Thermal power plant solid waste demand matching system and method based on big data driving

Abstract

The invention discloses a thermal power plant solid waste demand matching system and method based on big data driving, and relates to the technical field of data analysis, and the method comprises the following steps: calculating an inventory overstock rate and an operation load rate of each statistical period of each thermal power plant solid waste demand side, and forming an operation load abnormal time segment set of each demand side; the method comprises the following steps: planning and forming a solid waste transportation path based on geographical location information of a thermal power plant and demand sides, selecting a path to determine the sequence of accessing each demand side and the distance from a previous node in the path to the current demand side, predicting the time when a transportation team arrives at each demand side, and judging whether the transportation team is in a load abnormal period of the corresponding demand side; if the path is in the abnormal period, calculating expected retention duration, and calculating a matching value of the solid waste of the thermal power plant and the path; and calculating a comprehensive recommendation score of each path, traversing the comprehensive recommendation scores of all the paths, and selecting the path with the highest score as a preferred recommendation scheme, so that the solid waste demand matching of the thermal power plant is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a solid waste demand matching system and method for thermal power plants based on big data driving. BACKGROUND

[0002] With the continuous improvement of the production capacity of thermal power plants and the increasingly stringent environmental protection regulations, the resource disposal of solid waste in thermal power plants has become a core issue of green production. Such solid waste can be used as raw materials for production in demand parties such as building material plants and cement plants, realizing the transformation of waste into treasure. The matching of solid waste in thermal power plants and the demand of demand parties not only directly determines the disposal efficiency and environmental protection compliance of solid waste in thermal power plants, but also affects the stability of raw material supply in demand parties, and is a key link for the coordinated development of thermal power plants and demand parties.

[0003] However, the traditional solid waste demand matching method for thermal power plants often faces the following problems when dealing with multiple demand parties and dynamic operation scenarios. First, relying on manual experience decision-making, the data support is weak. The traditional matching is mainly based on the experience judgment of the historical cooperation relationship of the demand party, rough production capacity, etc. The historical operation data of the demand party are not systematically collected and analyzed, and the actual receiving capacity and abnormal period of load of the demand party cannot be identified. Second, the dimension of transportation path planning is single, and the load state of the demand party is not associated. The traditional path planning only takes the shortest transportation distance as the core target, and does not combine the geographical location information of the thermal power plant and the demand party for multi-path optimization, nor does it predict whether the arrival time of the transportation team is in the abnormal period of the load of the demand party. In addition, there is a lack of lag risk prediction and quantitative matching mechanism. The traditional method does not establish the calculation logic of the lag time of solid waste transportation. If the transportation team meets the abnormal load after arriving at the demand party, it can only be passive, and there is no unified quantitative standard for selecting multiple transportation paths, relying on subjective judgment to select the scheme, and the comprehensive adaptability of the path cannot be objectively measured. SUMMARY

[0004] The purpose of the present application is to provide a solid waste demand matching system and method for thermal power plants based on big data driving to solve the problems in the prior art.

[0005] To achieve the above purpose, the present application provides the following technical scheme: a solid waste demand matching method for thermal power plants based on big data driving, which comprises the following steps: acquiring a historical data set of solid waste demand parties in thermal power plants, calculating the inventory backlog rate and operation load rate of each solid waste demand party in thermal power plants in each statistical period, marking the periods with operation load rate exceeding the threshold value to form a set of abnormal load time periods of operation of each solid waste demand party in thermal power plants; The geographic position information of the thermal power plant and the thermal power plant solid waste demand side is acquired, and the solid waste transportation path is formed according to the transportation path planning. Any selected transportation path is used to determine the order of visiting each thermal power plant solid waste demand side and the route distance from the previous node on the path to the current thermal power plant solid waste demand side. The time when the solid waste transportation team reaches each thermal power plant solid waste demand side is predicted, and it is judged whether the time is in the abnormal period of operation load of the corresponding thermal power plant solid waste demand side. If it is in the abnormal period, the expected residence time is calculated, the expected residence time of each thermal power plant solid waste demand side is acquired, and the matching value of the thermal power plant solid waste and the transportation path is calculated. A solid waste transportation path recommendation model is constructed, the comprehensive recommendation score of each transportation path is calculated, all the planned solid waste transportation paths are traversed, the recommendation score set of all the paths is calculated according to the model, the path with the highest score is selected as the preferred recommendation scheme, if the preferred scheme cannot be executed due to temporary conditions, the path with the second highest score is selected from the remaining paths as a new recommendation scheme, and the operation is repeated until the thermal power plant confirms a scheme or all the generated paths are recommended.

