Inventory prediction method and system based on steelmaking production plan

Abstract

The application belongs to the technical field of steel production, and particularly discloses a kind of inventory prediction method and system based on steelmaking production plan, which comprises the following steps: obtaining existing inventory information, steelmaking production plan and hot rolling feeding plan information;According to the casting machine pulling speed and slab plan length, the slab output time is determined;According to the slab type and scene, the slab storage point is determined;According to the storage point, the roller conveyor time is determined, the slab output time is accumulated, and the slab storage time is obtained;According to the existing inventory information, slab in-out quantity, slab inventory prediction is carried out. By using the technical scheme, based on inventory prediction, the pouring plan component of each production line flow direction in the pouring plan is determined, the production continuity is ensured, and the accuracy of the plan and the executability of the site are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of steel production and relates to a stock prediction method and system based on a steelmaking production plan. BACKGROUND

[0002] As an important link and buffer for the transmission of material flow and energy flow between the steel-rolling interface, the efficient operation of the slab yard plays a crucial role in the efficiency of the interface connection and is an important means to improve the turnover efficiency of the slab yard and solve the bottleneck of the steel-rolling interface efficiency.

[0003] The steel production process is extremely complex and has many uncertainties. In the face of various unexpected and sudden situations in actual production, manual experience is currently mostly used to manage the slab yard. However, manual experience lacks scientificity and rationality and is prone to cause material flow congestion, which seriously affects the efficient execution of the production plan.

[0004] The stock prediction of the slab yard is of great significance to the operation and management of the enterprise:

[0005] (1) Stock prediction helps to more accurately understand the demand for slabs in the future period of time, so as to more reasonably allocate and schedule resources such as equipment and logistics.

[0006] (2) Excessive inventory levels will lead to high inventory costs, including storage costs, capital occupation costs, etc. Through stock prediction, the inventory quantity can be more accurately controlled to avoid inventory accumulation and waste, thereby reducing inventory costs.

[0007] (3) It helps to better plan and manage the stock area, break through the coordination relationship between steelmaking and hot rolling of the steel-rolling interface, ensure that the slabs can be timely stored and fed, and improve the logistics efficiency of the stock area.

[0008] The stock prediction of the slab yard has important application significance in optimizing resource allocation, reducing inventory costs, and improving logistics efficiency.

[0009] However, the existing technology still has the following technical problems:

[0010] (1) To solve the problem of multi-caster multi-flow prediction. The slab yard is divided into 1-4 spans and is responsible for the storage of 4 casters and the feeding of 2 rolling mills. The slab storage position and time need to be predicted according to factors such as slab type and operation scenario to improve the planning of the stock area operation.

[0011] (2) To solve the problem of inventory-steelmaking production plan linkage. The steelmaking production plan changes dynamically, and the inventory prediction function needs to identify the changes in the steelmaking scheduling to timely correct the prediction results and ensure the accuracy of the prediction. SUMMARY

[0012] The application aims to provide a steelmaking production plan-based inventory prediction method and system, and improve the accuracy of the plan and the executability of the site.

[0013] To achieve the above-mentioned purpose, the basic scheme of the application is a steelmaking production plan-based inventory prediction method, comprising the following steps:

[0014] Obtaining existing inventory information, steelmaking production plan and hot rolling feeding plan information;

[0015] According to the casting machine pulling speed and the slab plan length, the slab output time is determined;

[0016] According to the slab type, the slab storage point is determined;

[0017] According to the storage point, the roller conveying time is determined, the slab output time is accumulated, and the slab storage time is obtained;

[0018] According to the existing inventory information and the slab in-out information, the slab inventory prediction is performed.

[0019] The working principle and beneficial effects of the basic scheme are that, based on the contract analysis and inventory analysis, the pouring plan component of each production line flow in the pouring plan is determined based on the inventory balance target, so as to realize the production continuity. Based on the inventory prediction, the pouring plan is prepared, which can effectively improve the accuracy of the plan and the executability of the site, avoid the cost increase, process instability and unsmooth logistics caused by artificial influencing factors, and has an important role in improving product quality and contract delivery and reducing production cost.

