A data analysis method and system based on urban services

Abstract

The present application relates to a data analysis method and system based on urban services, and to the field of garbage disposal technology. The method includes obtaining the predicted amount of garbage at each garbage point; determining the predicted total amount of garbage based on all the predicted amounts of garbage, and determining the vehicle demand value based on the predicted total amount of garbage and the full load of a single vehicle; generating an initially empty induction set based on the vehicle demand value, and randomly planning each garbage point in an induction set to generate a garbage induction plan; determining the amount of collective garbage based on the garbage induction plan; defining the garbage induction plan when all the collective garbage amounts are less than the full load of a single vehicle as an effective treatment plan, and selecting the only effective treatment plan from all the effective treatment plans as the execution treatment plan; analyzing the garbage points in the same induction set based on the execution treatment plan to generate a garbage treatment path for garbage trucks to treat garbage. The present application has the effect of improving the treatment effect of garbage in urban services.

Description

Technical Field

[0001] The present application relates to the field of garbage disposal technology, and in particular to a data analysis method and system based on urban services. Background Art

[0002] Urban service data analysis refers to the process of processing and analyzing urban service-related data using statistics, data mining, machine learning and other methods to reveal the distribution patterns of urban services, optimize service configuration, and improve service efficiency and quality.

[0003] In the related art, urban services include garbage disposal, that is, determining the location where garbage disposal is required, planning the transportation route according to the garbage point, and assigning garbage trucks to move along the transportation route to collect garbage at the garbage point, and after the garbage trucks have completed the movement along the transportation route, assigning the garbage trucks to transport the garbage to the landfill or incineration plant to achieve effective garbage disposal.

[0004] In the above-mentioned related technologies, the transportation route of the garbage truck is generally fixed. However, with the increase in the number of migrant workers in some communities, some of whom are tenants and have a certain degree of mobility, the personnel in the communities and other places where garbage can be generated are constantly changing, resulting in changes in the overall structure of the garbage. Once the overall structure of the garbage changes significantly, the garbage truck cannot handle the garbage well (for example, the garbage truck is full), resulting in poor garbage treatment effect. There is still room for improvement. Summary of the invention

[0005] In order to improve the garbage treatment effect in urban services, the present application provides a data analysis method and system based on urban services.

[0006] In the first aspect, the present application provides a data analysis method based on urban services, which adopts the following technical solutions:

[0007] A data analysis method based on urban services, comprising:

[0008] Establishing a detection interval with the current time point as the starting point and a width of the preset detection duration on the preset time axis, and determining a similar time interval in the detection interval according to the current time point;

[0009] Obtain the garbage collection amount of each garbage point in each similar time interval;

[0010] Analyze the garbage collection volume at each similar time interval to determine the predicted garbage volume at each garbage point;

[0011] The total predicted amount of garbage is calculated by summing up all the predicted amounts of garbage, and the vehicle demand value is determined by calculating the total predicted amount of garbage and the preset single vehicle full load;

[0012] Generate an initially empty induction set according to the vehicle demand value, and randomly plan each garbage point in an induction set until all garbage points are in the induction set to generate a garbage induction plan;

[0013] According to the garbage classification scheme, the predicted garbage amounts of garbage points in the same classification set are summed up to determine the garbage amount of the set;

[0014] The garbage collection scheme when all the collected garbage volumes are less than the full load of a single vehicle is defined as an effective treatment scheme, and the only effective treatment scheme is selected from all the effective treatment schemes as the execution treatment scheme;

[0015] According to the execution of the processing plan, the garbage points in the same induction set are analyzed to generate a garbage processing path for the garbage truck to process the garbage.

[0016] Optionally, the step of analyzing the garbage collection amount in each similar time interval to determine the predicted garbage amount of each garbage point includes:

[0017] Randomly select a garbage collection amount from all garbage collection amounts at the same garbage point as the standard collection amount, and use the remaining garbage collection amounts as the comparison collection amount;

[0018] The difference between the standard collection amount and the comparative collection amount is calculated to determine the collection deviation amount, and the stability parameter is determined by calculating all the collection deviation amounts;

[0019] Determine the stability parameter with the smallest value according to the preset sorting rules, and define the standard collection amount corresponding to the stability parameter as the qualified collection amount;

[0020] The qualified collection volume and the preset similar collection volume are used to calculate the qualified interval, and the average of all garbage collection volumes within the qualified interval is calculated to determine the predicted garbage volume.

[0021] Optionally, after calculating the average of all garbage collection amounts within the qualified interval to determine the predicted garbage amount, the data analysis method based on urban services further includes:

[0022] Calculate the permitted adjustment range based on the predicted amount of garbage and the preset feasible adjustment amount;

[0023] Determine the similar interval duration according to the current time point and the similar time interval obtained by the garbage collection amount, and determine the calculation weight parameter corresponding to the similar interval duration according to the preset parameter matching relationship;

[0024] A simulated collection amount is randomly generated in the permitted adjustment interval, and a difference calculation is performed based on the simulated collection amount and each garbage collection amount to determine the difference collection amount;

[0025] Determine the deviation value based on all difference collection amounts and corresponding calculation weight parameters;

[0026] The smallest deviation value is determined according to the sorting rules, and the simulated collection amount corresponding to the deviation value is updated as a new garbage prediction amount.

