the solid waste amount variation data in a variety of ways. For example, the environmental analysis module 130 may construct a solid waste feature vector based on the solid waste variation data, the actual weather data, the season data, and the like, and determine the preparation environment influence degree based on the search result of the solid waste feature vector in the second vector database. The second vector database comprises a plurality of second reference vectors, and the recommended preparation environment influence degree corresponding to each second reference vector. The second reference vector is constructed based on the fixed waste amount change data, the weather data, the season data, and the recommended preparation environment influence degree in the history data. Specifically, the environmental analysis module may select, as the preparation environment influence degree, the recommended preparation environment influence degree corresponding to the second reference vector having the smallest vector distance by calculating the vector distance between the solid waste feature vector and the second reference vector.

The actual weather data may include temperature, precipitation, wind force, etc. The actual weather data may be obtained by a third party platform (e.g., weather forecast) or by human observation. The season data is data reflecting the season status. The damage condition of the land corresponding to different seasons, etc. may be different, and the damage condition of the corresponding land may be determined based on the season data. For example, autumn damaged land and summer damaged land may differ in the degree of damage and the degree of environmental impact may also differ, for example, autumn may more easily cause plant failure to reproduce, and thus the degree of damage is more serious.

In some embodiments, the background control module 140 may predict future solid waste variation data for future points in time; and determining a solid waste migration parameter at a future point in time based on the future solid waste amount change data.

In some embodiments, the future point in time includes a point in time when the vehicle is expected to be transported after loading.

The future solid waste amount change data is data reflecting the solid waste amount change situation at a future time point.

In some embodiments, the background control module 140 may predict future solid waste variation data for future points in time based on a variety of ways. For example, the background control module 140 may obtain the production speed of the solid waste based on the solid waste amount change data corresponding to a certain type of solid waste; and establishing a solid waste accumulation algorithm based on the solid waste amount, the solid waste production speed, the time and the traffic amount to obtain the solid waste amount of the type of solid waste at the corresponding future time point, and further predicting future solid waste amount change data of the future time point of the type of solid waste.

In some embodiments, the solid waste accumulation algorithm may be calculated according to equation (2):

y＝p×t-(t÷T)×M……(2)

wherein y is t ₀ The solid waste amount corresponding to a certain type of solid waste at a time point, p is the solid waste production speed of the type of solid waste, t is the current time and t ₀ The interval between time points, T transfer period, M is the transfer amount. In some embodiments, the production rate of the solid waste is calculated from the historical solid waste amount and the historical time. The solid waste transfer period is based on the influence degree of the preparation environment and the time point t ₀ Obtained by looking up a table. The transfer amount is the solid waste amount removed by the vehicle in unit time, and is calculated and determined according to the loading capacity and the loading speed of the vehicle.

And establishing a solid waste accumulation algorithm for each type of solid waste, so that future solid waste variable data at a future time point can be obtained.

In some embodiments, the background control module 140 may determine the future preparation environment impact level in a variety of ways. For example, the background control module may construct a degree of influence feature vector based on future fixed waste variation data, future weather data, seasonal data, and determine a future production environment influence degree using the second vector database. Future weather data is obtained and further details are provided in fig. 2 and related description. The second vector database includes a plurality of second reference vectors. The second reference vector is constructed based on the fixed waste amount change data, the weather data, the season data, and the recommended preparation environment influence degree in the history data. And selecting the recommended preparation environment influence degree corresponding to the second reference vector with the smallest vector distance of the influence degree feature vector as the future preparation environment influence degree.

In some embodiments, the background control module 140 may determine the solid waste migration parameters at a future point in time based on the future manufacturing environment impact and the future point in time via a preset table look-up. For more details on determining the solid waste migration parameters, see fig. 2 and the associated description.

Because the scheduling vehicle and the solid waste loading and unloading all need certain time, the scheme of adjusting the solid waste outward movement can be made in advance by predicting the future solid waste outward movement parameters, and further the transfer efficiency is improved, and the excessive accumulation of solid waste can be effectively prevented.

In some embodiments, the solid waste transfer period is also related to the prefabricated component demand.

The prefabricated part demand refers to the number of prefabricated parts required for construction. For example, when the demand of the prefabricated part is large, the solid waste transfer period can be properly increased; when the demand of the prefabricated parts is small, the solid waste transfer period can be appropriately reduced.