[0020] Further, the inventory information comprises:

[0021] The inventory information structure of all slabs is designed according to the inventory information, including slab number W.slab_no, warehouse number W.warehouse_id, cross number W.hall_id, logical partition W.zone, slot W.slot, steel grade W.st and weight W.weight;

[0022] According to the inventory information, the total inventory W0 is obtained by summarizing the weight W.weight of all slabs;

[0023] The steelmaking production plan information comprises:

[0024] The steelmaking heat structure is designed according to the steelmaking heat plan, including pouring number P.cast, plan heat number P.heat, plan start time P.T m , plan steel grade P.st;

[0025] Aiming at the slab cutting plan, a cutting plan structure is designed, including plan heat number P.heat, flow number P.strand_no, plan slab number P.slab_no, plan slab length P.len, weight P.weight, slab type P.type, and slab expected storage time P.in;

[0026] According to the pour number, the steelmaking heat plan and slab cutting plan information of the pour are extracted from the database;

[0027] The hot rolling loading plan information includes:

[0028] Aiming at the hot rolling loading plan information, a hot rolling loading plan structure is designed, including plan number L.plan, slab number L.slab_no, weight L.weight, and plan loading time L.loadtime;

[0029] According to the plan number, the hot rolling loading plan information of the plan is extracted from the database.

[0030] The relevant information of the inventory is obtained, facilitating subsequent use.

[0031] Further, for a multi-flow caster, Strand(s) = 1…s cast flows share one ladle and tundish, and the crystallizer, cooling section, straightening and cutting are all separate. According to the casting speed and slab plan length, the slab output time is predicted, and the specific method is as follows:

[0032] According to the steelmaking production plan, the mth heat plan start time T m is obtained, m = 1…M, and M is the total number of heats;

[0033] According to the caster where the pour is located, the parameter “pouring to blanking time length” d i is set, and i is the caster number;

[0034] According to the steel grade and caster number of the mth heat, the preset pouring model configuration base table is queried to obtain the theoretical casting speed S m of the caster, m = 1…M;

[0035] According to the steelmaking production plan, the plan cutting length L mn of the nth slab of Strand(s) flow of the mth heat is obtained; then the output time C m,n,Strand(s) of the nth slab of Strand(s) flow of the mth heat is:

[0036]

[0037] Wherein, N represents the slab block number of Strand(s) flow of heat m.

[0038] According to the casting speed and the slab plan length, the slab output time is predicted, and the operation is simple.

[0039] Further, according to the slab type, the slab storage point is decided, and the specific steps are as follows:

[0040] Obtain the slab type of the steelmaking furnace plan and the slab cutting plan information;

[0041] The probabilities of the storage slabs being stored in 1 span, 2 spans, 3 spans, 4 spans, …, r spans are calculated respectively;

[0042] The maximum probability is the slab storage point.

[0043] The slab storage point decision function is expressed as:

[0044] P w,r =P q,r *K w ,r∈{1,2,3…r}

[0045]

[0046] Wherein, the slab type set Q: {to be cleaned, to be checked, qualified blank};

[0047] The span set R of the slab storage: {1 span, 2 spans, 3 spans, 4 spans, …, r span storage};

[0048] w is the serial number of the slab, and W is the slab set, w∈W, and there are W slabs;

[0049] P q,r is the reference probability of the slab type q being stored to r span, which is generated by historical data statistics;

[0050] K w represents the weight coefficient of the w-th slab being stored to r span;

[0051] P w,r is the probability of the slab w being stored in 1 span, 2 spans, 3 spans, 4 spans, …, m spans;

[0052] P w,r ′ is the maximum probability of the slab w being stored in 1 span, 2 spans, 3 spans, 4 spans, …, m spans, and the corresponding span r is the slab offline point.

[0053] The slab type and the operation scene affect the slab storage point, and further affect the time when the slab is stored after being produced.

[0054] Further, according to the storage point, the roller conveying time is predicted, the slab production time is accumulated, and the method for predicting the slab storage time is:

[0055] The warehousing time T of the nth slab of the mth strand in the current pouring A mn is:

[0056] T mn = C m,n,Strand ( s )+R ab

[0057] wherein C m,n,Strand ( s) represents the production time of the nth slab of the mth strand, and R ab represents the time length from the slab production time of the caster to the delivery to the b span, a and b = 1, 2, 3, 4, … m.

[0058] The slab warehousing time is predicted, which facilitates the subsequent slab inventory prediction.

[0059] Further, according to the predicted slab in-out information, the slab inventory prediction is performed in the following steps:

[0060] At the current time T0, the slab inventory W t0 is set, and the inventory W t1 at the future time T1 is predicted.