[0027] Optionally, the step of selecting a unique valid processing solution from all valid processing solutions as the execution processing solution includes:

[0028] The predicted garbage amount determined at each garbage point in a similar time interval is defined as the historical predicted amount;

[0029] Perform difference calculation based on historical predictions and corresponding garbage collection to determine theoretical-actual difference;

[0030] The average difference is determined by averaging all theoretical-actual differences at a single garbage point;

[0031] In all effective treatment options, the excess capacity is determined by performing a difference calculation based on the amount of waste collected in each summary collection and the full load of a single vehicle;

[0032] The additional amount is determined by summing up the average difference of the garbage points in the induction set;

[0033] All effective treatment plans in which the additional amounts are less than the excess carrying capacity are defined as feasible treatment plans, and the only feasible treatment plan is selected from all feasible treatment plans as the execution treatment plan.

[0034] Optionally, the step of selecting the only feasible solution from all feasible solutions as the execution solution includes:

[0035] The idle adjustment amount is determined by performing difference calculation based on the excess load capacity of each summarized set in the feasible processing scheme and the corresponding additional amount;

[0036] Count the garbage points in the summary set to determine the number of collection points;

[0037] The difference between the idle adjustment amount and the number of assembly points is calculated to determine the unit adjustment amount, and a unit adjustment amount is randomly selected as the standard adjustment amount, and the remaining unit adjustment amounts are defined as comparison adjustments;

[0038] Calculate the adjustment satisfaction value based on the standard adjustment amount and all the comparative adjustment amounts;

[0039] The adjustment satisfaction value with the largest value is determined according to the sorting rule, and the feasible processing scheme corresponding to the adjustment satisfaction value is determined as the execution processing scheme.

[0040] Optionally, after the adjustment satisfaction value is determined, the data analysis method based on urban services further includes:

[0041] Analyze the garbage points in the induction set to determine the bicycle moving path, and determine the required moving distance based on the bicycle moving path;

[0042] The excess travel distance is determined by performing a difference calculation based on the required travel distance and the preset balanced travel distance;

[0043] The inductive set with excess travel distance greater than zero is defined as an irrational set, and the irrational number is determined by counting the irrational set;

[0044] A calculation is performed based on preset quantity parameters, unreasonable quantity, excess driving distance and preset distance parameters to determine a satisfactory correction value, and the adjusted satisfactory value is updated based on the satisfactory correction value.

[0045] Optionally, the step of analyzing the garbage points in the same summary set according to the execution processing scheme to generate the garbage processing path includes:

[0046] Obtain the garbage disposal time of each garbage point and the congestion period table of each area;

[0047] Randomly sort the garbage points to determine the garbage disposal order, and analyze the garbage disposal order, the preset starting point and the preset final destination on the preset road distribution map to determine the simulated disposal path and the simulated driving distance;

[0048] Determine the overall required time for the garbage truck to move to the final destination based on the simulated processing route, garbage processing time, congestion period table, preset normal driving speed, and preset congestion driving speed;

[0049] Perform calculation and analysis based on the overall demand time and simulated driving distance to determine the appropriate path value;

[0050] The path suitability value with the largest value is determined according to the sorting rule, and the simulation processing path corresponding to the path suitability value is determined as the garbage processing path.

[0051] In the second aspect, the present application provides a data analysis system based on urban services, which adopts the following technical solutions:

[0052] A data analysis system based on urban services, comprising:

[0053] An acquisition module, connected to the processing module, for acquiring information;

[0054] A processing module, connected to the acquisition module, for storing and processing information;

[0055] The processing module establishes a detection interval on a preset time axis with the current time point as the starting point and a width of the preset detection duration, and determines a similar time interval in the detection interval according to the current time point;

[0056] The acquisition module acquires the garbage collection amount of each garbage point in each similar time interval;

[0057] The processing module analyzes the garbage collection amount in each similar time interval to determine the predicted amount of garbage at each garbage point;

[0058] The processing module calculates the sum of all the predicted garbage quantities to determine the predicted total garbage quantity, and calculates the vehicle demand value based on the predicted total garbage quantity and the preset single vehicle full load;

[0059] The processing module generates an initially empty induction set according to the vehicle demand value, and randomly arranges each garbage point in an induction set until all garbage points are in the induction set, and generates a garbage induction plan;

[0060] The processing module calculates and sums the predicted garbage amounts of garbage points in the same summarized set according to the garbage summarization scheme to determine the aggregate garbage amount;

[0061] The processing module defines the garbage classification scheme when all the collected garbage volumes are less than the full load of a single vehicle as an effective processing scheme, and selects the only effective processing scheme from all the effective processing schemes as the execution processing scheme;

[0062] The processing module analyzes the garbage points in the same summary set according to the execution processing plan to generate a garbage processing path for the garbage truck to process the garbage.