[0006] The historical data set of the thermal power plant solid waste demand side is acquired, the inventory backlog rate and the operation load rate of each thermal power plant solid waste demand side in each statistical period are calculated, the periods with the operation load rate exceeding the threshold value are marked to form a set of abnormal time periods of the operation load of each thermal power plant solid waste demand side. The specific steps include: The thermal power plant and the thermal power plant solid waste demand side in the area to be monitored are connected to form a historical data set S of the thermal power plant solid waste demand side, S={s1, s2,...,s i ,...,s I},s i represents the historical data information of the i-th thermal power plant solid waste demand side, i=1, 2,...,I, I represents the number of thermal power plant solid waste demand sides; The historical data information of an arbitrary selected thermal power plant solid waste demand side is s k , which includes the historical solid waste receiving amount r kj and the historical inventory backlog rate e kj , r kj represents the historical amount of solid waste received by the k-th thermal power plant solid waste demand side in the j-th statistical period, e kj represents the historical inventory backlog rate of solid waste of the k-th thermal power plant solid waste demand side in the j-th statistical period, j=1, 2,...,J, J represents the total number of statistical historical periods, k∈{1, 2,...,I}; The historical inventory backlog rate of solid waste of the k-th thermal power plant solid waste demand side in the j-th statistical period is defined as follows: e kj =Q k，j_1 / Q k，j_0; wherein Q k，j_1 represents the actual inventory of solid waste of the solid waste demander k of the thermal power plant at the end of the jth statistical cycle; Q k，j_0 represents the actual consumption of solid waste of the solid waste demander k of the thermal power plant in the jth statistical cycle; The historical operation load rate o kj of the solid waste demander k of the thermal power plant in the jth statistical cycle is calculated kj , which is defined as shown below: o kj = [r kj * (1 + e k )] / C k ; wherein C i represents the theoretical maximum processing capacity of the solid waste of the solid waste demander k of the thermal power plant in a statistical cycle; The statistical cycles with the historical operation load rate greater than the preset operation load threshold are marked to form a set of time periods of historical operation load abnormality of the solid waste demander as T i , T i1 ,..., T i2 ,..., T ij’ , T iJ’}, T ij’ represents the historical operation load rate abnormality of the i th solid waste demander of the thermal power plant in the j' th statistical cycle, j' = 1, 2,..., J', J' represents the number of statistical cycles in which the historical operation load abnormality is detected, and T i represents the set of time periods of historical operation load abnormality of the i th solid waste demander of the thermal power plant. The historical operation load abnormality of the solid waste demander of the thermal power plant indicates that the operation load of the solid waste demander of the thermal power plant is continuously a times and above the maximum processing capacity thereof for a period of time, wherein a represents the preset maximum processing capacity threshold.

[0007] The geographical position information of the thermal power plant and the solid waste demander of the thermal power plant is obtained, based on which the transportation path is planned and the solid waste transportation path is formed. An arbitrary selected transportation path is determined to determine the order of visiting each solid waste demander of the thermal power plant and the route distance from the previous node on the path to the current solid waste demander of the thermal power plant. The time when the solid waste transportation team reaches each solid waste demander of the thermal power plant is predicted, and it is judged whether the time is in the operation load abnormal period of the corresponding solid waste demander of the thermal power plant. The specific steps include: The geographical position information of the thermal power plant and the solid waste demander of the thermal power plant is obtained, based on which the transportation path is planned and the solid waste transportation path is formed. An arbitrary selected transportation path is recorded as P p , and the order U p of visiting each solid waste demander of the thermal power plant is obtained according to the path P piand the route distance L between adjacent nodes pi Among them, L pi Indicates from transport route P p The route distance required to transport solid waste from the (i-1)th node to the i-th thermal power plant's solid waste demander, where nodes include the starting thermal power plant and the solid waste demanders of each thermal power plant, U pi Indicates according to transportation route P p The i-th visiting solid waste demander from a thermal power plant; Predict the arrival time of solid waste transportation teams at each thermal power plant's solid waste demander, and determine whether it is during a period of abnormal operating load for that thermal power plant's solid waste demander. When t0+(ΣL) pi / v)∈T i At that time, based on historical data from solid waste demanders at thermal power plants, the transportation path P of the solid waste was calculated. p The expected retention time D of solid waste at the i-th thermal power plant's solid waste demand side in the plan pi Where t0 represents the time point when the solid waste transportation team departs from the thermal power plant, v represents the average transportation speed of the solid waste transportation team, and T i This represents the set of historical abnormal operating load time periods for the i-th thermal power plant's solid waste demand side.

[0008] If it is an abnormal period, calculate the expected retention time, obtain the expected retention time at the solid waste demand sites of each thermal power plant, and calculate the matching value between the thermal power plant solid waste and the transportation route. The specific steps include: Solid waste in transportation route P p The expected retention time of solid waste at the i-th thermal power plant's solid waste demand side is defined as follows: D pi =max(0, o kj -α)*(T(i) / β); where α represents the preset normal load threshold, β represents the correlation coefficient between the load excess and the retention time, and T(i) represents the standard operating time for the i-th thermal power plant solid waste demander to unload and hand over the waste. For transport route P p The arrival times of the solid waste transportation teams at various thermal power plants' solid waste demanders are iterated to obtain the expected dwell time at each point along the route. The expected dwell time D is then analyzed. pi The number of times the operation takes longer than the standard operating time is accumulated and denoted as f; Calculate the solid waste and transportation route P of thermal power plant p The matching value is defined as follows: F k =1-(f / N k ); where F k P represents the solid waste and transportation route of thermal power plants. p The matching value between, N k Indicates the transportation route P p The number of solid waste demanders from thermal power plants included.

[0009] A solid waste transportation route recommendation model is constructed, and a comprehensive recommendation score is calculated for each transportation route. All planned solid waste transportation routes are traversed, and the set of recommendation scores for all routes is calculated based on the model. The route with the highest score is selected as the preferred recommended solution. If the preferred solution cannot be implemented due to unforeseen circumstances, the second-highest-scoring route from the remaining routes is selected as the new recommended solution. This process is repeated until the thermal power plant confirms a solution, or all generated routes have been recommended. Specific steps include: Construct a solid waste transportation route recommendation model and calculate P for any transportation route. p The overall recommendation score is defined as follows: C p =F k / [ΣL pi / v]; where C p Indicates the transportation route P p Overall recommendation score; All planned solid waste transportation routes are traversed. Based on the solid waste transportation route recommendation model, a set of recommended scores for all solid waste transportation routes is calculated. The transportation route with the highest recommended score is selected as the preferred recommended solution. If the preferred recommended solution cannot be executed due to temporary circumstances, the transportation route with the second highest recommended score from the remaining routes is selected as the new recommended solution and pushed. This process is repeated until the thermal power plant confirms a solution or all generated solid waste transportation routes have been recommended.