[0061] The planned warehousing slab set P: {planned slab number P.slab_no, weight P.weight, slab predicted warehousing time P.in} is planned;

[0062] The planned loading slab set L: {slab number L.slab_no, weight L.weight, planned loading time L.loadtime} is planned;

[0063] For all slabs P e in the P set, the parameter k e is set, when the slab predicted warehousing time P.in is less than T1, k e = 1; otherwise, k e = 0;

[0064] For all slabs L f in the L set, the parameter d f is set, when the planned loading time L.loadtime is less than T1, d f = 1; otherwise, d f = 0;

[0065] Then the slab warehousing amount W in and the slab delivery amount W out are:

[0066] Win = Σ{P e .weight*ke}e∈P

[0067] Wout=Σ{L f .weight*d f}f∈L

[0068] T1 time slab inventory W t1 is:

[0069] W t1 =W t0 +W in -W out .

[0070] According to the predicted slab in-out quantity, the slab inventory prediction is carried out.

[0071] The application also provides a kind of inventory prediction system based on steelmaking production plan, including data acquisition module and processing module, the data acquisition module is used to gather inventory information, steelmaking production plan, hot rolling loading plan information, the output of data acquisition module is connected with the input of processing module, the processing module executes the method described in the application, controls warehousing and discharging, realizes inventory prediction based on steelmaking production plan.

[0072] Utilize system to obtain relevant data information, and carry out data analysis processing, obtain accurate inventory prediction based on steelmaking production plan. BRIEF DESCRIPTION OF DRAWINGS

[0073] Figure 1 It is the flow schematic diagram of the inventory prediction method based on steelmaking production plan of the application;

[0074] Figure 2 It is the simulation experimental result schematic diagram of the inventory prediction method based on steelmaking production plan of the application. DETAILED DESCRIPTION

[0075] The embodiments of the application are described in detail below, and the examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary, only for explaining the application, and cannot be understood as the limitation of the application.

[0076] In the description of the present application, it is understood that the terms "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, which are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0077] In the description of the present application, unless otherwise specified and limited, it is pointed out that the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be mechanical connection or electrical connection, it can be the communication between two elements, it can be direct connection or indirect connection through intermediate medium, and the specific meaning of the above terms can be understood by those skilled in the art according to the specific circumstances.

[0078] The present application discloses a kind of based on inventory prediction method of steelmaking production plan, as shown in Figure 1 The present application discloses a kind of based on inventory prediction method of steelmaking production plan, as shown in

[0079] Obtain existing inventory information, steelmaking production plan and hot rolling loading plan information;

[0080] According to the casting machine pulling speed and slab plan length, the output time of slab is determined;

[0081] According to the slab type, the entry point of slab is determined;

[0082] According to the entry point, the roller conveying time is determined, the slab output time is accumulated, and the slab entry time is obtained;

[0083] According to the existing inventory information, slab entry and exit information, the slab inventory is predicted.

[0084] In a preferred scheme of the present application, the inventory information includes:

[0085] The inventory information structure of all slabs is designed according to the inventory information, including slab number W.s lab_no, warehouse number W.warehouse_id, aisle number W.hall_id, logical partition W.zone, stack position W.s lot, steel grade W.st, weight W.weight (for storage format);

[0086] According to the inventory information, the total inventory W0 is obtained by summarizing the weight W.weight of all slabs;

[0087] The steelmaking production plan information includes:

[0088] A steelmaking heat structure is designed for a steelmaking heat plan, including a casting number P.cast, a planned heat number P.heat, a planned starting time P.T m , a planned steel grade P.st

[0089] A cutting plan structure is designed for a slab cutting plan, including a planned heat number P.heat, a flow number P.strand_no, a planned slab number P.slab_no, a planned slab length P.length, a weight P.weight, a slab type P.type, and a slab expected storage time P.in

[0090] According to the casting number, the steelmaking heat plan and the slab cutting plan information of the casting are extracted from the database;

[0091] The hot rolling loading plan information includes:

[0092] A hot rolling loading plan structure is designed for the hot rolling loading plan information, including a plan number L.plan, a slab number L.slab_no, a weight L.weight, and a planned loading time L.load_time

[0093] According to the plan number, the hot rolling loading plan information of the plan is extracted from the database.