[0063] In summary, the present application includes at least one of the following beneficial technical effects:

[0064] 1. In the process of garbage disposal, the garbage disposal route of the garbage truck can be reasonably planned according to the recent garbage collection situation of each garbage point, so as to improve the garbage disposal effect in urban services;

[0065] 2. Analyze the recent garbage collection situation to eliminate abnormal data, thereby improving the accuracy of garbage prediction;

[0066] 3. Among the available garbage disposal options, the most appropriate option can be selected based on the capacity of the garbage truck and the driving distance, thereby further improving the garbage disposal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of the data analysis method based on urban services.

[0068] Figure 2 It is a module flow chart of the data analysis method based on urban services. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-Figure 2 It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0070] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0071] The embodiment of the present application discloses a data analysis method based on urban services. During the garbage disposal process, the recent garbage collection situation of each garbage point is analyzed to determine the appropriate garbage disposal plan for execution. At this time, the garbage disposal route of the garbage truck can be reasonably planned according to the selected garbage disposal plan, thereby ensuring that the garbage truck can better dispose of the garbage at each garbage point.

[0072] Reference Figure 1 ,The method flow of the data analysis method based on urban ,services includes the following steps:

[0073] Step S100: establishing a detection interval with the current time point as the starting point and a width of the preset detection duration on a preset time axis, and determining a similar time interval in the detection interval according to the current time point.

[0074] The time axis is a coordinate axis formed by the combination of various time points. The coordinate axis points from the time points that have passed to the time points that have not yet arrived. The detection duration is the duration set by the staff to obtain data on the recent garbage collection situation of each garbage point. The detection interval is established to facilitate the acquisition and analysis of data within the detection duration; the similar time interval is a time interval that is relatively close to the time interval of the current time point. For example, if the current time point is the morning of a weekday, then the similar time interval is the morning of each weekday in the detection interval.

[0075] Step S101: Obtaining the garbage collection amount of each garbage point in each similar time interval.

[0076] The garbage collection amount is the amount of garbage collected by the garbage truck when collecting garbage at the garbage point in a similar time interval, which can be determined by the change in the weight of the collected garbage.

[0077] Step S102: Analyze the garbage collection amount in each similar time interval to determine the predicted garbage amount of each garbage point.

[0078] The predicted amount of garbage is the amount of garbage that will be collected theoretically when garbage collection is performed at the garbage point in the interval corresponding to the current time point. The specific determination method can be determined through steps S200 to S203.

[0079] Step S103: performing a sum calculation based on all predicted garbage quantities to determine the predicted total garbage quantity, and performing a calculation based on the predicted total garbage quantity and a preset single vehicle full load to determine the vehicle demand value.

[0080] The predicted total amount of garbage is the total amount of garbage that needs to be processed in the time period corresponding to the current time point under theoretical circumstances, which is obtained by adding the predicted amounts of garbage at all garbage points; the full load of a single vehicle is the maximum amount of garbage that a garbage truck can carry. The vehicle demand value is obtained by dividing the predicted total amount of garbage by the full load of a single vehicle and rounding up. The vehicle demand value is also the number of garbage trucks required to effectively process the predicted total amount of garbage.

[0081] Step S104: Generate an initially empty summary set according to the vehicle demand value, and randomly plan each garbage point in a summary set until all garbage points are in the summary set and a garbage summary plan is generated.

[0082] Generate a summary set with the number of vehicle demand values ​​so that each garbage truck can correspond to a summary set. At this time, the garbage points are randomly planned in the summary set, which means that the garbage points are processed by different garbage trucks; when all garbage points are in the summary set, it means that all garbage points can be processed by garbage trucks. At this time, a garbage summary plan is generated to analyze the specific situation of garbage disposal.

[0083] Step S105: According to the garbage classification scheme, the predicted garbage amounts of garbage points in the same classification set are summed up to determine the aggregate garbage amount.

[0084] The aggregate garbage volume is obtained by adding the predicted garbage volumes of the garbage points in the same inductive set, that is, the amount of garbage that a garbage truck needs to handle when executing the garbage inductive plan.

[0085] Step S106: The garbage classification scheme when all collected garbage amounts are less than the full load of a single vehicle is defined as an effective treatment scheme, and a unique effective treatment scheme is selected from all effective treatment schemes as an execution treatment scheme.

[0086] When the amount of all collected garbage is less than the full load of a single vehicle, it means that the garbage at all garbage points can be processed by the corresponding garbage truck according to the garbage classification plan. At this time, the corresponding garbage classification plan is defined as an effective processing plan to achieve the distinction between different garbage classification plans, which is convenient for subsequent garbage processing; the execution processing plan is an execution plan for garbage processing at each garbage point. The execution processing plan can be determined by randomly selecting a plan from all effective processing plans, or it can be determined through steps S400-step S405.

[0087] Step S107: Analyze the garbage points in the same summary set according to the execution processing plan to generate a garbage processing path for the garbage truck to process the garbage.