[0010] This system, based on big data, is a demand matching system for solid waste from thermal power plants. It includes a load anomaly analysis module, a transportation route planning module, a route matching value evaluation module, and a transportation route recommendation module. The load anomaly analysis module calculates the inventory backlog rate and operating load rate of each thermal power plant's solid waste demanders for each statistical period, forming a set of abnormal operating load time segments for each demander. The transportation route planning module plans and forms solid waste transportation routes based on the geographical location information of the thermal power plants and demanders, selects a route to determine the order of visits to each demander and the distance from the previous node to the current demander, predicts the arrival time of the transportation team to each demander, and determines whether the corresponding demander is in a period of abnormal load. The route matching value evaluation module calculates the expected dwell time and the matching value between the thermal power plant's solid waste and the route if an abnormal period occurs. The transportation route recommendation module calculates the comprehensive recommendation score for each route, iterates through all route comprehensive recommendation scores, and selects the route with the highest score as the preferred recommendation.

[0011] The load anomaly analysis module includes a demand-side historical data acquisition unit, an inventory backlog rate calculation unit, an operating load rate calculation unit, and a load anomaly time segment set generation unit. The demand-side historical data acquisition unit is used to establish data links between thermal power plants and solid waste demanders in the monitored area, and to collect and integrate historical data from each demander. The inventory backlog rate calculation unit is used to calculate the historical inventory backlog rate of each demander in each statistical period. The operating load rate calculation unit is used to calculate the historical operating load rate in each statistical period. The load anomaly time segment set generation unit is used to compare the operating load rate with a preset operating load threshold, mark the statistical period in which the load exceeds the threshold, and form a historical operating load anomaly time segment set for each demander.

[0012] The transportation route planning module includes a geographic information acquisition unit, a transportation route generation unit, a route parameter parsing unit, an arrival time prediction unit, and an anomaly period matching and judgment unit. The geographic information acquisition unit is used to acquire the geographic location information of the thermal power plant and each solid waste demander. The transportation route generation unit is used to perform route optimization planning based on geographic information to generate solid waste transportation routes. The route parameter parsing unit, for any transportation route, parses the order of visits to each demander and the route distance. The arrival time prediction unit is used to predict the arrival time of each demander by combining the departure time of the transportation team, the average transportation speed, and the cumulative route distance. The anomaly period matching and judgment unit matches the predicted arrival time with the set of load anomaly time segments of the corresponding demander to determine whether the arrival time is within a load anomaly period.

[0013] The path matching value evaluation module includes an expected dwell time calculation unit, a dwell time overrun statistics unit, and a path matching value calculation unit. The expected dwell time calculation unit is used to calculate the expected dwell time of solid waste at the demander if the arrival time falls within a period of abnormal demand load, combining a preset normal load threshold, the correlation coefficient between the load excess and the dwell time, and the standard operation time for unloading and handover at the demander. The dwell time overrun statistics unit is used to iterate through the expected dwell times of all demanders in the transportation path and count the number of times the dwell time exceeds the standard operation time. The path matching value calculation unit is used to calculate the matching value between the thermal power plant solid waste and the current transportation path.

[0014] The transportation route recommendation module includes a recommendation model construction unit, a route comprehensive score calculation unit, and a scheme selection unit. The recommendation model construction unit is used to construct a solid waste transportation route recommendation model. The route comprehensive score calculation unit is used to traverse all planned transportation routes, calculate the comprehensive recommendation score of each route based on the recommendation model, and form a score set. The scheme selection unit is used to traverse all planned solid waste transportation routes, calculate the recommendation score set of all solid waste transportation routes according to the solid waste transportation route recommendation model, select the transportation route with the highest recommendation score as the preferred recommendation scheme, and if the preferred recommendation scheme cannot be executed due to temporary circumstances, the transportation route with the second highest recommendation score from the remaining routes is selected as a new recommendation scheme and pushed. This operation is repeated until the thermal power plant confirms a certain scheme or all generated solid waste transportation routes have been recommended.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. By analyzing historical data sets of solid waste demanders from thermal power plants using big data, this invention calculates the inventory backlog rate and operating load rate of each demander in each statistical period, forming a set of abnormal load time periods. This allows for the prediction of the actual solid waste receiving capacity of demanders at different times. Unlike existing technologies that rely on manual experience to judge demanders and lack data support, this invention captures the correlation between demander operating data to achieve dynamic assessment of demander receiving capacity. This avoids solid waste backlog or production disorder caused by "supply-demand mismatch," ensuring the suitability of solid waste disposal at thermal power plants and raw material reception by demanders. 2. This invention combines the geographical location information of thermal power plants and demanders to plan transportation routes, determine the order of visits and route distances, predict the arrival time of the transportation teams and determine whether they are in a period of abnormal demand. After calculating the expected delay time, it further obtains the route matching value. Unlike the single-dimensional route planning in the prior art that only aims at "shortest transportation distance", this invention comprehensively measures the route adaptability, reduces transportation delays caused by arrival times in a period of abnormal demand, improves the efficiency of solid waste transportation, and reduces the idle loss of transportation resources. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the big data-driven method for matching the demand of solid waste from thermal power plants, as described in this invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, this invention provides a technical solution: a big data-driven method for matching the demand for solid waste from thermal power plants. This method includes the following steps: Obtain historical data sets of solid waste demanders from thermal power plants, calculate the inventory backlog rate and operating load rate of each thermal power plant solid waste demander in each statistical period, mark the periods when the operating load rate exceeds the threshold, and form a set of abnormal load time segments for the operation of each thermal power plant solid waste demander. Obtain the geographical location information of thermal power plants and their solid waste demanders, plan transportation routes accordingly, and form solid waste transportation routes. Randomly select one of the formed transportation routes, determine the order of visiting each thermal power plant's solid waste demander, as well as the route distance from the previous node on the route to the current thermal power plant's solid waste demander, predict the time for the solid waste transportation team to arrive at each thermal power plant's solid waste demander, and determine whether the time is during the abnormal operating load period of the corresponding thermal power plant's solid waste demander. If it is in an abnormal period, calculate the expected retention time, obtain the expected retention time at the solid waste demand site of each thermal power plant, and calculate the matching value between the thermal power plant solid waste and the transportation route. A solid waste transportation route recommendation model is constructed, and a comprehensive recommendation score for each transportation route is calculated. All planned solid waste transportation routes are traversed, and the set of recommendation scores for all routes is calculated based on the model. The route with the highest score is selected as the preferred recommended solution. If the preferred solution cannot be implemented due to temporary circumstances, the second highest-scoring route is selected from the remaining routes as the new recommended solution. This operation is repeated until the thermal power plant confirms a solution or all generated routes have been recommended.