Vehicles at the worksite are limited, and if most of the vehicles are used to transfer the scrap, the transport efficiency of the prefabricated parts may be affected. The solid waste transfer period is adjusted based on the influence of the demand of the prefabricated parts, so that the on-site personnel can be helped to reasonably plan the vehicle use scheme, and the solid waste can be transferred in time on the premise of ensuring the transportation of the prefabricated parts.

In some embodiments, the background control module 140 may determine a transport path to transfer the solid waste.

In some embodiments, the background control module may determine a transportation path to transfer the solid waste based on the path characteristics determination model. Wherein the output of the path feature determination model does not include the preform failure probability. The destination of the recommended route is changed to a solid waste disposal site. For a detailed description of the path characteristics determination model, reference may be made to fig. 3 and the associated description.

And determining a transportation path for transferring solid wastes based on the path characteristic determination model, and accurately judging the transportation fault probability so as to recommend a proper transportation path. By adjusting the output of the model, the operation amount can be reduced, and the operation efficiency can be improved.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (10)

1. The environment-friendly monitoring system for the prefabricated part is characterized by comprising a data acquisition module, a monitoring module, an environment-friendly analysis module and a background control module,

the data acquisition module is deployed to acquire acquisition data in the preparation process and/or the transfer process of the prefabricated component, wherein the acquisition data comprises at least one of solid waste amount, noise intensity and dust amount;

the monitoring module is deployed to determine acquisition variation data of the acquisition data based on the acquisition data;

the environmental analysis module is deployed to determine a preparation environment influence degree based on the acquisition change data; the preparation environment influence degree is the influence degree on the environment in the preparation prefabrication process;

the background control module is deployed to:

Determining transfer parameters of the prefabricated part; the transfer parameters include a recommended transportation path;

determining a solid waste outward movement parameter based on the influence degree of the preparation environment; the solid waste outward movement parameters comprise a solid waste transfer period;

and sending the transfer parameters and the solid waste outward-moving parameters to a user terminal.

2. The system of claim 1, wherein the background control module is configured to:

determining candidate transportation paths based on preset conditions;

determining a path failure probability feature of the candidate transportation path based on future weather data, the candidate transportation path, road condition data of the candidate transportation path, and current traffic flow of the candidate transportation path;

and determining a recommended transportation path from the candidate transportation paths based on the path fault probability characteristics.

3. The system of claim 2, wherein the transfer parameters further comprise a transfer measure;

the background control module is further configured to:

and determining the transfer measure based on the estimated transfer environment influence degree of the recommended transportation path, the future weather data and the predicted influence parameter. The transferring measures comprise noise and/or dust loading control devices.

4. The system of claim 1, wherein the system further comprises a controller configured to control the controller,

the monitoring module is further configured to: determining solid waste amount change data based on the solid waste amount;

the environmental analysis module is further configured to: and determining the influence degree of the preparation environment based on the solid waste amount change data.

5. A method for environmental monitoring of a prefabricated component, characterized in that it is implemented based on the prefabricated component environmental monitoring system according to claim 1, comprising:

collecting data in the preparation process and/or the transfer process of the prefabricated part, wherein the collected data comprises at least one of solid waste amount, noise intensity and dust amount;

based on the acquired data, determining acquisition variation data of the acquired data;

determining the influence degree of the preparation environment based on the acquired change data;

determining transfer parameters of the prefabricated part; the transfer parameters include a recommended transportation path;

determining a solid waste outward movement parameter based on the influence degree of the preparation environment; the solid waste outward movement parameters comprise a solid waste transfer period;

and sending the transfer parameters and the solid waste outward-moving parameters to a user terminal.

6. The method of claim 5, wherein the determining the transfer parameters of the preform comprises:

Determining candidate transportation paths based on preset conditions;

determining a path failure probability feature of the candidate transportation path based on future weather data, the candidate transportation path, road condition data of the candidate transportation path, and current traffic flow of the candidate transportation path;

and determining a recommended transportation path from the candidate transportation paths based on the path fault probability characteristics.

7. The method of claim 6, wherein the transfer parameters further comprise a transfer measure;

the determining the transfer parameters of the prefabricated component comprises:

and determining the transfer measure based on the estimated transfer environment influence degree of the recommended transportation path, the future weather data and the predicted influence parameter.

8. The method of claim 5, wherein the method further comprises:

determining solid waste amount change data based on the solid waste amount;

and determining the preparation environment influence degree based on the solid waste amount change data.

9. A prefabricated component environmental protection monitoring device, characterized in that the device comprises at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the method of any one of claims 5-8.

10. A computer readable storage medium storing computer instructions which, when executed by a processor, implement a method as claimed in any one of claims 5 to 8.