[0088] The garbage disposal path is the moving path for the garbage truck when disposing of garbage. It can be determined by randomly sorting the garbage points in order and connecting the corresponding garbage points according to the order. It can also be determined by the method of steps S700-S704, which will not be repeated here.

[0089] The steps of analyzing the garbage collection amount at each similar time interval to determine the predicted amount of garbage at each garbage point include:

[0090] Step S200: randomly selecting a garbage collection amount from all garbage collection amounts at the same garbage point as a standard collection amount, and taking the remaining garbage collection amounts as comparison collection amounts.

[0091] Define standard collection amounts and comparative collection amounts to distinguish between different garbage collection amounts for subsequent analysis.

[0092] Step S201: performing difference calculation based on the standard collection amount and the comparison collection amount to determine the collection deviation amount, and performing calculation based on all the collection deviation amounts to determine the stability parameter.

[0093] The collection deviation is the difference between the standard collection amount and the comparison collection amount, and the difference is an absolute value; the stability parameter is a parameter that reflects the distance between the standard collection amount and all other comparison collection amounts. The smaller the stability parameter is, the closer it is to the other comparison collection amounts, and the more the standard collection amount can represent the other garbage collection amounts. The stability parameter is the average value of all collection deviations.

[0094] Step S202: determining the stability parameter with the smallest value according to a preset sorting rule, and defining the standard collection amount corresponding to the stability parameter as the qualified collection amount.

[0095] The sorting rule is a method set by the staff to sort the size of values, such as the bubble method. The sorting rule can be used to determine the stable parameter with the smallest value, that is, the standard collection amount corresponding to the stable parameter can best represent the remaining garbage collection amount. At this time, it is defined as the qualified collection amount to distinguish different standard collection amounts, which is convenient for subsequent analysis.

[0096] Step S203: Calculate the qualified collection amount and the preset similar collection amount to define a qualified interval, and calculate the average of all garbage collection amounts within the qualified interval to determine the predicted garbage amount.

[0097] The similar collection volume is the maximum difference allowed between the garbage collection volume that is relatively close to the qualified collection volume, which is set by the staff. The qualified interval is the interval within which the garbage collection volume that is relatively close to the qualified collection volume is allowed to appear. The upper endpoint of the qualified interval is determined by adding the qualified collection volume to the similar collection volume, and the lower endpoint of the qualified interval is determined by subtracting the similar collection volume from the qualified collection volume. The garbage collection volume within the qualified interval is the relatively normal garbage collection volume that usually occurs. The garbage prediction volume obtained by averaging the garbage collection volume within the qualified interval can effectively exclude the recent abnormal garbage volume, thereby improving the accuracy of the garbage prediction volume.

[0098] After calculating the average of all garbage collection amounts within the qualified range to determine the predicted amount of garbage, the data analysis method based on urban services also includes:

[0099] Step S300: Calculate based on the predicted amount of garbage and the preset feasible adjustment amount to define the permitted adjustment range.

[0100] The feasible adjustment amount is the value set by the staff to allow adjustment of the determined garbage forecast amount. The permitted adjustment interval is the interval of values ​​to which the garbage forecast amount can be adjusted. The upper and lower endpoints of the interval are determined by adding and subtracting the feasible adjustment amount from the garbage forecast amount, respectively.

[0101] Step S301: determining a similar interval duration according to the current time point and the similar time interval obtained by the garbage collection amount, and determining a calculation weight parameter corresponding to the similar interval duration according to a preset parameter matching relationship.

[0102] The similarity interval duration refers to the time interval between the current time point and the similar time interval, for example, how many days; the calculation weight parameter is the parameter used for calculation. Different similarity interval durations indicate different data reliability, and the corresponding calculation weight parameters are also different. When the similarity interval duration is short, it means that the two intervals are closer, and the data reliability is stronger. Therefore, the corresponding calculation weight parameter is larger. The parameter matching relationship between the two is determined by the staff in advance and will not be elaborated here.

[0103] Step S302: randomly generating a simulated collection amount in the permitted adjustment interval, and performing a difference calculation based on the simulated collection amount and each garbage collection amount to determine a differential collection amount.

[0104] The simulated collection amount is the value to which the predicted garbage amount needs to be adjusted in the simulation situation, and the difference collection amount is the difference between the simulated collection amount and each garbage collection amount, and the difference is an absolute value.

[0105] Step S303: Determine the deviation value based on all difference collection amounts and corresponding calculation weight parameters.

[0106] The degree of deviation is a parameter that reflects the reliability of the simulated collection volume as a garbage prediction volume. The smaller the value, the stronger the correlation of the existing data, that is, the higher the accuracy when used as a garbage prediction volume. The degree of deviation is determined by multiplying the difference collection volume by the corresponding calculation weight parameter and then adding them all up.

[0107] Step S304: determining the smallest deviation value according to the sorting rule, and updating the simulated collection amount corresponding to the deviation value as a new garbage prediction amount.

