Method and system for collaborative management and control of multifunctional modules in smart parks

Abstract

The present invention relates to the field of collaborative control technology, and is a collaborative management control method and system for the linkage of multifunctional modules in a smart park, comprising: estimating the initial effective loading margin of the current collaborative equipment; constructing a forward loading capacity prediction model, outputting the forward effective loading capacity prediction value of the current collaborative equipment, fusing the forward effective loading capacity prediction value with the real-time remaining effective loading capacity to obtain a real-time fused loading margin; performing a garbage seepage hazard analysis and a garbage escape hazard analysis on the garbage distribution based on real-time garbage status data; evaluating the processing urgency of the garbage collection points in each sub-collaborative area to obtain a processing urgency sequence; and determining whether to trigger a reverse loading instruction based on the processing urgency sequence. The present invention solves the problems of high operating costs and poor collaboration of collaborative equipment in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of collaborative control technology, and is a method and system for collaborative management control of multifunctional modules in a smart park. Background Art

[0002] In smart park waste collection systems, existing technologies generally employ fixed forward loading routes or single emergency response strategies, which suffer from the following drawbacks: While relying solely on fixed routes ensures efficient routing, it fails to dynamically respond to sudden environmental risks. When environmental hazards at waste collection points in specific sub-coordinated areas intensify due to the spread of seepage (e.g., a sudden increase in the decay index) or the escape of hazardous gases (e.g., excessive hydrogen sulfide concentrations), the system's rigid routes prevent it from prioritizing high-threat points, potentially leading to secondary hazards such as soil contamination from seepage and corrosion of production equipment from escaped gases. Conversely, while global scheduling based solely on urgency can mitigate environmental risks, it can also lead to disorganized movement of collection vehicles within the park. On the one hand, vehicles frequently travel back and forth between discrete, high-urgency points, significantly increasing idle mileage and energy consumption. On the other hand, due to a lack of coordinated prediction of remaining load capacity, vehicles may prematurely become fully loaded and be forced to return to the processing center (underutilizing remaining loading space), or prematurely perform low-urgency tasks, tying up transport capacity (resulting in delayed responses in high-urgency areas), resulting in an overall decrease in collection efficiency.

[0003] Crucially, existing methods struggle to achieve the collaborative goal of "arriving at the processing center fully loaded." If a vehicle is fully loaded too early, it must return empty to the processing center, increasing inefficient travel time. If it continues to carry garbage through the park for extended periods, pollutant exposure is prolonged, exacerbating the risk of secondary pollution. Therefore, a collaborative mechanism combining the efficiency of fixed routes with dynamic risk response is urgently needed. This ensures that after handling sudden high-risk locations, vehicles can still arrive fully loaded at the processing center along an optimized route, minimizing empty loads and avoiding inefficient transportation, thereby reducing environmental risks and operating costs at the source. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that in the existing technology, the operating cost of collaborative equipment is high and the collaboration is poor, and a method and system for collaborative management and control of multi-functional modules in a smart park are proposed.

[0005] To achieve the above-mentioned objectives, the technical solution of the present invention for the collaborative management and control method of multifunctional modules in a smart park includes the following steps:

[0006] Extract historical collaborative task logs of the park's garbage collection and transportation system to estimate the initial effective loading capacity of the current collaborative equipment;

[0007] Construct a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The forward effective loading capacity prediction value is integrated with the real-time remaining effective loading capacity to obtain the real-time integrated loading margin.

[0008] Obtain real-time garbage status data from garbage collection points in each sub-collaborative area of ​​the smart park, and conduct garbage seepage hazard analysis and garbage fugitive hazard analysis based on the real-time garbage status data.

[0009] Based on the results of the garbage leachate hazard analysis and the garbage fugitive hazard analysis, the treatment urgency of the garbage collection points in each sub-cooperative area is evaluated, and the treatment urgency of the garbage collection points in all sub-cooperative areas is ranked to obtain a treatment urgency sequence;

[0010] The real-time fused loading margin is compared with the total amount of waste to be processed at the waste collection points in the top three sub-cooperative areas in the processing urgency sequence, and whether to trigger the reverse loading instruction is determined based on the comparison result.

[0011] Preferably, extract the historical collaborative task logs of the park garbage removal system to estimate the initial effective loading margin of the current collaborative equipment, including:

[0012] A11: The smart park is evenly divided into multiple sub-cooperative areas, where the total number of sub-cooperative areas in the smart park is I, and i is the index of the sub-cooperative area;

[0013] A12: Extracting historical collaborative task logs of the current collaborative device in the garbage collection system, wherein the historical collaborative parameters recorded in the historical collaborative task logs include: pre-collaboration parameters and post-collaboration parameters;

[0014] The front-end coordination parameters include: maximum effective loading capacity;

[0015] The post-coordination parameters include: when the current cooperative device performs the cleaning task in each group of adjacent sub-coordination areas, the change rate of the moisture content of the garbage and the change in the full load rate of the garbage collection points in the sub-coordination area;

[0016] A13: Based on the maximum effective load capacity recorded in the historical collaborative task log of the current collaborative device, quantify the capacity loss factor η of the current collaborative device after each historical collaborative task execution. x ;

[0017] A14: Based on the capacity loss factor η of the current collaborative device after each historical collaborative task execution x , quantify the performance degradation factor of the current collaborative device

[0018] A15: Estimate the initial effective load margin PO of the current collaborative device when executing the current collaborative task based on the collaborative device performance attenuation factor X+1 ;

[0019] Preferably, a forward loading capacity prediction model is constructed, the real-time collaborative parameters of the current collaborative task log are input, the forward effective loading capacity prediction value of the current collaborative device is output, and the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform is simultaneously extracted. The forward effective loading capacity prediction value and the real-time remaining effective loading capacity are integrated to obtain the real-time integrated loading margin, including:

[0020] B21: Construct a forward loading capacity prediction model;

[0021] B22: Extract the output vector of the fully connected layer in the forward loading capacity prediction model, the forward effective loading capacity prediction value PS″(i). The output formula of the forward effective loading capacity prediction value is:

[0022] PS″(i)=PO X+1 ×ζ-L(i);

[0023] Among them, PO X+1 is the initial effective loading margin output in step A15; ζ is the processing efficiency fluctuation coefficient; L(i) is the cumulative processing volume when the current collaborative device travels to the i-th sub-collaborative area.