[0019] Obtain historical data sets of solid waste demanders from thermal power plants, calculate the inventory backlog rate and operating load rate of each thermal power plant's solid waste demander in each statistical period, mark periods where the operating load rate exceeds the threshold, and form a set of abnormal load time segments for each thermal power plant's solid waste demander. Specific steps include: Data connections will be established between thermal power plants and their solid waste demanders within the monitoring area to form a historical data set S, where S = {s1, s2, ..., s...}. i , ..., s I}, s i This represents the historical data information of the i-th thermal power plant solid waste demander, i=1,2,...,I, where I represents the number of thermal power plant solid waste demanders; Let s be any historical data information of a solid waste demander from a thermal power plant. k This includes the historical solid waste reception volume r of the demander. kj and historical inventory backlog rate e kj r kje represents the historical quantity of solid waste received by solid waste demander k of a thermal power plant in the j-th statistical period. kj Let J represent the historical inventory backlog rate of solid waste for solid waste demander k of thermal power plant in the j-th statistical period, where j = 1, 2, ..., J, and J represents the total number of historical periods in the statistics, k ∈ {1, 2, ..., I}; The historical inventory backlog rate of solid waste for thermal power plant solid waste demander k in the j-th statistical period is defined as follows: e kj =Q k，j_1 / Q k，j_0 ; where Q k，j_1 Q represents the actual inventory of solid waste held by solid waste demander k at the end of the j-th statistical period; k，j_0 This represents the actual amount of solid waste consumed by solid waste demander k of the thermal power plant during the j-th statistical period. Calculate the historical operating load rate of solid waste demand k from a thermal power plant in the j-th statistical period. kj The definition is as follows: o kj =[r kj *(1+e kj )] / C k Among them, C k This represents the theoretical maximum processing capacity of solid waste from a thermal power plant for the demand side k within a statistical period. The statistical periods in which the historical operating load rate exceeds the preset operating load threshold are marked to form a set of time periods of historical operating load anomalies for the solid waste demand side of thermal power plants, denoted as T. i T i ={T i1 ,T i2 , ..., T ij’ , ..., T iJ’}, T ij’ This indicates that the historical operating load rate of the i-th thermal power plant's solid waste demander is abnormal within the j'-th statistical period, where j' = 1, 2, ..., J', and J' represents the number of statistical periods in which the historical operating load abnormality was detected. i This represents the set of time periods during which the historical operating load of the i-th thermal power plant solid waste demander is abnormal. The historical operating load abnormality of the thermal power plant solid waste demander means that the operating load of the thermal power plant solid waste demander has been continuously at a times or more than its maximum processing capacity for a period of time, where a represents the preset maximum processing capacity threshold.

[0020] Obtain the geographical location information of thermal power plants and their solid waste demanders. Based on this, plan transportation routes and form solid waste transportation routes. Randomly select one of the formed transportation routes, determine the order of visiting each thermal power plant's solid waste demander, and the route distance from the previous node on the route to the current thermal power plant's solid waste demander. Predict the arrival time of the solid waste transportation team at each thermal power plant's solid waste demander, and determine whether this time falls within the abnormal operating load period of the corresponding thermal power plant's solid waste demander. Specific steps include: Obtain the geographical location information of thermal power plants and the geographical location information of solid waste demanders from thermal power plants, and plan transportation routes based on the above-mentioned relevant geographical location information to form solid waste transportation routes; Let P be any selected transportation route. p According to path P p The order U of the visits to the solid waste demanders of each thermal power plant was obtained respectively. pi and the route distance L between adjacent nodes pi Among them, L pi Indicates from transport route P p The route distance required to transport solid waste from the (i-1)th node to the i-th thermal power plant's solid waste demander, where nodes include the starting thermal power plant and the solid waste demanders of each thermal power plant, U pi Indicates according to transportation route P p The i-th visiting solid waste demander from a thermal power plant; Predict the arrival time of solid waste transportation teams at each thermal power plant's solid waste demander, and determine whether it is during a period of abnormal operating load for that thermal power plant's solid waste demander. When t0+(ΣL) pi / v)∈T i At that time, based on historical data from solid waste demanders at thermal power plants, the transportation path P of the solid waste was calculated. p The expected retention time D of solid waste at the i-th thermal power plant's solid waste demand side in the plan pi Where t0 represents the time point when the solid waste transportation team departs from the thermal power plant, v represents the average transportation speed of the solid waste transportation team, and T i This represents the set of historical abnormal operating load time periods for the i-th thermal power plant's solid waste demand side.