[0108] The sorting rules can be used to determine the smallest deviation value, that is, the simulated collection amount corresponding to the deviation value has a higher overall accuracy when used as the garbage prediction amount. At this time, it is updated to a new garbage prediction amount to further facilitate subsequent garbage processing.

[0109] The steps of selecting a unique valid treatment plan from all valid treatment plans as the execution treatment plan include:

[0110] Step S400: defining the garbage prediction amount determined at each garbage point in a similar time interval as a historical prediction amount.

[0111] Define historical forecasts to identify junk forecasts determined in similar time intervals for subsequent analysis.

[0112] Step S401: Perform difference calculation based on the historical prediction amount and the corresponding garbage collection amount to determine the theoretical-actual difference amount.

[0113] The theoretical-actual difference is the value obtained by subtracting the corresponding garbage collection amount under actual circumstances from the historical prediction amount.

[0114] Step S402: performing mean calculation based on all theoretical-actual differences at a single garbage point to determine an average difference.

[0115] The average difference is the average of all theoretical-actual differences, which reflects the garbage deviation between theory and reality under objective circumstances.

[0116] Step S403: performing a difference calculation based on the garbage volume of each summarized set and the full load of a single vehicle in all effective treatment solutions to determine the excess load.

[0117] The excess load capacity is the amount of garbage that a garbage truck can still load, which is determined by subtracting the collected garbage amount from the full load of a single truck.

[0118] Step S404: performing a summation calculation based on the average difference amount of the garbage points in the summary set to determine the additional amount.

[0119] The additional amount is the maximum amount of garbage that may increase in theoretical circumstances, and is determined by adding up all the average differences.

[0120] Step S405: define all effective processing solutions whose additional amounts are less than the excess loadable amount as feasible processing solutions, and select the only feasible processing solution from all feasible processing solutions as the execution processing solution.

[0121] When all the additional amounts are less than the excess loading capacity, it means that each garbage truck can effectively handle the abnormal increase in garbage, so as to reduce the situation where the garbage truck is full and cannot handle the garbage at some garbage points due to the abnormal increase in garbage. At this time, it is defined as a feasible treatment plan to distinguish different effective treatment plans, which is convenient for subsequent analysis; the only execution treatment plan can be randomly obtained from all feasible treatment plans, and can also be determined through steps S500-S504, which will not be repeated here.

[0122] The steps of selecting the only feasible solution from all feasible solutions as the execution solution include:

[0123] Step S500: performing difference calculation based on the excess load capacity of each summarized set in the feasible processing scheme and the corresponding additional amount to determine the idle adjustment amount.

[0124] The idle adjustment amount is the value that can be used for garbage volume adjustment, which is determined by subtracting the additional amount from the excess loadable capacity.

[0125] Step S501: Count the garbage points in the summary set to determine the number of set points.

[0126] The number of set points is the number of garbage points determined in the inductive set, that is, the number of garbage points that a garbage truck needs to handle, which can be determined by counting the garbage points in the inductive set one by one.

[0127] Step S502: performing difference calculation according to the idle adjustment amount and the number of set points to determine the unit adjustment amount, and randomly selecting a unit adjustment amount as the standard adjustment amount, and defining the remaining unit adjustment amounts as comparison adjustment amounts.

[0128] The unit adjustment is the average amount of garbage that can be adjusted for each garbage point, which is determined by dividing the idle adjustment by the number of collection points. The standard adjustment and comparison adjustment are defined to distinguish different unit adjustments for subsequent analysis.

[0129] Step S503: Calculate based on the standard adjustment amount and all the comparison adjustment amounts to determine the adjustment satisfaction value.

[0130] The adjustment satisfaction value reflects the reasonable degree of garbage volume that can be adjusted by each garbage truck. The larger the value, the more reasonable the allocation of garbage points. The calculation formula is: ,in To adjust the satisfaction value, are fixed parameters used for calculation, For the A comparison adjustment, is the standard adjustment amount, is the total number of comparison adjustments determined.

[0131] Step S504: determining the adjustment satisfaction value with the largest value according to the sorting rule, and determining the feasible processing solution corresponding to the adjustment satisfaction value as the execution processing solution.

[0132] The sorting rules are used to determine the adjustment satisfaction value with the largest value, that is, the allocation of garbage points at this time can achieve the best effect. At this time, the corresponding feasible treatment plan can be determined as the execution treatment plan.

[0133] After the adjusted satisfaction value is determined, the data analysis method based on urban services also includes:

[0134] Step S600: Analyze the garbage points in the summary set to determine the bicycle moving path, and determine the required moving distance based on the bicycle moving path.

[0135] The method for determining the bicycle moving path is the same as the garbage disposal path, which will not be repeated here; the required moving distance is the total distance that the garbage truck needs to travel when moving along the bicycle moving path.

[0136] Step S601: Calculate the difference between the required moving distance and the preset balanced moving distance to determine the excess driving distance.

[0137] The balanced moving distance is the maximum required moving distance set by the staff to ensure that the driver of the garbage truck can complete the garbage collection task within the specified time and the work process is relatively easy; the excess driving distance is the value obtained by subtracting the balanced moving distance from the required moving distance.