[0024] Preferably, a forward loading capacity prediction model is constructed, the real-time collaborative parameters of the current collaborative task log are input, the forward effective loading capacity prediction value of the current collaborative device is output, the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform is simultaneously extracted, and the effective loading capacity prediction value is integrated with the real-time effective loading capacity to obtain the real-time integrated loading margin, which also includes:

[0025] B23: extract the real-time remaining effective loading capacity PS′(i) of the current collaborative equipment displayed on the central control platform through the weight sensor;

[0026] B24: Fuse the forward effective loading capacity prediction value PS″(i) output by B22 with the real-time remaining effective loading capacity PS′(i) extracted by B23 to obtain the real-time fused loading margin; PS F (i);

[0027] Preferably, real-time garbage status data of garbage collection points in each sub-cooperative area in the smart park is obtained, and garbage seepage hazard analysis and garbage fugitive hazard analysis are performed on the garbage distribution based on the real-time garbage status data, including:

[0028] C31: Acquire real-time garbage status data, including garbage leachate analysis data and garbage gas analysis data;

[0029] The garbage seepage analysis data includes: garbage surface image data, unit layer gap length data divided on the garbage collection container, and seepage concentration data of each garbage monitoring layer;

[0030] The garbage gas analysis data includes: the types of gaseous pollutants in the gas escaping from the garbage collection container, the concentration ratio of each gaseous pollutant, the concentration of each gaseous pollutant, and the maximum concentration of each gaseous pollutant in the safety standard gas;

[0031] The garbage gas analysis data also includes: environmental wind speed and environmental wind direction factors at the outlet of the garbage collection container;

[0032] The garbage gas analysis data also includes: equivalent volume of gas emitted from garbage points;

[0033] The garbage gas analysis data also includes: the acid resistance of the materials of each production equipment in the risk workshop and the gas escape distance between each production equipment and the garbage collection point;

[0034] C32: Extract the currently collected garbage surface image data and arrange them in order according to their corresponding garbage monitoring layers. Perform physical state anomaly analysis based on the garbage surface image data: extract the HSV value of each pixel in the image and calculate the garbage decay index by superimposing it with the chromaticity deviation of the previous garbage monitoring layer.

[0035] C33: Analyze the spread degree s of garbage leachate based on the leachate concentration data of each garbage monitoring layer;

[0036] C34: The garbage decay index output from step C32 and the garbage leachate diffusion degree output from step C33 are weightedly summed to obtain the garbage leachate hazard value Q1.

[0037] Preferably, obtaining real-time garbage status data of garbage collection points in each sub-cooperative area in the smart park, and performing garbage seepage hazard analysis and garbage fugitive hazard analysis on garbage distribution based on the real-time garbage status data, further comprising:

[0038] C35: Extract garbage gas analysis data;

[0039] C36: Quantify the pollution intensity of the pollution sources at the garbage collection points based on the types of gaseous pollutants in the gas emitted from the garbage collection containers in the garbage gas analysis data, the concentration ratio of each gaseous pollutant, the concentration of each gaseous pollutant, and the maximum concentration of each gaseous pollutant allowed in the environmental protection standard gas. 1,i ;

[0040] The specific strategy for quantifying the pollution intensity of the pollution sources at the garbage collection points is as follows:

[0041]

[0042] Where K is the total number of gaseous pollutant types; k is the index of the gaseous pollutant;

[0043] ε k is the concentration ratio of the kth gas pollutant; C k,i represents the concentration of the kth gas pollutant in the ith sub-cooperative area; C k,std It is the maximum substance concentration of the kth gas pollutant allowed in the environmental standard gas.

[0044] Preferably, obtaining real-time garbage status data of garbage collection points in each sub-cooperative area in the smart park, and performing garbage seepage hazard analysis and garbage fugitive hazard analysis on garbage distribution based on the real-time garbage status data, further comprising:

[0045] C37: Ambient wind speed v at the outlet of the garbage collection container based on garbage gas analysis data i , environmental wind direction factor DF i,j , Equivalent volume of gas emitted from garbage point V0, Acid resistance SI of materials of each production equipment in risk workshop j And the gas escape distance d between each production equipment and the garbage collection point i,j , quantify the emission intensity of pollution sources at garbage collection points w 2,i ;

[0046] The pollution source emission intensity w at the garbage collection point 2,i The specific quantitative strategies are:

[0047]

[0048] Preferably, based on the results of the garbage leachate hazard analysis and the garbage fugitive hazard analysis, the treatment urgency of the garbage collection points in all the sub-cooperative areas is sorted to obtain a treatment urgency sequence, including:

[0049] D41: Extract the pollution intensity w of the pollution source at the garbage collection point output in step C36 1,i And the pollution source emission intensity w of the garbage collection point output in step C37 2,i ;

[0050] Use the emission intensity w of the pollution source 2,i Divide by the length of time garbage is retained at the garbage collection point to obtain the emission intensity of the pollution source per unit retention time;

[0051] D42: The pollution intensity of the pollution source w1,i , the emission intensity of the pollution source per unit residence time and the garbage leachate hazard value Q1 output in step C34, and the resulting product is used as the treatment urgency of the garbage collection point in each sub-cooperative area;

[0052] D43: Sort the processing urgency of the garbage collection points in all the sub-cooperative areas to obtain a processing urgency sequence.