[0021] If it is an abnormal period, calculate the expected retention time, obtain the expected retention time at the solid waste demand sites of each thermal power plant, and calculate the matching value between the thermal power plant solid waste and the transportation route. The specific steps include: Solid waste in transportation route P p The expected retention time of solid waste at the i-th thermal power plant's solid waste demand side is defined as follows: D pi =max(0, o kj-α)*(T(i) / β); where α represents the preset normal load threshold, β represents the correlation coefficient between the load excess and the retention time, and T(i) represents the standard operating time for the i-th thermal power plant solid waste demander to unload and hand over the waste. For transport route P p The arrival times of the solid waste transportation teams at various thermal power plants' solid waste demanders are iterated to obtain the expected dwell time at each point along the route. The expected dwell time D is then analyzed. pi The number of times the operation takes longer than the standard operating time is accumulated and denoted as f; Calculate the solid waste and transportation route P of thermal power plant p The matching value is defined as follows: F k =1-(f / N k ); where F k P represents the solid waste and transportation route of thermal power plants. p The matching value between, N k Indicates the transportation route P p The number of solid waste demanders from thermal power plants included.

[0022] A solid waste transportation route recommendation model is constructed, and a comprehensive recommendation score is calculated for each transportation route. All planned solid waste transportation routes are traversed, and the set of recommendation scores for all routes is calculated based on the model. The route with the highest score is selected as the preferred recommended solution. If the preferred solution cannot be implemented due to unforeseen circumstances, the second-highest-scoring route from the remaining routes is selected as the new recommended solution. This process is repeated until the thermal power plant confirms a solution, or all generated routes have been recommended. Specific steps include: Construct a solid waste transportation route recommendation model and calculate P for any transportation route. p The overall recommendation score is defined as follows: C p =F k / [ΣL pi / v]; where C p Indicates the transportation route P p Overall recommendation score; All planned solid waste transportation routes are traversed. Based on the solid waste transportation route recommendation model, a set of recommended scores for all solid waste transportation routes is calculated. The transportation route with the highest recommended score is selected as the preferred recommended solution. If the preferred recommended solution cannot be executed due to temporary circumstances, the transportation route with the second highest recommended score from the remaining routes is selected as the new recommended solution and pushed. This process is repeated until the thermal power plant confirms a solution or all generated solid waste transportation routes have been recommended.

[0023] This system, based on big data, is a demand matching system for solid waste from thermal power plants. It includes a load anomaly analysis module, a transportation route planning module, a route matching value evaluation module, and a transportation route recommendation module. The load anomaly analysis module calculates the inventory backlog rate and operating load rate of each thermal power plant's solid waste demanders for each statistical period, forming a set of abnormal operating load time segments for each demander. The transportation route planning module plans and forms solid waste transportation routes based on the geographical location information of the thermal power plants and demanders, selects a route to determine the order of visits to each demander and the distance from the previous node to the current demander, predicts the arrival time of the transportation team to each demander, and determines whether the corresponding demander is in a period of abnormal load. The route matching value evaluation module calculates the expected dwell time and the matching value between the thermal power plant's solid waste and the route if an abnormal period occurs. The transportation route recommendation module calculates the comprehensive recommendation score for each route, iterates through all route comprehensive recommendation scores, and selects the route with the highest score as the preferred recommendation.

[0024] The load anomaly analysis module includes a demand-side historical data acquisition unit, an inventory backlog rate calculation unit, an operating load rate calculation unit, and a load anomaly time segment set generation unit. The demand-side historical data acquisition unit is used to establish data links between thermal power plants and solid waste demanders in the monitored area, and to collect and integrate historical data from each demander. The inventory backlog rate calculation unit is used to calculate the historical inventory backlog rate of each demander in each statistical period. The operating load rate calculation unit is used to calculate the historical operating load rate in each statistical period. The load anomaly time segment set generation unit is used to compare the operating load rate with a preset operating load threshold, mark the statistical period in which the load exceeds the threshold, and form a historical operating load anomaly time segment set for each demander.

[0025] The transportation route planning module includes a geographic information acquisition unit, a transportation route generation unit, a route parameter parsing unit, an arrival time prediction unit, and an anomaly period matching and judgment unit. The geographic information acquisition unit is used to acquire the geographic location information of the thermal power plant and each solid waste demander. The transportation route generation unit is used to perform route optimization planning based on geographic information to generate solid waste transportation routes. The route parameter parsing unit, for any transportation route, parses the order of visits to each demander and the route distance. The arrival time prediction unit is used to predict the arrival time of each demander by combining the departure time of the transportation team, the average transportation speed, and the cumulative route distance. The anomaly period matching and judgment unit matches the predicted arrival time with the set of load anomaly time segments of the corresponding demander to determine whether the arrival time is within a load anomaly period.