[0138] Step S602: define the summary set with excess driving distance greater than zero as an irrational set, and count the irrational set to determine the irrational quantity.

[0139] When the excess driving distance is greater than zero, it means that the garbage truck in the inductive set cannot perform the garbage disposal task well. At this time, the physical set is defined to distinguish different inductive sets, and the irrational sets are counted to determine the irrational number of irrational sets for subsequent analysis.

[0140] Step S603: Calculate and determine the satisfaction correction value according to the preset quantity parameter, unreasonable quantity, excess driving distance and preset distance parameter, and update the adjusted satisfaction value according to the satisfaction correction value.

[0141] The satisfactory correction value is a parameter used to correct and update the adjustment satisfaction value. The calculation formula is: ,in is a satisfactory correction value, Quantity parameters preset for staff to use for calculations, The distance parameters preset for staff to calculate, is an irrational quantity, is the excess mileage; the adjustment satisfaction value is updated by subtracting the satisfaction correction value from the original adjustment satisfaction value, so that the most appropriate feasible treatment plan can be selected as the execution treatment plan in combination with the irrational number and the excess mileage.

[0142] The steps of analyzing the garbage points in the same induction set according to the execution processing scheme to generate the garbage processing path include:

[0143] Step S700: Obtain the garbage processing time of each garbage point and the congestion period table of each area.

[0144] The garbage disposal time is the time the garbage truck needs to operate to dispose of garbage at each garbage point. It can be determined by recording the processing time for the garbage points in the detection interval and then calculating the average value. The congestion period table is a timetable for when congestion will occur on the roads in the area where the garbage points are located. Generally, this period is the morning rush hour and the evening rush hour.

[0145] Step S701: Randomly sort the garbage points in order to determine the garbage processing order, and analyze the garbage processing order, the preset starting point and the preset final destination on the preset road distribution map to determine the simulated processing path and the simulated driving distance.

[0146] The garbage disposal order is the order of the garbage points obtained by sorting the garbage points in sequence; the starting point is the location of the garbage truck before it starts the operation, and the final destination is the location where the garbage truck processes the garbage after completing the garbage collection operation, such as a landfill and a garbage incineration plant. The road distribution map is the regional map of the area where the garbage points are located; the simulated processing path is the path that the garbage truck needs to travel from the starting point and moves along the garbage points corresponding to the garbage disposal order until the final destination. The simulated driving distance is the distance the garbage truck needs to travel along the simulated processing path.

[0147] Step S702: Determine the overall required time for the garbage truck to move to the final destination based on the simulated processing path, garbage processing time, congestion period table, preset normal driving speed, and preset congestion driving speed.

[0148] The normal driving speed is the average speed of the garbage truck driving on a non-congested road section, and the congested driving speed is the average speed of the garbage truck driving on a congested road section. Both are determined in advance by the staff based on previous driving conditions; the overall required time is the time required for the garbage truck to complete the entire simulated processing path after moving according to the simulated processing path and taking into account the congestion situation.

[0149] Step S703: Perform calculation and analysis based on the overall required time and the simulated driving distance to determine the appropriate path value.

[0150] The path suitability value is a numerical value reflecting the suitability of the determined simulation processing path, and is determined by multiplying the overall demand time by the simulated driving distance and taking the inverse.

[0151] Step S704: determining the path suitability value with the largest value according to the sorting rule, and determining the simulation processing path corresponding to the path suitability value as the garbage processing path.

[0152] The sorting rules can be used to determine the path suitability value with the largest value, that is, the simulated processing path corresponding to the path suitability value is most suitable for the garbage truck to move. In this case, it can be determined as the garbage processing path for the garbage truck to move.

[0153] Reference Figure 2 Based on the same inventive concept, an embodiment of the present invention provides a data analysis system based on city services, including:

[0154] An acquisition module, connected to the processing module, for acquiring information;

[0155] A processing module, connected to the acquisition module, for storing and processing information;

[0156] The processing module establishes a detection interval on a preset time axis with the current time point as the starting point and a width of the preset detection duration, and determines a similar time interval in the detection interval according to the current time point;

[0157] The acquisition module acquires the garbage collection amount of each garbage point in each similar time interval;

[0158] The processing module analyzes the garbage collection amount in each similar time interval to determine the predicted amount of garbage at each garbage point;

[0159] The processing module calculates the sum of all the predicted garbage quantities to determine the predicted total garbage quantity, and calculates the vehicle demand value based on the predicted total garbage quantity and the preset single vehicle full load;

[0160] The processing module generates an initially empty induction set according to the vehicle demand value, and randomly arranges each garbage point in an induction set until all garbage points are in the induction set, and generates a garbage induction plan;

[0161] The processing module calculates and sums the predicted garbage amounts of garbage points in the same summarized set according to the garbage summarization scheme to determine the aggregate garbage amount;

[0162] The processing module defines the garbage classification scheme when all the collected garbage volumes are less than the full load of a single vehicle as an effective processing scheme, and selects the only effective processing scheme from all the effective processing schemes as the execution processing scheme;

