NCV7450：集成CAN FD、LDO稳压与高速驱动的系统基 chip

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NCV7450

www.onsemi.com

NCV

7450

VR1

TxDC

VS1

GND

VS2

RSTN

WD_EN

HS_DIAG

CANL

CAN_EN

GND

HS_EN

WDI

CANH

RxDC

Table 1. PIN DESCRIPTION

No.

Pin Name

Pin Type

(LV = Low Voltage; HV = High Voltage)

Description

1 VR1 LV supply output Output of the 5 V / 250 mA low−drop regulator

2 WD_EN LV digital input; internal pull−up current Watchdog enable input

3 RSTN LV digital output; open drain; internal pull−up Reset signal to the MCU

4 TxDC LV digital input; internal pull−up CAN transmitter data input

5 HS_DIAG LV digital output; push−pull HS driver diagnostic output (active Low)

6 RxDC LV digital output; push−pull CAN receiver data output

7 WDI LV digital input; internal pull−down Watchdog trigger input

8 HS_EN LV digital input; internal pull−down HS driver enable input

9 CAN_EN LV digital input; internal pull−down CAN transceiver enable input

10 GND Ground connection Ground supply (all GND pins have to be connected externally)

11 CANH CAN bus interface CANH line of the CAN bus

12 CANL CAN bus interface CANL line of the CAN bus

13 GND Ground connection Ground supply (all GND pins have to be connected externally)

14 HS HV output; high−side High−side driver output

15 VS2 HV supply input Main supply input (HS Driver), keep floating if HS driver not used

16 VS1 HV supply input Main supply input (VR1, logic)

EP Exposed pad Substrate (has to be connected to all GND pins externally)

剩余17页未读，继续阅读

read(911,) Para_Inlst07 IF (TRIM(Para_Inlst07)=='POOL_STRUCTURE') THEN read(911,) NCV_Structure(N3Dpool) ENDIF allocate(OBCV_X(NCV_Structure(N3Dpool))) allocate(OBCV_Y(NCV_Structure(N3Dpool))) allocate(OBCV_Z(NCV_Structure(N3Dpool))) allocate(GAMA_OBCV(NCV_Structure(N3Dpool))) allocate(GAMA_OBX(NCV_Structure(N3Dpool))) allocate(GAMA_OBY(NCV_Structure(N3Dpool))) allocate(GAMA_OBZ(NCV_Structure(N3Dpool)))

接着，这段代码调用了allocate函数，分别为OBCV_X、OBCV_Y、OBCV_Z、GAMA_OBCV、GAMA_OBX、GAMA_OBY和GAMA_OBZ分配了内存空间，这些数组的大小都是NCV_Structure(N3Dpool)。这段代码的作用是动态地为这些数组分配...

tle94112es、

引用提到了TLE94106ES，它是一款受保护的驱动器，专为汽车运动控制而设计应用，比如供暖、通风和空调（HVAC）襟翼直流电机控制。而您提到的TLE94112ES也是由英飞凌（Infineon）生产的电机控制器。不过，根据我所了解...

物理：已知1mol的某种实际气体,在一定温度下,其压强随体积变化的函数关系为p=c/(v-b)-a/(v)^2式中a,b,c均为常量.求该气体系统经一准静态过程,体积由V1膨胀到V2时,气体对外界所做的功.

由于该气体系统经一准静态过程，根据热力学第二定律，气体内能的减少量等于热量的负值，即∆U=-Q。因此，气体对外界所做的功为∆W=Q=-∆U。由于气体是实际气体，其状态方程为pV=nRT，其中n为摩尔数，R为气体常数，...

read(911,) Para_Inlst07 IF (TRIM(Para_Inlst07)=='3DCV_AQ') THEN read(911,) AQ_Type_Input(N3Dpool) read(911,*) NCV_AQ(N3Dpool) ENDIF

首先读取一个字符串类型的参数 Para_Inlst07，如果该参数的值为 "3DCV_AQ"，则会继续读取两个参数 AQ_Type_Input 和 NCV_AQ，并将它们分别赋值给数组 AQ_Type_Input 和 NCV_AQ 的第 N3Dpool 个元素。这些数组可能会...