[0026] The path matching value evaluation module includes an expected dwell time calculation unit, a dwell time overrun statistics unit, and a path matching value calculation unit. The expected dwell time calculation unit is used to calculate the expected dwell time of solid waste at the demander if the arrival time falls within a period of abnormal demand load, combining a preset normal load threshold, the correlation coefficient between the load excess and the dwell time, and the standard operation time for unloading and handover at the demander. The dwell time overrun statistics unit is used to iterate through the expected dwell times of all demanders in the transportation path and count the number of times the dwell time exceeds the standard operation time. The path matching value calculation unit is used to calculate the matching value between the thermal power plant solid waste and the current transportation path.

[0027] The transportation route recommendation module includes a recommendation model construction unit, a route comprehensive score calculation unit, and a scheme selection unit. The recommendation model construction unit is used to construct a solid waste transportation route recommendation model. The route comprehensive score calculation unit is used to traverse all planned transportation routes, calculate the comprehensive recommendation score of each route based on the recommendation model, and form a score set. The scheme selection unit is used to traverse all planned solid waste transportation routes, calculate the recommendation score set of all solid waste transportation routes according to the solid waste transportation route recommendation model, select the transportation route with the highest recommendation score as the preferred recommendation scheme, and if the preferred recommendation scheme cannot be executed due to temporary circumstances, the transportation route with the second highest recommendation score from the remaining routes is selected as a new recommendation scheme and pushed. This operation is repeated until the thermal power plant confirms a certain scheme or all generated solid waste transportation routes have been recommended.

[0028] In Example 1: Historical data processing of demanders was carried out. Staff established data links between the thermal power plant and surrounding demanders to collect past operational data from each demander, including information such as solid waste reception, inventory changes, consumption rhythm, and the operating status of processing equipment. For each demander, the inventory backlog in different statistical periods was analyzed. By comparing the actual inventory at the end of the period with the solid waste consumption during that period, it was determined whether there was an inventory backlog. At the same time, the total amount of solid waste received by the demander and the maximum carrying capacity of its own processing equipment were combined to assess its operational load level. Periods in which the operational load exceeded the normal range were marked, and a set of abnormal time periods for the operational load of each demander was identified. Transportation route planning is carried out by collecting geographical location information of thermal power plants and various demanders. Taking into account the distribution of each demander, multiple solid waste transportation routes are planned. One route is selected for detailed analysis to determine the order of visiting each demander under this route. Priority is given to demanders that are relatively close to the thermal power plant and are in a non-abnormal period. At the same time, the route direction between adjacent nodes in the route is clarified. The nodes include the starting thermal power plant and each demander along the way. Predict the arrival time of the transport fleet and determine the load status. Based on the daily driving rhythm of the transport fleet and the driving conditions of each segment of the route, predict the arrival time of each demander. Compare the predicted time with the set of abnormal time segments of the operational load of each demander to determine whether the arrival time is in an abnormal period. If a demander is in an abnormal load period when the forecast arrives, the expected retention time is calculated by combining the demander's historical load abnormality and solid waste treatment efficiency. The expected retention of all demanders in the path is traversed, the number of demanders with overdue retention is counted, and the number of demanders is compared with the total number of demanders in the path to obtain the matching value between thermal power plant solid waste and the path. The comprehensive recommendation score of each route is calculated by the solid waste transportation route recommendation model. Taking into account the route matching value and the overall transportation time, the route with the highest score is selected as the preferred recommended solution. If the preferred solution cannot be implemented due to temporary circumstances (such as sudden traffic control on a certain road section), the second highest-scoring solution is selected from the remaining routes as the new recommended solution and pushed to the thermal power plant staff.

[0029] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A big data-driven method for matching the demand for solid waste from thermal power plants, characterized by: The method includes the following steps: Obtain historical data sets of solid waste demanders from thermal power plants, calculate the inventory backlog rate and operating load rate of each thermal power plant solid waste demander in each statistical period, mark the periods when the operating load rate exceeds the threshold, and form a set of abnormal load time segments for the operation of each thermal power plant solid waste demander. Obtain the geographical location information of thermal power plants and their solid waste demanders, plan transportation routes accordingly, and form solid waste transportation routes. Randomly select one of the formed transportation routes, determine the order of visiting each thermal power plant's solid waste demander, as well as the route distance from the previous node on the route to the current thermal power plant's solid waste demander, predict the time for the solid waste transportation team to arrive at each thermal power plant's solid waste demander, and determine whether the time is during the abnormal operating load period of the corresponding thermal power plant's solid waste demander. If it is in an abnormal period, calculate the expected retention time, obtain the expected retention time at the solid waste demand site of each thermal power plant, and calculate the matching value between the thermal power plant solid waste and the transportation route. A solid waste transportation route recommendation model is constructed, and a comprehensive recommendation score for each transportation route is calculated. All planned solid waste transportation routes are traversed, and the set of recommendation scores for all routes is calculated based on the model. The route with the highest score is selected as the preferred recommended solution. If the preferred solution cannot be implemented due to temporary circumstances, the second highest-scoring route is selected from the remaining routes as the new recommended solution. This operation is repeated until the thermal power plant confirms a solution or all generated routes have been recommended.