[0163] The processing module analyzes the garbage points in the same induction set according to the execution of the processing plan to generate a garbage processing path for the garbage truck to process the garbage;

[0164] A garbage prediction amount determination module is used to determine a more accurate garbage prediction amount based on the garbage collection amount of each garbage point in the detection interval;

[0165] A garbage prediction amount updating module is used to update the garbage prediction amount to improve the accuracy of the garbage prediction amount;

[0166] An effective processing scheme selection module is used to select a unique execution processing scheme from multiple effective processing schemes;

[0167] A feasible processing scheme selection module is used to select a unique execution processing scheme from multiple feasible processing schemes;

[0168] An adjustment satisfaction updating module is used to update the adjustment satisfaction;

[0169] The garbage disposal path generation module is used to generate a more suitable garbage disposal path according to the conditions of each garbage point for garbage trucks to handle garbage.

[0170] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device and unit described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

Claims

1. A data analysis method based on urban services, It is characterized in that include: Establishing a detection interval with the current time point as the starting point and a width of the preset detection duration on the preset time axis, and determining a similar time interval in the detection interval according to the current time point; Obtain the garbage collection amount of each garbage point in each similar time interval; Analyze the garbage collection volume at each similar time interval to determine the predicted garbage volume at each garbage point; The total predicted amount of garbage is calculated by summing up all the predicted amounts of garbage, and the vehicle demand value is determined by calculating the total predicted amount of garbage and the preset single vehicle full load; Generate an initially empty induction set according to the vehicle demand value, and randomly plan each garbage point in an induction set until all garbage points are in the induction set to generate a garbage induction plan; According to the garbage classification scheme, the predicted garbage amounts of garbage points in the same classification set are summed up to determine the garbage amount of the set; The garbage collection scheme when all the collected garbage volumes are less than the full load of a single vehicle is defined as an effective treatment scheme, and the only effective treatment scheme is selected from all the effective treatment schemes as the execution treatment scheme; According to the processing plan, garbage points in the same induction set are analyzed to generate garbage processing paths for garbage trucks to process garbage; The steps of selecting a unique valid treatment plan from all valid treatment plans as the execution treatment plan include: Get the historical predictions of each garbage point in a similar time interval; Perform difference calculation based on historical predictions and corresponding garbage collection amounts in similar time intervals to determine the theoretical-actual difference; The average difference is determined by averaging all theoretical-actual differences at a single garbage point; In all effective treatment options, the excess capacity is determined by performing a difference calculation based on the amount of waste collected in each summary collection and the full load of a single vehicle; The additional amount is determined by summing up the average difference of the garbage points in the induction set; All effective treatment plans with additional quantities less than the excess carrying capacity are defined as feasible treatment plans, and the only feasible treatment plan is selected from all feasible treatment plans as the implementation treatment plan; The steps of selecting the only feasible solution from all feasible solutions as the execution solution include: The idle adjustment amount is determined by performing difference calculation based on the excess load capacity of each summarized set in the feasible processing scheme and the corresponding additional amount; Count the garbage points in the summary set to determine the number of collection points; The unit adjustment amount is determined according to the ratio of the idle adjustment amount to the number of assembly points, and a unit adjustment amount is randomly selected as the standard adjustment amount, and the remaining unit adjustment amounts are defined as comparative adjustment amounts; The adjustment satisfaction value is determined by calculating the standard adjustment amount and all the comparative adjustment amounts. The adjustment satisfaction value reflects the reasonableness of the amount of garbage that can be adjusted by each garbage truck. The larger the value, the more reasonable the allocation of garbage points. The adjustment satisfaction value with the largest value is determined according to the sorting rule, and the feasible processing scheme corresponding to the adjustment satisfaction value is determined as the execution processing scheme.

2. The data analysis method based on urban services according to claim 1, It is characterized in that The steps of analyzing the garbage collection amount at each similar time interval to determine the predicted amount of garbage at each garbage point include: Randomly select a garbage collection amount from all garbage collection amounts at the same garbage point as the standard collection amount, and use the remaining garbage collection amounts as the comparison collection amount; The difference between the standard collection amount and the comparative collection amount is calculated to determine the collection deviation amount, and the stability parameter is determined by calculating all the collection deviation amounts; Determine the stability parameter with the smallest value according to the preset sorting rules, and define the standard collection amount corresponding to the stability parameter as the qualified collection amount; The qualified collection volume and the preset similar collection volume are used to calculate the qualified interval, and the average of all garbage collection volumes within the qualified interval is calculated to determine the predicted garbage volume.

3. The data analysis method based on urban services according to claim 2, It is characterized in that After calculating the average of all garbage collection amounts within the qualified range to determine the predicted amount of garbage, the data analysis method based on urban services also includes: Calculate the permitted adjustment range based on the predicted amount of garbage and the preset feasible adjustment amount; Determine the similar interval duration according to the current time point and the similar time interval obtained by the garbage collection amount, and determine the calculation weight parameter corresponding to the similar interval duration according to the preset parameter matching relationship; A simulated collection amount is randomly generated in the permitted adjustment interval, and a difference calculation is performed based on the simulated collection amount and each garbage collection amount to determine the difference collection amount; Determine the deviation value based on all difference collection amounts and corresponding calculation weight parameters; The smallest deviation value is determined according to the sorting rules, and the simulated collection amount corresponding to the deviation value is updated as a new garbage prediction amount.