[0053] Preferably, comparing the real-time fused loading margin with the total amount of waste to be processed at the waste collection points in the top three cooperative sub-areas in the processing urgency sequence comprises:

[0054] E51: Based on the processing urgency sequence, the total amount of waste to be processed PD of the waste collection points in the top three sub-cooperative areas in the processing urgency sequence is intercepted;

[0055] E52: Get the amount of waste to be processed (PN) in the next sub-cooperative area on the fixed forward loading route of the current cooperative device;

[0056] E53: Real-time fusion loading margin PS based on the output of steps E51, E52 and B24 F (i) Determine whether to trigger the reverse load instruction, wherein the judgment strategy for determining whether to trigger the reverse load instruction includes: when PS F (i) + PN + PD is less than or equal to When the reverse loading instruction is not triggered, the vehicle continues to move along the fixed forward loading route to the next sub-cooperative area for forward loading;

[0057] When PS F (i)+PN+PD is greater than When a reverse loading instruction is triggered, a reverse loading strategy for the current collaborative device is generated, wherein the reverse loading strategy includes sending a reverse loading instruction to the current collaborative device to sequentially go to the garbage collection points in the top three sub-collaborative areas in the processing urgency sequence;

[0058] in, The average value of the maximum effective loading capacity in the X historical collaborative task logs of the current collaborative device.

[0059] In addition, the present invention is used for the smart park multifunctional module linkage collaborative management and control system, which includes the following modules:

[0060] Initial margin quantification module, real-time margin quantification module, seepage hazard analysis module, escape hazard analysis module, treatment urgency assessment module and loading strategy reconstruction module;

[0061] The initial margin quantification module is used to extract the historical collaborative task logs of the park garbage removal system and estimate the initial effective loading margin of the current collaborative equipment;

[0062] The real-time margin quantification module is used to build a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The forward effective loading capacity prediction value is integrated with the real-time remaining effective loading capacity to obtain the real-time integrated loading margin;

[0063] The seepage hazard analysis module performs a garbage seepage hazard analysis on garbage distribution based on real-time garbage status data;

[0064] The waste fugitive hazard analysis performs a waste fugitive hazard analysis on waste distribution based on real-time waste status data;

[0065] The processing urgency assessment module couples the garbage leachate hazard analysis results and the garbage fugitive hazard analysis results to assess the processing urgency of the garbage collection points in each sub-cooperative area, and sorts the processing urgency of the garbage collection points in all sub-cooperative areas to obtain a processing urgency sequence;

[0066] The loading strategy reconstruction module compares the real-time fused loading margin with the total amount of garbage to be processed at the garbage collection points in the top three cooperative sub-areas in the processing urgency sequence, and determines whether to trigger a reverse loading instruction based on the comparison result.

[0067] Compared with the prior art, the technical effects of the present invention are as follows:

[0068] The present invention achieves efficient collaborative management of garbage collection and transportation in smart parks by dynamically integrating equipment loading capacity prediction and environmental risk urgency assessment. In terms of environmental risk control: based on the coupled calculation of the diffusion degree of garbage seepage (vertical concentration deviation analysis) and the fugitive hazard intensity (pollution exceedance ratio × distance attenuation exposure model), the processing urgency of each collection point is accurately quantified to ensure that high-hazard points (such as areas with a sudden increase in the seepage decay index or excessive hydrogen sulfide concentration) are treated first, reducing the risk of soil contamination by seepage and the probability of corrosion of production equipment by fugitive gases; in terms of transportation efficiency optimization: through the weighted superposition of the forward loading capacity prediction model (integrating the initial loading margin, moisture content and full load rate change parameters) and real-time sensor data, the remaining transportation capacity of the equipment is dynamically corrected; combined with the intelligent comparison of the total amount of garbage in the top three areas of the processing urgency sequence and the task volume of the next stop on the fixed route, the reverse loading instruction is triggered only when the integrated loading margin is insufficient, so that the vehicle can still return with a full load after dealing with sudden high-risk points. This move reduces ineffective mileage, improves equipment utilization, and avoids secondary pollution caused by long-term garbage transportation through the smart park. It ensures that the collaborative equipment is fully loaded when it arrives at the processing center, shortens the garbage retention time, and simultaneously reduces the energy consumption of transportation and the environmental management costs of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0070] in:

[0071] Figure 1 Schematic diagram of the process of the multifunctional module linkage collaborative management control method for a smart park according to the present invention;

[0072] Figure 2 A schematic diagram of a process for analyzing the hazards of landfill leachate according to the present invention;

[0073] Figure 3 A schematic diagram of a flow chart of garbage escape hazard analysis according to the present invention;

[0074] Figure 4 This is a structural diagram of the multifunctional module linkage collaborative management and control system for a smart park according to the present invention. DETAILED DESCRIPTION

[0075] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0076] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0077] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0078] Example 1:

[0079] like Figure 1 As shown, the embodiment of the present invention is used for the smart park multi-functional module linkage collaborative management control method, such as Figure 1 As shown, the specific steps are as follows:

[0080] Extract the historical collaborative task logs of the park's garbage collection and transportation system to estimate the initial effective load margin of the current collaborative equipment, including:

[0081] A11: The smart park is evenly divided into multiple sub-cooperative areas, where the total number of sub-cooperative areas in the smart park is I, and i is the index of the sub-cooperative area;

[0082] A12: Extracting historical collaborative task logs of the current collaborative device in the garbage collection system, wherein the historical collaborative parameters recorded in the historical collaborative task logs include: pre-collaboration parameters and post-collaboration parameters;

[0083] The front-end coordination parameters include: maximum effective loading capacity;

[0084] The post-coordination parameters include: when the current cooperative device performs the cleaning task in each group of adjacent sub-coordination areas, the change rate of the moisture content of the garbage and the change in the full load rate of the garbage collection points in the sub-coordination area;

[0085] A13: Based on the maximum effective load capacity recorded in the historical collaborative task log of the current collaborative device, quantify the capacity loss factor η of the current collaborative device after each historical collaborative task execution. x ;

[0086] For example, in this embodiment, a capacity loss factor η is provided. x The acquisition strategy is as follows:

[0087] Among them, η x is the capacity loss factor;

[0088] le x The maximum effective loading capacity of the current collaborative device in the xth historical collaborative task log;

[0089] Le0 is the maximum effective loading capacity of the current collaborative device in its factory state.

[0090] A14: Based on the capacity loss factor η of the current collaborative device after each historical collaborative task execution x , quantify the performance degradation factor of the current collaborative device

[0091] For example, in this embodiment, a strategy for obtaining a performance degradation factor is provided, specifically: Where X is the total number of tasks accumulated by the current collaborative device before executing the current collaborative task;

[0092] A15: Estimate the initial effective load margin PO of the current collaborative device when executing the current collaborative task based on the collaborative device performance attenuation factor X+1 ;

[0093] For example, in this embodiment,

[0094] PO0 is the average value of the initial effective load margin in all historical collaborative task logs of the current collaborative device.

[0095] Construct a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The forward effective loading capacity prediction value is integrated with the real-time remaining effective loading capacity to obtain the real-time integrated loading margin, including:

[0096] B21: Construct a forward loading capacity prediction model;

[0097] For example, in this embodiment, in step B21, the construction of the forward loading capacity prediction model includes:

[0098] B211: Constructing a forward load capacity prediction model, the forward load capacity prediction model comprising: an input layer, a GRU feature layer, an attention mechanism layer, a fully connected layer, and an output layer;

[0099] B212: Perform collaborative data preprocessing on the real-time post-collaboration parameters in the current collaborative task, and input the processed data into the input layer of the forward loading capacity prediction model. The collaborative data preprocessing includes: cleaning outliers and performing normalization. During the execution of the current collaborative task, the collaborative device maintains a fixed forward loading route within the smart park before triggering a reverse loading instruction.

[0100] B213: Perform feature extraction on each input real-time post-coordination parameter through the GRU feature layer. Each GRU feature layer has 24 unit gated kernels and exhibits a normal distribution with a mean of 0 and a variance of 0.05.

[0101] B214: The parameter features obtained from the GRU feature layer are sampled through the attention mechanism layer and activated using the ReLU function;

[0102] B215: Flatten the feature matrix output by the attention mechanism layer into a one-dimensional vector, input the flattened feature vector into the fully connected layer, and fit the evolution relationship between the effective loading capacity prediction value and the real-time post-coordination parameter through the fully connected layer. The fully connected layer uses the Swish function for activation.

[0103] B22: Extract the output vector of the fully connected layer in the forward loading capacity prediction model, the forward effective loading capacity prediction value PS″(i). The output formula of the forward effective loading capacity prediction value is:

[0104] PS″(i)=PO X+1 ×ζ-L(i);

[0105] Among them, PO X+1 is the initial effective loading margin output in step A15; ζ is the processing efficiency fluctuation coefficient; L(i) is the cumulative processing volume when the current collaborative device travels to the i-th sub-collaborative area.

[0106] For example, in this embodiment, a z acquisition strategy is provided, which is as follows:

[0107]

[0108] When the current collaborative device performs the cleaning task between the i-th and i-1-th sub-collaborative areas, the change in the moisture content of the garbage is ΔKG i,i-1 The change in the full load rate of the garbage collection point in the sub-cooperative area is ΔGD i,i-1 ;

[0109] When the current collaborative device performs the cleaning task in the i-1th sub-collaborative area, the moisture content of the garbage is KG i-1 , the full load rate of the garbage collection point in the sub-cooperative area is GDi-1 ;

[0110] For example, in this embodiment, it should be noted that moisture content is a key factor affecting the efficiency of waste disposal (especially subsequent incineration, composting, and landfill leachate generation). Waste with a high moisture content may be heavier and more viscous, directly affecting the loading capacity of waste collection equipment. Furthermore, the full load rate of a waste collection point reflects the degree of waste accumulation at the collection point. Collection points with different full load rates will have different efficiency in their waste collection operations.

[0111] B23: extract the real-time remaining effective loading capacity PS′(i) of the current collaborative equipment displayed on the central control platform through the weight sensor;

[0112] B24: Fuse the forward effective loading capacity prediction value PS″(i) output by B22 with the real-time remaining effective loading capacity PS′(i) extracted by B23 to obtain the real-time fused loading margin; PS F (i);

[0113] For example, in this embodiment, a strategy for obtaining the forward fusion loading margin is provided, specifically:

[0114] Obtain real-time garbage status data from garbage collection points in each sub-collaborative area of ​​the smart park, and conduct garbage seepage hazard analysis and garbage fugitive hazard analysis based on the real-time garbage status data.