2. The method for matching the demand for solid waste from thermal power plants based on big data as described in claim 1, characterized in that: Obtain historical data sets of solid waste demanders from thermal power plants, calculate the inventory backlog rate and operating load rate of each thermal power plant's solid waste demander in each statistical period, mark periods where the operating load rate exceeds the threshold, and form a set of abnormal load time segments for each thermal power plant's solid waste demander. Specific steps include: Data connections will be established between thermal power plants and their solid waste demanders within the monitoring area to form a historical data set S, where S = {s1, s2, ..., s...}. i , ..., s I }, s i This represents the historical data information of the i-th thermal power plant solid waste demander, i=1,2,...,I, where I represents the number of thermal power plant solid waste demanders; Let s be any historical data information of a solid waste demander from a thermal power plant. k This includes the historical solid waste reception volume r of the demander. kj and historical inventory backlog rate e kj r kj e represents the historical quantity of solid waste received by solid waste demander k of a thermal power plant in the j-th statistical period. kj Let J represent the historical inventory backlog rate of solid waste for solid waste demander k of thermal power plant in the j-th statistical period, where j = 1, 2, ..., J, and J represents the total number of historical periods in the statistics, k ∈ {1, 2, ..., I}; The historical inventory backlog rate of solid waste for thermal power plant solid waste demander k in the j-th statistical period is defined as follows: e kj =Q k，j_1 / Q k，j_0 ; where Q k，j_1 Q represents the actual inventory of solid waste held by solid waste demander k at the end of the j-th statistical period; k，j_0 This represents the actual amount of solid waste consumed by solid waste demander k of the thermal power plant during the j-th statistical period. Calculate the historical operating load rate of solid waste demand k from a thermal power plant in the j-th statistical period. kj The definition is as follows: o kj =[r kj *(1+e kj )] / C k Among them, C k This represents the theoretical maximum processing capacity of solid waste from a thermal power plant for the demand side k within a statistical period. The statistical periods in which the historical operating load rate exceeds the preset operating load threshold are marked to form a set of time periods of historical operating load anomalies for the solid waste demand side of thermal power plants, denoted as T. i T i ={T i1 ,T i2 , ..., T ij’ , ..., T iJ’ }, T ij’ This indicates that the historical operating load rate of the i-th thermal power plant's solid waste demander is abnormal within the j'-th statistical period, where j' = 1, 2, ..., J', and J' represents the number of statistical periods in which the historical operating load abnormality was detected. i This represents the set of time periods during which the historical operating load of the i-th thermal power plant solid waste demander is abnormal. The historical operating load abnormality of the thermal power plant solid waste demander means that the operating load of the thermal power plant solid waste demander has been continuously at a times or more than its maximum processing capacity for a period of time, where a represents the preset maximum processing capacity threshold.

3. The method for matching the demand for solid waste from thermal power plants based on big data as described in claim 2, characterized in that: Obtain the geographical location information of thermal power plants and their solid waste demanders. Based on this, plan transportation routes and form solid waste transportation routes. Randomly select one of the formed transportation routes, determine the order of visiting each thermal power plant's solid waste demander, and the route distance from the previous node on the route to the current thermal power plant's solid waste demander. Predict the arrival time of the solid waste transportation team at each thermal power plant's solid waste demander, and determine whether this time falls within the abnormal operating load period of the corresponding thermal power plant's solid waste demander. Specific steps include: Obtain the geographical location information of thermal power plants and the geographical location information of solid waste demanders from thermal power plants, and plan transportation routes based on the above-mentioned relevant geographical location information to form solid waste transportation routes; Let P be any selected transportation route. p According to path P p The order U of the visits to the solid waste demanders of each thermal power plant was obtained respectively. pi and the route distance L between adjacent nodes pi ; among which, L pi Indicates from transport route P p The route distance required to transport solid waste from the (i-1)th node to the i-th thermal power plant's solid waste demander, where nodes include the starting thermal power plant and the solid waste demanders of each thermal power plant, U pi Indicates according to transportation route P p The i-th visiting solid waste demander from a thermal power plant; Predict the arrival time of solid waste transportation teams at each thermal power plant's solid waste demander, and determine whether it is during a period of abnormal operating load for that thermal power plant's solid waste demander. When t0+(ΣL) pi / v)∈T i At that time, based on historical data from solid waste demanders at thermal power plants, the transportation path P of the solid waste was calculated. p The expected retention time D of solid waste at the i-th thermal power plant's solid waste demand side in the plan pi Where t0 represents the time point when the solid waste transportation team departs from the thermal power plant, v represents the average transportation speed of the solid waste transportation team, and T i This represents the set of historical abnormal operating load time periods for the i-th thermal power plant's solid waste demand side.

4. The method for matching the demand for solid waste from thermal power plants based on big data as described in claim 3, characterized in that: If it is an abnormal period, calculate the expected retention time, obtain the expected retention time at the solid waste demand sites of each thermal power plant, and calculate the matching value between the thermal power plant solid waste and the transportation route. The specific steps include: Solid waste in transportation route P p The expected retention time of solid waste at the i-th thermal power plant's solid waste demand side is defined as follows: D pi =max(0, o kj -α)*(T(i) / β); where α represents the preset normal load threshold, β represents the correlation coefficient between the load excess and the retention time, and T(i) represents the standard operating time for the i-th thermal power plant solid waste demander to unload and hand over the waste. For transport route P p The arrival times of the solid waste transportation teams at various thermal power plants' solid waste demanders are iterated to obtain the expected dwell time at each point along the route. The expected dwell time D is then analyzed. pi The number of times the operation takes longer than the standard operating time is accumulated and denoted as f; Calculate the solid waste and transportation route P of thermal power plant p The matching value is defined as follows: F k =1-(f / N k ); where F k P represents the solid waste and transportation route of thermal power plants. p The matching value between, N k Indicates the transportation route P p The number of solid waste demanders from thermal power plants included.