4. The data analysis method based on urban services according to claim 1, It is characterized in that After the adjusted satisfaction value is determined, the data analysis method based on urban services also includes: Analyze the garbage points in the induction set to determine the bicycle moving path, and determine the required moving distance based on the bicycle moving path; The excess travel distance is determined by performing a difference calculation based on the required travel distance and the preset balanced travel distance; The inductive set with excess travel distance greater than zero is defined as an irrational set, and the irrational number is determined by counting the irrational set; A calculation is performed based on preset quantity parameters, unreasonable quantity, excess driving distance and preset distance parameters to determine a satisfactory correction value, and the adjusted satisfactory value is updated based on the satisfactory correction value.

5. The data analysis method based on urban services according to claim 1, It is characterized in that The steps of analyzing the garbage points in the same induction set according to the execution processing scheme to generate the garbage processing path include: Obtain the garbage disposal time of each garbage point and the congestion period table of each area; Randomly sort the garbage points to determine the garbage disposal order, and analyze the garbage disposal order, the preset starting point and the preset final destination on the preset road distribution map to determine the simulated disposal path and the simulated driving distance; Determine the overall required time for the garbage truck to move to the final destination based on the simulated processing route, garbage processing time, congestion period table, preset normal driving speed, and preset congestion driving speed; Perform calculation and analysis based on the overall demand time and simulated driving distance to determine the appropriate path value; The path suitability value with the largest value is determined according to the sorting rule, and the simulation processing path corresponding to the path suitability value is determined as the garbage processing path.

6. A data analysis system based on urban services, It is characterized in that include: An acquisition module, connected to the processing module, for acquiring information; A processing module, connected to the acquisition module, for storing and processing information; The processing module establishes a detection interval on a preset time axis with the current time point as the starting point and a width of the preset detection duration, and determines a similar time interval in the detection interval according to the current time point; The acquisition module acquires the garbage collection amount of each garbage point in each similar time interval; The processing module analyzes the garbage collection amount in each similar time interval to determine the predicted amount of garbage at each garbage point; The processing module calculates the sum of all the predicted garbage quantities to determine the predicted total garbage quantity, and calculates the vehicle demand value based on the predicted total garbage quantity and the preset single vehicle full load; The processing module generates an initially empty induction set according to the vehicle demand value, and randomly arranges each garbage point in an induction set until all garbage points are in the induction set, and generates a garbage induction plan; The processing module calculates and sums the predicted garbage amounts of garbage points in the same summarized set according to the garbage summarization scheme to determine the aggregate garbage amount; The processing module defines the garbage classification scheme when all the collected garbage volumes are less than the full load of a single vehicle as an effective processing scheme, and selects the only effective processing scheme from all the effective processing schemes as the execution processing scheme; The processing module analyzes the garbage points in the same induction set according to the execution of the processing plan to generate a garbage processing path for the garbage truck to process the garbage; The steps of selecting a unique valid treatment plan from all valid treatment plans as the execution treatment plan include: The acquisition module obtains the historical prediction amount of each garbage point in a similar time interval; The processing module performs difference calculation based on the historical prediction amount and the corresponding garbage collection amount in a similar time interval to determine the theoretical-actual difference amount; The processing module calculates the average value of all theoretical-actual differences at a single garbage point to determine the average difference value; The processing module calculates the difference between the garbage volume of each summary collection and the full load of a single vehicle in all effective processing solutions to determine the excess load; The processing module performs summation calculation based on the average difference of the garbage points in the summary set to determine the additional amount; The processing module defines all effective processing solutions in which the additional amount is less than the excess load as feasible processing solutions, and selects the only feasible processing solution from all feasible processing solutions as the execution processing solution; The steps of selecting the only feasible solution from all feasible solutions as the execution solution include: The processing module performs difference calculation according to the excess load capacity of each summarized set in the feasible processing scheme and the corresponding additional amount to determine the idle adjustment amount; The processing module counts the garbage points in the summary set to determine the number of set points; The processing module determines the unit adjustment amount according to the ratio of the idle adjustment amount to the number of the set points, randomly selects a unit adjustment amount as the standard adjustment amount, and defines the remaining unit adjustment amounts as comparison adjustment amounts; The processing module calculates the adjustment satisfaction value based on the standard adjustment amount and all the comparative adjustment amounts. The adjustment satisfaction value reflects the reasonableness of the amount of garbage that can be adjusted by each garbage truck. The larger the value, the more reasonable the allocation of garbage points. The processing module determines the adjustment satisfaction value with the largest value according to the sorting rule, and determines the feasible processing solution corresponding to the adjustment satisfaction value as the execution processing solution.

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