[0115] Based on the results of the landfill leachate hazard analysis and the waste fugitive hazard analysis, the treatment urgency of the waste collection points in each sub-cooperative area is evaluated. The treatment urgency of the waste collection points in all sub-cooperative areas is ranked to obtain a treatment urgency sequence, including:

[0116] D41: Extract the pollution intensity w of the pollution source at the garbage collection point output in step C36 1,i And the pollution source emission intensity w of the garbage collection point output in step C37 2,i ;

[0117] Use the emission intensity w of the pollution source 2,i Divide by the length of time garbage is retained at the garbage collection point to obtain the emission intensity of the pollution source per unit retention time;

[0118] D42: The pollution intensity of the pollution source w 1,i , the emission intensity of the pollution source per unit residence time and the garbage leachate hazard value Q1 output in step C34, and the resulting product is used as the treatment urgency of the garbage collection point in each sub-cooperative area;

[0119] D43: Sort the processing urgency of the garbage collection points in all the sub-cooperative areas to obtain a processing urgency sequence.

[0120] Compare the real-time fused loading margin with the total amount of waste to be processed at the waste collection points in the top three cooperative sub-areas in the processing urgency sequence, and determine whether to trigger a reverse loading instruction based on the comparison result, including:

[0121] E51: Based on the processing urgency sequence, the total amount of waste to be processed PD of the waste collection points in the top three sub-cooperative areas in the processing urgency sequence is intercepted;

[0122] E52: Get the amount of waste to be processed (PN) in the next sub-cooperative area on the fixed forward loading route of the current cooperative device;

[0123] E53: Real-time fusion loading margin PS based on the output of steps E51, E52 and B24 F (i) Determine whether to trigger the reverse load instruction, wherein the judgment strategy for determining whether to trigger the reverse load instruction includes: when PS F (i) + PN + PD is less than or equal to When the reverse loading instruction is not triggered, the vehicle continues to move along the fixed forward loading route to the next sub-cooperative area for forward loading;

[0124] When PS F (i)+PN+PD is greater than When a reverse loading instruction is triggered, a reverse loading strategy for the current collaborative device is generated, wherein the reverse loading strategy includes sending a reverse loading instruction to the current collaborative device to sequentially go to the garbage collection points in the top three sub-collaborative areas in the processing urgency sequence;

[0125] in, The average value of the maximum effective loading capacity in the X historical collaborative task logs of the current collaborative device.

[0126] Example 2:

[0127] For example, in this embodiment, a specific implementation strategy for performing garbage leachate hazard analysis and garbage fugitive hazard analysis on garbage distribution based on real-time garbage status data is provided;

[0128] like Figure 2 As shown, the landfill leachate hazard analysis includes:

[0129] C31: Acquire real-time garbage status data, including garbage leachate analysis data and garbage gas analysis data;

[0130] The garbage seepage analysis data includes: garbage surface image data, unit layer gap length data divided on the garbage collection container, and seepage concentration data of each garbage monitoring layer;

[0131] It should be noted that the garbage surface image data collection strategy includes: when the garbage in the garbage collection container reaches any garbage monitoring layer, a garbage surface image is collected, that is, each garbage monitoring layer corresponds to a piece of garbage surface image data;

[0132] The garbage gas analysis data includes: the types of gaseous pollutants in the gas escaping from the garbage collection container, the concentration ratio of each gaseous pollutant, the concentration of each gaseous pollutant, and the maximum concentration of each gaseous pollutant in the safety standard gas;

[0133] The garbage gas analysis data also includes: environmental wind speed and environmental wind direction factors at the outlet of the garbage collection container;

[0134] The garbage gas analysis data also includes: equivalent volume of gas emitted from garbage points;

[0135] Exemplarily, in this embodiment, a strategy for obtaining the equivalent volume of gas escaping from a garbage point is provided, including: estimating by multiplying the gas escaping rate at the outlet of the garbage collection container by the residence time of the garbage;

[0136] The garbage gas analysis data also includes: the acid resistance of the materials of each production equipment in the risk workshop and the gas escape distance between each production equipment and the garbage collection point;

[0137] For example, in this embodiment, the production workshop closest to the garbage collection point is defined as a risk workshop;

[0138] C32: Extract the currently collected garbage surface image data and arrange them in order according to their corresponding garbage monitoring layers. Perform physical state anomaly analysis based on the garbage surface image data: extract the HSV value of each pixel in the image and calculate the garbage decay index by superimposing it with the chromaticity deviation of the previous garbage monitoring layer.

[0139] C33: Analyze the spread degree s of garbage leachate based on the leachate concentration data of each garbage monitoring layer;

[0140] For example, in this embodiment, a strategy for analyzing the diffusion degree of landfill leachate is provided, specifically:

[0141]

[0142] Where s is the diffusion degree of garbage leachate; N is the total number of garbage monitoring layers that have been accumulated by garbage collection containers during the current monitoring period; n is the index of the garbage monitoring layer;

[0143] They represent the actual seepage concentration of the garbage monitoring layer indexed by n and n-1 respectively;

[0144] is the global mean seepage concentration of N garbage monitoring layers;

[0145] The bottom seepage concentration of the bottom garbage monitoring layer.