5. The method for matching the demand for solid waste from thermal power plants based on big data as described in claim 4, characterized in that: A solid waste transportation route recommendation model is constructed, and a comprehensive recommendation score is calculated for each transportation route. All planned solid waste transportation routes are traversed, and the set of recommendation scores for all routes is calculated based on the model. The route with the highest score is selected as the preferred recommended solution. If the preferred solution cannot be implemented due to unforeseen circumstances, the second-highest-scoring route from the remaining routes is selected as the new recommended solution. This process is repeated until the thermal power plant confirms a solution, or all generated routes have been recommended. Specific steps include: Construct a solid waste transportation route recommendation model and calculate P for any transportation route. p The overall recommendation score is defined as follows: C p =F k / [ΣL pi / v]; where C p Indicates the transportation route P p Overall recommendation score; All planned solid waste transportation routes are traversed. Based on the solid waste transportation route recommendation model, a set of recommended scores for all solid waste transportation routes is calculated. The transportation route with the highest recommended score is selected as the preferred recommended solution. If the preferred recommended solution cannot be executed due to temporary circumstances, the transportation route with the second highest recommended score from the remaining routes is selected as the new recommended solution and pushed. This process is repeated until the thermal power plant confirms a solution or all generated solid waste transportation routes have been recommended.

6. A big data-driven solid waste demand matching system for thermal power plants, applied to the big data-driven solid waste demand matching method for thermal power plants as described in any one of claims 1-5, characterized in that: The system includes: a load anomaly analysis module, a transportation route planning module, a route matching value evaluation module, and a transportation route recommendation module. The load anomaly analysis module calculates the inventory backlog rate and operating load rate of each thermal power plant's solid waste demander for each statistical period, forming a set of abnormal operating load time segments for each demander. The transportation route planning module plans and forms solid waste transportation routes based on the geographical location information of the thermal power plants and demanders, selects a route to determine the order of visits to each demander and the distance from the previous node in the route to the current demander, predicts the arrival time of the transportation team to each demander, and determines whether the corresponding demander is in a period of load anomaly. The route matching value evaluation module calculates the expected dwell time and the matching value between the thermal power plant's solid waste and the route if an anomaly period is in effect. The transportation route recommendation module calculates the comprehensive recommendation score for each route, iterates through all route comprehensive recommendation scores, and selects the route with the highest score as the preferred recommendation.

7. The big data-driven solid waste demand matching system for thermal power plants according to claim 6, characterized in that: The load anomaly analysis module includes a demand-side historical data acquisition unit, an inventory backlog rate calculation unit, an operating load rate calculation unit, and a load anomaly time segment set generation unit. The demand-side historical data acquisition unit is used to establish data links between thermal power plants and solid waste demanders in the monitored area, and to collect and integrate historical data from each demander. The inventory backlog rate calculation unit is used to calculate the historical inventory backlog rate of each demander in each statistical period. The operating load rate calculation unit is used to calculate the historical operating load rate in each statistical period. The load anomaly time segment set generation unit is used to compare the operating load rate with a preset operating load threshold, mark the statistical period in which the load exceeds the threshold, and form a historical operating load anomaly time segment set for each demander.

8. The big data-driven solid waste demand matching system for thermal power plants according to claim 7, characterized in that: The transportation route planning module includes a geographic information acquisition unit, a transportation route generation unit, a route parameter parsing unit, an arrival time prediction unit, and an abnormal period matching judgment unit. The geographic information acquisition unit is used to obtain the geographic location information of the thermal power plant and each solid waste demander. The transportation route generation unit is used to perform route optimization planning based on geographic information to generate solid waste transportation routes; the route parameter parsing unit parses the order of visits to each demander and the route distance for any transportation route; the arrival time prediction unit is used to predict the arrival time of each demander by combining the departure time of the transportation team, the average transportation speed and the cumulative route distance; the abnormal period matching and judgment unit matches the predicted arrival time with the set of load abnormal time periods of the corresponding demander to determine whether the arrival time is in a load abnormal period.

9. The big data-driven solid waste demand matching system for thermal power plants according to claim 8, characterized in that: The path matching value evaluation module includes an expected dwell time calculation unit, a dwell time overrun statistics unit, and a path matching value calculation unit. The expected dwell time calculation unit is used to calculate the expected dwell time of solid waste at the demander if the arrival time falls within a period of abnormal demand load, combining a preset normal load threshold, the correlation coefficient between the load excess and the dwell time, and the standard operation time for unloading and handover at the demander. The dwell time overrun statistics unit is used to iterate through the expected dwell times of all demanders in the transportation path and count the number of times the dwell time exceeds the standard operation time. The path matching value calculation unit is used to calculate the matching value between the thermal power plant solid waste and the current transportation path.

10. The big data-driven solid waste demand matching system for thermal power plants according to claim 9, characterized in that: The transportation route recommendation module includes a recommendation model construction unit, a route comprehensive score calculation unit, and a scheme selection unit; the recommendation model construction unit is used to construct a solid waste transportation route recommendation model; the route comprehensive score calculation unit is used to traverse all planned transportation routes, calculate the comprehensive recommendation score of each route based on the recommendation model, and form a score set; The scheme selection unit is used to traverse all planned solid waste transportation routes, calculate the set of recommended scores for all solid waste transportation routes based on the solid waste transportation route recommendation model, select the transportation route with the highest recommended score as the preferred recommended scheme, and if the preferred recommended scheme cannot be executed due to temporary circumstances, the transportation route with the second highest recommended score is selected from the remaining routes as the new recommended scheme and pushed. The operation is repeated until the thermal power plant confirms a certain scheme, or all generated solid waste transportation routes have been recommended.

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