[0094] In a preferred scheme of the present application, for a multi-flow caster, Strand(s) = 1…s cast flows share a ladle and a tundish, and the crystallizer, the cooling section, the straightening and cutting are all separate, (for example, for a double-flow caster, 1 cast flow and 2 cast flow are cast at the same time, 1 and 2 cast flows share a ladle and a tundish, and the crystallizer, the cooling section, the straightening and cutting are all separate) according to the casting speed and the slab planned length, the slab output time is predicted with the casting signal of the casting number A as the trigger point, and the specific method is as follows:

[0095] According to the steelmaking production plan, the mth heat planned starting time T m is obtained, m = 1…M, and M is the total number of heats;

[0096] According to the caster 1#, 2#, 3# and 4# where the casting number is located, the parameter “casting to blanking time length” d i is set, i is the caster number (i = 1, 2, 3, 4), and d i is derived from the statistical average value in the past 30 days;

[0097] According to the steel grade and the caster number of the mth heat, the preset casting model configuration base table 1-1 is queried to obtain the theoretical casting speed S m of the caster, m = 1…M;

[0098] Table 1-1 Casting Model Configuration Base

[0099]

[0100] According to the steel production plan, the planned cutting length L of the nth slab of the mth strand(s) casting stream is obtained mn ;N is the output time C of the nth slab of the strand(s) casting stream m,n,Strand (s) is:

[0101]

[0102] Wherein, N represents the slab block number of the strand(s) stream of the mth heat. In actual steel production, S m is the theoretical pulling speed of the continuous casting machine, so it can be assumed that the casting time of the slab is proportional to the slab length. If the casting machine is a double casting stream, the two casting streams share a ladle and a tundish, and each is a separate casting stream from the crystallizer. The cooling section, the straightening and cutting are all separate, so the output time is calculated separately.

[0103] In a preferred embodiment of the present application, the slab storage point is determined according to the slab type, and the specific steps are as follows:

[0104] Obtain the slab type of the steelmaking heat plan and slab cutting plan information;

[0105] Calculate the probability of the incoming slab being stored in 1 span, 2 spans, 3 spans, 4 spans, …, r spans, respectively;

[0106] The maximum probability is the slab storage point;

[0107] The slab storage point decision function is expressed as:

[0108] P w,r =P q,r *K w ,r∈{1,2,3…r}

[0109]

[0110] Wherein, the slab type set Q: {to be cleaned, to be checked, qualified};

[0111] The span set R of the slab storage: {1 span, 2 span, 3 span, 4 span, …, m span storage};

[0112] w is the serial number of the slab, and W is the slab set, w∈W, and there are W slabs;

[0113] P q,r is the reference probability of the slab type q being stored in r spans, which is generated by statistical analysis of the last 6 months of historical data;

[0114] Kw represents the weight coefficient of the w-th slab into the r-th bay;

[0115] P w,r is the probability of the slab w into the 1st, 2nd, 3rd, 4th, …, mth bay;

[0116] P w,r is the maximum probability of the slab w into the 1st, 2nd, 3rd, 4th, …, mth bay, and the corresponding bay r is the slab off-line point.

[0117] The incoming slabs are of three types: to be cleaned, to be inspected, and qualified, which are respectively into the 1st, 2nd, 3rd, and 4th bays. The slab type and operation scenario affect the slab entry point, and further affect the time when the slab is put into the warehouse after production.

[0118] The scenario set is: {4th bay maintenance, 3rd / 4th bay simultaneous maintenance, 2nd bay without loading, 2nd bay loading, batch warehouse machine cleaning, quality inspection quantity < 20 pieces, cross bay material preparation, and empty crane}.

[0119] In a preferred scheme of the present application, the method for predicting the slab entry point and the roller conveying time is as follows:

[0120] Assuming that the conveying duration R ab represents the duration required for the slab produced by the caster a to be put into the bth bay, R ab obeys the normal distribution, the mean value is selected as the reference duration, and the historical data of the last 30 days is selected for statistics, as shown in Table 2:

[0121] Table 2 Conveying duration R ab value

[0122]

[0123] The entry time T of the n-th slab of the m-th furnace of the current casting A in Strand(s) casting stream into the warehouse is: mn

[0124] T mn = C m,n,Strand(s) + R ab

[0125] Wherein, C m,n,Strand(s) represents the production time of the n-th slab of the m-th furnace of Strand(s) casting stream, R ab represents the duration from the slab production time of the caster a to the conveying to the bth bay, a, b = 1, 2, 3, 4, …, m, and R ab can be obtained according to historical data statistics.