[0146] It should be noted that in this embodiment, the strategy for analyzing the diffusion of landfill leachate is to assess the vertical diffusion heterogeneity of leachate by calculating the deviation of the leachate concentration in each layer from the global average of its adjacent layer (the layer above) and the bottom layer. Because leachate flows downward in a waste container due to gravity, the bottom layer concentration is a key indicator. Furthermore, the concentration variation between adjacent layers also reflects the degree of diffusion.

[0147] C34: The garbage decay index output from step C32 and the garbage leachate diffusion degree output from step C33 are weightedly summed to obtain the garbage leachate hazard value Q1.

[0148] like Figure 3 As shown, the waste fugitive hazard analysis includes:

[0149] C35: Extract garbage gas analysis data;

[0150] C36: Quantify the pollution intensity of the pollution sources at the garbage collection points based on the types of gaseous pollutants in the gas emitted from the garbage collection containers in the garbage gas analysis data, the concentration ratio of each gaseous pollutant, the concentration of each gaseous pollutant, and the maximum concentration of each gaseous pollutant allowed in the environmental protection standard gas. 1,i ;

[0151] The specific strategy for quantifying the pollution intensity of the pollution sources at the garbage collection points is as follows:

[0152]

[0153] Where K is the total number of gaseous pollutant types; k is the index of the gaseous pollutant;

[0154] ε k is the concentration ratio of the kth gas pollutant; C k,i represents the concentration of the kth gas pollutant in the ith sub-cooperative area; C k,std It is the maximum substance concentration of the kth gas pollutant allowed in the environmental standard gas.

[0155] It should be noted that the nonlinear characteristics of pollutant hazards are captured by the exponential function exp. When the concentration of gaseous pollutants doubles, the system's response speed to the pollution intensity assessment of the pollution source accelerates;

[0156] It should also be noted that C k,i ,C k,std Respectively reflect the intensity of gaseous pollutants and the threshold value under environmental protection standards, so through The project realizes the quantification of the pollution exceeding standard ratio of pollution sources.

[0157] C37: Ambient wind speed v at the outlet of the garbage collection container based on garbage gas analysis data i , environmental wind direction factor DF i,j , Equivalent volume of gas emitted from garbage point V0, Acid resistance SI of materials of each production equipment in risk workshop j And the gas escape distance d between each production equipment and the garbage collection point i,j , quantify the emission intensity of pollution sources at garbage collection points w 2,i ;

[0158] The pollution source emission intensity w at the garbage collection point 2,i The specific quantitative strategies are:

[0159]

[0160] For example, in this example, when the production equipment is located downwind of the garbage collection point, the environmental wind direction factor DF i,j On the contrary, when the production equipment is located upwind of the garbage collection point, the environmental wind direction factor DF is i,j is 0.

[0161] It should be noted that for In the item, the larger the volume of pollutants, the greater the potential harm, and the higher the wind speed, that is, The smaller the value, the stronger the dilution effect, which reduces the concentration of waste gas that the production equipment is exposed to per unit time; Considering that the concentration of pollutants decays with the square of the distance, It shows that the impact on production equipment decreases as the distance increases;

[0162] It should also be noted that in this embodiment, the emission intensity of the pollution source w 2,i It is used to characterize the equivalent hazard exposure time of production equipment under the influence of garbage collection points. The larger the value, the higher the cumulative damage risk of production equipment.

[0163] Example 3:

[0164] like Figure 4As shown, the embodiment of the present invention is used for the smart park multifunctional module linkage collaborative management control system, such as Figure 4 As shown, it includes the following modules:

[0165] Initial margin quantification module, real-time margin quantification module, seepage hazard analysis module, escape hazard analysis module, treatment urgency assessment module and loading strategy reconstruction module;

[0166] The initial margin quantification module is used to extract the historical collaborative task logs of the park garbage removal system and estimate the initial effective loading margin of the current collaborative equipment;

[0167] The real-time margin quantification module is used to build a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The forward effective loading capacity prediction value is integrated with the real-time remaining effective loading capacity to obtain the real-time integrated loading margin;

[0168] The seepage hazard analysis module performs a garbage seepage hazard analysis on garbage distribution based on real-time garbage status data;

[0169] The waste fugitive hazard analysis performs a waste fugitive hazard analysis on waste distribution based on real-time waste status data;

[0170] The processing urgency assessment module couples the garbage leachate hazard analysis results and the garbage fugitive hazard analysis results to assess the processing urgency of the garbage collection points in each sub-cooperative area, and sorts the processing urgency of the garbage collection points in all sub-cooperative areas to obtain a processing urgency sequence;

[0171] The loading strategy reconstruction module compares the real-time fused loading margin with the total amount of garbage to be processed at the garbage collection points in the top three cooperative sub-areas in the processing urgency sequence, and determines whether to trigger a reverse loading instruction based on the comparison result.

[0172] Example 4:

[0173] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0174] The processor executes the above-mentioned method for the coordinated management and control of multi-functional modules in a smart park by calling the computer program stored in the memory.

[0175] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the method for collaborative management and control of multifunctional modules in a smart park provided by the above-mentioned method embodiment. The electronic device may also include other components for realizing the functions of the device. For example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for data input and output. This embodiment will not be described in detail here.

[0176] Embodiment 5:

[0177] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0178] When the computer program runs on a computer device, the computer device executes the above-mentioned collaborative management and control of the multi-functional modules of the smart park.