do ii=1,CircuitN_v do while(Logi(ii)==.false.) CondiPump0_v=CondiPump_v Dfr_max=0.0 Dp_max=0.0 CALL CalBCV(ii) CALL CalALS(ii) CALL CalIMP(ii) CALL CalArrayCD(ii) Dpressure_v(ii,1:NCV_v(ii))=Gauss(ArrayC_v(ii,1:NCV_v(ii),1:NCV_v(ii)),ArrayD_v(ii,1:NCV_v(ii)),NCV_v(ii)) CALL CalDflowRate(ii) do i=1,NCV_v(ii) if(Dp_max<abs(Dpressure_v(ii,i))) then Dp_max=abs(Dpressure_v(ii,i)) end if end do do i=1,NLS_v(ii) if(Dfr_max<abs(DflowRate_v(ii,i))) then Dfr_max=abs(DflowRate_v(ii,i)) end if end do if(Dfr_max>1000.0) then !CFR600!20180404wangjin修改 DT_v=DT_v0.75 !20200322 open(unit=36,file='输出卡_错误.dat') !20200322 write(36,) TimeNow_v,"Dfr_max=",Dfr_max,"DT_v=",DT_v !if(DT_v<1e-5) then ! DT_v=1e-5 !end if cycle else Logi(ii)=.true. end if do i=1,NCV_v(ii) CV_v(ii,i)%pressure=CV_v(ii,i)%pressure+Dpressure_v(ii,i) end do do i=1,NLS_v(ii) LS_v(ii,i)%flowRate=LS_v(ii,i)%flowRate+DflowRate_v(ii,i) !20190318 调试 !if(LS_v(ii,i)%flowRate<0.0) LS_v(ii,i)%flowRate=0.1 end do CondiPump_v=CondiPump0_v end do end do

这段代码是一个嵌套的循环结构，其中...需要注意的是，这段代码中并没有给出所有的子过程和变量的定义和实现，因此你需要根据实际情况进行适当的修改和补充。希望这能帮到你！如果你有任何进一步的问题，请随时提问。

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np.maximum(eigvals.real, 0) # Use complex detection of small eigenvalues from pinvh. t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) # Get a mask indicating which eigenpairs are not degenerate tiny, # and create a reordering array for thresholded singular values. above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh将这段代码放入一个.py文件中，用Spyder查看，有报错，可能是缩进有问题，无法被调用，根据这个问题，给出解决办法，给出改正后的完整代码

eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np....

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if self.which == 'LM': largest = True elif self.which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) #获得隐式定义的格拉米矩阵的低秩近似。 eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) #格拉米矩阵有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复数检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #获得一个指示哪些本征对不是简并微小的掩码， #并为阈值奇异值创建一个重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T) 将上述代码修改为使用LM，迭代器使用arpack

将代码修改为使用LM和迭代器使用arpack的方法如下： ```python class SVDRecommender: def __init__(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='...

class svd_recommender_py(): #svd矩阵推荐 def svds(A, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. #获得隐式定义的格拉米矩阵的低秩近似。 #这不是解决问题的稳定方法。 solver == 'arpack' eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) #格拉米矩阵具有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复杂检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #得到一个指示哪些本征对不是退化微小的掩码， #并创建阈值奇异值的重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh这段代码主要是为了将scipy包中的SVD计算方法封装成一个自定义类，是否封装合适？如果不合适，给出修改后的完整代码

这段代码的封装似乎没有问题，但是缺少了类的定义和初始化方法。以下是一个完整的代码示例： ```python import numpy as np from scipy.sparse.linalg import eigsh from scipy.sparse.linalg import ...

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Do i=1, NCV_Structure(N3Dpool) read(911,*)CV_Structure_x,CV_Structure_y,CV_Structure_z,Gama_CV,Gama_X,Gama_Y,Gama_z OBCV_X(i) =CV_Structure_x OBCV_Y(i) =CV_Structure_y OBCV_Z(i) =CV_Structure_z GAMA_OBCV(i) =Gama_CV GAMA_OBX(i) =Gama_X GAMA_OBY(i) =Gama_Y GAMA_OBZ(i) =Gama_z OBCV_X_Cal(N3Dpool,i) =OBCV_X(i) OBCV_Y_Cal(N3Dpool,i) =OBCV_Y(i) OBCV_Z_Cal(N3Dpool,i) =OBCV_Z(i) GAMA_OBCV_Cal(N3Dpool,i) =GAMA_OBCV(i) GAMA_OBX_Cal(N3Dpool,i) =GAMA_OBX(i) GAMA_OBY_Cal(N3Dpool,i) =GAMA_OBY(i) GAMA_OBZ_Cal(N3Dpool,i) =GAMA_OBZ(i) ENDDO