[0126] In a preferred scheme of the present application, according to the predicted slab entry and exit information, the steps for predicting the slab inventory are as follows:​

[0127] Let the current time T0, slab warehouse inventory W t0 , forecast future T1 time inventory W t1 ;

[0128] Slab set P: {planned slab number P.slab_no, weight P.weight, slab expected to enter the warehouse time P.in} is planned to enter the warehouse;

[0129] Slab set L: {slab number L.slab_no, weight L.weight, planned loading time L.loadtime} is planned to load;

[0130] For all P set of slabs P e , set parameter k e , when the slab expected to enter the warehouse time P.in is less than T1, then k e =1; otherwise k e =0;

[0131] For all L set of slabs L f , set parameter d f , when the planned loading time L.loadtime is less than T1, then d f =1; otherwise d f =0;

[0132] Then the slab inventory W in and the slab inventory W out are:

[0133] Win=Σ{P e .weight*k e}e∈P

[0134] Wout=Σ{L f .weight*d f}f∈L

[0135] The slab inventory W t1 at T1 time is:

[0136] W t1 =W t0 +W in -W out .

[0137] For example, let the 1# caster casting time of a certain furnace be the verification object, the furnace opening time is 15:37:36, and the average casting speed of the changed steel grade is 1.25, then according to the slab quality prediction and the warehouse entry point prediction results are as shown in Table 3:

[0138] Table 3 Slab entry time prediction results

[0139]

[0140]

[0141] Ten casting cycles (78 heats) from casting machines #1-#4 were selected for warehousing time prediction. Comparing the predicted and actual warehousing times, the prediction accuracy was approximately 98% within the range of -5 to +5. Figure 2 As shown.

[0142] This invention also provides an inventory forecasting system based on steelmaking production plans, including a data acquisition module and a processing module. The data acquisition module is used to collect inventory information, steelmaking production plans, and hot rolling material feeding plans. The output end of the data acquisition module is electrically connected to the input end of the processing module. The processing module executes the method described in this invention to control warehousing and outbound operations, thereby realizing inventory forecasting based on steelmaking production plans.

[0143] This invention, based on contract and inventory analysis, determines the component of the casting schedule for each production line flow within the casting schedule based on inventory balance targets, thereby achieving production continuity. This method of casting schedule preparation based on inventory forecasting can effectively improve the accuracy of the plan and its executability on-site, avoiding cost increases, process instability, and logistical disruptions caused by human factors. It plays a significant role in improving product quality and contract delivery, and reducing production costs.