[0179] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0180] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0181] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0182] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0183] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0184] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0185] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0186] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0187] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0188] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0189] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for collaborative management and control of multifunctional modules in a smart park, characterized in that: The method comprises: Extract historical collaborative task logs of the park's garbage collection and transportation system to estimate the initial effective loading capacity of the current collaborative equipment; Construct a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The forward effective loading capacity prediction value is integrated with the real-time remaining effective loading capacity to obtain the real-time integrated loading margin. Obtain real-time garbage status data from garbage collection points in each sub-collaborative area of ​​the smart park, and conduct garbage seepage hazard analysis and garbage fugitive hazard analysis based on the real-time garbage status data. Based on the results of the garbage leachate hazard analysis and the garbage fugitive hazard analysis, the treatment urgency of the garbage collection points in each sub-cooperative area is evaluated, and the treatment urgency of the garbage collection points in all sub-cooperative areas is ranked to obtain a treatment urgency sequence; The real-time fused loading margin is compared with the total amount of waste to be processed at the waste collection points in the top three sub-cooperative areas in the processing urgency sequence, and whether to trigger the reverse loading instruction is determined based on the comparison result.

2. The method for collaborative management and control of multifunctional modules in a smart park according to claim 1, characterized in that: Extract the historical collaborative task logs of the park's garbage collection and transportation system to estimate the initial effective load margin of the current collaborative equipment, including: A11: The smart park is evenly divided into multiple sub-cooperative areas, where the total number of sub-cooperative areas in the smart park is I, and i is the index of the sub-cooperative area; A12: Extracting historical collaborative task logs of the current collaborative device in the garbage collection system, wherein the historical collaborative parameters recorded in the historical collaborative task logs include: pre-collaboration parameters and post-collaboration parameters; The front-end coordination parameters include: maximum effective loading capacity; The post-coordination parameters include: when the current cooperative device performs the cleaning task in each group of adjacent sub-coordination areas, the change rate of the moisture content of the garbage and the change in the full load rate of the garbage collection points in the sub-coordination area; A13: Based on the maximum effective load capacity recorded in the historical collaborative task log of the current collaborative device, quantify the capacity loss factor η of the current collaborative device after each historical collaborative task execution. x ; A14: Based on the capacity loss factor η of the current collaborative device after each historical collaborative task execution x , quantify the performance degradation factor of the current collaborative device A15: Based on the performance attenuation factor of the collaborative device Estimate the initial effective load margin PO of the current collaborative device when performing the current collaborative task X+1 .

3. The method for collaborative management and control of multifunctional modules in a smart park according to claim 2 is characterized in that: Construct a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The forward effective loading capacity prediction value is integrated with the real-time remaining effective loading capacity to obtain the real-time integrated loading margin, including: B21: Construct a forward loading capacity prediction model; B22: Extract the output vector of the fully connected layer in the forward loading capacity prediction model, the forward effective loading capacity prediction value PS″(i). The output formula of the forward effective loading capacity prediction value is: PS″(i)=PO X+1 ×ζ-L(i); Among them, PO X+1 is the initial effective loading margin output in step A15; ζ is the processing efficiency fluctuation coefficient; L(i) is the cumulative processing volume when the current collaborative device travels to the i-th sub-collaborative area.

4. The method for collaborative management and control of multifunctional modules in a smart park according to claim 3 is characterized in that: Construct a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The effective loading capacity prediction value is integrated with the real-time effective loading capacity to obtain the real-time integrated loading margin. It also includes: B23: extract the real-time remaining effective loading capacity PS′(i) of the current collaborative equipment displayed on the central control platform through the weight sensor; B24: Fuse the forward effective loading capacity prediction value PS″(i) output by B22 with the real-time remaining effective loading capacity PS′(i) extracted by B23 to obtain the real-time fused loading margin; PS F (i).

5. The method for collaborative management and control of multifunctional modules in a smart park according to claim 4 is characterized in that: Obtain real-time garbage status data from garbage collection points in each sub-cooperative area of ​​the smart park, and conduct garbage seepage hazard analysis and garbage fugitive hazard analysis based on the real-time garbage status data, including: C31: Acquire real-time garbage status data, including garbage leachate analysis data and garbage gas analysis data; The garbage seepage analysis data includes: garbage surface image data, unit layer gap length data divided on the garbage collection container, and seepage concentration data of each garbage monitoring layer; The garbage gas analysis data includes: the types of gaseous pollutants in the gas escaping from the garbage collection container, the concentration ratio of each gaseous pollutant, the concentration of each gaseous pollutant, and the maximum concentration of each gaseous pollutant in the safety standard gas; The garbage gas analysis data also includes: environmental wind speed and environmental wind direction factors at the outlet of the garbage collection container; The garbage gas analysis data also includes: equivalent volume of gas emitted from garbage points; The garbage gas analysis data also includes: the acid resistance of the materials of each production equipment in the risk workshop and the gas escape distance between each production equipment and the garbage collection point; C32: Extract the currently collected garbage surface image data and arrange them in order according to their corresponding garbage monitoring layers. Perform physical state anomaly analysis based on the garbage surface image data: extract the HSV value of each pixel in the image and calculate the garbage decay index by superimposing it with the chromaticity deviation of the previous garbage monitoring layer. C33: Analyze the spread degree s of garbage leachate based on the leachate concentration data of each garbage monitoring layer; C34: The garbage decay index output from step C32 and the garbage leachate diffusion degree output from step C33 are weightedly summed to obtain the garbage leachate hazard value Q1.