这段代码使用了一个Do循环，循环的次数是NCV_Structure(N3Dpool)。在循环的每一次，从文件911中读取了7个数值，分别是CV_Structure_x、CV_Structure_y、CV_Structure_z、Gama_CV、Gama_X、Gama_Y和Gama_z。然后将...

Exception caught in handler {"worker_id": "0", "exc": "OpenCV(4.8.0) /io/opencv/modules/imgproc/src/contours.cpp:195: error: (-210:Unsupported format or combination of formats) [Start]FindContours supports only CV_8UC1 images when mode != CV_RETR_FLOODFILL otherwise supports CV_32SC1 images only in function 'cvStartFindContours_Impl'\n", "traceback": "Traceback (most recent call last):\n File \"/opt/nuclio/_nuclio_wrapper.py\", line 118, in serve_requests\n await self._handle_event(event)\n File \"/opt/nuclio/_nuclio_wrapper.py\", line 312, in _handle_event\n entrypoint_output = self._entrypoint(self._context, event)\n File \"/opt/nuclio/main.py\", line 51, in handler\n contours, _ = cv2.findContours(mask_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\ncv2.error: OpenCV(4.8.0) /io/opencv/modules/imgproc/src/contours.cpp:195: error: (-210:Unsupported format or combination of formats) [Start]FindContours supports only CV_8UC1 images when mode != CV_RETR_FLOODFILL otherwise supports CV_32SC1 images only in function 'cvStartFindContours_Impl'\n\n"} 2023-07-16 10:23:22 /opt/nuclio/_nuclio_wrapper.py:395: DeprecationWarning: There is no current event loop 2023-07-16 10:23:22 loop = asyncio.get_event_loop() 2023-07-16 10:24:38 2023-07-16 10:24:40 0: 480x640 1 car, 1 truck, 82.9ms 2023-07-16 10:24:40 Speed: 16.6ms preprocess, 82.9ms inference, 24.2ms postprocess per image at shape (1, 3, 480, 640)

`cv2.findContours()` 函数需要传入一个二值图像（只包含0和255两个像素值）作为输入，而且图像的数据类型应为 `np.uint8`。你可以尝试在调用 `cv2.findContours()` 函数之前，对 `mask_binary` 进行以下处理，...

r语言adf.test参数type

adf.test函数是用于进行单位根检验的函数，在进行检验时需要传入type参数来确定检验的类型。该参数共有五种取值： 1. type="nc": 不考虑常数项，只考虑趋势...5. type="ncv": 包含趋势项和自回归误差项的单位根检验。

1、2 mol双原子理想气体从始态300K，50 dm3 ，先恒容加热至 400 K，再恒压加热至体积增大至 100m3，求整个过程的Q，W，△U，△H及△S。

其中，P2、V2、P1、V1分别为末态时的压强、体积和始态时的压强、体积。根据理想气体状态方程，有： P1V1/T1 = P2V2/T2 结合已知条件可得到： T2 = P2V2T1/P1V1 代入式子可得： T2 = 400 K x 100 m3 / (2 atm ...

from scipy.sparse.linalg import eigsh, LinearOperator from scipy.sparse import isspmatrix, is_pydata_spmatrix class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): largest = self.which == 'LM' if not largest and self.which != 'SM': raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) eigvals = np.maximum(eigvals.real, 0) t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T)这段代码中使用的scipy包太旧了，导致会出现报错信息为：cannot import name 'is_pydata_spmatrix' from 'scipy.sparse' (D:\Anaconda\lib\site-packages\scipy\sparse_init.py)，将这段代码修改为使用最新版的scipy包

Mycat1.6.7.3 for windows版本

Mycat1.6.7.3 for windows版本，windows版本下载

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ncv_contrib-master\modules\wechat_qrcode\src\wechat_qrcode.cpp:56: error