[0144] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0145] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for inventory forecasting based on a steelmaking production plan, characterized in that, The method comprises the following steps: obtaining existing inventory information, steelmaking production plan and hot rolling feeding plan information; determining slab output time according to casting machine pulling speed and slab plan length; determining slab storage point according to slab type; determining slab storage time by adding slab output time according to roller conveying time and the storage point; carrying out slab inventory prediction according to existing inventory information and slab in-out information, determining slab plan components of each production line flow in the casting plan based on inventory balance target, so as to realize production continuity; carrying out casting plan preparation based on inventory prediction, effectively improving plan accuracy and on-site executability; the steps of carrying out slab inventory prediction according to predicted slab in-out information are as follows: Let the current time T0, slab warehouse inventory W t0 , forecast future T1 time inventory W t1 ; planned slab set P: {planned slab number P.slab_no, weight P.weight, slab predicted storage time P.in}; planned feeding slab set L: {slab number L.slab_no, weight L.weight, planned feeding time L.loadtime}; Slab P for all P set e , set parameter k e , when slab P.in is less than T1, then k e = 1; otherwise k e = 0; Slab L for all L set f , set parameter d f , when the planned loading time L.loadtime is less than T1, then d f =1; otherwise d f =0; Then the quantity of slabs entering the warehouse W in and slab output W out for: W in =∑{P e· weight*ke} e∈P W out =∑{L f· weight*d f} f∈L The slab inventory W at time T1 is: t1 is: W t1 = W t0 + W in - W out ; determining slab storage point according to slab type, the specific steps are as follows: obtaining slab type of steelmaking heat plan and slab cutting plan information; calculating the probability of storage of the slab in 1st cross, 2nd cross, 3rd cross, 4th cross, …, rth cross, respectively; the maximum probability is the slab storage point; the slab storage point decision function is expressed as: P w，r = P q.r K w , r e {1,2,3...r} P w，r ′=max{P w，r} wherein, slab type set Q: {to-be-cleaned slab, to-be-inspected slab, qualified slab}; slab storage cross set R: {1st cross, 2nd cross, 3rd cross, 4th cross, …, rth cross storage}; w is the serial number of the slab, W is the slab set, w∈W, and there are W slabs in total; P q，r The reference probability of the slab of the slab type q into the r cross is generated by historical data statistics; K w represents the weight coefficient of the w-th slab bank entering the r-th cross. P w，r P is the probability of slab w entering the yard at 1st bay, 2nd bay, 3rd bay, 4th bay, …, mth bay. P w，r r is the maximum value of the slab w in the 1st, 2nd, 3rd, 4th, …, mth cross-in warehouse probability, and the corresponding cross r is the slab off-line point; the method for predicting the roll conveying time according to the cross-in point, accumulating the slab output time, and predicting the slab cross-in time is as follows: The time T for the nth slab of the Strand(s) cast in the mth furnace of the current casting cycle A is when it enters the warehouse. mn for: T mn = C m,n,Strand(s) + R ab wherein C m,n,Strand(s) represents the output time of the nth slab of the mth casting strand (s), R ab represents the time length from the slab output time of the casting machine a to the delivery to the b span, a, b = 1, 2, 3, 4, … m; the inventory information comprises: designing an inventory information structure body of all slabs according to inventory information, including slab number W.slab_no, warehouse number W.warehouse_id, cross number W.hall_id, logical partition W.zone, slot W.slot, steel grade W.st, weight W.weight; obtaining total inventory W0 according to inventory information by summarizing the weight W.weight of all slabs; the steelmaking production plan information comprises: The design of the steelmaking furnace structure is based on the planned furnace design, including the casting number P.cast, the planned furnace number P.heat, and the planned start time PT. m , Planned steel grade P.st; designing a cutting plan structure body according to slab cutting plan, including planned heat number P.heat, flow number P.strand_no, planned slab number P.slab_no, slab planned length P.len, weight P.weight, slab type P.type, slab predicted storage time P.in; extracting the steelmaking heat plan and slab cutting plan information of the heat from the database according to the heat number; the hot rolling feeding plan information comprises: designing a hot rolling feeding plan structure body according to hot rolling feeding plan information, including plan number L.plan, slab number L.slab_no, weight L.weight, planned feeding time L.loadtime; extracting the hot rolling feeding plan information of the plan from the database according to the plan number; For multi-stream caster, Strand(s) = 1…s streams share a ladle and tundish, crystallizer, cooling section, straightening and cutting are separate, according to the casting speed and slab plan length, the slab output time is predicted, the specific method is as follows: According to the steelmaking production plan, obtain the starting time T of the mth heat plan m , m = 1…M, M is the total number of heats; According to the casting machine where the casting is located, the parameter "length of time from start of casting to end of casting" d is set i i is the casting machine number; According to the steel grade of the mth heat and the caster number, a preset pouring model configuration base table is inquired to obtain a theoretical casting speed S of the caster m , m = 1…M; According to the steelmaking production plan, the planned cutting length L of the nth slab of the mth strand(s) casting strand(s) is obtained mn ; Cn = Cn-1 + Tn m，n，Strand (s) is: Wherein, N represents the slab block number of Strand(s) stream of furnace m.

2. An inventory forecasting system based on a steelmaking production plan, characterized by, The application relates to a steel inventory prediction method and system, comprising a data acquisition module and a processing module, the data acquisition module is used for acquiring inventory information, steelmaking production plan and hot rolling feeding plan information, the output end of the data acquisition module is connected with the input end of the processing module, the processing module executes the method in claim 1, controls warehousing and delivery, and realizes inventory prediction based on the steelmaking production plan. The application relates to a steel inventory prediction method and system, comprising a data acquisition module and a processing module, the data acquisition module is used for acquiring inventory information, steelmaking production plan and hot rolling feeding plan information, the output end of the data acquisition module is connected with the input end of the processing module, the processing module executes the method in claim 1, controls warehousing and delivery, and realizes inventory prediction based on the steelmaking production plan.

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