6. The method for collaborative management and control of multifunctional modules in a smart park according to claim 5, characterized in that: Obtain real-time garbage status data from garbage collection points in each sub-cooperative area of ​​the smart park, and conduct garbage seepage hazard analysis and garbage fugitive hazard analysis based on the real-time garbage status data. This also includes: C35: Extract garbage gas analysis data; C36: Quantify the pollution intensity of the pollution sources at the garbage collection points based on the types of gaseous pollutants in the gas emitted from the garbage collection containers in the garbage gas analysis data, the concentration ratio of each gaseous pollutant, the concentration of each gaseous pollutant, and the maximum concentration of each gaseous pollutant allowed in the environmental protection standard gas. 1,i ; The specific strategy for quantifying the pollution intensity of the pollution sources at the garbage collection points is as follows: Where K is the total number of gaseous pollutant types; k is the index of the gaseous pollutant; ε k is the concentration ratio of the kth gas pollutant; C k,i represents the concentration of the kth gas pollutant in the ith sub-cooperative area; C k,std It is the maximum substance concentration of the kth gas pollutant allowed in the environmental standard gas.

7. The method for collaborative management and control of multifunctional modules in a smart park according to claim 6, characterized in that: Obtain real-time garbage status data from garbage collection points in each sub-cooperative area of ​​the smart park, and conduct garbage seepage hazard analysis and garbage fugitive hazard analysis based on the real-time garbage status data. This also includes: C37: Ambient wind speed v at the outlet of the garbage collection container based on garbage gas analysis data i , environmental wind direction factor DF i,j , Equivalent volume of gas emitted from garbage point V0, Acid resistance SI of materials of each production equipment in risk workshop j And the gas escape distance d between each production equipment and the garbage collection point i,j , quantify the emission intensity of pollution sources at garbage collection points w 2,i ; The pollution source emission intensity w at the garbage collection point 2,i The specific quantitative strategies are:

8. The method for collaborative management and control of multifunctional modules in a smart park according to claim 7, characterized in that: Based on the results of the landfill leachate hazard analysis and the waste fugitive hazard analysis, the treatment urgency of the waste collection points in all sub-cooperative areas is sorted to obtain a treatment urgency sequence, including: D41: Extract the pollution intensity w of the pollution source at the garbage collection point output in step C36 1,i And the pollution source emission intensity w of the garbage collection point output in step C37 2,i ; Use the emission intensity w of the pollution source 2,i Divide by the length of time garbage is retained at the garbage collection point to obtain the emission intensity of the pollution source per unit retention time; D42: The pollution intensity of the pollution source w 1,i , the emission intensity of the pollution source per unit residence time and the garbage leachate hazard value Q1 output in step C34, and the resulting product is used as the treatment urgency of the garbage collection point in each sub-cooperative area; D43: Sort the processing urgency of the garbage collection points in all the sub-cooperative areas to obtain a processing urgency sequence.

9. The method for collaborative management and control of multifunctional modules in a smart park according to claim 3 is characterized in that: Compare the real-time fused loading margin with the total amount of waste to be processed at the waste collection points in the top three sub-coordination areas in the processing urgency sequence, including: E51: Based on the processing urgency sequence, the total amount of waste to be processed PD of the waste collection points in the top three sub-cooperative areas in the processing urgency sequence is intercepted; E52: Get the amount of waste to be processed (PN) in the next sub-cooperative area on the fixed forward loading route of the current cooperative device; E53: Real-time fusion loading margin PS based on the output of steps E51, E52 and B24 F (i) Determine whether to trigger the reverse load instruction, wherein the judgment strategy for determining whether to trigger the reverse load instruction includes: when PS F (i) + PN + PD is less than or equal to When the reverse loading instruction is not triggered, the vehicle continues to move along the fixed forward loading route to the next sub-cooperative area for forward loading; When PS F (i)+PN+PD is greater than When a reverse loading instruction is triggered, a reverse loading strategy for the current collaborative device is generated, wherein the reverse loading strategy includes sending a reverse loading instruction to the current collaborative device to sequentially go to the garbage collection points in the top three sub-collaborative areas in the processing urgency sequence; in, The average value of the maximum effective loading capacity in the X historical collaborative task logs of the current collaborative device.

10. A system for the coordinated management and control of multifunctional modules in a smart park, for implementing the method for the coordinated management and control of multifunctional modules in a smart park as claimed in any one of claims 1 to 9, characterized in that: The system includes the following modules: Initial margin quantification module, real-time margin quantification module, seepage hazard analysis module, escape hazard analysis module, treatment urgency assessment module and loading strategy reconstruction module; The initial margin quantification module is used to extract the historical collaborative task logs of the park garbage removal system and estimate the initial effective loading margin of the current collaborative equipment; The real-time margin quantification module is used to build a forward loading capacity prediction model, input the real-time collaborative parameters of the current collaborative task log, output the forward effective loading capacity prediction value of the current collaborative device, and simultaneously extract the real-time remaining effective loading capacity of the current collaborative device displayed on the central control platform. The forward effective loading capacity prediction value is integrated with the real-time remaining effective loading capacity to obtain the real-time integrated loading margin; The seepage hazard analysis module performs a garbage seepage hazard analysis on garbage distribution based on real-time garbage status data; The waste fugitive hazard analysis performs a waste fugitive hazard analysis on waste distribution based on real-time waste status data; The processing urgency assessment module couples the garbage leachate hazard analysis results and the garbage fugitive hazard analysis results to assess the processing urgency of the garbage collection points in each sub-cooperative area, and sorts the processing urgency of the garbage collection points in all sub-cooperative areas to obtain a processing urgency sequence; The loading strategy reconstruction module compares the real-time fused loading margin with the total amount of garbage to be processed at the garbage collection points in the top three cooperative sub-areas in the processing urgency sequence, and determines whether to trigger a reverse loading instruction based on the comparison result.

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